Although global brain science research has progressed rapidly in recent decades, several fundamental questions in neuroscience remain unresolved. In particular, the physical mechanism underlying neural signal transmission remains controversial, and the carriers responsible for neural information storage and retrieval have not yet been fully clarified. These unresolved issues motivate us to re-examine the processes of neural information generation, transmission, integration, storage, and retrieval from multiple perspectives. A key observation is that neural electromagnetic activities are closely associated with time. Their duration, temporal structure, and dynamic evolution play crucial roles in neural information processing. In this work, we analyze neural electromagnetic activities from the perspective of temporal scales (referred to here as the “time course”). By reviewing and integrating findings from previous studies, we examine the characteristic time requirements and dynamic features of neural processes occurring at different stages of information processing. These stages include neural signal generation, signal transmission along axons, synaptic integration, synaptic plasticity, and memory formation and retrieval. Based on this temporal analysis, we outline a framework describing neural electromagnetic activities across a wide range of time scales, spanning from microseconds to minutes, hours, or even longer periods associated with long-term memory, which suggests that neural information processing involves multiple physical processes operating at different time levels. Rapid electromagnetic events may occur on microsecond scales, whereas electrophysiological phenomena such as action potentials typically last on the order of milliseconds. Longer time scales are associated with synaptic plasticity and memory-related processes. From this perspective, we propose that the physical carrier of neural information may be transient electromagnetic pulses with durations on the microsecond scale. In this framework, action potentials can be interpreted as the macroscopic electrophysiological manifestation of underlying electromagnetic processes triggered by ionic currents across neuronal membranes. Rather than being the fundamental neural signal itself, the action potential may represent a measurable membrane-level response associated with the successful activation of these electromagnetic events. Moreover, we discuss a possible mechanism for long-term memory storage. Considering the apparent temporal contradiction between the millisecond-scale excitation of neurons and the long-term persistence of memories, we believe that long-term memory information may be stored within neural network topologies formed by electrical synapse coupling. Such structures, referred to as electrically coupled memory networks (ECMNs), may enable neurons within the same network to respond rapidly and synchronously to stimuli, thereby facilitating efficient memory retrieval. Overall, this study emphasizes the importance of considering the temporal organization of neural electromagnetic activities when interpreting neural signaling mechanisms. It may provide new insights into the physical nature of neural information carriers and the mechanisms of memory storage and retrieval. Furthermore, highlighting the potential role of electromagnetic interactions in neural activity may contribute to the development of new theoretical frameworks and experimental approaches in neuroscience. Such perspectives may also offer valuable references for future research on neural coding, brain function mechanisms, and neuromodulation technologies.

ObjectiveTo analyze the output and efficiency of chronic disease management services for hypertension and diabetes in community health service institutions in Shanghai, to evaluate the allocation and efficiency of chronic disease management resources, and to provide references for improving the efficiency of chronic disease management in Shanghai’s communities. MethodsA total of 73 community health institutions from 15 districts of Shanghai were selected. Based on the chronic disease management resource data of community health institutions in 2019, 2021, and 2023, descriptive methods and the BCC model in Data Envelopment Analysis (DEA) were used to analyze the resource allocation, service output, and the efficiency of chronic disease management services in community health institutions in Shanghai. ResultsFrom 2019 to 2023, the indicators of resource allocation and service output of community health service institutions in Shanghai all showed upward trends. In terms of service efficiency, the average number of chronic disease patients managed by each health technician decreased from 237 to 231, and the number of patients served per 1 000 working hours decreased from 54 to 44. DEA efficiency analyses showed that the average overall efficiency increased from 0.867 in 2019 to 0.886 in 2021 and then decreased to 0.811 in 2023. The average technical efficiency decreased from 0.901 to 0.847, which was the main reason for the decline in overall efficiency. The average scale efficiency showed an initial increase followed by a decrease, rising from 0.961 in 2019 to 0.977 in 2021 and dropping back to 0.955 in 2023. The number of strong effective districts (overall efficiency=1.000) decreased from 6 in 2019 to 3 in 2023, and the efficiency differences between districts expanded. The range of overall efficiency widened from 0.622‒1.000 to 0.513‒1.000, indicating a decline in overall efficiency and an increase in inter-district heterogeneity. ConclusionFrom 2019 to 2023, the total volume of resource allocation and service output for chronic disease management in community health service institutions in Shanghai increased continuously. However, both the service output efficiency and the overall resource allocation efficiency declined, driven primarily by a decrease in technical efficiency, and accompanied by widening inter‑district disparities.

Thyroid diseases are common clinical endocrine disorders， and their pathogenesis is generally considered to be closely related to genetic predisposition factors， immune system disorders， hormone levels， etc. Xiaoyaosan is widely used in the treatment of various thyroid diseases with excellent effects. This study summarized the relevant literature on the treatment of thyroid diseases with modified Xiaoyaosan prescriptions and their active ingredients from aspects such as theoretical analysis， clinical research， and mechanism research. Theoretical analysis revealed that Xiaoyaosan could not only disperse stagnated liver qi but also replenish deficient spleen Qi， which was consistent with the etiology and pathogenesis of thyroid diseases. Clinical studies found that Xiaoyaosan and its modified prescriptions could be widely used in the treatment of multiple thyroid diseases， such as hyperthyroidism， Hashimoto's thyroiditis， and thyroid nodules. Both the use of modified Xiaoyaosan alone and in combination with medications such as methimazole， propylthiouracil， and euthyrox could effectively improve patients' clinical symptoms. In the mechanism research， this study discovered that the whole formula of Xiaoyaosan and its modified prescriptions could inhibit inflammatory reactions， regulate immune balance， and delay liver damage during the treatment of thyroid diseases. The research on Xiaoyaosan for treating thyroid diseases mainly focused on thyroid cancer， autoimmune thyroiditis， hyperthyroidism， and hypothyroidism. The mechanisms of action mainly involved promoting cell apoptosis， inhibiting cell proliferation and migration， arresting the cell cycle， and regulating thyroid hormone levels. In conclusion， this study systematically combs and summarizes the research status of Xiaoyaosan in treating thyroid diseases through literature retrieval， aiming to provide new perspectives and new ideas for the prevention and treatment of thyroid diseases with traditional Chinese medicine.

Objective:To explore the value of the vesical imaging-reporting and data system (VI-RADS) score based on multiparametric MRI (mpMRI) combined with quantitative tumor MRI parameters in assessing the muscle invasion of bladder cancer.Methods:The study was a case-control study. The data of 87 bladder cancer patients confirmed by pathology who underwent mpMRI of the bladder were retrospectively collected from the First Medical Center of Chinese PLA General Hospital between January 2019 and April 2023 The pathological findings were used as the gold standard to categorize them into the muscle invasive bladder cancer (MIBC) group (29 cases) and non-muscle invasive bladder cancer (NMIBC) group (58 cases). Quantitative parameters were measured based on preoperative mpMRI images, including the length of tumor bladder wall contact, the perpendicular distance between the bladder tumor and the tangent of the bladder wall, the maximal diameter of the bladder tumor, and the volume of the bladder tumor. Bladder cancer was classified according to the VI-RADS scoring criteria. The Mann-Whitney U test was used for intergroup comparisons. Multivariate logistic regression analysis was performed to obtain the independent risk factors related to muscle invasion of bladder cancer and to establish the model. The receiver operating characteristic curves were analyzed for MRI quantitative parameters and logistic regression models, and area under the curve (AUC) comparisons were performed using the DeLong test. Results:The differences in tumor bladder wall contact length, perpendicular distance from the tumor to the tangent line of the bladder wall, maximum diameter, bladder tumor volume, and the VI-RADS scores were statistically significant between the MIBC group and the NMIBC group ( P<0.05). Multifactorial logistic regression analysis showed that tumor bladder wall contact length ( OR=21.07, 95% CI 3.56-124.89, P=0.001) and VI-RADS score ( OR=11.90, 95% CI 3.53-40.12, P<0.001) were the independent risk factors for evaluating the muscle invasion of bladder cancer. The difference between the VI-RADS score and the tumor bladder wall contact length for assessing muscular infiltration of bladder cancer had AUCs of 0.802 (95% CI 0.704-0.899) and 0.759 (95% CI 0.652-0.865). The combined model of VI-RADS score combined with tumor bladder wall contact length had an AUC of 0.891 (95% CI 0.812-0.970), which was higher than the diagnostic efficacy of applying tumor bladder wall contact length or VI-RADS score alone ( Z=3.05, 2.37, P=0.002, 0.018). Conclusion:Tumor contact length with the bladder wall is an independent risk factor for assessing muscle invasion of bladder cancer and the combination of VI-RADS score may enhances diagnostic accuracy.

In recent years,there has been a significant rise in the incidence of peripheral nerve injury(PNI),highlighting the urgent need for effective treatment strategies.The inflammation and pain hypersensitivity associated with PNI greatly diminish patients'quality of life.Although there are promising treatment approaches for nerve injury,the com-plex pathological mechanisms underlying neuropathic pain caused by PNI present significant challenges for clinical manage-ment.Extensive research has established that the development of neuropathic pain is closely linked to nerve conduction and related signaling molecules.Among these,P2X4 receptor(P2X4R),an ATP-dependent ion channel,is involved in nerve signal transmission and associated pathways-plays a crucial role in the progression of neuropathic pain.This article offers a comprehensive overview of the function and distribution of P2X4R,investigates its pathological mechanisms in PNI-induced neuropathic pain,and elucidates its relationship with peripheral neuropathic pain disorders.Through this explo-ration,we aim to provide valuable insights that could inform the development of novel clinical strategies for pain management.

Objective To construct a mammography image classification assistant system suitable for Chinese population,and explore the potential of artificial intelligence technology to assist early screening of breast cancer in China.Methods Curated breast imaging subset of digital database for screening mammography(CBIS-DDSM),Mammographic image analysis society database(MIAS)and other international open datasets were used to conduct model training respectively in order to reproduce the mainstream in-depth learning methods in the current literature.The model was also tested on the Chinese breast mammography database(CBMD)provided by Huajiao Technology Co.,Ltd,and the performance was compared.Aiming at the problem that the Chinese population data are not ideal in the performance test of the open dataset training model,an optimization strategy based on the sliding window adjustment mechanism was implemented in combination with the characteristics of Chinese population data.Then a two-stage migration learning method was designed to improve the overall performance of the model,and then development of our system was carried out.Results With the sliding window adjustment mechanism and the CBMD training model after two-stage transfer learning,the accuracy of our developed system was improved from 0.50 of the open datasets to 0.80,precision from 0.54 to 0.82,sensitivity from 0.52 to 0.80,F1 value from 0.52 to 0.80,and AUC value from 0.51 to 0.89 based on the Chinese population dataset as the test set.Conclusion Through the introduction of sliding window adjustment mechanism and two-stage migration learning strategy,the performance of the breast molybdenum target image classification model has been significantly improved in the Chinese population dataset,and our system primarily achieves the purpose of assisting the classification of breast molybdenum target images for the Chinese population.

Objective To verify the accuracy of a Non-Contact Sleep Monitoring Mattress(NCSMM)based on body movement during sleep in assessing sleep quality of patients before neurosurgery in order to provide a more portable and efficient assessment tool for clinical staff.Methods A single-arm trial was conducted on 114 inpatients admitted in our department selected with convenience sampling.Sleep quality data of 1 night were collected through 5 sleep quality assessment tools,including NCSMM,polysomnography(PSG),Patient-Reported Outcome Measurement Information System(PROMIS)Sleep Disturbance scale,Richards-Campbell Sleep Scale(RCSQ),and a wearable device(smart watch for body movements and sleep quality monitoring).The sleep efficiency(≤85%)obtained by PSG was used as the diagnostic standard for sleep disorders.The area under the receiver operating characteristic curve(AUC),sensitivity,specificity,positive predictive value,negative predictive value,and Youden index were calculated for the other 4 tools to evaluate and compare their diagnostic effectiveness.Results The AUC value for NCSMM,PROMIS,RCSQ and smart watch was 0.788(95%CI:0.687～0.888,P<0.001),0.664(95%CI:0.543～0.784,P=0.02),0.723(95%CI:0.600～0.846,P=0.001)and 0.750(95%CI:0.654～0.846,P<0.001),respectively.The diagnostic accuracy rate was 0.774,0.559,0.742 and 0.602,with corresponding Youden index value of 0.488,0.321,0.456,and 0.459.NCSMM demonstrated the best AUC value,sensitivity and Youden index when compared with the other 3 tools.Conclusion NCSMM shows high accuracy in assessing sleep quality in pre-neurosurgery inpatients,and it is a viable portable and efficient assessment tool in clinical practice.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

The aim of this updated guideline is to provide comprehensive recommendations for the management of gout in patients with common comorbidities, such as chronic kidney disease(CKD), cardiovascular disease(CVD), diabetes, osteoarthritis(OA), and gastrointestinal disorders. This guideline was developed by a multidisciplinary expert panel consisting of specialists in endocrinology, rheumatology, nephrology, cardiology, gastroenterology, and methodology. The development process adhered to standard methodologies, including PICO(population, intervention, comparator, and outcomes) question deconstruction, systematic literature review, the Grading of Recommendations Assessment, Development and Evaluation(GRADE) for evidence and recommendation evaluation, Delphi voting, and expert consensus. The guideline presents 26 evidence-based recommendations addressing 7 clinical questions for patients with hyperuricemia and gout in the context of comorbidities. Key recommendations include the maintenance of strict serum urate targets, particularly for patients with CKD stage≥3, chronic gouty arthritis, and OA, in order to prevent disease progression. In patients with CVD or diabetes, intra-articular triamcinolone is preferred over systemic glucocorticoids. Prioritized anti-inflammatory treatments for patients with CKD, gastrointestinal diseases and OA are recommended. The guideline also introduces emerging therapies, such as interleukin-1 inhibitors and selective urate transport inhibitors, as potential treatment options for refractory cases. The update offers a comprehensive, patient-centered approach to managing gout, particularly in individuals with associated comorbidities. Multidisciplinary collaboration and emerging new treatments and evidence ensure the optimization of the recommendations.

To investigate the molecular mechanisms underlying the mechanosensitive ion channel PI-EZO2 in osteoarthritis(OA),we developed a lentiviral vector for endogenous PIEZO2 overexpression and established a stable PIEZO2-high-expressing immortalized human primary chondrocyte line.By map-ping the open reading frame of the PIEZO2 locus and designing sequence-specific sgRNA,we employed the CRISPR/Cas9 synergistic activation mediator(SAM)system to precisely integrate transcriptional ac-tivation elements into the PIEZO2 promoter region.Lentiviral-mediated targeted genomic integration en-sured endogenous PIEZO2 overexpression,confirmed by mCherry fluorescence tracing coupled with flow cytometric sorting,which revealed membrane-specific localization of PIEZO2 protein(localization effi-ciency:78.49％).Quantitative PCR demonstrated a 17-fold upregulation of PIEZO2 mRNA,while Western blotting validated enhanced membrane-localized protein expression.Strikingly,PIEZO2-overex-pressing chondrocytes exhibited hallmark OA metabolic phenotypes compared to wild-type controls:typeⅡ collagen mRNA expression decreased to 50％of baseline levels,whereas matrix metalloproteinase 13(MMP13)mRNA surged by 20-fold.These alterations recapitulated the pathological matrix metabolic phenotype observed in biomechanical OA models induced by cyclic mechanical stress(10％strain,0.5 Hz,8 h/day for 2 consecutive days).Collectively,we successfully generated a human chondrocyte model with stable PIEZO2 overexpression,which faithfully mirrors mechanotransduction-driven OA progression.This engineered cellular system provides a robust platform for dissecting PIEZO2-mediated mechanosig-naling networks and advancing targeted therapeutic discovery.