Objective·To investigate the causal relationship between type 2 diabetes mellitus(T2DM)and cognitive dysfunction using two-sample Mendelian randomisation(MR).Methods·Instrumental variables associated with T2DM were pooled from a large-scale genome-wide association study(GWAS)dataset.Inverse variance weighting was used as the primary analytical technique,supplemented by MR-Egger regression,weighted median and simple median analyses.Meta-analysis was jointly applied to combine different endpoints and to analyse the possibility of a causal relationship between T2DM and dementia,Alzheimer's disease,and Parkinson's dementia.Horizontal pleiotropy was examined by MR-PRESSO global test and MR-Egger analysis.Results·There was a causal relationship between genetically predicted T2DM and dementia(OR=1.11,95%CI 1.02～1.20,P=1.96×10-2)and AD(OR=1.16,95%CI 1.04～1.30,P=8.41×10-3).Meta-analysis also supported the association between T2DM and cognitive impairment(OR=1.12,95%CI 1.05～1.20,P=4.22×10-4).A series of sensitivity analyses suggested the absence of heterogeneity and horizontal pleiotropy.Reverse MR analysis showed no significant causal relationship of various types of dementia on T2DM.Conclusion·T2DM is positively associated with the risk of developing various types of dementia,suggesting that T2DM may be an important risk factor for cognitive impairment.

Objective:To explore the role and related mechanism of myeloid related differentiation markers (MYADM) in lung adenocarcinoma metastasis induced by high cholesterol diet.Methods:(1) Cell experiments: Using lung adenocarcinoma A549 and H1975 cells, the cells were treated with 0.8 mg/ml cholesterol and then transfected with a lentivirus to knock down MYADM. The overexpression of MYADM was achieved by transfecting the cells with an overexpression plasmid. Western blotting was used to detect the expression levels of MYADM, E-cadherin, β-catenin, MMP-2, MMP-9, and vimentin in the cells. The proliferation ability of the cells was assessed using the plate clonal formation assay, while the migration and invasion ability were evaluated using the Transwell assay. Western blot was used to determine the effects of MYADM knockdown or overexpression on these proteins. Western blot and immunofluorescence assays were conducted to investigate the impact of Akt phosphorylation on the expression of MYADM and Rac1 in cholesterol-treated lung adenocarcinoma cells, as well as the phosphorylation of c-Myc. Western blot was also used to assess the effect of c-Myc knockdown on the expression of MYADM and MCT1 in lung adenocarcinoma cells. Chromatin immunoprecipitation (ChIP) assays were performed to investigate the impact of cholesterol on the binding between c-Myc and the promoters of MYADM and MCT1 in lung adenocarcinoma cells. (2) Animal experiment: A549 cells or A549 cells with MYADM knockdown were intravenously inoculated into BALB/c nude mice, which were then divided into a normal diet group and a high cholesterol diet group. Using a live imaging system, the growth and metastasis of tumors in the mice were monitored. After 42 days, lung tissues were collected for immunohistochemical staining to detect changes in relevant proteins.Results:After cholesterol treatment, the expression level of MYADM in A549 cells increased from 1.00±0.18 to 3.28±0.28 ( P<0.001), and in H1975 cells, it increased from 1.00±0.06 to 2.03±0.10 ( P<0.001). Compared with the control group, the expression of E-cadherin in lung adenocarcinoma cells after MYADM knockdown increased ( P<0.01), while the expressions of β-catenin, MMP-2, MMP-9, and vimentin decreased (all P<0.01). After MYADM knockdown, the number of clonal plates decreased in A549 cells (203±23 vs 60±18, t=8.48, P=0.001) and H1975 cells (298±64 vs 137±51, t=3.41, P=0.271). The number of invasive cells also decreased in A549 cells (212±18 vs 99±34, t=5.09, P=0.007) and H1975 cells (268±34 vs 134±14, t=6.31, P=0.003). Additionally, the number of migratory cells decreased in A549 cells (353±37 vs 124±29, t=8.44, P=0.001) and H1975 cells (279±41 vs 79±19, t=7.67, P=0.002). In the lung adenocarcinoma cells overexpressing MYADM, the expression of E-cadherin decreased ( P<0.01), while the levels of β-catenin, MMP-2, MMP-9, and vimentin increased (all P<0.01). The number of plate clonal colonies formed by lung adenocarcinoma cells overexpressing MYADM increased significantly in A549 cells, (94±26 vs 298±34, t=8.26, P=0.001) and H1975 cells (83±13 vs 331±24, t=15.74, P<0.001). The number of invasive A549 cells also increased (118±17 vs 193±24, t=4.41, P=0.012) and (156±19 vs 321±12, t=12.72, P<0.001). Additionally, the number of migrating cells increased in A549 cells (171±22 vs 284±15, t=7.35, P=0.002) and in H1975 cells (178±7 vs 263±12, t=10.6, P<0.001). Experiments related to the molecular mechanism showed that overexpression of MYADM promotes the expression of MCT1 in lung adenocarcinoma cells (all P<0.01). Cholesterol not only enhances the expression of MYADM in lung adenocarcinoma cells, but also boosts the expression of Rac1 and MCT1, as well as the phosphorylation of Akt and c-Myc (all P<0.05). Immunoprecipitation experiments revealed that in A549 cells treated with cholesterol, MYADM-Rac1 interaction levels increased from (100.0±15.9)% to (191.0±26.7)% ( P=0.007), while in H1975 cells, the levels increased from (100.0±18.2)% to (170.0±27.5)% ( P=0.021). ChIP confirmed that cholesterol treatment enhances the binding of c-Myc to the promoters of MYADM and MCT1. In vivo experiments demonstrated that a high-cholesterol diet promotes the metastasis of lung adenocarcinoma cells in mice, inducing the expression of MYADM, MCT1, and Rac1, as well as the phosphorylation of Akt and c-Myc in mouse lung tissue. Conversely, knocking down MYADM inhibits the metastasis of lung adenocarcinoma cells in mice, suppressing the expression of MYADM, MCT1, and Rac1, as well as the phosphorylation of Akt and c-Myc in mouse lung tissues. Conclusion:Cholesterol may induce lung adenocarcinoma cells proliferation and metastasis by regulating the MYADM/Rac1/Akt/c-Myc/MCT1 axis.

Objective:To investigate the clinical value of enhanced magnetic resonance imaging (MRI)-based deep learning model in preoperative prediction of proliferative hepatocellular carcinoma (HCC).Methods:The retrospective cohort study was conducted. The clinical data of 906 HCC patients who were admitted to The First Affiliated Hospital of Army Medical University and The Second Affiliated Hospital of Chongqing Medical University from May 2017 to October 2022 were collected. There were 769 males and 137 females, aged (53.2±10.9)years. Of the 906 patients, 815 cases who were admitted to The First Affiliated Hospital of Army Medical University were divided into the training set of 634 patients and the internal validation set of 181 patients using a random number table method with a ratio of 8:2, and 91 patients who were admitted to The Second Affiliated Hospital of Chongqing Medical University were divided into the external validation set. The training set was used to construct the prediction model, while the validation set was used to validate the prediction model. Observation indicators: (1) analysis of factors influencing the pathological classification of HCC patients; (2) deep learning imaging features of HCC patients; (3) evaluation of the efficacy of prediction model for proliferative HCC; (4) validation of the prediction model for proliferative HCC; (5) prognosis of HCC patients. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Multivariate analysis was conducted using the binary Logistic regression model. The model perfor-mance was evaluated through five-fold cross-validation, and receiver operating characteristic (ROC) curve was plotted to assess the diagnostic value of the model based on the area under curve (AUC), sensitivity, and specificity. The Delong test was used to compare the diagnostic performance of models. The Hosmer-Lemeshow test was employed to evaluate the calibration of models. The optimal cutoff value of the prediction model was determined by the maximum Youden index, with the value >0.175 indicating high-risk patients and value ≤0.175 indicating low-risk patients.The Kaplan-Meier method was used to calculate the survival rate and the Log-rank test was used for survival analysis. Results:(1) Analysis of factors influencing the pathological classification of HCC patients. Of 634 patients in the training set, there were 190 cases of proliferative HCC and 444 cases of non-proliferative HCC. Results of multivariate analysis showed that alpha fetoprotein (AFP) ≥400 μg/L and tumor diameter >5 cm were independent risk factors for pathological type of HCC as proli-ferative [ odds ratio=1.73, 1.88, 95% confidence interval ( CI) as 1.19-2.50, 1.30-2.71, P<0.05]. (2) Deep learning imaging features of HCC patients. In the training set of 634 patients, the probability predicted by MRI-based deep learning model was 84.8%(30.5%,95.4%) for proliferative HCC and 5.8%(3.2%,12.5%) for non-proliferative HCC, showing a significant difference between them ( Z=-16.01, P<0.05). (3) Evaluation of the efficacy of prediction model for proliferative HCC. In the training set, the AUC of clinical prediction model for proliferative HCC was 0.63(95% CI as 0.59-0.68, P<0.05), with sensitivity of 54.74% and specificity of 64.19%. The AUC of MRI-based deep learning prediction model was 0.90(95% CI as 0.87-0.93, P<0.05), with sensitivity of 80.53% and specificity of 86.94%. The AUC of combined MRI-based deep learning with clinical prediction model was 0.90 (95% CI as 0.87-0.93, P<0.05), with sensitivity of 83.16% and specificity of 86.04%. Results of Delong test showed that there was a significant difference between the combined MRI-based deep learning with clinical prediction model and the clinical prediction model ( P<0.05), and there was no signifi-cant difference between the combined MRI-based deep learning with clinical prediction model and the MRI-based deep learning prediction model ( P>0.05). Results of Hosmer-Lemeshow test showed good calibration for the clinical prediction model, the MRI-based deep learning prediction model and the combined MRI-based deep learning with clinical prediction model ( χ2=0.84, 6.38, 3.93, P>0.05), indicating that the predicted probabilities of these three prediction models matched the actual risk well. (4) Validation of the prediction model for proliferative HCC. Results of validation of the prediction model in internal validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.84(95% CI as 0.77-0.91, P<0.05), with sensitivity of 82.35% and specificity of 77.69%. Results of validation of the prediction model in external validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.81(95% CI as 0.71-0.92, P<0.05), with sensitivity of 70.00% and specificity of 81.69%. (5) Prognosis of HCC patients. Of the 906 patients, the 1-, 3-, and 5-year recurrence-free survival rates for 645 proliferative HCC patients were 56.9%, 31.4%, and 29.1%, respectively, and the 1-, 3-, and 5-year recurrence-free survival rates for 261 non-proliferative HCC patients were 88.8%, 68.6%, and 56.0%, respectively. There were significant differences in recurrence-free survival time between proliferative HCC and non-proliferative HCC patients of the training set, internal validation set and external validation set ( P<0.05). The 1-, 3-, 5-year recurrence-free survival rates for 331 high-risk HCC patients were 64.6%, 50.4%, 43.6%, versus 88.5%, 71.9%, 62.7% for 575 low-risk HCC patients. There were significant differences in recurrence-free survival time between high-risk HCC patients and low-risk HCC patients of the training set, internal validation set and external validation set ( P<0.05). Conclusion:The MRI-based deep learning model can effectively predict proliferative HCC and recurrence-free survival of patients before the surgery.

Objective:To explore the role and related mechanism of myeloid related differentiation markers (MYADM) in lung adenocarcinoma metastasis induced by high cholesterol diet.Methods:(1) Cell experiments: Using lung adenocarcinoma A549 and H1975 cells, the cells were treated with 0.8 mg/ml cholesterol and then transfected with a lentivirus to knock down MYADM. The overexpression of MYADM was achieved by transfecting the cells with an overexpression plasmid. Western blotting was used to detect the expression levels of MYADM, E-cadherin, β-catenin, MMP-2, MMP-9, and vimentin in the cells. The proliferation ability of the cells was assessed using the plate clonal formation assay, while the migration and invasion ability were evaluated using the Transwell assay. Western blot was used to determine the effects of MYADM knockdown or overexpression on these proteins. Western blot and immunofluorescence assays were conducted to investigate the impact of Akt phosphorylation on the expression of MYADM and Rac1 in cholesterol-treated lung adenocarcinoma cells, as well as the phosphorylation of c-Myc. Western blot was also used to assess the effect of c-Myc knockdown on the expression of MYADM and MCT1 in lung adenocarcinoma cells. Chromatin immunoprecipitation (ChIP) assays were performed to investigate the impact of cholesterol on the binding between c-Myc and the promoters of MYADM and MCT1 in lung adenocarcinoma cells. (2) Animal experiment: A549 cells or A549 cells with MYADM knockdown were intravenously inoculated into BALB/c nude mice, which were then divided into a normal diet group and a high cholesterol diet group. Using a live imaging system, the growth and metastasis of tumors in the mice were monitored. After 42 days, lung tissues were collected for immunohistochemical staining to detect changes in relevant proteins.Results:After cholesterol treatment, the expression level of MYADM in A549 cells increased from 1.00±0.18 to 3.28±0.28 ( P<0.001), and in H1975 cells, it increased from 1.00±0.06 to 2.03±0.10 ( P<0.001). Compared with the control group, the expression of E-cadherin in lung adenocarcinoma cells after MYADM knockdown increased ( P<0.01), while the expressions of β-catenin, MMP-2, MMP-9, and vimentin decreased (all P<0.01). After MYADM knockdown, the number of clonal plates decreased in A549 cells (203±23 vs 60±18, t=8.48, P=0.001) and H1975 cells (298±64 vs 137±51, t=3.41, P=0.271). The number of invasive cells also decreased in A549 cells (212±18 vs 99±34, t=5.09, P=0.007) and H1975 cells (268±34 vs 134±14, t=6.31, P=0.003). Additionally, the number of migratory cells decreased in A549 cells (353±37 vs 124±29, t=8.44, P=0.001) and H1975 cells (279±41 vs 79±19, t=7.67, P=0.002). In the lung adenocarcinoma cells overexpressing MYADM, the expression of E-cadherin decreased ( P<0.01), while the levels of β-catenin, MMP-2, MMP-9, and vimentin increased (all P<0.01). The number of plate clonal colonies formed by lung adenocarcinoma cells overexpressing MYADM increased significantly in A549 cells, (94±26 vs 298±34, t=8.26, P=0.001) and H1975 cells (83±13 vs 331±24, t=15.74, P<0.001). The number of invasive A549 cells also increased (118±17 vs 193±24, t=4.41, P=0.012) and (156±19 vs 321±12, t=12.72, P<0.001). Additionally, the number of migrating cells increased in A549 cells (171±22 vs 284±15, t=7.35, P=0.002) and in H1975 cells (178±7 vs 263±12, t=10.6, P<0.001). Experiments related to the molecular mechanism showed that overexpression of MYADM promotes the expression of MCT1 in lung adenocarcinoma cells (all P<0.01). Cholesterol not only enhances the expression of MYADM in lung adenocarcinoma cells, but also boosts the expression of Rac1 and MCT1, as well as the phosphorylation of Akt and c-Myc (all P<0.05). Immunoprecipitation experiments revealed that in A549 cells treated with cholesterol, MYADM-Rac1 interaction levels increased from (100.0±15.9)% to (191.0±26.7)% ( P=0.007), while in H1975 cells, the levels increased from (100.0±18.2)% to (170.0±27.5)% ( P=0.021). ChIP confirmed that cholesterol treatment enhances the binding of c-Myc to the promoters of MYADM and MCT1. In vivo experiments demonstrated that a high-cholesterol diet promotes the metastasis of lung adenocarcinoma cells in mice, inducing the expression of MYADM, MCT1, and Rac1, as well as the phosphorylation of Akt and c-Myc in mouse lung tissue. Conversely, knocking down MYADM inhibits the metastasis of lung adenocarcinoma cells in mice, suppressing the expression of MYADM, MCT1, and Rac1, as well as the phosphorylation of Akt and c-Myc in mouse lung tissues. Conclusion:Cholesterol may induce lung adenocarcinoma cells proliferation and metastasis by regulating the MYADM/Rac1/Akt/c-Myc/MCT1 axis.

Objective·To investigate the causal relationship between type 2 diabetes mellitus(T2DM)and cognitive dysfunction using two-sample Mendelian randomisation(MR).Methods·Instrumental variables associated with T2DM were pooled from a large-scale genome-wide association study(GWAS)dataset.Inverse variance weighting was used as the primary analytical technique,supplemented by MR-Egger regression,weighted median and simple median analyses.Meta-analysis was jointly applied to combine different endpoints and to analyse the possibility of a causal relationship between T2DM and dementia,Alzheimer's disease,and Parkinson's dementia.Horizontal pleiotropy was examined by MR-PRESSO global test and MR-Egger analysis.Results·There was a causal relationship between genetically predicted T2DM and dementia(OR=1.11,95%CI 1.02～1.20,P=1.96×10-2)and AD(OR=1.16,95%CI 1.04～1.30,P=8.41×10-3).Meta-analysis also supported the association between T2DM and cognitive impairment(OR=1.12,95%CI 1.05～1.20,P=4.22×10-4).A series of sensitivity analyses suggested the absence of heterogeneity and horizontal pleiotropy.Reverse MR analysis showed no significant causal relationship of various types of dementia on T2DM.Conclusion·T2DM is positively associated with the risk of developing various types of dementia,suggesting that T2DM may be an important risk factor for cognitive impairment.

Objective:To investigate the clinical value of enhanced magnetic resonance imaging (MRI)-based deep learning model in preoperative prediction of proliferative hepatocellular carcinoma (HCC).Methods:The retrospective cohort study was conducted. The clinical data of 906 HCC patients who were admitted to The First Affiliated Hospital of Army Medical University and The Second Affiliated Hospital of Chongqing Medical University from May 2017 to October 2022 were collected. There were 769 males and 137 females, aged (53.2±10.9)years. Of the 906 patients, 815 cases who were admitted to The First Affiliated Hospital of Army Medical University were divided into the training set of 634 patients and the internal validation set of 181 patients using a random number table method with a ratio of 8:2, and 91 patients who were admitted to The Second Affiliated Hospital of Chongqing Medical University were divided into the external validation set. The training set was used to construct the prediction model, while the validation set was used to validate the prediction model. Observation indicators: (1) analysis of factors influencing the pathological classification of HCC patients; (2) deep learning imaging features of HCC patients; (3) evaluation of the efficacy of prediction model for proliferative HCC; (4) validation of the prediction model for proliferative HCC; (5) prognosis of HCC patients. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Multivariate analysis was conducted using the binary Logistic regression model. The model perfor-mance was evaluated through five-fold cross-validation, and receiver operating characteristic (ROC) curve was plotted to assess the diagnostic value of the model based on the area under curve (AUC), sensitivity, and specificity. The Delong test was used to compare the diagnostic performance of models. The Hosmer-Lemeshow test was employed to evaluate the calibration of models. The optimal cutoff value of the prediction model was determined by the maximum Youden index, with the value >0.175 indicating high-risk patients and value ≤0.175 indicating low-risk patients.The Kaplan-Meier method was used to calculate the survival rate and the Log-rank test was used for survival analysis. Results:(1) Analysis of factors influencing the pathological classification of HCC patients. Of 634 patients in the training set, there were 190 cases of proliferative HCC and 444 cases of non-proliferative HCC. Results of multivariate analysis showed that alpha fetoprotein (AFP) ≥400 μg/L and tumor diameter >5 cm were independent risk factors for pathological type of HCC as proli-ferative [ odds ratio=1.73, 1.88, 95% confidence interval ( CI) as 1.19-2.50, 1.30-2.71, P<0.05]. (2) Deep learning imaging features of HCC patients. In the training set of 634 patients, the probability predicted by MRI-based deep learning model was 84.8%(30.5%,95.4%) for proliferative HCC and 5.8%(3.2%,12.5%) for non-proliferative HCC, showing a significant difference between them ( Z=-16.01, P<0.05). (3) Evaluation of the efficacy of prediction model for proliferative HCC. In the training set, the AUC of clinical prediction model for proliferative HCC was 0.63(95% CI as 0.59-0.68, P<0.05), with sensitivity of 54.74% and specificity of 64.19%. The AUC of MRI-based deep learning prediction model was 0.90(95% CI as 0.87-0.93, P<0.05), with sensitivity of 80.53% and specificity of 86.94%. The AUC of combined MRI-based deep learning with clinical prediction model was 0.90 (95% CI as 0.87-0.93, P<0.05), with sensitivity of 83.16% and specificity of 86.04%. Results of Delong test showed that there was a significant difference between the combined MRI-based deep learning with clinical prediction model and the clinical prediction model ( P<0.05), and there was no signifi-cant difference between the combined MRI-based deep learning with clinical prediction model and the MRI-based deep learning prediction model ( P>0.05). Results of Hosmer-Lemeshow test showed good calibration for the clinical prediction model, the MRI-based deep learning prediction model and the combined MRI-based deep learning with clinical prediction model ( χ2=0.84, 6.38, 3.93, P>0.05), indicating that the predicted probabilities of these three prediction models matched the actual risk well. (4) Validation of the prediction model for proliferative HCC. Results of validation of the prediction model in internal validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.84(95% CI as 0.77-0.91, P<0.05), with sensitivity of 82.35% and specificity of 77.69%. Results of validation of the prediction model in external validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.81(95% CI as 0.71-0.92, P<0.05), with sensitivity of 70.00% and specificity of 81.69%. (5) Prognosis of HCC patients. Of the 906 patients, the 1-, 3-, and 5-year recurrence-free survival rates for 645 proliferative HCC patients were 56.9%, 31.4%, and 29.1%, respectively, and the 1-, 3-, and 5-year recurrence-free survival rates for 261 non-proliferative HCC patients were 88.8%, 68.6%, and 56.0%, respectively. There were significant differences in recurrence-free survival time between proliferative HCC and non-proliferative HCC patients of the training set, internal validation set and external validation set ( P<0.05). The 1-, 3-, 5-year recurrence-free survival rates for 331 high-risk HCC patients were 64.6%, 50.4%, 43.6%, versus 88.5%, 71.9%, 62.7% for 575 low-risk HCC patients. There were significant differences in recurrence-free survival time between high-risk HCC patients and low-risk HCC patients of the training set, internal validation set and external validation set ( P<0.05). Conclusion:The MRI-based deep learning model can effectively predict proliferative HCC and recurrence-free survival of patients before the surgery.

Objective:To study the distribution and antibiotics resistance of the main pathogens of neonatal purulent meningitis in different regions of China.Methods:A retrospective descriptive clinical epidemiological study was conducted in children with neonatal purulent meningitis which admitted to 18 tertiary hospitals in different regions of China between January 2015 to December 2019. The test results of blood and cerebrospinal fluid, and drug sensitivity test results of the main pathogens were collected. The distributions of pathogenic bacteria in children with neonatal purulent meningitis in preterm and term infants, early and late onset infants, in Zhejiang Province and other regions outside Zhejiang Province, and in Wenzhou region and other regions of Zhejiang Province were analyzed. The chi-square test was used for statistical analysis.Results:A total of 210 neonatal purulent meningitis cases were collected. The common pathogens were Escherichia coli ( E. coli)(41.4%(87/210)) and Streptococcus agalactiae ( S. agalactiae)(27.1%(57/210)). The proportion of Gram-negative bacteria in preterm infants (77.6%(45/58)) with neonatal purulent meningitis was higher than that in term infants (47.4%(72/152)), and the difference was statistically significant ( χ2=15.54, P=0.001). There were no significant differences in the constituent ratios of E. coli (36.5%(31/85) vs 44.8%(56/125)) and S. agalactiae (24.7%(21/85) vs 28.8%(36/125)) between early onset and late onset cases (both P>0.05). The most common pathogen was E. coli in different regions, with 46.7%(64/137) in Zhejiang Province and 31.5%(23/73) in other regions outside Zhejiang Province. In Zhejiang Province, S. agalactiae was detected in 49 out of 137 cases (35.8%), which was significantly higher than other regions outside Zhejiang Province (11.0%(8/73)). The proportions of Klebsiella pneumoniae, and coagulase-negative Staphylococcus in other regions outside Zhejiang Province (17.8%(13/73) and 16.4%(12/73)) were both higher than those in Zhejiang Province (2.9%(4/137) and 5.1%(7/137)). The differences were all statistically significant ( χ2=14.82, 12.26 and 7.43, respectively, all P<0.05). The proportion of Gram-positive bacteria in Wenzhou City (60.8%(31/51)) was higher than that in other regions in Zhejiang Province (38.4%(33/86)), and the difference was statistically significant ( χ2=6.46, P=0.011). E. coli was sensitive to meropenem (0/45), and 74.4%(32/43) of them were resistant to ampicillin. E. coli had different degrees of resistance to other common cephalosporins, among which, cefotaxime had the highest resistance rate of 41.8%(23/55), followed by ceftriaxone (32.4%(23/71)). S. agalactiae was sensitive to penicillin, vancomycin and linezolid. Conclusions:The composition ratios of pathogenic bacteria of neonatal purulent meningitis are different in different regions of China. The most common pathogen is E. coli, which is sensitive to meropenem, while it has different degrees of resistance to other common cephalosporins, especially to cefotaxime.

Objective:To investigate the relationship between the volume ratio of ischemic leukoaraiosis (LA) and cognitive level and arterial perfusion.Methods:Fifty-four patients, who was hospitalized in Dalian Central Hospital and diagnosed as LA clinically during the time of March to December in 2012, were selected to collect the information of the volume ratio of white matter disease, MoCa score and the average flow rate of carotid artery. The correlation between the volume ratio of white matter disease and MoCa score, cognitive impairment and the average flow rate of carotid artery were analyzed.Results:The volume ratio of LA lesions was negatively correlated with MOCA score ( r = -0.59, P<0.01); the volume ratio of LA lesions was negatively correlated with the mean flow rate of internal carotid artery ( r = -0.37, P<0.01). Quantity order of the area under receiver operating characteristic (ROC) curve of MoCA cognitive subgroup was as following: delayed memory (1.000)> visual space/executive function (0.970) = abstract force (0.970)> language ability (0.960)> attention (0.888). Conclusions:The larger the volume ratio of leukopathy in LA patients, the more serious the cognitive impairment, especially the cognitive impairment of impairment of memory delay, visual space/executive function, abstract ability and language ability.

Objective To explore the status-quo and possible constraints of scientific research in a municipal public hospital,provide countermeasures for the improvement of scientific research administration capacity.Methods Questionnaire survey was conducted to 1 356 medical staff in a tertiary hospital in Zhejiang province,information collected including the statusquo of scientific research,attitude towards scientific research,difficulties and suggestions.Results Among 1 316 valid questionnaires,61 ％ of the respondents agreed that scientific research was very important for the development of hospitals,83 ％ of the respondents were willing to use their spare time to conduct scientific research projects,and 61.2％ of them usually had plans to do research but did not know where to start.The main constraints identified were the poor academic atmosphere,lack of scientific research training,and lack of scientific research facilities and resources.Conclusions The medical staffs in a municipal public hospital have high subjective enthusiasm for scientific research,low personal research ability and poor research environment.It is suggested to improve the scientific research ability of the staff on the basis of improving the objective environmental conditions for research.

Objective To investigate the effect of the different rehabilitation training method on the first ray of postoperative hallux valgus (HV). Methods Based on medical images of HV patient, a comprehensive three-dimensional finite element model of HV foot was established, including bones, sesamoid, cartilage, ligaments, soft tissues, Achilles tendon. The passive/active plantar flexion and dorsal flexion as well as standing were simulated to investigate the biomechanical behavior of distal osteotomy fragment of the postoperative HV. Results The stress distribution on distal osteotomy fragment during passive training was more uniform, and the peak stress (7.78 MPa) was greater than that during stance phase and active training. The distal osteotomy fragment displacement during passive training (0.98 mm) in anterior-posterior direction was greater than that during stance phase (0.69 mm) and active training (0.38 mm). Conclusions The passive training could promote the contact of osteotomy surface and reduce the healing time of osteotomy, which would be beneficial for rehabilitation of postoperative HV.