This paper systematically reviews the current status of integrated traditional Chinese and western medicine in the treatment of Helicobacter pylori （Hp） infection， as well as recent progress in clinical and basic research both in China and internationally. It summarizes the advantages of traditional Chinese medicine （TCM） in Hp infection management， including improving Hp eradication rates， enhancing antibiotic sensitivity， reducing antimicrobial resistance， decreasing drug-related adverse effects， and ameliorating gastric mucosal lesions. These advantages are particularly evident in patients who are intolerant to bismuth-containing regimens， those with refractory Hp infection， and individuals with precancerous gastric lesions. An integrated， whole-process management approach and individualized， staged comprehensive treatment strategies combining TCM and western medicine are proposed for Hp infection. Future prevention and control of Hp infection should adopt an integrative Chinese-western medical strategy， emphasizing prevention， strengthening primary care， implementing proactive long-term monitoring， optimizing screening strategies， and advancing the development of novel technologies and mechanistic studies of Chinese herbal interventions. These efforts aim to provide a theoretical basis and practical pathways for the establishment and improvement of Hp infection prevention and control systems.

Objective The aim of this study was to investigate the correlation between the interaction of uric acid and inflammatory factors and hyperuricemia in overweight/obese patients. Methods The personnel with hyperuricemia who underwent physical examination in our hospital from September 2021 to September 2022 were selected as the study subjects, and they were divided into 100 cases of overweight group and 90 cases of obese group according to the BMI index; 120 cases of healthy and non-hyperuricemic personnel were randomly selected as the control group; venous blood of the three groups was collected in 5 mL after 8 h of fasting, and were tested respectively for serum uric acid, lipid indexes and inflammatory factors: IL-6, IL-2, IFN-γ, TNF-α, IL-4, IL-10. Results Glucose, triglycerides, total cholesterol, and LDL were significantly higher in the obese group versus the overweight group (P<0.001), while HDL was significantly lower than the control group (P<0.001), and these changes were more pronounced in the obese group (P<0.001).The Pearson correlation coefficient pointed out that the levels of serum uric acid in patients with hyperuricosuric acid were significantly associated with the pro-inflammatory factors IL- 6, IL-2, IFN-γ, and TNF-α were significantly positively correlated (P<0.001), whereas they were significantly negatively correlated with the anti-inflammatory factors IL-4, IL-10 (P<0.001). Conclusion High uric acid levels in overweight/obese patients can cause enhanced inflammatory responses and reduced expression levels of anti-inflammatory factors, and the interaction between uric acid and pro-inflammatory factors aggravates the condition of patients with hyperuricemia.

Objective To analyze the relationship between cognitive function, motor ability and the risk of falls in patients during the rehabilitation period after stroke. Methods A total of 330 patients with stroke who received rehabilitation treatment in the hospital from February 2022 to February 2025 were classified into fall group (n=105) and non-fall group (n=225) based on the occurrence of falls during rehabilitation period. The clinical data of patients were collected, and the motor function of patients was evaluated by Fugl-Meyer scale, and the cognitive function of patients was assessed by mini-mental state examination (MMSE). Univariate and logistic multivariate regression analyses were applied to analyze the factors affecting the occurrence of falls. Spearman correlation analysis was performed to analyze the relationship between motor ability, cognitive function and risk of falls. Results The proportions of osteoporosis, malnutrition, sleep disorder and history of falls in the fall group were higher while the education level, Fugl-Meyer score and MMSE score were lower compared to the non-fall group (P<0.05). After logistic multivariate analysis, it was found that osteoporosis, malnutrition, sleep disorder, history of falls, low education level, low Fugl-Meyer score and low MMSE score were risk factors for falls in patients during post-stroke rehabilitation period (P<0.05). Correlation analysis indicated that the motor ability and cognitive function in patients during post-stroke rehabilitation period were negatively associated with the occurrence of falls (r=-0.579, -0.438, P<0.001). Conclusion The cognitive function, motor ability of patients in the post-stroke rehabilitation period are closely related to the risk of falls. Clinically, the risk of falls in patients during post-stroke rehabilitation period can be reduced by improving the motor ability and cognitive function.

The human norepinephrine transporter(hNET)is expressed in neural crest-derived tumors, such as neuroblastoma, pheochromocytoma, and paraganglioma. 18F-meta-fluorobenzylguanidine (MFBG) has high affinity and specificity for hNET, and has been gradually applied to PET/CT imaging in neural crest-derived tumors. The application of 18F-MFBG PET/CT imaging in the diagnosis and treatment of neural crest-derived tumors is reviewed in this article.

Objective To compare the diagnostic performance of three breast MRI measurement methods—RECIST 1.1,the optimal method,and three-dimensional(3D)volumetric assessment—in assessing the efficacy of neoadjuvant chemotherapy(NAC)in breast cancer patients,with the objective of identifying the most clinically practical approach.Methods A total of 110 breast cancer patients who underwent NAC followed by surgical treatment between 2019 and 2023 were included in the study.Breast magnetic resonance imaging(MRI)was conducted within one week before and after the completion of NAC.Tumor response was evaluated using RECIST 1.1 criteria,widely recognized as the optimal method,as well as 3D volume measurement.Pathological response was determined according to the Miller-Payne grading system.Sensitivity,specificity,accuracy,and the area under the receiver operating characteristic curve(AUC)were computed and compared using the DeLong test.Results The AUC values for RECIST 1.1,the optimal method,and 3D volumetric assessment were 0.768,0.795,and 0.883,respectively.The 3D volumetric assessment exhibited significantly better discriminative performance(P＜0.05),with the highest sensitivity(98.9％),specificity(77.8％),and accuracy(95.5％).Additionally,the optimal method demonstrated superior performance over RECIST 1.1 across multiple parameters.Conclusions 3D volumetric mea-surement demonstrates superior performance compared to RECIST 1.1 and the optimal method in evaluating the response to NAC,offering a more accurate and comprehensive assessment tool.Additionally,the optimal method shows advantages over RECIST 1.1 and may serve as a practical alternative in settings where 3D software is not available.

Objective To develop a deep learning method for small sample multi-omics data using attention mechanism and Meta-learning for the establishment of autism diagnosis model.Methods MLAN(Meta-learning based attentive network)consisting of the omics feature pre-reduction module,the multi-omics data fusion and feature learning module,and the parameter optimization module was designed.Firstly,differential expression analysis was performed on high-dimensional multi-omics data to preliminarily screen out unimportant features.Secondly,a multi-channel attention mechanism was used to learn the importances of every set of omics data and to realize data fusion,and a two-layer fully connected network was constructed to further extract latent features and realize the diagnosis task.Finally,the Meta-learning algorithm Reptile was used to optimize the initial parameters of the above model to obtain the optimal parameters.A total of 58 children's saliva samples were collected,including 21 children diagnosed with autism,12 children with social disorders,and 25 healthy controls,and the protein and metabolomics data were detected by mass spectrometry.All data were randomly divided into training set and test set by 4∶1,and the training set was divided into training data and validation data in the same way for model training and validation.The test set was used for the final evaluation of the model effect.Five baseline models and three ablated models were constructed and evaluated along with MLAN based on metrics including multi-classification accuracy,F1-macro and F1-weighted scores.Results The constructed multi-classification autism diagnosis model MLAN achieved multi-classification accuracy,F1-macro and F1-weighted scores of 0.850±0.066,0.817±0.103 and 0.834±0.087.The values of all three indicators were better than those of baseline models and the ablated models.Conclusion The proposed MLAN can effectively deal with heterogeneous multi-omics data with small samples and achieve good results,which is expected to provide assistance for the clinical diagnosis of autism.

Objective To compare the diagnostic performance of three breast MRI measurement methods—RECIST 1.1,the optimal method,and three-dimensional(3D)volumetric assessment—in assessing the efficacy of neoadjuvant chemotherapy(NAC)in breast cancer patients,with the objective of identifying the most clinically practical approach.Methods A total of 110 breast cancer patients who underwent NAC followed by surgical treatment between 2019 and 2023 were included in the study.Breast magnetic resonance imaging(MRI)was conducted within one week before and after the completion of NAC.Tumor response was evaluated using RECIST 1.1 criteria,widely recognized as the optimal method,as well as 3D volume measurement.Pathological response was determined according to the Miller-Payne grading system.Sensitivity,specificity,accuracy,and the area under the receiver operating characteristic curve(AUC)were computed and compared using the DeLong test.Results The AUC values for RECIST 1.1,the optimal method,and 3D volumetric assessment were 0.768,0.795,and 0.883,respectively.The 3D volumetric assessment exhibited significantly better discriminative performance(P＜0.05),with the highest sensitivity(98.9％),specificity(77.8％),and accuracy(95.5％).Additionally,the optimal method demonstrated superior performance over RECIST 1.1 across multiple parameters.Conclusions 3D volumetric mea-surement demonstrates superior performance compared to RECIST 1.1 and the optimal method in evaluating the response to NAC,offering a more accurate and comprehensive assessment tool.Additionally,the optimal method shows advantages over RECIST 1.1 and may serve as a practical alternative in settings where 3D software is not available.

Objective To develop a deep learning method for small sample multi-omics data using attention mechanism and Meta-learning for the establishment of autism diagnosis model.Methods MLAN(Meta-learning based attentive network)consisting of the omics feature pre-reduction module,the multi-omics data fusion and feature learning module,and the parameter optimization module was designed.Firstly,differential expression analysis was performed on high-dimensional multi-omics data to preliminarily screen out unimportant features.Secondly,a multi-channel attention mechanism was used to learn the importances of every set of omics data and to realize data fusion,and a two-layer fully connected network was constructed to further extract latent features and realize the diagnosis task.Finally,the Meta-learning algorithm Reptile was used to optimize the initial parameters of the above model to obtain the optimal parameters.A total of 58 children's saliva samples were collected,including 21 children diagnosed with autism,12 children with social disorders,and 25 healthy controls,and the protein and metabolomics data were detected by mass spectrometry.All data were randomly divided into training set and test set by 4∶1,and the training set was divided into training data and validation data in the same way for model training and validation.The test set was used for the final evaluation of the model effect.Five baseline models and three ablated models were constructed and evaluated along with MLAN based on metrics including multi-classification accuracy,F1-macro and F1-weighted scores.Results The constructed multi-classification autism diagnosis model MLAN achieved multi-classification accuracy,F1-macro and F1-weighted scores of 0.850±0.066,0.817±0.103 and 0.834±0.087.The values of all three indicators were better than those of baseline models and the ablated models.Conclusion The proposed MLAN can effectively deal with heterogeneous multi-omics data with small samples and achieve good results,which is expected to provide assistance for the clinical diagnosis of autism.

The human norepinephrine transporter(hNET)is expressed in neural crest-derived tumors, such as neuroblastoma, pheochromocytoma, and paraganglioma. 18F-meta-fluorobenzylguanidine (MFBG) has high affinity and specificity for hNET, and has been gradually applied to PET/CT imaging in neural crest-derived tumors. The application of 18F-MFBG PET/CT imaging in the diagnosis and treatment of neural crest-derived tumors is reviewed in this article.

Objective To explore the prevalence rate and related factors of urban and rural residents with hyperuricemia (HUA). Methods A total of 360 subjects in physical examination department of Sanliusan Hospital from January 2020 to January 2023 were selected and divided into urban residents and rural residents according to their permanent residence addresses, and the demographic information, living habits and underlying diseases were collected. Fasting blood glucose (FBG), serum uric acid (SUA), body mass index (BMI) and triglyceride (TG) were measured. The risk factors of HUA were analyzed by logistics regression analysis. Results The incidence rates of HUA in urban and rural residents were 12.18% and 12.88%. There were statistically significant differences in education level, occupation, BMI, sleep time, alcohol drinking, FBG and TG between urban and rural residents (all P<0.05). Logistics regression analysis showed that male, BMI>24 kg/m², alcohol drinking and chronic kidney disease were independent risk factors for HUA occurrence among urban residents (all P<0.05). Chronic kidney disease, FBG≥7.0 mmol/L and TG≥2.3 mmol/L were independent risk factors for hyperuricemia occurrence among rural residents (all P<0.05). Conclusion Rural residents should strengthen health education and blood glucose and lipid control, and urban residents should pay more attention to reasonable exercise, control alcohol consumption and reduce HUA occurrence.