Analysis of the Spectrum of Common Pathogens and the Resistance of Mycoplasma in Sialidase-Positive Bacterial Vaginosis.

Background: The objective of this study is to understand the characteristics of the common spectrum of pathogen and the resistance of Mycoplasma in Sialidase-positive bacterial vaginosis.

Methods: The vaginal secretion specimens collected from August 2018 to October 2018 for the analysis of bacterial vaginosis (BV) were subjected to various techniques. These included routine leukorrhea examination, bacterial vaginosis sialidase testing, routine culture for common pathogens, mass spectrometry identification, and Mycoplasma resistance testing.

Microbiology and Clinical Outcome of Bloodstream Infections in Patients After Hematopoietic Stem Cell Transplantation.

Purpose: Patients after hematopoietic stem cell transplantation (HSCT) are often followed by bloodstream infections (BSIs). BSI is an important cause of non-relapse mortality (NRM) in HSCT patients.

Methods: We conducted a retrospective cohort study of patients (aged >14 years) who underwent HSCT at our hospital from 2017 to 2021.

SCCPMD: Probability matrix decomposition method subject to corrected similarity constraints for inferring long non-coding RNA-disease associations.

Accumulating evidence has demonstrated various associations of long non-coding RNAs (lncRNAs) with human diseases, such as abnormal expression due to microbial influences that cause disease. Gaining a deeper understanding of lncRNA-disease associations is essential for disease diagnosis, treatment, and prevention. In recent years, many matrix decomposition methods have also been used to predict potential lncRNA-disease associations.

DRPADC: A novel drug repositioning algorithm predicting adaptive drugs for COVID-19.

Given that the usual process of developing a new vaccine or drug for COVID-19 demands significant time and funds, drug repositioning has emerged as a promising therapeutic strategy. We propose a method named DRPADC to predict novel drug-disease associations effectively from the original sparse drug-disease association adjacency matrix. Specifically, DRPADC processes the original association matrix with the WKNKN algorithm to reduce its sparsity.

HBRWRLDA: predicting potential lncRNA-disease associations based on hypergraph bi-random walk with restart.

Accumulating evidence indicates that the regulation of long non-coding RNAs (lncRNAs) is closely related to a variety of diseases. Identifying meaningful lncRNA-disease associations will help to contribute to the understanding of the molecular mechanisms underlying these diseases. However, only a limited number of associations between lncRNAs and diseases have been inferred from traditional biological experiments due to the high cost and highly specialized.

BGMSDDA: a bipartite graph diffusion algorithm with multiple similarity integration for drug-disease association prediction.

Drug repositioning, a method that relies on the information from the original drug-disease association matrix, aims to identify new indications for existing drugs and is expected to greatly reduce the cost and time of drug development. However, most current drug repositioning methods make use of the original drug-disease association matrix directly without preconditioning. As relatively only a few associations between drugs and diseases have been determined from actual observations, the original drug-disease association matrix used in the prediction is sparse, which affects the performance of the prediction method.

HOPMCLDA: predicting lncRNA-disease associations based on high-order proximity and matrix completion.

In recent years, emerging evidence has shown that long noncoding RNAs (lncRNAs) have important roles in the biological processes of complex diseases. However, experiments to determine the associations between diseases and lncRNAs are time consuming and costly. Therefore, there is a need to develop effective computational methods for exploring potential lncRNA-disease associations.

Mitochondrial DNA 4977 bp deletion is a common phenomenon in hair and increases with age.

Mitochondrial DNA (mtDNA) is believed to be particularly susceptible to oxidative damage during aging, resulting in mtDNA point mutations, duplications, and deletions. Although mtDNA deletions have been reported in various human tissues, e.g.