XLV Mexican Conference on Biomedical Engineering

For an elderly adult, a fall can be fatality by the fact that his bones are fragile and propense to fracture. The present device is intended to detect a change of posture prior to a fall by using a sensor composed of an accelerometer and a triaxial gyroscope. The signal processing used multiple classification neural networks with hyper-parameters of five hidden layers and 500 interactions, training and validation showed an accuracy of 83% respectively. In the results, the device detects fall postures efficiently and with a minimum margin of error.

There are certain parameters in the human body that may be indicators of the presence of breast cancer, these can be assessed with different algorithms such as the Support-Vector Machine (SVM), the Naïve Bayes Algorithm (BA) and Artificial Neural Networks (ANN) to determine whether the laboratory tests are positive or not. Machine Learning (ML) has gained more uses across fields as it proposes a cost-effective classifier with versatility to be developed for any type of application, such as early breast cancer detection. This paper shows an ensemble of the algorithms previously mentioned that, based on a database consisting of 8 characteristics, can provide a high accuracy result. The highest F1 Score obtained was 78.261% from the BA, followed by the ANN’s score of 77.273% and a 72.34% from the SVM, resulting in a compositive F1 Score of 80.851%. All the data used on this article was trained using supervised machine learning techniques and variables of interest for breast cancer proliferation.

In order to analyze brain functional connectivity (FC) in humans, this work focuses on resting state functional magnetic resonance imaging (Rs-fMRI) studies to measure functional connectivity within default-mode network (DMN). The objective of the present study is to compare functional connectivity networks in healthy subjects with a drug-refractory epilepsy patient. Since the FC has a dynamic nature, evidence of variations over time and pathology are explored. The FC graph in Rs was obtained using the visibility algorithm. Based on the resulting graph, it was possible to determine how each voxel is connected to others and it is also possible to identify the hubs in healthy subjects and compare them with the DMN and how connectivity networks are altered in epilepsy patients.

Brain-computer interface (BCI) systems have gained significant interest given the different biomedical applications in which they can be used to help disabled individuals to communicate or control external devices. Imagined speech is related to BCI systems controlled only by thinking about a vowel, phoneme, or word without any physical movement. In this paper, a Prototypical Network approach, named Proto-imEEG, is presented for the automatic classification of seven imagined phonemic/syllabic prompts and four imagined words by analyzing EEG data of the KaraOne dataset. The Prototypical Network is selected because of its ability to learn from a few samples, a common issue in EEG data. The embedding function of our Prototypical Network is based on a 1D-convolutional layer and bidirectional recurrent networks. The average classification accuracy achieved by Proto-imEEG is 92.04% and 96% by using a Long-Short Term Memory or a Gated Recurrent Unit, respectively, with an average inference time of 0.2 s. These results demonstrate superior performance to state-of-the-art methods in classifying the eleven classes of the KaraOne dataset. As far as we know, this is the first time that a Prototypical Network approach is used in imagined speech classification tasks.

Breast cancer early detection is a critical factor associated to patient survival and treatment cost reduction. Nevertheless, it is difficult to obtain a diagnose at earliest stages since it does not cause any symptoms. Recently, artificial intelligence field has demonstrated to be a suitable alternative to improve classification and early detection for this affection. Therefore, this paper proposes the design and comparison of three artificial intelligent algorithms for breast cancer classification. The algorithms used for classification were a naive Bayesian network (NBN), a support vector machine (SVM), and an artificial neural network (ANN). These algorithms were trained and validated in the Breast Cancer Prediction Database, located on the Kaggle platform. This database contains ten real-valued features computed from benign and malignant tumors. The evaluation results in F1 score shown 94%, 92% and 91% for the NBN, ANN and SVM respectively. These scores were compared with state-of-the-art algorithms to demonstrate the robustness of the proposed algorithms. The comparison was made considering feature-based and image-based models. Findings shown that our feature-based algorithms obtained competitive results requiring less computational resources than image-based models. Therefore, algorithms here proposed are a good option for the development of a high-fidelity system to classify the mentioned database into the cancer and non-cancer categories.

The simulation of electrophysiological signals has been a challenging task for the computer sciences for a long time due to their complex morphology and the high environmental noise present within the human body. In an attempt to replicate these signals a variety of conventional models have been developed but all of those presented complexity restrictions that prevented them to reach an acceptable similarity with the real signals. Viewing this situation the goal of this work was set to demonstrate that it is possible to simulate electrophysiological registers using machine learning algorithms, to achieve this goal electromyographic registers (EMG) from the Biceps Brachii muscle of three subjects were recorded using surface electrodes and each register was split in time fragments with a length of 1 s to be used as a data set for the model. Subsequently, the proposed machine learning model based in a recurrent neural network (RNN) and an autoencoder was trained to simulate EMG signals based on the previously obtained data set. Finally, the resultant simulations of this model were analyzed using digital signal processing techniques and the results showed that the simulations behaved in a very similar way to the real electromyographic registers.

The team of plastic surgeons that collaborates in the Smile Train Cleft Leadership Center (CLC) SUMA in Mexico, is interested in developing a quantitative classification for representing the surgical complexity of clefts and provides more objective criteria for the treatment of patients. The objective of this work was to propose a cleft classification and a relevance factor for each cleft. The classification is a problem of multiple attribute decision making, and that is why we use the Analytic Hierarchy Method for developing the relevance factor for each cleft. The relevance factor was validated in a sample of fifty patients treated at the SUMA-CLC. A total of twenty-nine classes were defined: nine for primary palate, two classes for secondary palate, and eighteen classes for labial-palatal clefts. The relevance factors obtained for the clefts grouped in these classes are in the interval (3, 298), where three means the lowest relevance and 298 means the highest. In general, the values increase as the cleft characteristics become more complex. Surgeons consider that the class relevance factor effectively represents the complexity of the cleft, and the surgical treatment requires by the patient.

One in eight women is likely to develop breast cancer at some stage in her life, with a 12.5% average risk rate of developing breast cancer. Early detection and treatment are of vital importance to ensure the patient's survival. Currently, mammography is the main diagnostic study to identify breast cancer. However, since mammography requires a human, medical radiologist, to make a diagnosis, it is prone to errors. Recently, deep learning techniques have proven to be a suitable tool for breast cancer classification and detection. Therefore, this research proposes an algorithm based on convolutional neural networks (CNN) for screening classification of cancer in mammography images. The evaluation results of the proposed algorithm respect state-of-the-art algorithms demonstrate competitive accuracy results of up to 99% and the fastest training time. Therefore, our algorithm is well suitable for automatic breast cancer detection using the public All-MIAS database.

Experimentation as a teaching technique allows the understanding and relationship of concepts as well as the acquisition of problem-solving skills. Computational chemistry is a tool for studying chemical phenomena through computational experiments. The use of simulation in chemistry and biochemistry education is evolving the teaching techniques and developing computational skills.Teaching chemistry and biology through simulations and structural analysis is mainly limited to graduate students. However, we are moving toward a future where computational skills, including programming and simulation, will no longer be optional.In the present research, we use a pharmaceutical example for computational modeling and molecular docking to study and design drugs. Physicochemical characterization of the drug Remdesivir was carried out to demonstrate that the acquisition and learning of theoretical concepts are more practical when performing computational experiments.

The brain is a complex network of anatomic functional modules called brain circuits. A brain circuit comprises structures that share transcriptional dynamics, enabling them to interact together to meet cognitive functions. The brain circuit emergence and its complexification begins at the early stages of neurodevelopment. Studies that approach transcriptional dynamics only focus on a few genes in limited structures. Thus, complex transcriptional behaviors underlying the brain circuits’ emergence remain hidden. In this work, we examine the whole-brain spatiotemporal transcriptomes to capture transcriptional dynamics during neurodevelopment. Through hyperbolic representations like Poincaré maps, we illustrate a transcriptional machinery difference in embryonic development and adulthood. Examples like dendrogenesis and axonogenesis indicate that this difference results from constituting and later striving transcriptional dynamics. This qualitative analysis evidences expression dynamics that lead to brain circuit emergence.

Intracytoplasmic sperm injection (ICSI) is one of the most commonly applied techniques for in vitro fertilization. This technique consists of the single selection of a spermatozoon followed by the injection of this sample into the oocyte’s plasma. The embryologists perform the ICSI by using their judgment to select the spermatozoon to inject. Additionally, they decide the best technique to penetrate the oocyte with the needle. Therefore, the success of an ICSI procedure can be affected by subjective decisions such as the characteristics of the sperm selected, the angle at which the needle pierces the oocyte, and the speed at which it is performed. The main objective of this project is to develop a computational tool that can automatically identify the different stages of the ICSI procedure. A tool like this will automate the activation of other artificial intelligence tools that can assist the embryologist while performing the ICSI (e.g., assistants to select the best sperm to inject and guides for the injection technique). Our results indicate the feasibility of employing a deep neural network architecture to determine the stage of the ICSI procedure from a video stream from a camera attached to a microscope.

Parkinson’s disease (PD) is a progressive condition that affects dopaminergic neurons, causing motor alterations. Motor disturbances, such as gait impairment, can be used to assess the disease. Unfortunately, gait disturbances, such as decreased walking speed and step variability, can also occur due to aging, affecting the identification of abnormal PD gait. Therefore, developing an adequate tool to evaluate PD patients’ gait is essential. This paper proposes a deep learning algorithm to differentiate between PD gaits and normal walking using vertical ground reaction force (VGRF) signals. CLDNN is a single framework composed of a convolutional neural network, a long-short term memory network, and a deep neural network. To train and validate a CLDNN classifier gait cycles were obtained from VGRF signals. The VGRF signals were from a public database with recordings from 93 PD patients and 73 healthy adult controls. The CLDNN performance was evaluated by five-fold cross-validation. The combined spatial and temporal methods in CLDNN enabled the effective identification of PD gait with less complex architecture. The best weighted accuracy was 98.28 ± 0.38. Thus, our model is compact and efficient for future embedded or portable implementations.

Chlamydia trachomatis (CT) is the most frequent bacterial sexually transmitted infection (STI) in the world. Therefore, the identification of serovars is essential for epidemiological surveillance and the development of prevention methods in our population. We aimed to demonstrate the diversity and frequency of serovars of CT in the urogenital tract in Mexican patients. We carried out an observational, prospective and cross-sectional study through 40 samples positive for CT. For the serovars identification, PCR multiplex, PCR of the ompA gene, automated sequencing, multiple sequence alignment, and phylogenetic analysis were used. Moreover, the variables like serovars, sex, aged groups, anatomical sites and, concomitant pathogens, were statistically analyzed. We reported that the prevalence of the most common serovars in our Mexican population were F (38.1%), E (27.3%) and, D (18.2%). Serovar F was the most prevalent in men, meanwhile, serovar D was in women and, E was equally prevalent in both genders. The most common concomitant pathogens were Ureaplasma spp. (22.7%), Mycoplasma spp. (13.6%) and, Haemophilus spp. (9.1%). In summary, epidemiological surveillance remarks the necessity of detecting serovars CT to elucidate the molecular implication and reduce sexual and reproductive complications.

Due to the growing need for specialized care in communities that do not have a local radiology service, and the long response times of diagnostic departments, there is a need to provide technologies that help medical diagnosis. In this project, we implement convolutional neural network models for the identification of mammography’s with data suggestive of breast cancer, considering the BI-RADS scale system in a binary classification based on its scales. We considered BI-RADS scales from 1–3 as benign and from 4–5 as malign, binary interpretation for both models (VGG16 and ResNet50) obtained results of 0.87 in accuracy.

An aminoglycoside antibiotic is used to treat different pulmonary illnesses. These drugs save the lives of millions of people around the world, but they also could cause ototoxicity. Kanamycin is an aminoglycoside antibiotic that causes loss or damage of Hair Cell receptors in the inner ear, resulting in hearing loss. The megalin is an endocytic receptor for aminoglycosides in the cochlea.On the other hand, different studies use flutamide to protect the HCs from kanamycin. Androgen receptor inhibition protects against cochlear injuries in kanamycin-induced hearing loss. This work determined the stability of megalin-kanamycin and megalin-flutamide by calculating the binding energy (ΔGb). Our results showed that the megaline-kanamycin interaction was more favorable (with a stronger binding affinity); by contrast, megalin-flutamide was less favorable. These results characterize the driving forces responsible for binding kanamycin to megalin, allowing kanamycin to be internalized by megalin-mediated endocytosis, causing ototoxicity. Our results suggest flutamide does not undergo endocytosis and does not contribute to hearing loss. Currently, we are carrying out molecular docking studies considering megalin-ototoxic receptors to bind flutamide to understand the signaling pathway of aminoglycoside-induced ototoxicity.

The study of new biomaterials that can interact directly with cellular components seeks to improve the integration and functionality of the damaged organs or tissues in which they are implanted. Tissue engineering seeks materials that can provide a suitable microenvironment for proliferation, cell adhesion and differentiation; the interaction with components of the extracellular matrix is also of great interest. In this paper, the interactions generated between a collagen peptide and Plasma-Synthesized Polypyrrole (PPPy), a structure proposed by Kumar et al., were analyzed. Molecular docking analysis and computational determinations of the free energy of binding were performed. The PPPy structure used in this work has in its terminal branches an amino, nitrile and hydroxyl groups; direct interactions between the material and collagen were found, being the amino group the one that generated conformers with favorable binding free energy (ΔGb <0). The findings of this work allow us to propose PPPy as a new biomaterial capable of form important interactions with elements of the extracellular matrix of various tissues.

Conventional audiometry reliability is reached after a variable length period when is applied to implanted pediatric patients, hence delaying auditory rehabilitation therapy decision-making. This work deals with the implanted pediatric patient sound field audiometry early estimation by using the Electrical Cochlear Response (ECR) threshold, which is obtained while patient is using her/his cochlear implant (CI) functioning in everyday mode. In a transversal study, sound field audiometry and ECR thresholds were obtained in a group of 34 prelingual pediatric implanted patients, ranged in age from 1.8 to 6 years, and grouped by age and CI use time. ECR thresholds profile and sound field audiometry show the same tendency over patient age and device use time, however ECR thresholds show a stable behavior across age and device use in contrast to sound field audiometry. Sound field audiometry had higher standard deviation for short age patients and scanty device use. There was a significant difference between ECR thresholds and audiometry across frequency, 16.5 dBHL on average by age, and 14.6 dBHL on average by CI use time (p < 0.05). In contrast, there was an average difference no significant of 4.1 dBHL and 3.1 dBHL when patient age and cochlear implant use time increases, respectively. These results indicate the feasibility to get patient hearing thresholds as soon as CI is activated. Early audiometry estimation by using ECR thresholds allows an opportune therapy decision-making, increasing patient rehabilitation expectations and avoiding audiometry limitations due to patient short age, general health condition and scanty CI use time.

Bone tumors account for less than 1% of all diagnosed cancer, however their morbidity and mortality are significant. Conventional treatments produce a variety of side-effects that decrease the patient’s life quality. Therefore, thermal ablation is proposed as a new treatment for bone malignancies. Commonly, computational models use constant values of tissue properties to reduce computational time. Hence, in this study the performance of a triple slot antenna to treat bone tumors, with thermal ablation, was predicted by considering the temperature dependence of several tissue properties. A parametric modeling study was implemented in COMSOL Multiphysics, based on the Finite Element Method. The Standing Wave Ratio SWR, the area of ablated bone and muscle (T > 60 $$^\circ $$ ∘ C), the area of fat and skin at hyperthermia temperatures (T > 42 $$^\circ $$ ∘ C), etc. were obtained. The SWR remained constant in all simulations with a value of 1.23. Skin and fat do not reach hyperthermia temperatures; moreover, less than 0.2 cm $$^2$$ 2 of muscle reach thermal ablation. The area of ablated bone varies from 3.69 cm $$^2$$ 2 to 5.2 cm $$^2$$ 2 , when thermal conductivity function goes from a positive slope to a negative one ( $$k_1$$ k 1 and $$k_2$$ k 2 ), showing no difference when changing the functions for the other parameters.

When entering the bloodstream, any exogenous biomaterial is covered by proteins, forming a coating known as protein crown. The opsonization of these biomaterials depends on the formation of the protein crown, within which immunoglobulin M (IgM) is the first antibody expressed during the primary immune response. It has been reported that plasma-polymerized pyrrole (PPPy)-coated biomaterials promote cell adhesion due to molecular interactions established between extracellular matrix proteins and amino groups (NH2) present on the PPPy surface. In this project, we model the interaction of a PPPy structure with a binding site of an IgM model through computational studies such as molecular docking, molecular dynamics, and binding energy. These studies allowed us to conclude that the binding between IgM and PPPy is favorable and driven by electrostatic interactions. Our results suggest that the immune system will be able to recognize PPPy-coated biomaterials, possibly unleashing an opsonic response. Our results should be considered in the design of PPPy-coated scaffolds for use in tissue engineering.

Due to the accelerated emergence of drug-resistant bacterial strains, better techniques are needed to enable intelligent antibiotic design. In 2019, drug-resistant bacterial strains were the direct cause of at least 1.27 million deaths internationally. Thus, new strategies are being developed for a faster and more cost-effective process focusing on analyzing so-called older antibiotics. Nitrofurans are documented as the family of antibiotics least prone to bacterial resistance. Therefore, they are molecules from which we can learn by analyzing their structure along with their action mechanism. In this work, we use computational chemistry to calculate the minimum conformations energies, highest occupied molecular orbital, lowest unoccupied molecular orbital, and gaps energies of nitrofurans and their derivative products. Subsequently, the structure of each molecule and its charge distribution were analyzed to understand their reactivity. The charge distribution in the nitrofurans and their nitroso derivatives was concentrated in the Furan-nitro group, which explains the antibacterial properties.

Ca2+ and cAMP are the most extended intracellular second messengers playing as transducers of physiological activity in the form of oscillations. In pancreatic α- and β-cells they are directly related to glucagon and insulin secretion, respectively, which are the main hormones involved in blood glucose regulation. The crosstalk between these messengers is very complex and of great relevance to infer abnormalities in both hormones’ secretion. In this work, we simulate the interaction between the Ca2+ and the cAMP pathways when considering either positive or negative effects of one pathway on the other one. As long as Ca2+ inhibits cAMP synthesis, out-of-phase oscillations were reached, regardless of positive or negative effect of cAMP on Ca2+ dynamics. In contrast, when Ca2+ enhances cAMP synthesis, oscillations were always in phase. We thus conclude that Ca2+ effect on cAMP pathway can switch these phase relationships, which agrees with recent experimental observations in pancreatic β-cells.

Type II diabetes (T2D) is a disorder defined by an impaired insulin secretion and insulin resistance. Throughout the progression of the disease, $$\beta $$ β -cells are lost due to the high-demanding environment produced by a prolonged hyperglycemic state. Moreover, T2D has been associated to structural alterations of the architecture of pancreatic islets, which might interfere negatively in the gap-junctional communication between $$\beta $$ β -cells. In this work, aiming to evaluate the effects of the loss of $$\beta $$ β -cell mass on connectivity metrics and on the synchronization of the cells’ electrical signals, we performed computational simulations of the network formed by $$\beta $$ β -cells in a human islet. Our results indicate that the loss of 15 and 30% of $$\beta $$ β -cells of a human islet would have a negative impact on the connectivity, integration and efficiency of the network, with a marked negative effect on the overall synchronization of the electrical signals of the islet $$\beta $$ β -cells.

Sensory hair cells, located at the cochlea, convert sound into a depolarizing stimulus that, as a response to an increase in the intracellular Ca $$^{2+}$$ 2 + concentration, triggers the release of glutamate. Experimental observations have shown that depending on their location in the inner ear, sensory hair cells respond differently to sounds of different frequencies. The origin of this behavior is still a matter of debate but, given the importance of the dynamics of intracellular Ca $$^{2+}$$ 2 + for the exocytotic response, it has been hypothesized that the availability of endogenous Ca $$^{2+}$$ 2 + buffers at the active zone, along with the size of the readily releasable pool of glutamate vesicles, could be associated to the frequency-dependent tuning of the exocytotic response. Here, we implemented a computational model of the active zone of a sensory hair cell with the main objective of evaluating the effects of the endogenous Ca $$^{2+}$$ 2 + buffers and the readily releasable pool on the fast exocytosis of glutamate.

Pancreatic islets are highly vascularized micro-organs composed of glucagon-secreting $$\alpha $$ α cells, insulin-secreting $$\beta $$ β -cells and somatostatin-secreting $$\delta $$ δ -cells. Within the islets, the exchange of nutrients and hormones, as well as paracrine communication between islet cells, rely on a dense network of capillaries that allows the pancreatic hormones to travel throughout the islet before reaching the systemic circulation. Based on experimental observations, three models for islet vasculature have been proposed: pole-to-pole, center-to-periphery and periphery-to-center. However, despite its importance, the structure and functional role of the islet vasculature remains an open question. In this work, we developed computational models of the three vasculature models, aiming to evaluate the effect of the vasculature on the concentration of insulin reached within the islets. According to our results, the direction of blood flow could have a key role for the intraislet paracrine signaling by directing the hormones signals towards certain regions of the islet.

Hypoglycemia is caused by low glucose levels due to the type of diabetes that the patient has, also due to the inadequate dose of medications they take or due to chronic-degenerative diseases, and being constant puts the patient’s life in danger. The authors carried out this research in order to avoid this type of event thanks to a non-invasive transdermal patch without causing discomfort or pain to patients who have to have multiple punctures to check their glycemia values or correct severe hypoglycemia. They carried out a design and simulation in the COMSOL Multiphysics (version 6.0) software of a transdermal patch that meets the objective of passing glucagon mixed with glycerin by passive facilitated diffusion and that is absorbed through a porous graphene membrane until it reaches the skin. This patch sticks to the skin, which was also simulated in the software COMSOL. As an example, an electrochemical biological sensor will be used, to obtain blood glucose samples, in addition a heat gradient is activated to activate the absorption of glucagon in the skin, the transducer consists of an electrode that translates the signal emitted by the sensor and the detector that gives the response to the emitted signal.

The snake’s venom conformation consists of several enzymes and other proteins which all contribute to the envenomation phenomena. Our objective consists in finding an enzymatic core along a snake-population study in order to achieve a better understanding of the role played by these enzymes. Such identification will build up our hypothesis that all venomous organisms share an enzymatic core. For enzymes identification, transcriptomic data available from selected snake species was processed, followed by an intersection analysis. An enzymatic core composed by 50 enzyme classes was found with an overall high presence of hydrolases. Unexpectedly, among the core components, an elevated amount of serine endopeptidases was identified and associated to the enhancement of the venom’s action in its host, which has been described in the properties of Furin in Loxosceles’ venom. Evidence was found of a correlation between previously described scorpion’s enzymatic core and snake’s enzymatic core described herein, supporting the idea of a shared enzymatic core among all venomous animals.

To what extend, sequence homology determines the functionality of a Protein?. Through the residues chain study, one of the most used tools is the pairwise sequence alignment (PSA), and in many cases it yields good results when correlating AAs sequences with its functionality. Nevertheless, we consider this should not be the only analysis performed for protein homology determination. Therefore, we proposed a computational methodology using Alpha Fold, Colab fold, CCP4 suite and RMSD calculus to analyze the structural similarity between enzymes. The proposal represents a first challenge to determine, with the use of a new Deep Learning tools, the validity of the classical argument about its functional determination. Thus, the present work seeks to prove (computationally) that even in sequences with low identity, we can find structural and functional homology. This has allowed us to identify the active sites of five terpene cyclase enzymes (with highly distinguishable sequences) from different taxonomic kingdoms. Our study thus contributes to the discussion on the insufficiency of a sequential analysis for the identification of functionality.

Cancer is a serious public health problem that consists of exponential cell growth that forms tumors. There are different techniques to deal with tumors, such as chemotherapy or surgery. Over time it has been observed that ablation is a highly effective technique of treatment that consists of reaching tissue temperatures of 60 to 100 ℃, destroying cancerous tissue. Micro-coaxial antennas have been studied to achieve bone ablation. Monopole antenna, previously designed and optimized by using constant thermal properties, was chosen to perform a more realistic 3D simulation. A thermal ablation treatment in a leg with a bone tumor including tissue thermal dependence properties was modeled by means of the finite element method. Therefore, it was evaluated how thermal dependence properties can modify the antenna performance. Tissue heating volumes by using thermal dependence of electrical and thermal conductivity, blood perfusion, and metabolism were obtained. 56 different cases scenarios were evaluated. The most favorable scenario reaches 6.326 cm3 of tumor at ablation temperatures.

Tissue engineering has emerged as a tool to propose solutions to the main diseases that cause death in Mexico, achieving different cell reproduction techniques such as the use of bioreactors. Mathematics can determine and collect biokinetic parameters, providing the necessary skills to optimize different operating conditions. Therefore, this study aims to design a dynamic mathematical model that describes the behavior of liver cells, protein, and substrate production in a bioreactor. The results showed that the proposed model can predict the behavior of different variables over time. Some of the biokinetic parameters found are YXS = 2.94 gx/gs, YPS = 5.46 gp/gs, and μmáx = 1.84 1/h. Contrastingly, a parametric sensitivity analysis was carried out to find parameters that have the greatest effect on the concentration of biomass and product. The parameters are YXS, α and β for biomass concentration, and μmáx, YXS and KPS for product concentration. As part of the study, different simulations were carried out to determine the performance at different initial substrate concentrations. The results show that the higher the initial substrate concentration, the lower the yield within the first 8 days. After this time, the yields are similar for all types of initial substrate concentration.

This work presents the development of a gamma radiation detection simulation system, which is composed of a java application and a physical detector that simulates gamma radiation detection through distance measurement. The objective of the system is to simulate the behavior of 14 of the most commonly used radioactive isotopes in medical physics and nuclear medicine, using technologies that are harmless to health. This project arises to provide an educational tool in the field of medical physics and nuclear medicine that facilitates the understanding of the behavior of radioactive isotopes that generate gamma radiation. Currently the only people who have access to gamma radiation practices are people certified with a POE designation (occupationally exposed personnel), so this device is proposed as another tool in the knowledge of gamma radiation. This device is not limited to any type of public, since it is designed so that anyone can work with it, and interact with the behavior of isotopes and gamma radiation, since everything is simulated and does not generate any health risk, providing radiological safety and respecting the mathematical models that describe the behavior of radioactive isotopes.

A new readout system for radiochromic film dosimetry was developed, based on virtual instrumentation, a broadband light-emitting diode and a camera. This experimental densitometry system works in transmission mode as does the well-known flatbed scanner used in clinical dosimetry tasks. Square samples of 3 cm were cut from an EBT3 film batch and irradiated to doses up to 50 Gy, sampling 100-pixel data from each image acquisition for analysis of noise, optical density and uncertainty for the three color channels. Current protocols for film manipulation and scanning were employed as reference to compare the resulting data from both devices. Overall, the sensitivity of the imaging prototype to optical density was increased more than twofold compared to the scanner, with a maximum of 1.7 corresponding to the red channel. The best noise rejection is in the green channel for both digitizers, with a signal-to-noise up to 431. The lowest uncertainty was found in the scanner, 1% in average. This performance can be improved when reducing the noise by implementing better opto-electronic instrumentation and image processing algorithms to enhance the image quality.

The simplest form of communication between people is done through speech. However, there are situations in which this communication is not possible, hence, there is great interest in decoding imagined speech. To address this problem, this work proposes a method for recognizing imagined speech from electroencephalographic signals applying artificial intelligence models, focusing on covering daily use words. We used the OpenBCI system with a reduced number of channels using dry electrodes and machine learning methods. Mean, variance, skewness, RMS and kurtosis were extracted as characteristics for each channel of the EEG. A Decision Tree and a Support Vector Machine were tested to classify the words via a one-vs-rest approach. The SVM gave the best results for the task of imagined speech classification, with an accuracy of 92.5%, 76.3%, 70% and 70% for “Descansar", “Baño”, “Comida” and “Okay" respectively. The Decision Tree presents lower accuracy results, but allows us to identify the decision criteria used for each classification.

Electrohysterogram or uterine electromyogram (EHG) is a non-invasive record that has been shown to provide information about uterine electrical dynamics in labor. This study aimed to compare the irregularity and nonlinearity of uterine electrical dynamics at term and preterm labor using entropy-based methods. We analyzed a dataset of EHG recordings corresponding to parturient women who attended the Maternal-Perinatal Hospital “Mónica Pretelini Sáenz,” Toluca, Mexico. Participants were classified as women in active labor at term (T = 30) and preterm labor (PT = 18). The raw EHG signals were analyzed using nonlinear entropy-based methods such as Multiscale Sample Entropy (MSE) and Phase Entropy (PhEn); MSE was evaluated in a range s = 1 − 20 and PhEn for j = 2 − 40.Additionally, nonlinearity tests were performed on the EHG data by applying a surrogate analysis. The main statistical differences were found in the lower scales of s and j for MSE and PhEn, respectively. Interestingly, nonlinearity was significantly diminished in the preterm group than in the term group. We concluded that reduced nonlinearity of EHG dynamics might indicate lower regulation in control systems that generate uterine action potentials in preterm labor. A clinical approach for assessing uterine activity in preterm labor may be established using nonlinear analysis of EHG.

Concentration is understood as focusing on a specific task. In this work, a study was done in order to find out if there are differences or similarities between male and female, while doing a fast 3-level game as a concentration task. For the study, electroencephalographic signals of ten healthy subjects, five men and five women between 21 and 28 years old, were recorded. The analysis was carried out in time and frequency domains, by computing variance, kurtosis, correlation, and an average power spectrum. According to the results, men tend to have lower values of power in beta-band, than women, indicating that their concentration levels change from stage to stage. Moreover, there are certain differences in the variance and kurtosis between men and women in the last level.

It is believed that excessive loads in the backpack have a negative effect on the posture and on the gait of undergraduate students. Therefore, this study aims to analyze the cyclic gait of university students who carry different weights in the backpack. The study population consisted of female and male young adults (n = 6) with a mean age of 21 ± 2 years. The mean heights and weights for men were 1.75 ± 0.10 m and 65 ± 6 kg, and for women were 1.65 ± 0.05 m, 56 ± 10 kg. A smartphone camera was used to obtain images of the subjects walking, catching each phase of cyclic gait and, through the software Kinovea® and the MATLAB® mathematical software, the images were processed. By means of statistical methods such as One-way ANOVA and t-student test, an analysis of the possible effects on the cyclic gait and body posture was performed, taking as a reference the subjects’ normal body posture without carrying out weight on their back versus backpack loads based on different percentages of their body weights (BW). Significative differences between female and male subjects were founded, and these suggested that natural neck and hip angles are compromised differently in men and women.

Alteration of the circadian rhythm of the sleep-wake cycle is observed in shift workers, causing sleep disorders and changes in the cardiac autonomic nervous system. The multifractal-multiscale structure of inter-beat intervals (IBI) during sleep was analyzed in healthy shift working females by the multiscale-multifractal detrended fluctuation analysis (MMF-DFA) method. The MMF-DFA was applied to estimate the self-similarity coefficients $$\alpha (q,\tau )$$ α ( q , τ ) considering moment orders q between $$-5$$ - 5 and $$+5$$ + 5 , and scales $$\tau $$ τ between 8 and 150 s. During daytime sleep, for $$q > 2$$ q > 2 , the $$\alpha (q,\tau )$$ α ( q , τ ) coefficients were very similar to those of nighttime sleep along $$\tau $$ τ . However, significantly higher scaling coefficients were observed during daytime sleep than during nighttime sleep, at small scales $$44\le \tau \le 68$$ 44 ≤ τ ≤ 68 for $$1 \le q \le 2$$ 1 ≤ q ≤ 2 , and at large scales $$219 \le \tau \le 414$$ 219 ≤ τ ≤ 414 for negative moment orders q. The results suggest an alteration in the autonomic nervous system of shift workers, which could increase the risk of cardiovascular disease. Also, multifractal surface assessment of scale coefficients during sleep could be a tool to complement and improve the assessment of HRV alterations due to shift work.

EEG recordings have been used to study the spatio-temporal dynamics of brain processes. In motion intention, these analyses have detected EEG patterns present before the movement begins. This dynamics can be mapped using network models based on global functional brain connectivity, estimated from temporal connectivity matrices of all electrodes. However, this temporal matrix representation leads to dense networks containing redundant information. Brain-computer interfaces (BCI) are systems using brain patterns as control signals. Therefore, the simplification of calculations in devices or applications that need brain information with the least number of electrodes, without losing significant information, identifying the most significant channels associated with motion intention, becomes an important task. The aim of this work is to propose a methodology to select EEG electrodes containing significant information during the motor intention. Thus, a single-layer model was fitted to the BNCI Horizon 2020 motor imagery task database (2a of BCI Competition IV), including EEG recordings of four different motor imagery tasks: left hand, right hand, feet, and tongue. The correlation index was computed to estimate the pairwise correlation between EEG channels in $$\alpha _1$$ α 1 (8–10 Hz), $$\alpha _2$$ α 2 (10–13 Hz), and $$\beta _1$$ β 1 (13–18 Hz) bands. To simplify, the adjacency matrices were thresholded. Then, a graph analysis was conducted using the degree and the eigenvector centrality as graph metrics. Later, a statistical analysis was made to identify the most significant channels associated with motion intention. Our results show that the electrodes with significant differences are located in the fronto-central area (C2, C3, FC1, Fz). These results agree with other works but also estimate the electrodes associated with the motor imagery tasks.

At all times, the brain is capable of integrating and processing multiple sensory inputs, so knowing how listening comprehension is represented in the brain is essential in areas such as linguistics and neuroscience. In the present work, an electroencephalographic (EEG) recording was performed on 5 subjects to identify the brain response to auditory stimuli. The auditory stimuli correspond to 3 short stories in order to find the comprehension of the words within a narrative. Processing of words occurs in a range of 300 to 400 ms after they are heard. After preprocessing the signals, a time-frequency analysis was performed to find cerebral auditory activation in two cerebral parameters of the gamma rhythm (35, 45 Hz). The comprehension was determined by the two maximum values of the spectral density and we located the specific electrode that corresponds to the said word. Word comprehension was distributed over different areas of the cortex, therefore we did not find a specific location in the brain for listening comprehension.

Intracellular calcium [Ca2+]i is key to many sperm functions including swimming, locating the egg, and fertilizing it. However, the 3D motion of the cell complicates measuring the [Ca2+]i of freely-swimming cells, mainly because a sperm rotates on its axis of movement as it swims (head and flagellum rolling), which may add spurious fluctuations (artifacts) to the fluorescence images. Here we implement an experimental device and software that for the first time allow quantifying the effect of the head spin on the [Ca2+]i signal. The device is a 3D + t multi-focal-plane optical microscope that permits simultaneous recording of bright-field and fluorescence images. The scripts we developed allow measurement of the head’s spin time series from the bright-field images and the [Ca2+]i signal from the fluorescence images. We found that the fluorescence signal of cells labeled with a [Ca2+]i-insensitive dye significantly resemble the rotation signal, while cells with a calcium-sensitive dye have a high-frequency harmonic related to the beat of the flagellum.

In this paper we present a new approach to morphological analysis of magnetic resonance brain images based on graphs. The construction of the graphs is based on the segmentation of brain subregions and measurement of two invariant metrics: discrete compactness and discrete tortuosity. The aim is to construct a graph for a subject at different time instants and quantify the brain changes using network characteristics. When a temporal graph analysis is applied to control subjects, patients with mild cognitive impairment and patients with Alzheimer’s disease (AD), decreases in average degree (up to 25%) and average closeness (up to 28%) were found. Preliminary results suggest that the proposed analysis may be useful in the study and follow-up of patients with AD, as a diagnostic tool or as features in an automatic classification strategy.

Positron emission tomography is widely used for tumor detection and treatment monitoring in oncology. However, the quality of the images depends, among other factors, on the amount of radiopharmaceutical ingested by the patient. In this sense, the quality suffers degradation because there is a limit on the amount of radiation the patient can tolerate. Because of this, image reconstruction algorithms are required to generate images of adequate quality even if the amount of radiopharmaceutical to produce the image is small. In this study, a reconstruction algorithm is proposed based on deep learning using a GRU recurrent network which is expected to model the series of projections produced by the PET scanner as an input sequence to the recurrent network and is capable of reconstructing an image even with low amounts of the radiopharmaceutical. In comparisons using image quality metrics, our proposal achieves a SIMM of 0.95, outperforming other state-of-the-art methods. Additionally, tests were performed for the evaluation of the task of lesion detection; the proposed method obtained a better contrast of the lesion with a value of 0.54 when using the weber contrast metric, very similar to the ground truth contrast of 0.55.

Although real time polymerase chain reaction test (RT-PCR) is the gold standard method for the diagnosis of COVID-19 patients, the use of Computed Tomography (CT) images for diagnosis, assessment of the severity of this disease and its evolution is widely accepted due to the possibility to observe the lungs damage. This evaluation is mainly made qualitatively, therefore, techniques have been proposed to obtain relevant additional clinical information, such as texture features. In this work, CT scans from 46 patients with COVID-19 were used to characterize the lungs by means of textural features. In the proposed approach, pulmonary parenchyma was delimited using a U-NET previously trained with images from different pulmonary diseases. Texture metrics were calculated using co-occurrence and run-length matrices considering both lungs, right and left lung, as well as apex, middle zone and base lung regions. A boxplot descriptive analysis was performed looking for significant differences between regions of each estimated texture metric. Results show that Gray Level Non-Uniformity (GLNU) and Run-Length Non-Uniformity (RLNU) features have more significant differences between regions, suggesting that these metrics may provide a proper characterization of the pulmonary damage caused by COVID-19.

In this work, a new normalized reflectance calibration proposal is presented for hyperspectral (HS) images, and evaluated through a nonlinear unmixing classification method. This evaluation was performed on craniotomy HS images to classify regions affected by grade IV glioblastoma tumor. The classification methodology follows a semi-supervised strategy where some pixels in the HS images were manually labeled by a clinical expert. The nonlinear unmixing of the HS images is carried out by using a multilinear model, and the abundances of the estimated end-members are the distinctive features for classification purposes. The evaluation results show that the proposed calibration decreases the variability of the spectral signatures, increasing the classification accuracy compared to the standard methodology of the state of the art. These results demonstrate that the new formulation allows reflectances calibration without losing characteristic features, which allows better separability among classes than with the standard calibration.

Huntington’s Disease, known for the presence of extended polyQ repeats in the huntingtin protein, has pathological effects on cellular membrane organelles. Here, we describe disturbances in membrane organization caused by the expression of mutant polyQ. We use the environment-sensitive fluorescent probe, LAURDAN, to assess variations in membrane lipid order of SH-SY5Y cells. The cells were induced to express labeled non-pathogenic (Q18mApple) and pathogenic (Q53mApple) proteins. Our approach takes advantage of LAURDAN’s affinity for hydrophobic regions, such as membranes, where it displays a red shift in emission associated with higher membrane fluidity (MF) instigated by changes in dipolar relaxation (DR) from the penetration of water molecules into the lipid bilayer. To assess fluidity in membranes, we use the Phasor analysis where we analyze LAURDAN fluorescence lifetime. In the phasor analysis plot, we identify two axes, one sensitive to MF and another to DR. Here we show that expression of pathogenic polyQ, correlates with increasing membrane fluidity, with no changes in DR processes, that suggests a disturbance in water penetration but not in membrane-lipid composition. Moreover, we show MF and DR processes are not inversely proportional and can be distinguished apart using lifetime measurements.

The diagnosis of stenosis, characterized by narrowing the lumen on arteries, requires the inspection of medical images acquired using technics such as X-Ray angiography. Recently, convolutional neural networks (CNN) have been successfully applied to automate segmenting arteries on angiographic images. The main challenge of using these models relies on counting many images where the vessels of interest have been manually annotated. Besides being difficult and expensive to produce, obtaining consent for its use in clinical investigations is even more complicated. Thus, this work presents an automatic angiography segmentation method that does not rely on a CNN architecture. Our results indicate the feasibility of using the proposed method for segmenting vascular components on angiographies with accuracies comparable to other CNN-based state-of-the-art methods.

The analysis of COVID-19 by tomographic imaging has been a standard for pandemic management. The application of different types of artificial intelligence algorithms has proven to be an accurate method for disease detection. This study presents a method of lung segmentation and a classification algorithm that allows to discriminate between images that show signs of the disease and those that don’t. In addition, the article seeks to establish what kind of features are relevant when feeding a machine learning algorithm. Texture features extracted from Gray Label Concurrence Matrix (GLCM) and a Gabor filter are used for this purpose. Then, we trained and evaluated a SVM algorithm using different combinations of features. It is found that the features extracted from the Gabor filter work better than those extracted from the GLCM, finding that those features focused exclusively on intensity description work better than those focused on spatial description, at least in early stages.

Since the SARS-CoV-2 transmission can occur by contact with surfaces contaminated with respiratory secretions and other fluids like faeces or saliva, the superficial disinfection has been one of the main problems during the COVID-19 pandemic. Cross-contagion has been observed between health personnel and cleaning staff from hospitals attending COVID-19 patients. The problem was solved through the implementation of a contact-less disinfection system that reduces the COVID-19 exposition of sanitation workers from healthcare facilities. This work presents the results observed from the implementation of an Ultraviolet-C (UV-C) disinfection method controlled and monitored using an Internet of Things (IoT) scheme. Also, implementation experiences obtained from the application of the proposed solution at the Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán (INCMNSZ) are discussed in this article. The main contribution of this work relies in the fulfillment of a disinfection proceeding that helps reducing the cross-contagion between the cleaning staff of hospitals attending the COVID-19 pandemic.

Meibography is described as the specialized imaging study developed exclusively to visualize the morphology of meibomian glands in vivo. Numerous technologies have been developed, mostly involving the use of infrared light. Despite these advancements, core issues like device size and accessibility remain. In this project an alpha prototype was developed and presented as a portable alternative to traditional equipment for meibography. The prototype was tested and assembled with the proposed components: buttons, touch screen, lighting and trigger control buttons, battery and CPU cardboard. It contains a lighting control system of up to 16 levels, which can be operated by the user through physical and virtual buttons in an application. The battery selected is considered as adequate as it allows continuous use for 5.5 h, although intermittent use of the device is expected enlarging its functional operation. The quality of the images taken without a lens is considered as sufficient for the tests performed, but the decision must be confirmed by experimentation in the clinical environment. With this prototype, testing can proceed in a clinical environment to assess the feasibility of the project.

Telemonitoring has been useful during the COVID19 pandemic, to monitor patients avoiding risk of infection, and patients living in remote areas. Monitoring of SpO2 has been a fundamental parameter to preserve health and go to hospitals only in emergencies. This research develops a prototype of pulse oximetry for telemonitoring using an open-source platform and low-cost elements, the Atmega328 microcontroller and MAX30102 sensor to measure the reflection of light in the blood were used. Our device displays the SpO2 and HR on a screen and through an ESP8266 WiFi module sends the data automatically to a free cloud platform “ThingSpeak”, where it is stored, visualized, or can be exported for analysis in other software. A comparison with a commercial clinical grade device was performed and Bland-Altman charts and ICC to verify the concordance between both. The results are ICC of 0.97 and 0.73 corresponding ±2 BPM and ±3% SpO2 have been obtained.

This paper presents the experimental result of a portable and wearable system that measures the vertical component of the Ground Reaction Forces that are generated during the human gait cycle. This system consists of an instrumented insole with four Force-sensing resistors that are strategically placed, the data measured is sent to any mobile device across Bluetooth® communication. These sensors were characterized, obtaining the mathematical model that represent the relationship between the voltage and the force applied on the sensor. To verify the behavior of the system, forty samples were taken from twenty individuals with different physical characteristics, indicating that these forces are directly related to the terrain, weight and walking of each user. With the data obtained is possible to identify in which section of the foot of everyone is applied a greater pressure, offering to the orthopedist an alternative to a system of gait analysis.

Implementation of a pulse oximeter with measurement of altitude, heart rate (HR) and oxygen saturation (SpO2) with bluetooth communication, in the need of real-time as well as remote monitoring by the therapist with the purpose of avoiding the spread of the SARS-CoV-2 virus. With the use of bluetooth technology, the data is transmitted in real-time to a mobile phone through an application in the Android operating system, a medical history record is generated using the MIT App Inventor platform, likewise an audible alarm is activated in case the saturation level is out of range, taking into consideration the variation in oxygen saturation depending on the altitude where measurement is taken.

Currently, the exact number of people who use the Braille system in Mexico is unknown, however, it is known that the quantity is much lower than the number of people with visual impairment in the country. Some of the traditional methods for learning Braille use basic and outdated teaching materials that turn out to be unprofessional.The aim of this device development is to be implemented in teaching systems, as a new technological method easy to use, effective and completely autonomous, which helps the user to have a first contact with Braille by learning the alphabet.The device consists of a rechargeable electronic device whose main function is the reading and writing of letters, words and phrases, and getting a response through an audio system by pressing different buttons. For its operation, basic and easily acquired electronic components are employed, based on the use of the microcontroller Atmega328P, in conjunction with two memory modules and a speaker.After the validation tests, the obtained results show that the device users had a greater number of correct answers in the learning process compared to the people who learned using a common method used by special education centers in Mexico.

The signal-to-noise ratio (SNR) and the current consumption of a photoplethysmography (PPG) system based on a LED-LED configuration were estimated. The emitting LED was powered in switched mode using frequencies between 100 Hz and 400 Hz and duty cycles (D) between 5% and 30%. The signal from the detector LED was analog conditioned using synchronous demodulation implemented with a zero-order hold circuit that worked at the same frequencies and D. An important change in the SNR was observed when the switching frequency and D varied. This change was mainly due to the change in the amplitude of the PPG signal because of the changes in the light emitted by the emitting LED, and also by the changes in the frequency response of the synchronous demodulator. Regarding current consumption, the greatest contribution came from the conditioning circuit of the detector LED, since the maximum current consumed by the emitting LED was 51 μA. At 100 Hz and D = 5%, a SNR of 67 dB and current consumption of 2.71 mA were obtained. With the system used in this study, no tradeoff between the SNR and power consumption was observed when the frequency and D were changed.

Nowadays there is not an explicit rule on when the soda lime should be changed in an anesthesia machine. It is well known from experience with soda lime and manuals of supply stores that this should be done when it has a violet color but there is not a formal guide that tells the recommended color so that the patient does not suffer damage. The prototype presented helps to have a clearer idea of when this change should be made since, with the help of two TCS-230 sensors located in the canister, we can visualize via Wi-Fi the color acquired by the soda lime and with a MQ-7 sensor located in the environmental output of the canister it can be observed the process of this due to the absorption of carbon dioxide. According to the prototype and the ANOVA results obtained, the most advisable is to change the soda lime when it is in lilac color, since the absorption of carbon dioxide begins to decrease after this color, and could be harmful to the patient.

Phonocardiography is routinely used in clinical practice as a tool for detecting heart diseases by listening and analyzing heart sounds (HS). Several electronic phonocardiograph (PCG) devices are commercially available, sometimes including a companion software for personal computers or smartphones. In this study, the design, implementation, and pilot testing of a smartphone-based digital and affordable PCG is addressed. The proposed system incorporates an electret microphone, analog signal conditioning, analog-to-digital conversion, and Bluetooth wireless transmission to a custom designed mobile application (app) for Android devices. The case and acoustic bell was designed in 3D, satisfying the dimensions of the air cavity stipulated in the literature. The app oversaw the real-time recording, digital signal processing and displaying of the PCG signals. The results of a series of pilot tests performed to the PCG, including tests with Gaussian white noise, pure tones, and simulated heart sounds, are presented, together with an example of a PCG signal recorded at the mitral auscultation focus of a healthy volunteer. The preliminary results pointed out to the feasibility of implementing the low-cost, custom designed, smartphone-based PCG system, but more exhaustive tests and real data acquisitions are required.

Around 25% of newborns presents apnea, this is a pathology characterized by the cessation of respiratory effort for more than 20 s, these episodes can lead to neurological damage and in most cases to death; therefore, a constant review of vital signs is required to detect these events in time. This work proposes to use the movement generated by breathing as a parameter to measure in order to determine that the neonate is susceptible to present an apnea event. An analog vibration sensor and an ESP32 microcontroller are used to monitor breathing. The sensor is placed on the newborn’s chest using a support vest to detect the vibrations generated by breathing and to detect when breathing has stopped, activating a visual and audible alarm. To determine the presence of these apnea events, an algorithm was implemented on the ESP32 microcontroller using an open source language. The algorithm compares 5 samples every 100 ms and if there is any variation between samples the sensor detects movement and therefore breathing. It was decided to take 5 samples in order to avoid detection errors. The proposal presented is intended to be implemented in hospitals in Mexico where there are not enough resources for the acquisition of specialized monitoring equipment for the detection of apneas. The presented device allows detection and alert of apnea events and accompanies the work done by caregivers.

Exergames are video games combined with physical activity with the purpose of making exercise more enjoyable and more likely to become a habit. However, most require a separate console like the Wii or Xbox to play, and none offer the option of measuring vital signs such as heart rate in-game. Because of this, we developed an exergame that can be played using any computer with access to a python editor and a webcam and only needs an extra band containing a circuit with accelerometers and ARDUINO to work. Using the measurement of the player’s heart rate pre and post-game, we found that, on average, the heart rate increased by 43.2 bpm, making the game physically challenging enough to be considered an adequate form of exercise but not overly challenging to discourage the player from playing again. This game paves the way to make exergames more widely available to everyone, needing only a computer and webcam and thus increasing the average activity levels of the entire population.

Capacitive electrical bone stimulators are, used as complementary therapy for recent high-risk fractures. They induce bone growth for late or non-union fractures and are devices accepted by the FDA. This paper presents the prototype design of a bone capacitive electrostimulator based on the waveforms: square, triangle, and sine, with an amplitude of 6 V, stable IRMS output currents in the following ranges: square (30 μA–23 mA), triangle (9 μA–10 mA) and sine (13 μA–15 mA), at frequencies between 15 Hz and 1.5 kHz. This prototype allows for modifying the parameters of current, voltage, waveform, and frequency as required; it complies the conditions of electrical safety and with the parameters to stimulate the bone growth factors since the use of voltages from 1 to 10 V with frequencies between 20 Hz, and 200 kHz have proven to be efficient for bone stimulation, so it can be used in experiments with an animal model and constitute another possible alternative in electrotherapy. It is important to mention that in this work are presented the first findings of this electrostimulation design.

Stress is a disease that affects a large part of the world population. The type of life that is led in cities and the current pandemic that we are experiencing have increased this disease and the inconveniences in both physical and emotional health. Using a sensor for the stress detection would be a valuable tool in point-of-care (POC) testing. The use of the paper-based microanalytical device (µPAD) as a platform for the detection of hormonal stress would be a great advantage since use human saliva samples without pretreatment. A nano-ELISA (enzyme-linked immunosorbent assay + 20 nm AuNP) was used to make it sensitive to the detection of cortisol (COR), a stress-linked hormone. The response obtained by the nano-ELISA was a color change after adding a standard sample of artificial saliva with hydrocortisone. Subsequently, the characterization of the nanostructured surface was carried out using atomic force microscopy (AFM), hyperspectral microscopy (HM) and Fourier Transform Infrared spectroscopy (FT-IR) to support the colorimetric detection of salivary COR. Finally, clear evidence was obtained that nanostructured modification for the detection of cortisol in freshly extracted human saliva works label on the colorimetric change with the naked eye, is possible.

Electrochemical genosensors have improved the diagnosis of genetic and epigenetic diseases due to their ability to detect specific DNA sequences. Moreover, they provide significant advantages such as high sensitivity, selectivity, low cost, portability and excellent reproducibility. Herein, we have constructed an electrochemical nanogenosensor to identify the k12p.1 mutation related to K-RAS oncogene. To this end, we modified the surface of a Screen-Printed Gold Electrode (SPGE) and detected the hybridization reaction by Cyclic Voltammetry (CV). Finally, the morphological changes of the SPGE surface after each modification were analyzed with Atomic Force Microscopy (AFM). The device selectively detected the k12p.1 mutation within a measuring range of 10 fM–1 μM with a limit of detection of 7.96 fM in the presence of doxorubicin (Dox) as an indicator of the hybridization reaction. Hence, the electrochemical nanogenosensor developed in this study could be helpful in the detection of diseases related to K-RAS oncogene mutations.

This work presents the development of a 3D model in COMSOL Multiphysics® for fast study of thin films deposited on interdigitated microelectrodes (IMEs). A reduction in computation time is achieved by simulating only one micrometric piece of the total IME array. The measurement technique was electrical impedance spectroscopy. Hydrogenated amorphous silicon-carbon (a-SiC:H) was selected as a material under study. The electrical conductivity, thickness, and surface topology of the films were varied to know their effects on the corresponding electrical impedance spectra. According to the results, the deposition of the a-SiC:H thin film on the IMEs generates a plateau in the Bode graph at intermediate frequencies, whose impedance is reduced subsequently in frequency by its own capacitive effect. The thickness of the film impacts both on the impedance magnitude of the plateau and on the value of the capacitance associated to the film. The surface topology was modified removing rectangular blocks from the inwards surface of the films. The effects of this modification were inverse with respect to that obtained with the increase in thickness. This 3D model could be used to correlate changes in impedance with the progressive dissolution of films immersed in biological fluids or define design parameters in sensors and biosensors that uses this type of structures.

Uric acid (UA) detection is a problem of great relevance, since this biomarker has been linked to diseases like gout, Diabetes Mellitus and Nephrolithiasis. Although clinical assays are available, the development of simple, accessible and low-cost methods for rapid UA detection is highly desirable. This work presents the development of a non-enzymatic electrochemical sensor for UA based on gold nanoparticles (AuNPs) electrodeposited on laser induced graphene (LIG) electrode. The materials were characterized by scanning electron microscopy (SEM) to verify the presence and morphology of AuNPs. The electrochemical characterization was carried out by cyclic voltammetry (CV) using (K3[Fe(CN6)]/K4[Fe(CN6)]) redox probe to evaluate the efficiency of surface modification stages. With optimized conditions, the LIG/AuNPs sensor showed a linear range from 1.68–16.81 mg/dL, a sensibility of 4.6919 µA/(mg/dL)cm2 (0.0788 µA/(µM * cm2)), a response time of 10 s, and a limit of detection (LOD) of 0.2098 mg/dL. Common interference of UA like glucose, urea and ascorbic acid showed negligible signals towards the designed LIG/AuNPs sensor. The simple design of the sensor and the achieved sensibility and selectivity showed that the proposed sensing platform could be a reliable tool for routine UA monitoring.

The presence of Bisphenol A (BPA) as a component in products for human use represents a risk to public health because it is an endocrine disruptor. For this reason, it is necessary to detect the presence of this compound in an easy, fast, and economical way. To aid in the detection of this substance, this work presents an unmodified screen-printed electrode-based sensor for electrochemical detection, simple, low-cost and affordable to detect BPA. The electrochemical detection is based on methylene blue as a redox probe, a sample analysis is carried out using three different electrochemical techniques, cyclic voltammetry, linear-sweep voltammetry and square-wave voltammetry. The results show the sensor’s ability to detect and quantify BPA. In this sense, our unmodified screen-printed electrode-based sensor is an attractive, cost-effective and low-cost tool to determine the presence of BPA.

Monitoring glucose levels is a very important task in diabetic patients to avoid health complications. Sensors for sweat glucose detection are a promising tool as a non-invasive method to quantify glucose concentration. However, due to the low glucose concentration, sensing based on sweat remains a challenge for low-cost, simple, and affordable devices. Thus, this work introduces a resistive paper-based sensor for sensing glucose in sweat using the molecularly imprinted polymer (MIP) method, which allows to create specific molecular templates for target detection. Our sensor is based on MIP over eco-friendly paper strips and screen-printed electrodes, coupled with a simple read-out hardware for wireless data streaming, ideal for wearable devices. We show the sensor fabrication stage and characterization tests in glucose levels from 0 to 80 $$\mu $$ μ M, achieving a detection limit of 4.2 $$\mu $$ μ M. The results show that our sensor is a low-cost and attractive alternative towards a sweat glucose wearable sensor, with high reliability, low detection limit, and easy fabrication process.

Some febrile diseases, such as Dengue, among others whit vector-associated febrile syndrome (VAFS), can result in severe febrile syndromes leading to death if not intervened early. BIA can be useful for detecting severe febrile syndromes with the advantage of being an extremely fast and inexpensive noninvasive technique.Objective: To evaluate the ability to predict severe VAFS of diverse etiologies by BIA parameters.Materials and Methods: A cross-sectional analytical study included 23 subjects as a healthy control group, 23 patients with a VASF according to the format used in Mexico to identify VAFS (ETV format for its acronym in Spanish) recruited from August to November 2021. Clinical data were collected in a clinical record, BIA was performed at multi-frequency (kHz) using Biody Xpert ZM device of AMINOGram ®.Results: PhA obtained a value of p < 0.004 compared to other BIA parameters between groups. The AUC of the ROC analysis = 0.818 for PhA and 0.913 for the predictive model.Conclusions: The PhA is an independent prognostic factor for identifying severe VAFS. A logistic regression model including muscle, age, fat-free water, and PhA predicts with high accuracy severe VASF (AUC = 0.913). These BIA changes reflect impaired cellular integrity.

The electrical bioimpedance technique is a diagnostic method that is increasingly recognized and accepted by numerous medical areas, due to the great benefits that it offers since it allows performing the necessary measurements without the need to subject the patient to invasive interventions; benefits that facilitate obtaining results in a faster and painless way. Despite this, there is the possibility of obtaining signals from biological tissue located in a perimeter close to the organ or biological system from which the measurement is going to be obtained, or on some occasions, it is complicated to obtain signals from specific organs. Therefore, the main objective of this project is the implementation of palladium nanoparticles as a contrast agent to facilitate the identification of the study area, guaranteeing the accuracy in each of the measurements, complementing the bioimpedance technique, and thus obtaining a more robust and safe diagnostic method for its use in the health area. For this purpose, several biological tissues were analyzed by the electrical impedance spectroscopy technique, obtaining significant and favorable results with reference to the use of these nanoparticles as a contrast agent for the optimal use of the bioimpedance measurement procedure.

Currently, there is no non-invasive method for estimating bladder volume in the urology field. The gold standard used in health centers is urodynamics, which is highly invasive and expensive. For this reason, our research group proposes the use of the electrical bioimpedance (EB) technique to monitor the volume bladder. In this study, a group of 10 healthy male participants was analyzed. The impedance changes were obtained by an EB device EBI100C BIOPAC®. The volumes were estimated by SONOSITE®Ultrasound equipment. The calibration equations were based on a resistor-capacitor (RC) model, and by this model, a capacitance parameter was also measured. All parameters were analyzed by IBM SPSS Statistics and the signals were processed by Python 3.9. From obtained results, the mathematical adjustment of calibration equations was over 75 $$\%$$ % . The bladder capacitance estimations evidenced an adjustment of roughly 80 $$\%$$ % with respect to impedance changes. These results evidenced that bladder volume can be measured through EB changes by using a calibration equation. The capacitance values must be compared with parameters of bladder pressure to validate this parameter.

This study proposes the mechanical characterization of the patellar tendon of the knee submitted to traction by using electrical bioimpedance (EB) equipment. This is a first approach to detecting knee swelling and possible injuries. All determinations were compared with the patellar tendon volume estimated by an ultrasound used as the gold-standard. The study has a correlational design in which a group of 8 healthy women without knee injuries was analyzed. Two knee stages were analyzed: basal and traction. For each stage, EB determinations and volume measures were acquired. From the obtained results, it was evidenced that the mean (±SD) EB-module determinations at basal and traction stages were $$32\pm 22\,\Omega $$ 32 ± 22 Ω and $$76\pm 26\,\Omega $$ 76 ± 26 Ω , respectively. The mean (±SD) EB-phase measures at basal and traction were $$36 \pm 22^\circ $$ 36 ± 22 ∘ and $$76 \pm 36^\circ $$ 76 ± 36 ∘ , respectively. Finally, the mean (±SD) patellar volume measurements at basal and traction were $$18 \pm 6\,\textrm{cm}^3$$ 18 ± 6 cm 3 and $$22\pm 6\,\textrm{cm}^3$$ 22 ± 6 cm 3 , respectively. The statistical analysis of all determinations between stages evidenced significant statistical differences (p $${<}$$ < 0.05). The EB-module and EB-phase determinations increase roughly at 43% and 47% with respect to the basal stage, respectively. The changes in the patellar tendon distension and the space formed by the separation between the tibia and femur might affect the EB determinations.

Glioblastoma multiforme is a primary brain tumor whose diagnosis carries with it a dismal prognosis for survival. The development of nanomedicine would lay a path to cross the hurdles that current treatments fail to overcome: the blood-brain barrier (BBB) and the tumor’s immune microenvironment. Targeted drug delivery systems are responsible for releasing the chemotherapeutic drug into specific tumor cells, which in addition to allowing crossing the BBB, reduces the damage caused to healthy cells in conventional chemotherapy. However, this type of therapy is still in its infancy and its health effects are still being studied using murine models. The present project aims to determine whether the use of nanoparticles in targeted drug delivery for the treatment of glioblastoma has an inhibitory effect on tumor cell growth, so a systematic review was developed using a defined search strategy using the key terms focused on the research question. The steps and guidelines defined in the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) for systematic reviews were followed. The analysis of the data extracted from the articles included in the review indicates that there is an inhibitory effect on the proliferative activity of tumor cells and a reduction in tumor size when nanoparticles are used to encapsulate drugs in targeted delivery.

Several studies have showed that Deoxyribonucleic Acids (DNA) molecule has a natural electronegative condition and its electrical properties, such as electrical bioimpedance (EBI’s), are strongly associated to DNA concentration. In this work, we propose a simple and preliminary analysis by the use of multifrequency EBI’s measurements to detect amplicons of gene-expression in final Transcriptase Reverse Polymerase Chain Reaction (RT-PCR) products, and the identification of a potential sensitivity threshold point in a specific EBI’s frequency and parameter. Final RT-PCR products for two genes amplification were analyzed by EBIs in a broad frequency range of 100 Hz to 10 MHz. Relative gene expression values were correlated with bioimpedance measurements at every frequency point. The findings suggest a relative gene over-expression threshold sensitivity point in the limit of 0.3, particularly in the phase parameter at 10 MHz.

Tissue engineering seeks to obtain functional organs in laboratories due to the scarcity and difficulty of obtaining organs and tissues for donations. We must consider that there are patients who require organs and/or tissues for which there is currently no transplant protocol for them, for example, blood vessels. Currently, a scaffold biofabrication technique used in tissue engineering known as decellularization is used, with which we obtain a natural cellular scaffold in which we can seed cells to obtain a functional blood vessel. The aim of this work is to make the biochemical process of decellularization more efficient using a bioreactor that generates mechanical and hydraulic stimulations. A radial flow hydraulic circuit was designed and simulated in Solidworks by solving the Navier-Stokes equations to have a non-turbulent laminar behavior and to stimulate cell detachment without damaging the collagen matrix of the blood vessel. Finally, the bioreactor was printed using additive printing techniques using a photosensitive resin as shown in this work.

Metallic nanoparticles perform as therapeutic agents because of their precise response to optical, electrical, magnetic, physicochemical fields, and other stimulation processes at the nanometric scale. Targeted drug delivery using nanoparticles has been one of the most researched fields in recent years to improve the degree of precision, decrease treatment times, and reduce side effects compared to existing conventional methods.Nowadays, one nanoparticle synthesis and encapsulation process is improving green chemistry with surfactants conducive to increasing the biodegradability and biocompatibility of metal nanoparticles. Amino acid surfactants can provide functional groups with high affinity to the application site in drug delivery applications and reduce toxicity originating from the characteristic aggregation of metal nanoparticles. In that regard, functionalization by amino acids increases the use of metal nanoparticles. Arginine, as a surfactant in the nanometric scale and, at low concentrations, is used in cancer therapy, hormone deliberation, and drug delivery in biological systems.In this work, we evaluate polypeptides potentially used in various applications with metallic nanoparticles, such as separator agents in biological environments. Herein, oleic acid (OA) was bioconjugated with L-Arginine (OAR) to change iron carbide@ iron oxide nanoparticles (ICIONPs) core-shell type from hydrophobic to hydrophilic. The synthesis route is based on bioconjugation techniques, using N'N-Dicyclohexilcarbodimiide by nucleophilic substitution of the carboxyl group performed at low temperatures. This study shows OAR nanoparticles size 55 ± 24 nm of oleic acid.

Bioactive properties and hemolytic behavior of sintered bioceramics of natural wollastonite and synthetic hydroxyapatites (B-type carbonated hydroxyapatite and stoichiometric hydroxiapatites with different crystallization level) were tested. In order to investigate their bioactive properties, the materials were immersed in SBF solution for 3 weeks. Hemolysis percentage was carried out conforming to ASTM protocol E2524-08. Natural wollastonite showed a higher bioactivity than the rest of the biomaterials evaluated. Stoichiometric hydroxyapatites exhibited less than 1% hemolysis. Particle size, shape and composition materials were analyzed.

Hydroxyapatite (HAp) is the main mineral component of bones and has various applications in the biomedical area. The objective of this work was first to synthesize and characterize hydroxyapatite nanofibers with a crystalline growth orientation and with a Ca/P ratio similar to bone and secondly to elaborate composite materials with HAp nanofibers that possess mechanical properties similar to trabecular human bone. For this, we synthesized HAp nanofibers by microwave-assisted hydrothermal technique and performed the structural characterization by means of the X-ray powder diffraction method. The morphological, topological, and microstructural characterization was performed with scanning electron microscopy and high-resolution electron microscopy. With HAp as a base, 3 types of porous ceramic materials were obtained using the modified gel casting method. Finally, these composite materials were subjected to mechanical compression tests. Given the results, synthetic HAp nanofibers exhibited a hexagonal morphology, a preferential crystal orientation in the [300] direction, and a purity level of [001]. In addition, a Ca/P ratio similar to that of human bone was obtained. In the mechanical tests, the HAp composite materials got maximum compressive stress of 20 MPa, which corresponds to that required by trabecular bone. In conclusion, the composite material obtained has physicochemical, structural, morphological, and mechanical properties similar to those of natural bone tissue, so it is possible to consider the use of this type of material as a bone replacement.

Heart disease is one of the leading causes of death in the world. Heart disease has a wide variety of causes. Recently, the development of biological pacemakers obtained by cell differentiation for the restoration of sinoatrial node function has emerged for those heart diseases that are related to the sinoatrial pacemaker. The research is aimed at observing the action potential (AP) and If current relationship based on their depolarization and hyperpolarization in preclinical models. The present work aims to identify AP and If current values from recent reports to determine whether cardiomyocytes derived from embryonic hESC-CMs or induced pluripotent iPSC-CMs fulfill the function of pacemaker cells in the sinoatrial node in preclinical models based on the criteria of the Preferred Reporting Items for Systematic Reviews. Results show that the PA values were reflected between −40 mV and 60 mV and the If current ratio between −60 mV and −80 mV increased heart rate in preclinical models. Findings from this review will be informative for researchers seeking to prioritize future advances in the development of biological pacemakers.

Standard treatments for chronic-diseases face limitations related to drug schemes difficult to accomplish by patients, as well as poor drug effectivity. Self-assembling peptide hydrogels made from amino acids, form secondary structures, which ultimately form nanofibril networks that mimic the architecture of the extracellular matrix for three-dimensional (3D) cell culture, drug delivery, and regenerative medicine applications. The aims of this study are evaluating the self-assembly of the nano-peptide FEFEFKFKK (F9) to form a hydrogel and the capability of such a hydrogel to support the 3D culture of human cells in vitro. The gelation of F9 was investigated using the tilt tube test. Cell viability in the F9 hydrogel, was monitored using lived/dead assay and confocal microscopy. The sol-gel transition of the peptide F9 was held at pH 5 after the progressive addition of salts, where a self-supported and transparent gel was formed at room temperature. Human cells were viable and distributed throughout the F9 gel at day 5 of 3D culture. The outcomes reported here, suggest that the F9 hydrogel is a promising alternative to act as a 3D scaffolding for cell culture and regenerative medicine applications.

The 3D bioprinting technique consists of obtaining three-dimensional structures of fibers stacked layer after layer, and this work presents the use of an alginate/gelatin hydrogel to print porous scaffolds. However, the bioprinting process requires a customized approach in the crosslinking stage, and that is why our work lies in the implementation of a support material that provides structural stability by using hydrophobic sands and calcium chloride (CaCl2). Showing results of the bioprinted hydrogel matrices within the support bath with 1.05% error with respect to the designed in the printing gcode.

Shoulder injuries in volleyball are one of the most common due to the extreme articular ranges involved in the plays of this sport. Currently, there are not many studies that can provide information for the resolution of this problem. This study proposed using surface electromyography and accelerometry to analyze flexion and abduction gestures in a group of 13 players, whit electromyography it evaluated muscle contribution in right and left shoulder and accelerometry evaluated motor control index. It was found that this group of players does not present a predisposition to pain, or pathological movements because the muscle contribution and motor control index don’t have alterations. The anterior deltoid contributes mainly to the flexion movement and the middle deltoid for abduction.

More than five million people in Mexico have some type of disability to move. These problems can be grouped in difficulty moving the affected area and in pain when carrying the weight of the body. Walking sticks are a walking assistance technology with proven benefits. However, the use of the cane can cause an excessive charge to the joints, contractures, and arthritis in the shoulder. The present project applies biomechanics to develop a cane where the arms are not used. In the design process, a weight load analysis was performed. The prototype consists of a rigid part in the hip area attached to a belt which allows the device to be adjusted to the size of the user, a lateral mechanism that performs a biomechanical movement imitation to the opposite limb, a cane that mimics the movement of the contralateral leg, and a thigh section which consists of a thread system with a band placed around the thigh of the weakened limb. A list of improvements necessary for its efficient operation is obtained based on the placement and adjustment of the prototype in the user’s body.

In areas such as rehabilitation and assistance, the use of robotic systems has increased, so several methodologies have been proposed to regulate human-robot interaction. However, despite its adequate performance many times the sensitivity to singularities, the physical limits of the robot actuators and the dependence on user-specific parameters are often overlooked. In this regard, this work proposes an approach to estimate the torque generated during human-robot interaction and test its effectiveness within an impedance controller that regulates this interaction. The torque estimation methodology uses the electromyographic (EMG) signal and the Hill muscle model, and the user-specific parameters are obtained from an optimization process. Experimental tests were performed to validate the estimation algorithm for continuous movements, obtaining correlation coefficients greater than 0.9. In addition, a simulation test was carried out using the estimated torque within an impedance controller to regulate robot-assisted elbow flexion movements, our controller maintaining the impedance error convergence to zero while regulate the human-robot interaction. These results corroborate the effectiveness of our torque estimation and the controller, thus this methodology may be able to improve the design and safety of rehabilitation and assistance robotic systems. As future work, it is planed to perform other tests to optimize the parameters of Hill’s model and prove the performance of the impedance controller under different user conditions.

Symbrachydactyly is a congenital pathology characterized by the deformation or absence of finger phalanges of the hand, as well as a deformity in the palm. Consequently, affected people have difficulties performing daily activities, in addition hand prostheses are usually designed for total amputations or do not consider the finger remnants, for this reason, this research work presents a prototype of a customized active partial hand prosthesis. As a starting point, a 3D scanning of the affected hand is carried out to be processed in CAD software, then a socket is designed and printed in a flexible material to couple the affected hand with the prosthesis. For the motion performing, prosthetic fingers contemplate a four-bar mechanism, 4R, to mimic as far as possible the natural hand movement. Preliminary tests show that the prototype is able to adapt to the deformity of the hand and to performing the cylindrical grip.

The development of an electronic system for force measurement in a hand exoskeleton robot is presented, where the placement of these force sensors is performed in confined spaces with the objective of taking measurements in the proximal and distal phalanges of the user to carry out controls focused on motor rehabilitation of the hand and also to provide support and strength enhancement to users. The results obtained indicate that measurements of force exerted by the user can be obtained up to 0.9 kg in flexion movements and up to 1 kg in extension movements.

The National Institute of Respiratory Diseases (INER. Its acronym in Spanish) is a public healthcare institution that provides medical attention, teaching and scientific research centered on diseases of the respiratory system. In 2019, the hospital held 228 beds and over six thousand medical devices for diagnosis, treatment, and rehabilitation of patients. In 2020, the COVID-19 pandemic pushed the Hospital to convert 215 conventional care beds to intensive care beds, assigned exclusively to critical COVID-19 patients. This resulted in a higher demand of Health Technology Management (HTM) resources which were reflected by a 60% increase in the Hospital’s medical devices. Therefore, the technical staff of the Department of Biomedical Engineering grew by 300%. Thus, the objective of this work was to innovate HTM procedures through the application of Six Sigma Methodology and two Lean tools: 5S and Kanban, to control the activities carried out at the Hospital. Procedures for dealing with work orders and inventory control of supplies (accessories, consumables, and spare parts) that medical equipment require for its operation were analyzed. Additionally, three activities related to HTM were analyzed: tool control, work area control and information flow among all 5 personnel work shifts. In total, seven innovation strategies were proposed and implemented in the Department of Biomedical Engineering at the INER.

The “Nicolas San Juan” General Hospital, is a second level public hospital located at Toluca City, belonging to the Health Institute of the State of Mexico. It has a technological capacity of 534 medical devices, most of which (69%) are older than eleven years. In 2021, the total maintenance interventions performed by the Biomedical Engineering Service technical staff was 724. It was observed that more than 50% interventions were corrective maintenance (CM). The high demand for this maintenance could be related to the age of the devices.The objective of this work was to carry out an Exploratory Data Analysis (EDA) using the R Studio suite, to find out if there is any relation between different variables relate to medical devices, and to discover patterns or anomalies that provide auxiliary criteria for decision-making in preventive and corrective maintenance management. The EDA was realized based on data from 349 work orders for medical devices located in six critical areas of the Hospital: surgical suite, intensive care and intermediate therapy units, emergency room, neonatology, and the central sterilization unit. Among the main results, it was detected a strong relationship between the total of corrective maintenance and the age of medical devices.

Support treatment for patients with severe Covid19 symptoms is based on mechanical ventilation. As a result of the spread of the coronavirus, various medical institutions found it necessary to acquire more mechanical ventilators, causing an increase in the demand for their production. The production, distribution, and use conditions present a potential medium- and long-term risk concerning the equipment’s operation, safety, and effectiveness. In the face of the pandemic, the care that must be implemented to guarantee patient safety, the effectiveness of the equipment, and the quality of care, should be a high priority. This paper presents a first approach to analyzing the causes that can lead to a potential state of obsolescence in mechanical ventilators used to care for patients with Covid19. We collected information about mechanical ventilators, identified the factors that may cause a failure in the device, applied Multi-Criteria Decision Analysis for three possible outcomes, and compared the results with the expert opinion of clinical engineers. We found a total of 30 factors and classified them into associated, operation, and external concerning the ventilator. The results show that the factors found are associated with the technical field. The knowledge of the operation of the equipment’s subsystems and familiarity with its components is essential. The contrast with the expert opinion and the non-inclusion of factors such as the economic ones led to the development of more in-depth work on this topic.

The Quality Function Deployment (QFD) methodology focuses on the voice f the customer (VOC) to create or improve a product/service; it is a system that allows the identification of customers’ needs and expectations. This study aims to apply the QFD methodology for identifying areas of opportunity in a Clinical Analysis Laboratory (CAL) based on the analysis of patient satisfaction obtained through the Service Quality Model or SERVQUAL questionnaires. We applied 382 SERVQUAL questionnaires to patients who underwent a phlebotomy procedure in the CAL of a public hospital in Toluca, Mexico. The SERVQUAL tool measured the quality of expectations and perceptions of the patients. Consequently, a planning QFD matrix was built to implement a design matrix, and the main improvement areas were identified. The main results of the QFD revealed that the requirements of sampling area optimization had the highest relative weight of 30.8%, and engineering needs should focus on corrective maintenance and comfort of phlebotomy chairs (14.1%). Thus, the QFD methodology allowed an analysis of the service quality in the CAL and identified the main areas of opportunity to improve the care provided to the patient.

This research summarizes the strategies employed in reconfiguring healthcare facilities in OECD member countries to care for patients with COVID-19. The findings were organized by highlighting each country’s hospital reconfiguration strategies, strategies targeting medical devices for treating COVID-19 patients, and medical devices classified by patient severity. Specific hospitals or new units were designated to treat patients in 79% of member countries, 47% reported having reoriented hospital areas for patient care, and 57% reported having increased capacity to treat patients in intensive care units. Telematic consultations (57%) and postponement of non-urgent interventions (76%) were reported strategies for reducing contagion. The 38 countries reported increased personal protective equipment, hospital beds, ventilators, and oxygenation supplies. Significantly few countries reported an increase in ECMO machines, negative pressure systems and rooms, and the availability of imaging equipment.

In this work, CO2 levels were estimated in the naso-buccal area due to the use of face masks. Tests were performed on a healthy volunteer subject sitting at rest and breathing regularly, who used five types of face masks in well-ventilated and poorly ventilated rooms. The ventilation conditions were determined by the natural ventilation of the room. Each of the tests lasted one hour. To estimate the CO2 level, a sensor based on the Non-dispersive Infrared (NDIR) principle was used. The results revealed that while wearing a face mask, the ventilation conditions affected the CO2 concentration levels in the naso-buccal area of the user, especially in those that offered a higher level of protection, and in those that best fit the face of the subject. A multiple comparison method (Tukey) revealed significant differences in the levels of CO2 between all the facemask tested (p < 0.0001). The CO2 levels were also compared with the exposure limits recommended by NIOSH, showing that the use of N95 for 1 h exceeded the recommended 5,000 ppm for an 8-h workday. None of the masks tested exceeded the NIOSH-recommended short-term limit in the first 15 min of use.

The evaluation of needs is a crucial process in the acquisition of medical equipment; a permissive execution can lead to waste in the short and medium-term, which is even more important in the case of high-technology equipment or equipment requiring infrastructure specification whose price is considerable. This work presents the development of a system to identify and provide the necessary basis for a centralized public health institution to validate requests for the acquisition of the aforementioned equipment. The system, which consists of a website and a needs detection evaluation, was built in three stages: first, the equipment considered as high technology or requiring mechanical guides was identified, then all the necessary requirements were investigated as well as the current regulations for the correct installation and operation of the medical equipment obtained from the previous stage, finally, the system was developed and tested for usability and functionality by approvers and capturers to measure its performance, which provided feedback to make the necessary modifications and thus obtain the final version.

In recent years, the education has been influenced by the implementation of new learning strategies due to the confinement caused by the spread of COVID-19. Teachers adopted audiovisual resources that allow them to teach their classes remotely. However, the transition to this modality has been sudden, forced, and evolving “on the fly" in response to the pedagogical adaptations. A structured implementation of audiovisual media in education is required, since it represents an important part of access to information in our times. For this goal, we propose strategies for the development of complementary offline and online audiovisual content to help teach practical courses in a Biomedical Engineering bachelor program. In particular, we present content created for a Medical Imaging Systems course.

Skin cancer is one of the diseases that doubles its incidence every ten years, becoming since 2004 the fifth most frequent in Mexico. Among the main types are melanoma and squamous and basal cell carcinoma caused by solar radiation, tanning beds, exposure to X-rays, etc. However, to treat and study this type of disease, there is a non-invasive in vivo diagnostic technique called dermatoscopy. Dermoscopy allows the magnification of skin lesions and establishes diagnostic patterns of structures that are not identifiable to the naked eye. This is done through a device called a dermatoscope, which is made up of an achromatic lens, a light source, and a power source. These types of equipment are classified as non-polarized, cross-polarized and hybrid dermatoscopes. However, the price of this equipment is a key factor to consider. Therefore, this article shows the mechanical design and additive manufacturing of a low-cost hybrid dermatoscope that helps dermatologists to identify skin diseases through a light source (polarized and non-polarized) and two optical lenses of different magnifications each. The device’s design by employing SolidWorks software and construction through 3D printing is also presented along with the instrumentation. Finally, dermatologists help with the evaluation of the proposed device.