Pulmonary fibrosis（PF） is a progressive and life-threatening disease with limited therapeutic options， highlighting the urgent need for innovative drug discovery strategies. To address this challenge， the authors propose the formula-originated rational intelligent screening&translation（FIRST）， a systematic framework for developing anti-fibrotic monomers derived from classical traditional Chinese medicine（TCM）. The strategy integrates three key dimensions， including tissue-oriented intelligent screening of active compounds， structural optimization based on drug-target spatial interactions and plant biosynthetic pathways， and cross-scale validation of drug. We further highlight its applications in discovering tissue-oriented novel drugs from clinically validated TCM， the development and mechanistic elucidation of anti-fibrotic therapeutics， as well as the clinical translation and secondary development of candidate drugs. This strategy paves the way for first-in-class， formula-derived monomeric drugs with defined structures， clarified mechanisms， and proven safety， offering a transformative avenue to meet the urgent therapeutic needs of PF and setting a new paradigm for TCM-based drug innovation.

Pulmonary fibrosis（PF） is a progressive and life-threatening disease with limited therapeutic options， highlighting the urgent need for innovative drug discovery strategies. To address this challenge， the authors propose the formula-originated rational intelligent screening&translation（FIRST）， a systematic framework for developing anti-fibrotic monomers derived from classical traditional Chinese medicine（TCM）. The strategy integrates three key dimensions， including tissue-oriented intelligent screening of active compounds， structural optimization based on drug-target spatial interactions and plant biosynthetic pathways， and cross-scale validation of drug. We further highlight its applications in discovering tissue-oriented novel drugs from clinically validated TCM， the development and mechanistic elucidation of anti-fibrotic therapeutics， as well as the clinical translation and secondary development of candidate drugs. This strategy paves the way for first-in-class， formula-derived monomeric drugs with defined structures， clarified mechanisms， and proven safety， offering a transformative avenue to meet the urgent therapeutic needs of PF and setting a new paradigm for TCM-based drug innovation.

Objective To explore a method for rapid and differential diagnosis of rejection after kidney transplantation through urine hyperspectral imaging technology. Methods Hyperspectral data information from urine samples of 118 recipients after kidney transplantation was collected, and a deep learning model was constructed to diagnose and classify the types of rejection. Results A deep learning diagnostic model based on the 34-layer residual network (ResNet-34) was constructed, and 118 patients were included and divided into the training set and the test set. Based on the pathological results of the transplanted kidney puncture, the urine samples of the patients were classified into five groups: the non-rejection group, the T-cell-mediated rejection group, the antibody-mediated rejection group, the mixed rejection group and the nephropathy recurrence group. The results showed that the diagnostic sensitivities of the model for the above five groups were 0.960, 0.980, 0.930, 0.940 and 0.943 respectively, and the diagnostic specificities were 0.983, 0.993, 0.997, 0.989 and 0.989 respectively. The overall diagnostic accuracy rate reached 95.7%. Conclusions The study provides a non-invasive, rapid and accurate auxiliary diagnostic method for the differential diagnosis of rejection after kidney transplantation.

ObjectiveTo explore the function and related molecular mechanisms of magnoflorine against colon cancer via network pharmacology， molecular docking， and in vitro cell experiments. MethodsIn this study， the canonical SMILES of magnoflorine was obtained from the PubChem database， and the potential targets of magnoflorine were predicted by the Swiss Target Prediction database， while the disease targets of colon cancer were obtained from the DisGeNET， GeneCards and OMIM databases. The intersecting targets between magnoflorine's predicted targets and colon cancer disease targets were taken， and a protein-protein interaction （PPI） network was constructed and analyzed. The DAVID online database was employed to conduct Gene Ontology （GO） and KEGG pathway enrichment analyses on core targets. The top five key targets screened were docked with magnoflorine using AutoDock software. Finally， cellular experiments including CCK-8 assays， EdU experiments， cell scratch assays， and Transwell assays were conducted to validate the results from network pharmacology and molecular docking. Results44 key targets of magnoflorine in resisting colon cancer were acquired. The molecular docking results showed that magnoflorine had a strong binding activity with the core target signal transducer and activator of transcription 3 （STAT3） in the top five of the PPI network. Cellular experiments confirmed that magnoflorine could inhibit the proliferation and migration of colon cancer cells by suppressing the JAK/STAT3 signaling pathway. ConclusionMagnoflorine may inhibit the proliferation and migration of colon cancer cells by regulating the JAK/STAT3 signaling pathway.

Heart failure （HF） is the end stage of various cardiovascular diseases， manifested as ventricular filling or impaired left ventricular ejection fraction （LVEF）. Type 2 diabetes mellitus （T2DM） is a metabolic disorder， mainly caused by insufficient insulin secretion and insulin resistance. The incidence rates of the two common clinical diseases are increasing year by year. HF and T2DM are mutually causal chronic diseases， and their mutual deterioration can form a HF-T2DM comorbidity. The comorbidity status significantly increases the mortality and readmission risk of patients， and seriously affects the quality of life of patients. At present， western medicine treatment mainly involves the use of anti HF drugs and hypoglycemic drugs， which can significantly alleviate the clinical symptoms of patients. However， these two categories of drugs are numerous in variety and differ in their mechanisms， and blind combination may produce adverse reactions. With its unique theoretical system， traditional Chinese medicine exhibits the advantages of multiple components， multiple targets， and high safety through traditional Chinese medicine monomers， Chinese patent medicine， traditional Chinese medicine compounds， and acupuncture， which significantly delays the development of HF-T2DM and shows great potential in clinical treatment. A large number of clinical studies have shown that the integrated traditional Chinese and western medicine in the treatment of HF-T2DM has a significant effect in improving cardiac function， controlling blood glucose levels， and improving the quality of life of patients. Based on relevant research at home and abroad， this paper reviewed the HF-T2DM pathogenesis， pharmacological effects， research status of traditional Chinese and western medicine， and early prevention strategies， and discussed current problems and future research directions， in order to provide new ideas and useful references for clinical prevention and treatment of HF-T2DM.

Heart failure （HF） is the end stage of various cardiovascular diseases， manifested as ventricular filling or impaired left ventricular ejection fraction （LVEF）. Type 2 diabetes mellitus （T2DM） is a metabolic disorder， mainly caused by insufficient insulin secretion and insulin resistance. The incidence rates of the two common clinical diseases are increasing year by year. HF and T2DM are mutually causal chronic diseases， and their mutual deterioration can form a HF-T2DM comorbidity. The comorbidity status significantly increases the mortality and readmission risk of patients， and seriously affects the quality of life of patients. At present， western medicine treatment mainly involves the use of anti HF drugs and hypoglycemic drugs， which can significantly alleviate the clinical symptoms of patients. However， these two categories of drugs are numerous in variety and differ in their mechanisms， and blind combination may produce adverse reactions. With its unique theoretical system， traditional Chinese medicine exhibits the advantages of multiple components， multiple targets， and high safety through traditional Chinese medicine monomers， Chinese patent medicine， traditional Chinese medicine compounds， and acupuncture， which significantly delays the development of HF-T2DM and shows great potential in clinical treatment. A large number of clinical studies have shown that the integrated traditional Chinese and western medicine in the treatment of HF-T2DM has a significant effect in improving cardiac function， controlling blood glucose levels， and improving the quality of life of patients. Based on relevant research at home and abroad， this paper reviewed the HF-T2DM pathogenesis， pharmacological effects， research status of traditional Chinese and western medicine， and early prevention strategies， and discussed current problems and future research directions， in order to provide new ideas and useful references for clinical prevention and treatment of HF-T2DM.

Objective:To investigate the effect of uridine diphosphate ceramide galactosyltransferase 8(UGT8)on colorectal cancer(CRC)cell growth and migration,elucidate an underlying mechanism,and assess the potential regulatory role of SRY-box transcription factor 9(SOX9)on UGT8.Methods:UGT8 and SOX9 mRNA expression levels in CRC tissues,and correlation between their expression levels,were analyzed using GEPIA2,UALCAN,and TIMER 2.0 online databases.UGT8 and SOX9 protein expression in CRC and adjacent tissues was detec-ted using immunohistochemistry,and relationships between their expression and clinicopathological characteristics were analyzed.Impact of UGT8 knockdown on CRC cell proliferation was assessed using a CCK-8 assay,and cell migration was evaluated using Transwell and wound healing assays.Western blot was performed to detect expression of epithelial-mesenchymal transition(EMT)markers(E-cadherin and ZEB1).RT-qPCR and Western blot were used to measure UGT8 mRNA and protein expression levels after SOX9 knockdown.The JASPAR online database was used to assess SOX9 potential for binding to the UGT8 promoter.Results:Bioinformatics analyses revealed significantly higher mRNA expression levels of both UGT8 and SOX9 in CRC tissues than in normal tissues.Positive correlation was observed between expres-sion levels.Immunohistochemistry results showed that tumor UGT8 and SOX9 protein levels were significantly higher than those in adjacent tissues.UGT8 protein level was found to correlates with N stage,and SOX9 protein level correlated with T stage.A positive correlation was observed between UGT8 and SOX9 expression levels.Following UGT8 knockdown,cell proliferation capacity was attenuated and cell migra-tion ability was reduced.E-cadherin expression concurrently increased and ZEB1 expression decreased.RT-qPCR and Western blot results showed that SOX9 knockdown significantly reduced UGT8 mRNA and protein levels.The JASPER website predicts that SOX9 will bind to the UGT8 promoter.Conclusions:UGT8 and SOX9 are highly expressed in CRC tissues,and their expression levels correlate with clinicopatholo-gical features.UGT8 and SOX9 expression levels display significant positive correlation.Mechanistically,UGT8 promotes CRC cell prolifera-tion and migration by facilitating epithelial-mesenchymal transition(EMT).SOX9 enhances UGT8 mRNA and protein expression and may bind to the UGT8 promoter region.

Metabolomics covers a wide range of applications in life sciences, biomedicine, and phytology. Data acquisition (to achieve high coverage and efficiency) and analysis (to pursue good classification) are two key segments involved in metabolomics workflows. Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups. However, insufficient feature extraction, inappropriate feature selection, overfitting, or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused. Using two ginseng varieties, namely Panax japonicus (PJ) and Panax japonicus var. major (PJvm), containing the similar ginsenosides, we integrated pseudo-targeted metabolomics and deep neural network (DNN) modeling to achieve accurate species differentiation. A pseudo-targeted metabolomics approach was optimized through data acquisition mode, ion pairs generation, comparison between multiple reaction monitoring (MRM) and scheduled MRM (sMRM), and chromatographic elution gradient. In total, 1980 ion pairs were monitored within 23 min, allowing for the most comprehensive ginseng metabolome analysis. The established DNN model demonstrated excellent classification performance (in terms of accuracy, precision, recall, F1 score, area under the curve, and receiver operating characteristic (ROC)) using the entire metabolome data and feature-selection dataset, exhibiting superior advantages over random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP). Moreover, DNNs were advantageous for automated feature learning, nonlinear modeling, adaptability, and generalization. This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples. This established approach holds promise for plant metabolomics and is not limited to ginseng.

Metabolomics covers a wide range of applications in life sciences,biomedicine,and phytology.Data acquisition(to achieve high coverage and efficiency)and analysis(to pursue good classification)are two key segments involved in metabolomics workflows.Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups.However,insufficient feature extraction,inappropriate feature selection,overfitting,or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused.Using two ginseng varieties,namely Panax japonicus(PJ)and Panax japonicus var.major(PJvm),containing the similar ginsenosides,we integrated pseudo-targeted metabolomics and deep neural network(DNN)modeling to achieve accurate species differentiation.A pseudo-targeted metabolomics approach was optimized through data acquisition mode,ion pairs generation,comparison between multiple reaction monitoring(MRM)and scheduled MRM(sMRM),and chromatographic elution gradient.In total,1980 ion pairs were monitored within 23 min,allowing for the most comprehensive ginseng metabolome analysis.The established DNN model demonstrated excellent classification performance(in terms of accuracy,precision,recall,F1 score,area under the curve,and receiver operating characteristic(ROC))using the entire metabolome data and feature-selection dataset,exhibiting superior advantages over random forest(RF),support vector ma-chine(SVM),extreme gradient boosting(XGBoost),and multilayer perceptron(MLP).Moreover,DNNs were advantageous for automated feature learning,nonlinear modeling,adaptability,and generalization.This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples.This established approach holds promise for plant metabolomics and is not limited to ginseng.

Objective To explore the expression characteristics of BAIAP2L1 in cervical cancer (CC) and its regulatory role in tumor cell metastasis. Methods The correlation between BAIAP2L1 expression and clinical prognosis was analyzed by using a public database. GO pathway enrichment and clinicopathological correlation analyses were conducted by employing R language. The effect of BAIAP2L1 knockdown on CC cell proliferation, invasion, migration, and epithelial-mesenchymal transition (EMT) were further investigated through gene silencing approaches. Results BAIAP2L1 expression was significantly upregulated in CC tissues (P_adj <0.001) and it was identified as an independent risk factor for patient mortality (HR=2.808, P=0.03). Elevated BAIAP2L1 levels showed significant correlations with poor overall survival, advanced T/N stage, recurrence, and metastasis (all P<0.05). Functional enrichment analysis revealed its involvement in tumor metastasis-related pathways. The knockdown of BAIAP2L1 significantly attenuated CC cell proliferation, invasion, and migration and suppressed key EMT processes (all P<0.05). Conclusion BAIAP2L1 is overexpressed in CC tissues and associated with patient prognosis and metastasis. The targeted inhibition of BAIAP2L1 can effectively curb tumor progression.