Prediction of new HIV infection in men who have sex with men based on machine learning: secondary analysis of a prospective cohort study from Western China.

Objective: This study aimed to construct a model based on machine learning to predict new HIV infections in HIV-negative men who have sex with men (MSM).

Methods: This is a secondary analysis of a previous random clinical trial aiming to evaluate the preventive effects of PrEP on new HIV infection in MSM. During 2013-2015, 1455 HIV-negative MSM were enrolled.

MaKAN-Mixer: Channel Interaction-Based Mamba Method for rPPG Extraction.

Remote photoplethysmography (rPPG) achieves non-contact heart rate monitoring by detecting subtle skin color variations in facial videos, offering significant potential in healthcare, fitness, and security applications.However, accurately extracting rPPG signals in complex environments-especially under variable lighting and motion artifacts-remains challenging. The main difficulties are capturing spatio-temporal dynamics and modeling long-term dependencies across channels.

KRT14 is a promising prognostic biomarker of breast cancer related to immune infiltration.

Background: Breast cancer (BC) is the leading cancer among women globally, which has the highest incidence and mortality rate in over a hundred countries. This study was intended to discover a new prognostic biomarker, facilitating personalized treatment approaches.

Methods: RNA sequencing data from The Cancer Genome Atlas database and Gene Expression Omnibus database were utilized to download to evaluate expression levels and prognostic significance of Keratin 14 (KRT14).

Development and validation of a neutrophil extracellular traps-related gene signature for lower-grade gliomas.

There is growing evidence linking neutrophil extracellular traps (NETs) to tumor genesis, growth, distant metastasis, and tumor-related thrombosis. However, the roles of NETs-related genes (NETRGs) on LGG prognosis remain unclear. The purpose of this study was to integrate multiple machine learning techniques and experiment validation to develop a reliable NETs-based signature that opens up novel approaches for assessing the prognosis and treatment response of LGG patients.

Screening of Oxidative Stress-related Biomarkers and Antioxidant Drugs for Polycystic Ovary Syndrome Based on Bioinformatics and Molecular Docking.

Polycystic ovary syndrome (PCOS) is a prevalent endocrine and metabolic disorder affecting women of reproductive age. Oxidative stress (OS) is suggested to play a significant role in the development of PCOS. Using antioxidants to reduce OS and maintain a healthy balance in the body could be a novel treatment approach for PCOS.

A transcriptome-wide association study integrating multi-omics bioinformatics and Mendelian randomization reveals the prognostic value of ADAMDEC1 in colon cancer.

An abundant amount of colon cancers is diagnosed every year, accounting for 9% of malignant tumors. Even with the progress of relevant research, the 5-year survival rate for colon cancer is still less than 60%, indicating that improving the prognosis of colon cancer is still a challenge that needs to be overcome. This study employed the algorithm "scissor" to integrate the single-cell sequencing data and bulk transcriptome data with prognosis information to predict prognosis-associated cells (PAC).

Hypertension and 28-day mortality in sepsis patients: An observational and mendelian randomization study.

Background: Predicting and reducing the 28-day mortality in sepsis remains a challenge in this research field.

Objective: This study aimed to explore the association between hypertension and 28-day mortality in sepsis.

Methods: This study is a cross-sectional approach with Mendelian Randomization (MR).

Single-nucleus sequencing unveils heterogeneity in renal cell carcinomas microenvironment: Insights into pathogenic origins and treatment-responsive cellular subgroups.

Background: Different individuals with renal cell carcinoma (RCC) exhibit substantial heterogeneity in histomorphology, genetic alterations in the proteome, immune cell infiltration patterns, and clinical behavior.

Objectives: This study aims to use single-nucleus sequencing on ten samples (four normal, three clear cell renal cell carcinoma (ccRCC), and three chromophobe renal cell carcinoma (chRCC)) to uncover pathogenic origins and prognostic characteristics in patients with RCC.

Methods: By using two algorithms, inferCNV and k-means, the study explores malignant cells and compares them with the normal group to reveal their origins.

Artificial intelligence in ovarian cancer drug resistance advanced 3PM approach: subtype classification and prognostic modeling.

Background: Ovarian cancer patients' resistance to first-line treatment posed a significant challenge, with approximately 70% experiencing recurrence and developing strong resistance to first-line chemotherapies like paclitaxel.

Objectives: Within the framework of predictive, preventive, and personalized medicine (3PM), this study aimed to use artificial intelligence to find drug resistance characteristics at the single cell, and further construct the classification strategy and deep learning prognostic models based on these resistance traits, which can better facilitate and perform 3PM.

Methods: This study employed "Beyondcell," an algorithm capable of predicting cellular drug responses, to calculate the similarity between the expression patterns of 21,937 cells from ovarian cancer samples and the signatures of 5201 drugs to identify drug-resistance cells.

Using structural equation modeling to investigate students' satisfaction with an undergraduate tutorial system.

Background: The undergraduate tutorial system (UTS) is a crucial measure in China for adhering to the principle of prioritizing foundational education, innovating the undergraduate talent training mode, and building a powerful country of higher education. This study investigated undergraduate students' satisfaction with UTS and the influencing factors, aiming to promote the healthy and sustainable development of UTS and provide practical implications and suggestions for universities.

Methods: Based on relevant theories, we conducted a survey study and leveraged structural equation modeling to assess students' satisfaction with UTS and explore the influencing factors.

Psychological status of pregnant women during the omicron pandemic outbreak in China.

Background: Pregnant women faced great challenges and psychological and physiological changes of varying degrees during the omicron epidemic outbreak. It is important to recognize the potential impact of these challenges on the mental health of pregnant women and to provide appropriate resources and support to mitigate their effects.

Method: By using the convenience sampling approach, a total of 401 pregnant women from two hospitals of different grades in two cities were included in the survey.

Effects of dietary supplementation with alkaline mineral complex on ruminal fermentation and bacterial composition.

Introduction: Dairy industry growth faces challenges in China due to inadequate forage, leading to high-concentrate diets and potential rumen issues. Buffering agents, like sodium bicarbonate, play a crucial role in stabilizing rumen pH. Alkaline Mineral Complex (AMC), a liquid additive with a pH of 14, shows promise in supporting dairy cow health and mitigating heat stress through ionization.

Causal association of monocytes with chronic kidney disease and the mediation role of frailty: A study integrating large-scale two-sample Mendelian randomization and single-cell analysis.

Background: Recent research reported that frailty was prevalent among adults with chronic kidney disease (CKD) in clinical trials, and monocytes illustrated a similar difference in these two diseases compared to the normal. However, the scientific evidence for a causal relationship between these two diseases was lacking, with further exploration into whether monocytes co-regulate them.

Methods: We aimed to integrate large-scale Mendelian randomization (MR) and single-cell transcriptome analysis to determine whether there was a causal relationship between frailty and CKD (Bidirectional two-sample Mendelian determined the causal direction), whether monocytes impacted them, and whether the two diseases shared genetic variation sites.

Landscape of infiltrated immune cell characterization in COVID-19.

Purpose: Although the role of SARS-CoV-2-specfic immune cells has been revealed, a comprehensive understanding of immune patterns remains unknown.

Methods: In this work, unsupervised consensus clustering analysis was used to classify 240 coronavirus disease 2019 (COVID-19) patients into different immune subtypes. Next, we performed differentially expressed analysis between different immune subtypes.

Psychological Status of Medical Staff in Obstetrics and Gynecology Hospitals during the Omicron Pandemic Outbreak in China.

Background: Medical staff in China faced great challenges and psychological and physiological changes of varying degrees during the omicron epidemic outbreak. It is important to recognize the potential impact of these challenges on the mental health of medical staff and to provide appropriate resources and support to mitigate their effects.

Methods: A total of 354 medical staff in two obstetrics and gynecology hospitals of different grades were included in this survey using convenience sampling.

A robust primary liver cancer subtype related to prognosis and drug response based on a multiple combined classifying strategy.

The recurrence or resistance to treatment of primary liver cancer (PLL) is significantly related to the heterogeneity present within the tumor. In this study, we integrated prognosis risk score, mRNAsi index, and immune characteristics clustering to classify patients. The four subtypes obtained from the combined classification are associated with PLC's prognosis and drug response.

Mononuclear phagocyte system-related multi-omics features yield head and neck squamous cell carcinoma subtypes with distinct overall survival, drug, and immunotherapy responses.

Background: Recent research reported that mononuclear phagocyte system (MPS) can contribute to immune defense but the classification of head and neck squamous cell carcinoma (HNSCC) patients based on MPS-related multi-omics features using machine learning lacked.

Methods: In this study, we obtain marker genes for MPS through differential analysis at the single-cell level and utilize "similarity network fusion" and "MoCluster" algorithms to cluster patients' multi-omics features. Subsequently, based on the corresponding clinical information, we investigate the prognosis, drugs, immunotherapy, and biological differences between the subtypes.

Integrating Machine Learning and Mendelian Randomization Determined a Functional Neurotrophin-Related Gene Signature in Patients with Lower-Grade Glioma.

Recent researches reported that neurotrophins can promote glioma growth/invasion but the relevant model for predicting patients' survival in Lower-Grade Gliomas (LGGs) lacked. In this study, we adopted univariate Cox analysis, LASSO regression, and multivariate Cox analysis to determine a signature including five neurotrophin-related genes (NTGs), CLIC1, SULF2, TGIF1, TTF2, and WEE1. Two-sample Mendelian Randomization (MR) further explored whether these prognostic-related genes were genetic variants that increase the risk of glioma.