OTUCD: Unsupervised GCN based metagenomics non-overlapping community detection.

Metagenomics is a discipline that studies the genetic material of all tiny organisms in the biological environment. In recent years, the interaction between metagenomic microbial communities, the transfer of horizontal genes, and the dynamic changes of microbial ecosystems have attracted more and more attention. It is of great significance to use the community detection algorithm to divide the metagenomic microbes into modules, and it has a positive guiding role for the follow-up research on human, drug, microbial interaction study and drug prediction and development.

The Effects of Patient Education on Psychological Status and Clinical Outcomes in Rheumatoid Arthritis: A Systematic Review and Meta-Analysis.

Background: Rheumatoid arthritis (RA) is a common systemic inflammatory autoimmune disease. The disease has a serious impact on mental health and requires more effective non-pharmacological interventions.

Objective: This study aims to systematically evaluate the effectiveness of patient education on psychological status and clinical outcomes in rheumatoid arthritis.

Identification of Novel Loci Shared by Juvenile Idiopathic Arthritis Subtypes Through Integrative Genetic Analysis.

Objective: Juvenile idiopathic arthritis (JIA) is the most common chronic immune-mediated joint disease among children and encompasses a heterogeneous group of immune-mediated joint disorders classified into 7 subtypes according to clinical presentation. However, phenotype overlap and biologic evidence suggest a shared mechanistic basis between subtypes. This study was undertaken to systematically investigate shared genetic underpinnings of JIA subtypes.

Deep learning for microscopic examination of protozoan parasites.

The infectious and parasitic diseases represent a major threat to public health and are among the main causes of morbidity and mortality. The complex and divergent life cycles of parasites present major difficulties associated with the diagnosis of these organisms by microscopic examination. Deep learning has shown extraordinary performance in biomedical image analysis including various parasites diagnosis in the past few years.

Berberine ameliorates DSS-induced intestinal mucosal barrier dysfunction through microbiota-dependence and Wnt/β-catenin pathway.

Ulcerative colitis (UC) is an idiopathic, chronic inflammatory disorder of the colon, and it has become one of the world-recognized medical problems as it is recurrent and refractory. Berberine (BBR) is an effective drug for UC treatment. However, the underlying mechanism and targets remain obscure.

Sanguinarine induces apoptosis in Eimeria tenella sporozoites via the generation of reactive oxygen species.

Eimeria tenella (E. tenella) is the most pathogenic genus in Eimeria and can lead to a huge number of deaths of chickens, causing significant economic losses in the poultry industry worldwide. As a natural alkaloid, sanguinarine has many medicinal effects; to a certain extent, it can replace antibiotics and has good application prospects in veterinary medicine.

Quantifying the Colonization of Environmental Microbes in the Fish Gut: A Case Study of Wild Fish Populations in the Yangtze River.

In aquatic animals, gut microbial communities shift with host development and living environments. Understanding the mechanism by which the environment impacts the gut microbial communities of aquatic animals is crucial for assessing and managing aquatic ecosystem health. Here, we proposed a simplified framework for the colonization and dynamics of gut microbial communities.

Synthetic lethality of cyclin-dependent kinase inhibitor Dinaciclib with -deficiency allows for selective targeting of clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (CC-RCC) remains one of the most deadly forms of kidney cancer despite recent advancements in targeted therapeutics, including tyrosine kinase and immune checkpoint inhibitors. Unfortunately, these therapies have not been able to show better than a 16% complete response rate. In this study we evaluated a cyclin-dependent kinase inhibitor, Dinaciclib, as a potential new targeted therapeutic for CC-RCC.

Asymmetric Biomechanical Properties of the Paravertebral Muscle in Elderly Patients With Unilateral Chronic Low Back Pain: A Preliminary Study.

Clinical incidences of chronic low back pain among the elderly are increasing. However, studies have not fully elucidated on changes in biomechanical properties of paravertebral muscles in patients with unilateral chronic low back pain. We evaluated the changes in biomechanical properties of painful and non-painful paravertebral muscles in elderly patients with unilateral chronic low back pain.

Prediction of the disease causal genes based on heterogeneous network and multi-feature combination method.

At present, the prediction of disease causal genes is mainly based on heterogeneous. Research shows that heterogeneous network contains more information and have better prediction results. In this paper, we constructed a heterogeneous network including four node types of disease, gene, phenotype and gene ontology.

SmileGNN: Drug-Drug Interaction Prediction Based on the SMILES and Graph Neural Network.

Concurrent use of multiple drugs can lead to unexpected adverse drug reactions. The interaction between drugs can be confirmed by routine in vitro and clinical trials. However, it is difficult to test the drug-drug interactions widely and effectively before the drugs enter the market.

Molecular Characterization of the miR156/MsSPL Model in Regulating the Compound Leaf Development and Abiotic Stress Response in Alfalfa.

Plant leaf patterns and shapes are spectacularly diverse. Changing the complexity of leaflet numbers is a valuable approach to increase its nutrition and photosynthesis. Alfalfa () is the most important forage legume species and has diversified compound leaf patterns, which makes it a model species for studying compound leaf development.

Characterization and identification of microplastics using Raman spectroscopy coupled with multivariate analysis.

The manufacture and use of plastic products have resulted in the release and spread of a massive amount of microplastics. Identifying and quantifying microplastics is challenging due to their small size and complicated composition. Although vibrational spectroscopy has been applied to analyze microplastics, its reliability and throughput are limited by the challenges to distinguish the pending alterations manually and the lack of a spectra-based automated microplastic classification model.

The gut microbiota of bats confers tolerance to influenza virus (H1N1) infection in mice.

Pathogens from wild animals cause approximately 60% of emerging infectious diseases (EIDs). Studies on the immune systems of natural hosts are helpful for preventing the spread of EIDs. Bats are natural hosts for many emerging infectious pathogens and have a unique immune system that often coexists with pathogens without infection.

Intestinal morphology and microflora to Vibrio alginolyticus in pacific white shrimp (Litopenaeus vannamei).

In recent years, the shrimp farming industry encountered significant economic losses induced by Vibrio alginolyticus. In this study, the influence of Vibrio alginolyticus on intestinal histomorphology and microbiome composition in Litopenaeus vannamei were studied. The results showed that the intestinal mucosal epithelial cells of Vibrio group (VA group) injected only with Vibrio alginolyticus showed large area exfoliation at 12 h, and the tissue morphology of intestine recovered at 48 h.

Transcriptomic profiling suggests candidate molecular responses to waterlogging in cassava.

Owing to climate change impacts, waterlogging is a serious abiotic stress that affects crops, resulting in stunted growth and loss of productivity. Cassava (Manihot esculenta Grantz) is usually grown in areas that experience high amounts of rainfall; however, little research has been done on the waterlogging tolerance mechanism of this species. Therefore, we investigated the physiological responses of cassava plants to waterlogging stress and analyzed global gene transcription responses in the leaves and roots of waterlogged cassava plants.

Prot2GO: Predicting GO Annotations From Protein Sequences and Interactions.

Protein is the main material basis of living organisms and plays crucial role in life activities. Understanding the function of protein is of great significance for new drug discovery, disease treatment and vaccine development. In recent years, with the widespread application of deep learning in bioinformatics, researchers have proposed many deep learning models to predict protein functions.

Phosphate removal by a La(OH) loaded magnetic MAPTAC-based cationic hydrogel: Enhanced surface charge density and Donnan membrane effect.

Cationic hydrogels have received great attention to control eutrophication and recycle phosphate. In this study, a type of La(OH) loaded magnetic MAPTAC-based cationic hydrogel (La(OH)@MMCH) was developed as a potential adsorbent for enhanced phosphate removal from aqueous environment. La(OH)@MMCH exhibited high adsorption capacity of 105.

Effectiveness assessment of using riverine water eDNA to simultaneously monitor the riverine and riparian biodiversity information.

Both aquatic and terrestrial biodiversity information can be detected in riverine water environmental DNA (eDNA). However, the effectiveness of using riverine water eDNA to simultaneously monitor the riverine and terrestrial biodiversity information remains unidentified. Here, we proposed that the monitoring effectiveness could be approximated by the transportation effectiveness of land-to-river and upstream-to-downstream biodiversity information flows and described by three new indicators.

Clinical Pharmacology Perspectives for Adoptive Cell Therapies in Oncology.

Adoptive cell therapies (ACTs) have shown transformative efficacy in oncology with five US Food and Drug Administration (FDA) approvals for chimeric antigen receptor (CAR) T-cell therapies in hematological malignancies, and promising activity for T cell receptor T-cell therapies in both liquid and solid tumors. Clinical pharmacology can play a pivotal role in optimizing ACTs, aided by modeling and simulation toolboxes and deep understanding of the underlying biological and immunological processes. Close collaboration and multilevel data integration across functions, including chemistry, manufacturing, and control, biomarkers, bioanalytical, and clinical science and safety teams will be critical to ACT development.

Uncovering the action mechanism of homoharringtonine against colorectal cancer by using network pharmacology and experimental evaluation.

Homoharringtonine (HHT), an Food and Drug Administration (FDA)-approved anti-leukemia drug, exerts anti-tumor activity in several solid tumors, including colorectal cancer (CRC). However, its mechanism of action in CRC progression has not been comprehensively elucidated. The drug-disease targets were obtained using publicly available databases.

Riverbed Substrate Requirements for Natural Reproduction of .

(i.e., Qinghai Lake naked carp) is a migratory fish species that lives in highland brackish water.

Deep Learning Driven Drug Discovery: Tackling Severe Acute Respiratory Syndrome Coronavirus 2.

Deep learning significantly accelerates the drug discovery process, and contributes to global efforts to stop the spread of infectious diseases. Besides enhancing the efficiency of screening of antimicrobial compounds against a broad spectrum of pathogens, deep learning has also the potential to efficiently and reliably identify drug candidates against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Consequently, deep learning has been successfully used for the identification of a number of potential drugs against SARS-CoV-2, including Atazanavir, Remdesivir, Kaletra, Enalaprilat, Venetoclax, Posaconazole, Daclatasvir, Ombitasvir, Toremifene, Niclosamide, Dexamethasone, Indomethacin, Pralatrexate, Azithromycin, Palmatine, and Sauchinone.

Student's Learning Strategies and Academic Emotions: Their Influence on Learning Satisfaction During the COVID-19 Pandemic.

Based on the control-value theory (CVT), learning strategies and academic emotions are closely related to learning achievement, and have been considered as important factors influencing student's learning satisfaction and learning performance in the online learning context. However, only a few studies have focused on the influence of learning strategies on academic emotions and the interaction of learning strategies with behavioral engagement and social interaction on learning satisfaction. The participants were 363 pre-service teachers in China, and we used structural equation modeling (SEM) to analyze the mediating and moderating effects of the data.