Identification of serum tRNA-derived small RNAs biosignature for diagnosis of tuberculosis.

The tRNA-derived small RNAs (tsRNAs) are a new class of non coding RNAs, which are stable in body fluids and can be used as potential biomarkers for disease diagnosis. However, the exact value of tsRNAs in the diagnosis of tuberculosis (TB) is still unclear. The objective of the present study was to evaluate the performance of the serum tsRNAs biosignature to distinguish between active TB, healthy controls, latent TB infection, and other respiratory diseases.

Coexistence of immunoglobulin G4-related kidney disease and acute hematogenous disseminated pulmonary tuberculosis: a case report.

Background: Immunoglobulin G4-related disease (IgG4-RD) is an immune-mediated fibrous inflammatory disease. Recently, an association between IgG4-RD and tuberculosis (TB) has been reported.

Case Summary: We report a 56-year-old man complaining of a cough and poor appetite for 2 months and oliguria for 1 day.

Utilizing sc-linker to integrate single-cell RNA sequencing and human genetics to identify cell types and driver genes associated with non-small cell lung cancer.

Background: Genome-wide association studies (GWAS) provide a powerful method for identifying the loci and genes that contribute to disease. However, in many cases, the specific cell types and states that confer disease risk through these genes remain unknown. Determining this relationship is crucial for identifying pathogenic processes and developing therapeutic strategies.

Development and application of a machine learning-based antenatal depression prediction model.

Background: Antenatal depression (AND), occurring during pregnancy, is associated with severe outcomes. However, there is a lack of objective and universally applicable prediction methods for AND in clinical practice. We leveraged sociodemographic and pregnancy-related data to develop and validate a machine learning-based AND prediction model.

Automatic visual detection of activated sludge microorganisms based on microscopic phase contrast image optimisation and deep learning.

The types and quantities of microorganisms in activated sludge are directly related to the stability and efficiency of sewage treatment systems. This paper proposes a sludge microorganism detection method based on microscopic phase contrast image optimisation and deep learning. Firstly, a dataset containing eight types of microorganisms is constructed, and an augmentation strategy based on single and multisamples processing is designed to address the issues of sample deficiency and uneven distribution.