Role of heat shock proteins in response to temperature stress and their effect on apoptosis in Drosophila melanogaster.

Temperature is a key ecological factor influencing insect development and survival. Temperature stress triggers insect cell apoptosis. However, factors surrounding the response of insects to various temperature stresses at different developmental stages remain unclear.

Deciphering the Genetic Basis of Sugar Cane ( L) Root System and Related Traits under Nitrogen Stress through the Integration of Genome-Wide Association Studies and RNA-seq.

Nitrogen (N) is an essential element for plant growth and development. Identifying functional gene loci associated with nitrogen absorption and utilization in sugar cane can facilitate the development of nutrient-efficient sugar cane varieties. In this study, sugar cane seedlings were subjected to normal and low nitrogen stress treatments within a hydroponic system for the identification of candidate genes related to six root-associated traits using a diversity population of 297 accessions.

[Wangbi Tablets reduce inflammation in rat model of collagen-induced arthritis with syndrome of kidney deficiency by regulating cGAS-STING signaling pathway].

This study aims to investigate the therapeutic effect of Wangbi Tablets(WBT) on the inflammation in the rat model of collagen-induced arthritis(CIA) with the syndrome of kidney deficiency based on the cyclic guanosine monophosphate-adenosine monophosphate synthase(cGAS)-stimulator of interferon genes(STING) signaling pathway. Eighteen rats were randomly chosen from 24 SPF-grade rats for the modeling of CIA with the syndrome of kidney deficiency. The 24 SPF-grade rats were randomized into 4 groups: control(normal rats), model(CIA with syndrome of kidney deficiency), model+WBT(M+WBT), and model+methotrexate(M+MTX).

The multifaceted anti-atherosclerotic properties of herbal flavonoids: A comprehensive review.

Atherosclerosis (AS) is a major etiological factor underpinning a spectrum of cardiovascular diseases, leading to cerebral infarction, coronary artery disease, and peripheral vascular disease. The chronic progression of AS, spanning from initial plaque formation to the occurrence of acute cardiovascular events, underscores the complexity of AS and the challenges it presents in terms of treatment. Currently, the clinical management of AS relies predominantly on statins and proprotein convertase subtilisin/kexin type 9 inhibitors, which primarily aim to reduce low-density lipoprotein levels and have demonstrated some therapeutic efficacy.

The Different Predictive Effects of Multiple Body Fat Indexes on Metabolic Dysfunction-Associated Fatty Liver Disease.

Purpose: The aim of this study was to comprehensively compare the predictive effect of 10 body fat indexes on MAFLD in different sex, age and BMI subgroups.

Patients And Methods: A total of 5403 physical examination data were included and divided into the MAFLD group (N=2632) and non-MAFLD group (N=2771). The differences and correlation of 10 promising indicators between the two groups were compared, including fatty liver index (FLI), hepatic steatosis index (HSI), lipid accumulation product (LAP), visceral fat index (VAI), cardiometabolic index (CMI), body adiposity index (BAI), and triglyceride-glucose index (TyG), waist circumference index (WC), body mass index (BMI), waist to height ratio (WHtR).

Multi-trait analysis reveals risk loci for heart failure and the shared genetic etiology with blood lipids, blood pressure, and blood glucose.

Phenotypic associations have been reported between heart failure (HF) and blood lipids (BLs), blood pressure (BP), and blood glucose (BG). However, the shared genetic etiology underlying these associations remains incompletely understood. Conducting a large-scale multi-trait association study for HF with these traits, we discovered 143 previously unreported genomic risk loci for HF.

Personalized surgical recommendations and quantitative therapeutic insights for patients with metastatic breast cancer: Insights from deep learning.

Background: The role of surgery in metastatic breast cancer (MBC) is currently controversial. Several novel statistical and deep learning (DL) methods promise to infer the suitability of surgery at the individual level.

Objective: The objective of this study was to identify the most applicable DL model for determining patients with MBC who could benefit from surgery and the type of surgery required.

Construction of high-quality genomes and gene catalogue for culturable microbes of sugarcane (Saccharum spp.).

Microbes living inside or around sugarcane (Saccharum spp.) are crucial for their resistance to abiotic and biotic stress, growth, and development. Sequences of microbial genomes and genes are helpful to understand the function of these microbes.

Light-induced Extracellular Vesicle Adsorption.

The role of extracellular vesicles (EVs) in human health and disease has garnered considerable attention over the past two decades. However, while several types of EVs are known to interact dynamically with the extracellular matrix and there is great potential value in producing high-fidelity EV micropatterns, there are currently no label-free, high-resolution, and tunable platform technologies with this capability. We introduce Light-induced Extracellular Vesicle Adsorption (LEVA) as a powerful solution to rapidly advance the study of matrix- and surface-bound EVs and other particles.

Determining individual suitability for neoadjuvant systemic therapy in breast cancer patients through deep learning.

Background: The survival advantage of neoadjuvant systemic therapy (NST) for breast cancer patients remains controversial, especially when considering the heterogeneous characteristics of individual patients.

Objective: To discern the variability in responses to breast cancer treatment at the individual level and propose personalized treatment recommendations utilizing deep learning (DL).

Methods: Six models were developed to offer individualized treatment suggestions.

Deep learning-guided adjuvant chemotherapy selection for elderly patients with breast cancer.

Purpose: The efficacy of adjuvant chemotherapy in elderly breast cancer patients is currently controversial. This study aims to provide personalized adjuvant chemotherapy recommendations using deep learning (DL).

Methods: Six models with various causal inference approaches were trained to make individualized chemotherapy recommendations.

Genetic Predisposition, Sedentary Behavior, and Incident Coronary Artery Disease: A Prospective Chinese Cohort Study.

Purpose: Whether the association of sedentary behaviors with coronary artery disease (CAD) can be influenced by genetic susceptibility remains unclear. We aimed to investigate the joint and interplay effects between genetic risk and sedentary time (ST) and to further explore the extent to which the risk for CAD can be counteracted by reducing ST in different genetic groups.

Methods: This prospective cohort study included 39,164 Chinese adults without CAD history.

Federated Data Quality Assessment Approach: Robust Learning With Mixed Label Noise.

Federated learning (FL) has been an effective way to train a machine learning model distributedly, holding local data without exchanging them. However, due to the inaccessibility of local data, FL with label noise would be more challenging. Most existing methods assume only open-set or closed-set noise and correspondingly propose filtering or correction solutions, ignoring that label noise can be mixed in real-world scenarios.

Iterative Self-Training Based Domain Adaptation for Cross-User sEMG Gesture Recognition.

Surface electromyography (sEMG) based gesture recognition has received broad attention and application in rehabilitation areas for its direct and fine-grained sensing ability. sEMG signals exhibit strong user dependence properties among users with different physiology, causing the inapplicability of the recognition model on new users. Domain adaptation is the most representative method to reduce the user gap with feature decoupling to acquire motion-related features.

Clinical nutrition service capacity of 445 secondary or above hospitals in Sichuan, China: the 2021 annual survey.

Background And Objectives: To investigate the capacity of clinical nutrition services in secondary and tertiary hospitals in the Sichuan Province, China.

Methods And Study Design: Convenience sampling was used. E-questionnaires were distributed to all eligible medical institutions in Sichuan through the official network of provincial and municipal clinical nutrition quality control centers.

Path-ATT-CNN: A Novel Deep Neural Network Method for Key Pathway Identification of Lung Cancer.

Attention convolutional neural networks (ATT-CNNs) have got a huge gain in picture operating and nature language processing. Shortage of interpretability cannot remain the adoption of deep neural networks. It is very conspicuous that is shown in the prediction model of disease aftermath.

Causal associations of alcohol consumption with cardiovascular diseases and all-cause mortality among Chinese males.

Background: The causal effects of moderate alcohol consumption on cardiovascular diseases (CVDs) are continuously debated, especially on coronary artery disease (CAD).

Objectives: We aimed to explore the causal associations of alcohol consumption with CVDs and all-cause mortality among Chinese males.

Methods: A prospective cohort study was conducted in 40,386 Chinese males, with 17,676 being genotyped for the rs671 variant in the aldehyde dehydrogenase 2 (ALDH2) gene.

Pan-Cancer Bioinformatics Analysis of Gene UBE2C.

Ubiquitin-Conjugating Enzyme E2 C (UBE2C) is a gene that encodes protein. Disorders associated with UBE2C include methotrexate-related lymphatic hyperplasia and complement component 7 deficiency. The encoded protein is necessary for the destruction of mitotic cell cyclins and cell cycle progression, and may be involved in cancer progression.

Applications of deep learning in fundus images: A review.

The use of fundus images for the early screening of eye diseases is of great clinical importance. Due to its powerful performance, deep learning is becoming more and more popular in related applications, such as lesion segmentation, biomarkers segmentation, disease diagnosis and image synthesis. Therefore, it is very necessary to summarize the recent developments in deep learning for fundus images with a review paper.

Enhancing the production of poly-γ-glutamate in ZJS18 by the heat- and osmotic shock and its mechanism.

Poly-γ-glutamate (γ-PGA) is a natural macromolecule peptide, and is widely used in the food, medicine, and pharmaceutical industries. In this study, heat- and osmotic shock were used to improve the production of γ-PGA in ZJS18, and its molecular mechanism was explored. The results indicated that the heat- and osmotic shock significantly promoted the production of γ-PGA owing to the stress response of cells to adverse environment.

Synthetic Lethality-based Identification of Targets for Anticancer Drugs in the Human Signaling Network.

Chemotherapy agents can cause serious adverse effects by attacking both cancer tissues and normal tissues. Therefore, we proposed a synthetic lethality (SL) concept-based computational method to identify specific anticancer drug targets. First, a 3-step screening strategy (network-based, frequency-based and function-based screening) was proposed to identify the SL gene pairs by mining 697 cancer genes and the human signaling network, which had 6306 proteins and 62937 protein-protein interactions.

TLR2/TLR4 activation induces Tregs and suppresses intestinal inflammation caused by Fusobacterium nucleatum in vivo.

Toll-like receptors (TLRs) 2 and 4 play critical roles in intestinal inflammation caused by Fusobacterium nucleatum (F. nucleatum) infection, but the role of TLR2/TLR4 in regulation of proinflammatory cytokines remains unknown. In this study, through microarray analysis and qRT-PCR, we showed that TLR2/TLR4 are involved in the F.

RNALocate: a resource for RNA subcellular localizations.

Increasing evidence has revealed that RNA subcellular localization is a very important feature for deeply understanding RNA's biological functions after being transported into intra- or extra-cellular regions. RNALocate is a web-accessible database that aims to provide a high-quality RNA subcellular localization resource and facilitate future researches on RNA function or structure. The current version of RNALocate documents more than 37 700 manually curated RNA subcellular localization entries with experimental evidence, involving more than 21 800 RNAs with 42 subcellular localizations in 65 species, mainly including Homo sapiens, Mus musculus and Saccharomyces cerevisiae etc.