Aging is becoming a severe social phenomenon globally, and the improvements in health care and increased health awareness among the elderly have led to a dramatic increase in the number of surgical procedures. Because of the degenerative changes in the brain structure and function in the elderly, the incidence of perioperative neurocognitive disorders (PND) is much higher in elderly patients than in young people following anesthesia/surgery. PND is attracting more and more attention, though the exact mechanisms remain unknown. A growing body of evidence has shown that the gut microbiota is likely involved. Recent studies have indicated that the gut microbiota may affect postoperative cognitive function via the gut-brain axis. Nonetheless, understanding of the mechanistic associations between the gut microbiota and the brain during PND progression remains very limited. In this review, we begin by providing an overview of the latest progress concerning the gut-brain axis and PND, and then we summarize the influence of perioperative factors on the gut microbiota. Next, we review the literature on the relationship between gut microbiota and PND and discuss how gut microbiota affects cognitive function during the perioperative period. Finally, we explore effective early interventions for PND to provide new ideas for related clinical research.

Motivation: The UK Biobank data holds immense potential for human health research. However, the complex data preparation and interpretation processes often act as barriers for researchers, diverting them from their core research questions. Results: We developed LUKB, an R Shiny-based web tool that simplifies UK Biobank data preparation by automating these preprocessing tasks. LUKB reduces preprocessing time and integrates functions for initial data exploration, allowing researchers to dedicate more time to their scientific endeavors. Detailed deployment and usage can be found in the Supplementary Data. Availability and implementation: LUKB is freely available at https://github.com/HaiGenBuShang/LUKB.

As the volume of electronic medical data grows, understanding disease progression and identifying risk factors have become central to public health research. Disease trajectories provide valuable insights into disease progression and risk factors. LUKB DT (LUKB Disease Trajectories) is a user-friendly web tool designed to facilitate disease trajectory analysis using electronic medical records (EMR), particularly the UK Biobank data. LUKB DT processes EMR data, including patient ID, disease diagnoses, corresponding timestamps, and other relevant variables, combining Cox regression, binomial tests, and conditional logistic regression to identify disease trajectories. This tool offers a quick and efficient way to prepare and analyze disease trajectories, contributing to the expanding field of disease trajectory research and providing valuable insights for risk factor identification and disease progression studies. Detailed deployment and usage can be found in the Supplementary Material. LUKB DT is freely available at https://github.com/HaiGenBuShang/LUKB .

Background Intrinsic subtypes have played an important role in breast cancer research. The accuracy of breast cancer research findings depends on the accuracy of intrinsic subtype classification of breast cancer samples. Popular intrinsic subtype models, such as PAM50 and AIMS, were mainly developed on Microarray but are widely used in other platforms. The transferability of these models to RNA-seq and other platforms has rarely been studied. We aim to assess the effectiveness of popular intrinsic subtype models on RNA-seq data and improve the accuracy of breast cancer subtyping on this platform.Methods Assuming that one breast cancer sample only belongs to one subtype irrespective of the expression measuring platform, we assessed the consistency of subtype predictions of PAM50 and AIMS for TCGA Microarray and RNA-seq data from the same samples using Kappa statistic. We also built 12 models using common and intrinsic genes on both Microarray and RNA-seq data, and evaluated their performance under the same assumption.Results Both PAM50 and AIMS failed to produce consistent predictions for Microarray and RNA-seq data from the same samples, with Kappa values of 0.33 and 0.21, respectively. Data normalization improved prediction consistency but introduced false classification. Microarray-developed models showed low prediction consistency when applied to both Microarray and RNA-seq profiles of the same samples. However, prediction consistency between predictions of RNA-seq developed model predicting RNA-seq data and Microarray developed model predicting Microarray data was high with most Kappa values above 0.85.Conclusion Great caution should be taken when using PAM50 and AIMS for the subtyping of breast cancer RNA-seq data. Breast cancer subtyping models should be developed and applied on the same platform, using unnormalized expression data for accurate subtype prediction purpose.

Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide. In the U.S., HCC incidence is highest among Hispanic populations. With the exception of well-known PNPLA3 variants, the genetic risk factors underlying HCC in Hispanic individuals, particularly rare germline variants, are largely unexplored. Methods: We performed whole-exome sequencing in 719 Hispanic Americans, including 455 HCC cases and 264 controls. We analyzed rare to low frequency, putatively deleterious variants in 86 genes previously implicated in HCC susceptibility. Genetic associations were assessed at single-variant, gene, and cumulative multi-variant burden levels using Fisher’s exact tests and weighted burden tests. Results: We identified 29 rare deleterious variants across 16 genes enriched in HCC cases compared with controls. Single-variant tests identified statistically robust associations in pathways related to lipids metabolism, TM6SF2 R138W, immune regulation, HLA-DRB1 W38X, and Wnt signaling pathway, WNT9A R375H. Exploratory analyses showed that TM6SF2 R138W was further enriched among cases with alcohol and metabolic dysfunction-associated steatotic liver disease. Gene-based burden tests revealed five associated genes, including TM6SF2 and HLA-DRB1. A strong dose-effect relationship was observed, with HCC risk increasing ~4-fold per candidate variant carried (odds ratio, 3.84; 95% confidence interval, 2.40–6.53). Conclusions: Rare deleterious germline variants across lipid metabolism and immune pathways contribute to increased HCC risk in Hispanic individuals. These findings provide a genetic framework for understanding HCC disparities and support future mechanistic and epidemiologic studies in this high-risk population.