Metabolic dysfunction-associated fatty liver disease （MAFLD）， as one of the most common chronic liver diseases in the world， poses a severe challenge to precision diagnosis and treatment due to its complex pathogenesis and highly heterogeneous disease progression. Existing clinical classification systems cannot meet the needs for comprehensively analyzing the complexity of the disease and the heterogeneity of its adverse outcomes. In recent years， data-driven prognostic risk classification methods have gradually emerged， optimizing the ability for predicting adverse outcomes and enhancing the accuracy of identifying different endpoint outcomes. However， such paradigm of “classify first， associate outcomes later” suffers from a “black-box” nature， and there are various indicators for classification， leading to limited stability and generalizability in clinical application. Future research needs to integrate or establish large-scale population cohorts， develop outcome-oriented prognostic risk classification models， incorporate dynamic data， refine classification algorithms， and validate their generalizability across multiple populations， thereby providing reliable support for the precision diagnosis and treatment of MAFLD.

ObjectiveTo preliminarily explore the active components and target pathways of Angelicae Sinensis Radix-Polygonati Rhizoma （ASR-PR） herb pair in the treatment of simple obesity through network pharmacology and molecular docking， and to verify and investigate its mechanism of action via animal experiments. MethodsThe chemical constituents and targets of ASR and PR were predicted using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform （TCMSP）. Targets related to simple obesity were identified by retrieving the GeneCards， Online Mendelian Inheritance in Man （OMIM）， Pharmacogenomics Knowledgebase （PharmGKB）， and DisGeNET databases. The intersection of drug and disease targets was used to construct an active component-target network using Cytoscape software. This network was imported into the STRING database to construct a protein-protein interaction （PPI） network， and topological analysis was conducted to identify core genes. Gene Ontology （GO） analysis and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analysis and mapping were performed using the DAVID database and the Microbioinformatics platform. AutoDock 1.5.7 software was used to perform molecular docking between the top five active components and core targets. An animal model of simple obesity was established by feeding C57BL/6J mice a high-fat diet. The mice were administered ASR （2.06 g·kg^-1）， PR （2.06 g·kg^-1）， or ASR-PR （4.11 g·kg^-1） for 10 weeks， while the model group received an equal volume of purified water by gavage. After the administration period， the mice were sacrificed to measure body fat weight and serum levels of total cholesterol （TC）， triglycerides （TG）， high-density lipoprotein （HDL）， and low-density lipoprotein （LDL）. Hematoxylin-eosin （HE） staining was used to observe histopathological sections of liver and adipose tissue. Serum levels of leptin， interleukin-6 （IL-6）， and tumor necrosis factor-α （TNF-α） were determined by enzyme-linked immunosorbent assay （ELISA）， and the mRNA expression levels of epidermal growth factor receptor （EGFR） and signal transducer and activator of transcription 3 （STAT3） in liver tissue were detected by real-time quantitative polymerase chain reaction （Real-time PCR）. ResultsNetwork pharmacology and molecular docking results indicated that the treatment of simple obesity by ASR-PR may involve the regulation of protein expression of core targets EGFR and STAT3 by its main components MOL009760 （Siberian glycoside A_qt）， MOL003889 （methyl protodioscin_qt）， MOL009766 （resveratrol）， MOL006331 （4′，5-dihydroxyflavone）， and MOL004941 （baicalin）， thereby modulating the PI3K/Akt and JAK/STAT signaling pathways. The animal experiment results showed that compared with the normal group， the model group had significantly increased body weight， body fat weight， and serum levels of TG， TC， TNF-α， IL-6， and leptin （P<0.01）. EGFR mRNA expression was significantly elevated （P<0.05）， while STAT3 mRNA expression was significantly decreased （P<0.01）. Histological analysis revealed disordered hepatic architecture in the model group， with pronounced lipid vacuoles， cytoplasmic loosening， lipid accumulation， and steatosis. Adipocytes in white adipose tissue （WAT） and brown adipose tissue （BAT） of the model group exhibited markedly increased diameters， reduced cell counts per unit area， and irregular morphology. Compared with the model group， the ASR-PR group significantly reduced body weight， body fat weight， serum TC， IL-6， TNF-α， leptin levels， and EGFR mRNA expression （P<0.01）. TG levels were also significantly decreased （P<0.05）， while STAT3 mRNA expression was significantly increased （P<0.01）. Histopathological improvements included reduced size and number of hepatic lipid vacuoles and restoration of liver cell morphology toward that of the normal group. The diameter of adipocytes significantly decreased， and the number of adipocytes per unit area increased. ConclusionASR-PR may regulate the expression of key target proteins such as EGFR and STAT3 via its core active components， modulate the PI3K/Akt and JAK/STAT signaling pathways， repair damaged liver and adipose tissues， and thereby alleviate the progression of obesity in mice.

ObjectiveTo preliminarily explore the active components and target pathways of Angelicae Sinensis Radix-Polygonati Rhizoma （ASR-PR） herb pair in the treatment of simple obesity through network pharmacology and molecular docking， and to verify and investigate its mechanism of action via animal experiments. MethodsThe chemical constituents and targets of ASR and PR were predicted using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform （TCMSP）. Targets related to simple obesity were identified by retrieving the GeneCards， Online Mendelian Inheritance in Man （OMIM）， Pharmacogenomics Knowledgebase （PharmGKB）， and DisGeNET databases. The intersection of drug and disease targets was used to construct an active component-target network using Cytoscape software. This network was imported into the STRING database to construct a protein-protein interaction （PPI） network， and topological analysis was conducted to identify core genes. Gene Ontology （GO） analysis and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analysis and mapping were performed using the DAVID database and the Microbioinformatics platform. AutoDock 1.5.7 software was used to perform molecular docking between the top five active components and core targets. An animal model of simple obesity was established by feeding C57BL/6J mice a high-fat diet. The mice were administered ASR （2.06 g·kg^-1）， PR （2.06 g·kg^-1）， or ASR-PR （4.11 g·kg^-1） for 10 weeks， while the model group received an equal volume of purified water by gavage. After the administration period， the mice were sacrificed to measure body fat weight and serum levels of total cholesterol （TC）， triglycerides （TG）， high-density lipoprotein （HDL）， and low-density lipoprotein （LDL）. Hematoxylin-eosin （HE） staining was used to observe histopathological sections of liver and adipose tissue. Serum levels of leptin， interleukin-6 （IL-6）， and tumor necrosis factor-α （TNF-α） were determined by enzyme-linked immunosorbent assay （ELISA）， and the mRNA expression levels of epidermal growth factor receptor （EGFR） and signal transducer and activator of transcription 3 （STAT3） in liver tissue were detected by real-time quantitative polymerase chain reaction （Real-time PCR）. ResultsNetwork pharmacology and molecular docking results indicated that the treatment of simple obesity by ASR-PR may involve the regulation of protein expression of core targets EGFR and STAT3 by its main components MOL009760 （Siberian glycoside A_qt）， MOL003889 （methyl protodioscin_qt）， MOL009766 （resveratrol）， MOL006331 （4′，5-dihydroxyflavone）， and MOL004941 （baicalin）， thereby modulating the PI3K/Akt and JAK/STAT signaling pathways. The animal experiment results showed that compared with the normal group， the model group had significantly increased body weight， body fat weight， and serum levels of TG， TC， TNF-α， IL-6， and leptin （P<0.01）. EGFR mRNA expression was significantly elevated （P<0.05）， while STAT3 mRNA expression was significantly decreased （P<0.01）. Histological analysis revealed disordered hepatic architecture in the model group， with pronounced lipid vacuoles， cytoplasmic loosening， lipid accumulation， and steatosis. Adipocytes in white adipose tissue （WAT） and brown adipose tissue （BAT） of the model group exhibited markedly increased diameters， reduced cell counts per unit area， and irregular morphology. Compared with the model group， the ASR-PR group significantly reduced body weight， body fat weight， serum TC， IL-6， TNF-α， leptin levels， and EGFR mRNA expression （P<0.01）. TG levels were also significantly decreased （P<0.05）， while STAT3 mRNA expression was significantly increased （P<0.01）. Histopathological improvements included reduced size and number of hepatic lipid vacuoles and restoration of liver cell morphology toward that of the normal group. The diameter of adipocytes significantly decreased， and the number of adipocytes per unit area increased. ConclusionASR-PR may regulate the expression of key target proteins such as EGFR and STAT3 via its core active components， modulate the PI3K/Akt and JAK/STAT signaling pathways， repair damaged liver and adipose tissues， and thereby alleviate the progression of obesity in mice.

Purpose: Cancer has become a significant major public health concern, making the discovery of new cancer markers or therapeutic targets exceptionally important. Elevated expression of tumor necrosis factor receptor superfamily member 12A (TNFRSF12A) expression has been observed in certain types of cancer. This project aims to investigate the function of TNFRSF12A in tumors and the underlying mechanisms. Materials and Methods: Various websites were utilized for conducting the bioinformatics analysis. Tumor cell lines with stable knockdown or overexpression of TNFRSF12A were established for cell phenotyping experiments and subcutaneous tumorigenesis in BALB/c mice. RNA-seq was employed to investigate the mechanism of TNFRSF12A. Results: TNFRSF12A was upregulated in the majority of cancers and associated with a poor prognosis. Knockdown TNFRSF12A hindered the colorectal cancer progression, while overexpression facilitated malignancy both in vitro and in vivo. TNFRSF12A overexpression led to increased nuclear factor кB (NF-κB) signaling and significant upregulation of baculoviral IAP repeat containing 3 (BIRC3), a transcription target of the NF-κB member RELA, and it was experimentally confirmed to be a critical downstream factor of TNFRSF12A. Therefore, we speculated the existence of a TNFRSF12A/RELA/BIRC3 regulatory axis in colorectal cancer. Conclusion TNFRSF12A is upregulated in various cancer types and associated with a poor prognosis. In colorectal cancer, elevated TNFRSF12A expression promotes tumor growth, potentially through the TNFRSF12A/RELA/BIRC3 regulatory axis.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Purpose: Cancer has become a significant major public health concern, making the discovery of new cancer markers or therapeutic targets exceptionally important. Elevated expression of tumor necrosis factor receptor superfamily member 12A (TNFRSF12A) expression has been observed in certain types of cancer. This project aims to investigate the function of TNFRSF12A in tumors and the underlying mechanisms. Materials and Methods: Various websites were utilized for conducting the bioinformatics analysis. Tumor cell lines with stable knockdown or overexpression of TNFRSF12A were established for cell phenotyping experiments and subcutaneous tumorigenesis in BALB/c mice. RNA-seq was employed to investigate the mechanism of TNFRSF12A. Results: TNFRSF12A was upregulated in the majority of cancers and associated with a poor prognosis. Knockdown TNFRSF12A hindered the colorectal cancer progression, while overexpression facilitated malignancy both in vitro and in vivo. TNFRSF12A overexpression led to increased nuclear factor кB (NF-κB) signaling and significant upregulation of baculoviral IAP repeat containing 3 (BIRC3), a transcription target of the NF-κB member RELA, and it was experimentally confirmed to be a critical downstream factor of TNFRSF12A. Therefore, we speculated the existence of a TNFRSF12A/RELA/BIRC3 regulatory axis in colorectal cancer. Conclusion TNFRSF12A is upregulated in various cancer types and associated with a poor prognosis. In colorectal cancer, elevated TNFRSF12A expression promotes tumor growth, potentially through the TNFRSF12A/RELA/BIRC3 regulatory axis.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Coronavirus disease 2019 (COVID-19) is an acute infectious respiratory disease that has been prevalent since December 2019. Chinese medicine (CM) has demonstrated its unique advantages in the fight against COVID-19 in the areas of disease prevention, improvement of clinical symptoms, and control of disease progression. This review summarized the relevant material components of CM in the treatment of COVID-19 by searching the relevant literature and reports on CM in the treatment of COVID-19 and combining with the physiological and pathological characteristics of the novel coronavirus. On the basis of sorting out experimental methods in vivo and in vitro, the mechanism of herb action was further clarified in terms of inhibiting virus invasion and replication and improving related complications. The aim of the article is to explore the strengths and characteristics of CM in the treatment of COVID-19, and to provide a basis for the research and scientific, standardized treatment of COVID-19 with CM.
Humans ; Drugs, Chinese Herbal/pharmacology* ; COVID-19 Drug Treatment ; SARS-CoV-2/drug effects* ; COVID-19/therapy* ; Medicine, Chinese Traditional/methods* ; Antiviral Agents/pharmacology* ; Animals

Cytotoxicity, usually represented by cell viability, is a crucial parameter for evaluating drug safety in vitro. Accurate prediction of cell viability/cytotoxicity could accelerate drug development in the early stage. In this study, by integrating cellular transcriptome and cell viability data using four machine learning algorithms (support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM)) and two ensemble algorithms (voting and stacking), highly accurate prediction models of 50% and 80% cell viability were developed with area under the receiver operating characteristic curve (AUROC) of 0.90 and 0.84, respectively; these models also showed good performance when utilized for diverse cell lines. Concerning the characterization of the employed Feature Genes, the models were interpreted, and the mechanisms of bioactive compounds with a narrow therapeutic index (NTI) can also be analyzed. In summary, the models established in this research exhibit superior capacity to those of previous studies; these models enable accurate high-safety substance screening via cytotoxicity prediction across cell lines. Moreover, for the first time, Cytotoxicity Signature (CTS) genes were identified, which could provide additional clues for further study of mechanisms of action (MOA), especially for NTI compounds.

Drug development encompasses multiple processes, wherein protein subcellular localization is essential. It promotes target identification, treatment development, and the design of drug delivery systems. In this research, a deep learning framework called LocPro is presented for predicting protein subcellular localization. Specifically, LocPro is unique in (a) combining protein representations from the pre-trained large language model (LLM) ESM2 and the expert-driven tool PROFEAT, (b) implementing a hybrid deep neural network architecture that integrates convolutional neural network (CNN), fully connected (FC) layer, and bidirectional long short-term memory (BiLSTM) blocks, and (c) developing a multi-label framework for predicting protein subcellular localization at multiple granularity levels. Additionally, a dataset was curated and divided using a homology-based strategy for training and validation. Comparative analyses show that LocPro outperforms existing methods in sequence-based multi-label protein subcellular localization prediction. The practical utility of this framework is further demonstrated through case studies on drug target subcellular localization. All in all, LocPro serves as a valuable complement to existing protein localization prediction tools. The web server is freely accessible at https://idrblab.org/LocPro/.