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 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.

To clarify the key quality attributes of substance benchmarks in Danggui Buxue Decoction(DBD), this study prepared 21 batches of DBD substance benchmarks, and established two methods for detecting their fingerprints, followed by the identification of peak attribution and similarity range as well as the determination of extract and transfer rate ranges and contents of index components ferulic acid, calycosin-7-O-β-D-glucoside, and astragaloside Ⅳ. The mass fractions and transfer rates of DBD substance benchmarks from different batches were calculated as follows: ferulic acid(index component in Angelicae Sinensis Radix): 0.037%-0.084% and 31.41%-98.88%; astragaloside Ⅳ(index component in Astragali Radix): 0.021%-0.059% and 32.18%-118.57%; calycosin-7-O-β-D-glucoside: 0.002%-0.023% and 11.51%-45.65%, with the extract rate being 18.4%-36.1%. The similarity of fingerprints among 21 batches of DBD substance benchmarks was all higher than 0.9. The quality control method for DBD substance benchmarks was preliminarily established based on the HPLC fingerprint analysis and index component determination, which has provided a basis for the subsequent development of DBD and the quality control of novel related preparations.
Chromatography, High Pressure Liquid/methods* ; Drugs, Chinese Herbal/standards* ; Quality Control

OBJECTIVE: To identify the causative variants in 13 Chinese pedigrees affected with oculocutaneous albinism (OCA) so as to provide genetic counseling and prenatal diagnosis to them. METHODS: Thirteen unrelated pedigrees with clinically diagnosed OCA were collected and classified based on the manifestation of skin and eyes. With informed consent obtained from the participants, peripheral blood samples were collected from the probands and their family members for the extraction of genomic DNA. Candidate variants were screened by targeted capture and next generation sequencing, and the results were validated by Sanger sequencing. Prenatal diagnosis was provided to the families upon their subsequent pregnancies. RESULTS: Causative variants were detected in all probands, including 10 with compound heterozygotes or homozygotes for TYR gene variants and 3 with compound heterozygotes for OCA2 gene variants. Among these, two variants [TYR: c.650G>C (p.Arg217Pro) and OCA2: c.516-2A>T] were unreported previously. The pathogenicity of the novel TYR: c.650G>C (p.Arg217Pro) variant was verified through bioinformatic analysis and prediction of three dimensional structure of the protein. Prenatal diagnosis was provided to 6 fetuses with a high risk for OCA. Four fetuses were found to be carriers, one did not carry the variants of the proband, and one was affected with OCA. CONCLUSION Identification of the pathogenic variants in the 13 probands, including 2 novel ones, has expanded the mutational spectrum of OCA and enabled genetic counseling and prenatal diagnosis for the families.
Albinism, Oculocutaneous/genetics* ; China ; Female ; Genetic Testing ; Humans ; Membrane Transport Proteins/genetics* ; Monophenol Monooxygenase/genetics* ; Mutation ; Pedigree ; Pregnancy ; Prenatal Diagnosis

OBJECTIVE: To illustrate the molecular mechanisms underlying the therapeutic effects of electroacupuncture (EA) on knee osteoarthritis (OA). METHODS: Twenty-seven six-month-old New Zealand white rabbits were allocated into three groups in accordance with a random number table: normal group (no surgery-induced OA; without treatment), model group (surgery-induced OA; without treatment) and EA group [surgery-induced OA; received treatment with EA at acupoints Dubi (ST 35) and Neixiyan (EX-LE 5), 30 min twice a day]. After eight consecutive weeks of treatment, the histopathological alterations in cartilage were observed using optical microscopy and transmission electron microscopy, cartilage degeneration was evaluated by modified Mankin's score principles, the synovial fluid concentration of interleukin-1β (IL-1β), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α) and matrix metalloproteinase-3 (MMP-3) were evaluated by enzyme-linked immunosorbent assay, and the protein expression levels of IL-1β, IL-6, TNF-α, MMP-3, IκB kinase-β (IKK-β), nuclear factor of α light polypeptide gene enhancer in B-cells inhibitor α (IκB-α) and nuclear factor-κB (NF-κB) p65 were quantified by Western blot analysis. RESULTS: EA treatment significantly improved cartilage structure arrangement and reduced cellular degeneration. The IL-1β, IL-6, TNF-α and MMP-3 of synovial fluid in the EA-treated group were significantly decreased compared with the model group (all P<0.01). Compared with the model group, the IL-1β, IL-6, TNF-α, MMP-3, IKK-β and NF-κB p65 protein expressions in cartilage of EA-treated group were significantly decreased (all P<0.01), whereas IκB-α expression was significantly up-regulated (P<0.01). CONCLUSION EA treatment may delay cartilage degeneration by down-regulating inflammatory factors through NF-κB signaling pathway, which may, in part, explain its clinical efficacy in the treatment of knee OA.

Objective: To evaluate the impact of KIT D816 mutation on the salvage therapy in relapsed acute myeloid leukemia (AML) with t(8;21) translocation. Method: The characteristics of the first relapsed AML with t(8;21) translocation from 10 hospitals were retrospectively collected, complete remission (CR(2)) rate after one course salvage chemotherapy and the relationship between KIT mutation and CR(2) rate was analyzed. Results: 68 cases were enrolled in this study, and 30 cases (44.1%) achieved CR(2). All patients received KIT mutation detection, and KIT D816 mutation was identified in 26 cases. The KIT D816 positive group had significantly lower CR(2) compared with non-KIT D816 group (23.1% vs 57.1%, χ(2)=7.559, P=0.006), and patients with longer CR(1) duration achieved significantly higher CR(2) than those with CR(1) duration less than 12 months (74.1% vs 31.9%, χ(2)=9.192, P=0.002). KIT D816 mutation was tightly related to shorter CR(1) duration. No significant difference of 2 years post relapse survival was observed between KIT D816 mutation and non-KIT D816 mutation group. Conclusion: KIT D816 mutation at diagnosis was an adverse factor on the salvage therapy in relapsed AML with t(8;21) translocation, significantly related to shorter CR1 duration, and can be used for prediction of salvage therapy response. KIT D816 mutation could guide the decision-making of salvage therapy in relapsed AML with t(8;21) translocation.
Antineoplastic Combined Chemotherapy Protocols ; Cytarabine ; Humans ; Leukemia, Myeloid, Acute/therapy* ; Prognosis ; Retrospective Studies ; Salvage Therapy

Objective To explore the role of remote ischemic preconditioning(RIPC)in prevention of contrast -induced nephropathy(CIN)in elderly patients undergoing coronary artery angiography(CAA).Methods 106 elderly patients were enrolled in this randomized control trial.According to random number table,the patients were randomized into control group (n =53)and RIPC group(n =53).All of the patients received 1 000mL of 0.9% sodium chloride injection before CAA.The RIPC group patients underwent RIPC in their right arms with sphygmomanometer cuff infla-tion for 5 minutes prior to the CAA,three cycles were repeated.Serum creatinine was detected before and 48 hours after CAA.Results CIN was reported in 10 cases in the control group and 3 cases in the RIPC group(χ2 =4.30, P =0.04).The levels of serum creatinine were increased[(96.38 ±9.50)μmol/L vs (88.87 ±10.24)μmol/L] after CAA in the control group(t =2.28,P =0.03),and there was no difference in the RIPC group(t =1.17,P =0.24).Conclusion RIPC has a protective effect on CIN in elderly patients in our study.Since this method is harm-less and cost effective,further studies is required to popularize PIPC to our clinical practice for prevention of CIN.

Column chromatographies over silica gel, Sephadex LH-20, reverse phase C18, and MCI, and semi-preparative HPLC were used for separation and purification of constituents from Inula cappa. The 22 compounds were obtained and their strutures were determined by NMR and MS spectra data as nine flavonoids: luteolin (1), apigenin (2), chrysoeriol (3), artemetin (4), 2', 5-di- hydroxy-3, 6, 7, 4', 5'-pentamethoxyflavone (5), chrysosplenol C (6), apigenin-5-0-β-D-glucopyranoside (7), luteolin-3-methyl, luteolin-3-methylether-4'-0-β-D-glucopyranoside (8), luteolin-4'-0-β-D-glucopyranoside (9); four triterpenes: darma-20, 24-dien- 3β-0-acetate (10), darma-20, 24-dien-3β-ol (11), epirfiedelanol (12), friedelin (13); three coumarins: scopoletin (14) , isosco- poletin (15) , scopolin(16) , and other types of compounds stigmasta-5, 22-dien-3β-0-7-one (17), stigmasterol (18), palmitic acid (19), linoleic acid (20), linoleic acid methyl ester (21), (E) -9, 12, 13-trihydroxyoetadee-10-enoie acid (22). Compound 5 is a new natural product. Compounds 3-9, 15, 17, 21, and 22 were isolated from this genus for the first time.
Drugs, Chinese Herbal ; chemistry ; isolation & purification ; Inula ; chemistry ; Molecular Structure ; Spectrometry, Mass, Electrospray Ionization

To prepare monoclonal antibody of carbohydrate antigen 19-9(CA19-9).Methods: Based on the titer test results of mouse ascites and its IC 50 values ,the mouse that prepare for fusion was identified.Positive monoclonal cell strains were established by cell fusing and screening.Monoclonal antibody from ascites was produced by peritoneal injection monoclonal cell , and then purified by octoic acid-ammonium sulfate precipitation method.After determine the protein concentrations by UV-spectrophotometry ,the monoclonal antibody against CA 19-9 was labelled with horseradish peroxidase.Based on antibody pairing test , DAS-ELISA method was established .To compared with abroad kit , analyzing performance of this method.Results: Three strains of monoclonal antibody were obtained.And the optimal working concentrations of mAb (ZJY3-1G9) ,as coated antibody,McAb(ZJY2-7F10),as HRP-IgG,were assured.Limit of detection was 26.4 U/ml.Linear range was 30-300 U/ml.By detecting patients with serum 33 , confirmed the correlation coefficient of r=0.950 4 , compared with abroad kit that measure simultaneously.Conclusion:Monoclonal antibody prepared for CA 19-9 can be used to develop a kit.

Objective To construct and identify norepinephrine ( NE) complete antigen for the preparation of high sensitive and specific anti-NE monoclonal antibody .Methods Glutaraldehyde ( GA) and 1-Ethyl-3-(3-Dimethylaminopropyl ) carbodiimide ( EDC) were used to cross-link NE with carrier pro-teins (BSA, OVA) for NE complete antigen preparation under conditions of pH 4.5 or pH9.0.Three assays including UV scanning , SDS-PAGE and FeCl3 color reaction were performed for identification of NE com-plete antigen.Serum antibody titers were evaluated in mice model induced by intraperitoneal immunization with NE complete antigen .Results NE complete antigens were successfully prepared as indicated by the three identification assays .The coupling ratio was significantly increased in a time-depended manner under the condition of pH9.0 in comparison to that in the condition of pH 4.5.Indirect ELISA results showed that , when coating antigens and serum antibodies were prepared with the same cross -linking method , the serum antibody titers were significantly higher than those with different methods .Conclusion Anti-NE antibodies were successfully prepared by immunizing mice with NE complete antigens .