Biomedical and Computational Biology

Due to the highly complex chemical structure of biomolecules, the extensive understanding of the electronic information for proteomics can be challenging. Here, we construct a charge transfer database at residue level derived from tens of thousands of electronic structure calculations among 20 × 20 possible amino acid side-chains combinations, which are extracted from available high-quality structures of thousands of protein complexes. Then, we propose the data driven network (D2Net) procedure to quickly identify the critical residue or residue groups for any possible protein structure. As an initial evaluation, we apply this model to scrutinize the charge transfer networks for randomly selected a protein which is associated with signal transduction. This D2Net model highlights the global view of the charge transfer topology in representative proteins, for which the most critical residues show the largest number of degrees acting as network hubs. This work provides us a promising tool for efficiently understanding the electronic information in the growing number of high-quality experimental proteins structures, with minor computational costs.

For vascular interventional surgery, the preoperative 3D computed tomography (CT) has complete information of vessels but is not convenient for obvervation, while the intraoperative 2D digital subtraction angiography (DSA) is easy for doctors to monitor the vascular conditions real-timely but information is incomplete in each frame. As a result, 2D/3D registration, which is the technology to fuse information from images of different modal, is useful for the guidance of vascular interventional surgery. In this paper, we proposed a self-supervised 2D/3D vascular registration method to improve the performance on DSAs with incomplete vessels. The proposed method contains a rigid and an elastic registration stage, for regressing the 6-dim parameters to obtain a center image and fine-tuning respectively. In addition, a patch-based content loss is introduced to the rigid registration step to give an appropriate similarity measure for images with incomplete vessels, and a masked elastic module is introduced to simulate the incompletion and deformation caused by breath or heart beats on the real vessels in elastic registration. We evaluated our method on both simulated and real images. Experiments prove that our proposed method is effective to register CT and DSA images.

Complex signatures expressed at the genetic, transcriptional, and epigenetic levels influence tumorigenesis and evolution in breast cancer. A growing body of evidence supports the close association of RNA modification with the epigenetic regulation of the immune response. However, the mechanism of RNA modification “writers” in the immunity of breast cancer remains indeterminacy. We analyzed genomic alterations in 8236 breast cancer samples from the cBio portal database. Correlations between RNA “writers” and the expression of immunomodulators, including immunosuppressants, immunostimulants, and MHC molecules, were calculated using the TIMER and TISIDB databases. Our analysis confirmed that abnormalities in four classes of RNA “writers” were significantly correlated with poor prognosis in breast cancer. In addition, abnormal expression of TRMT61B, a tumor-associated RNA “writer”, may be associated with patients prognosis, immune infiltration levels, and expression of immunomodulators in breast cancer patients. Our results suggest that TRMT61B may serve as a biological marker of breast cancer prognosis and a potential drug target, providing a novel idea for the future therapy of breast cancer.

Objective: Rana chensinensis ovum is one of the main by-products of Rana chensinensis oil, which has a large annual output but has not been fully utilized. Research shows that it contains a variety of bioactive substances that are beneficial to human health. Therefore, it is of far-reaching significance to explore its effective components, pharmacological activities and reveal its medicinal and health care value. Methods: In this study, RCOO was extracted by supercritical CO2 extraction, and the extraction conditions were optimized to improve oil yield. The composition of RCOO was analyzed by GC/MS. At the same time, the RCOO obtained by extraction was used to intervene in the hyperlipidemia rat model to verify its blood lipid-lowering effect. The hyperlipidemia model rats were established by feeding high-fat diet for 28 days, and the rats were treated by gavage with RCOO for 42 days (with simvastatin as the control). Results: Under the optimum extraction conditions (extraction temperature 32 °C, extraction time 2 h, extraction pressure 25 MPa, particle size 40 mesh), the oil yield of RCOO was 15.39%. The experimental results of the rat model of hyperlipidemia showed that RCOO can effectively reduce contents of triglyceride (31.9%), total cholesterol (35.1%) and low-density lipoprotein cholesterol (78%) in the serum of hyperlipidemic rats, and its effect is similar to that of the lipid-lowering drug simvastatin. Conclusion: This study provides a new idea for the efficient extraction of RCOO, as well as a new reference for the development of blood lipid-lowering products.

Background: This study aimed to explore the mechanism by which arabinogalactan (AG) inhibited N-acetyl-para-aminophenol (APAP)-induced acute liver injury in mice. The balance of the mouse intestinal flora and the relationship between AG treatment and the PI3K/AKT and NF-κB signaling pathways were evaluated to confirm a liver-gut interaction. Methods: Mice were administered 2 different doses of AG (150 or 300 mg/kg body weight) by gavage for 7 days and liver injury was induced by a single injection of APAP (250 mg/Kg). Hematoxylin-eosin staining, terminal deoxynucleotidyl transferase dUTP nick-end labeling, and Hoechst 33258 fluorescence staining of liver tissue were used to analyze liver damage. Western blots were used to evaluate expression of proteins related to PI3K/AKT and NF-κB signaling pathways, and changes in the hierarchical structure of the intestinal flora were determined. Results: AG pretreatment increased the proportion of Lactobacillus and decreased the abundance of species from norank_o_Clostridiaceae and Prevotella in mouse feces compared with APAP-only treated mice. The AG pretreatment reversed glutathione depletion and CYP2E1 overexpression, reduced the production of malondialdehyde and 4-hydroxynonenal, and decreased the levels of alanine aminotransferase, aspartate aminotransferase, tumor necrosis factor-α and interleukin-1β compared with the APAP-only treated mice. The levels of proteins related to the PI3K/AKT signaling pathway were similar between the AG and control groups. AG pretreatment significantly reduced APAP-induced hepatocyte apoptosis and necrosis and inflammatory infiltration into the liver. Conclusion: PI3K/AKT pathway-mediated BAX expression and the NF-κB signaling cascade were inhibited by AG. AG protected the intestinal flora composition, which subsequently suppressed oxidative stress in the liver, improved the inflammatory response, and reduced hepatocyte apoptosis and necrosis.

Background: Lignocellulose resources are abundant, but the utilization rate is very low, and wheat straw is one of them. The main reason is that the compact structure of lignin limits the conversion and utilization of cellulose and hemicellulose. Objectives: Thus, optimizing the pretreatment process of wheat straw is one of the effective means to improve the conversion rate. Methods: Firstly, the pretreatment method of wheat straw was optimized, and the optimal pretreatment method was determined by the analysis components of wheat straw before and after pretreatment and the enzymatic hydrolysis and saccharification experiments. On this basis, the pretreatment conditions were optimized to further improve the hydrolysis efficiency of wheat straw; Finally, Scanning Electron Microscopy was used to characterize the surface structure of wheat straw before and after pretreatment to verify the pretreatment effect again. Results: In this study, 2% H3Cit + 1% H2O2 was selected to pretreated wheat straw finally. This method removed lignin relatively mildly while retained cellulose and hemicellulose components well; the mixed sugar content in the enzymatic hydrolysis solution was 91 mmol/L (glucose 53 mmol/L, xylose 38 mmol/L). The experimental results of pretreatment optimization showed that the total amount of glucose and xylose in the hydrolysate of pretreated wheat straw was highest, which could reach 97 mmol/L, under the conditions of pretreatment temperature of 120 ℃, liquid ratio of 1:11 and time of 50 min; The results of scanning electron microscopy showed that the surface of the wheat straw pretreated by the method in this study had fractures, roughness and hollow, and the lignin was effectively removed. The relatively mild pretreatment method adopted in this study effectively removed the lignin of wheat straw, at the same time retained the cellulose and hemicellulose components well, improved the utilization rate of wheat straw. It provided a favorable basis for the improvement of wheat straw fermentation utilization and comprehensive utilization efficiency, and laid a foundation for the efficient transformation of wheat straw biomass resources.

Conventional MRI, RS-fMRI, and DTI were performed on 15 patients with MTBI (GCS, 13–15) and 17 healthy individuals. REST software was used to analyze the RS-fMRI data after DPARSF’s preprocessing. In this study, to determine if the functional deficiencies in concussed people are congruent with structural changes, we utilized RS-fMRI to identify areas characterized by ReHo and ALFF in MTBI patients and controls. We then used these anomalous ALFF regions to guide ROI placement for DTI analysis utilizing ADC and FA. In order to look at the relationship between functional changes and cognitive performance, Modified Mini-Mental State tests were administered twice in all the participants involved in this study. The first time was administered within 12 h after functional MRI; the second time was performed at 6 months after injury. Our study was designed to determine whether BOLD signal abnormalities in the acute phase of injury help predict chronic function impairment after MTBI. According to RS-fMRI BOLD signals, MTBI patients showed higher bilateral frontal lobe and cerebellar posterior lobe activation during rest compared to healthy people, but lower activation in the right thalamus, right hippocampus, brainstem, bilateral occipital lobe, left post-central gyrus, and right corona radiata. Despite the fact that both ALFF and ReHo observed increasing or decreasing BOLD signals in some brain regions, ALFF was more perceptive of abnormal activation than ReHo under the identical conditions (P ≤ 0.005, K ≥ 18). We noticed generally lower diffusivity in areas of the brain where rs-fMRI suggested abnormalities. This was shown by decreased FA in the left thalamus, right cerebellar posterior lobe, bilateral hippocampus, and brainstem, as well as decreased ADC in the left occipital gyrus. MTBI scored significantly lower on the second MMSE as compared to the control group. Alterations in ALFF were linked to diminished cognitive function in MTBI patients. In conclusion, RS-fMRI and DTI both have the capacity to recognize anomalies that conventional MRI and CT overlook. Combining RS-fMRI and DTI not only increases the number of lesions that can be detected by providing a focused and objective method for regional DTI analyses, but it also provides the clinician with knowledge that they can use to more effectively explain the patient’s symptoms in light of the abnormal areas identified by fMRI and DTI.

Cerebrovascular disease, which is one of the primary sources of harm to human health, may not only cause symptoms such as cerebral hemorrhage and cerebral hypoxia-ischemia but also result in disability and even death in severe cases. Medical imaging of the brain plays an essential role in cerebrovascular surgery of clinical medicine. The modeling of three-dimensional (3D) medical images allows doctors to better understand the patient’s condition to improve the probability of successful surgery. However, the cerebral vessels are extremely complex. It is hard to reconstruct a realistic cerebral vessel model which should be consistent with the real human anatomy. The paper introduces a method for reconstructing 3D models of cerebral arteries based on Mimics 21.0 and Computed Tomography Angiography (CTA) images of different sequences. A clear 3D model of cerebral arteries with complete structure and obvious details can be obtained after threshold segmentation, image import, Boolean operation, 3D calculation, etc., and segmented, meshed, or exported for other analyzes such as fluid mechanics, simulated surgery, and more.

Background: Lignocellulose resources are abundant, but the utilization of lignocellulose is seriously hindered by the existence of lignin. Microorganisms rely on lignin-degrading enzymes to degrade lignin efficiently, so screening strains with high-yielding lignin-degrading enzymes will surely lay the foundation for the full utilization of lignocellulose. Objective: Therefore, this study expects to screen strains with high production of manganese peroxidase by effective means, and conduct preliminary evaluation of their efficacy. Methods: First, the strains with high production of manganese peroxidase were first screened by aniline blue medium; then some strains that produced decolorized transparent circles were screened again by enzyme activity assay, so as to screen out the strains with high production of manganese peroxidase (Take P. chrysosporium as the control); observe the growth state of the strain and identify the strain; the enzyme activity was further improved by optimizing the fermentation medium; finally, the enzyme properties were analyzed to evaluate the stability of manganese peroxidase. Results: In this study, a strain (L7) with high production of manganese peroxidase was successfully screened through primary screening and re-screening. After 6 days of fermentation, its enzyme activity was as high as 39.63 U/mL. On PDA solid medium, the strain was a light brown, round colony with brittle and slender mycelia, and it was identified as Fusarium proliferatum. The optimization test of fermentation medium showed that the highest activity of manganese peroxidase was 46.06 IU/mL under the conditions of glucose 10 g, beef extract 10 g, pH 6.5 and rotation speed 100 r/min. The analysis of enzymatic properties showed that the optimum reaction temperature of manganese peroxidase in this study was 20–40 ℃; When the pH is between 4.8–5.6, the stability of manganese peroxidase is good; Cu2+, Fe2+, Mn2+ can promote the activity of manganese peroxidase in this study, while K2+ has obvious inhibition, which can reach 95.346%. The strain screened in this study had not only high manganese peroxidase activity and enzyme production efficiency, but also had stable properties, therefore it is very valuable and worthy for further development and utilization. As one of the key enzymes in lignin degradation, it will certainly provide new ideas for the efficient utilization of biomass resources also.

Objective: Energy and environmental issues are one of the most severe challenges for human survival and development in the 21st century. Therefore, the global interest in developing lignocellulosic biomass resources is growing, and cellobiohydrolase is one of the key enzymes for converting cellulose into sugars. Based on this, we want to breed strains with high cellobiohydrolase production. Methods: The strains with high production of cellobiohydrolase were screened by Congo red medium firstly. Then, strains producing transparent circle were screened again by enzyme activity assay, so as to screen out the strain with high production of cellobiohydrolase (with Trichoderma reesei as control). Observed the growth state of the strain and identified the strain. Atmospheric and Room Temperature Plasma mutagenesis was used to mutagenize the strain, and selected dominant mutants in order to further improve the enzyme activity. Finally, the enzymatic property analysis was carried out to evaluate the stability of cellobiohydrolase. Results: 12 strains were obtained through the primary screening by Congo red staining. After fermentation and re-screening, a strain with high enzyme activity was obtained, which was H9, and the maximum enzyme activity was 32.4 IU/mL. The colony of the strain nearly round, thick and fluffy, with a slight bulge in the middle, it was light gray flocculent in the initial stage of culture, and turns light green in the later stage; It was identified as Cladosporium. In this study, 36 positive mutants were screened, of which H9-18 mutant strain had the highest enzyme activity of 43.2 IU/mL, which was 33.33% higher than that before mutation. The results of the enzymatic property analysis showed that the cellobiohydrolase produced by the H9-18 strain selected in this study showed highest enzyme activity of 43.2 IU/mL when the temperature was 50 ℃. And the highest enzyme activity was 44.1 IU/mL when the pH was 4.8. Fe2+ had the strongest promoting effect on the enzyme activity of 38.67%, while Na+ had the most obvious inhibitory effect of 34.03%. Conclusion: This experiment finally obtained a strain H9-18 with high cellobiohydrolase production efficiency and stable enzyme activity, which further laid a theoretical and experimental foundation for the efficient utilization of lignocellulosic biomass resources.

Background: The left bundle branch (LBB) in human heart is very thin and certainly individually different, so the corresponding anatomical data is relatively rare. The gradual spread of His-Purkinje system pacing and aortic valve replacement is in great need of support from the morphological data of LBB. Our pre-investigation suggested that the morph of LBB may be ethnically different. Therefore, we herein focus on exploring and summarizing the morphological characteristics of LBB in the Chinese adult hearts. Methods: The LBB of 35 Chinese adult hearts were microanatomized by the system of stereomicroscope & camera, and then the anatomical elements of the LBB were examined, measured and statistically analyzed under this system. Results: The widths of proximal portion of the LBB were approximately 1 cm ( $${\overline{\text{x}}}$$  ± S, 0.92 ± 0.22 cm), and the shapes of it were flat and broad. This characteristic of shape bears traces of natural selection. On the other hand, in the view from the left ventricular septum, the minimum distances between the anterior boundary of LBB and the right coronary aortic valve ring (RCAR) were only about 1 mm ( $${\overline{\text{x}}}$$  ± S, 1.02 ± 0.58 mm), and the posterior boundary almost coincided with the posterior angle of the membranous septum (Near the central fibrous body). By comparison, some anatomical elements of LBB in the Chinese adult heart should be different from those in other ethnic heart. Conclusion: The RCAR and the posterior angle of the membranous septum can be used to estimate the range of the proximal portion of LBB in Chinese hearts.

Retinoblastoma (RB) is an intraocular malignancy with a high incidence and very severe symptoms in children and is a rare life-threatening ophthalmic disease. Screening for key genes in retinoblastoma to identify the main molecular mechanisms of RB pathogenesis. The high-throughput sequencing data GSE111168, gene expression microarray data sets GSE24673 and GSE41321 were obtained from the GEO database. Differential genes were screened using the “limma” package, the threshold was set “padj < 0.05 & |log2FoldChange| ≥ 2”, and the differential gene volcanic map and clustering heat map were drawn. The protein-protein interaction (PPI) network of differentially expressed genes (DEG) was constructed using the STRING database, and followed by functional enrichment analysis to predict biological function. The co-expression analysis obtained the DELs-DEGs relationship pairs, the miRcode website obtained the DEMs-DELs relationship pairs, and the miRDB and miRWalk websites obtained the DEMs-DEGs relationship pairs. According to the ceRNA theory, the DELs-DEMs-DEGs network was obtained by the intersection of pairwise representation of the Venn diagram and imported into Cytoscape software for visualization. PPI network results showed that 20 key genes out of 475 DEGs were likely to serve as new biomarkers to indicate the occurrence, development, and disease staging of retinoblastoma. The ceRNA regulatory network is composed of DELs-DEMs-DEGs consisting of one DEL (LINC00518), one DEM (hsa-miR-129-5p), and three DEGs (FBXO32, MEF2C, WLS). The components of the ceRNA regulatory network have been reported to have abnormal expressions in a variety of cancers, which can affect the growth and metastasis of some cancer cells. These results provide a theoretical basis for the research on the mechanism of retinoblastoma.

Resveratrol can initiate anti-alcoholic functions in the human body. Magnetic resonance diffusion kurtosis imaging (DKI) can comprehensively evaluate ethanol associated brain changes at different time points. This study aimed at utilizing the DKI technique to evaluate the protective effects of resveratrol on brain. Ten volunteers were recruited into this study. Participants in Group A were orally administered with a high alcoholic dose. Those in Group R only took resveratrol, and each participant in Group AR received resveratrol before taking alcohol with the same dose in Group A. Each group included the same 10 volunteers. Each volunteer was subjected to 3 batches of DKI tests. DKI data and images were obtained before drinking, 1.5 h after drinking and at 2 h after drinking. The data obtained before drinking were set as the control group, and the rest data were compared with it. Both Group A and Group AR showed the decrease of mean diffusion (MD) values in multiple brain regions (Group A, n = 12; Group AR, n = 3), Group A showed increased fractional anisotropy (FA) values (n = 16) and Group AR showed increased mean kurtosis (MK) values (n = 7) in multiple brain regions. The MK values of Group A and FA values of Group AR showed biphasic changes in different brain regions. Comparisons between Group A and Group AR, Group AR showed lower FA values in 9 brain regions. The results implied that alcohol caused less damage to the brains of the AR group. DKI can be used to quantify changes in brain tissue microstructure during acute alcohol exposure. The ability of resveratrol consumption to mitigate fluctuations in DKI parameters associated with acute alcohol intake suggests that resveratrol has a protective effect on the brain in a state of alcohol exposure.

This paper mainly studies the effect of different styles of music on subjective and objective mental fatigue through continuous cognitive tasks. The experiment used music styles (soothing music, exciting music) as different intervention conditions, subjective mental fatigue and behavioral performance indicators as experimental observation values, and an experimental control group was set up. Participants performed a 75-min N-Back cognitive task (the first 60-min fatigue-inducing period; the second 15-min music conditioning period). The results showed that: (a) a 60-min continuous cognitive task successfully induced mental fatigue, and the fatigue indicators in period 4 (45–60 min) changed significantly compared with period 1. (b) Compared with the no-music control group, the soothing and exciting music groups both decreased subjective and objective fatigue to varying degrees during the regulation period. (c) Due to its high rhythm and high energy characteristics, exciting music has a higher arousal level and a more obvious compensation effect on mental fatigue.

Saccharomyces cerevisiae engineered strain of β-glucosidase was constructed constitutive in this study. The complete PαBC expression cassette was constructed by overlap- extension PCR technique; then the complete PαBC expression cassette was connected with the pYES2 expression vector and rDNA core sequence to construct the pYES2-PαBC-rDNA vector, the new vector was transferred into Saccharomyces cerevisiaeINVSc1 by r-DNA integration method subsequently. A total of 23 positive mutants were obtained by SC-U nutrient deficient medium and strain solution PCR screening. Digital droplet PCR (dd-PCR) technique was used to identify the copy numbers of positive transformants. Copy numbers of the β-glucoside gene sequence were: 1, 2, 3, 5, 6, 9, 10, 12, 13, 15, analyzing the relationship between the number of copies and the expression of β-glucoside enzyme activity, the result showed that β-glucoside have the highest activity at 10 copies (29.8 IU/mL). The β-glucoside enzyme activities of transformants were verified stable inheritance. The molecular weight of β- glucosidase is 60 kDa, and it is relatively stable at 40–55 °C and pH5.0–6.0 slightly acidic environment. Mn2+ has a significant effect on the promotion of β-glucosidase activity (138%), while Ag+ and Cu2+ had an inhibitory effect on the activity of β-glucosidase, only 13.3% and 39% of pre-addition.

Objective: Laccase is a copper-containing polyphenol oxidase, which is widely used in papermaking, food, environmental treatment and bioenergy. At present, white rot fungi is one of the important laccase-producing microbes, but there is still a big gap between laccase large-scale industrial production and use. Therefore, screening efficient laccase producing strains and improving laccase production are one of the important links to promote its development and utilization. Methods: firstly, the strains with high laccase yield were screened by aniline blue medium, and then strains with decolorizing transparent circle were re-screened by enzyme activity determination, so as to screen the strains with high laccase yield. The growth state of the strain was observed, and strain identification was carried out. ARTP mutagenesis technology was used to mutagenize the strain, and the dominant mutant strain with high laccase production was screened; the fermentation conditions of the dominant mutant strain were optimized to further improve enzymatic activity. Results: A dominant strain W11 with high laccase production was obtained through the primary screening of aniline blue medium and the secondary screening of fermentation enzyme production test, and the enzyme activity was up to 17.3 IU/mL, which was identified as Mucor fragilis. Through mutation breeding, a total of 42 forward mutants were screened in this study, among which the M28 mutant had the highest enzyme activity of 41.2 IU/mL, which was 138.2% higher than that before mutagenesis, and could be inherited stably. In this study, the highest enzyme activity of Mucor fragilis was 49.5 IU/mL under the fermentation conditions of 10g xylose, 10g yeast powder, pH6.5 and 100r/min. Conclusion: A strain with high laccase production was obtained through strain breeding, and the laccase activity was further improved by optimizing the fermentation conditions, which will surely provide new ideas for the large-scale production and utilization of laccase.

Many existed Chinese text classification solutions are successful, but the gap is that they are also limited by the models applied by themselves, so it’s available to consider a solution for advancing the Chinese text classification performance, especially in TCM (Traditional Chinese Medicine) text classification task. Assembled by Encoder element and Decoder element, Transformer and others X-former models have shown an outstanding performance in different NLP tasks, and among them BERT has succeeded in text representation and text classification tasks, but it has the possibility to be improved. Here we show our solution and experiment. In many NLP tasks, RoBERTa, which is based on BERT, has s a state-of-the-art performance than BERT. The classified sample data is selected from TCM workbench and tokenized by the Tokenizer we build based on pretrained RoBERTa, which was processed by RoBERTa_TCM, the RoBERTa model fine-tuned with our own data. In order to evaluate the vectorization performance and text classification performance of our Tokenizer-RoBERTa_TCM solution, we select some wild-range-applied language model: Word2Vec, LSTM, Bi-LSTM, contributing 4 baselines: Word2Vec-LSTM, Word2Vec-BiLSTM, Tokenizer-LSTM, Tokenizer-BiLSTM. We find out that the Tokenizer-RoBERTa_TCM model has shown a state-of-the-art classification ability with 90.88% average precision, 91.05% average recall and 90.72% average F1. All of them were the highest results among the baselines. It means that compared to regular text classification models (LSTM, Bi-LSTM, etc.), our RoBERTa_TCM model has an obvious improvement. This solution has the potential application research value in the text classification of TCM text.

Hierarchical Medical System (HMS) is the core link of medical and health system reform, and it is also one of the important mechanisms to prevent and control public health crisis. In 2015, in order to encourage residents to visit grassroots medical institutions, China began to implement the HMS. Based on the quantitative research method, this paper describes implementation status of HMS in Wuhan, and finds out the problems existing in the implementation of HMS. According to the survey results, the corresponding strategies are put forward from the aspects of policy subjects and policy stakeholders to provide theoretical basis and practical guidance for the implementation of HMS, and further improve the existing medical model.

To study the influence and correlation of meteorological factors on the incidence of Henoch-SchÖnlein purpura (HSP), and to establish the grade prediction equation for the number of HSP incidents based on meteorological factors. Methods The data of 7027 HSP cases and meteorological data in the same period from January 2010 to December 2019 in the No.1 People’s hospital of Jining were analysed the number of HSP incidents was classified by K-means cluster analysis. Correlation analysis was used to explore the correlation between various meteorological factors and the number of HSP incidents in the same period, the stepwise regression method was applied to establish the grade prediction equation for the number of HSP incidents in different periods, and the prediction equation was used to verify the grade regression verification. Results The number of HSP incidents in Jining presented different distribution under the influence of different meteorological factors, according to the actual number of HSP incidents, the whole year was divided into three periods, and the number of HSP incidents in each period was different from the meteorological factors with high correlation in the corresponding period. The daily grade prediction equation of the number of HSP incidents based on significant meteorological factors was used to verify the grade back substitution, and the prediction results proved that it had a strong prediction ability. Conclusions The number of HSP incidents in Jining was correlated with the meteorological factors in the same period, the grade prediction equation of the number of HSP incidents in different periods based on meteorological factors had strong prediction ability.

A novel human machine interface is presented that ‘self-adapts’ to accommodate for changes in position between an operator and a non-contact sensor. Zero force sensing has been especially suitable for people with small amounts of movement force, making switch operation difficult or impossible. A common issue with existing switches concerned maintaining a workable operating position for a user. Testing of new “auto adapting” sensors demonstrated the viability of the approach and optical sensors provided a workable solution, but problems were encountered in strong light. Further work addressed this problem.

Because transportation is strongly linked to public health, there is a need to better understand the factors that contribute to increasing or decreasing risk-taking. For understanding behavior, several theoretical approaches can be studied such as personality or decision-making. These approaches can be seen as complementary to understanding the complexity of human behavior. Existing literature highlights the relation between personality variables and decision theory which contributes to a better understanding of decision-making. This study aims to present how personality traits and decision-making are related. The study of personality rests on the Big-Five models which highlight the following facets: excitement-seeking, anxiety, anger, altruism, and morality. To investigate the decision-making approach, starting from the game theory, a lottery with ten trials has been experimented with. Our results based on 4011 young drivers highlight the links between economic preferences, personality, and driving behavior. Some differences were found between female and male drivers.

This investigation is aimed at conducting a fundamental experimental research of impact of Anglo-Americanisms of a wide semantic range used in virtual network communication. The main subject of the research is the network communication of young native speakers of the Russian language, which is now replete with an increasing number of Anglo-Americanisms replacing Russian spoken language and hypothetically influencing: a) the final effect of communication; b) the emergence of non-chemical dependence (addiction) on the used Anglo-Americanisms among network communicants, native speakers of the Russian language; c) gradual rejection of neurophysiological and cognitive connections within the semantic models of the native language; d) cognitive and neurophysiological re-coding of the processes in the brain reinforced by the constant and long-term use of the same foreign language stimuli-patterns, which leads to a change in the behavioral reactions of Internet users in the process of virtual network communication, as well as real communication.

Hepatocellular carcinoma is the most common primary liver cancer, accounting for 90% of cases, and a major cause of death worldwide. Despite this, alpha-fetoprotein tests are the only blood-based diagnostic tools available, and their use is limited by their low sensitivity. DNA methylation changes offer an alternative method of diagnosis through their measurement in circulating cell-free DNA. A genetic programming-based symbolic regression approach was applied to gain the benefits of machine learning while avoiding the opacity drawbacks of “black-box” models. The data included plasma samples from 36 patients with hepatocellular carcinoma as well as a control group of 55 that contained patients with and without cirrhosis. A 75–25 train-test splitting was done before training. The symbolic regression methodology developed an equation utilizing the methylation levels of three biomarkers, with an accuracy of 91.3%, a sensitivity of 100%, and a specificity of 87.5% on the test data. The ML approach identified a novel three-gene biomarker, consisting of NR2F1, GET1, and CYP2A7P1, and its performance was able to match prior research while also providing the added benefits of transparency. Circulating cell-free DNA presents opportunities for minimally invasive early diagnosis of hepatocellular carcinoma, and utilizing transparent machine learning approaches like symbolic regression can allow accurate diagnosis by combining biological and mathematical principles. Future validation of the model obtained here on a larger and more diverse dataset can reveal the potential for such approaches in cancer diagnosis and open the way for further research.

In the present paper, we explore the possibility of creating a uniform approach to form a factual representation for health information on the basis of multimodal medical examination data. We propose to use a formal approach based on multi-agent neurocognitive architectures to integrate data of this kind in a unified graph of facts and actions. The situationally determined self-organization of program agents-neurons, organized at the nodes of the cognitive architecture, makes it possible to connect afferent data flows of different modalities with the functional representation of semantic ontologies. There are some results of an ongoing multistage computational experiment in the paper.

Heart Failure is a syndromal disease affecting about 2% of the world’s adult population and about 10% of adults over 70 years of age. Because of the progressive nature of this disease, continuous and close monitoring of disease progression is an important approach to managing patient quality of life once data suggest that patients who develop Heart Failure have difficulty maintaining self-care routines. The use of technology to assist physicians and patients in managing Heart Failure offers great opportunities related to the challenges of managing the disease. Several papers have shown that the use of follow-up protocols by patients from Heart Failure reduces hospital readmissions by approximately 26%. Therefore, regular follow-up of patients is crucial to keep the disease stable and maintain the quality of life of these people. However, through the use of technologies like electronic follow-up protocols and Decision Support Technologies, disease management could be more assertive and precise. For this reason, in this paper we present an overview of how prognostic variables can be important for system development and patient monitoring.

As an innovative monitoring system, methods for non-contact human vital signs detection have been on the rise recently. Although different technologies use different principles, the chief purpose is physical health assessment. Nevertheless, in practice, the position and angle of an individual are not always in an ideal condition for the current vital signs monitoring programs to obtain reliable information. Therefore, this study proposed a method of automatic gain of low-frequency signals used to track the monitoring signals of the human vital signs dynamically. This study also designed a web system that processes and stores millimetre-wave radar technology data to detect the patient’s heartbeat and breathing rate with a touch-free approach. Additionally, this system proved to be helpful for non-sensory perception and information gathering in daily life. Afterwards, the algorithm that monitors the vital signs analyses the breathing pattern, heart rate, and their variations after the human signs enter a static state. The reference design was a customised respiratory and heart rate signal extraction based on the STM32F401 chip and 24G Doppler radar sensor. Our method has successfully detected the presence of apnea events and has thus alerted the corresponding individual accordingly. Ultimately, the contactless method is particularly suitable for the pandemic as it is the best way to prevent the transmission of infectious illnesses; furthermore, it provides a steady stream of data to be stored in the server for further use and analysis.

The need to indicate the significant adverse effects of COVID-19 and the behaviour desired from the population to address this offer an excellent context to consider the varied approaches to providing such information. More specifically, it offers the opportunity to consider the potential utility of neuroscience and what could be usefully added when thinking about the design and presentation of warnings and information. With the understandable wish to neutralise the threat of the COVID-19 pandemic, countries have been displaying miscellaneous messages against the spread of SARS-CoV-2, perhaps some with untested assumptions that those messages would be effective. Despite this, there seems to be highly variable effectiveness in conveying protective messages. Primary causes of poor compliance with various preventive messages might involve a lack of clear vision and direction if the aim is to change citizens’ responsibility for their behaviour, consistency of such changes, and people having confidence in the information they are presented with. It would seem beneficial, in terms of effectiveness, for information presentation to be tailored to target community groups and for this to come from the governments or authorities after determining achievable practical interventions, understanding the citizen’s perceptions of the messages and how science, and particularly neuroscience, shows that the words in language alter behaviour. Last and not least, this allows suitable stylistic consistency to be applied to aid with messaging efficiency and recognisability.

In the last years, the entire world has been affected by the SARS-COV-2 pandemic, that represents the etiologic agent of Coronavirus disease 2019 (CoViD-19), which degenerated into a global pandemic in 2020. CoViD-19 has also had a strong impact on cancer patients. Our analysis has been performed at the Department of Oncology of the AORN “Cardarelli” in Naples, collecting data from all patients who had access in 2019–2020. We aim to understand how CoViD-19 affected hospital admissions. The statistical analysis showed that between 2019 and 2020 there was an increase in urgent hospitalizations and a decrease in scheduled hospitalization, probably to decrease the risk of infection, particularly in this category of susceptible patients. Indeed, as recommended by the European Society of Medical Oncology, during the pandemic, it was necessary to reorganize healthcare activities, ensure adequate care for patients infected with CoViD-19. Therefore telemedicine services were implemented and clinic visits were reduced.

Overcrowded Emergency Department (ED) is a common as well as widespread public health problem around the world. In order to implement strategies to counter the most critical situations, however, it is first necessary to expand knowledge on the subject. Among the strategies proposed in the literature, scoring systems are widely used to detect the problem. In this study, the National ED Overcrowding Scale (NEDOCS) and ED Work Index (EDWIN) indices are used to study the ED situation in the Evangelical Hospital “Betania” in Naples (Italy) in a typical week in the year 2019. The results show that among the indices the most accurate is NEDOCS, which is able to highlight an overcrowding situation in 11% of the cases.

In December 2019, SARS-CoV-2 broke out, which raised great attention worldwide. In fact, it was essential to reorganize the management of economic, infrastructural and medical resources to deal with the inadequate preparation of medical practitioners for this emergency. It was evident that the global health, medical and scientific communities were not adequately prepared for this emergency, so during the pandemic. In this paper, data extracted from hospital discharge records of the Department of Urology of the A.O.R.N “Cardarelli” in Naples, Italy, were used. This work is an extension of a previous work, whose goal concerned how admission procedure in the Urology department of the “San Giovanni di Dio and Ruggi d’Aragona” hospital has been affected by COVID-19 pandemic. In this work we compare the results obtained for the patients of the University Hospital “San Giovanni di Dio and Ruggi d’Aragona” of Salerno and the patients of the A.O.R.N. “Antonio Cardarelli” of Naples (Italy). Data have been extracted from both hospitals discharge records of the Departments of Urology. Experimental analysis performed comparing pre-pandemic data with those collected during the epidemic showed an in-crease in the number of emergency hospitalizations and a decrease in planned pre-admission hospitalizations.

Beginning in December 2019, a new epidemic, called COVID-19 has disrupted our lives. Tt started from the city of Wuhan in China to affect the whole world. This epidemic has changed the health care systems around the world, revealing their shortcomings and bringing attention to effective and efficient management of wards. In this paper, our aim is to investigate how COVID-19 pandemic affecting the Emergency Medicine ward of “San Giovanni di Dio and Ruggi d'Aragona,” also comparing the obtained outcome with respect to the same sample of Cardarelli for unveiling and analyze possible similarities and differences in procedures and suggested possible future directions.

Lean Six Sigma (LSS) is a methodological approach that originated in industry and has, over time, become increasingly popular in healthcare. Its tool-to, the DMAIC cycle, consisting of 5 main steps, offers methodological rigor that helps improve processes by comparing results quantitatively. In this study, the LSS and in particular the DMAIC cycle was used to investigate the impact of COVID-19 on patients’ length of stay in the Emergency Department (ED-LOS) of the Evangelical Hospital “Betania” of Naples (Italy). The study revealed a general increase in ED-LOS due mainly to the new steps that the hospital added to the standard flow, such as those for performing screening swabs, and the reduction of treatment stations, with the exception of patients discharged home for whom there was a statistically significant reduction.

Aortocoronary bypass surgery is an open-heart procedure that involves a significant hospital stay and therefore an increase in costs. Length of stay (LOS) is an important parameter for monitoring patients and is a useful tool for doctors and hospital administrators to assess the efficiency of the hospital. For the correct management of beds and to reduce costs, it is necessary to analyze and evaluate the procedures for reducing hospital admissions. This study was conducted with the aim of predicting LOS for all patients undergoing aortocoronary bypass surgery in the University Hospital “San Giovanni di Dio e Ruggi d’Aragona” of Salerno (Italy) and the A.O.R.N. “Antonio Cardarelli” of Naples (Italy). The analysis was conducted with the implementation of Machine Learning algorithms: Decision Tree (DT), Random Forest (RF) and Gradient Boosted Trees (GBT). Accuracy and error have been analyzed to demonstrate the accuracy of the model.

Ensuring satisfactory service for a patient in the emergency department (ED) setting is challenging. Long wait times have been shown to be associated with dissatisfaction with ED care. A prolonged stay may be closely related to phenomena such as overcrowding and abandonment, which result in significant consequences for both staff and patients. Therefore, many studies in the literature have focused on optimizing ED flow, using parameters such as ED-LOS and abandonment rate. In this study, tools such as statistical analysis and logistic regression were implemented to analyze ED-LOS at the ED of Evangelical Hospital “Betania” of Naples (Italy) in the year 2019. The results showed that an ED-LOS beyond the expected limits was observed in patients with higher mean age, discharged at home or hospitalized and with non-critical conditions recorded during triage.

The Emergency Department (ED) has been in a critical situation for years to manage with limited capacity a rapidly growing demand. This condition, aggravated by overcrowding, pushes patients to abandon the ED. This phenomenon, called “left without being seen” (LWBS), in addition to being dangerous for patients who thus lose the possibility of contact with the systems of care, can also be used as a quality indicator for performance evaluation. In this work, the LWBS rate for the year 2019 of the Evangelical Hospital “Betania” of Naples (Italy) was analyzed. To do so, statistical analysis and Firth logistic regression were used. The results show that patients with non-critical conditions and accessing in the evening hours are the most likely to abandon the ED.

The purpose of the present work was to assess the impact of the Covid-19 epidemic on the activity of the Department of General Medicine in the University Hospital “San Giovanni di Dio and Ruggi d’Aragona” of Salerno and the hospital “A.O.R.N. A. Cardarelli” of Naples (Italy). COVID-19 is a specific disease affecting subject respiratory system is a respiratory infection that changed the health context. Because of the pandemic hospitals had to reorganize departments to better manage resources. In order to make a comparison with and without Covid-19, the data for the year 2019 (in the absence of Covid-19) and in the year of the pandemic 2020 have been collected. In the work was used the logistic regression technique to study the following variables: age, sex, LOS, weight of DRG, mode of discharge and type of hospitalization. In addition, the results of the two hospitals were used to make a comparison. For both hospitals in the year 2020 the number of patients admitted is lower than the previous year, and this shows that there has been appropriate management and control to establish patients who really needed hospitalization.

Cost-containment and efficiency are aspects that have more and more weight in the evaluation of the performance of healthcare facilities. This trend, coupled with the ever-rising complexity of the services and quality standards, has called for a great attention to the rationalization of resources. Our aim is to predict the Length Of Stay (LOS) by investigating several variabilities both intrinsic (i.e. age, comorbidities) and extrinsic (i.e. complications, pre-operative LOS) to the patient and have great impact on the economic expenditure. Therefore, healthcare facilities are in dire need of new tools to know a priori patient’s needs. This study has the purpose to design and compare different Artificial Intelligence (AI) models for predicting the subject’s LOS under appendectomy. In particular, the AI model has been designed in a previous work using data extracted from an Italian hospital, the University Hospital “San Giovanni di Dio e Ruggi d’Aragona” of Salerno through Multiple Linear Regression. In this paper the results were compared with a similar sample from the AORN “Antonio Cardarelli” of Napoli to evaluate its efficacy.

One of the most common causes of mortality and morbidity in neonatal intensive care units (NICU) are healthcare associated infections (HAIs). HAIs in newborns were recognized in 2016 as one of the six most common HAIs from the European Centre for Disease Prevention and Control (ECDC). Neonatal sepsis is a major contributor to neonatal morbidity and mortality. Predicting the onset of infections from a few characteristics of newborns can be crucial in combating this health problem. In this study, conducted at the NICU of the “Federico II” University Hospital in Naples between 2019 and 2020, four different Machine Learning (ML) algorithms was implemented and compared to assess their ability to predict the occurrence of infections from seven variables: Decision Tree (DT), Random Forest (RF), Logistic Regression (LR) and Gradient Boosted Tree (GBT). The four algorithms achieved very high percentages of accuracy, the best in particular was DT.

Pathologies involving the heart valves lead to alterations that can be restrictive (valve stenosis) or incontinence (valve insufficiency). Valvular heart disease led often to make surgery, in the case of the subject or disease is symptomatic or severe, respectively. Operative risk is influenced by the type of valve lesions and by other factors such as age and comorbidities. The length of stay (LOS) is the parameter that is used to describe the path of care of a patient and is an index of hospital management. The LOS for patients undergoing percutaneous valvuloplasty was evaluated for the following study, and for these patients may be affected by different parameters. In fact, in this work a Multiple Linear Regression has been designed for predicting LOS for subjects under valvuloplasty at the University Hospital “San Giovanni di Dio and Ruggi d’Aragona” of Salerno (Italy) and at the A.O.R.N. “Antonio Cardarelli” of Naples (Italy).

This work aims to report how COVID-19 pandemic affects the operations of the department of Otolaryngology, in two hospitals in Campania: University Hospital “San Giovanni di Dio and Ruggi d’Aragona” of Salerno and at the hospital “A.O.R.N. A. Cardarelli” of Naples (Italy). In the last years, COVID-19 has become the main type of disease affecting subjects with possible lung infections (pneumonia). SARS-cov-2 infection has been reported as severe acute respiratory syndrome that mainly affects the respiratory system and lungs, but the virus also involved other organs such as cardiac, renal and nervous ones. In the study the attention is turned to the department of Otolaryngology because the operators are very exposed. Data were collected for the year 2019, in the absence of Covid-19, and in the year of the pandemic, 2020. The purpose of the work was to make a comparison between the situation of the department before and during the epidemic from Covid-19 to the individual hospital, in addition a comparison was made between the two hospitals.

The increase in emergency access causes overcrowding, which leads to an increase in waiting times and consequently in the rate of abandonment. LWBS patients are the patients who register the cure, but then leave ED without any visit to a doctor. Studying the number of LWBS is effective to improve first aid flows and better manage healthcare staff. In this work we investigate some of the factors that can contribute to the increase of LWBS patients in the ED. Data were collected at the University Hospital “San Giovanni di Dio and Ruggi d’Aragona” of Salerno and at the hospital “A.O.R.N. A. Cardarelli” of Naples (Italy) and then analyzed through advanced machine learning algorithms. The results demonstrate once again that ML is a valuable predictor.

Cholecystectomy and Appendectomy are the most frequent procedures in emergency general surgery. Emergency surgeries represent particularly important procedures in health care as they include various surgical specialties and represent a significant percentage of surgeries sustained in a hospital. This will have a reply is in terms of management of the resources that of costs supported from the hospital. The value of the average hospital stay, LOS, is an important parameter for the proper management of hospitals, providing support to clinicians. This study was conducted with the aim of predicting LOS for all patients undergoing appendectomy and cholecystectomy in the University Hospital “San Giovanni di Dio e Ruggi d’Aragona” of Salerno (Italy) and the A.O.R.N. “Antonio Cardarelli” of Naples (Italy).

The estimation of the length of stay (LOS) can support the analysis of practitioners for improving the hospital efficiency. For the appropriate management of beds and to reduce costs, it is necessary to analyse and evaluate procedures for reducing hospital stays. For patients with orthopedic trauma, especially with lower limb fractures, LOS becomes an important parameter to estimate. The aim of this study is to study LOS for all patients with lower limb fractures in the University Hospital “San Giovanni di Dio and Ruggi d’Aragona” of Salerno and A.O.R.N. “Antonio Cardarelli” of Naples. The analysis was conducted by implementing different well-known Artificial Intelligence models, whose accuracy for the Hospital “San Giovanni di Dio and Ruggi d’Aragona” was 92,0% (NB algorithm) while for the A.O.R.N. “A. Cardarelli” the best performance was obtained with RF algorithm with an accuracy of 95,92%. These results obtained from the estimation of LOS show promise and therefore can be a valuable help in the management process of the hospital.

CoViD-19 pandemic caused a severe changing of healthcare facilities activities. Specifically, one of the most affected areas are the Department of Emergency Surgery that have been reorganized to face the emergency giving priority to urgent procedures at cost of those which could be deferred. This study evaluates the impact of the pandemic on the departments of two different Italian Hospitals: “San Giovanni di Dio and Ruggi d’Aragona” University Hospital in Salerno and the AORN “A. Cardarelli” of Napoli. Two different analyses (statistical and machine learning) have been provided for investigating patients in 2019, as an example of the normal activity before the pandemic, and those recorded in 2020, in which the pandemic reached its peak. The evaluation performed showed an increase in the urgent hospitalization and Diagnostic Related Group while transfers to Social Care Residences (RSA) decreased in both the Hospitals, even if the steepness of these changes are consistent with the starting values.

Coronavirus disease has spread throughout the world rapidly and has changed the world health scenario. Each hospital department was faced with an emergency and then reorganized services. The aim of the present work is to assess the impact of the Covid-19 epidemic on the activity of the transplant center in the A.O.R.N. “Antonio Cardarelli” of Naples (Italy). This study was conducted considering all patients undergoing skin transplantation in the years 2019 (in the absence of Covid-19) and 2020 (in the pandemic emergency). In the work, the logistical regression was used to analyze the tie among hospitalization year (as a dependent variable) and the following independent variables: gender, age, Length of stay (LOS), relative weight DRG, discharge mode and admission procedure.

A pneumonia outbreak of unknown origin was reported in Wuhan, China in late December. This virus, called coronavirus-2, has an impact on the respiratory tract, leading to acute respiratory syndromes. In 2020, this virus was declared a pandemic by the World Health Organization since it caused a high number of deaths worldwide. In addition, this pandemic has had a negative impact on the world economy, focusing the attention of the practitioners on the resource management in health structures. This work was carried out to evaluate the effects of the pandemic on the ordinary hospitalization activities of the Department of Ophthalmology at “A. Cardarelli” based in Naples (Italy). The dataset was evaluated using statistical analysis techniques and logistic regression. The results, for this department, did not show significant differences when comparing the health variables of the pre-pandemic year (2019) with the pandemic year (2020).

High Length of Stay values (LOS) have a significant impact on the economy of the national health system. The LOS is influenced by numerous factors such as poor resources management and surgical interventions. In this work Machine Learning models have been built to predict LOS. We collect information about 138 patients from the hospital information system of the Complex Operating Units of Ophthalmology of the A.O.R.N. “Antonio Cardarelli” of Naples (Italy). The analysis was conducted with the implementation of the following Machine Learning algorithms. All models obtained an accuracy greater than 80%. The algorithms that obtained a higher accuracy were RF and GBT, with a value of 85.71%.

The pandemic related to the Covid-19 virus that began in 2019 in China and then extended to the rest of the world has led to changes in the management of almost all clinical specializations. The main adaptations are due not only to changes in managerial management to better address organizational difficulties but there have also been variations from a treatment and care management point of view with respect to different clinical sectors including that relating to the Neurosurgery sector. In our analysis, the activity of the Department of Neurosurgery in AORN “A. Cardarelli” in Naples (Italy) was analysed. In particular, our analysis aims to investigate variables pre and post pandemic, comparing information gathered in 2019 and 2020. In the specific case, the hospitalizations of 2177 patients were considered in order to understand the influences that the Department has suffered due to the difficulties linked to the pandemic.

Knee replacement surgery is one of the most interesting procedures in surgery. Patients generally have an LOS of about 5 days and when it increases it is related to clinical factors and the patient’s comorbidity. Duration of stay (LOS) is a useful tool for monitoring patients and useful for hospital administrators to assess the efficiency of the hospital. This study was conducted with the aim of analyzing LOS for all patients who underwent a procedure for the insertion or review of knee prostheses at the University Hospital “San Giovanni di Dio e Ruggi d’Aragona” in Salerno (Italy) and the A.O.R.N. “Antonio Cardarelli” in Naples (Italy). The goal of the work was to make a comparison between the two hospitals. The analysis was conducted with Multiple Linear Regression analysis and the implementation of Machine Learning algorithms: Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM) and Gradient Boosted Trees (GBT).

In the last few years, the COVID-19 pandemic has strongly affected different hospital departments, revealing their major weaknesses. For this reason, this emergency has been a driver for healthcare transformation in a very short interval of time in order to optimize the resources, minimize costs and simultaneously increase the caring services, also limiting over-occupancy in wards, especially emergency ones. One of the main factors for assessing the efficiency of a department is associated with how long a patient stays in the facility (LOS). This bicentric study investigated how COVID-19 has modified the activity of the Complex Operative Unit (C.O.U.) of Neurology and Stroke of the University Hospital “San Giovanni di Dio e Ruggi d’Aragona” of Salerno (Italy) and the hospital A.O.R.N. “Antonio Cardarelli” of Naples (Italy). In the work data for the year 2019 (in the absence of Covid-19) and in the year of Covid-19 pandemic 2020 were considered. This work used the logistic regression technique to study the following variables: age, gender, length of stay (LOS), relative weight of DRG and mode of discharge. The analysis shows that in 2020 there was a greater adequacy of admissions, with an increase in the relative weight of DRG. And the statistical analysis obtained the following significant variables: gender, age, relative weight of DRG and discharge mode.

In the last few decades there has been considerable interest in facing the challenging problem of improving emergency general surgery management.The most crowded wards is the emergency one, especially due to population aging which results in higher mortality rates and prolonged hospital stay (LOS) w.r.t. the elective surgery intervention. This is mostly due to the intrinsic stochastic nature of patience arrival, and the heterogeneity of the medical procedures required. In this context, our work aims at reducing the LOS by using predictive algorithms to improve the emergency department management. In particular, we examine the LOS variation for cholecystectomy interventions in the emergency general surgery through three different machine learning algorithms and the linear regression analysis, with the purpose of identifying the best prediction model as long as those factors that have the highest contribution in enhancing the LOS, in order to reduce it and improve both the subject satisfaction and the overall quality of the provided health services.

The main phenomenon that impacted people’s lives was the COVID-19 pandemic, having strong consequences on national health systems. Since the beginning of the Covid-19 pandemic, hospital admissions dropped precipitously in 2020. Our aim concerns the analysis about how the COVID-19 affects the activity of the Department of General Surgery, Day Surgery and Breast Unit in the University Hospital “San Giovanni di Dio and Ruggi d’Aragona” of Salerno and the hospital “A.O.R.N. Antonio Cardarelli” of Naples (Italy). In the work data for the year 2019 (in the absence of pandemic) and in the year of pandemic 2020 were considered. This work used the logistic regression technique to study the following variables: age, gender, length of stay (LOS), relative weight of DRG, admission procedure, mode of discharge and the results about both hospitals were used to make a comparison.

The aim of the present work is to assess the impact of the Covid-19 epidemic on the activity of the Department of Plastic Surgery in the University Hospital “San Giovanni di Dio and Ruggi d’Aragona” of Salerno and the hospital “A.O.R.N. Antonio Cardarelli” of Naples (Italy). Coronavirus disease 2019 (COVID-19), has spread since December 2019 and is a respiratory infection that has changed the healthcare environment, in fact hospitals have had to reorganize departments to better manage resources and make processes efficient. In order to assess the effect that Covid-19 had on the department, data were collected for the year 2019 (in the absence of Covid-19) and in the year of the pandemic 2020. In the work was used the logistic regression technique to study the following variables: age, gender, length of stay (LOS), relative weight of DRG, admission procedure, and the results of the two hospitals were used to make a comparison.

Emergency medicine is a discipline that today is increasingly the focus of attention. In the emergency department, to avoid overcrowding, it is important to assess the Length of the Stay (LOS). The length of stay (LOS) is a useful tool to monitor patients and for evaluating the efficiency and quality of the services offered. This study was conducted with the aim of providing LOS for all patients of Emergency Medicine unit of the University Hospital “San Giovanni di Dio e Ruggi d’Aragona” in Salerno (Italy) and the A.O.R.N. “Antonio Cardarelli” in Naples (Italy). Our aim is to evaluate the procedures about LOS in two different hospitals, also comparing the obtained results The analysis was conducted with Multiple Linear Regression. In particular for the second an R2 equal to 0.880 was obtained.

Today, fracture surgery is a key part of a hospital’s orthopaedic department and usually involves significant clinical cost implications. The evaluation of hospitalization time for subjects suffering of hip fracture assumes a key role in the last years because it can affect the postoperative course and recovery of the patient. Length of stay (LOS) is a useful tool for monitoring patients and useful for hospital administrators to assess the efficiency of the hospital. Our aim is to investigate the LOS prediction for all patients with hip fracture hospitalized in two hospitals located in Campania Region, also comparing the obtained results. Different machine learning models and data analysis methodologies have been applied on a cohort of patients hospitalized in two different hospitals, also evaluating them in terms of accuracy and error.

The Department of General Medicine deals with patients suffering from various acute or chronic pathologies coming from home, from the emergency room or from specialized departments. The length of stay (LOS) is a useful tool to monitor patients and for evaluating the efficiency and quality of the services offered. This study was conducted with the aim of providing LOS for all patients who were admitted in the General Medicine Department at the University Hospital “San Giovanni di Dio e Ruggi d’Aragona” in Salerno (Italy) and the A.O.R.N. “Antonio Cardarelli” in Naples (Italy). Our aim concerns the comparison between the LOS estimation in two different hospitals located in Campania Region. The analysis was conducted with Multiple Linear Regression analysis, in particular for the former an R2 equal to 0.764 was obtained and for the latter a value of R2 equal to 0.712.

Kidney disease is a very important disease in the hospital context, in fact, the kidneys play a fundamental role for the whole body by maintaining the homeostasis of water for removing excess fluid and toxic substances. Therefore, it is necessary to assess all possible comorbidities of the patient before kidney surgery and possible complications. It is therefore necessary to analyze the length of stay (LOS) for proper hospital planning to ensure efficient and effective services. In this work the Length of Stay (LOS) of 70 patients of the AORN “A. Cardarelli” Hospital in Naples in the years 2019–2021 were analyzed. Machine Learning techniques were applied, and the algorithms implemented were Decision Tree, Random Forest and Naïve Bayes.

The purpose of this study is to present a novel small animal PET imaging system, which can be integrated with a small animal CT or MRI imaging in a PET/CT or PET/MRI schemes. The specific small animal PET imaging design consists of two planar detectors, with LYSO crystals, followed by Position Sensitive Photomultiplier tubes (PSPMTs). The output of the each PSPMTS is led to the corresponding modern electronics for analysis and digitization while at the end is stored on a host computer for further processing. In order to evaluate the average performance of the aforementioned PET system, SIMSET simulation tool was used. So, the small animal PET imaging system was simulated, taking into account that the radioactive source inside it’s field of view was a Derenzo-like phantom full of FDG. Attenuation and scatter phenomena, as well as random events were also considered during simulation process. The simulated data (Derenzo-like phantom sinogram) was then reconstructed with the ordered subsets versions of EM-ML, ISRA and WLS algorithms, considering regularization and penalized schemes. Regularization techniques accelerate reconstruction procedure while penalized methods increase signal to noise ratios with small image resolution degradation. Reconstructed images are further analyzed and evaluated so that a general conclusion on final system resolution output to be obtained.