Transcriptomics and metabolomics profiling of Paris polyphylla var. yunnanensis with different growth years.

Paris polyphylla var. yunnanensis is a widely cultivated rhizomatous perennial woody plant known for producing highly valued steroidal saponins. Steroidal saponins serve as the primary active components and exhibit a variety of pharmacological effects.

BiTS-SleepNet: An Attention-Based Two Stage Temporal-Spectral Fusion Model for Sleep Staging With Single-Channel EEG.

Automated sleep staging is crucial for assessing sleep quality and diagnosing sleep-related diseases. Single-channel EEG has attracted significant attention due to its portability and accessibility. Most existing automated sleep staging methods often emphasize temporal information and neglect spectral information, the relationship between sleep stage contextual features, and transition rules between sleep stages.

Dissection of transcriptome and metabolome insights into the polyphyllin biosynthesis in Paris.

Background: Polyphyllins are significant medicinal compounds found in Paris species, with different polyphyllins fulfilling distinct medicinal roles. Although some genes involved in polyphyllin synthesis have been identified, further exploration of the genes in the polyphyllin synthesis pathway is necessary due to the extensive genome of Paris species. The content and composition of polyphyllins vary among different Paris species, and the variations in specific polyphyllin levels across these plants make them promising candidates for identifying metabolites and genes associated with the biosynthesis of specific polyphyllins.

Low-Rank Tensor Fusion for Enhanced Deep Learning-Based Multimodal Brain Age Estimation.

A multimodal brain age estimation model could provide enhanced insights into brain aging. However, effectively integrating multimodal neuroimaging data to enhance the accuracy of brain age estimation remains a challenging task. In this study, we developed an innovative data fusion technique employing a low-rank tensor fusion algorithm, tailored specifically for deep learning-based frameworks aimed at brain age estimation.

Disrupted dynamic brain functional connectivity in male cocaine use disorder: Hyperconnectivity, strongly-connected state tendency, and links to impulsivity and borderline traits.

Cocaine use is a major public health problem with serious negative consequences at both the individual and societal levels. Cocaine use disorder (CUD) is associated with cognitive and emotional impairments, often manifesting as alterations in brain functional connectivity (FC). This study employed resting-state functional magnetic resonance imaging (rs-fMRI) to examine dynamic FC in 38 male participants with CUD and 31 matched healthy controls.

Improving brain age prediction with anatomical feature attention-enhanced 3D-CNN.

Currently, significant progress has been made in predicting brain age from structural Magnetic Resonance Imaging (sMRI) data using deep learning techniques. However, despite the valuable structural information they contain, the traditional engineering features known as anatomical features have been largely overlooked in this context. To address this issue, we propose an attention-based network design that integrates anatomical and deep convolutional features, leveraging an anatomical feature attention (AFA) module to effectively capture salient anatomical features.

Associations between physical activity and all-cause and cardiovascular mortality in adults with type 2 diabetes mellitus: A prospective cohort study from NHANES 2007-2018.

Aims: To investigate the dose-response association between physical activity and all-cause and cardiovascular mortality in adults with type 2 diabetes mellitus and the effects of replacing sedentary behavior with physical activity.

Methods: 4808 adults with type 2 diabetes mellitus were included in NHANES 2007-2018. Cox proportional hazards models were used to calculate hazard ratios and 95% confidence intervals.

An attention-based multi-modal MRI fusion model for major depressive disorder diagnosis.

Major depressive disorder (MDD) is one of the biggest threats to human mental health. MDD is characterized by aberrant changes in both structure and function of the brain. Although recent studies have developed some deep learning models based on multi-modal magnetic resonance imaging (MRI) for MDD diagnosis, the latent associations between deep features derived from different modalities were largely unexplored by previous studies, which we hypothesized may have potential benefits in improving the diagnostic accuracy of MDD.

Integrated analysis of single-cell and bulk RNA-sequencing identifies a signature based on T-cell marker genes to predict prognosis and immunotherapy response in bladder cancer.

Background: T cells have been proven to play important roles in anti-tumor and tumor microenvironment shaping, while these roles have not been explained in bladder cancer (BLCA).

Methods: Single-cell RNA-sequencing (scRNA-seq) data were downloaded from the gene expression omnibus (GEO) database to screen T-cell marker genes. Bulk RNA-sequencing data and clinical information from BLCA patients were downloaded from the cancer genome atlas (TCGA) database to develop a prognosis signature.

Multiomics strategies for decoding seed dormancy breakdown in Paris polyphylla.

Background: The disruption of seed dormancy is a complicated process and is controlled by various factors. Among these factors, membrane lipids and plant hormones are two of the most important ones. Paris polyphylla is an important Chinese herbaceous species, and the dormancy trait of its seed limits the cultivation of this herb.

The brain activation of anxiety disorders with emotional stimuli-an fMRI ALE meta-analysis.

Numerous studies have analyzed the state of brain activation about anxiety disorders under emotional stimuli. However, there is no meta-analysis to assess the commonality and specificity activation concerning different subtypes of anxiety. Here, we used ALE to assess this.

Mining of immunological and prognostic-related biomarker for cervical cancer based on immune cell signatures.

Background: Immunotherapy has changed the therapeutic landscape of cervical cancer (CC), but has durable anti-tumor activity only in a subset of patients. This study aims to comprehensively analyze the tumor immune microenvironment (TIME) of CC and to mine biomarkers related to immunotherapy and prognosis.

Methods: The Cancer Genome Atlas (TCGA) data was utilized to identify heterogeneous immune subtypes based on survival-related immune cell signatures (ICSs).

Integrated analysis of single-cell and bulk RNA-sequencing identifies a signature based on T-cell marker genes to predict prognosis and therapeutic response in lung squamous cell carcinoma.

Cancer immunotherapy is an increasingly successful strategy for treating patients with advanced or conventionally drug-resistant cancers. T cells have been proved to play important roles in anti-tumor and tumor microenvironment shaping, while these roles have not been explained in lung squamous cell carcinoma (LUSC). In this study, we first performed a comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data from the gene expression omnibus (GEO) database to identify 72 T-cell marker genes.

Prognostic characteristics of immune subtypes associated with acute myeloid leukemia and their identification in cell subsets based on single-cell sequencing analysis.

Immune genes play an important role in the development and progression of acute myeloid leukemia (AML). However, the role of immune genes in the prognosis and microenvironment of AML remains unclear. In this study, we analyzed 151 AML patients in the TCGA database for relevant immune cell infiltration.

Germplasm resources of three wood plant species enriched with nervonic acid.

Nervonic acid (NA) is a very-long-chain monounsaturated fatty acid with pharmaceutical and nutraceutical functions that plays an important role in treating several neurological disorders. One major source of NA is plant seed oil. Here we report fatty acid profiles of seeds and germplasm diversity of six plant species, including three woody plants with high amounts of NA-enriched seed oil, , , and .

Comprehensive Analysis of N6-Methyladenosine-Related Long Noncoding RNA Prognosis of Acute Myeloid Leukemia and Immune Cell Infiltration.

N6-Methyladenosine-related long noncoding RNAs play an essential role in many cancers' development. However, the relationship between m6A-related lncRNAs and acute myelogenous leukemia (AML) prognosis remains unclear. We systematically analyzed the association of m6A-related lncRNAs with the prognosis and tumor immune microenvironment (TME) features using the therapeutically applicable research to generate effective treatment (TARGET) database.

A transformer-based multi-features fusion model for prediction of conversion in mild cognitive impairment.

Mild cognitive impairment (MCI) is usually considered the early stage of Alzheimer's disease (AD). Therefore, the accurate identification of MCI individuals with high risk in converting to AD is essential for the potential prevention and treatment of AD. Recently, the great success of deep learning has sparked interest in applying deep learning to neuroimaging field.

Aberrant functional network topology and effective connectivity in burnout syndrome.

Objective: Recent neuroimaging studies have demonstrated that burnout is linked to specific anatomical and functional abnormalities in the brain. However, topological alterations of brain networks are not yet characterized in burnout.

Methods: Resting-state functional magnetic resonance imaging (rs-fMRI) was performed on 32 female participants with burnout and 30 matched healthy controls.

Better Medications Adherence Lowers Cardiovascular Events, Stroke, and All-Cause Mortality Risk: A Dose-Response Meta-Analysis.

Aims: We investigated the association between vascular medication adherence, assessed by different methods, and the risk of cardio-cerebrovascular events and all-cause mortality.

Methods: A meta-analysis with a systematic search of PubMed, Web of Science, EMBASE, and Cochrane databases from inception date to 21 June 2021 was used to identify relevant studies that had evaluated the association between cardiovascular medication adherence levels and cardiovascular events (CVEs), stroke, and all-cause mortality risks. Pooled relative risks (RRs) and 95% confidence intervals (CIs) were calculated using a random-effects meta-analysis.

Metabolically healthy obesity increases the prevalence of stroke in adults aged 40 years or older: Result from the China National Stroke Screening survey.

Debate over the cardio-cerebrovascular risk associated with metabolically healthy obesity (MHO) continues. In this study we investigated the association of MHO with the risk of stroke among 221,114 individuals aged 40 years or older based on data from the China National Stroke Screening and Prevention Project (CNSSPP), a nationally representative cross-sectional study, during 2014 to 2015. Different metabolic health and obesity phenotypes were defined according to the Adult Treatment Panel III (ATP III) criteria, where obesity was defined as a body mass index (BMI) ≥28 kg/m.

Analysis of changes in the glycerolipidome in response to long-term chilling and heat.

Long-term moderately high or low temperatures can damage economically important plants. In the present study, we treated , an important traditional Chinese medicine, with temperatures of 10, 20, and 30 °C for 30 days. We then investigated glycerolipidome responses to these moderate temperature stresses using an ESI/MS-MS-based lipidomic approach.

Impacts of CO elevation on the physiology and seed quality of soybean.

Understanding the responses of crops to elevated atmospheric carbon dioxide concentrations (E[CO]) is very important in terms of global food supplies. The present study investigates the effects of CO enrichment (to 800 μmol mol) on the physiology of soybean plants and the nutritional value of their seeds under growth chamber conditions. The photosynthesis of soybean was significantly promoted by E[CO] at all growth stages, but leaf area and specific leaf weight were not affected.

DUSP1 induces apatinib resistance by activating the MAPK pathway in gastric cancer.

Dual‑specificity phosphatase‑1 (DUSP1) is an oncogene that is associated with cancer progression following drug resistance. In order to investigate the potential relationship between DUSP1 and apatinib resistance in gastric cancer cells, we preformed many assays to study this problem. DUSP1 gene was detected by RT‑qPCR assay, proteins in MAPK pathway were quantified by western blot assay, and CCK‑8 assay, flow cytometry and Hoechest 33342 stain were performed to detect the resistance of cells, cell cycles and apoptosis, respectively.

Specialized metabolites from Ageratina adenophora and their inhibitory activities against pathogenic fungi.

The Asteraceae plant Ageratina adenophora (also called Eupatorium adenophorum) has became the most destructive invasive species in China, especially the southwestern region, and is gravely threatening the native biodiversity. Its high reproductive capacity is partly due to the developed root system. From the roots of A.