OBJECTIVE To explore the effect and mechanism of Zhiqiao gancao decoction （ZQGCD） on pathological angiogenesis of degenerative intervertebral disc. METHODS The rats were randomly divided into sham operation group （normal saline）， model group （normal saline）， hypoxia inducible factor-1α （HIF-1α） inhibitor （YC-1） group [2 mg/（kg·d）， tail vein injection]， and ZQGCD low-dose， medium-dose and high-dose groups [3.06， 6.12， 12.24 g/（kg·d）]， with 8 rats in each group. Except for sham operation group， lumbar disc degeneration model of rat was constructed in all other groups. After modeling， they were given relevant medicine once a day， for consecutive 3 weeks. After the last medication， pathological changes and angiogenesis of the intervertebral disc tissue in rats were observed； the levels of inflammatory factors [interleukin-1β （IL-1β）， IL-6， tumor necrosis factor-α （TNF-α）] and the expressions of angiogenesis-related proteins [HIF-1α， vascular endothelial growth factor （VEGF）， VEGF receptor 2 （VEGFR2）， angiotensin 1（Ang 1）， Ang 2] in the com intervertebral disc tissue in rats were all determined. In cell experiment， the primary nucleus pulposus cells were isolated and cultured from rats， and cellular degeneration was induced using 50 ng/mL TNF-α. The cells were divided into blank control group （10% blank control serum）， TNF-α group （10% blank control serum）， YC-1 group （10% blank control serum+0.2 mmol/L YC-1）， and 5%， 10%， 15% drug-containing serum group （5%， 10%， 15% drug-containing serum）. After 24 hours of intervention， the nucleus pulposus cells were co-cultured with HUVEC. The expressions of Collagen Ⅱ， matrix metalloproteinase-3 （MMP-3） in nucleus pulposus cells were detected. HUVEC proliferation， migration and tube forming ability were detected， and the expression levels of the HIF-1α/VEGF/Ang signal axis and angiogenesis- related proteins （add MMP-2， MMP-9） in HUVEC were detected. RESULTS Animal experiments had shown that compared with model group， the positive expression of CD31 in the intervertebral disc tissues of rats in each drug group was down-regulated （P＜ 0.05）， the levels of inflammatory factors and angiogenesis-related proteins were decreased significantly （P＜0.05）， and the pathological changes in the intervertebral disc were alleviated. Cell experiments had shown that compared with TNF-α group， the expression of Collagen Ⅱ in nucleus pulposus cells of all drug groups was significantly up-regulated （P＜0.05）， and the expression of MMP-3 was significantly down-regulated （P＜0.05）； the proliferation， migration and tubulogenesis of HUVEC were significantly weakened （P＜0.05）. The mRNA and protein expressions of HIF-1α， VEGF， Ang 2 as well as the expression of angiogenesis-related proteins （except for the expression of Ang 2 mRNA and HIF-1α， VEGFR2， Ang 2 protein in 5% drug- containing serum group） were significantly down-regulated （P＜0.05）. CONCLUSIONS ZQGCD may inhibit the HIF-1α/VEGF/ Ang signal axis to weaken the angiogenic ability of vascular endothelial cells， improve pathological angiogenesis in the intervertebral disc， and delay the degeneration of the intervertebral disc.

Objective:To explore the application value of machine learning prediction model for neural invasion in gallbladder cancer based on enhanced computed tomography (CT) and clinical characteristics.Methods:The retrospective cohort study was conducted. The clinical and imaging data of 502 patients with gallbladder cancer who were admitted to The First Affiliated Hospital of Xinxiang Medical University from January 2010 to June 2024 were collected. There were 171 males and 331 females, aged 65(range, 35?91)years. All patients underwent preoperative abdominal enhanced CT and radical resection. The 502 patients were randomly divided into a training set of 351 cases and a test set of 151 cases at a 7:3 ratio. The training set was used to construct prediction model, and the test set was used to validate prediction model. Observation indicators: (1)neural invasion in gallbladder cancer and influencing factor analysis; (2) construction and validation of machine learning prediction models for neural invasion in gallbladder cancer. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the Mann-Whitney U test. Logistic regression model was performed for univariate and multivariate analyses. Independent influencing factors were incor-porated to construct machine learning models using the standard library modules based on Python 3.9. Receiver operating characteristic (ROC) curves were plotted, and the accuracy, sensitivity, specificity, area under the curve (AUC), precision, F1 score, positive predictive value, negative predic-tive value, and Kappa value were calculated to evaluate the predictive performance of the models. The Delong test was used to assess the differences in AUC among different models in the test set. The Hosmer-Lemeshow test and Brier score were used to evaluate the calibration of the models. Results:(1) Neural invasion in gallbladder cancer and influencing factor analysis. Of the 502 patients with gallbladder cancer, 131 cases had neural invasion, and 371 cases had no neural invasion. Results of multivariate analysis showed that total bilirubin, carcinoembryonic antigen, CA199, CA125, neutrophil-lymphocyte ratio, liver invasion detected by CT, vascular invasion detected by CT, hilar or retroperi-toneal lymph node metastasis detected by CT, and tumor stages T3 and T4 were independent influencing factors for neural invasion in patients with gallbladder cancer [ odds ratios=3.747, 2.395, 3.917, 3.596, 2.805, 2.377, 3.523, 2.774, 5.080, 6.809, 95% confidence interval ( CI) as 1.890?7.430, 1.154?4.971, 2.054?7.472, 1.807?7.155, 1.506?5.225, 1.241?4.553, 1.666?7.449, 1.483?5.189, 2.050?12.589, 2.552?18.168, P<0.05]. (2) Construction and validation of machine learning predic-tion models for neural invasion in gallbladder cancer. Based on the independent influencing factors, seven machine learning models were constructed, including logistic regression, K-nearest neighbors, support vector machine, random forest, decision tree, back-propagation neural network, and gradient boosting machine. The ROC curves of seven machine learning models in the test set were plotted, and the AUC were 0.900(95% CI as 0.851?0.948), 0.741(95% CI as 0.646?0.829), 0.836(95% CI as 0.762?0.895), 0.782(95% CI as 0.701?0.855), 0.839(95% CI as 0.770?0.901), 0.817(95% CI as 0.738?0.887), 0.843(95% CI as 0.770?0.909), respectively. Results of Delong test showed that the logistic regression model had the highest AUC. The sensitivity and specificity of the logistic regression model were 0.868 and 0.805 respectively, indicating the best balance. Results of Hosmer-Lemeshow test showed that the logistic regression model had a good goodness-of-fit ( χ2=5.320, P>0.05). The Brier score of the logistic regression model was relatively low, as 0.168, which verified its calibration advantage. Conclusion:Total bilirubin, carcinoembryonic antigen, CA199, CA125, neutrophil-to-lymphocyte ratio, liver invasion detected by enhanced CT, vascular invasion detected by enhanced CT, hilar or retroperitoneal lymph node metastasis detected by enhanced CT, and tumor stages T3 and T4 are independent influencing factors for nerve invasion in patients with gallbladder cancer. Seven machine learning models are constructed based on enhanced CT and clinical characteristics to predict neural invasion in gallbladder cancer, of which the logistic regression model demonstrates good predictive performance.

Objective:To explore the application value of machine learning prediction model for neural invasion in gallbladder cancer based on enhanced computed tomography (CT) and clinical characteristics.Methods:The retrospective cohort study was conducted. The clinical and imaging data of 502 patients with gallbladder cancer who were admitted to The First Affiliated Hospital of Xinxiang Medical University from January 2010 to June 2024 were collected. There were 171 males and 331 females, aged 65(range, 35?91)years. All patients underwent preoperative abdominal enhanced CT and radical resection. The 502 patients were randomly divided into a training set of 351 cases and a test set of 151 cases at a 7:3 ratio. The training set was used to construct prediction model, and the test set was used to validate prediction model. Observation indicators: (1)neural invasion in gallbladder cancer and influencing factor analysis; (2) construction and validation of machine learning prediction models for neural invasion in gallbladder cancer. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the Mann-Whitney U test. Logistic regression model was performed for univariate and multivariate analyses. Independent influencing factors were incor-porated to construct machine learning models using the standard library modules based on Python 3.9. Receiver operating characteristic (ROC) curves were plotted, and the accuracy, sensitivity, specificity, area under the curve (AUC), precision, F1 score, positive predictive value, negative predic-tive value, and Kappa value were calculated to evaluate the predictive performance of the models. The Delong test was used to assess the differences in AUC among different models in the test set. The Hosmer-Lemeshow test and Brier score were used to evaluate the calibration of the models. Results:(1) Neural invasion in gallbladder cancer and influencing factor analysis. Of the 502 patients with gallbladder cancer, 131 cases had neural invasion, and 371 cases had no neural invasion. Results of multivariate analysis showed that total bilirubin, carcinoembryonic antigen, CA199, CA125, neutrophil-lymphocyte ratio, liver invasion detected by CT, vascular invasion detected by CT, hilar or retroperi-toneal lymph node metastasis detected by CT, and tumor stages T3 and T4 were independent influencing factors for neural invasion in patients with gallbladder cancer [ odds ratios=3.747, 2.395, 3.917, 3.596, 2.805, 2.377, 3.523, 2.774, 5.080, 6.809, 95% confidence interval ( CI) as 1.890?7.430, 1.154?4.971, 2.054?7.472, 1.807?7.155, 1.506?5.225, 1.241?4.553, 1.666?7.449, 1.483?5.189, 2.050?12.589, 2.552?18.168, P<0.05]. (2) Construction and validation of machine learning predic-tion models for neural invasion in gallbladder cancer. Based on the independent influencing factors, seven machine learning models were constructed, including logistic regression, K-nearest neighbors, support vector machine, random forest, decision tree, back-propagation neural network, and gradient boosting machine. The ROC curves of seven machine learning models in the test set were plotted, and the AUC were 0.900(95% CI as 0.851?0.948), 0.741(95% CI as 0.646?0.829), 0.836(95% CI as 0.762?0.895), 0.782(95% CI as 0.701?0.855), 0.839(95% CI as 0.770?0.901), 0.817(95% CI as 0.738?0.887), 0.843(95% CI as 0.770?0.909), respectively. Results of Delong test showed that the logistic regression model had the highest AUC. The sensitivity and specificity of the logistic regression model were 0.868 and 0.805 respectively, indicating the best balance. Results of Hosmer-Lemeshow test showed that the logistic regression model had a good goodness-of-fit ( χ2=5.320, P>0.05). The Brier score of the logistic regression model was relatively low, as 0.168, which verified its calibration advantage. Conclusion:Total bilirubin, carcinoembryonic antigen, CA199, CA125, neutrophil-to-lymphocyte ratio, liver invasion detected by enhanced CT, vascular invasion detected by enhanced CT, hilar or retroperitoneal lymph node metastasis detected by enhanced CT, and tumor stages T3 and T4 are independent influencing factors for nerve invasion in patients with gallbladder cancer. Seven machine learning models are constructed based on enhanced CT and clinical characteristics to predict neural invasion in gallbladder cancer, of which the logistic regression model demonstrates good predictive performance.

Pharmaceutical analysis is a discipline based on chemical, physical, biological, and information technologies. At present, biotechnological analysis is a short branch in pharmaceutical analysis; however, bioanalysis is the basis and an important part of medicine. Biotechnological approaches can provide information on biological activity and even clinical efficacy and safety, which are important characteristics of drug quality. Because of their advantages in reflecting the overall biological effects or functions of drugs and providing visual and intuitive results, some biotechnological analysis methods have been gradually applied to pharmaceutical analysis from raw material to manufacturing and final product analysis, including DNA super-barcoding, DNA-based rapid detection, multiplex ligation-dependent probe amplification, hyperspectral imaging combined with artificial intelligence, 3D biologically printed organoids, omics-based artificial intelligence, microfluidic chips, organ-on-a-chip, signal transduction pathway-related reporter gene assays, and the zebrafish thrombosis model. The applications of these emerging biotechniques in pharmaceutical analysis have been discussed in this review.

Background Bromadiolone is the second-generation anticoagulant rodenticide widely used all over the world. Exposure to bromadiolone in early life stage can lead to neurodevelopmental toxicity, but its toxic mechanism of neurodevelopment is not clear so far. Objective To investigate the developmental neurotoxicity and mechanism of bromadiolone to zebrafish embryos. Methods Zebrafish embryos were randomly divided into four groups: a solvent control group (dimethylsulphoxide) and three bromadiolone exposure groups (0.39, 0.78, and 1.18 mg·L⁻¹). The exposure period was from 4 h to 120 h post-fertilization. The number of spontaneous movement per minute was recorded at 24 h post-treatment. The locomotor ability of zebrafish larvae and the activity of acetylcholinesterase (AChE) were tested at 120 h post-treatment. The relative expression levels of neurodevelopment-related genes (elavl3, gap43, mbp, and syn2a) were measured by fluorescence quantitative PCR. Results Compared with the control group, the number of spontaneous movement per minute at 24 h decreased significantly in the 1.18 mg·L⁻¹ bromadiolone exposure group (P<0.05). Compared with the control group, the total distance travelled of the zebrafish larvae in the 0.78 and 1.18 mg·L⁻¹ bromadiolone exposure groups decreased by 60% and 69% respectively (P<0.05, P<0.01), and the total movement time decreased by 34% and 65% respectively (P<0.05, P<0.01). The AChE activity in the 1.18 mg·L⁻¹ bromadiolone exposure group increased by 36% when compared with the control group (P<0.05). The fluorescence quantitative PCR results showed that compared with the control group, the expression levels of neurodevelopment-related genes elavl3, syn2a, and mbp were significantly down-regulated by 66%, 69%, and 65% in the 1.18 mg·L⁻¹ bromadiolone exposure group respectively (P<0.01), the expression level of gap43 was up-regulated by 56% in the 0.78 mg·L⁻¹ bromadiolone exposure group (P<0.01) and down-regulated by 34% in the 1.18 mg·L⁻¹ bromadiolone exposure group (P<0.05). Conclusion Bromadiolone exposure could inhibit spontaneous movement and locomotive behavior, down-regulate the expression levels of neurodevelopment-related genes, hinder the release of neurotransmitters, and result in neurodevelopmental toxicity in the early-staged zebrafish.

[Abstract] Objective: To develop a new type of CD7 chimeric antigen receptor modified T cell (CD7-CAR-T) for the treatment of CD7 positive acute myeloid leukemia (AML), and to observe its killing effect on CD7 positive AML cells. Methods: The CD7-CAR lentiviral vector was constructed based on the CD7 Nanobody sequence and costimulatory domain sequence of CD28 and 4-1BB. The lentiviral particles were packaged and used to co-transfect human T cells with protein expression blocker (PEBL), so as to prepare CD7- CAR-T cells. Real time cellular analysis (RTCA) was used to monitor the cytotoxicity of CD7-CAR-T cells on CD7 overexpressed 293T cells. Flow cytometry assay was used to detect the effect of CD7-CAR-T cells on proliferation and cytokine secretion of AML cells with high, medium and low CD7 expressions (KG-1, HEL and Kasumi-1 cells, respectively). Results: CD7-CAR-T cell was successfully constructed and its surface expression of CD7 was successfully blocked. Compared with T cells, CD7-CAR-T cells could significantly inhibit the proliferation of CD7-293T cells and promote the release of TNF, Granzyme B and INF-γ; in addition, CD7-CAR-T cells also significantly promoted the apoptosis (t=147.1, P<0.01; t=23.57, P<0.01) and cytokine release (P<0.05 or P<0.01) in CD7 positive KG-1 and HEL cells, but had little effect on Kasumi-1 cells that only expressed minimal CD7 antigen (t=0.7058, P>0.05). Conclusion: CD7-CAR-T cells can specifically kill CD7-positive AML cells in vitro.

Non-small cell lung cancer (NSCLC) accounts for 85% of total cases of lung cancer, which has the highest incidence and mor-tality in China. Most patients with lung cancer present with advanced stage disease at the time of diagnosis. With the limited develop-ment of cytotoxic chemotherapy for NSCLC therapy, median overall survival in patients receiving platinum-based doublet chemothera-py has been less than one year in several trials. To date, anti-angiogenesis agents combined with chemotherapy, small molecule tyro-sine kinase inhibitors (TKI) and immune checkpoint inhibitors were commonly applied in NSCLC instead of purely chemotherapy, which makes a great breakthrough in NSCLC therapy. This review summarizes and discusses the application of anti-angiogenic therapy in ad-vanced NSCLC.

Objective To examine the prevalence ofHelicobacter pylori in Shanghai and relevant risk factors, evaluate the resistance proifle ofH. pylori isolates to antibiotics used in ifrst-line therapy in two hospitals in Shanghai.MethodsH. pylori were isolated from the biopsy samples of gastric mucosa collected from September 2013 to January 2015. Antimicrobial susceptibility test was performed by E-test method for 131H. pylori strains to 4 antibiotics, clarithromycin, metronidazole, amoxicillin and tetracycline. Results A total of 955 patients receiving gastroscopy were enrolled. And 248 (26.0%)H. pylori strains were isolated from the biopsy samples of gastric mucosa. Overall, 14.5%, 64.1%, 0 and 0.8% of the strains were resistant to clarithromycin, metronidazole, amoxicillin and tetracycline, respectively. Resistance to two drugs was found in 10.7%(14/131) of the strains, and majority (92.8%, 13/14) of which were resistant to clarithromycin and metronidazole.Conclusions The prevalence ofH. pylori in gastric mucosa is rather lower compared with the data reported previously. It is associated with the sex, age and clinical outcome of patients, however, antibiotic resistance profile is not related to these factors.H. pylori eradication therapy should be individualized according to the results of susceptibility test in Shanghai.

Objective To investigate and analyze the data of continuing medical education in order to understand the real need of the learners.Methods Reviewing and making statistic analysis of the data of Renji hospital's continuing medical education courses from 2007 to 2012,including the amount,title,discipline,geographical distribution,training content and the questionnaire survey.Results It shows that the learners from coastal and developed areas have a higher demand for cuttingedge technology and the new progress in medicine(49.1％),while the learners from inland and western regions prefer to learn the basic medical theory and technology (41.9％).Senior doctors tend to study cutting-edge technology and new progress medical courses (51.7％) while the junior persons prefer the basic medical knowledge (46.6％).These mean that the needs of content of continuing medical education is associated with the regions and the level of the doctors (P＜0.05).Conclusion The conclusions prompt that medical development between different regions of China may be unbalanced.Chinese government should pay more attention in terms of allocation of health resources and personnel training.It also provides advice for future courses so that it may improve the efficiency of the medical education resources.

OBJECTIVETo improve the occupational health management levels in electroplating enterprises with quantitative classification measures and to provide a scientific basis for the prevention and control of occupational hazards in electroplating enterprises and the protection of workers' health.

METHODSA quantitative classification table was created for the occupational health management in electroplating enterprises. The evaluation indicators included 6 items and 27 sub-items, with a total score of 100 points. Forty electroplating enterprises were selected and scored according to the quantitative classification table. These electroplating enterprises were classified into grades A, B, and C based on the scores.

RESULTSAmong 40 electroplating enterprises, 11 (27.5%) had scores of >85 points (grade A), 23 (57.5%) had scores of 60∼85 points (grade B), and 6 (15.0%) had scores of <60 points (grade C).

CONCLUSIONQuantitative classification management for electroplating enterprises is a valuable attempt, which is helpful for the supervision and management by the health department and provides an effective method for the self-management of enterprises.