Objective:Based on the activities of Helicobacter pylori ( HP) screening in Jiangsu Province in 2024, to evaluate the overall management ability in HP screening, testing, treatment and follow-up in primary medical facilities. Methods:From May 15 to October 18, 2024, the data of HP screening and treatment were retrospectively collected from 79 township health centers, community hospitals, and community health service centers in Jiangsu Province. The rates of screening completion, urea breath test (UBT) completion, treatment rate, UBT follow-up completion, and HP eradication were analyzed. Chi-square test was used for statistical analysis. Results:The completion rate of HP screening was 94.45% (15 489/16 400). There were 6 604 cases (42.64%) with serum HP antibody positive among the 15 489 individuals who completed screening. The positive rate of serum HP antibody in males was higher than that in females (44.77%, 2 643/5 904 vs. 41.32%, 3 961/9 585), and the difference was statistically significant ( χ2=17.69, P<0.001). The positive rates of serum HP antibody in screened individuals aged 18 to 19, 20 to 39, 40 to 59, and 60 to 75 years old were 22.38% (32/143), 36.12% (1 168/3 234), 45.01% (3 240/7 199), and 44.05% (2 164/4 913), respectively, and the difference was statistically significant( χ2=100.73, P<0.001). Among the 6 604 HP antibody-positive individuals, 4 381 cases completed UBT, with a UBT completion rate of 66.34% (4 381/6 604). There were 3 197 individuals with both HP serum antibody and UBT positive, the consistency rate of the 2 tests was 72.97% (3 197/4 381). Totally 2 737 cases received treatment, with a treatment completion rate of 85.61% (2 737/3 197); 2 327 individuals underwent UBT follow-up, with a follow-up completion rate of 85.02% (2 327/2 737). During follow-up, the result of UBT was negative in 1 982 individuals, and the HP eradication rate was 85.17% (1 982/2 327). Conclusions:There are deficiencies in the completion rate of HP screening, testing, treatment, and follow-up in primary hospitals, especially in the completion rate of UBT, which may be related to cognitive insufficiency for HP in residents. It is necessary to strengthen the training of physicians′ abilities in primary hospitals, optimize the allocation of drug resources, enhance health education, and increase residents′ participation and compliance.

Objective:To analyze risk factors for early trauma-induced coagulopathy (TIC) in pediatric patients with moderate-to-severe traumatic brain injury (msTBI), develop a predictive model and evaluate its predictive performance.Methods:A retrospective cohort study was conducted to analyze the clinical data of 290 pediatric patients with msTBI who were admitted to Children′s Hospital of Soochow University between January 2016 and December 2024, including 188 boys and 102 girls, aged 0.2-15.7 years [5.2(2.8, 9.3)years]. Based on the coagulation test results at admission, the patients were divided into TIC group ( n=162) and non-TIC group ( n=128). The patients were randomly allocated into training set ( n=203) and validation set ( n=87) at a ratio of 7∶3. Demographic characteristics, clinical data, vital signs, imaging findings, arterial blood gas analysis results, and coagulation profiles of the patients were collected. Univariate analysis and Lasso regression analysis were used to identify risk factors associated with early TIC in children with msTBI and multivariate Logistic regression analysis was performed to determine independent risk factors and construct a predictive model. The model′s discrimination and calibration were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), Hosmer-Lemeshow (H-L) test, and calibration curve. Its clinical utility was assessed through decision curve analysis (DCA). Results:Significant differences were observed between the TIC group and non-TIC group in terms of age, weight, time from injury to admission, child′s Glasgow coma scale (CGCS) score, pediatric trauma score (PTS), shock index, heart rate, respiratory rate, systolic blood pressure, Rotterdam CT score, intraventricular hemorrhage, cerebral contusion, brain herniation, long bone fracture, pelvic fracture, hemopneumothorax, pulmonary contusion, intra-abdominal organ injury, actual bicarbonate, base excess, base excess in the extracellular fluid, blood glucose, hemoglobin (Hb), osmolarity, blood calcium, anion gap, blood lactate, prothrombin time, activated partial thromboplastin time, international normalized ratio, and platelet count ( P<0.05). With coagulation-related variables excluded, the following features were identified with Lasso regression including CGCS score, PTS, heart rate, systolic blood pressure, long bone fracture, blood glucose, and Hb. Multivariate Logistic regression analysis revealed that CGCS score≤8 points ( OR=3.05, 95% CI 1.65, 5.63), PTS>5 points ( OR=0.45, 95% CI 0.23, 0.89), systolic blood pressure ( OR=0.98, 95% CI 0.97, 0.99), blood glucose ( OR=1.09, 95% CI 1.01, 1.17), and long bone fracture ( OR=2.47, 95% CI 1.13, 5.42) were influencing factors for early TIC in children with msTBI ( P<0.05). The regression equation of the predictive model was established as follows: Logit[ P/(1- P)]=1.01×"CGCS score≤8 points"-0.69×"PTS>5 points"- 0.02×"systolic blood pressure"+0.89×"long bone fracture"+0.08×"blood glucose"+1.32. The ROC curve analysis showed that the training set had an AUC of 0.86 (95% CI 0.78, 0.94), with sensitivity and specificity of 76.6% and 92.5%, while the AUC was 0.80 (95% CI 0.74, 0.86), with sensitivity and specificity of 75.7% and 79.6% in the validation set. H-L test results showed a χ2 value of 8.18 ( P=0.416) in the training set and 5.30 ( P=0.216) in the validation set. The calibration curves for both sets demonstrated good agreement with the actual curves, indicating that the predicted probabilities closely matched the observed probabilities. The DCA results indicated that both the training set and validation set demonstrated positive net benefits within threshold probabilities ranges of 10%-100% and 15%-96%. Conclusions:Independent risk factors for early TIC in pediatric msTBI patients include CGCS score≤ 8 points, PTS≤5 points, low systolic blood pressure, long bone fracture, and high blood glucose. The predictive model constructed based on these factors demonstrates favorable predictive performance and clinical application value.

Objective To investigate the clinical characteristics and related risk factors of severe Mycoplasmal pneumoniae pneumonia(SMPP)in children.Methods Analysis of clinical data of 240 children with Mycoplasmal pneumoniae pneumonia admitted to Guangxi Hospital Division of the First Affiliated Hospital,Sun Yat-sen University from October 2023 to March 2024.According to the severity of the disease,they were divided into SMPP group and general Mycoplasmal pneumoniae pneumonia(GMPP)group,multivariate Logistic regression analysis was used to analyze the risk factors related to the occurrence of SMPP.The receiver operating characteristic(ROC)curve was plotted to analyze the predictive value of each independent risk factor of SMPP.Results Compared the data during hospitalisation between GMPP group and SMPP group,the SMPP group had longer fever course,hospitalization time,azithromycin treatment time,and glucocorticoid use time(P＜0.05).The proportion of patients with unilateral lung lesions and large patchy shadows in SMPP group were significantly higher than those in GMPP group(P＜0.05).The levels of C-reactive protein,interleukin(IL)-6,and D-dimer in SMPP group were significantly higher than those in GMPP group,while the peripheral blood lymphocyte count was significantly lower than that in GMPP group(P＜0.05).The results of multiple Logistic regression analysis showed that the duration of fever,D-dimer,IL-6,and large patchy shadows in the lungs were independent risk factors for SMPP.The ROC curve results showed that fever duration ≥ 6.5 days,D-dimer≥0.495mg/L,IL-6≥11.05pg/ml,and large patchy shadows in the lungs had good application value in distinguishing SMPP and GMPP children.Conclusion Prolonged fever duration(≥6.5 days),significantly elevated levels of D-dimer and IL-6,and large patchy shadows in the lungs are the main clinical features of children with SMPP,which may be independent risk factors for predicting SMPP.

Objective:Middle ear cholesteatoma is a common otolaryngological disease, and traditional diagnostic methods have certain limitations. This study aims to construct a computer-aided diagnosis model for middle ear cholesteatoma based on integrated convolutional neural networks (CNNs) to improve diagnostic accuracy and efficiency.Methods:Firstly, Data were collected from patients who visited the Department of Otorhinolaryngology Head and Neck Surgery at the First People′s Hospital of Yinchuan between January 2020 and December 2021. 8 000 temporal bone CT images were collected, including 5 000 images diagnosed pathologically as middle ear cholesteatoma and 3 000 normal images. A five-fold cross-validation method was used to divide the dataset into training and testing sets. Next, a transfer learning approach was used to initialize model parameters, and the AlexNet, GoogleNet, and ResNet networks were pre-trained to extract deep features from the images. Then, the Softmax classification algorithm was applied to classify the features, resulting in three independent classifiers. These classifiers were combined using an ensemble learning method with a weighted voting approach to obtain the final diagnostic results. Finally, the model was evaluated by comparing the ensemble classifier with individual classifiers to assess its accuracy, precision, sensitivity, specificity, and diagnostic time, and a comparison with low-mid-and high-experience physician groups was conducted to comprehensively evaluate the model′s diagnostic performance.Results:The experimental results showed that the model achieved an accuracy of 88.8%(178/200), precision of 92.9%,(112/120) sensitivity of 89.8%(108/120), and specificity of 88.1%(70/80). The average diagnostic time for individual patient temporal bone CT images was reduced to 2-3 seconds. Compared to the diagnostic results from low-mid-and high-experience physician groups, the model demonstrated significant advantages and effectively assisted clinicians in making rapid and accurate middle ear cholesteatoma diagnoses.Conclusion:The proposed middle ear cholesteatoma diagnostic model based on integrated convolutional neural networks exhibits high recognition accuracy and rapid diagnostic speed, significantly improving clinical diagnostic efficiency, especially in early screening and auxiliary diagnosis, making it of considerable value in clinical practice.

Objective:To analyze risk factors for early trauma-induced coagulopathy (TIC) in pediatric patients with moderate-to-severe traumatic brain injury (msTBI), develop a predictive model and evaluate its predictive performance.Methods:A retrospective cohort study was conducted to analyze the clinical data of 290 pediatric patients with msTBI who were admitted to Children′s Hospital of Soochow University between January 2016 and December 2024, including 188 boys and 102 girls, aged 0.2-15.7 years [5.2(2.8, 9.3)years]. Based on the coagulation test results at admission, the patients were divided into TIC group ( n=162) and non-TIC group ( n=128). The patients were randomly allocated into training set ( n=203) and validation set ( n=87) at a ratio of 7∶3. Demographic characteristics, clinical data, vital signs, imaging findings, arterial blood gas analysis results, and coagulation profiles of the patients were collected. Univariate analysis and Lasso regression analysis were used to identify risk factors associated with early TIC in children with msTBI and multivariate Logistic regression analysis was performed to determine independent risk factors and construct a predictive model. The model′s discrimination and calibration were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), Hosmer-Lemeshow (H-L) test, and calibration curve. Its clinical utility was assessed through decision curve analysis (DCA). Results:Significant differences were observed between the TIC group and non-TIC group in terms of age, weight, time from injury to admission, child′s Glasgow coma scale (CGCS) score, pediatric trauma score (PTS), shock index, heart rate, respiratory rate, systolic blood pressure, Rotterdam CT score, intraventricular hemorrhage, cerebral contusion, brain herniation, long bone fracture, pelvic fracture, hemopneumothorax, pulmonary contusion, intra-abdominal organ injury, actual bicarbonate, base excess, base excess in the extracellular fluid, blood glucose, hemoglobin (Hb), osmolarity, blood calcium, anion gap, blood lactate, prothrombin time, activated partial thromboplastin time, international normalized ratio, and platelet count ( P<0.05). With coagulation-related variables excluded, the following features were identified with Lasso regression including CGCS score, PTS, heart rate, systolic blood pressure, long bone fracture, blood glucose, and Hb. Multivariate Logistic regression analysis revealed that CGCS score≤8 points ( OR=3.05, 95% CI 1.65, 5.63), PTS>5 points ( OR=0.45, 95% CI 0.23, 0.89), systolic blood pressure ( OR=0.98, 95% CI 0.97, 0.99), blood glucose ( OR=1.09, 95% CI 1.01, 1.17), and long bone fracture ( OR=2.47, 95% CI 1.13, 5.42) were influencing factors for early TIC in children with msTBI ( P<0.05). The regression equation of the predictive model was established as follows: Logit[ P/(1- P)]=1.01×"CGCS score≤8 points"-0.69×"PTS>5 points"- 0.02×"systolic blood pressure"+0.89×"long bone fracture"+0.08×"blood glucose"+1.32. The ROC curve analysis showed that the training set had an AUC of 0.86 (95% CI 0.78, 0.94), with sensitivity and specificity of 76.6% and 92.5%, while the AUC was 0.80 (95% CI 0.74, 0.86), with sensitivity and specificity of 75.7% and 79.6% in the validation set. H-L test results showed a χ2 value of 8.18 ( P=0.416) in the training set and 5.30 ( P=0.216) in the validation set. The calibration curves for both sets demonstrated good agreement with the actual curves, indicating that the predicted probabilities closely matched the observed probabilities. The DCA results indicated that both the training set and validation set demonstrated positive net benefits within threshold probabilities ranges of 10%-100% and 15%-96%. Conclusions:Independent risk factors for early TIC in pediatric msTBI patients include CGCS score≤ 8 points, PTS≤5 points, low systolic blood pressure, long bone fracture, and high blood glucose. The predictive model constructed based on these factors demonstrates favorable predictive performance and clinical application value.

Objective To investigate the clinical characteristics and related risk factors of severe Mycoplasmal pneumoniae pneumonia(SMPP)in children.Methods Analysis of clinical data of 240 children with Mycoplasmal pneumoniae pneumonia admitted to Guangxi Hospital Division of the First Affiliated Hospital,Sun Yat-sen University from October 2023 to March 2024.According to the severity of the disease,they were divided into SMPP group and general Mycoplasmal pneumoniae pneumonia(GMPP)group,multivariate Logistic regression analysis was used to analyze the risk factors related to the occurrence of SMPP.The receiver operating characteristic(ROC)curve was plotted to analyze the predictive value of each independent risk factor of SMPP.Results Compared the data during hospitalisation between GMPP group and SMPP group,the SMPP group had longer fever course,hospitalization time,azithromycin treatment time,and glucocorticoid use time(P＜0.05).The proportion of patients with unilateral lung lesions and large patchy shadows in SMPP group were significantly higher than those in GMPP group(P＜0.05).The levels of C-reactive protein,interleukin(IL)-6,and D-dimer in SMPP group were significantly higher than those in GMPP group,while the peripheral blood lymphocyte count was significantly lower than that in GMPP group(P＜0.05).The results of multiple Logistic regression analysis showed that the duration of fever,D-dimer,IL-6,and large patchy shadows in the lungs were independent risk factors for SMPP.The ROC curve results showed that fever duration ≥ 6.5 days,D-dimer≥0.495mg/L,IL-6≥11.05pg/ml,and large patchy shadows in the lungs had good application value in distinguishing SMPP and GMPP children.Conclusion Prolonged fever duration(≥6.5 days),significantly elevated levels of D-dimer and IL-6,and large patchy shadows in the lungs are the main clinical features of children with SMPP,which may be independent risk factors for predicting SMPP.

Objective:Middle ear cholesteatoma is a common otolaryngological disease, and traditional diagnostic methods have certain limitations. This study aims to construct a computer-aided diagnosis model for middle ear cholesteatoma based on integrated convolutional neural networks (CNNs) to improve diagnostic accuracy and efficiency.Methods:Firstly, Data were collected from patients who visited the Department of Otorhinolaryngology Head and Neck Surgery at the First People′s Hospital of Yinchuan between January 2020 and December 2021. 8 000 temporal bone CT images were collected, including 5 000 images diagnosed pathologically as middle ear cholesteatoma and 3 000 normal images. A five-fold cross-validation method was used to divide the dataset into training and testing sets. Next, a transfer learning approach was used to initialize model parameters, and the AlexNet, GoogleNet, and ResNet networks were pre-trained to extract deep features from the images. Then, the Softmax classification algorithm was applied to classify the features, resulting in three independent classifiers. These classifiers were combined using an ensemble learning method with a weighted voting approach to obtain the final diagnostic results. Finally, the model was evaluated by comparing the ensemble classifier with individual classifiers to assess its accuracy, precision, sensitivity, specificity, and diagnostic time, and a comparison with low-mid-and high-experience physician groups was conducted to comprehensively evaluate the model′s diagnostic performance.Results:The experimental results showed that the model achieved an accuracy of 88.8%(178/200), precision of 92.9%,(112/120) sensitivity of 89.8%(108/120), and specificity of 88.1%(70/80). The average diagnostic time for individual patient temporal bone CT images was reduced to 2-3 seconds. Compared to the diagnostic results from low-mid-and high-experience physician groups, the model demonstrated significant advantages and effectively assisted clinicians in making rapid and accurate middle ear cholesteatoma diagnoses.Conclusion:The proposed middle ear cholesteatoma diagnostic model based on integrated convolutional neural networks exhibits high recognition accuracy and rapid diagnostic speed, significantly improving clinical diagnostic efficiency, especially in early screening and auxiliary diagnosis, making it of considerable value in clinical practice.

Objective:Based on the activities of Helicobacter pylori ( HP) screening in Jiangsu Province in 2024, to evaluate the overall management ability in HP screening, testing, treatment and follow-up in primary medical facilities. Methods:From May 15 to October 18, 2024, the data of HP screening and treatment were retrospectively collected from 79 township health centers, community hospitals, and community health service centers in Jiangsu Province. The rates of screening completion, urea breath test (UBT) completion, treatment rate, UBT follow-up completion, and HP eradication were analyzed. Chi-square test was used for statistical analysis. Results:The completion rate of HP screening was 94.45% (15 489/16 400). There were 6 604 cases (42.64%) with serum HP antibody positive among the 15 489 individuals who completed screening. The positive rate of serum HP antibody in males was higher than that in females (44.77%, 2 643/5 904 vs. 41.32%, 3 961/9 585), and the difference was statistically significant ( χ2=17.69, P<0.001). The positive rates of serum HP antibody in screened individuals aged 18 to 19, 20 to 39, 40 to 59, and 60 to 75 years old were 22.38% (32/143), 36.12% (1 168/3 234), 45.01% (3 240/7 199), and 44.05% (2 164/4 913), respectively, and the difference was statistically significant( χ2=100.73, P<0.001). Among the 6 604 HP antibody-positive individuals, 4 381 cases completed UBT, with a UBT completion rate of 66.34% (4 381/6 604). There were 3 197 individuals with both HP serum antibody and UBT positive, the consistency rate of the 2 tests was 72.97% (3 197/4 381). Totally 2 737 cases received treatment, with a treatment completion rate of 85.61% (2 737/3 197); 2 327 individuals underwent UBT follow-up, with a follow-up completion rate of 85.02% (2 327/2 737). During follow-up, the result of UBT was negative in 1 982 individuals, and the HP eradication rate was 85.17% (1 982/2 327). Conclusions:There are deficiencies in the completion rate of HP screening, testing, treatment, and follow-up in primary hospitals, especially in the completion rate of UBT, which may be related to cognitive insufficiency for HP in residents. It is necessary to strengthen the training of physicians′ abilities in primary hospitals, optimize the allocation of drug resources, enhance health education, and increase residents′ participation and compliance.

Objective:To explore the application value of patient data-based real-time quality control (PBRTQC) in enhancing the effectiveness of internal quality control (IQC) management.Methods:From the PBRTQC real-time quality control intelligent monitoring platform integrated with the laboratory information system (LIS), a total of 35,631 test results of red blood cell (RBC) count, white blood cell (WBC) count, and dehydroepiandrosterone sulfate (DHEA-S) were collected from patients of the Department of General Xi'an Area Medical Laboratory Center from August 1, 2023, to April 1, 2024. The platform was used in patient data distribution characteristics test, EWMA real-time quality control chart procedure establishment, performance validation, effect evaluation, best procedure selection, and real-time operation. The performance evaluation indexes of the best PBRTQC procedure establishment, the cut-off limit range, weighting coefficient, cumulative mean, standard deviation (SD), coefficient of variation ( CV) of the EWMA real-time quality control chart, and the cumulative mean, SD, and CV of its internal quality control data in the same period were counted, and at the same time compared with the quality target (1/3TEa). Coefficient of variation analyses were performed to compare the quality control status of PBRTQC and conventional internal quality control in the presence of warning or alarm prompts based on quality control process records, and alarm messages. Results:The evaluation indexes of the optimal procedures for RBC count, WBC count, and DHEA-S were the probability of error detection (Ped) between 93%-97% and greater than 90%, the false positive rate (FPR) between 0.0%-0.5%, the false negative rate (FNR) between 3.0%-7.0%, and the average number of the patient sample until error detection (ANPed) between 5-11, which is in line with the optimal quality control efficacy quality requirements for the PBRTQC procedure. The patient outcome cut-off concentrations for the optimal procedure EWMA quality control charts ranged from RBC count (3.92-5.16)×10 12/L, WBC count (4.28-7.50)×10 9/L, and DHEA-S (830-2 160) μg/L; (2 160-4 210) μg/L. The weighting coefficients were 0.05, 0.03, and 0.03, respectively. The real-world application of the EWMA real-time quality control charts showed stable and excellent analytical performance of the measurement system, such as out-of-control alarm: RBC count, 1 true alarm, Ped of 95.85%, and FPR of 0%. The cumulative CV of EWMA was less than the quality target; the cumulative CV of DHEA-S was 7.66% and 9.47%, respectively, and the cumulative CV of low level was greater than the quality target (8.33%), and the cumulative CV of high and low levels were 4.12% and 6.25%. Conclusion:The PBRTQC EWMA method can monitor the patient data - in real-time and continuous way. It can also dynamically identify and provide early indication of small changes in analytical performance during the analysis process, and can be used as a supplement to quality control products to improve the efficacy of laboratory quality management.

Objective : To examine the diagnostic and prognostic value of long non-coding RNA (lncRNA) JPX in mesothelioma, so as to provide insights into diagnosis and prognosis of mesothelioma. Methods: Patients with clinically definitive diagnosis of mesothelioma from 2015 to 2019 that were sampled from asbestos processing plants in Zhejiang Province from 2015 to 2019 were recruited in the mesothelioma group, while healthy residents without asbestos exposure or asbestos-related diseases in the same area served as controls. Participants' demographics, pathologic diagnosis and imaging features were collected, and the expression of blood lncRNA JPX was detected using lncRNA microarrays. The diagnostic value of lncRNA JPX for mesothelioma was evaluated using the receiver operating characteristic (ROC) curve, and the correlation between lncRNA JPX expression and prognosis was examined among mesothelioma patients using survival analysis. Results: There were 17 subjects in the mesothelioma group, with a mean age of (65.71±8.36) years, and 34 subjects in the controls, with a mean age of (64.24±8.70) years. LncRNA microarray detected significantly high lncRNA JPX expression in mesothelioma patients, and higher blood lncRNA JPX expression was detected in the mesothelioma group than in the control group [median (interquartile range), 1.10 (1.31) vs. 0.89 (0.54); t'=-2.300, P=0.034]. The area under the ROC curve was 0.673 (95%CI: 0.507-0.839, P=0.046), and if the cutoff was 1.759, the sensitivity and specificity were 35.3% and 100.0%, respectively. Survival analysis showed no significant difference in the survival rate of mesothelioma patients between the high lncRNA JPX expression group and the low expression group (χ2=0.212, P=0.645). Conclusions LncRNA JPX overexpression is detected in the blood of patients with mesothelioma, and lncRNA JPX expression presents a diagnostic value for mesothelioma; however, it shows little prognostic value for mesothelioma.