Objective:To construct classification models of traumatic brain injury (TBI) based on the injury data collected at the scene of the accidents and validate its efficacy.Methods:A retrospective cohort study was conducted to analyze the pre-hospital treatment data of 368 TBI patients admitted to the Second Affiliated Hospital of Army Military Medical University from January 2019 to December 2023, including 243 males and 125 females, aged 18-82 years [(48.1±20.8)years]. The patients′ Glasgow coma scale (GCS) scores were 3-15 points [11.0(3.0, 15.0)points] at emergency medical service arrival. The patients were randomly assigned to the training set ( n=257) and test set ( n=111) at a ratio of 7∶3. According to the admission diagnosis, the patients fell into the mild TBI group ( n=62), medium TBI group ( n=137), severe TBI group ( n=120), and extremely severe TBI group ( n=49). In the training set, 44 patients fell into mild TBI group, 98 into medium TBI group, 82 into severe TBI group and 33 into extremely severe TBI group, while in the test set, 18 patients fell into mild TBI group, 39 into medium TBI group, 38 into severe TBI group and 16 into extremely severe TBI group. The following 12 kinds of injury data, including MARCH [massive hemorrhage (M), airway obstruction (A), respiratory failure (R), circulatory failure (C) and hypothermia (H)], GCS, pre-hospital index (PHI), shock index (SI), reverse SI multiplied by GCS (rSIG), optic nerve sheath diameter (ONSD) measured by ultrasound, scalp and skull injuries were collected at the scene of the accidents. Three machine algorithm including random forest (RF), support vector machine (SVM) and logistic regression (LR) were used to construct scene data-based TBI classification models. The accuracy rate, precision rate, recall rate, F1 value and area under receiver operating characteristic (ROC) curve (AUC) of the 3 models were used to verify the efficiency of the models for TBI classification. Shapley additive explanations (SHAP) method was used to interpret the results of the optimal model. The 12 kinds of injury data in the models were sorted according to their contribution to the TBI classification and the injury data with greater contribution were selected. Results:In the test set, the accuracy rate of the RF, SVM and LR models was 0.93, 0.92 and 0.87, respectively; the precision rate was 0.93, 0.92 and 0.89, respectively; the recall rate was 0.93, 0.92 and 0.87, respectively; the F1 value was 0.93, 0.92 and 0.87, respectively. In the mild, medium, severe and extremely severe TBI groups in the test set, the AUC of the RF model was 0.96 (95% CI 0.92, 0.98), 0.98 (95% CI 0.94, 0.99), 0.97 (95% CI 0.95, 0.98), and 0.97 (95% CI 0.96, 0.98), respectively; the AUC of the SVM model was 0.90 (95% CI 0.88, 0.94), 0.95 (95% CI 0.92, 0.97), 0.96 (95% CI 0.94, 0.98), and 0.95 (95% CI 0.92, 0.99), respectively; the AUC of the LR model was 0.90 (95% CI 0.83, 0.96), 0.90 (95% CI 0.84, 0.95), 0.96 (95% CI 0.95, 0.98), and 0.95 (95% CI 0.94, 0.97), respectively. The RF model demonstrated optimal discriminative performance for TBI classification. As the SHAP′s interpretation of the RF model indicated, among the 12 kinds of injury data, those with greater contributions to the TBI classification were GCS, rSIG, SI, PHI, respiratory failure, ONSD, and circulatory failure in sequence. Conclusions:Of the scene data-based TBI classification models, the RF model achieves good predictive performance for TBI classification when compared with the SVM model and LR model. Besides, GCS, rSIG, SI, PHI, respiratory failure, ONSD and circulatory failure contribute significantly to the classification of TBI in the RF model, which may assist emergency medical personnel in field triage and management of TBI at accident scenes.

Objective:To construct classification models of traumatic brain injury (TBI) based on the injury data collected at the scene of the accidents and validate its efficacy.Methods:A retrospective cohort study was conducted to analyze the pre-hospital treatment data of 368 TBI patients admitted to the Second Affiliated Hospital of Army Military Medical University from January 2019 to December 2023, including 243 males and 125 females, aged 18-82 years [(48.1±20.8)years]. The patients′ Glasgow coma scale (GCS) scores were 3-15 points [11.0(3.0, 15.0)points] at emergency medical service arrival. The patients were randomly assigned to the training set ( n=257) and test set ( n=111) at a ratio of 7∶3. According to the admission diagnosis, the patients fell into the mild TBI group ( n=62), medium TBI group ( n=137), severe TBI group ( n=120), and extremely severe TBI group ( n=49). In the training set, 44 patients fell into mild TBI group, 98 into medium TBI group, 82 into severe TBI group and 33 into extremely severe TBI group, while in the test set, 18 patients fell into mild TBI group, 39 into medium TBI group, 38 into severe TBI group and 16 into extremely severe TBI group. The following 12 kinds of injury data, including MARCH [massive hemorrhage (M), airway obstruction (A), respiratory failure (R), circulatory failure (C) and hypothermia (H)], GCS, pre-hospital index (PHI), shock index (SI), reverse SI multiplied by GCS (rSIG), optic nerve sheath diameter (ONSD) measured by ultrasound, scalp and skull injuries were collected at the scene of the accidents. Three machine algorithm including random forest (RF), support vector machine (SVM) and logistic regression (LR) were used to construct scene data-based TBI classification models. The accuracy rate, precision rate, recall rate, F1 value and area under receiver operating characteristic (ROC) curve (AUC) of the 3 models were used to verify the efficiency of the models for TBI classification. Shapley additive explanations (SHAP) method was used to interpret the results of the optimal model. The 12 kinds of injury data in the models were sorted according to their contribution to the TBI classification and the injury data with greater contribution were selected. Results:In the test set, the accuracy rate of the RF, SVM and LR models was 0.93, 0.92 and 0.87, respectively; the precision rate was 0.93, 0.92 and 0.89, respectively; the recall rate was 0.93, 0.92 and 0.87, respectively; the F1 value was 0.93, 0.92 and 0.87, respectively. In the mild, medium, severe and extremely severe TBI groups in the test set, the AUC of the RF model was 0.96 (95% CI 0.92, 0.98), 0.98 (95% CI 0.94, 0.99), 0.97 (95% CI 0.95, 0.98), and 0.97 (95% CI 0.96, 0.98), respectively; the AUC of the SVM model was 0.90 (95% CI 0.88, 0.94), 0.95 (95% CI 0.92, 0.97), 0.96 (95% CI 0.94, 0.98), and 0.95 (95% CI 0.92, 0.99), respectively; the AUC of the LR model was 0.90 (95% CI 0.83, 0.96), 0.90 (95% CI 0.84, 0.95), 0.96 (95% CI 0.95, 0.98), and 0.95 (95% CI 0.94, 0.97), respectively. The RF model demonstrated optimal discriminative performance for TBI classification. As the SHAP′s interpretation of the RF model indicated, among the 12 kinds of injury data, those with greater contributions to the TBI classification were GCS, rSIG, SI, PHI, respiratory failure, ONSD, and circulatory failure in sequence. Conclusions:Of the scene data-based TBI classification models, the RF model achieves good predictive performance for TBI classification when compared with the SVM model and LR model. Besides, GCS, rSIG, SI, PHI, respiratory failure, ONSD and circulatory failure contribute significantly to the classification of TBI in the RF model, which may assist emergency medical personnel in field triage and management of TBI at accident scenes.

The amino acid sequences of the OmpA protein isolated from Escherichia coli QML2206-1(E.coli QML2206-1)in our laboratory were analyzed for homology with different strains of OmpA proteins using bioinformatics software,and the OmpA protein was analyzed for its physicochemical properties,transmembrane structure and signal peptide prediction,B-cell anti-genic epitope prediction,secondary and tertiary structure prediction.The OmpA gene fragment was ligated with pET-32a vector to construct a prokaryotic expression vector,which was purified by a nickel column affinity purification system after prokaryotic expression and optimization of ex-pression conditions in BL21(DE3).The purified recombinant protein was fully mixed with Freund's adjuvant to immunize mice,and the levels of mouse-specific IgG antibody and the expression levels of cytokines CD4,CD8 and IL-4 in mouse serum were detected by ELISA,and the immuno-protective effect was evaluated by mouse attack protection test.OmpA protein is a hydrophilic protein with no transmembrane structural domains and a secondary structure consisting mainly of irregular coils(47.98％)and α-helices(29.77％),with 12 antigenic epitopes that can bind to anti-bodies produced by B cells.The recombinant protein OmpA with a relative molecular mass of a-bout 55 kDa was successfully obtained by prokaryotic expression,and the highest expression was induced by IPTG concentration of 0.000 4 mmol/L for 6 h at 37 ℃.The serum-specific IgG anti-body potency of recombinant protein immunized mice was up to 1∶32 000;the expression levels of CD4,CD8 and IL-4 in the serum of immunized mice were elevated compared with those of the con-trol group.The survival rate of mice was 80％and 40％after attack with minimum lethal dose(MLD)and 2 times minimum lethal dose(2MLD),respectively.OmpA recombinant protein has good antigenicity and certain immunoprotective effects,and this study provides a technical basis for the next step in the development of a genetically engineered subunit vaccine against yak-appli-cable E.coli based on OmpA protein.

The amino acid sequences of the OmpA protein isolated from Escherichia coli QML2206-1(E.coli QML2206-1)in our laboratory were analyzed for homology with different strains of OmpA proteins using bioinformatics software,and the OmpA protein was analyzed for its physicochemical properties,transmembrane structure and signal peptide prediction,B-cell anti-genic epitope prediction,secondary and tertiary structure prediction.The OmpA gene fragment was ligated with pET-32a vector to construct a prokaryotic expression vector,which was purified by a nickel column affinity purification system after prokaryotic expression and optimization of ex-pression conditions in BL21(DE3).The purified recombinant protein was fully mixed with Freund's adjuvant to immunize mice,and the levels of mouse-specific IgG antibody and the expression levels of cytokines CD4,CD8 and IL-4 in mouse serum were detected by ELISA,and the immuno-protective effect was evaluated by mouse attack protection test.OmpA protein is a hydrophilic protein with no transmembrane structural domains and a secondary structure consisting mainly of irregular coils(47.98％)and α-helices(29.77％),with 12 antigenic epitopes that can bind to anti-bodies produced by B cells.The recombinant protein OmpA with a relative molecular mass of a-bout 55 kDa was successfully obtained by prokaryotic expression,and the highest expression was induced by IPTG concentration of 0.000 4 mmol/L for 6 h at 37 ℃.The serum-specific IgG anti-body potency of recombinant protein immunized mice was up to 1∶32 000;the expression levels of CD4,CD8 and IL-4 in the serum of immunized mice were elevated compared with those of the con-trol group.The survival rate of mice was 80％and 40％after attack with minimum lethal dose(MLD)and 2 times minimum lethal dose(2MLD),respectively.OmpA recombinant protein has good antigenicity and certain immunoprotective effects,and this study provides a technical basis for the next step in the development of a genetically engineered subunit vaccine against yak-appli-cable E.coli based on OmpA protein.

Objective To present a named entity recognition method referred to as BioBERT-Att-BiLSTM-CRF for eligibility criteria based on the BioBERT pretrained model.The method can automatically extract relevant information from clinical trials and provide assistance in efficiently formulating eligibility criteria.Methods Based on the UMLS medical semantic network and expert-defined rules,the study established medical entity annotation rules and constructed a named entity recognition corpus to clarify the entity recognition task.BioBERT-Att-BiLSTM-CRF converted the text into BioBERT vectors and inputted them into a bidirectional long short-term memory network to capture contextual semantic features.Meanwhile,attention mechanisms were applied to extract key features,and a conditional random field was used for decoding and outputting the optimal label sequence.Results BioBERT-Att-BiLSTM-CRF outperformed other baseline models on the eligibility criteria named entity recognition dataset.Conclusion BioBERT-Att-BiLSTM-CRF can efficiently extract eligibility criteria-related information from clinical trials,thereby enhancing the scientific validity of clinical trial registration data and providing assistance in the formulation of eligibility criteria for clinical trials.

Objective To explore the clinical features,treatment and prognosis of glioblastomas(GBM)in adults.Methods A retrospective cohort study was performed on 176 adult GBM patients admitted to our department from January 2015 to December 2021.Chi-square test was used to investigate the clinical differences between isocitrate dehydrogenase(IDH)mutant and wild-type GBM.Kaplan-Meier and Log-Rank tests were employed to plot survival curve and compute the survival analysis.Multivariate Cox regression model was applied to identify the independent prognostic factors.Results IDH wild-type GBM account for 89.2％and had significantly differences from the IDH-mutant GBM in terms of age of onset,Karnofsky(KPS)score at admission,symptoms of neurological deficit,and methylation status of O6-methylguanine-DNA-methyltransferase(MGMT)promoter(P＜0.05).For the IDH wild-type GBM patients receiving conventional therapy,univariate Cox hazard analysis showed gross total resection,methylation of MGMT promoter,initiation of radiation within the 5th to 6th week after surgery,and adjuvant temozolomide(TMZ)chemotherapy ≥6 cycles were favorable prognostic factors for overall survival(OS);GBMs in the left hemisphere,involvement of single lobe,methylation of MGMT promoter,and initiation of radiation within the 5th to 6th week after surgery were favorable prognostic factors for progression free survival(PFS)(all P＜0.05).Moreover,multivariate Cox hazard regression analysis indicated that methylation of MGMT promoter,and initiation of radiation within the 5th to 6th week after surgery,and adjuvant TMZ chemotherapy ≥6 cycles were independent protective factors for OS,and GBMs in the left hemisphere,involvement of single lobe and methylation of MGMT promoter were independent protective factors for PFS in the GBM patients(all P＜0.05).Conclusion The clinical and prognostic features are totally different between IDH mutant and wild-type GBM,and molecular detections are needed for the further pathological classification.Methylation of MGMT promoter is a primary marker of favorite prognosis for IDH wild-type GBM,and slightly delay in radiotherapy(the 5th to 6th week after surgery)can effectively improve the survival prognosis of IDH wild-type GBM.

The condition of patients with severe traumatic brain injury (sTBI) complicated by corona virus 2019 disease (COVID-19) is complex. sTBI can significantly increase the probability of COVID-19 developing into severe or critical stage, while COVID-19 can also increase the surgical risk of sTBI and the severity of postoperative lung lesions. There are many contradictions in the treatment process, which brings difficulties to the clinical treatment of such patients. Up to now, there are few clinical studies and therapeutic norms relevant to sTBI complicated by COVID-19. In order to standardize the clinical treatment of such patients, Critical Care Medicine Branch of China International Exchange and Promotive Association for Medical and Healthcare and Editorial Board of Chinese Journal of Trauma organized relevant experts to formulate the Chinese expert consensus on clinical treatment of adult patients with severe traumatic brain injury complicated by corona virus infection 2019 ( version 2023) based on the joint prevention and control mechanism scheme of the State Council and domestic and foreign literatures on sTBI and COVID-19 in the past 3 years of the international epidemic. Fifteen recommendations focused on emergency treatment, emergency surgery and comprehensive management were put forward to provide a guidance for the diagnosis and treatment of sTBI complicated by COVID-19.

Background: Peste des petits ruminants (PPR), caused by the PPR virus (PPRV), is an acute and fatal contagious disease that mainly infects goats, sheep, and other artiodactyls.Peripheral blood mononuclear cells (PBMCs) are considered the primary innate immune cells. Objectives: PBMCs derived from goats were infected with PPRV and analyzed to detect the relationship between PPRV replication and apoptosis or the inflammatory response. Methods: Quantitative real-time polymerase chain reaction was used to identify PPRV replication and cytokines expression. Flow cytometry was conducted to detect apoptosis and the differentiation of CD4+ and CD8+T cells after PPRV infection. Results: PPRV stimulated the differentiation of CD4+ and CD8+ T cells. In addition, PPRV induced apoptosis in goat PBMCs. Furthermore, apoptosis and the inflammatory response induced by PPRV could be suppressed by Z-VAD-FMK and Z-YVAD-FMK, respectively.Moreover, the virus titer of PPRV was attenuated by inhibiting caspase-1-dependent apoptosis and inflammation. Conclusions This study showed that apoptosis and the inflammatory response play an essential role in PPR viral replication in vitro, providing a new mechanism related to the cell host response.

Here, we took base construction of neurosurgery as example to discuss and analyze according to requirements and evaluation indexes of base construction in Xinqiao Hospital, and put forward the specific objectives, measures and implementations of base construction. Foremost, we summarized experiences and overcame shortcomings through interpreting and implementing scheme of our base construction, which would help to improve the construction of standardized residency training base in China.

Targeted protein degradation(TPD)has rapidly emerged as a therapeutic modality to eliminate previously undruggable proteins by repurposing the cell's endogenous protein degrada-tion machinery.However,the susceptibility of proteins for targeting by TPD approaches,termed"degradability",is largely unknown.Here,we developed a machine learning model,model-free anal-ysis of protein degradability(MAPD),to predict degradability from features intrinsic to protein tar-gets.MAPD shows accurate performance in predicting kinases that are degradable by TPD compounds[with an area under the precision-recall curve(AUPRC)of 0.759 and an area under the receiver operating characteristic curve(AUROC)of 0.775]and is likely generalizable to inde-pendent non-kinase proteins.We found five features with statistical significance to achieve optimal prediction,with ubiquitination potential being the most predictive.By structural modeling,we found that E2-accessible ubiquitination sites,but not lysine residues in general,are particularly associated with kinase degradability.Finally,we extended MAPD predictions to the entire proteome to find 964 disease-causing proteins(including proteins encoded by 278 cancer genes)that may be tractable to TPD drug development.