Based on whole-genome identification of the TPS gene family in Perilla frutescens and screening, cloning, bioinformatics, and expression analysis of the synthetic enzyme for the insect-resistant component germacrene D, this study lays the foundation for understanding the biological function of the TPS gene family and the insect resistance mechanism in P. frutescens. This study used bioinformatics tools to identify the TPS gene family of P. frutescens based on its whole genome and predicted the physicochemical properties, systematic classification, and promoter cis-elements of the proteins. The relative content of germacrene D was detected in both normal and insect-infested leaves of P. frutescens, and the germacrene D synthase was screened and isolated. Gene cloning, bioinformatics analysis, and expression profiling were then performed. The results showed that a total of 99 TPS genes were identified in the genome, which were classified into the TPS-a, TPS-b, TPS-c, TPS-e/f, and TPS-g subfamilies. Conserved motif analysis showed that the TPS in P. frutescens has conserved structural characteristics within the same subfamily. Promoter cis-element analysis predicted the presence of light-responsive elements, multiple hormone-responsive elements, and stress-responsive elements in the TPS family of P. frutescens. Transcriptome data revealed that most of the TPS genes in P. frutescens were highly expressed in the leaves. GC-MS analysis showed that the relative content of germacrene D significantly increased in insect-damaged leaves, suggesting that it may act as an insect-resistant component. The germacrene D synthase gene was screened through homologous protein binding gene expression and was found to belong to the TPS-a subfamily, encoding a 64.89 kDa protein. This protein was hydrophilic, lacked a transmembrane structure and signal peptide, and was predominantly expressed in leaves, with significantly higher expression in insect-damaged leaves compared to normal leaves. In vitro expression results showed that germacrene D synthase tended to form inclusion bodies. Molecular docking showed that farnesyl pyrophosphate(FPP) fell into the active pocket of the protein and interacted strongly with six active sites. This study provides a foundation for further research on the biological functions of the TPS gene family in P. frutescens and the molecular mechanisms underlying its insect resistance.
Perilla frutescens/chemistry* ; Plant Proteins/chemistry* ; Multigene Family ; Sesquiterpenes, Germacrane/metabolism* ; Alkyl and Aryl Transferases/chemistry* ; Phylogeny ; Gene Expression Regulation, Plant

Objective To explore the role and possible molecular mechanism of Isocitrate dehydrogenase 1(IDH1)gene in proliferation and migration of intrahepatic cholangiocarcinoma(iCCA)cell HuCCT1.Methods HuCCT1 cells with IDH1 gene knockout(HuCCT1IDH1-/-)were constructed by CRISPR/Cas9 gene editing technology.To investigate the capacities of proliferation,migration and invasion of HuCCT1WT(HuCCT1 cells with wild-type IDH1 gene)and HuCCT1IDH1-/-cells,assays of CCK-8,clone formation,scratch and transwell were performed.Western blotting was used to detect the expression levels of epithelial-mesenchymal transition(EMT)associated proteins E-cadherin,N-cadherin,Vimentin,MMP-9,Wnt3a and β-catenin in two groups of cells.The transcriptome sequencing data of HuCCT1WT and HuCCT1IDH1-/-cells were analyzed by bioinformatics methods,Western blotting was used to verify the expression of signaling pathway-related proteins.Results Compared with HuCCT1WT cells,HuCCT1IDH1-/-cells showed the number of proliferation and clone formation significantly reduced(P<0.05),the proportion of cells blocked in G2/M phase was significantly increased(P<0.01),the rate of scratch healing was significantly decreased(P<0.01),and the number of migrated cells(P<0.001)and invaded cells(P<0.05)was significantly reduced.qRT-PCR assay showed that the expression levels of IDH1,Vimentin,MMP-9 and genes related to the regulation of G2/M cycle proliferation,Cyclin A2,Cyclin B1 and CDK1 mRNA were down-regulated in HuCCT1IDH1-/-cells(P<0.05),and the expression of CDH1 mRNA encoding E-cadherin was up-regulated(P<0.01);Western blotting assay showed that the expression level of E-cadherin in HuCCT1IDH1-/-cells was significantly increased(P<0.05),and the expression level of N-cadherin,Vimentin and MMP-9 protein was significantly decreased(P<0.05)than that in HuCCT1WT cells.Data of transcriptome sequencing revealed 1476 differentially expressed genes(DEGs)between two groups of HuCCT1 cells.Go enrichment analysis showed the DEGs were significantly enriched in cell biological processes associated with inflammatory response,cell signaling and cell metabolism.Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analysis suggested that the DEGs may be involved in some signaling pathways such as Wnt,MAPK,Rap1,Hippo and TNF,which are closely related to the regulation of proliferation and invasion of tumor cells.Western blotting verification results showed that compared with HuCCT1WT cells,the relative expression of Wnt3a and β-catenin proteins of HuCCT1IDH1-/-cells was significantly decreased(P<0.05).Conclusions IDH1 gene may participate in the control of biological functions of HuCCT1 cells,including cell proliferation,migration,invasion and epithelial mesenchymal transition.The mechanism may be related to the activation of the Wnt/β-catenin signaling pathway.

Objective: To assess the role of dynamic contrast-enhanced (DCE)-MRI of the extraocular muscles (EOMs) for determining the activity of thyroid-associated ophthalmopathy (TAO) and treatment response to glucocorticoids (GCs). Materials and Methods: We prospectively enrolled 65 patients with TAO (41 active, 82 eyes; 24 inactive, 48 eyes). Twenty-two active patients completed the GC treatment and follow-up assessment, including 15 patients (30 eyes) and 7 patients (14 eyes), defined as responsive and unresponsive, respectively. Model-free (time to peak [TTP], area under the curve [AUC], and Slope max) and model-based (Ktrans , Kep, and Ve) parameters of EOMs in embedded simplified histogram analyses were calculated and compared between groups. Multivariable logistic regression analysis was used to identify the independent predictors. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the diagnostic performance. Results: Active patients exhibited significantly higher TTP at the 10th percentile (-10th), TTP-mean, and TTP at the 90th percentile (-90th); AUC-10th, AUC-mean, AUC-90th, and AUC-max; Ktrans -10th and Ktrans -mean; and Ve-10th, Ve-mean, Ve-90th, and Ve-max than inactive patients (P < 0.05). Responsive patients exhibited significantly lower TTP-min; higher Ktrans -mean and Ktrans -max; and higher Kep-10th, Kep-mean, and Kep-max than unresponsive patients (P < 0.05). TTP-mean and Ve-mean were independent variables for determining disease activity (P = 0.017 and 0.022, respectively). A combination of the two parameters could determine active TAO with moderate performance (AUROC = 0.687). TTP-min and Ktrans -mean were independent predictors of the response to GCs (P = 0.023 and 0.004, respectively), uniting which could determine the response to GCs with decent performance (AUROC = 0.821). Conclusion DCE-MRI-derived model-free and model-based parameters of EOMs can assist in the evaluation of TAO. In particular, TTP-mean and Ve-mean could be useful for determining the activity of TAO, whereas TTP-min and K trans -mean could be promising biomarkers for determining the response to GCs.

Objective: To assess the role of dynamic contrast-enhanced (DCE)-MRI of the extraocular muscles (EOMs) for determining the activity of thyroid-associated ophthalmopathy (TAO) and treatment response to glucocorticoids (GCs). Materials and Methods: We prospectively enrolled 65 patients with TAO (41 active, 82 eyes; 24 inactive, 48 eyes). Twenty-two active patients completed the GC treatment and follow-up assessment, including 15 patients (30 eyes) and 7 patients (14 eyes), defined as responsive and unresponsive, respectively. Model-free (time to peak [TTP], area under the curve [AUC], and Slope max) and model-based (Ktrans , Kep, and Ve) parameters of EOMs in embedded simplified histogram analyses were calculated and compared between groups. Multivariable logistic regression analysis was used to identify the independent predictors. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the diagnostic performance. Results: Active patients exhibited significantly higher TTP at the 10th percentile (-10th), TTP-mean, and TTP at the 90th percentile (-90th); AUC-10th, AUC-mean, AUC-90th, and AUC-max; Ktrans -10th and Ktrans -mean; and Ve-10th, Ve-mean, Ve-90th, and Ve-max than inactive patients (P < 0.05). Responsive patients exhibited significantly lower TTP-min; higher Ktrans -mean and Ktrans -max; and higher Kep-10th, Kep-mean, and Kep-max than unresponsive patients (P < 0.05). TTP-mean and Ve-mean were independent variables for determining disease activity (P = 0.017 and 0.022, respectively). A combination of the two parameters could determine active TAO with moderate performance (AUROC = 0.687). TTP-min and Ktrans -mean were independent predictors of the response to GCs (P = 0.023 and 0.004, respectively), uniting which could determine the response to GCs with decent performance (AUROC = 0.821). Conclusion DCE-MRI-derived model-free and model-based parameters of EOMs can assist in the evaluation of TAO. In particular, TTP-mean and Ve-mean could be useful for determining the activity of TAO, whereas TTP-min and K trans -mean could be promising biomarkers for determining the response to GCs.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective: To assess the role of dynamic contrast-enhanced (DCE)-MRI of the extraocular muscles (EOMs) for determining the activity of thyroid-associated ophthalmopathy (TAO) and treatment response to glucocorticoids (GCs). Materials and Methods: We prospectively enrolled 65 patients with TAO (41 active, 82 eyes; 24 inactive, 48 eyes). Twenty-two active patients completed the GC treatment and follow-up assessment, including 15 patients (30 eyes) and 7 patients (14 eyes), defined as responsive and unresponsive, respectively. Model-free (time to peak [TTP], area under the curve [AUC], and Slope max) and model-based (Ktrans , Kep, and Ve) parameters of EOMs in embedded simplified histogram analyses were calculated and compared between groups. Multivariable logistic regression analysis was used to identify the independent predictors. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the diagnostic performance. Results: Active patients exhibited significantly higher TTP at the 10th percentile (-10th), TTP-mean, and TTP at the 90th percentile (-90th); AUC-10th, AUC-mean, AUC-90th, and AUC-max; Ktrans -10th and Ktrans -mean; and Ve-10th, Ve-mean, Ve-90th, and Ve-max than inactive patients (P < 0.05). Responsive patients exhibited significantly lower TTP-min; higher Ktrans -mean and Ktrans -max; and higher Kep-10th, Kep-mean, and Kep-max than unresponsive patients (P < 0.05). TTP-mean and Ve-mean were independent variables for determining disease activity (P = 0.017 and 0.022, respectively). A combination of the two parameters could determine active TAO with moderate performance (AUROC = 0.687). TTP-min and Ktrans -mean were independent predictors of the response to GCs (P = 0.023 and 0.004, respectively), uniting which could determine the response to GCs with decent performance (AUROC = 0.821). Conclusion DCE-MRI-derived model-free and model-based parameters of EOMs can assist in the evaluation of TAO. In particular, TTP-mean and Ve-mean could be useful for determining the activity of TAO, whereas TTP-min and K trans -mean could be promising biomarkers for determining the response to GCs.

Objective: To assess the role of dynamic contrast-enhanced (DCE)-MRI of the extraocular muscles (EOMs) for determining the activity of thyroid-associated ophthalmopathy (TAO) and treatment response to glucocorticoids (GCs). Materials and Methods: We prospectively enrolled 65 patients with TAO (41 active, 82 eyes; 24 inactive, 48 eyes). Twenty-two active patients completed the GC treatment and follow-up assessment, including 15 patients (30 eyes) and 7 patients (14 eyes), defined as responsive and unresponsive, respectively. Model-free (time to peak [TTP], area under the curve [AUC], and Slope max) and model-based (Ktrans , Kep, and Ve) parameters of EOMs in embedded simplified histogram analyses were calculated and compared between groups. Multivariable logistic regression analysis was used to identify the independent predictors. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the diagnostic performance. Results: Active patients exhibited significantly higher TTP at the 10th percentile (-10th), TTP-mean, and TTP at the 90th percentile (-90th); AUC-10th, AUC-mean, AUC-90th, and AUC-max; Ktrans -10th and Ktrans -mean; and Ve-10th, Ve-mean, Ve-90th, and Ve-max than inactive patients (P < 0.05). Responsive patients exhibited significantly lower TTP-min; higher Ktrans -mean and Ktrans -max; and higher Kep-10th, Kep-mean, and Kep-max than unresponsive patients (P < 0.05). TTP-mean and Ve-mean were independent variables for determining disease activity (P = 0.017 and 0.022, respectively). A combination of the two parameters could determine active TAO with moderate performance (AUROC = 0.687). TTP-min and Ktrans -mean were independent predictors of the response to GCs (P = 0.023 and 0.004, respectively), uniting which could determine the response to GCs with decent performance (AUROC = 0.821). Conclusion DCE-MRI-derived model-free and model-based parameters of EOMs can assist in the evaluation of TAO. In particular, TTP-mean and Ve-mean could be useful for determining the activity of TAO, whereas TTP-min and K trans -mean could be promising biomarkers for determining the response to GCs.

Objective To explore the risk factors of early femoral head necrosis in patients with femoral neck fracture after operation,and to establish a nomogram prediction model.Methods A total of 167 patients with femoral neck fracture from Jan-uary 2020 to April 2022 were selected and divided into necrosis group and non-necrosis group according to whether femoral head necrosis occurred in the early postoperative period.There were 21 males and 17 females in the necrosis group,aged from 33 to 72 years old,with an average of(53.49±10.96)years old,and the time from injury to operation ranged from 40 to 67 hours,with average time of(53.46±7.23)hours.There were 72 males and 57 females in the non-necrosis group,aged from 18 to 83 years,with an average of(52.78±12.55)years old,and the time from injury to operation was 18 to 65 hours,with an aver-age time of(39.88±7.79)hours.The potential influencing factors,including patient gender,diabetes mellitus,hypertension,chronic liver disease,posterior inclination angle of the femoral head,operation mode,fracture displacement,fracture line loca-tion,preoperative braking traction,screw arrangement mode,reduction quality,age,body mass index(BMI),and injury to operation time were subjected to single factor analysis.Logistic multivariate regression analysis was conducted for factors with a significance level of P＜0.05.Results The incidence of femoral head necrosis in 167 patients with femoral neck fracture was 22.76％.The following factors were identified as independent risk factors for early postoperative femoral head necrosis in pa-tients with femoral neck fractures:coexisting diabetes[OR=5.139,95％CI(1.405,18.793),P=0.013],displaced fracture[OR=3.723,95％CI(1.105,12.541),P=0.034],preoperative immobilization[OR=3.444,95％CI(1.038,11.427),P=0.043],quality of reduction[OR=3.524,95％CI(1.676,7.411),P=0.001],and time from injury to surgery[OR=1.270,95％CI(1.154,1.399),P=0.000].The Hosmer-Lemeshow goodness-of-fit test(x2=3.951,P=0.862),the area under the receiver operator characteristic(ROC)curve was 0.944[P＜0.001,95％CI(0.903,0.987)],with a sensitivity of 89.50％,the specificity was 88.40％,the maxi-mum Youden index was 0.779,and the overall trend of the model correction curve was close to the ideal curve.Model regres-sion equation was Z=1.637 × diabetes+1.314× fracture displacement+1.237 × preoperative braking traction+1.260 × reduc-tion quality+0.239 ×injury to operation time-18.310.Conclusion The occurrence of early femoral head necrosis in patients with femoral neck fracture postoperatively is affected by multiple factors.The risk early warning model established according to the factors has good predictive efficacy.

Objective: To assess the role of dynamic contrast-enhanced (DCE)-MRI of the extraocular muscles (EOMs) for determining the activity of thyroid-associated ophthalmopathy (TAO) and treatment response to glucocorticoids (GCs). Materials and Methods: We prospectively enrolled 65 patients with TAO (41 active, 82 eyes; 24 inactive, 48 eyes). Twenty-two active patients completed the GC treatment and follow-up assessment, including 15 patients (30 eyes) and 7 patients (14 eyes), defined as responsive and unresponsive, respectively. Model-free (time to peak [TTP], area under the curve [AUC], and Slope max) and model-based (Ktrans , Kep, and Ve) parameters of EOMs in embedded simplified histogram analyses were calculated and compared between groups. Multivariable logistic regression analysis was used to identify the independent predictors. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the diagnostic performance. Results: Active patients exhibited significantly higher TTP at the 10th percentile (-10th), TTP-mean, and TTP at the 90th percentile (-90th); AUC-10th, AUC-mean, AUC-90th, and AUC-max; Ktrans -10th and Ktrans -mean; and Ve-10th, Ve-mean, Ve-90th, and Ve-max than inactive patients (P < 0.05). Responsive patients exhibited significantly lower TTP-min; higher Ktrans -mean and Ktrans -max; and higher Kep-10th, Kep-mean, and Kep-max than unresponsive patients (P < 0.05). TTP-mean and Ve-mean were independent variables for determining disease activity (P = 0.017 and 0.022, respectively). A combination of the two parameters could determine active TAO with moderate performance (AUROC = 0.687). TTP-min and Ktrans -mean were independent predictors of the response to GCs (P = 0.023 and 0.004, respectively), uniting which could determine the response to GCs with decent performance (AUROC = 0.821). Conclusion DCE-MRI-derived model-free and model-based parameters of EOMs can assist in the evaluation of TAO. In particular, TTP-mean and Ve-mean could be useful for determining the activity of TAO, whereas TTP-min and K trans -mean could be promising biomarkers for determining the response to GCs.

OBJECTIVES: Venous thromboembolism is a highly prevalent condition after polytrauma, and recognized as an important factor contributing to poor prognosis. The aim of this study was to investigate the risk factors for lower extremity deep venous thrombosis (LEDVT) in a severely traumatized population and to evaluate their predictive value for LEDVT. METHODS: This was a retrospective, single-center observational study. All subjects were severely traumatized patients who were admitted to the Traumatic Intensive Care Unit from January 2021 to May 2024. Based on Doppler ultrasound findings of both lower extremities from the time of injury to 30 days post-injury, patients who developed LEDVT were enrolled in the LEDVT group, and those who did not develop LEDVT were enrolled in the NLEDVT group. Demographic, clinical, and laboratory data were collected upon admission. Multivariable logistic regression analysis was performed to identify risk factors for LEDVT. Receiver operating characteristic (ROC) curve was used to evaluate the overall fit of the final model. RESULTS: There were 56 patients enrolled in the LEDVT group and 81 patients in the NLEDVT group.Age, Aggregate Index of Systemic Inflammation (AISI), Systemic Inflammation Response Index (SIRI), ICU length of stay, and albumin were identified as independent risk factors for LEDVT (all P < 0.05). The area under their ROC curves were 0.604, 0.657, 0.694, 0.668, and 0.405, respectively. Combined model for early clinical prediction of LEDVT in severely traumatized patients by age, SIRI, AISI, and albumin resulted in an area under the ROC curve of 0.805 (95%CI: 0.73-0.88, SE = 0.037). CONCLUSIONS The combination of age, SIRI, AISI, and albumin has a predictive value for LEDVT in severely traumatized patients.
Humans ; Venous Thrombosis/blood* ; Male ; Female ; Retrospective Studies ; Adult ; Lower Extremity/blood supply* ; Middle Aged ; Inflammation ; Wounds and Injuries/blood* ; Risk Factors ; Predictive Value of Tests ; ROC Curve