Objective To examine the time window effect of high-load exerciseon skeletal muscle in-flammation genes,and identify the key genes and signaling pathways involved in this process.Methods Forty-eight Sprague-Dawley rats were randomly assigned to a control group(group C,n=8)and an ex-ercise group(group E,n=40).Gastrocnemius muscles were collected immediately(group E0),12h(group E12),24h(group E24),48h(group E48),and 72 h(group E72)after exercise for transcrip-tome sequencing.Differentially expressed genes(DEGs)were identified,and enrichment analysis was carried out using the Gene Ontology(GO)and the Kyoto Encyclopedia of Genes and Genomes(KEGG)annotations.Meanwhile,inflammation-related genes were obtained from databases,and differ-entially expressed inflammation-related genes(DEIRGs)were done through identifying their intersec-tion with DEGs.Moreover,the Mfuzz algorithm was used for time series clustering to obtain subsets with similar characteristics.GO and KEGG analyses,along with protein interaction network analysis,were performed to obtain key DEIRGs,followed by secondary functional enrichment to analyze changes in expression of key genes over time and identify key signaling pathways.Results Seven DEIRG clus-ters were obtained through Mfuzz time series clustering of skeletal muscle inflammation genes after high-load exercise.Overall,the expression of DEGs in cluster 5 was downregulated,while that in cluster 7 was upregulated.The expression of DEGs in clusters 3 and 4 was upregulated at E0 and rap-idly downregulated at E12.In contrast,the expression of DEGs in cluster 2 and 6 were downregulated at E0 and rapidly upregulated at E12.The expression of DEGs in cluster 1 was upregulated at E0,rapidly downregulated at E12,and remained upregulated at E24.Screening identified TP53,STAT3,CD44,AKT1,KDR,GJA1,CYCS,HIF1A,IQGAP3,FASN,and TFRC as key DEIRGswhich were enriched in apoptosis,HIF-1,apoptosis,ferroptosis,MAPK,VEGF,PI3K-Akt,insulin resistance,FoxO,AMPK and the JAK-STAT signaling pathway.Conclusion Inflammation-related genes exhibit temporal dynamic changes in exercise-induced muscle damage and show significant time window effects at 12 h after exercise.The key targets STAT3,AKT1 and HIF-1A react to exercise-induced muscle damage through the JAK-STAT,PI3K-Akt,HIF-1 and VEGF signaling pathways,and promote tissue repair.

AIM:To investigate the mechanism through which aerobic exercise improves inflammatory senes-cence in SAMP8 mice via Janus kinase 3(JAK3)-signal transducer and activator of transcription 5α(STAT5α)signaling pathway and to provide novel insights for anti-inflammatory aging interventions.METHODS:Sixteen 28-week-old SAMP8 mice were randomly divided into model(Mod)group and exercise(Exe)group,with 8 mice in each group.Addi-tionally,8 senile SAMR1 mice served as control(Con)group.The mice in Exe group underwent an 8-week aerobic training regimen on a treadmill.We monitored the changes in hair quality and body weight,immune organ indexes,histomorpho-logical alterations in immune organs,the expression levels of spleen aging marker P16,and the serum and spleen levels of inflammatory factors[tumor necrosis factor-α(TNF-α)and interleukin-1β(IL-1β)]and aging indicator IL-2.Moreover,we examined the alterations in the mRNA and protein expression of IL-2,JAK3 and STAT5α in the spleen.RESULTS:(1)Compared with Con group,the mice in Mod group exhibited decreased hair luster and volume,with some instances of alopecia areata,increased body weight,markedly reduced spleen and thymus indexes,evident atrophy and senescence of immune organs,and markedly decreased concentration of IL-2 in the serum and spleen.The expression of P16 in the spleen,and the serum and spleen levels of inflammatory factors TNF-α and IL-1β were significantly increased.The mRNA expression of IL-2 and IL-2 receptor subunit gamma(IL-2RG)in the spleen was markedly decreased,whereas that of JAK3 and STAT5α was markedly increased.Furthermore,the protein expression of IL-2 in the spleen was markedly de-creased,whereas that of STAT5α and JAK3 was markedly increased.(2)Compared with Mod group,the mice in Exe group showed relatively vigorous hair growth and better hair luster,lower body weight,markedly increased spleen and thy-mus indexes,and delayed immune organ aging.The concentration of IL-2 in the serum and spleen was markedly in-creased,while the expression of P16 in the spleen was markedly decreased.The levels of TNF-α and IL-1β in the serum and spleen were markedly decreased.The mRNA expression of IL-2 and IL-2RG in the spleen was markedly increased,whereas that of JAK3 and STAT5α was markedly decreased.The protein levels of IL-2 were markedly increased,whereas those of STAT5α and JAK3 were markedly decreased.CONCLUSION:Aerobic exercise can delay inflammatory aging in mice possibly through the JAK3-STAT5α signaling pathway.

Objective To analyze the differences in structural integrity of cerebral white matter fiber bundles in vegetative state(VS)patients with different prognoses,and to construct an early-stage prognostic prediction model for 1-year post-stabilization prognosis.Methods A retrospective analysis was conducted on 52 VS patients admitted to the Department of Rehabilitation Medicine at Zhujiang Hospital of Southern Medical University.Patients were stratified into good prognosis(n=22)and poor prognosis(n=30)at 1-year follow-up based on Coma Recovery Scale-Revised(CRS-R)scores.The fractional anisotropy values of cerebral white matter fiber bundles were derived from diffusion tensor imaging,and for the first time,the scores of the visual subscales of CRS-R were combined with FA values as input features for the prognostic model.To optimize model construction,the least absolute shrinkage and selection operator regression was employed for feature screening,and synthetic minority over-sampling technique for data balancing.The prognostic prediction model was subsequently developed using a support vector machine algorithm and validated via leave-one-out cross-validation.Model performance was evaluated using area under receiver operating characteristic curve,along with accuracy,sensitivity,specificity,and F1 score metrics.Results Following LASSO regression feature screening,the pontine crossing tract,medial lemniscus,tapetum,splenium of corpus callosum,and visual subscale scores were identified as key predictors.A multimodal SVM-based prediction model constructed with the above features could effectively predict the 1-year prognosis of VS patients,achieving a high predictive performance(AUC=0.894).Conclusion The SVM-based model integrating FA values of specific white matter fiber bundles and visual subscale scores demonstrates excellent predictive performance in predicting the 1-year prognosis of VS patients.

Objective:To investigate the differences in brain age and brain age gap (BAG) between patients with prolonged disorders of consciousness (pDoC) and healthy controls (HC).Methods:A retrospective cross-sectional study was performed; 43 patients with pDoC admitted to Rehabilitation Medicine Center, Zhujiang Hospital, Southern Medical University from January 2020 to October 2022 were enrolled; 26 healthy volunteers recruited at the same time and 187 healthy subjects from the publicly available healthy control dataset Nathan Kline Institute-Rockland Sample (NKI-RS) were chosen as HC group. The clinical and imaging data of these subjects were collected. A brain age estimation model was constructed based on amplitude of low-frequency fluctuation (ALFF) in resting-state functional magnetic resonance imaging (rs-fMRI) for healthy individuals, and the pDoC group was used as the test set. A two-sample t-test was used to compare the brain age and BAG differences between the pDoC group and HC group. Pearson correlation analysis was used to explore the correlation between BAG and coma recovery scale-revised (CRS-R) in the pDoC group. Results:The chronological age and estimated brain age in the HC group were (41.54±9.61) and (42.32±10.65) years, respectively, without significant difference ( t=-0.254, P=0.801). The chronological age and estimated brain age in the pDoC group were (49.91±12.03) and (62.79±15.00) years, respectively, with significant difference ( t=-4.341, P<0.001). The BAG in the HC and pDoC groups were (0.78±4.59) and (12.88±7.17) years, respectively, with significant difference ( t=-7.822, P<0.001). Correlation analysis results showed that in the pDoC patients, no correlation was noted between BAG and CRS-R score on the day of imaging scan or 6 months after the day of imaging scan ( r=0.090, P=0.738; r=0.205, P=0.674); no correlation was noted between BAG and difference in CRS-R score (difference value of CRS-R score 6 months after the day of imaging scan-CRS-R score on the day of imaging scan, r=0.246, P=0.687). Conclusion:Compared with the HC subjects, patients with pDoC exhibit an abnormal increase in brain age, suggesting that pDoC caused by severe brain injury may lead to accelerated brain aging.

Objective To examine the time window effect of high-load exerciseon skeletal muscle in-flammation genes,and identify the key genes and signaling pathways involved in this process.Methods Forty-eight Sprague-Dawley rats were randomly assigned to a control group(group C,n=8)and an ex-ercise group(group E,n=40).Gastrocnemius muscles were collected immediately(group E0),12h(group E12),24h(group E24),48h(group E48),and 72 h(group E72)after exercise for transcrip-tome sequencing.Differentially expressed genes(DEGs)were identified,and enrichment analysis was carried out using the Gene Ontology(GO)and the Kyoto Encyclopedia of Genes and Genomes(KEGG)annotations.Meanwhile,inflammation-related genes were obtained from databases,and differ-entially expressed inflammation-related genes(DEIRGs)were done through identifying their intersec-tion with DEGs.Moreover,the Mfuzz algorithm was used for time series clustering to obtain subsets with similar characteristics.GO and KEGG analyses,along with protein interaction network analysis,were performed to obtain key DEIRGs,followed by secondary functional enrichment to analyze changes in expression of key genes over time and identify key signaling pathways.Results Seven DEIRG clus-ters were obtained through Mfuzz time series clustering of skeletal muscle inflammation genes after high-load exercise.Overall,the expression of DEGs in cluster 5 was downregulated,while that in cluster 7 was upregulated.The expression of DEGs in clusters 3 and 4 was upregulated at E0 and rap-idly downregulated at E12.In contrast,the expression of DEGs in cluster 2 and 6 were downregulated at E0 and rapidly upregulated at E12.The expression of DEGs in cluster 1 was upregulated at E0,rapidly downregulated at E12,and remained upregulated at E24.Screening identified TP53,STAT3,CD44,AKT1,KDR,GJA1,CYCS,HIF1A,IQGAP3,FASN,and TFRC as key DEIRGswhich were enriched in apoptosis,HIF-1,apoptosis,ferroptosis,MAPK,VEGF,PI3K-Akt,insulin resistance,FoxO,AMPK and the JAK-STAT signaling pathway.Conclusion Inflammation-related genes exhibit temporal dynamic changes in exercise-induced muscle damage and show significant time window effects at 12 h after exercise.The key targets STAT3,AKT1 and HIF-1A react to exercise-induced muscle damage through the JAK-STAT,PI3K-Akt,HIF-1 and VEGF signaling pathways,and promote tissue repair.

AIM:To investigate the mechanism through which aerobic exercise improves inflammatory senes-cence in SAMP8 mice via Janus kinase 3(JAK3)-signal transducer and activator of transcription 5α(STAT5α)signaling pathway and to provide novel insights for anti-inflammatory aging interventions.METHODS:Sixteen 28-week-old SAMP8 mice were randomly divided into model(Mod)group and exercise(Exe)group,with 8 mice in each group.Addi-tionally,8 senile SAMR1 mice served as control(Con)group.The mice in Exe group underwent an 8-week aerobic training regimen on a treadmill.We monitored the changes in hair quality and body weight,immune organ indexes,histomorpho-logical alterations in immune organs,the expression levels of spleen aging marker P16,and the serum and spleen levels of inflammatory factors[tumor necrosis factor-α(TNF-α)and interleukin-1β(IL-1β)]and aging indicator IL-2.Moreover,we examined the alterations in the mRNA and protein expression of IL-2,JAK3 and STAT5α in the spleen.RESULTS:(1)Compared with Con group,the mice in Mod group exhibited decreased hair luster and volume,with some instances of alopecia areata,increased body weight,markedly reduced spleen and thymus indexes,evident atrophy and senescence of immune organs,and markedly decreased concentration of IL-2 in the serum and spleen.The expression of P16 in the spleen,and the serum and spleen levels of inflammatory factors TNF-α and IL-1β were significantly increased.The mRNA expression of IL-2 and IL-2 receptor subunit gamma(IL-2RG)in the spleen was markedly decreased,whereas that of JAK3 and STAT5α was markedly increased.Furthermore,the protein expression of IL-2 in the spleen was markedly de-creased,whereas that of STAT5α and JAK3 was markedly increased.(2)Compared with Mod group,the mice in Exe group showed relatively vigorous hair growth and better hair luster,lower body weight,markedly increased spleen and thy-mus indexes,and delayed immune organ aging.The concentration of IL-2 in the serum and spleen was markedly in-creased,while the expression of P16 in the spleen was markedly decreased.The levels of TNF-α and IL-1β in the serum and spleen were markedly decreased.The mRNA expression of IL-2 and IL-2RG in the spleen was markedly increased,whereas that of JAK3 and STAT5α was markedly decreased.The protein levels of IL-2 were markedly increased,whereas those of STAT5α and JAK3 were markedly decreased.CONCLUSION:Aerobic exercise can delay inflammatory aging in mice possibly through the JAK3-STAT5α signaling pathway.

Objective:To explore the method for selecting characteristic prices of Chinese patent medicines based on informa-tion entropy theory.It involves analyzing the connotative differences among various price indicators and utilizing information entropy metrics to validate the scientific rigor of characteristic price selection so as to optimize the pricing model for Chinese patent medi-cines and improve the accuracy of price evaluation.Methods:A correlation analysis and information entropy calculation are con-ducted on the median price of the smallest preparation unit,average daily cost,and average course cost of TCM.It compares the information diversity and uncertainty of different pricing indicators.Results:The average daily cost exhibits the highest information diversity and uncertainty among all the pricing indicators examined.Conclusion:It is recommended that the average daily cost be used as the dependent variable for characteristic prices in TCM pricing research.This choice plays an important role in optimizing TCM pricing models and enhancing the accuracy of price evaluation.

Objective:To establish a scientific,systematic,and objective indicator system for the characteristic price variables of Traditional Chinese Patent Medicines(TCPM),providing a reference framework for the pricing mechanism of TCPM.Methods:The brainstorming method was initially used to screen related variable indicators.The Nominal Group Technique(NGT)and Delphi methods were applied to gather expert opinions,and SPSS 28.0 was employed for data statistical analysis.It led to the development of a TCPM characteristic price variable indicator system consisting of 6 dimensions,14 characteristic variables and 26 measurement indicators.Results:The authority coefficient of the experts exceeded 0.7,indicating the representativeness of the results.Expert opinions were generally concentrated.Based on the collected opinions and statistical analysis,the scope of selected TCPM characteristic price variables was preliminarily established.Conclusion:The TCPM characteristic price variable indicator system was initially developed.However,due to the complexity of the pricing mechanism and divergent expert opinions,further qualitative and quantitative research methods,along with a dynamic adjustment mechanism,are needed to verify and refine the system.

The National Essential Medicines System could protect public health and ensure access to essential medications.Although the current selection methods for China's National Essential Medicines Lists(NEMLs)are becoming more scientific and standardized,there are still problems such as much emphasis on expert experience and the lack of transparency of decision-making basis.To address these issues,it proposes an evidence-based decision-making pathway for NEMLs selection guided by clinical value.This approach ensures a strong integration of evidence and decision-making,offering valuable insights for improving the adjustment procedures and selection criteria of the NEMLs in China.

Objective To analyze the differences in structural integrity of cerebral white matter fiber bundles in vegetative state(VS)patients with different prognoses,and to construct an early-stage prognostic prediction model for 1-year post-stabilization prognosis.Methods A retrospective analysis was conducted on 52 VS patients admitted to the Department of Rehabilitation Medicine at Zhujiang Hospital of Southern Medical University.Patients were stratified into good prognosis(n=22)and poor prognosis(n=30)at 1-year follow-up based on Coma Recovery Scale-Revised(CRS-R)scores.The fractional anisotropy values of cerebral white matter fiber bundles were derived from diffusion tensor imaging,and for the first time,the scores of the visual subscales of CRS-R were combined with FA values as input features for the prognostic model.To optimize model construction,the least absolute shrinkage and selection operator regression was employed for feature screening,and synthetic minority over-sampling technique for data balancing.The prognostic prediction model was subsequently developed using a support vector machine algorithm and validated via leave-one-out cross-validation.Model performance was evaluated using area under receiver operating characteristic curve,along with accuracy,sensitivity,specificity,and F1 score metrics.Results Following LASSO regression feature screening,the pontine crossing tract,medial lemniscus,tapetum,splenium of corpus callosum,and visual subscale scores were identified as key predictors.A multimodal SVM-based prediction model constructed with the above features could effectively predict the 1-year prognosis of VS patients,achieving a high predictive performance(AUC=0.894).Conclusion The SVM-based model integrating FA values of specific white matter fiber bundles and visual subscale scores demonstrates excellent predictive performance in predicting the 1-year prognosis of VS patients.