Clinical characteristics and prediction model of re-positive nucleic acid tests among Omicron infections by machine learning: a real-world study of 35,488 cases.

Background: During the Omicron BA.2 variant outbreak in Shanghai, China, from April to May 2022, PCR nucleic acid test re-positivity (TR) occurred frequently, yet the risk factors and predictive models for TR remain unclear. This study aims to identify the factors influencing Omicron TR and to develop machine learning models to predict TR risk.

Relationship of body mass index and kidney outcomes in patients with primary glomerulonephritis: A systematic review and meta-analysis.

Background: Primary glomerulonephritis, is a major contributor to the development of chronic kidney disease and end-stage renal disease. Previous studies on the relationship between overweight/obesity with primary glomerulonephritis in the chronic kidney disease was inconsistent. This study aimed to systematically review and meta-analyze the relationship between body mass index and adverse kidney outcomes in patients with primary glomerulonephritis.

Impact of body mass index on adverse kidney events in diabetes mellitus patients: A systematic-review and meta-analysis.

Background: The incidence of chronic kidney disease among patients with diabetes mellitus (DM) remains a global concern. Long-term obesity is known to possibly influence the development of type 2 diabetes mellitus. However, no previous meta-analysis has assessed the effects of body mass index (BMI) on adverse kidney events in patients with DM.

Deep evolutionary fusion neural network: a new prediction standard for infectious disease incidence rates.

Background: Previously, many methods have been used to predict the incidence trends of infectious diseases. There are numerous methods for predicting the incidence trends of infectious diseases, and they have exhibited varying degrees of success. However, there are a lack of prediction benchmarks that integrate linear and nonlinear methods and effectively use internet data.

Identification of associated risk factors for serological distribution of hepatitis B virus via machine learning models.

Background: The provincial-level sero-survey was launched to learn the updated seroprevalence of hepatitis B virus (HBV) infection in the general population aged 1-69 years in Chongqing and to assess the risk factors for HBV infection to effectively screen persons with chronic hepatitis B (CHB).

Methods: A total of 1828 individuals aged 1-69 years were investigated, and hepatitis B surface antigen (HBsAg), antibody to HBsAg (HBsAb), and antibody to B core antigen (HBcAb) were detected. Logistic regression and three machine learning (ML) algorithms, including random forest (RF), support vector machine (SVM), and stochastic gradient boosting (SGB), were developed for analysis.

OEDL: an optimized ensemble deep learning method for the prediction of acute ischemic stroke prognoses using union features.

Background: Early stroke prognosis assessments are critical for decision-making regarding therapeutic intervention. We introduced the concepts of data combination, method integration, and algorithm parallelization, aiming to build an integrated deep learning model based on a combination of clinical and radiomics features and analyze its application value in prognosis prediction.

Methods: The research steps in this study include data source and feature extraction, data processing and feature fusion, model building and optimization, model training, and so on.

Spatio-temporal pattern, matching level and prediction of ageing and medical resources in China.

Objective: Population ageing, as a hot issue in global development, increases the burden of medical resources in society. This study aims to assess the current spatiotemporal evolution and interaction between population ageing and medical resources in mainland China; evaluate the matching level of medical resources to population ageing; and forecast future trends of ageing, medical resources, and the indicator of ageing-resources (IAR).

Methods: Data on ageing (EPR) and medical resources (NHI, NBHI, and NHTP) were obtained from China Health Statistics Yearbook and China Statistical Yearbook (2011-2020).

Discovery of potential biomarkers for lung cancer classification based on human proteome microarrays using Stochastic Gradient Boosting approach.

Purpose: Early identification of lung cancer (LC) will considerably facilitate the intervention and prevention of LC. The human proteome micro-arrays approach can be used as a "liquid biopsy" to diagnose LC to complement conventional diagnosis, which needs advanced bioinformatics methods such as feature selection (FS) and refined machine learning models.

Methods: A two-stage FS methodology by infusing Pearson's Correlation (PC) with a univariate filter (SBF) or recursive feature elimination (RFE) was used to reduce the redundancy of the original dataset.

COVID-19 epidemic and public health interventions in Shanghai, China: Statistical analysis of transmission, correlation and conversion.

Background: The Shanghai COVID-19 epidemic is an important example of a local outbreak and of the implementation of normalized prevention and disease control strategies. The precise impact of public health interventions on epidemic prevention and control is unknown.

Methods: We collected information on COVID-19 patients reported in Shanghai, China, from January 30 to May 31, 2022.

Using machine learning models to predict the duration of the recovery of COVID-19 patients hospitalized in Fangcang shelter hospital during the Omicron BA. 2.2 pandemic.

Background: Factors that may influence the recovery of patients with confirmed SARS-CoV-2 infection hospitalized in the Fangcang shelter were explored, and machine learning models were constructed to predict the duration of recovery during the Omicron BA. 2.2 pandemic.

E-DU: Deep neural network for multimodal medical image segmentation based on semantic gap compensation.

Background: U-Net includes encoder, decoder and skip connection structures. It has become the benchmark network in medical image segmentation. However, the direct fusion of low-level and high-level convolution features with semantic gaps by traditional skip connections may lead to problems such as fuzzy generated feature maps and target region segmentation errors.

Integrating Internet multisource big data to predict the occurrence and development of COVID-19 cryptic transmission.

With the recent prevalence of COVID-19, cryptic transmission is worthy of attention and research. Early perception of the occurrence and development risk of cryptic transmission is an important part of controlling the spread of COVID-19. Previous relevant studies have limited data sources, and no effective analysis has been carried out on the occurrence and development of cryptic transmission.

Construction of a Medical Micro-Object Cascade Network for Automated Segmentation of Cerebral Microbleeds in Susceptibility Weighted Imaging.

The detection and segmentation of cerebral microbleeds (CMBs) images are the focus of clinical diagnosis and treatment. However, segmentation is difficult in clinical practice, and missed diagnosis may occur. Few related studies on the automated segmentation of CMB images have been performed, and we provide the most effective CMB segmentation to date using an automated segmentation system.

N-mytistoyltransferase 1 and 2 are potential tumor suppressors and novel targets of miR-182 in human non-small cell lung carcinomas.

Background & Aims: Non-small cell lung cancer (NSCLC) accounts for about 80% of lung cancer diagnoses across the world. Despite recent appreciable improvements in treatment plans for patients with NSCLC, the prognosis for those with the cancer still remains poor. Recently, a growing number of studies have shown that N-myristoyltransferases (NMTs) may be critical in carcinogenesis, however, the functional and clinical significance of this pathway in NSCLC remains unclear and requires further research.

Restoring Exhibits Therapeutic Potential for Ameliorating Malignant Progression and 5-Fluorouracil Resistance in Colorectal Cancer.

Emerging evidence suggests that hypermethylation of plays an important role in human cancers. However, the biological and clinical impacts of overmethylation and its downstream targets in colorectal cancer remain unknown. We evaluated the methylation level of in paired cancer and normal tissues ( = 42) by using pyrosequencing, followed by validation of the methylation status of from The Cancer Genome Atlas (TCGA) datasets with 302 cancer tissues and 38 normal tissues.

An immunoscore nomogram for predicting survival in patients with oesophageal cancer.

This study aimed to construct and validate an immunoscore nomogram that may be used to predict the prognosis of oesophageal cancer. With the gene expression data of oesophageal cancer in a public database, we used CIBERSORT to estimate the fractions of 22 infiltrating immune cell types. We then built an immunoscore signature based on 12 types of infiltrating immune cells using the least absolute shrinkage and selection operator (LASSO) model.

High incidence of stroke in COVID-19 patients.

A retrospective analysis of 11 COVID-19 patients complicated with stroke was performed. It was found that the incidence of stroke in patients with COVID-19 was significantly higher than the average level of the general population (P=0.003), and the D-dimer levels of 11 stroke patients were significantly higher than other patients (P=0.

Combined use of the neutrophil-to-lymphocyte ratio and CRP to predict 7-day disease severity in 84 hospitalized patients with COVID-19 pneumonia: a retrospective cohort study.

Background: Coronavirus disease 2019 (COVID-19) has spread rapidly worldwide from Wuhan. An easy-to-use index capable of the early identification of inpatients who are at risk of becoming critically ill is urgently needed in clinical practice. Hence, the aim of this study was to explore an easy-to-use nomogram and a model to triage patients into risk categories to determine the likelihood of developing a critical illness.

Cumulative Evidence for Associations between Genetic Variants and Risk of Esophageal Cancer.

Background: A large number of studies have been conducted to investigate associations between genetic variants and esophageal cancer risk in the past several decades. However, findings from these studies have been generally inconsistent. We aimed to provide a summary of the current understanding of the genetic architecture of esophageal cancer susceptibility.

Toxicity responses of different organs of zebrafish (Danio rerio) to silver nanoparticles with different particle sizes and surface coatings.

Silver nanoparticles (AgNPs) in aquatic ecosystems are toxic to aquatic organisms. In this study, we aimed to investigate the toxicities and molecular mechanisms of AgNPs with different surface coatings (sodium citrate and polyvinylpyrrolidone) and particle sizes (20 nm and 100 nm) in the gills, intestines, and muscles of zebrafish after 96 h of exposure. Our results indicated that the contribution of particle size to AgNP toxicity was greater than that of the surface coating.

Development and Preliminary Validation of a Depression Assessment Tool for Maintenance Hemodialysis Patients.

Patients undergoing maintenance hemodialysis (MHD) are subject to a higher-than-usual prevalence of depressive disorders. However, the lack of consensus regarding the best assessment method remains an important problem. Thus, there is a clear need for more effective screening tools and an easily administered, disease-specific self-report measure of depression in MHD patients.

Toxic effects of different types of zinc oxide nanoparticles on algae, plants, invertebrates, vertebrates and microorganisms.

Concerns about the potential environmental risks of zinc oxide nanoparticles (ZnO NPs) are becoming an important issue because of their rapid growth in different fields. ZnO NPs are inevitably released in the environment during the production, transport, use and disposal process. Therefore, it is necessary to understand their toxicities and mode of actions.