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.

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.

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.

Objective: To analyze the characteristics of first-visit cases of herpes zoster in Zhoushan City, Zhejiang Province from 2021 to 2023, so as to provide the reference for improving herpes zoster prevention and control measures. Methods: Data on the incidence and vaccination of first-visit herpes zoster cases at all levels of public medical institutions in Zhoushan City from 2021 to 2023 were collected through the Zhoushan Comprehensive Health Information Platform and Zhoushan Immunization Program Information Management System. The incidence and outpatient proportion were calculated. The population distribution, seasonal distribution, and clinical consultation status of first-visit herpes zoster cases were described. Results: From 2021 to 2023, a total of 15 156 first-visit herpes zoster cases were reported in Zhoushan City, with an average annual incidence of 5.36‰. The incidences for each year were 5.78‰, 5.29‰ and 5.02‰, respectively, and the outpatient proportions were 0.15%, 0.14% and 0.11%, respectively, showed decreasing trends (both P<0.05). The number of doses of recombinant herpes zoster vaccine or live attenuated herpes zoster vaccine administered were 130, 312, and 633, respectively. The main consultation department was dermatology, with 11 004 cases (72.60%). The primary clinical diagnosis was visceral herpes zoster, with 5 901 cases (38.94%). A total of 1 936 cases (12.77%) had at least one underlying medical condition. The mean age of onset was (56.08±16.23) years, and the incidence showed an upward trend with increasing age (P<0.05). There were 7 386 male cases and 7 770 female cases, with a male-to-female ratio of 0.95∶1. The incidence among males aged ≥50 years was lower than that among females (6.53‰ vs. 8.69‰, P<0.05). The onset of the disease exhibited a significant seasonal pattern, with a peak period from June 21st to August 21st, covering 75% of the cases Conclusions From 2021 to 2023, the incidence and outpatient proportion of herpes zoster in Zhoushan City decreased. Summer was the peak season for onset, and women and the elderly were the key populations. It is necessary to strengthen the collaborative diagnostic and treatment capabilities of key departments such as dermatology and enhance the enthusiasm for vaccination among key populations.

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 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.

To construct a recombinant fowl adenovirus 4(FAdV-4)expressing the Cap protein of goose astrovirus genotype 2(GoAstV-2),the expression cassette of Cap gene was inserted into the natural 1 966 bp deletion region of the FAdV-4 genome in the infectious clone p15A-cm-FAdV4-HNJZ.The resulted recombinant plasmid p15A-cm-FAdV4-HNJZ-Cap/GoAstV-2 was linearized with restriction enzyme and transfected into chicken hepatoma cell line(LMH)to rescue the recombinant FAdV-4 expressing the Cap protein of GoAstV-2,rF Ad V4-Cap/GoAstV-2.After 15 passages in LMH cells,the recombinant rFAdV4-Cap/GoAstV-2 was identified by PCR using primers flanking the insertion site of the Cap gene expression cassette and using viral genome DNA extracted from rFAdV4-Cap/GoAstV-2 infected LMH cells as template.LMH cells were in-fected with 15th passage rFAdV4-Cap/GoAstV-2 and indirect immunofluorescence was performed with a polyclonal antibody against Cap protein as the primary antibody.Western blot was carried out with lysates of rFAdV4-Cap/GoAstV-2 infected LMH cells.The in vitro replication dynamic of the 15th passage of the rFAdV4-Cap/GoAstV-2 was also investigated in LMH cells.The results demonstrated that the Cap gene of GoAstV-2 was presented in the genome of the recombinant vi-rus rF AdV4-Cap/Go Ast V-2,and could be expressed stably.The prepared recombinant virus in this study will lay a foundation for developing inactivated bivalent vaccine candidate against co-in-fection of FAdV-4 and GoAstV-2 in goose.

Objective:To explore the longitudinal relationship between adolescents' future self-continuity and depression, providing insights into their future development.Methods:A total of 419 adolescents from a school in Shandong Province were selected, and two followed-up surveys were conducted at 6-month intervals in April (T1) and October (T2) of 2023 using the future self-continuity questionnaire and Beck depression inventory-Ⅱ. Cross-lagged analysis was performed using Mplus 8.0.Results:The scores of future self-continuity were 36.01±8.69 at baseline (T1) and 35.89±9.92 after six months (T2), while the scores of depression were 2.00(0, 14.00) and 1.00(0, 9.00), respectively. There was a negative correlation between future self-continuity and depression at both time points (T1: r=-0.195, T2: r=-0.239, both P<0.01). Adolescents' future self-continuity could predict depression six months later ( β=-0.098, P<0.01), and depression also could predict the level of future self-continuity six months later ( β=-0.114, P<0.01). Conclusion:There is an interactively influence effect between adolescents' future self-continuity and depression.While enhancing adolescents' future self-continuity level to alleviate depression, attention should also be paid attention to the impact of depression on their future self-continuity, which can help adolescents overcome the interference of depressive emotions and grow into a better future self.

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.