Discovery of a New Anti-Inflammatory Agent from Anemoside B4 Derivatives and Its Therapeutic Effect on Colitis by Targeting Pyruvate Carboxylase.

Anemoside B4 (AB4), a triterpenoidal saponin from , shows significant anti-inflammatory activity, and may be used for treating inflammatory bowel disease (IBD). Nevertheless, its application is limited due to its high molecular weight and pronounced water solubility. To discover new effective agents for treating IBD, we synthesized 28 AB4 derivatives and evaluated their cytotoxic and anti-inflammatory activities .

Comprehensive untargeted lipidomics study of black morel (Morchella sextelata) at different growth stages.

The black morel (Morchella sextelata) is a valuable edible and medicinal mushroom appreciated worldwide. Here, lipidomic profiles and lipid dynamic changes during the growth of M. sexletata were analyzed using ultra-performance liquid chromatography coupled with mass spectrometry.

Prediction of blood-brain barrier penetrating peptides based on data augmentation with Augur.

Background: The blood-brain barrier serves as a critical interface between the bloodstream and brain tissue, mainly composed of pericytes, neurons, endothelial cells, and tightly connected basal membranes. It plays a pivotal role in safeguarding brain from harmful substances, thus protecting the integrity of the nervous system and preserving overall brain homeostasis. However, this remarkable selective transmission also poses a formidable challenge in the realm of central nervous system diseases treatment, hindering the delivery of large-molecule drugs into the brain.

Antibiotic combinations prediction based on machine learning to multicentre clinical data and drug interaction correlation.

Background: With increasing antibiotic resistance and regulation, the issue of antibiotic combination has been emphasised. However, antibiotic combination prescribing lacks a rapid identification of feasibility, while its risk of drug interactions is unclear.

Methods: We conducted statistical descriptions on 16 101 antibiotic coprescriptions for inpatients with bacterial infections from 2015 to 2023.

Predicting the role of the human gut microbiome in type 1 diabetes using machine-learning methods.

Gut microbes is a crucial factor in the pathogenesis of type 1 diabetes (T1D). However, it is still unclear which gut microbiota are the key factors affecting T1D and their influence on the development and progression of the disease. To fill these knowledge gaps, we constructed a model to find biomarker from gut microbiota in patients with T1D.

Predicting coronary heart disease in Chinese diabetics using machine learning.

Diabetes, a common chronic disease worldwide, can induce vascular complications, such as coronary heart disease (CHD), which is also one of the main causes of human death. It is of great significance to study the factors of diabetic patients complicated with CHD for understanding the occurrence of diabetes/CHD comorbidity. In this study, by analyzing the risk of CHD in more than 300,000 diabetes patients in southwest China, an artificial intelligence (AI) model was proposed to predict the risk of diabetes/CHD comorbidity.

Benefits and risks of drug combination therapy for diabetes mellitus and its complications: a comprehensive review.

Diabetes is a chronic metabolic disease, and its therapeutic goals focus on the effective management of blood glucose and various complications. Drug combination therapy has emerged as a comprehensive treatment approach for diabetes. An increasing number of studies have shown that, compared with monotherapy, combination therapy can bring significant clinical benefits while controlling blood glucose, weight, and blood pressure, as well as mitigating damage from certain complications and delaying their progression in diabetes, including both type 1 diabetes (T1D), type 2 diabetes (T2D) and related complications.

Anemoside B4, a new pyruvate carboxylase inhibitor, alleviates colitis by reprogramming macrophage function.

Objectives: Colitis is a global disease usually accompanied by intestinal epithelial damage and intestinal inflammation, and an increasing number of studies have found natural products to be highly effective in treating colitis. Anemoside B4 (AB4), an abundant saponin isolated from Pulsatilla chinensis (Bunge), which was found to have strong anti-inflammatory activity. However, the exact molecular mechanisms and direct targets of AB4 in the treatment of colitis remain to be discovered.

Accurately identifying hemagglutinin using sequence information and machine learning methods.

Introduction: Hemagglutinin (HA) is responsible for facilitating viral entry and infection by promoting the fusion between the host membrane and the virus. Given its significance in the process of influenza virus infestation, HA has garnered attention as a target for influenza drug and vaccine development. Thus, accurately identifying HA is crucial for the development of targeted vaccine drugs.

P450Rdb: A manually curated database of reactions catalyzed by cytochrome P450 enzymes.

Introduction: Cytochrome P450 enzymes (P450s) are recognized as the most versatile catalysts worldwide, playing vital roles in numerous biological metabolism and biosynthesis processes across all kingdoms of life. Despite the vast number of P450 genes available in databases (over 300,000), only a small fraction of them (less than 0.2 %) have undergone functional characterization.

A gender specific risk assessment of coronary heart disease based on physical examination data.

Large-scale screening for the risk of coronary heart disease (CHD) is crucial for its prevention and management. Physical examination data has the advantages of wide coverage, large capacity, and easy collection. Therefore, here we report a gender-specific cascading system for risk assessment of CHD based on physical examination data.

iPADD: A Computational Tool for Predicting Potential Antidiabetic Drugs Using Machine Learning Algorithms.

Diabetes mellitus is a chronic metabolic disease, which causes an imbalance in blood glucose homeostasis and further leads to severe complications. With the increasing population of diabetes, there is an urgent need to develop drugs to treat diabetes. The development of artificial intelligence provides a powerful tool for accelerating the discovery of antidiabetic drugs.

Bitter-RF: A random forest machine model for recognizing bitter peptides.

Introduction: Bitter peptides are short peptides with potential medical applications. The huge potential behind its bitter taste remains to be tapped. To better explore the value of bitter peptides in practice, we need a more effective classification method for identifying bitter peptides.

Construction of a Combined Hypoxia-related Genes Model for Hepatocellular Carcinoma Prognosis.

Background: Hepatocellular carcinoma (HCC) is the most common liver malignancy where tumorigenesis and metastasis are believed to be tied to the hallmarks of hypoxia and tumor microenvironment (TME).

Methods: In this study, to investigate the relationships among hypoxia, TME, and HCC prognosis, we collected two independent datasets from a public database (TCGA-LIHC for identification, GSE14520 for validation) and identified the hypoxia-related differentially expressed genes (DEGs) from the TCGA data, and the univariable Cox regression and lasso regression analyses were performed to construct the prognosis model. An HCC prognosis model with 4 hypoxiarelated DEGs ("NDRG1", "ENO1", "SERPINE1", "ANXA2") was constructed, and high- and low-risk groups of HCC were established by the median of the model risk score.

DeepIDC: A Prediction Framework of Injectable Drug Combination Based on Heterogeneous Information and Deep Learning.

Background And Objective: In clinical practice, injectable drug combination (IDC) usually provides good therapeutic effects for patients. Numerous clinical studies have directly indicated that inappropriate IDC generates adverse drug events (ADEs). The clinical application of injections is increasing, and many injections lack relevant combination information.

Identification of adaptor proteins using the ANOVA feature selection technique.

The adaptor proteins play a crucially important role in regulating lymphocyte activation. Rapid and efficient identification of adaptor proteins is essential for understanding their functions. However, biochemical methods require not only expensive experimental costs, but also long experiment cycles and more personnel.

TCM2COVID: A resource of anti-COVID-19 traditional Chinese medicine with effects and mechanisms.

In China, traditional Chinese medicine (TCM) has been widely used for coronavirus infectious disease 2019 (COVID-19) prevention, treatment, and recovery and has played a part in the battle against the disease. A variety of TCM treatments have been recommended for different stages of COVID-19. But, to the best of our knowledge, a comprehensive database for storing and organizing anti-COVID TCM treatments is still lacking.

Integration of Transcriptomics and Metabolomics for Understanding the Different Vegetative Growth in .

is an edible and medicinal fungus with high nutritional, medicinal, and economic value. Recently, . has been produced through artificial cultivation in China, but its stable production remains problematic because the details of its growth and development process are limitedly understood.

iRice-MS: An integrated XGBoost model for detecting multitype post-translational modification sites in rice.

Post-translational modification (PTM) refers to the covalent and enzymatic modification of proteins after protein biosynthesis, which orchestrates a variety of biological processes. Detecting PTM sites in proteome scale is one of the key steps to in-depth understanding their regulation mechanisms. In this study, we presented an integrated method based on eXtreme Gradient Boosting (XGBoost), called iRice-MS, to identify 2-hydroxyisobutyrylation, crotonylation, malonylation, ubiquitination, succinylation and acetylation in rice.

[Exploration on rationality evaluation approach of drug combination medication based on sequential analysis and machine learning].

Drug combination is a common clinical phenomenon. However, the scientific implementation of drug combination is li-mited by the weak rational evaluation that reflects its clinical characteristics. In order to break through the limitations of existing evaluation tools, examining drug-to-drug and drug-to-target action characteristics is proposed from the physical, chemical and biological perspectives, combining clinical multicenter case resources, domestic and international drug interaction public facilities with the aim of discovering the common rules of drug combination.

Untargeted Metabolomics Reveals Alterations in the Primary Metabolites and Potential Pathways in the Vegetative Growth of Morchella sextelata.

one of the true morels, has recently been artificially cultivated with stable production in China. Analysis of the variations in primary metabolites during the vegetative stages of is critical for understanding the metabolic process. In this study, three developmental stages were categorized based on morphological and developmental changes, including the young mushroom period, marketable mature period, and physiological maturity period.

Chemical Composition of Aromas and Lipophilic Extracts from Black Morel () Grown in China.

Morels ( spp.) are valuable medicinal and edible mushrooms. In this study, chemical profiles of aromas and lipophilic extracts of black morel () grown in China were analyzed by gas chromatography/mass spectroscopy, along with the evaluation of antioxidant and antimicrobial activities for the lipophilic extracts.