Mutations in the IARS1 gene are rare in clinical practice， and up to now， only ten cases with detailed clinical and genetic data have been recorded in the literature. This article reports a case of growth retardation， intellectual developmental disorder， hypotonia， and hepatopathy （GRIDHH） associated with single heterozygous mutations in the IARS1 gene and summarizes the clinical and genetic features of GRIDHH， thereby expanding the genetic spectrum of GRIDHH.

In order to explore the effect of PGD2/DP1 receptor pathway on the expression of cyto-kines and injury-related factors in Escherichia coli(E.coli)induced bovine bone marrow derived macrophages,an in vitro model of E.coli induced bovine bone marrow derived macrophages was established.The effects of DP1 receptor agonist on phagocytosis and killing ability,mRNA expres-sion,secretion of pro-inflammatory cytokines(TNF-α,IL-1β)and activation of signaling pathway(MAPK,NF-κB)in cow bone marrow derived macrophages induced by E.coli were examined.The results showed that compared with the E.coli infection group,the phagocytosis and killing ability of BW-245C+E.coli group and 15 d-PGJ2+E.coli group were enhanced(P＜0.01).Compared with the blank control group,mRNA expression was at a higher level(P＜0.001),and the secre-tion of pro-inflammatory cytokines(TNF-α,IL-1β)was significantly increased after adding E.coli solution.The mRNA expression of BW-245C+E.coli group and 15 d-PGJ2+E.coli group were significantly decreased(P＜0.001),and the secretion of pro-inflammatory cytokines(TNF-α,IL-1β)was significantly decreased(P＜0.001).and signaling pathway(MAPK,NF-κB)were sig-nificantly down-regulated(P＜0.001).This study showed that DP1receptor agonist plays an inhib-itory role in the inflammatory response of cow bone marrow-derived macrophages induced by E.coli.This finding provides a potential target for future treatment of cow endometritis,laying the foundation for the development of novel anti-inflammatory treatment strategies.

Objective:To construct a prediction model for postoperative poor in-hospital prognosis in patients with traumatic brain injury (TBI) and evaluate its predictive performance.Methods:A retrospective case control study was conducted to analyze the clinical data of 1 120 TBI patients admitted to Changde Hospital Affiliated to Xiangya Medical College of Central South University from May 2019 to December 2024. The patients were divided into the training set ( n=784) and verification set ( n=336) at a ratio of 7∶3. Based on the Glasgow outcome scale-extended (GOS-E) at discharge, the training set was stratified into favorable prognosis group ( n=335, GOS-E 5-8 points) and poor prognosis group ( n=449, GOS-E 1-4 points). The two groups in the training set were compared in terms of general baseline indicators, TBI-related clinical indicators, and admission laboratory blood test results. Univariate analysis and Lasso regression analysis were employed to screen risk factors associated with postoperative poor in-hospital prognosis in TBI patients. Multivariate Logistic regression analysis was used to determine independent risk factors and construct a regression equation. The regression equation was presented using R language to create a visual nomogram for predicting postoperative poor in-hospital prognosis in TBI patients. In both the training set and verification set, the predictive performance of the model was evaluated by calculating the area under the receiver operating characteristic (ROC) curve (AUC), plotting calibration curves, and performing decision curve analysis (DCA). Results:The results of the univariate analysis indicated that the age, Charlson complication index (CCI), time from trauma to admission, time from trauma to operation, cause of injury, abbreviated injury scale (AIS) (head and neck), injury severity score (ISS), admission Glasgow coma scale (GCS), admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraventricular hemorrhage, subarachnoid hemorrhage, decompressive craniotomy, intraoperative blood loss, intraoperative blood transfusion, traumatic cerebral infarction, postoperative delayed bleeding, epilepsy seizures, as well as the following admission tested results including red blood cell count, white blood cell count, platelet count, neutrophil percentage, percentage of lymphocytes, albumin, total bilirubin, urea nitrogen, thrombin time (TT), prothrombin time (PT), international standardized ratio (INR), glutamic aminotransferase, alanine aminotransferase, creatinine, and blood glucose were statistically different between the two groups in the training set ( P<0.05). Lasso regression analysis suggested 14 risk factors of age, CCI, cause of injury, head and neck AIS, ISS, admission GCS, admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraoperative blood loss, admission platelet count, admission albumin, admission blood glucose for postoperative poor in-hospital prognosis. The results of the multivariate Logistic regression analysis showed that age ( OR=1.02, 95% CI 1.00, 1.03, P<0.01), CCI ( OR=1.46, 95% CI 1.02, 2.09, P<0.05), head and neck AIS ( OR=1.43, 95% CI 1.11, 1.85, P<0.01), ISS ( OR=2.16, 95% CI 1.39, 3.35, P<0.01), admission GCS ( OR=1.59, 95% CI 1.19, 2.13, P<0.01), intracerebral hematoma ( OR=4.41, 95% CI 2.15, 9.44, P<0.01), intraoperative blood loss ( OR=1.05, 95% CI 1.00, 1.09, P<0.05), admission platelet count ( OR=0.98, 95% CI 0.97, 0.99, P<0.01), admission blood glucose ( OR=1.08, 95% CI 1.02, 1.15, P<0.05) could be the main risk factors to construct a prediction model for postoperative poor in-hospital prognosis in TBI patients. Meanwhile, a regression equation was constructed: Logit[ P/(1- P)]=-2.4+ 0.02×"age"+0.38×"CCI"+0.36×"head and neck AIS"+0.77×"ISS"+0.47×"admission GCS"+1.48×"intracerebral hematoma"+0.05×intraoperative blood loss-0.02×admission platelet count+0.08×admission blood glucose. In the training set, the predictive model for poor postoperative in-hospital prognosis in TBI patients achieved an AUC of 0.87 (95% CI 0.84, 0.89), with a Youden′s index of 0.57, sensitivity of 73.70%, and specificity of 83.00%. In the verification set, the model showed an AUC of 0.80 (95% CI 0.76, 0.85), with a Youden′s index of 0.63, sensitivity of 65.20%, and specificity of 77.90%. In the training set, the Brier score for the calibration curve was 0.14 (95% CI 0.13, 0.16). In the verification set, the Brier score for the calibration curve was 0.18 (95% CI 0.15, 0.20). The DCA diagram indicated that the nomogram prediction model provided high clinical net benefit for predicting postoperative poor in-hospital prognosis in TBI patients. Conclusion:The prediction model for postoperative poor in-hospital prognosis in TBI patients, constructed based on age, CCI, head and neck AIS, ISS, admission GCS, intracerebral hematoma, intraoperative blood loss, admission platelet count, and admission blood glucose, exhibits good predictive performance.

Objective:To assess the efficacy and safety profile of antaitasvir phosphate combined with yiqibuvir in the treatment of chronic hepatitis C (CHC) of various genotypes, without cirrhosis or with compensated cirrhosis.Methods:394 cases with CHC from 22 centers were collected from October 2021 to April 2023. They were randomly assigned to receive either the experimental drugs (antaitasvir phosphate 100 mg+yiqibuvir 600 mg) or placebo treatment in a 3∶1 ratio. The patients were administered drugs once a day for 12 consecutive weeks, and then followed up for 24 weeks after treatment cessation. All subjects were unblinded at the four-week follow-up following drug discontinuation, with the experimental drug group continuing to complete subsequent post-discontinuation follow-up. The placebo group was switched to receive the experimental drugs for a repeated 12-week treatment period and followed up for another 24 weeks after discontinuation of the drug (placebo delayed treatment phase).The sustained virologic response rate (SVR12) was observed for subjects in the double-blind phase and the placebo delayed-treatment phase at 12 weeks after treatment cessation.Virological resistance analysis was performed on subjects who failed treatment. The primary efficacy endpoint was SVR12. The number and percentage of subjects who achieved "HCV RNA

Corynebacterium pseudotuberculosis(C.pseudotuberculosis)is a group of intracellular Gram-positive bacteria that can cause zoonotic diseases.This study investigated the mechanisms of inflammatory mediator secretion and the phagocytic and bactericidal functions of mouse peritoneal macrophages following C.pseudotuberculosis infection.Initially,transcriptomic sequencing was em-ployed to identify genes critical for C.pseudotuberculosis infection in macrophages.Subsequently,gene knockout mice were utilized to assess the impact of these key genes on inflammatory media-tor secretion,activation of inflammatory signaling pathways,and the phagocytic and bactericidal functions of macrophages infected with C.pseudotuberculosis.Techniques such as ELISA,Western blot,and immunofluorescence were employed in this analysis.Further,transcriptomic sequencing was conducted to identify key downstream genes.Following C.pseudotuberculosis infection,GO enrichment analysis was performed,and TLR2 was identified as the focal point of the study.Perito-neal macrophages from C57BL/6J and TLR2 knockout(TLR2-/-)mice were infected with C.pseudotuberculosis.ELISA results revealed that the levels of TNF-α,IL-1β,and IL-10 were signifi-cantly downregulated in TLR2-/-macrophages compared to C57BL/6J macrophages post-infec-tion.Western blot demonstrated that the absence of TLR2 led to a marked decrease in M APK(p38 and ERK)signaling pathway phosphorylation following C.pseudotuberculosis infection.Immuno-fluorescence results indicated that the phagocytic rate of TLR2-/-macrophages was significantly higher than that of C57BL/6J macrophages after infection.Subsequently,transcriptomic analysis of C57BL/6J and TLR2-/-macrophages infected with C.pseudotuberculosis was performed,followed by GO enrichment analysis of differential genes.IL-36a,Cx3cr1,TLR1,and TLR2 were identified as key differential genes.TLR2 plays a crucial role in the inflammatory response induced by C.pseudotuberculosis infection in mice,influencing the progression of the inflammatory response and host outcomes through the secretion of inflammatory mediators,activation of signaling pathways,and modulation of phagocytic and bactericidal functions.IL-36a and Cx3cr1 were identified as key downstream factors in this process.

The implementation of the Good Pharmacovigilance Practice(GVP)in 2021 has laid a solid foundation for improving China's pharmacovigilance system,a matter of paramount importance currently emphasized by Marketing Authorization Holders(MAHs).Given the unique organizational structures of corporate groups,establishing a centralized pharmacovigilance system can optimize resource utilization.This paper analyzes the challenges faced by corporate groups in developing such central-ized systems and proposes an organizational framework with defined operational divisions.The findings aim to provide actionable insights for vaccine manufacturers in establishing and refining life cycle pharmacovigilance systems.

Escherichia coli(E.coli)is a key pathogenic bacterium responsible for postpartum endo-metritis,with its colonization in the reproductive tract closely associated with endometrial damage and disruption of the ovarian cycle.This ultimately leads to infertility,causing significant economic losses to the dairy industry.Macrophages play a pivotal role in the inflammatory response.This study aims to investigate the mRNA expression profile of bovine bone marrow-derived macropha-ges following E.coli infection using RNA sequencing(RNA-seq)technology.Additionally,it seeks to identify the biological functions and signaling pathways of differentially expressed genes(DEGs)through Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analyses.The results demonstrated that E.coli infection induced differential expression of 4 522 genes,with 2 141 upregulated and 2 381 downregulated.These genes were primarily asso-ciated with inflammatory responses,where TLR2,TLR4,NLRP3,and PTGS2 played pivotal roles.PGD2 synthesis was mediated by TLR2,TLR4,and NLRP3.Transcriptome sequencing of bovine bone marrow-derived macrophages infected with E.coli and treated with a PGD2 inhibitor revealed a marked downregulation of TLR2 and TLR4 gene expression.qPCR validation results were highly consistent with the RNA-seq findings.This study elucidates the interactive regulatory roles of TLR2,TLR4,and NLRP3 in conjunction with PGD2,which collectively modulate bovine endome-tritis.These findings offer significant molecular insights that enhance our understanding of the pathological mechanisms underlying bovine endometritis,thereby informing its prevention and treatment strategies.

Corynebacterium pseudotuberculosis(C.pseudotuberculosis)is a group of intracellular Gram-positive bacteria that can cause zoonotic diseases.This study investigated the mechanisms of inflammatory mediator secretion and the phagocytic and bactericidal functions of mouse peritoneal macrophages following C.pseudotuberculosis infection.Initially,transcriptomic sequencing was em-ployed to identify genes critical for C.pseudotuberculosis infection in macrophages.Subsequently,gene knockout mice were utilized to assess the impact of these key genes on inflammatory media-tor secretion,activation of inflammatory signaling pathways,and the phagocytic and bactericidal functions of macrophages infected with C.pseudotuberculosis.Techniques such as ELISA,Western blot,and immunofluorescence were employed in this analysis.Further,transcriptomic sequencing was conducted to identify key downstream genes.Following C.pseudotuberculosis infection,GO enrichment analysis was performed,and TLR2 was identified as the focal point of the study.Perito-neal macrophages from C57BL/6J and TLR2 knockout(TLR2-/-)mice were infected with C.pseudotuberculosis.ELISA results revealed that the levels of TNF-α,IL-1β,and IL-10 were signifi-cantly downregulated in TLR2-/-macrophages compared to C57BL/6J macrophages post-infec-tion.Western blot demonstrated that the absence of TLR2 led to a marked decrease in M APK(p38 and ERK)signaling pathway phosphorylation following C.pseudotuberculosis infection.Immuno-fluorescence results indicated that the phagocytic rate of TLR2-/-macrophages was significantly higher than that of C57BL/6J macrophages after infection.Subsequently,transcriptomic analysis of C57BL/6J and TLR2-/-macrophages infected with C.pseudotuberculosis was performed,followed by GO enrichment analysis of differential genes.IL-36a,Cx3cr1,TLR1,and TLR2 were identified as key differential genes.TLR2 plays a crucial role in the inflammatory response induced by C.pseudotuberculosis infection in mice,influencing the progression of the inflammatory response and host outcomes through the secretion of inflammatory mediators,activation of signaling pathways,and modulation of phagocytic and bactericidal functions.IL-36a and Cx3cr1 were identified as key downstream factors in this process.

Escherichia coli(E.coli)is a key pathogenic bacterium responsible for postpartum endo-metritis,with its colonization in the reproductive tract closely associated with endometrial damage and disruption of the ovarian cycle.This ultimately leads to infertility,causing significant economic losses to the dairy industry.Macrophages play a pivotal role in the inflammatory response.This study aims to investigate the mRNA expression profile of bovine bone marrow-derived macropha-ges following E.coli infection using RNA sequencing(RNA-seq)technology.Additionally,it seeks to identify the biological functions and signaling pathways of differentially expressed genes(DEGs)through Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analyses.The results demonstrated that E.coli infection induced differential expression of 4 522 genes,with 2 141 upregulated and 2 381 downregulated.These genes were primarily asso-ciated with inflammatory responses,where TLR2,TLR4,NLRP3,and PTGS2 played pivotal roles.PGD2 synthesis was mediated by TLR2,TLR4,and NLRP3.Transcriptome sequencing of bovine bone marrow-derived macrophages infected with E.coli and treated with a PGD2 inhibitor revealed a marked downregulation of TLR2 and TLR4 gene expression.qPCR validation results were highly consistent with the RNA-seq findings.This study elucidates the interactive regulatory roles of TLR2,TLR4,and NLRP3 in conjunction with PGD2,which collectively modulate bovine endome-tritis.These findings offer significant molecular insights that enhance our understanding of the pathological mechanisms underlying bovine endometritis,thereby informing its prevention and treatment strategies.

Objective:To construct a prediction model for postoperative poor in-hospital prognosis in patients with traumatic brain injury (TBI) and evaluate its predictive performance.Methods:A retrospective case control study was conducted to analyze the clinical data of 1 120 TBI patients admitted to Changde Hospital Affiliated to Xiangya Medical College of Central South University from May 2019 to December 2024. The patients were divided into the training set ( n=784) and verification set ( n=336) at a ratio of 7∶3. Based on the Glasgow outcome scale-extended (GOS-E) at discharge, the training set was stratified into favorable prognosis group ( n=335, GOS-E 5-8 points) and poor prognosis group ( n=449, GOS-E 1-4 points). The two groups in the training set were compared in terms of general baseline indicators, TBI-related clinical indicators, and admission laboratory blood test results. Univariate analysis and Lasso regression analysis were employed to screen risk factors associated with postoperative poor in-hospital prognosis in TBI patients. Multivariate Logistic regression analysis was used to determine independent risk factors and construct a regression equation. The regression equation was presented using R language to create a visual nomogram for predicting postoperative poor in-hospital prognosis in TBI patients. In both the training set and verification set, the predictive performance of the model was evaluated by calculating the area under the receiver operating characteristic (ROC) curve (AUC), plotting calibration curves, and performing decision curve analysis (DCA). Results:The results of the univariate analysis indicated that the age, Charlson complication index (CCI), time from trauma to admission, time from trauma to operation, cause of injury, abbreviated injury scale (AIS) (head and neck), injury severity score (ISS), admission Glasgow coma scale (GCS), admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraventricular hemorrhage, subarachnoid hemorrhage, decompressive craniotomy, intraoperative blood loss, intraoperative blood transfusion, traumatic cerebral infarction, postoperative delayed bleeding, epilepsy seizures, as well as the following admission tested results including red blood cell count, white blood cell count, platelet count, neutrophil percentage, percentage of lymphocytes, albumin, total bilirubin, urea nitrogen, thrombin time (TT), prothrombin time (PT), international standardized ratio (INR), glutamic aminotransferase, alanine aminotransferase, creatinine, and blood glucose were statistically different between the two groups in the training set ( P<0.05). Lasso regression analysis suggested 14 risk factors of age, CCI, cause of injury, head and neck AIS, ISS, admission GCS, admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraoperative blood loss, admission platelet count, admission albumin, admission blood glucose for postoperative poor in-hospital prognosis. The results of the multivariate Logistic regression analysis showed that age ( OR=1.02, 95% CI 1.00, 1.03, P<0.01), CCI ( OR=1.46, 95% CI 1.02, 2.09, P<0.05), head and neck AIS ( OR=1.43, 95% CI 1.11, 1.85, P<0.01), ISS ( OR=2.16, 95% CI 1.39, 3.35, P<0.01), admission GCS ( OR=1.59, 95% CI 1.19, 2.13, P<0.01), intracerebral hematoma ( OR=4.41, 95% CI 2.15, 9.44, P<0.01), intraoperative blood loss ( OR=1.05, 95% CI 1.00, 1.09, P<0.05), admission platelet count ( OR=0.98, 95% CI 0.97, 0.99, P<0.01), admission blood glucose ( OR=1.08, 95% CI 1.02, 1.15, P<0.05) could be the main risk factors to construct a prediction model for postoperative poor in-hospital prognosis in TBI patients. Meanwhile, a regression equation was constructed: Logit[ P/(1- P)]=-2.4+ 0.02×"age"+0.38×"CCI"+0.36×"head and neck AIS"+0.77×"ISS"+0.47×"admission GCS"+1.48×"intracerebral hematoma"+0.05×intraoperative blood loss-0.02×admission platelet count+0.08×admission blood glucose. In the training set, the predictive model for poor postoperative in-hospital prognosis in TBI patients achieved an AUC of 0.87 (95% CI 0.84, 0.89), with a Youden′s index of 0.57, sensitivity of 73.70%, and specificity of 83.00%. In the verification set, the model showed an AUC of 0.80 (95% CI 0.76, 0.85), with a Youden′s index of 0.63, sensitivity of 65.20%, and specificity of 77.90%. In the training set, the Brier score for the calibration curve was 0.14 (95% CI 0.13, 0.16). In the verification set, the Brier score for the calibration curve was 0.18 (95% CI 0.15, 0.20). The DCA diagram indicated that the nomogram prediction model provided high clinical net benefit for predicting postoperative poor in-hospital prognosis in TBI patients. Conclusion:The prediction model for postoperative poor in-hospital prognosis in TBI patients, constructed based on age, CCI, head and neck AIS, ISS, admission GCS, intracerebral hematoma, intraoperative blood loss, admission platelet count, and admission blood glucose, exhibits good predictive performance.