ObjectiveThis paper aims to explore the risk factors for chronic atrophic gastritis （CAG） with turbidity toxin accumulation syndrome and establish a prediction model. MethodsClinical data of 180 patients with CAG who participated in the "clinical study of Xianglian Huazhuo Particles blocking CAG cancer transformation" of Hebei Sheng Zhong Yi Yuan from July 2021 to March 2022 were collected. After confounding factors were controlled by propensity score matching， patients were divided into a training set （namely dev） and a validation set （namely vad） in a seven to three ratio. The risk factors for CAG with turbidity toxin accumulation syndrome in the training set were investigated by using univariate Logistic regression analysis and least absolute shrinkage and selection operator （namely Lasso） regression algorithms. Subsequently， a model， named model 1se， was developed by using the training set data to predict the risk factors for CAG with turbidity toxin accumulation syndrome. The accuracy of the prediction model was assessed by using various methods， including the receiver operating characteristic （ROC） curve， Hosmer-Lemeshow test （H-L）， calibration plot， and decision curve analysis （DCA）. ResultsAge， body mass index （BMI）， family history of cancer， job and life satisfaction， yellow and greasy fur with slippery pulse， and heavy body sensation were independent risk factors of the model. The prediction model showed excellent predictive value for both the training and validation sets. ConclusionThe established prediction model for CAG with turbidity toxin accumulation syndrome has high discrimination and excellent calibration， which could provide an excellent clinical basis for disease diagnosis and individualized treatment of patients.

ObjectiveThis paper aims to explore the risk factors for chronic atrophic gastritis （CAG） with turbidity toxin accumulation syndrome and establish a prediction model. MethodsClinical data of 180 patients with CAG who participated in the "clinical study of Xianglian Huazhuo Particles blocking CAG cancer transformation" of Hebei Sheng Zhong Yi Yuan from July 2021 to March 2022 were collected. After confounding factors were controlled by propensity score matching， patients were divided into a training set （namely dev） and a validation set （namely vad） in a seven to three ratio. The risk factors for CAG with turbidity toxin accumulation syndrome in the training set were investigated by using univariate Logistic regression analysis and least absolute shrinkage and selection operator （namely Lasso） regression algorithms. Subsequently， a model， named model 1se， was developed by using the training set data to predict the risk factors for CAG with turbidity toxin accumulation syndrome. The accuracy of the prediction model was assessed by using various methods， including the receiver operating characteristic （ROC） curve， Hosmer-Lemeshow test （H-L）， calibration plot， and decision curve analysis （DCA）. ResultsAge， body mass index （BMI）， family history of cancer， job and life satisfaction， yellow and greasy fur with slippery pulse， and heavy body sensation were independent risk factors of the model. The prediction model showed excellent predictive value for both the training and validation sets. ConclusionThe established prediction model for CAG with turbidity toxin accumulation syndrome has high discrimination and excellent calibration， which could provide an excellent clinical basis for disease diagnosis and individualized treatment of patients.

Objective:To investigate the expressions of cystathionine-β-synthase(CBS)and cystathionine-γ-lyase(CSE)and their effects on the malignant biological behaviours of breast cancer cells,and to elucidate their mechanisms.Methods:The breast cancer tissue and paracancerous normal tissue from 15 cases of patients were selected,and RT-qPCR and Western blotting methods were used to detect the mRNA and protein expression levels of CBS and CSE in breast cancer tissue,paracancerous normal tissue,MCF-7 cells,and MDA-MB-231 cells.The MCF-7 cells were divided into siNC group(transfected with siNC)and siCBS group(transfected with siCBS),and the MDA-MB-231 cells were divided into ovNC group(transfected with CSE over-expression empty plasmid)and ovCSE group(transfected with CSE over-expression plasmid).CCK8 assay was used to detect the proliferation activities of breast cancer cells in various groups,Transwell assay was used to detect the numbers of migration and invasion cells in various groups,and Western blotting method was used to detect the protein expression levels of E-cadherin,N-cadherin and Vimentin proteins in the breast cancer cells in various groups.Results:Compared with paracancerous normal tissue,the expression levels of CBS and CSE mRNA and proteins in breast cancer tissue were increased(P＜0.05 or P＜0.01).Compared with MDA-MB-231 cells,the CBS mRNA expression level in the MCF-7 cells was increased(P＜0.05);compared with MCF-7 cells,the expression level of CSE protein in the MDA-MB-231 cells was decreased(P＜0.05).Compared with siNC group,the proliferation activity,the numbers of migration and invasion cells,the expression levels of N-cadherin and Vimentin proteins in the MCF-7 cells in siCBS group were significantly decreased(P＜0.05),and the expression level of E-cadherin protein was increased(P＜0.05).Compared with ovNC group,the proliferation activity,the numbers of migratoin and invasion cells,and the expression levels of N-cadherin and Vimentin proteins in the MDA-MB-231 cells in ovCSE group were increased(P＜0.05),while the expression level of E-cadherin protein was significantly decreased(P＜0.05).Conclusion:The expressions of CBS and CSE are upregulated in breast cancer tissue,and high levels of CBS and CSE promote proliferation,migration,invasion and epithelial-mesenchymal transition(EMT)of breast cancer cells.

Cognitive impairment is the main clinical manifestation of many nervous system diseases such as stroke,multiple sclerosis,and neurodegeneration,and neuroinflammation is one of the key mechanisms for the onset of cognitive disorders.Chemokines are a class of highly conserved small-molecule secretory proteins that bind to the corresponding chemokine receptors located on cell mem-brane,activating downstream signaling pathways and playing an important role in cell migration,proliferation,differentiation,and sur-vival.In the central nervous system,chemokines and their receptors are involved in immune response and can exert a certain regulatory effect on neuroinflammation.This article reviews the research advances in chemokines and their receptors in cognitive disorders,in or-der to provide new insights and targets for the early diagnosis and treatment of related diseases.

Objective To assess the clinical value of a novel surgical technique—Tubeless subxiphoid uniportal video-assisted thoracoscopic surgery with percutaneous suspension technique via balance-shaped sternal elevation device in the resection of anterior mediastinal masses. Methods Patients who underwent tubeless subxiphoid uniportal video-assisted thoracoscopic surgery via balance-shaped sternal elevation device in anterior mediastinal masses process at the Department of Thoracic Surgery, West China Hospital, Sichuan University from March to April 2025 were included, and their clinical data were analyzed. Results A total of 4 patients were included, with 2 males and 2 females, aged 58-75 years. The diameter of the tumor was 2.5-3.0 cm. The operation time was 60.0-150.0 min, intraoperative blood loss was 5-10 mL, pain score on the 3rd day after surgery was 0 points, and postoperative hospital stay was 2-3 days. All patients achieved complete resection of the masses and thymus without perioperative complications. Conclusion The tubeless subxiphoid uniportal video-assisted thoracoscopic surgery with percutaneous suspension technique via balance-shaped sternal elevation device technique optimizes surgical visualization and instrument maneuverability while avoiding complications related to conventional anesthesia and tubing, thereby markedly enhancing the minimally invasive profile of anterior mediastinal masses resections. In addition to maintaining procedural safety, this approach effectively reduces postoperative pain and accelerates patient recovery, highlighting its potential for widespread clinical adoption.

Objective To understand the instrument distribution and capacities of inter-laboratory comparisons among occupational medical examination (OME) institutions in Guangdong Province. Methods A total of 291 OME institutions in Guangdong Province that were within the validity period of filing in June 2024 and 101 OME institutions that participated in inter-laboratory comparisons in 2023 were selected as the study subjects. Their filing categories, instrument distribution and external quality assessment in laboratories were collected and analyzed. Results The location of 291 OME institutions distributed in 21 prefectural-level cities in the province, mainly in the Pearl River Delta (PRD) region, accounting for 68.4% of the total. The public hospitals, private medical institutions, the center for disease control and prevention and occupational disease prevention and treatment institutions (hereinafter referred to as "occupational prevention institutions") accounted for 61.2%, 28.5% and 10.3%, respectively. The filing rates for physical factors, chemical factors, and dust categories among OME institutions were 91.1%, 86.9%, and 86.6%, respectively. The average number of institutional instruments, from high to low, was 53.8, 38.8 and 38.2 per unit in occupational prevention institutions, private medical institutions and public hospitals, respectively. The passing rates of inter-laboratory comparisons among OME institutions of external quality assessment in laboratories among OME institutes in Guangdong Province, from high to low were occupational prevention institutions, public hospitals and private medical institutions (96.2% vs 86.8% vs 73.0%, P<0.05). Conclusion The regional distribution of OME institutes in Guangdong Province is unbalanced, mainly concentrated in the PRD region. Low overall level of OME capacity is found in public hospitals and private medical institutions. Leading by the occupational prevention institutions, public hospitals and private medical institutions should strengthen the development and investment in OME hardware, in order to improve the OME level.

Objective:To investigate the risk factors and construct a nomogram prediction model for neonatal bacterial meningitis (BM).Methods:A retrospective cohort study was conducted on 1 228 neonates who underwent lumbar puncture for cerebrospinal fluid examination in the Department of Neonatology at Gansu Provincial Women and Child Healthcare Hospital from December 2019 to February 2024. The subjects were randomly divided into a training cohort and a validation cohort at a ratio of 7∶3 using a computer program. Rank sum test or Chi-square tests were used to compare differences between the two cohorts. The subjects were divided into BM and non-BM groups based on the presence or absence of BM. Multivariate logistic regression analysis (forward stepwise regression method) was used in the training cohort to identify risk factors for BM. The area under the receiver operating characteristic curve (AUC) and the Hosmer-Lemeshow goodness-of-fit test were used to assess the discrimination and calibration of the model, respectively. Calibration curves were used to evaluate the accuracy of the model and to construct the nomogram. Internal validation was performed using the Bootstrap resampling method. Decision curve analysis was used to assess the clinical utility of the model. Results:Among the 1 228 neonates, 151 (12.3%) had BM. The training cohort included 859 neonates, of whom 106 (12.3%) had BM and 753 (87.7%) did not. The validation cohort included 369 neonates, of whom 45 (12.2%) had BM and 324 (87.8%) did not. The results of the multivariate logistic regression analysis in the training cohort showed that sepsis ( OR=4.446, 95% CI:2.583-7.653), convulsions ( OR=3.749, 95% CI:1.930-7.280), high maximum body temperature ( OR=2.027, 95% CI:1.636-2.513), and elevated C-reactive protein ( OR=1.007, 95% CI:1.003-1.012) were independent risk factors for BM, while greater gestational age at birth ( OR=0.946, 95% CI: 0.898-0.995) and higher hemoglobin levels ( OR=0.990, 95% CI:0.981-0.998) were protective factors for BM (all P<0.05). Based on these findings, a nomogram prediction model for neonatal BM was constructed and validated for accuracy. The AUC values of the nomogram model in the training and validation cohorts were 0.796 (95% CI: 0.750-0.843) and 0.781 (95% CI: 0.700-0.862), respectively. The Hosmer-Lemeshow goodness-of-fit test showed P>0.05 in both cohorts. The clinical decision curve analysis demonstrated good net benefit across most threshold ranges. Conclusions:Sepsis, convulsions, high maximum body temperature, and elevated C-reactive protein increase the risk of neonatal BM. The nomogram model constructed based on these factors, combined with gestational age and hemoglobin levels, provides a reference value for predicting the risk of neonatal BM.

Objective:To analyze the accuracy of nine estimation methods for umbilical venous catheterization (UVC) insertion depth in neonates.Methods:This prospective study enrolled neonates who underwent successful UVC placement in the Department of Neonatology at Gansu Provincial Women and Child Healthcare Hospital between September 2023 and October 2024. The standard catheter tip position was defined as the junction of the inferior vena cava and right atrium, with a deviation of ≤0.5 cm considered accurate. Patients were stratified by birth weight (BW) into three groups: <1 500 g, 1 500- 2 499 g, and ≥2 500 g. The actual UVC depth was compared with depths estimated using nine methods: Shukla formula, modified Shukla formula, JSS formula, BW formula, Tambasco formula, modified Tambasco formula, Dunn's nomogram, body surface measurement, and ultrasonographic measurement. Accuracy was evaluated using nonparametric tests and Bland-Altman agreement analysis.Results:The study included 111 neonates: 41 (36.9%) in the <1 500 g group, 55 (49.6%) in the 1 500-2 499 g group, and 15 (13.5%) in the ≥2 500 g group. In the <1 500 g group, accuracy rates ranged from 24% to 56%, with body surface measurement showing the highest accuracy (56%); the mean difference from actual depth was－0.073 cm, with 95% limits of agreement (LOA) of－1.764 to 1.618 cm. In the 1 500-2 499 g group, accuracy rate ranged from 15% to 51%, with the modified Tambasco formula being most accurate (51%); the mean difference was 0.113 cm (95%LOA:－1.558-1.783 cm). In the ≥2 500 g group, accuracy rate ranged from 0/15 to 10/15, with Dunn's nomogram being most accurate (10/15); the mean difference was－0.120 cm (95%LOA:－1.380-1.140 cm).Conclusions:The accuracy of the nine UVC depth estimation methods varied across different BW groups and among methods within the same group. Selection of an estimation method should be tailored to the neonate's birth weight.

Objective:To investigate the risk factors and construct a nomogram prediction model for neonatal bacterial meningitis (BM).Methods:A retrospective cohort study was conducted on 1 228 neonates who underwent lumbar puncture for cerebrospinal fluid examination in the Department of Neonatology at Gansu Provincial Women and Child Healthcare Hospital from December 2019 to February 2024. The subjects were randomly divided into a training cohort and a validation cohort at a ratio of 7∶3 using a computer program. Rank sum test or Chi-square tests were used to compare differences between the two cohorts. The subjects were divided into BM and non-BM groups based on the presence or absence of BM. Multivariate logistic regression analysis (forward stepwise regression method) was used in the training cohort to identify risk factors for BM. The area under the receiver operating characteristic curve (AUC) and the Hosmer-Lemeshow goodness-of-fit test were used to assess the discrimination and calibration of the model, respectively. Calibration curves were used to evaluate the accuracy of the model and to construct the nomogram. Internal validation was performed using the Bootstrap resampling method. Decision curve analysis was used to assess the clinical utility of the model. Results:Among the 1 228 neonates, 151 (12.3%) had BM. The training cohort included 859 neonates, of whom 106 (12.3%) had BM and 753 (87.7%) did not. The validation cohort included 369 neonates, of whom 45 (12.2%) had BM and 324 (87.8%) did not. The results of the multivariate logistic regression analysis in the training cohort showed that sepsis ( OR=4.446, 95% CI:2.583-7.653), convulsions ( OR=3.749, 95% CI:1.930-7.280), high maximum body temperature ( OR=2.027, 95% CI:1.636-2.513), and elevated C-reactive protein ( OR=1.007, 95% CI:1.003-1.012) were independent risk factors for BM, while greater gestational age at birth ( OR=0.946, 95% CI: 0.898-0.995) and higher hemoglobin levels ( OR=0.990, 95% CI:0.981-0.998) were protective factors for BM (all P<0.05). Based on these findings, a nomogram prediction model for neonatal BM was constructed and validated for accuracy. The AUC values of the nomogram model in the training and validation cohorts were 0.796 (95% CI: 0.750-0.843) and 0.781 (95% CI: 0.700-0.862), respectively. The Hosmer-Lemeshow goodness-of-fit test showed P>0.05 in both cohorts. The clinical decision curve analysis demonstrated good net benefit across most threshold ranges. Conclusions:Sepsis, convulsions, high maximum body temperature, and elevated C-reactive protein increase the risk of neonatal BM. The nomogram model constructed based on these factors, combined with gestational age and hemoglobin levels, provides a reference value for predicting the risk of neonatal BM.

Objective:To analyze the accuracy of nine estimation methods for umbilical venous catheterization (UVC) insertion depth in neonates.Methods:This prospective study enrolled neonates who underwent successful UVC placement in the Department of Neonatology at Gansu Provincial Women and Child Healthcare Hospital between September 2023 and October 2024. The standard catheter tip position was defined as the junction of the inferior vena cava and right atrium, with a deviation of ≤0.5 cm considered accurate. Patients were stratified by birth weight (BW) into three groups: <1 500 g, 1 500- 2 499 g, and ≥2 500 g. The actual UVC depth was compared with depths estimated using nine methods: Shukla formula, modified Shukla formula, JSS formula, BW formula, Tambasco formula, modified Tambasco formula, Dunn's nomogram, body surface measurement, and ultrasonographic measurement. Accuracy was evaluated using nonparametric tests and Bland-Altman agreement analysis.Results:The study included 111 neonates: 41 (36.9%) in the <1 500 g group, 55 (49.6%) in the 1 500-2 499 g group, and 15 (13.5%) in the ≥2 500 g group. In the <1 500 g group, accuracy rates ranged from 24% to 56%, with body surface measurement showing the highest accuracy (56%); the mean difference from actual depth was－0.073 cm, with 95% limits of agreement (LOA) of－1.764 to 1.618 cm. In the 1 500-2 499 g group, accuracy rate ranged from 15% to 51%, with the modified Tambasco formula being most accurate (51%); the mean difference was 0.113 cm (95%LOA:－1.558-1.783 cm). In the ≥2 500 g group, accuracy rate ranged from 0/15 to 10/15, with Dunn's nomogram being most accurate (10/15); the mean difference was－0.120 cm (95%LOA:－1.380-1.140 cm).Conclusions:The accuracy of the nine UVC depth estimation methods varied across different BW groups and among methods within the same group. Selection of an estimation method should be tailored to the neonate's birth weight.