Driven by the growing practical need to accelerate drug development and the continuous innovation of trial design in recent years， the number of protocol amendments during clinical trials have gradually increased， and the changed contents have become more flexible and complex， which significantly heightens the difficulty of ethical review on amendments. Against this backdrop， it is of great importance to fully leverage the role and responsibilities of ethics committees， effectively control clinical trial risks， and ensure subject safety. This paper analyzed development trends of protocol amendments in recent years， sorted out requirements for protocol amendments in Chinese regulations and guiding principles， and examined difficulties of amendment ethical review in practical work. Based on these， targeted strategies and recommendations were proposed， namely， strengthening the integration with scientific review， enhancing the formal review， adjusting the scope of review according to approval notifications， and adopting appropriate review methods， with a view to providing insights and references for the management of the amendment ethical review in drug clinical trials.

Objective: To explore the association between the HLA antibody positivity rate in female blood donors and pregnancy history, number of pregnancies, interval from the last pregnancy to blood donation, and age, to identify associated variables using a univariate generalized additive model (GAM), and to further analyze the predictive role of characteristic variables for HLA antibody positivity using a random forest model. Methods: HLA antibody detection was performed on 391 female blood donors using the Luminex immunomagnetic bead method. The correlation between pregnancy-related factors and HLA antibodies was analyzed using the Chi-square test. Based on R software, a univariate GAM was first constructed to analyze the association types between characteristic variables and the HLA antibody positivity rate, followed by the construction of a random forest model to evaluate the predictive value of the variables. Results: Among the 391 female blood donors without a transfusion history, the overall HLA antibody positivity rate was 26.34%. The positivity rate in donors with a pregnancy history was significantly higher than that in those without (30.09% vs 9.72%, P<0.05), and HLA antibody positivity rate increased linearly with the number of pregnancies (P<0.05). In the univariate GAM, age and number of deliveries exhibited a non-linear association with the HLA antibody positivity rate (the positivity rate increased sharply between 25-35 years of age and stabilized after 3 deliveries). Besides, the interval from the last pregnancy to blood donation showed a linear association with the HLA antibody positivity rate, and the positivity rate decreased as the interval prolonged (P<0.05). In the random forest model, age (mean decrease gini=29.26) and interval from the last pregnancy to blood donation (mean decrease gini=22.02) were core predictive variables: age was more conducive to identifying positive samples, while the interval from the last pregnancy to blood donation was more helpful for excluding negative samples. The number of deliveries (mean decrease accuracy=16.98) made a significant contribution to predicting positive samples, whereas the number of abortions had no impact. The model had an AUC of 0.583 (95% CI: 0.593 8-0.770 2), indicating a certain predictive value. Conclusion: The associated variables identified by the univariate GAM model, including age, interval from the last pregnancy to blood donation, and number of deliveries, provide a basis for key variables in the random forest model. All three variables have predictive value for HLA antibody positivity, which can provide evidence-based support for personalized transfusion management and stratified screening of female blood donors in this region.

ObjectiveTo systematically assess the public health governance capacity in Zhejiang Province, to conduct an in-depth analysis of its strengths and weaknesses, so as to provide scientific basis and strategic recommendations for further enhancement. MethodsA systematic collection of policy documents, public information reports, and research literature related to public health governance capacity in Zhejiang Province from 2002 to 2023 was conducted (encompassing a total of 1 263 policy documents, 138 pieces of information reports and 631 research articles). Based on the evaluation criteria suitable for public health systems previously developed by the research team, the basic status and magnitude of change in public health governance capacity in Zhejiang Province was evaluated. Additionally, normative gap analyses were employed to identify the strengths and weaknesses. ResultsZhejiang Province ranked 4th nationwide in terms of public health governance capacity with a score of 733.4 points (1 000.0-point maximum). The province has effectively implemented the principle of health first (scoring 698.5 points in the assessment of health-first strategy implementation) and attached sufficient importance to health-related goals (scoring 658.2 points in the scientific rationality of goal setting). However, the implementation of inter-departmental coordination and incentive mechanisms only scored 178.7 points, the feasibility of management and monitoring mechanisms scored even lower at only 144.0 points, and the coverage of incentive mechanisms scored 286.0 points. ConclusionZhejiang Province has effectively implemented its health first strategy and attached great importance to health targets, but still needs to strengthen cross-departmental coordination mechanisms and health-oriented incentives.

Objective: To develop a prediction model for mild cognitive impairment (MCI) risk among the elderly, so as to provide a tool for MCI early screening. Methods : From July 2022 to September 2024, a multi-stage stratified random cluster sampling method was used to recruit permanent residents aged ≥65 years from the Xinjiang Uygur Autonomous Region as study participants. Data on sociodemographic characteristics, nutritional status, body composition indices, bone mineral density, and handgrip strength were collected through questionnaires and physical examinations. Sarcopenia was defined based on appendicular skeletal muscle index and handgrip strength. MCI was assessed using the Mini-Mental State Examination, with adjustments for educational level. Participants were randomly divided into a training set and a validation set in a 7∶3 ratio. LASSO regression and multivariable logistic regression models were employed to screen for predictors and construct an MCI risk prediction model. The predictive performance of the model was evaluated using receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Results: A total of 1 641 participants were surveyed, including 755 males (46.01%) and 886 females (53.99%). The majority of participants were aged 65-<75 years, comprising 1 154 individuals (70.32%). MCI was detected in 517 participants, corresponding to a detection rate of 31.51%. Resultsfrom LASSO regression and multivariate logistic regression analysis showed that residence (rural, OR = 2.323, 95% CI: 1.682-3.210), age (75-<85 years, OR = 1.405, 95% CI: 1.019-1.937; ≥85 years, OR = 3.655, 95% CI: 1.696-7.875), educational level (primary school, OR = 0.341, 95% CI: 0.247-0.472; junior high school, OR = 0.255, 95% CI: 0.160-0.408; high school, OR = 0.286, 95% CI: 0.154-0.531; bachelor's degree or above, OR = 0.120, 95% CI: 0.041-0.351), history of alcohol consumption (yes, OR = 3.216, 95% CI: 2.164-4.779), risk of malnutrition (yes, OR = 1.464, 95% CI: 1.064-2.014), sarcopenia (yes, OR = 3.197, 95% CI: 2.332-4.385), and waist-to-hip ratio (abnormal, OR = 1.540, 95% CI: 1.159-2.048) were identified as predictive factors for MCI among the elderly. In the training set, the area under the ROC curve, sensitivity, and specificity were 0.788, 0.719, and 0.712, respectively. In the validation set, the corresponding values were 0.784, 0.913, and 0.542, respectively. DCA demonstrated that the model provided a higher clinical net benefit for predicting MCI risk when the risk threshold probability ranged from 0.124 to 0.764. Conclusion The prediction model developed in this study demonstrates good discriminative ability and clinical utility, indicating its substantial value for predicting the MCI risk among the elderly.

BACKGROUND:Fibromyalgia syndrome,as a common rheumatic disease,is related to central sensitization and immune abnormalities.However,the specific mechanism has not been elucidated,and there is a lack of specific diagnostic markers.Exploring the possible pathogenesis of this disease has important clinical significance. OBJECTIVE:To screen the potential diagnostic marker genes of fibromyalgia syndrome and analyze the possible immune infiltration characteristics based on bioinformatics methods,such as weighted gene co-expression network analysis(WGCNA),and machine learning. METHODS:Gene expression profiles in peripheral serum of fibromyalgia syndrome patients and healthy controls were obtained from the gene expression omnibus(GEO)database.The differentially co-expressed genes were screened in the expression profile by differential analysis and WGCNA analysis.Least absolute shrinkage and selection operator(LASSO)and support vector machine-recursive feature elimination(SVM-RFE)machine learning algorithm were further used to identify hub biomarkers,and draw receiver operating characteristic curve(ROC)to evaluate the accuracy of diagnosing fibromyalgia syndrome.Finally,single sample gene set enrichment analysis(ssGSEA)and gene set enrichment analysis(GSEA)were used to evaluate the immune cell infiltration and pathway enrichment in patients with fibromyalgia syndrome. RESULTS AND CONCLUSION:Eight down-regulated differentially expressed genes(DEGs)were obtained after differential analysis of the GSE67311 dataset according to the conditions of log2|(FC)|>0 and P<0.05.After WGCNA analysis,497 genes were included in the module(MEdarkviolet)with the highest positive correlation(r=0.22,P=0.04),and 19 genes were included in the module(MEsalmon2)with the highest negative correlation(r=-0.41,P=6×10-5).After intersecting DEGs and the module genes of WGCNA,seven genes were obtained.Four genes were screened out by LASSO regression algorithm and five genes were screened out by SVM-RFE machine learning algorithm.After the intersection of the two,three core genes were identified,which were germinal center associated signaling and motility like,integrin beta-8,and carboxypeptidase A3.The areas under the ROC curve of the three core genes were 0.744,0.739,and 0.734,respectively,indicating that they have good diagnostic value and can be used as biomarkers for fibromyalgia syndrome.The results of immune infiltration analysis showed that memory B cells,CD56 bright NK cells,and mast cells were significantly down-regulated in patients with fibromyalgia syndrome compared with the control group(P<0.05),and were significantly positively correlated with the above three biomarkers(P<0.05).The enrichment analysis suggested that there were nine fibromyalgia syndrome enrichment pathways,mainly related to olfactory transduction pathway,neuroactive ligand-receptor interaction,and infection pathway.The above results showed that the occurrence and development of fibromyalgia syndrome are related to the involvement of multiple genes,abnormal immune regulation,and multiple pathways imbalance.However,the interactions between these genes and immune cells,as well as their relationships with various pathways need to be further investigated.

BACKGROUND:Rev-erbα is involved in the regulation of inflammation,but pharmacological activation of Rev-erbα increases the risk for cardiovascular diseases.To reduce the relevant risk,an exploration on SR9009,a Rev-erbα agonist,combined with other drugs to relieve inflammation in skeletal myoblasts was conducted,laying the theoretical foundation for the treatment of inflammation-associated skeletal muscle atrophy. OBJECTIVE:To investigate the relationship of SR9009,indolepropionic acid and nuclear factor-κB signaling pathways in lipopolysaccharide-induced C2C12 myoblasts. METHODS:(1)C2C12 myoblasts were induced to differentiate in the presence of lipopolysaccharide(1 μg/mL).RNA-seq and KEGG pathway analysis were used to study signaling pathways.(2)C2C12 myoblast viability was assessed using the cell counting kit-8 assay to determine optimal concentrations of indolepropionic acid.Subsequently,cells were categorized into control group,lipopolysaccharide(1 μg/mL)group,SR9009(10 μmol/L)+lipopolysaccharide group,indolepropionic acid(80μmol/L)+lipopolysaccharide group,and SR9009+indolepropionic acid+lipopolysaccharide group.ELISA was employed to measure protein expression levels of interleukin-6 in the cultured supernatant.Real-time quantitative PCR were employed to measure mRNA expression levels of interleukin-6,tumor necrosis factor α,TLR4 and CD14.Western blot assay were employed to measure protein expression levels of NF-κB p65 and p-NF-κB p65.(3)After Rev-erbα was knocked down by siRNA,knockdown efficiency was assessed by RT-qPCR.And mRNA levels of interleukin-6 and tumor necrosis factor α were also measured. RESULTS AND CONCLUSION:Compared with the blank control group,lipopolysaccharide time-dependently inhibited myofibroblast fusion to form myotubes,the mRNA expression levels of interleukin-6 and tumor necrosis factor α were elevated,and the level of interleukin-6 in the cell supernatant was significantly increased.The results of KEGG pathway showed that the nuclear factor-κB signaling pathway was activated by lipopolysaccharide.Indolepropionic acid exhibited significant suppression of C2C12 myoblasts viability when its concentration exceeded 80 μmol/L.Indolepropionic acid and SR9009 inhibited the activation of NF-κB signaling pathway,thereby played an anti-inflammatory role,and suppressed the mRNA expression levels of interleukin-6,tumor necrosis factor α,TLR4 and CD14.Compared with the lipopolysaccharide group,the ratio of p-NF-κB p65/NF-κB p65 protein expression were downregulated.SR9009 combined with indolepropionic acid notably reduced lipopolysaccharide-induced inflammation,further downregulated the mRNA expression levels of interleukin-6,tumor necrosis factor α,TLR4 and CD14.The ratio of p-NF-κB p65/NF-κB p65 protein expression was significantly lower than that in the SR9009+lipopolysaccharide group or indolepropionic acid+lipopolysaccharide group.Rev-erbα increases time-dependently with lipopolysaccharide induction.The knockdown efficiency of Rev-erbα by siRNA reached over 58%,and lipopolysaccharide was added after Rev-erbα was successfully knocked down.Compared with the lipopolysaccharide group,the mRNA expression levels of interleukin-6 and tumor necrosis factor α were significantly up-regulated.These results conclude that Rev-erbα may act as a promising pharmacological target to reduce inflammation.SR9009 targeted activation of Rev-erbα combined with indolepropionic acid significantly inhibits the nuclear factor-κB signaling pathway and attenuates the inflammatory response of C2C12 myofibroblasts.Moreover,the combined anti-inflammatory effect is superior to that of the intervention alone.

Objective To investigate the effects of paeoniflorin(PF)on lipopolysaccharide(LPS)-induced aerobic glycolysis in renal tubular epithelial cell line HK-2 and its underlying mechanism of action.Methods This study consists of a preliminary experiment and a formal experiment.Preliminary experiment:CCK-8 assay and RT-qPCR were used respectively to measure cell viability and mRNA expression levels of inflammatory factors in HK-2 cells after LPS stimulation to determine the optimal LPS concentration for modeling as well as to evaluate the toxicity of PF and screen for its appropriate concentration.Formal experiment:HK-2 cells were divided into control group(CON group),LPS group,LPS+PF group and LPS+PF+740Y-P group.LPS was used to establish a cell model of sepsis associated-acute kidney injury(SA-AKI)in HK-2 cells,and then the cell model was treated with PF and PI3K activator 740Y-P,correspondingly for 24 h.CCK-8 assay was employed to detect cell viability,and Extracellular Acidification Rate(ECAR)Kit was utilized to measure the rate.The contents of IL-1β,IL-18,lactic acid(Lac)and lactate dehydrogenase A(LDHA)were determined with ELISA.Western blotting was applied to detect the expression of p-PI3K,p-AKT,HIF-1α,pyruvate kinase 2(PKM2,a key enzyme in aerobic glycolysis)and NOD-like receptor thermal protein domain associated protein 3(NLRP3),and immunofluorescence assay was performed to observe the expression and distribution of PKM2.Results ① Our preliminary experiment identified that the optimal concentration of LPS for modeling was 20.0 μg/mL,a safe dosage range of PF was 0～100.0 μmol/L,and its optimal therapeutic concentration was 25.0 μmol/L.② Compared with the CON group,LPS stimulation resulted in significantly decreased cell viability(P<0.05),increased ECAR(P<0.05),elevated contents of IL-1β,IL-18,Lac and LDHA(P<0.05),up-regulated protein levels of p-PI3K,p-AKT,HIF-1α,p-PKM2 and NLRP3(P<0.05),and enhanced fluorescence intensity of PKM2 in the nucleus of cells(P<0.05).Compared with the model group,PF treatment reversed all above effects induced by LPS stimulation(all P<0.05).Compared with the LPS+PF group,in the LPS+PF+740Y-P group,ECAR was elevated(P<0.05),the contents of IL-1β,IL-18,Lac and LDHA were increased(P<0.05),and the relative expression levels of p-PI3K,p-AKT,HIF-1α,p-PKM2 and NLRP3 were increased(P<0.05),and the fluorescence intensity of PKM2 was strengthened(P<0.05)and enhanced in the nucleus(P<0.05).Conclusion PF reduces aerobic glycolysis in HK-2 cells and alleviates the inflammatory response by inhibiting the PI3K/AKT/HIF-1α signaling pathway.

ObjectiveTo systematically evaluate the public health governance capacity of 40 cities in Jiangsu, Zhejiang, and Anhui Provinces, providing a scientific evaluation basis for building a "Healthy Yangtze River Delta". MethodsA comprehensive collection of policy documents, public information reports, and research literature related to public health governance capacity in Jiangsu, Zhejiang, and Anhui Provinces was conducted, totaling 6 920 policy documents, 1 720 information reports, and 1 200 literature pieces. Based on the evaluation standards for an appropriate public health system established by the research team, the basic status of public health governance capacity was assessed to identify the strengths and weaknesses of the 40 cities. ResultsIn 2022, the public health governance capacity score for the 40 cities in Jiangsu, Zhejiang, and Anhui Provinces was (562.5±38.0) points. In terms of specific areas, the emergency response field received the highest score of (791.4±49.7) points, while the chronic disease prevention and control field received the lowest score of (368.2±29.6) points. The Jiangsu-Zhejiang-Anhui region has largely achieved the strategic priority of health, gradually improved public health legal regulations, and established a basic organizational framework with a solid foundation for information and data infrastructure. However, challenges still need to be addressed, such as unstable government funding for public health, unclear departmental responsibilities, and barriers to information interoperability. ConclusionThe public health governance capacity of the 40 cities in Jiangsu, Zhejiang, and Anhui Province has been at a moderate level, but disparities have still existed across regions and fields. In the future, while continuing to deepen existing advantages, it is essential to accurately identify the causes of problems, establish a long-term and stable investment mechanism, enhance information connectivity mechanisms, further clarify departmental responsibilities, and promote the achievement of the "Healthy Yangtze River Delta" goal.

Objective:Explore the protective effect and mechanism of methyl badosolone (CDDO-Me) on rats with traumatic brain injury (TBI).Methods:A total of 72 SPF-grade SD rats aged 8 weeks were randomly (random number) divided into 4 groups ( n=18) using the random number table method: Sham, TBI, TBI+Vehicle, and TBI+CDDO-Me. The rat TBI model was established using the hydraulic impact head injury method. The TBI+CDDO-Me group was administered CDDO-Me (dissolved in 1% DMSO, at a dose of 10 mg/kg) via intraperitoneal injection 30 minutes after modeling, twice a day for a total of 3 days. On the third day after modeling, brain tissue was collected for pathological and water content detection after mNSS scoring. Immunofluorescence double staining was used to detect the expression of nuclear factor erythroid2 related factor 2 (Nrf2); immunohistochemical staining was used to detect the expression of ionized calcium binding adapter molecule-1(Iba-1); ELISA was used to detect the levels of tumor necrosis factor-α(TNF-α), interleukin (IL)-1β, and IL-18 in serum; kits were used to detect the levels of malondialdehyde (MDA) and reactive oxygen species (ROS); Western blot was used to detect the expression of the Nrf2 pathway, B-cell lymphoma-2 (BCL-2), and BCL-2 associated X protein (BAX). Results:(1) Compared with the Sham group, the mNSS scores and water content in the injured cortex of the TBI group rats were significantly increased (both P<0.05), and both significantly decreased after CDDO-Me intervention (both P<0.05). (2) Compared with the Sham group, the proportion of Nissl-stained injured neurons and apoptotic positive cells in the TBI group rats were significantly increased (both P<0.05), and both significantly decreased after CDDO-Me intervention (both P<0.05), accompanied by a decrease in BAX protein expression and upregulation of BCL-2 protein expression (both P<0.05). (3) Immunofluorescence and Western blot results showed that compared with the Sham group, the expression of total Nrf2, nuclear Nrf2, HO-1, and NQO1 proteins in the TBI group were all increased (all P<0.05), and the increase was more significant after CDDO-Me intervention (all P<0.05). (4) Immunohistochemistry and ELISA results showed that compared with the Sham group, the levels of MDA, ROS, Iba-1 in brain tissue and the levels of TNF-α, IL-1β, and IL-18 in serum in the TBI group rats were all significantly increased (all P<0.05), and all significantly decreased after CDDO-Me intervention (all P<0.05). Conclusion:CDDO-Me helps to reduce oxidative stress and inflammatory responses in TBI rats, and the mechanism may be related to the activation of the Nrf2/HO-1 antioxidant stress pathway.

Objective:s To investigate the risk factors for delayed encephalopathy after acute carbon monoxide poisoning (DEACMP) in patients with acute carbon monoxide poisoning (ACOP) and to develop predictive models based on machine learning algorithms.Methods:Patients with ACOP hospitalized at the First Affiliated Hospital of Zhengzhou University from August 2019 to October 2024 were included, with the occurrence of DEACMP as the outcome measure. The dataset was randomly divided into training and validation sets at a ratio of 7:3. Lasso regression was used to select features influencing the outcome in training sets. Nine machine learning models—including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM)—were constructed. Receiver operating characteristic (ROC) curves were plotted and the area under the curve (AUC) calculated for each model. Calibration curves were used to assess accuracy, and decision curve analysis (DCA) was applied to evaluate clinical utility. The SHapley Additive exPlanations (SHAP) method was employed to visualize and interpret the best-performing model.Results:A total of 264 ACOP patients were included, of whom 54 (20.5%) developed DEACMP. Lasso regression identified eight key feature variables. Based on these factors, predictive models were constructed, showing good AUC stability across the nine machine learning models in both training (0.92–0.99) and validation sets (0.85–0.91). The RF model performed best, with an AUC of 0.99 in the training set and 0.90 in the validation set; its calibration curve and DCA curve also demonstrated excellent performance. SHAP analysis of the RF model revealed the importance ranking of factors from highest to lowest as follows: Glasgow Coma Scale (GCS) score, duration of coma, age, history of coronary heart disease, CK-MB level, monocyte count, diastolic blood pressure (DBP), and drinking history.Conclusions:The RF model exhibited the highest predictive performance for DEACMP occurrence in ACOP patients. The influencing factors, ranked in order of importance from highest to lowest, are as follows: GCS score, duration of coma, age, history of coronary heart disease, CK-MB level, monocyte count, DBP, and drinking history.