Autonomous International Classification of Diseases Coding Using Pretrained Language Models and Advanced Prompt Learning Techniques: Evaluation of an Automated Analysis System Using Medical Text.

Background: Machine learning models can reduce the burden on doctors by converting medical records into International Classification of Diseases (ICD) codes in real time, thereby enhancing the efficiency of diagnosis and treatment. However, it faces challenges such as small datasets, diverse writing styles, unstructured records, and the need for semimanual preprocessing. Existing approaches, such as naive Bayes, Word2Vec, and convolutional neural networks, have limitations in handling missing values and understanding the context of medical texts, leading to a high error rate.

Characterization of the wall-associated kinase (WAK) gene family in Gossypium barbadense reveals the positive role of GbWAK5 in salt tolerance.

We characterized the WAK gene family in Gossypium barbadense and revealed the potential function of GbWAK5 in regulating salt tolerance by modulating ion homeostasis. Soil salinization is one of the main factors restricting cotton production. Although the role of the wall-associated kinases (WAKs) in plants has been extensively studied, its response to salt stress in sea-island cotton (Gossypium barbadense L.

[Discussion about Testing Scheme of Intelligent Medical Devices].

Intelligent medical devices are flourishing with the deep integration of modern information and artificial intelligence technologies into healthcare. Testing is an important means of performance evaluation and quality control for intelligent medical devices. Compared with traditional medical devices, the testing methods and technologies of intelligent medical devices are still immature, and need active research to promote the progress in this area.

Erratum: AMPD3 promotes doxorubicin-induced cardiomyopathy through HSP90α-mediated ferroptosis.

[This corrects the article DOI: 10.1016/j.isci.

Insulin Resistance-Nutritional Index: A Simple Index and Potential Predictor of Mortality Risk in Patients with Chronic Heart Failure and Type 2 Diabetes.

Background: Patients with chronic heart failure (CHF) and type 2 diabetes mellitus (DM) are prone to insulin resistance and malnutrition, both of which are significant prognostic factors for CHF. However, the combined effect of the triglyceride-glucose index (TyG index) and prognostic nutritional index (PNI) on the mortality risk in patients with CHF and type 2 DM has not yet been studied.

Methods: We enrolled 3,315 patients with CHF and type 2 DM.

AMPD3 promotes doxorubicin-induced cardiomyopathy through HSP90α-mediated ferroptosis.

Doxorubicin (DOX), a widely used anticancer drug, can induce myocardial damage, and current treatments are limited. Our research identified AMPD3 upregulation in DOX-induced cardiotoxicity (DIC), and we hypothesized that AMPD3 may contribute to cardiac injury by regulating mitochondrial dynamics and ferroptosis. We generated AMPD3 knockout (KO) mice and AC16 cell models with AMPD3 knockdown/overexpression, using various methods to explore underlying mechanisms.

Associations between metabolic overweight/obesity phenotypes and mortality risk among patients with chronic heart failure.

Background: Metabolic disorders and overweight or obesity are highly prevalent and intricately linked in patients with chronic heart failure (CHF). However, it remains unclear whether there is an interactive effect between these conditions and the prognosis of heart failure, and whether such an interaction is influenced by stratification based on age and sex.

Methods: A total of 4,955 patients with CHF were enrolled in this study.

scEGG: an exogenous gene-guided clustering method for single-cell transcriptomic data.

In recent years, there has been significant advancement in the field of single-cell data analysis, particularly in the development of clustering methods. Despite these advancements, most algorithms continue to focus primarily on analyzing the provided single-cell matrix data. However, within medical contexts, single-cell data often encompasses a wealth of exogenous information, such as gene networks.

Medical Federated Model with Mixture of Personalized and Shared Components.

Although data-driven methods usually have noticeable performance on disease diagnosis and treatment, they are suspected of leakage of privacy due to collecting data for model training. Recently, federated learning provides a secure and trustable alternative to collaboratively train model without any exchange of medical data among multiple institutes. Therefore, it has draw much attention due to its natural merit on privacy protection.

Association of preoperative antiviral treatment with incidences of post-hepatectomy liver failure in hepatitis B virus-related hepatocellular carcinoma.

Background: Post-hepatectomy liver failure (PHLF) is a common consequence of radical partial hepatectomy in hepatocellular carcinoma (HCC).

Aims: To investigate the relationship between preoperative antiviral therapy and PHLF, as well as assess the potential efficacy of hepatitis B virus (HBV) DNA level in predicting PHLF.

Methods: A retrospective study was performed involving 1301 HCC patients with HBV who underwent radical hepatectomy.

A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multi-Modal.

Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research direction. It has been proven to significantly benefit the usage of KGs in many AI applications, such as question answering, recommendation systems, and etc. According to the graph types, existing KGR models can be roughly divided into three categories, i.

Assessing and Optimizing Large Language Models on Spondyloarthritis Multi-Choice Question Answering: Protocol for Enhancement and Assessment.

Background: Spondyloarthritis (SpA), a chronic inflammatory disorder, predominantly impacts the sacroiliac joints and spine, significantly escalating the risk of disability. SpA's complexity, as evidenced by its diverse clinical presentations and symptoms that often mimic other diseases, presents substantial challenges in its accurate diagnosis and differentiation. This complexity becomes even more pronounced in nonspecialist health care environments due to limited resources, resulting in delayed referrals, increased misdiagnosis rates, and exacerbated disability outcomes for patients with SpA.

RedCDR: Dual Relation Distillation for Cancer Drug Response Prediction.

Based on multi-omics data and drug information, predicting the response of cancer cell lines to drugs is a crucial area of research in modern oncology, as it can promote the development of personalized treatments. Despite the promising performance achieved by existing models, most of them overlook the variations among different omics and lack effective integration of multi-omics data. Moreover, the explicit modeling of cell line/drug attribute and cell line-drug association has not been thoroughly investigated in existing approaches.

Self-Supervised Temporal Graph Learning With Temporal and Structural Intensity Alignment.

Temporal graph learning aims to generate high-quality representations for graph-based tasks with dynamic information, which has recently garnered increasing attention. In contrast to static graphs, temporal graphs are typically organized as node interaction sequences over continuous time rather than an adjacency matrix. Most temporal graph learning methods model current interactions by incorporating historical neighborhood.

On the Consistency and Large-Scale Extension of Multiple Kernel Clustering.

Existing multiple kernel clustering (MKC) algorithms have two ubiquitous problems. From the theoretical perspective, most MKC algorithms lack sufficient theoretical analysis, especially the consistency of learned parameters, such as the kernel weights. From the practical perspective, the high complexity makes MKC unable to handle large-scale datasets.

Longitudinal multi-omics analysis uncovers the altered landscape of gut microbiota and plasma metabolome in response to high altitude.

Background: Gut microbiota is significantly influenced by altitude. However, the dynamics of gut microbiota in relation to altitude remains undisclosed.

Methods: In this study, we investigated the microbiome profile of 610 healthy young men from three different places in China, grouped by altitude, duration of residence, and ethnicity.

HyperTMO: a trusted multi-omics integration framework based on hypergraph convolutional network for patient classification.

Motivation: The rapid development of high-throughput biomedical technologies can provide researchers with detailed multi-omics data. The multi-omics integrated analysis approach based on machine learning contributes a more comprehensive perspective to human disease research. However, there are still significant challenges in representing single-omics data and integrating multi-omics information.

Effective multi-modal clustering method via skip aggregation network for parallel scRNA-seq and scATAC-seq data.

In recent years, there has been a growing trend in the realm of parallel clustering analysis for single-cell RNA-seq (scRNA) and single-cell Assay of Transposase Accessible Chromatin (scATAC) data. However, prevailing methods often treat these two data modalities as equals, neglecting the fact that the scRNA mode holds significantly richer information compared to the scATAC. This disregard hinders the model benefits from the insights derived from multiple modalities, compromising the overall clustering performance.

The Associations of Two Novel Inflammation Biomarkers, SIRI and SII, with Mortality Risk in Patients with Chronic Heart Failure.

Background: The associations of two novel inflammation biomarkers, systemic inflammation response index (SIRI) and systemic immune inflammation index (SII), with mortality risk in patients with chronic heart failure (CHF) are not well-characterized.

Methods: This retrospective cohort study included patients with CHF in two medical centers of Chinese People's Liberation Army General Hospital, Beijing, China. The outcomes of this study included in-hospital mortality and long-term mortality.

Chronic diseases spectrum and multimorbidity in elderly inpatients based on a 12-year epidemiological survey in China.

Background: The number and proportion of the elderly population have been continuously increasing in China, leading to the elevated prevalence of chronic diseases and multimorbidity, which ultimately brings heavy burden to society and families. Meanwhile, the status of multimorbidity tends to be more complex in elderly inpatients than community population. In view of the above concerns, this study was designed to investigate the health status of elderly inpatients by analyzing clinical data in Chinese People's Liberation Army (PLA) General Hospital from 2008 to 2019, including the constitution of common diseases, comorbidities, the status of multimorbidity, in-hospital death and polypharmacy among elderly inpatients, so as to better understand the diseases spectrum and multimorbidity of elderly inpatients and also to provide supporting evidence for targeted management of chronic diseases in the elderly.

Impact of body mass index on long-term outcomes in patients undergoing percutaneous coronary intervention stratified by diabetes mellitus: a retrospective cohort study.

Background: Patients with diabetes mellitus (DM) caused by obesity have increased in recent years. The impact of obesity on long-term outcomes in patients undergoing percutaneous coronary intervention (PCI) with or without DM remains unclear.

Methods: We retrospectively analysed data from 1918 patients who underwent PCI.

Methylation of NRIP3 Is a Synthetic Lethal Marker for Combined PI3K and ATR/ATM Inhibitors in Colorectal Cancer.

Introduction: The aim of this study was to investigate the epigenetic regulation and underlying mechanism of NRIP3 in colorectal cancer (CRC).

Methods: Eight cell lines (SW480, SW620, DKO, LOVO, HT29, HCT116, DLD1, and RKO), 187 resected margin samples from colorectal cancer tissue, 146 cases with colorectal adenomatous polyps, and 308 colorectal cancer samples were used. Methylation-specific PCR, Western blotting, RNA interference assay, and a xenograft mouse model were used.

Multi-omics reveals changed energy metabolism of liver and muscle by caffeine after mice swimming.

In recent years, numerous studies have investigated the effects of caffeine on exercise, and provide convincing evidence for its ergogenic effects on exercise performance. However, the precise mechanisms underlying these ergogenic effects remain unclear. In this study, an exercise swimming model was conducted to investigate the effects of orally administered with caffeine before swimming on the alterations of proteome and energy metabolome of liver and muscle after swimming.