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.

Objective To conduct a scoping review of the intervention design and application status of intelligent interactive cognitive-motor training(IICMT)in fall prevention among older adults,providing references for subsequent research in this field.Methods Following the scoping review framework,systematic searches were conducted in PubMed,Web of Science,Embase,Cochrane Library,CINAHL,Scopus,IEEE Xplore,Chinese Biomedical Literature Database,CNKI,Wanfang Database,and VIP Database from their inception to September 17,2024.The included studies were analyzed and summarized.Results A total of 19 articles were included.The main intelligent interactive technologies included virtual reality,commercial games,computer touch sensing,wearable devices,and infrared sensing,typically combining basic cognitive ability training,advanced cognitive function training,spatial cognitive training,and comprehensive application training with functional activity training such as balance and coordination,gait and strength,stair climbing,and obstacle avoidance.Assessment indicators included balance and gait,fall risk and self-efficacy,physiological indicators,muscle strength,postural stability,physical function,as well as feasibility and safety assessments.Conclusion IICMT is safe and feasible among older adults,and can improve balance and fall efficacy,reducing the risk of falls.

Objective:To explore the value of quantitative parameters of diffusion weighted imaging (DWI) combined with dynamic enhanced magnetic resonance imaging (DCE-MRI) in predicting the efficacy of neoadjuvant chemotherapy for locally advanced breast cancer (LABC) .Methods:A total of 97 patients with LABC admitted to the hospital from Mar. 2020 to Mar. 2023 were studied and received neoadjuvant chemotherapy to evaluate the therapeutic effect, and DWI and DCE-MRI scans were performed before and after treatment. The difference of DWI and DCE-MRI quantitative parameters before treatment in patients with different therapeutic effects was compared, and the correlation between the difference of DWI and DCE-MRI quantitative parameters and therapeutic effect was analyzed. The predictive value of quantitative parameters of DWI and DCE-MRI before treatment was analyzed. The quantitative parameters of DWI and DCE-MRI in patients with different pathological reactions were compared before treatment, and the quantitative parameters of DWI and DCE-MRI were compared before and after treatment.Results:The apparent diffusion coefficient (ADC) of patients with effective chemotherapy before treatment was higher, but transport constant (Ktrans) , extracellular space volume percentage (Ve) and rate constant (Kep) were lower ( t=5.0, 3.27, 3.55, 3.89, P < 0.05) ; Spearman correlation analysis showed that ADC was positively correlated with chemotherapy efficacy before treatment (r=0.66; P < 0.05) , while Kep, Ve, Ktrans were negatively correlated with it (r=-0.58, -0.47, -0.60; P < 0.05) ; ROC curves showed that the area under the curve (AUC) values of ADC, Kep, Ve and Ktrans in predicting chemotherapy efficacy before treatment were 0.771, 0.797, 0.664 and 0.715, respectively, while the combined AUC value of each indicator was 0.832; Compared with patients with non-significant pathological response, ADC before treatment was higher in patients with significant pathological response, Kep, Ve and Ktrans were lower ( t=4.46, 3.32, 3.60, 3.95, P < 0.05) ; Compared with before treatment, ADC value increased after treatment, while Kep, Ve and Ktrans decreased ( t=8.77, 6.22, 9.34, 10.26, P < 0.05) . Conclusion:Quantitative parameters of DWI and DCE-MRI can reflect the changes in the condition of patients with locally advanced neoadjuvant chemotherapy, and the combination of the two can help to improve the predictive value of chemotherapy efficacy in patients.

Objective:To explore the value of material separation technique based on energy spectrum CT in predicting the recurrence of osteoporotic vertebral fractures after operation, in order to provide reference for the evaluation of postoperative recurrence of fractures.Methods:A total of 80 cases with percutaneous vertebroplasty (PVP) treated in the First People’s Hospital of Shangqiu, Henan Province from Jan. 2020 to Dec. 2021 were selected for pre-operative CT material separation examination and postoperative follow-up for at least 2 years. The patients were divided into recurrence group and no recurrence group according to the recurrence of fracture. The clinical data and the results of material separation technique by energy spectrum CT were compared. The relevant factors for fracture recurrence was assessed by Logistic regression analysis, and ROC curve was used to evaluate the predictive value of material separation technique based on energy spectrum CT in the recurrence of fracture.Results:After a follow-up of at least 2 years, 26 of the 80 patients had a recurrence of the fracture. The age, calcium water and hydroxyapatite (HAP) -water values in recurrent group were higher than those in no recurrent group, and the proportion of bone cement leakage was higher ( t/ Fisher=2.57, 5.40, 3.96, - P = 0.012 < 0.001, < 0.001, 0.033) . Logistic regression analysis showed that calcium-water ( OR=2.321, 95%CI: 1.464-3.679) and HAP-water ( OR=1.784, 95%CI: 1.246-2.554) values were the factors for postoperative fracture recurrence in osteoporotic fractures ( P<0.001) . ROC curve showed that AUC, sensitivity and specificity of combined calcium-water and HAP-water values in predicting postoperative fracture recurrence were 0.868, 88.46% and 79.63%, respectively ( P<0.001) . Conclusions:The material separation technique based on energy spectrum CT in predicting the recurrence of osteoporotic vertebral fractures after operation has high clinical application value, and provides a new idea for the clinical evaluation of postoperative recurrence of fractures.

Anti-leucine-rich glioma-inactivated 1 protein (LGI1) antibody-associated encephalitis is an autoimmune encephalitis mediated by LGI1 antibodies, which can occur in both adults and children. Its common clinical manifestations include epileptic seizures, cognitive and psychiatric disorders; rare symptoms include sleep disorders and autonomic disorders; and its characteristic manifestations are faciobrachial dystonic seizures and refractory hyponatremia. Since anti-LGI1 antibody-associated encephalitis is relatively rare in clinical practice, this article reviews the disease in terms of etiology and pathogenesis, clinical manifestations, auxiliary examinations, diagnosis and differential diagnosis, treatment, recurrence and prognosis. It aims to improve clinicians′ understanding of this disease, provide references for its early diagnosis and treatment, and thereby improve patients′ prognosis.

Objective:To explore the value of quantitative parameters of diffusion weighted imaging (DWI) combined with dynamic enhanced magnetic resonance imaging (DCE-MRI) in predicting the efficacy of neoadjuvant chemotherapy for locally advanced breast cancer (LABC) .Methods:A total of 97 patients with LABC admitted to the hospital from Mar. 2020 to Mar. 2023 were studied and received neoadjuvant chemotherapy to evaluate the therapeutic effect, and DWI and DCE-MRI scans were performed before and after treatment. The difference of DWI and DCE-MRI quantitative parameters before treatment in patients with different therapeutic effects was compared, and the correlation between the difference of DWI and DCE-MRI quantitative parameters and therapeutic effect was analyzed. The predictive value of quantitative parameters of DWI and DCE-MRI before treatment was analyzed. The quantitative parameters of DWI and DCE-MRI in patients with different pathological reactions were compared before treatment, and the quantitative parameters of DWI and DCE-MRI were compared before and after treatment.Results:The apparent diffusion coefficient (ADC) of patients with effective chemotherapy before treatment was higher, but transport constant (Ktrans) , extracellular space volume percentage (Ve) and rate constant (Kep) were lower ( t=5.0, 3.27, 3.55, 3.89, P < 0.05) ; Spearman correlation analysis showed that ADC was positively correlated with chemotherapy efficacy before treatment (r=0.66; P < 0.05) , while Kep, Ve, Ktrans were negatively correlated with it (r=-0.58, -0.47, -0.60; P < 0.05) ; ROC curves showed that the area under the curve (AUC) values of ADC, Kep, Ve and Ktrans in predicting chemotherapy efficacy before treatment were 0.771, 0.797, 0.664 and 0.715, respectively, while the combined AUC value of each indicator was 0.832; Compared with patients with non-significant pathological response, ADC before treatment was higher in patients with significant pathological response, Kep, Ve and Ktrans were lower ( t=4.46, 3.32, 3.60, 3.95, P < 0.05) ; Compared with before treatment, ADC value increased after treatment, while Kep, Ve and Ktrans decreased ( t=8.77, 6.22, 9.34, 10.26, P < 0.05) . Conclusion:Quantitative parameters of DWI and DCE-MRI can reflect the changes in the condition of patients with locally advanced neoadjuvant chemotherapy, and the combination of the two can help to improve the predictive value of chemotherapy efficacy in patients.

Objective:To explore the value of material separation technique based on energy spectrum CT in predicting the recurrence of osteoporotic vertebral fractures after operation, in order to provide reference for the evaluation of postoperative recurrence of fractures.Methods:A total of 80 cases with percutaneous vertebroplasty (PVP) treated in the First People’s Hospital of Shangqiu, Henan Province from Jan. 2020 to Dec. 2021 were selected for pre-operative CT material separation examination and postoperative follow-up for at least 2 years. The patients were divided into recurrence group and no recurrence group according to the recurrence of fracture. The clinical data and the results of material separation technique by energy spectrum CT were compared. The relevant factors for fracture recurrence was assessed by Logistic regression analysis, and ROC curve was used to evaluate the predictive value of material separation technique based on energy spectrum CT in the recurrence of fracture.Results:After a follow-up of at least 2 years, 26 of the 80 patients had a recurrence of the fracture. The age, calcium water and hydroxyapatite (HAP) -water values in recurrent group were higher than those in no recurrent group, and the proportion of bone cement leakage was higher ( t/ Fisher=2.57, 5.40, 3.96, - P = 0.012 < 0.001, < 0.001, 0.033) . Logistic regression analysis showed that calcium-water ( OR=2.321, 95%CI: 1.464-3.679) and HAP-water ( OR=1.784, 95%CI: 1.246-2.554) values were the factors for postoperative fracture recurrence in osteoporotic fractures ( P<0.001) . ROC curve showed that AUC, sensitivity and specificity of combined calcium-water and HAP-water values in predicting postoperative fracture recurrence were 0.868, 88.46% and 79.63%, respectively ( P<0.001) . Conclusions:The material separation technique based on energy spectrum CT in predicting the recurrence of osteoporotic vertebral fractures after operation has high clinical application value, and provides a new idea for the clinical evaluation of postoperative recurrence of fractures.

Cancer patients often experience worsening pain due to the limitations of traditional pharmacological treatments. This paper explores the innovative application of multi-agent systems in this field. As a key component of artificial intelligence, multi-agent systems assist healthcare providers in making intelligent decisions and interventions based on patients' conditions, and can collaborate with other smart devices to provide personalized care. This review discusses the concept of multi-agent systems, their application in pain management, and the potential challenges and countermeasures, aiming to provide guidance for the intelligent management of cancer patient pain.

Anti-leucine-rich glioma-inactivated 1 protein (LGI1) antibody-associated encephalitis is an autoimmune encephalitis mediated by LGI1 antibodies, which can occur in both adults and children. Its common clinical manifestations include epileptic seizures, cognitive and psychiatric disorders; rare symptoms include sleep disorders and autonomic disorders; and its characteristic manifestations are faciobrachial dystonic seizures and refractory hyponatremia. Since anti-LGI1 antibody-associated encephalitis is relatively rare in clinical practice, this article reviews the disease in terms of etiology and pathogenesis, clinical manifestations, auxiliary examinations, diagnosis and differential diagnosis, treatment, recurrence and prognosis. It aims to improve clinicians′ understanding of this disease, provide references for its early diagnosis and treatment, and thereby improve patients′ prognosis.

Cancer patients often experience worsening pain due to the limitations of traditional pharmacological treatments. This paper explores the innovative application of multi-agent systems in this field. As a key component of artificial intelligence, multi-agent systems assist healthcare providers in making intelligent decisions and interventions based on patients' conditions, and can collaborate with other smart devices to provide personalized care. This review discusses the concept of multi-agent systems, their application in pain management, and the potential challenges and countermeasures, aiming to provide guidance for the intelligent management of cancer patient pain.