Global, regional, and national trends in the incidence of pneumoconiosis among populations aged 20 and above from 1990 to 2021.

Background: Pneumoconiosis remains one of the most critical occupational health hazards globally. Utilizing data from the Global Burden of Disease (GBD) 2021, we have updated the epidemiological trends of pneumoconiosis.

Methods: We conducted and analyzed pneumoconiosis-related data from the GBD 2021 study for individuals aged ≥20 years.

BiVAE-CPI: An Interpretable Generative Model Using a Bilateral Variational Autoencoder for Compound-Protein Interaction Prediction.

Predicting compound-protein interaction (CPI) plays a critical role in drug discovery and development, but traditional screening experiments consume much time and resources. Therefore, deep learning methods for CPI prediction are popular now. However, many existing methods treat CPI pairs as independent inputs, ignoring the correlations among different CPI pairs, and do not capture their latent representations well.

Molecular function validation and prognostic value analysis of the cuproptosis-related gene ferredoxin 1 in papillary thyroid carcinoma.

Cuproptosis, a copper-dependent distinct form of cell death, holds a critical role in tumor development. However, further investigation is needed to elucidate the impact of the cuproptosis signaling on thyroid cancer. In this study, comprehensive bioinformatics analyses with six independent cohorts and in vitro experiments were performed to explore the expression, prognostic significance, and molecular function of the cuproptosis key regulator FDX1 in papillary thyroid carcinoma (PTC).

RPI-GGCN: Prediction of RNA-Protein Interaction Based on Interpretability Gated Graph Convolution Neural Network and Co-Regularized Variational Autoencoders.

RNA-protein interactions (RPIs) play an important role in several fundamental cellular physiological processes, including cell motility, chromosome replication, transcription and translation, and signaling. Predicting RPI can guide the exploration of cellular biological functions, intervening in diseases, and designing drugs. Given this, this study proposes the RPI-gated graph convolutional network (RPI-GGCN) method for predicting RPI based on the gated graph convolutional neural network (GGCN) and co-regularized variational autoencoder (Co-VAE).

Gut Microbiome-Based Therapeutics in Critically Ill Adult Patients-A Narrative Review.

The gut microbiota plays a crucial role in the human microenvironment. Dysbiosis of the gut microbiota is a common pathophysiological phenomenon in critically ill patients. Therefore, utilizing intestinal microbiota to prevent complications and improve the prognosis of critically ill patients is a possible therapeutic direction.