ObjectiveThe frequency difference limen (FDL) serves as a fundamental metric utilized for effectively quantifying the precise perceptual capabilities of the central auditory system. However, traditional measurement methods rely heavily on the active behavioral responses of subjects and are consequently highly susceptible to the negative influence of confounding subjective factors. Furthermore, existing research paradigms frequently employ uniform stimulus configurations that overlook critical individual perceptual differences. Based on brain-computer interface (BCI) technology, this comprehensive study aims to establish an objective and quantitative evaluation method for auditory frequency discrimination by systematically analyzing and decoding the specific neural responses elicited at the exact threshold state. MethodsWe designed a personalized rapid serial auditory presentation (RSAP) paradigm customized based on each individual’s precise FDL. A cohort of eleven healthy participants was recruited to evaluate the paradigm using pure-tone sequences at a baseline frequency of 4 000 Hz. This experimental paradigm simulates a realistic auditory perception environment through the continuous presentation of acoustic stimuli, thereby allowing for an in-depth investigation into the specific neural representations evoked by weak frequency deviations at the threshold state. Given that auditory stimulus-evoked response features exhibit complex and differentiated spatiotemporal distribution patterns across multiple frequency domains, this study further deeply integrates the cross-scale feature interaction module with the dynamic spatiotemporal attention allocation strategy, innovatively proposing the Multi-Scale Spatial-Temporal Dual Attention Network (MS-STAMNet). Specifically, the network constructs parallel processing branches with multiple receptive fields and introduces a dynamic adaptive weighting strategy to precisely localize core neural activity signals, further deeply integrating multi-scale information through cross-branch feature information interaction to achieve robust single-trial decoding of weak auditory evoked responses. ResultsThe comprehensive electrophysiological data analysis demonstrated that subtle auditory frequency deviation stimuli presented at the threshold level successfully elicited pronounced N2 and P3 event-related potential features, reflecting pre-attentive mismatch detection and subsequent cognitive evaluation, which were prominently distributed over the frontal, central, and temporal regions of the scalp. In the complex time-frequency domain, the extracted neural response characteristics exhibited distinct, statistically significant event-related synchronization within both the low-frequency δ and θ frequency bands, which was simultaneously accompanied by a widespread, prominent event-related desynchronization within the higher α band. A comparative analysis of model performance demonstrated that MS-STAMNet achieved an average unweighted average recall (UAR) of (69.67±6.12)% and area under the curve (AUC) of 0.761 8±0.07, significantly outperforming the established baseline models such as EEGNet and PLNet. Furthermore, a distinct dissociation phenomenon was verified between neural decoding and behavioral performance through regression analysis (R²=0.016, P=0.709), indicating that this model can effectively capture the implicit features of subtle frequency deviations, even when they fail to trigger explicit conscious responses. Additionally, attention weight visualization analysis further reveals the highly accurate focus of the network on key features concentrated over the bilateral temporal and fronto-parietal regions. ConclusionThis study systematically and comprehensively uncovers the multi-dimensional spatiotemporal evolutionary patterns of complex neural responses processing subtle acoustic variations under long-sequence threshold auditory stimulation. Concurrently, it verifies the efficacy and robustness of the proposed MS-STAMNet architecture in accurately deciphering weak, single-trial electroencephalogram signals amidst complex background noise. Ultimately, these neurophysiological and algorithmic findings lay a solid theoretical and methodological foundation for the objective and quantitative evaluation of individual auditory cognitive capabilities in clinical applications, transcending the fundamental limitations of traditional behavioral paradigms and providing robust technical support for future auditory research and related clinical assessments.

By summarizing the mass spectrometric fragmentation patterns of oxyphenisatin substances,an analytical method was established for screening of illegally added oxyphenisatin compounds in weight-loss health foods using high performance liquid chromatography-in-source-fragmentation-quadrupole time-of-flight mass spectrometry(HPLC-ISF-QTOF-MS),along with a quantitative method for 11 kinds of oxyphenisatin compounds.Based on the developed screening method,an oxyphenisatin derivative was discovered in the reference standards,which was tentatively identified as 4-(3-(4-hydroxyphenyl)-2-oxoindolin-3-yl)phenyl acetate and confirmed by MS/MS analysis.The results showed that all 11 kinds of oxyphenisatin compounds had correlation coefficients greater than 0.9971,with limits of detection(LODs)ranging from 0.12 to 0.68 μg/L and limits of quantification(LOQ)from 0.21 to 2.29 μg/L.The LODs for 11 kinds of characteristic ions ranged from 0.45 to 9.11 μg/kg.At spiking levels of 25,50 and 100 μg/kg,the recoveries ranged from 78.9%to 117.3%.The instrumental precision,intra-day method precision and inter-day method precision were 0.23%?1.70%,0.7%?2.4%,and 1.1%?3.3%,respectively.The developed targeted and non-targeted detection method demonstrated high sensitivity,strong stability,rapid analysis,and an expanded screening range for oxyphenisatin substances,and provided robust technical support for regulatory authorities in combating illegal adulteration.

Objective:To explore the correlation between skeletal muscle mass and strength with metabolic syndrome in children.Methods:This cross-sectional study was conducted involving 383 children aged 10 to 15 years who visited the Department of Child Health Care, Children′s Hospital of Nanjing Medical University from June 2021 to December 2022. Their height, weight, waist circumference, body composition, grip strength and blood pressure were measured. Relative skeletal muscle mass, muscle-to-fat ratio, and grip strength-to-body weight index were calculated. The levels of fasting blood glucose, lipids and insulin were tested. Homeostasis model assessment of insulin resistance (HOMA-IR) was calculated. Children were divided into the metabolic syndrome group and the non-metabolic syndrome group. Independent t test or Mann-Whitney U test etc. was used to compare the difference between groups. Spearman correlation analysis and binary Logistic regression were used to investigate the correlation between skeletal muscle mass and strength and metabolic syndrome. The area under the curve (AUC) of receiver operating characteristic (ROC) curve was used to compare the accuracy of the index of skeletal muscle in predicting metabolic syndrome in children. Results:Among the 383 children, 282 (73.6%) were male, at the age of 11.4 (10.6, 12.5) years. There were 216 children (56.4%) diagnosed with obesity and 90 children (23.5%) diagnosed with metabolic syndrome. Relative skeletal muscle mass, muscle-to-fat ratio, and grip strength-to-body weight index of the metabolic syndrome group were all lower than those in the non-metabolic syndrome group (all P<0.001). After adjusting for sex and age, relative skeletal muscle mass, muscle-to-fat ratio, and grip strength-to-body weight index were all negatively correlated with body mass index ( r=-0.84, -0.38, -0.63), waist circumference ( r=-0.76, -0.36, -0.70), systolic blood pressure ( r=-0.42, -0.21, -0.38), diastolic blood pressure ( r=-0.33, -0.18, -0.24), triglycerides ( r=-0.29, -0.13, -0.23), fasting insulin ( r=-0.28, -0.20, -0.29), and HOMA-IR ( r=-0.26, -0.18, -0.26) (all P<0.05), and positively correlated with high-density lipoprotein cholesterol ( r=0.38, 0.13, 0.31, all P<0.01). After adjusting for sex and age, high relative skeletal muscle mass, high muscle-to-fat ratio, and high grip strength-to-body weight index all decreased the risks of metabolic syndrome ( OR=0.80, 0.55, 0.90), obesity ( OR=0.53, 0.64, 0.82), hypertension ( OR=0.86, 0.58, 0.92), low high-density lipoprotein cholesterol ( OR=0.83, 0.62, 0.92), hypertriglyceridemia ( OR=0.88, 0.78, 0.96). After adjusting for sex and age, high relative skeletal muscle mass and high grip strength-to-body weight index all decreased the risks of hyperglycemia ( OR=0.93 and 0.95, all P<0.05). ROC curve analysis showed that the relative skeletal muscle mass, muscle-to-fat ratio, and grip strength-to-body weight index all had good predictive accuracy of metabolic syndrome in children (AUC=0.79, 0.71, 0.76), with optimal cutoff values of 40%, 1.2, and 35%, respectively. Conclusions:High relative skeletal muscle mass, high muscle-to-fat ratio, and high grip strength-to-body weight index are all protective factors for metabolic syndrome in children. Regular measurement of skeletal muscle mass and grip strength can aid in the early identification and prevention of obesity and metabolic syndrome during childhood .

OBJECTIVE: To investigate the clinical efficacy and safety of single Kirschner wire assisted poking and closed reduction in the treatment of Gartland type Ⅲ supracondylar humeral fractures in children. METHODS: A retrospective analysis was performed on patients diagnosed with Gravland type Ⅲ supracondylar humeral fractures between January 2022 and June 2023. A total of 46 patients were treated with closed reduction assisted by Kirschner wires and percutaneous Kirschner wire internal fixation.There were 25 males and 21 females. The age ranged from 5 to 10 years old, with an average of (5.8±1.8) years old. The left side was involved in 28 patients and the right side in 18 patients. Record the operative duration for patients, the number of fluoroscopic exposures, fracture healing time, postoperative carrying angle, Baumann angle, elbow joint function score at three months post-operation, and any associated complications. RESULTS: All 46 patients were followed up for a period of 12 to 16 weeks, with an average of (13.74±1.44 )weeks. The operation duration was (30.7±5.1) minutes, the fluoroscopy count was (10.2±2.7) times, the postoperative carrying angle of the elbow joint was (8.7±2.2) degrees, and the Baumann angle was (71.5±2.9) degrees. All fractures achieved successful union in all patients, with a mean healing time of (25.5±1.7) days.At the final follow-up, elbow joint function was assessed using the Flynn criteria, with 43 patients rated as excellent and 3 patients rated as good. No complications were observed, including cubitus varus, nerve injury, or local infection. CONCLUSION The use of a single Kirschner wire assisted prying reduction for treating Gartland type Ⅲ supracondylar humeral fractures in children demonstrates excellent clinical efficacy and safety.
Humans ; Male ; Female ; Child ; Bone Wires ; Child, Preschool ; Humeral Fractures/physiopathology* ; Retrospective Studies ; Fracture Fixation, Internal/instrumentation* ; Treatment Outcome ; Fracture Healing

BACKGROUND: Blood glucose and serum albumin have been associated with cardiovascular disease prognosis, but the impact of admission-blood-glucose-to-albumin ratio (AAR) on adverse outcomes in critical ill coronary artery disease (CAD) patients was not investigated. METHODS: Patients diagnosed with CAD were non-consecutively selected from the MIMIC-IV database and categorized into quartiles based on their AAR. The primary outcome was 1-year mortality, and secondary endpoints were in-hospital mortality, acute kidney injury (AKI), and renal replacement therapy (RRT). A restricted cubic splines model and Cox proportional hazard models assessed the association between AAR and adverse outcomes in CAD patients. Kaplan-Meier survival analysis determined differences in endpoints across subgroups. RESULTS: A total of 8360 patients were included. There were 726 patients (8.7%) died in the hospital and 1944 patients (23%) died at 1 year. The incidence of AKI and RRT was 63% and 4.3%, respectively. High AAR was markedly associated with in-hospital mortality (HR = 1.587, P = 0.003), 1-year mortality (HR = 1.502, P < 0.001), AKI incidence (HR = 1.579, P < 0.001), and RRT (HR = 1.640, P < 0.016) in CAD patients in the completely adjusted Cox proportional hazard model. Kaplan-Meier survival analysis noted substantial differences in all endpoints based on AAR quartiles. Stratified analysis and interaction test demonstrated stable correlations between AAR and outcomes. CONCLUSIONS The results highlight that AAR may be a potential indicator for assessing in-hospital mortality, 1-year mortality, and adverse renal prognosis in critical CAD patients.

Background Swimming is becoming increasingly popular for its combined leisure and fitness benefits. However, polluted swimming pool water may pose various health risks. Previous studies have indicated that health indicators of swimming venues have lower qualification rates compared to other public places, highlighting the urgent need to optimize hygiene management measures. Objective To assess the overall hygiene status and identify the key factors influencing water quality in Shanghai’s swimming venues from 2010 to 2024, and to provide a scientific basis for optimizing water quality management. Methods Water quality was assessed in three stages (2010—2019, 2020—2022, and 2023—2024) based on the monitoring data of Shanghai’s swimming venues (2010—2024). The influences of monitoring stage, region, season, scale, day of week, and per capita attendance on water quality were analyzed using chi-square tests and logistic regression. Results From 2010 to 2024, water quality was monitored in 1478 swimming venues. The compliance rates of turbidity, pH, urea, oxidation-reduction potential, free residual chlorine, combined residual chlorine, total bacteria, coliforms, and heat-resistant coliforms in swimming pool water, and free residual chlorine in the foot disinfection basin water samples were 99.56%, 94.73%, 73.00%, 47.50%, 56.55%, 54.12%, 97.74%, 99.49%, 99.90%, and 69.19%, respectively. The overall water quality compliance rate was 85.28%. The main non-compliant indicators for swimming pool water were urea, ORP, free residual chlorine, and combined residual chlorine, along with free chlorine residual in the foot disinfection basin water samples. Using the period before the implementation of the new national standard as reference, the results of logistic regression revealed that the qualification rates of urea, ORP, and free residual chlorine in the swimming pool water samples, as well as free residual chlorine in foot disinfection basin water samples, were significantly higher during the two phases after launching the new standard (2020–2022 and 2023–2024), and the associated odds ratios (95%CI) were 1.28 (1.00, 1.64) and 1.48 (1.12, 1.95), 13.35 (7.63, 23.37) and 10.78 (7.00, 16.60), 2.54 (1.81, 3.58) and 3.18 (2.41, 4.20), and 3.14 (2.14, 4.60) and 2.83 (1.89, 4.23), respectively. Compared with the reference group, those sampled in urban pools and in non-summer seasons showed higher qualification rates of urea and ORP; in contrast, the pools with high visitor flow showed lower qualification rates of free residual chlorine in both water samples of swimming pools and foot disinfection basins (P<0.05). Conclusion The implementation of the new national standard has effectively enhanced the management standards and significantly improved overall water quality of swimming venues in Shanghai. However, the compliance rates of free residual chlorine and urea in swimming pool water still need further improvement. The compliance of some indicators is influenced by region, season, scale, and average passenger flow, particularly in small suburban swimming venues. Future efforts should focus on refining water quality management strategies and enhancing public health management.

OBJECTIVE: As an age-related neurodegenerative disease, the prevalence of mild cognitive impairment (MCI) increases with age. Within the framework of traditional Chinese medicine, spleen-kidney deficiency syndrome (SKDS) is recognized as the most frequent MCI subtype. Due to the covert and gradual onset of MCI, in community settings it poses a significant challenge for patients and their families to discern between typical aging and pathological changes. There exists an urgent need to devise a preliminary diagnostic tool designed for community-residing older adults with MCI attributed to SKDS (MCI-SKDS). METHODS: This investigation enrolled 312 elderly individuals diagnosed with MCI, who were randomly distributed into training and test datasets at a 3:1 ratio. Five machine learning methods, including logistic regression (LR), decision tree (DT), naive Bayes (NB), support vector machine (SVM), and gradient boosting (GB), were used to build a diagnostic prediction model for MCI-SKDS. Accuracy, sensitivity, specificity, precision, F1 score, and area under the curve were used to evaluate model performance. Furthermore, the clinical applicability of the model was evaluated through decision curve analysis (DCA). RESULTS: The accuracy, precision, specificity and F1 score of the DT model performed best in the training set (test set), with scores of 0.904 (0.845), 0.875 (0.795), 0.973 (0.875) and 0.973 (0.875). The sensitivity of the training set (test set) of the SVM model performed best among the five models with a score of 0.865 (0.821). The area under the curve of all five models was greater than 0.9 for the training dataset and greater than 0.8 for the test dataset. The DCA of all models showed good clinical application value. The study identified ten indicators that were significant predictors of MCI-SKDS. CONCLUSION The risk prediction index derived from machine learning for the MCI-SKDS prediction model is simple and practical; the model demonstrates good predictive value and clinical applicability, and the DT model had the best performance. Please cite this article as: Ai YT, Zhou S, Wang M, Zheng TY, Hu H, Wang YC, Li YC, Wang XT, Zhou PJ. Development of a machine learning-based risk prediction model for mild cognitive impairment with spleen-kidney deficiency syndrome in the elderly. J Integr Med. 2025; 23(4): 390-397.
Humans ; Cognitive Dysfunction/diagnosis* ; Aged ; Male ; Female ; Machine Learning ; Spleen ; Aged, 80 and over ; Kidney ; Medicine, Chinese Traditional

OBJECTIVE: Lipid oxidation is involved in the pathogenesis of atherosclerosis and may be contribute to the development of Ischemic stroke (IS). However, the lipid profiles associated with IS have been poorly studied. We conducted a pilot study to identify potential IS-related lipid molecules and pathways using lipidomic profiling. METHODS: Serum lipidomic profiling was performed using LC-MS in 20 patients with IS and 20 age- and sex-matched healthy controls. Univariate and multivariate analyses were simultaneously performed to identify the differential lipids. Multiple testing was controlled for using a false discovery rate (FDR) approach. Enrichment analysis was performed using MetaboAnalyst software. RESULTS: Based on the 294 lipids assayed, principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) models were used to distinguish patients with IS from healthy controls. Fifty-six differential lipids were identified with an FDR-adjusted P less than 0.05 and variable influences in projection (VIP) greater than 1.0. These lipids were significantly enriched in glycerophospholipid metabolism (FDR-adjusted P = 0.009, impact score = 0.216). CONCLUSIONS Serum lipid profiles differed significantly between patients with IS and healthy controls. Thus, glycerophospholipid metabolism may be involved in the development of IS. These results provide initial evidence that lipid molecules and their related metabolites may serve as new biomarkers and potential therapeutic targets for IS.
Humans ; Pilot Projects ; Lipidomics ; Male ; Female ; Biomarkers/blood* ; Middle Aged ; Ischemic Stroke/blood* ; Aged ; China ; Lipids/blood* ; Adult ; Case-Control Studies ; East Asian People

Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.
Humans ; Schizophrenia/pathology* ; Diffusion Tensor Imaging/methods* ; Male ; Female ; Adult ; Brain/metabolism* ; Young Adult ; Middle Aged ; White Matter/pathology* ; Gene Expression ; Nerve Net/diagnostic imaging* ; Graph Neural Networks