Rho proteins and the Rho-associated protein kinase(ROCK)signaling pathway are cruci-al components of intracellular signaling cascades.Rho proteins,which belong to the small GTPase family,play a pivotal role in regulating essential elements of the cytoskeleton within cells.ROCK functions as a downstream effector protein kinase of Rho,modulating various biological processes,including cell morphology,migration,and proliferation.Recent studies have underscored the signifi-cance of the ROCK signaling pathway in the replication of a diverse group of viruses.Furthermore,it has been discovered that some viruses disrupt cellular contraction,adhesion,and migration through the Rho/ROCK pathway,subsequently influencing the immune response triggered by vi-ral infections and affecting the tight junctions between cells.This article primarily reviews the re-search progress regarding the Rho/ROCK signaling pathway and its key signaling molecules,Rho and ROCK,in terms of their activation and regulation of viral replication and tight junction pro-teins between cells.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Aim To verify the mechanism of action of Carthami Flos-Lycopodii Herba in treating KOA carti-lage inflammation based on network pharmacology and in vitro and in vivo experiments.Methods The effec-tive ingredients of Carthami Flos-Lycopodii Herba were screened through the database,the core targets of"drug disease"were analyzed,and pathway enrichment analy-sis and molecular docking verification were conducted.Experimental verification:Primary chondrocytes were extracted from mice and divided into the control group,IL-1β group,treatment group,and treatment+TLR4 agonist group.CCK-8 method was used to screen the optimal intervention concentration of Carthami Flos-Ly-copodii Herba.ELISA was used to detect the content of inflammatory factors in chondrocytes.Western blot was employed to detect the protein expression related to cellular pathways.Subsequently,a KOA mouse model was constructed using the DMM method.After admin-istration,the knee joint injury of mice was evaluated u-sing safranin O-green staining.ELISA was used to de-tect the levels of inflammatory factors in serum.West-ern blot was employed to detect collagen Ⅱ,MMP13,Aggrecan,and apoptosis related protein expression in cartilage tissue.TUNEL staining was used to detect the apoptosis rate of cells.Results A total of 26 active ingredients of Carthami Flos-Lycopodii Herba were screened,as well as 123 potential targets for treating KOA.The enrichment analysis results indicated that it mainly involved mechanisms such as Toll like receptors and cell apoptosis.The experimental results showed that Carthami Flos-Lycopodii Herba alleviated the in-flammatory response of chondrocytes and affected the expression of pathway related proteins.Compared with KOA mice,safflower stretched muscle grass could im-prove cartilage damage and reduce the concentration of serum inflammatory factors,regulate the expression of collagen Ⅱ,MMP13,Aggrecan,and apoptosis related proteins in cartilage tissue,and reduce the fluorescence intensity of TUNEL staining in the tissue.Conclusions Carthami Flos-Lycopodii Herba can improve KOA cartilage inflammation,and its mechanism may be relat-ed to the TLR4/MyD88/NF-κB signaling pathway.

Aim To investigate the therapeutic effects of cinnamaldehyde on synovial lesions in mice with knee osteoarthritis(KOA)and its regulatory mecha-nism in the phagocytic function of synovial macropha-ges.Methods In the animal experiments,mouse ser-um and synovial tissue were extracted.HE staining was used to evaluate the inflammatory cell infiltration in the synovial tissue.ELISA was employed to detect the lev-els of inflammatory factors such as interleukins in the serum.Western blot was used to detect the expression of Ras homolog family member A(RhoA),Rho-associ-ated protein kinase 1(ROCK1),myosin light chain(MLC),and p-MLC proteins in the synovial tissue.RT-qPCR was utilized to detect the expression of in-flammatory factors and pathway-related mRNA in the synovial tissue.TUNEL staining was used to detect ap-optosis in the synovial tissue.In the cellular experi-ments,after the intervention,RAW267.4 cells were subjected to Western blot and RT-qPCR for the detec-tion of the aforementioned indicators,and confocal mi-croscopy was used to assess phagocytic function.Re-sults After cinnamaldehyde intervention,the synovial inflammatory infiltration was significantly reduced,the protein and mRNA expression of the RhoA/ROCK1/MLC signaling pathway was markedly downregulated,the fluorescence intensity of TUNEL staining signifi-cantly decreased,and the phagocytic function of macro-phages was enhanced.Conclusion Cinnamaldehyde can inhibit RhoA/Rock1/MLC signaling pathway,en-hance macrophage burial,improve synovial inflamma-tion,and delay the progression of KO A mice.

Rho proteins and the Rho-associated protein kinase(ROCK)signaling pathway are cruci-al components of intracellular signaling cascades.Rho proteins,which belong to the small GTPase family,play a pivotal role in regulating essential elements of the cytoskeleton within cells.ROCK functions as a downstream effector protein kinase of Rho,modulating various biological processes,including cell morphology,migration,and proliferation.Recent studies have underscored the signifi-cance of the ROCK signaling pathway in the replication of a diverse group of viruses.Furthermore,it has been discovered that some viruses disrupt cellular contraction,adhesion,and migration through the Rho/ROCK pathway,subsequently influencing the immune response triggered by vi-ral infections and affecting the tight junctions between cells.This article primarily reviews the re-search progress regarding the Rho/ROCK signaling pathway and its key signaling molecules,Rho and ROCK,in terms of their activation and regulation of viral replication and tight junction pro-teins between cells.

Aim To investigate the therapeutic effects of cinnamaldehyde on synovial lesions in mice with knee osteoarthritis(KOA)and its regulatory mecha-nism in the phagocytic function of synovial macropha-ges.Methods In the animal experiments,mouse ser-um and synovial tissue were extracted.HE staining was used to evaluate the inflammatory cell infiltration in the synovial tissue.ELISA was employed to detect the lev-els of inflammatory factors such as interleukins in the serum.Western blot was used to detect the expression of Ras homolog family member A(RhoA),Rho-associ-ated protein kinase 1(ROCK1),myosin light chain(MLC),and p-MLC proteins in the synovial tissue.RT-qPCR was utilized to detect the expression of in-flammatory factors and pathway-related mRNA in the synovial tissue.TUNEL staining was used to detect ap-optosis in the synovial tissue.In the cellular experi-ments,after the intervention,RAW267.4 cells were subjected to Western blot and RT-qPCR for the detec-tion of the aforementioned indicators,and confocal mi-croscopy was used to assess phagocytic function.Re-sults After cinnamaldehyde intervention,the synovial inflammatory infiltration was significantly reduced,the protein and mRNA expression of the RhoA/ROCK1/MLC signaling pathway was markedly downregulated,the fluorescence intensity of TUNEL staining signifi-cantly decreased,and the phagocytic function of macro-phages was enhanced.Conclusion Cinnamaldehyde can inhibit RhoA/Rock1/MLC signaling pathway,en-hance macrophage burial,improve synovial inflamma-tion,and delay the progression of KO A mice.

This study aims to investigate the anti-inflammatory and anti-apoptotic effects of total flavonoids of hawthorn leaves(TFHL)on lipopolysaccharide(LPS)-induced inflammatory injury in rat intestinal epithelial(IEC-6)cells,as well as the underlying mechanisms.An in vitro inflam-mation model was first established by treating IEC-6 cells with lipopolysaccharide(LPS).IEC-6 cells were then incubated with three concentrations of TFHL for 24 h prior to a further 24 h LPS treatment.RT-qPCR was used to quantify mRNA levels of the inflammatory genes COX-2 and iN-OS,while Western blotting was used to assess protein levels of the apoptotic markers Bax,cleaved Caspase-3,Bcl-2,and the JNK/p-JNK signaling pathway.Finally,cells were pretreated with TFHL and/or the JNK inhibitor SP600125 for 24 h before LPS exposure for 24 h,in order to evaluate the combined effects of TFHL and SP600125 on LPS-induced inflammatory cytokine expression and apoptotic protein levels in IEC-6 cells.The results showed that,compared with the LPS group,the mRNA level of COX-2 and iNOS in the 2.5,5.0,10.0 mg/L TFHL group and the Bax and Caspase-3 protein levels decreased significantly(P＜0.01),and the Bcl-2 protein level was significantly higher(P＜0.01),p-JNK protein level and p-JNK/JNK ratio decreased significantly(P＜0.01);compared with the LPS group,the COX-2 and iNOS mRNA levels of the TFHL+LPS group de-creased significantly(P＜0.01),Bax,and Caspase-3 protein levels decreased significantly(P＜0.01),and the level of Bcl-2 protein increased significantly(P＜0.05);compared with the LPS group,the COX-2 and iNOS mRNA levels of the TFHL+SP600125 group decreased significantly(P＜0.01),Bax and Caspase-3 protein levels decreased significantly(P＜0.01),and Bcl-2 protein level increased significantly(P＜0.01).These findings indicate that TFHL exerts anti-inflammato-ry and anti-apoptotic effects in LPS-challenged IEC-6 cells by inhibiting the JNK signaling path-way.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.