This study was aimed at establishing a method of ultra-fast quantitative PCR for Brucella detection.We used an exogenous recombinant plasmid as the internal reference and targeted the T4SS secretion system,an important Brucella viru-lence factor,to design specific primers and probes.The sensitivity,specificity,and repeatability of this method were evaluated,and a standard curve was constructed.The coincidence rate of detection findings with this method versus quantitative PCR was determined.This method markedly decreased the detection time to only 10 minutes.The standard curve demonstrated a good linear relationship(Y=-3.410 7x+38.357,R2=0.998 5)with a low minimum detection limit of 10 copies/μL.The method exhibited good specificity and did not specifically amplify several common clinical bacteria other than Brucella.The de-tection of three concentrations of positive plasmids yielded coefficients of variation(CVs)of 0.20％to 0.91％,thus demonstra-ting the method's excellent repeatability.Furthermore,140 clinical samples were analyzed concurrently with the fluorescence PCR method,which yielded a 100％compliance rate and consistent results.Our findings indicated that the Brucella ultra-fast quantitative PCR was ultrafast;had high sensitivity,high specificity,and good specificity;and can be used for the clinical de-tection of Brucella and emergency investigation of epidemics.Therefore,this method is valuable for the early diagnosis of Bru-cella.

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.

Objective:This study aims to explore the risk factors of Multi-center Investigator-Initiated Clinical Trials (MIITs), and provide a basis for developing study management strategies.Methods:The original draft of MIIT risk evaluation factors was determined through literature analysis and internal discussions of the research group. Thirty five experts were consulted using the Delphi method, and then the MIIT risk evaluation elements were finally determined. Analytic Hierarchy Process (AHP) was used to calculate the weights of each index.Results:The recovery rates of both rounds of expert consultation were 100%, and the degree of expert authority was 0.856. The study ultimately formed an MIIT risk evaluation framework consisting of three first-class indexes, twelve second-class indexes, and thirty-eight third-class indexes. The weight values of the first-class indexes (start-up period, implementation period, and summary period) were 0.209 8, 0.710 6, and 0.079 6, respectively. Meanwhile, the weight values of the second-class indexes and third-class indexes were determined.Conclusions:Exploring the risk evaluation factors of MIIT provides valuable insights into identifying critical risk points, which, in turn, contributes to enhancing MIIT management efficiency, research progress, and quality.

This work was aimed at analyzing the protein characteristics of Coiled-Coil Domain-Containing Protein 115(CCDC115)and using Ccdc115-deficient mouse monocyte-macrophage leukemia cells(RAW264.7)to explore the influence of CCDC115 on the intracellular survival of Salmonella Typhimurium.Bioinformatics analysis was conducted to examine the fundamental attributes of CCDC115,which was determined to be an unstable protein consisting of two α-helices and an intervening disordered re-gion,devoid of any transmembrane structural domains.A RAW264.7-Ccdc115-KO cell line was successfully established with CRISPR/Cas9 gene-editing technology.To elucidate the effects of CCDC115 on the intracellular survival of Salmonella Typhimurium,we infected RAW264.7 cells with Salmonella Typhimurium.The expression of CCDC115 was found to be upregulated at both the mRNA and protein levels post-infection,according to RT-qPCR and western blot analysis.Via counting of colony-forming units(CFU),the proliferation rate of Salmonella Typhimurium within RAW264.7-Ccdc115-KO cells was found to be 1.5-fold higher than that in RAW264.7 cells.Acidification imaging studies indicated that,whereas Salmonella Typhimurium phagosomes underwent acidifi-cation in RAW264.7 cells,this process was absent in RAW264.7-Ccdc115-KO cells.In conclusion,the study successfully estab-lished a RAW264.7-Ccdc115-KO cell line and demonstrated that the expression of CCDC115 is elevated during Salmonella Ty-phimurium infection,thus potentially inhibiting the intracellular survival of Salmonella Typhimurium by facilitating phagosome acidifi-cation.This study lay a theoretical foundation for functional studies of CCDC115 and the investigation of mechanisms regulating the survival of intracellular Salmonella Typhimurium.

This work was aimed at analyzing the protein characteristics of Coiled-Coil Domain-Containing Protein 115(CCDC115)and using Ccdc115-deficient mouse monocyte-macrophage leukemia cells(RAW264.7)to explore the influence of CCDC115 on the intracellular survival of Salmonella Typhimurium.Bioinformatics analysis was conducted to examine the fundamental attributes of CCDC115,which was determined to be an unstable protein consisting of two α-helices and an intervening disordered re-gion,devoid of any transmembrane structural domains.A RAW264.7-Ccdc115-KO cell line was successfully established with CRISPR/Cas9 gene-editing technology.To elucidate the effects of CCDC115 on the intracellular survival of Salmonella Typhimurium,we infected RAW264.7 cells with Salmonella Typhimurium.The expression of CCDC115 was found to be upregulated at both the mRNA and protein levels post-infection,according to RT-qPCR and western blot analysis.Via counting of colony-forming units(CFU),the proliferation rate of Salmonella Typhimurium within RAW264.7-Ccdc115-KO cells was found to be 1.5-fold higher than that in RAW264.7 cells.Acidification imaging studies indicated that,whereas Salmonella Typhimurium phagosomes underwent acidifi-cation in RAW264.7 cells,this process was absent in RAW264.7-Ccdc115-KO cells.In conclusion,the study successfully estab-lished a RAW264.7-Ccdc115-KO cell line and demonstrated that the expression of CCDC115 is elevated during Salmonella Ty-phimurium infection,thus potentially inhibiting the intracellular survival of Salmonella Typhimurium by facilitating phagosome acidifi-cation.This study lay a theoretical foundation for functional studies of CCDC115 and the investigation of mechanisms regulating the survival of intracellular Salmonella Typhimurium.

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.

This study was aimed at establishing a method of ultra-fast quantitative PCR for Brucella detection.We used an exogenous recombinant plasmid as the internal reference and targeted the T4SS secretion system,an important Brucella viru-lence factor,to design specific primers and probes.The sensitivity,specificity,and repeatability of this method were evaluated,and a standard curve was constructed.The coincidence rate of detection findings with this method versus quantitative PCR was determined.This method markedly decreased the detection time to only 10 minutes.The standard curve demonstrated a good linear relationship(Y=-3.410 7x+38.357,R2=0.998 5)with a low minimum detection limit of 10 copies/μL.The method exhibited good specificity and did not specifically amplify several common clinical bacteria other than Brucella.The de-tection of three concentrations of positive plasmids yielded coefficients of variation(CVs)of 0.20％to 0.91％,thus demonstra-ting the method's excellent repeatability.Furthermore,140 clinical samples were analyzed concurrently with the fluorescence PCR method,which yielded a 100％compliance rate and consistent results.Our findings indicated that the Brucella ultra-fast quantitative PCR was ultrafast;had high sensitivity,high specificity,and good specificity;and can be used for the clinical de-tection of Brucella and emergency investigation of epidemics.Therefore,this method is valuable for the early diagnosis of Bru-cella.

Objective:This study aims to explore the risk factors of Multi-center Investigator-Initiated Clinical Trials (MIITs), and provide a basis for developing study management strategies.Methods:The original draft of MIIT risk evaluation factors was determined through literature analysis and internal discussions of the research group. Thirty five experts were consulted using the Delphi method, and then the MIIT risk evaluation elements were finally determined. Analytic Hierarchy Process (AHP) was used to calculate the weights of each index.Results:The recovery rates of both rounds of expert consultation were 100%, and the degree of expert authority was 0.856. The study ultimately formed an MIIT risk evaluation framework consisting of three first-class indexes, twelve second-class indexes, and thirty-eight third-class indexes. The weight values of the first-class indexes (start-up period, implementation period, and summary period) were 0.209 8, 0.710 6, and 0.079 6, respectively. Meanwhile, the weight values of the second-class indexes and third-class indexes were determined.Conclusions:Exploring the risk evaluation factors of MIIT provides valuable insights into identifying critical risk points, which, in turn, contributes to enhancing MIIT management efficiency, research progress, and quality.

The surface plasmon resonance(SPR)biosensor technology is a novel optical analysis method for studying intermolecular interactions.Owing to in-depth research on traditional Chinese medicine(TCM)in recent years,comprehensive and specific identification of components and target interactions has become key yet difficult tasks.SPR has gradually been used to analyze the active components of TCM owing to its high sensitivity,strong exclusivity,large flux,and real-time monitoring capabilities.This review sought to briefly introduce the active components of TCM and the principle of SPR,and provide historical and new insights into the application of SPR in the analysis of the active components of TCM.

Objective To explore the correlation between colorectal cancer tissue Janus kinase 2(JAK2)gene mutations and T cytokine 3(TCF3)protein expression with clinical pathological characteristics and prognosis,and to provide laboratory reference indicators for early evaluation of the illness severity and prognosis of colorectal cancer.Methods A retrospective analysis was conducted on the data of 50 colorectal cancer patients who were admitted from January 2016 to April 2021 and retained colon cancer and adjacent tissue wax blocks.Basic information,clinical and pathological features such as TNM staging,lymph node metastasis,and 3-year survival prognosis of the patients were collected.The wax blocks of colon cancer and adjacent tissues of patients were detected,in which Taqman fluorescence probe method was applied to detect the distribution of JAK2 gene at the rs2230724 locus AA,AG,and GG genotypes in colon cancer tissues,and immunohistochemistry method was applied to detect the positive expression rate of TCF3 protein in colon cancer and adjacent tissues.The basic information,JAK2 rs2230724 gene mutation,and TCF3 protein expression of patients with different clinical and pathological characteristics were compared,and the influencing factors of clinical and pathological characteristics of colon cancer was analyzed by logistic regression model.Kaplan Meier survival curve was applied to compare the survival prognosis of patients with JAK2 gene mutations and TCF3 protein expression in colorectal cancer tissue,and Cox regression model was applied to analyze the risk factors affecting the prognosis of colorectal cancer patients.Results The positive expression rate of TCF3 protein in colon cancer tissues was higher than that in adjacent tissues(P＜0.05).The age,BMI,proportion of GG genotype at rs2230724 locus of JAK2 gene and positive expression rate of TCF3 protein in TNM stage Ⅲ colon cancer patients were higher than those in TNM stage Ⅰ-Ⅱ patients(P＜0.05);The age,BMI,smoking rate,proportion of GG type at the rs2230724 locus of JAK2 gene in colon cancer tissue,and positive expression rate of TCF3 protein in patients with lymph node metastasis were higher than those without lymph node metastasis(P＜0.05);The results of the logistic regression model analysis showed that the influencing factors of clinical pathological features such as TNM staging and lymph node metastasis in colon cancer were age,mutation of JAK2 gene rs2230724 site in colon cancer tissue,and positive expression rate of TCF3 protein(P＜0.05).The Kaplan Meier survival curve analysis showed that patients with JAK2 gene rs2230724 GG genotype and TCF3 protein positivity in colon cancer tissue had higher cumulative all-cause mortality rates(P＜0.05).The results of univariate and multivariate Cox regression model analysis showed that independent risk factors affecting the prognosis of colorectal cancer patients include JAK2 gene rs2230724 site GG type,TCF3 protein positive expression,TNM stage Ⅲ,lymph node metastasis,and age.Conclusion The proportion of JAK2 gene rs2230724 GG type and TCF3 protein expression in colorectal cancer tissues are related to their clinical pathological characteristics and prognosis,and can be used as reference indicators for evaluating clinical pathological characteristics and predicting prognosis of colorectal cancer.