ObjectiveTo delineate imaging findings using an imaging platform and investigate the correlation between MRI characteristics of spinal cord atrophy and clinical diagnosis in children with spinal cord injury (SCI). MethodsImaging data of 150 children with SCI admitted to Beijing Bo'ai Hospital, China Rehabilitation Research Center, from January, 2002 to March, 2024 were collected and imported into the imaging platform. The anteroposterior and transverse diameters of the middle part of the spinal cord at the cross-section with the most severe atrophy were measured, and the relevant indicators of the previous normal spinal cord segment were measured as controls; the radiomic features were extracted. Clinical data of the children including gender, age, cause of injury, sensory level, motor level, spinal cord injury level, injury severity and disease course were collected. ResultsSpinal cord atrophy was identified in 81 cases (54%), among which 78 cases (96%) were American Spinal Injury Association Impairment Scale (AIS) grade A and 3 cases (4%) were AIS grade C. The upper boundary of the spinal cord atrophy site strongly correlated with the injury level, motor level and sensory level (r > 0.8, P < 0.001). ConclusionMore than half of children with SCI may develop secondary spinal cord atrophy, the vast majority of whom suffer from complete spinal cord injury; the upper boundary of spinal cord atrophy is correlated with the injury level.

ObjectiveTo investigate the mechanisms by which Renshentang treats atherosclerosis （AS） in mice， focusing on the regulation of endothelial inflammatory responses mediated by transient receptor potential vanilloid subtype 1 （TRPV1）. MethodsAn AS model was established in apolipoprotein E knockout （ApoE^-/-） mice fed a high-fat diet. The mice were randomly divided into a simvastatin group （0.02 g·kg^-1·d^-1） and low-， medium-， and high-dose Renshentang groups （1.77， 3.54， 7.08 g·kg^-1·d^-1）， with 12 mice in each group. ApoE^-/- mice were fed a high-fat diet and treated simultaneously. C57BL/6J mice fed a normal diet served as the normal group （n=9）. After continuous administration for 12 weeks， mice were anesthetized and the aortas were collected. Oil Red O staining was used to observe lipid plaque formation in the aorta. Hematoxylin-eosin （HE） staining was performed to examine pathological changes in the aortic root. Immunohistochemistry was used to analyze the levels of pro-inflammatory factors tumor necrosis factor-α （TNF-α） and interleukin-1β （IL-1β）， as well as the expression of TRPV1， phosphorylated phosphoinositide 3-kinase （p-PI3K）， and phosphorylated protein kinase B （p-Akt） in the aortic root. Real-time quantitative polymerase chain reaction （Real-time PCR） was used to detect endothelial nitric oxide synthase （eNOS） mRNA expression in the aorta， and Western blot was used to detect TRPV1 protein expression. ResultsCompared with the normal group， the model group showed a significant increase in aortic plaque formation （P<0.01） and significantly elevated levels of TNF-α and IL-1β in the aortic root （P<0.01）. The expression levels of TRPV1， p-PI3K， and p-Akt were decreased （P<0.05， P<0.01）， and eNOS mRNA expression was reduced （P<0.05， P<0.01）. Compared with the model group， all Renshentang groups significantly reduced aortic plaque formation （P<0.01）， significantly decreased TNF-α and IL-1β levels （P<0.01）， and markedly increased the expression levels of TRPV1， p-PI3K， p-Akt， and eNOS mRNA （P<0.05， P<0.01）. ConclusionRenshentang may inhibit endothelial inflammation and suppress the formation of AS by increasing TRPV1 protein expression and up-regulating the PI3K/Akt/eNOS signaling pathway， which may be one of the molecular mechanisms underlying its therapeutic effect against AS.

Objective To analyze the case data of farmers' foodborne disease surveillance reports in Guizhou Province from 2022 to 2024, and to provide reference for the precise prevention and control of foodborne diseases among farmers in Guizhou Province. Methods Case data of foodborne disease surveillance reports of farmers were systematically collected from 2022 to 2024 in Guizhou Province. Descriptive epidemiological methods were used to analyze the temporal, geographical, and demographic distribution of foodborne diseases among farmers, along with their primary clinical symptoms and pathogen detection results. Results From 2022 to 2024, a total of 22,882 cases of foodborne diseases were reported among farmers in Guizhou Province. The majority of clinical symptoms (97.81%) were related to the digestive system, with summer being the peak season. While females outnumbered males, the gender difference was statistically insignificant (P >0.05). The 36-55 age group accounted for the highest proportion (38.83%), with Zunyi City (34.89%) and Qiandongnan Prefecture (23.21%) reporting the most cases. Fungal products were the most frequently reported suspected food items (26.96%), and home-made preparation was the primary processing method (58.63%). A total of 1 210 fecal samples were collected through active monitoring with an overall detection rate of 13.22%. Norovirus showed the highest detection rate (9.92%, 120/1 210). Statistically significant differences were observed among different seasons, age groups, regions, types and processing methods of suspected food exposure, and pathogen detection rates (P <0.001). Conclusion Foodborne disease prevention and control among farmers in Guizhou Province should focus on the risks of wild mushroom poisoning in summer and homemade foods, and continuously improve farmers' awareness of the dangers of foodborne diseases and food safety.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Pathologic myopia(PM)is characterized by progressive axial elongation, posterior staphyloma, and fundus degenerative changes, representing a leading cause of blindness in Asian countries.Posterior scleral reinforcement(PSR)is currently the only surgical intervention to slow axial elongation. Its mechanisms involve mechanical reinforcement, improved blood circulation, and biological stimulation of scleral remodeling. This article reviews recent advances in PSR: surgical techniques have evolved from complex, classic procedures to standardized single-strip methods, incorporating 3D MRI to quantify posterior staphyloma morphology for personalized graft design and facilitating minimally invasive, precision surgery. Among implant materials, biomaterials such as allogeneic dura mater are being continuously optimized, while biomimetic piezoelectric smart materials actively promote scleral collagen synthesis through electrical signals, achieving a paradigm shift from “passive support”to “active reinforcement”. Clinical outcomes indicate advantages in regulating axial length, enhancing visual function and blood circulation, and facilitating early intervention in children. Combined procedures with ICL implantation, cataract surgery or vitrectomy provide comprehensive treatment regimens for individual patients with varied conditions. This article also analyzes long-term issues and core controversies including ectopic posterior staphyloma and reoperation, and prospects future research directions such as artificial intelligence assistance, translation of smart materials and full-cycle prevention and treatment systems, so as to provide references for clinical practice and scientific research.

Objective: To analyze the epidemilogical and seasonal characteristics of foodborne Salmonella-associated diarrhea among children aged 0-6 years in Guizhou Province from 2016 to 2023, so as to provide a basis for the prevention and control of foodborne diseases. Methods: Data were extracted from the Foodborne Disease Survellance System for cases reported between January 1, 2016, and December 31, 2023. The incidence, seasonal characteristics, and peak periods were analyzed by the method of concentration and circular distribution. Results: A total of 6 434 cases of diarrhea in children aged 0-6 years were collected, and 455 cases of Salmonella were detected, with a positive detection rate of 7.07%. Salmonella typhimurium was the main serotype causing diarrhea (59.34%). The peak of the disease was from May 3 to September 30, with certain seasonal characteristics. The highest detection rate was found in children aged 1-3 years (8.66%). Among food types, the positive detection rates of Salmonella were relatively high in other foods (17.39%), fruits and their products (10.22%), infant and toddler foods (10.09%), and aquatic animals and their products (9.80%). The processing and packaging methods of food were mainly home-made (9.38%) and bulk food (7.54%). Conclusions The detection rate of Salmonella in children aged 0-6 years is high in Guizhou Province, with strong seasonal characteristics. The detection rates of other foods, fruits and their products, infant and toddler foods, and aquatic animals and their products are high. Enhanced pathogen surveillance for susceptible populations and high-risk foods, coupled with public health education during summer/autumn, is recommended.

Objective To explore the recognition capabilities of electronic nose combined with machine learning in identifying the breath odor map of benign and malignant pulmonary nodules and Traditional Chinese Medicine (TCM) syndrome elements. Methods The study design was a single-center observational study. General data and four diagnostic information were collected from 108 patients with pulmonary nodules admitted to the Department of Cardiothoracic Surgery of Hospital of Chengdu University of TCM from April 2023 to March 2024. The patients' TCM disease location and nature distribution characteristics were analyzed using the syndrome differentiation method. The Cyranose 320 electronic nose was used to collect the odor profiles of oral exhalation, and five machine learning algorithms including random forest (RF), K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) were employed to identify the exhaled breath profiles of benign and malignant pulmonary nodules and different TCM syndromes. Results (1) The common disease locations in pulmonary nodules were ranked in descending order as liver, lung, and kidney; the common disease natures were ranked in descending order as Yin deficiency, phlegm, dampness, Qi stagnation, and blood deficiency. (2) The electronic nose combined with the RF algorithm had the best efficacy in identifying the exhaled breath profiles of benign and malignant pulmonary nodules, with an AUC of 0.91, accuracy of 86.36%, specificity of 75.00%, and sensitivity of 92.85%. (3) The electronic nose combined with RF, LR, or XGBoost algorithms could effectively identify the different TCM disease locations and natures of pulmonary nodules, with classification accuracy, specificity, and sensitivity generally exceeding 80.00%.Conclusion Electronic nose combined with machine learning not only has the potential capabilities to differentiate the benign and malignant pulmonary nodules, but also provides new technologies and methods for the objective diagnosis of TCM syndromes in pulmonary nodules.

Objectives:This study aims to establish a calcified aortic valve disease(CAVD)-related gene regulatory network,and clarify the impact of interactions between CAVD-related genes on CAVD.Methods:Differential expression gene method was used to screen candidate genes,and STRING software was used to construct protein-protein interaction networks for screened genes.The networks and genes with the highest scores were obtained.Monte Carlo method was used to rank the importance of genes in CAVD,and the top 1%genes were identified.Genes identified by these two methods are intersected to obtain the key genes.The correlation between the key genes and CAVD were confirmed by biological verification method.Then,key genes were used as query genes to establish CAVD gene regulatory module with a co-expression patterns-based gene regulatory network identifying method.Specifically,we first detected the underlying co-expression patterns of the seemingly uncorrelated genes,and then pieced the isolated gene pairs up into gene regulatory network with the help of the bridging genes.Finally,we verified the function of the established gene regulatory network through enrichment analysis and literature analysis.Results:We identified the CAVD gene regulatory network containing 211 genes from 18 084 candidate genes.Functional enrichment analysis indicate that the established gene regulatory network included genes with both known interactions and potential novel pathway interactions related to CAVD,serving as candidates for further experimental CAVD studies.Conclusions:Methodologically,the proposed method avoids the problem of complex computation,and relies only on available biological priors.It is also able to detect underlying co-expression patterns.The results will advance the understanding of the interactions of the CAVD-related genes and this method provides a novel way to identify underlying gene co-expression patterns in the setting of CAVD.

Objective To explore the influencing factors of cognitive impairment in non-dialysis patients with chronic kidney disease(CKD).Methods A total of 60 hospitalized non-dialysis patients with CKD in the Department of Nephrology of Northern Jiangsu People's Hospital Affiliated to Yangzhou University from September 2022 to September 2023 were enrolled as research objects.According to the estimated glomerular filtration rate(eGFR),they were divided into stage 1 to 2 of CKD group[eGFR ≥60 mL/(min·1.73 m2)]with 23 cases,the stage 3 of CKD group[eGFR 30～＜60 mL/(min·1.73 m2)]with 20 cases,and stage 4 to 5 of CKD group[eGFR＜30 mL/(min·1.73 m2)]with 17 cases.The Montreal Cognitive Assessment Scale(MoCA)was used to evaluate the cognitive function of the patients.Basic data and common clinical laboratory in-dicators on hospital admission were collected to analyze the differences in cognitive function levels under different renal function statuses and to explore the influencing factors of cognitive impairment.Results The incidence rates of cognitive impairment in the stage 1 to 2 of CKD group,stage 3 of CKD group,and stage 4 to 5 of CKD group were 47.8％,85.0％,and 94.1％respectively,the median MoCA scored 26,24 and 20 respectively,with statistically significant between-group differ-ences(P＜0.05).Cognitive function was significantly negatively correlated with age(r=-0.634,P＜0.001),blood urea nitrogen(BUN)(r=-0.574,P＜0.001),serum creatinine(Cr)(r=-0.417,P＜0.001),cystatin C(Cys-C)(r=-0.327,P=0.011),serum β2-microglobulin(β2-MG)(r=-0.259,P=0.046),and N-terminal pro-brain natriuretic peptide(NT-proBNP)(r=-0.474,P＜0.001),and was significantly positively correlated with hemoglobin(HB)(r=0.401,P=0.001)and eGFR(r=0.485,P＜0.001).Multivariate Logistic regression analysis showed that age(P=0.006)and NT-proBNP(P=0.041)were influencing factors of cognitive im-pairment in non-dialysis patients with CKD.Receiver operating characteristic(ROC)curve analysis showed that the area under the curve(AUC),sensitivity,and specificity of age for prediction were 0.860,0.864 and 0.812 respectively,the AUC,sensitivity,and specificity of NT-proBNP for pre-diction were 0.808,0.795 and 0.875 respectively,and the combined prediction of age and NT-proBNP had an AUC,sensitivity,and specificity of 0.893,0.955,and 0.750,respectively.Conclusion As renal function deteriorates,the incidence rate and severity of cognitive impairment in non-dialysis patients with CKD tend to increase.Advanced age,renal function deterioration,high NT-proBNP level,and anemia are associated with the occurrence of cognitive impairment in non-di-alysis patients with CKD,among which age and NT-proBNP are influencing factors for cognitive im-pairment.

Objective To assess functional abnormalities in frontoparietal attention network of patients with traumatic brain injury(TBI),and to analyze neural mechanism of its attention disorders based on independent component analysis(ICA).Methods Totally 84 TBI patients were prospectively enrolled and divided into mild(n=33),moderate(n=27)and severe TBI groups(n=24)according to symptoms,non-contrast enhanced MRI findings,Glasgow coma scale(GCS)and Mayo craniocerebral trauma grading criteria.ICA was used to extract and analyze the optimal independent components(IC)of the dorsal attention network(DAN)and ventral attention network(VAN).Multiple linear regression analyses were performed to explore the correlations of functional connectivity(FC)of brain regions being significantly different among groups within attention network and the scores of trail making test A/B(TMT-A/B).Results Compared with control group and mild TBI group,spatial distribution of DAN(especially bilateral frontal eye field[FEF])in moderate and severe TBI groups were significantly smaller,the clusters tended to be discrete,and the intensity were significantly weakened.The right temporal parietal junction(TPJ)and right ventral frontal cortex(VFC)of VAN in control group,mild and moderate TBI groups were significantly affected,and VFC connection strength was the highest in severe TBI group.FC intensity in right VFC of TBI patients was positively correlated with TMT-A score(r=0.654,P＜0.001),which in left FEF was negatively correlated with TMT-B score(r=—0.383,P＜0.001).Conclusion FC of frontoparietal attention network was abnormal in TBI patients,which might be neural mechanism for its attention behavior disorders.