Brain-computer interface (BCI) is a neuro-engineering technology that establishes a direct communication pathway between the brain and external devices. It can realize bidirectional information interaction between the brain and external devices through real-time acquisition and decoding of brain signals, thereby completing brain state recognition and precise feedback regulation. This technology is promoting the transformation of the diagnosis and treatment model of neuropsychiatric diseases from “open-loop stimulation” to “closed-loop adaptation”. The brain regions and circuits involved in the neural mechanisms of addiction and sleep disorder are highly intertwined. Sleep-related brain rhythms provide a key time window for the intervention of addiction memory, and both have abnormal electrophysiological activities and functional imbalances in core neural circuits. This article reviews the latest research progress of closed-loop BCI in the fields of addiction and sleep disorder, explains the neural circuits and electrophysiological mechanisms of its regulation, and its limitations in diagnosis and treatment of the two diseases. In addition, it prospects individualized closed-loop intervention from the perspective of brain-body interaction, so as to provide reference for the clinical transformation of closed-loop BCI.

Abstract To promote adolescents active participation in physical activity and improve sleep quality, the article analyzes the relationship of adolescent physical activity with subjective sleep satisfaction, sleep latency, sleep continuity, sleep efficiency, and sleep duration. It explores potential mechanisms underlying the link between physical activity and sleep quality, including physiological mechanisms (circadian rhythms, body temperature, neuroendocrine systems, and immune function), and psychological mechanisms (stress relief, improvement of negative emotions, and promotion of mental relaxation). Based on existing research, it is recommended that adolescents engage in moderate to vigorous physical activity daily to promote improved sleep quality.

Electromagnetic fields can regulate the fundamental biological processes involved in bone remodeling. As a non-invasive physical therapy, electromagnetic fields with specific parameters have demonstrated therapeutic effects on bone remodeling diseases, such as fractures and osteoporosis. Electromagnetic fields can be generated by the movement of charged particles or induced by varying currents. Based on whether the strength and direction of the electric field change over time, electromagnetic fields can be classified into static and time-varying fields. The treatment of bone remodeling diseases with static magnetic fields primarily focuses on fractures, often using magnetic splints to immobilize the fracture site while studying the effects of static magnetic fields on bone healing. However, there has been relatively little research on the prevention and treatment of osteoporosis using static magnetic fields. Pulsed electromagnetic fields, a type of time-varying field, have been widely used in clinical studies for treating fractures, osteoporosis, and non-union. However, current clinical applications are limited to low-frequency, and research on the relationship between frequency and biological effects remains insufficient. We believe that different types of electromagnetic fields acting on bone can induce various “secondary physical quantities”, such as magnetism, force, electricity, acoustics, and thermal energy, which can stimulate bone cells either individually or simultaneously. Bone cells possess specific electromagnetic properties, and in a static magnetic field, the presence of a magnetic field gradient can exert a certain magnetism on the bone tissue, leading to observable effects. In a time-varying magnetic field, the charged particles within the bone experience varying Lorentz forces, causing vibrations and generating acoustic effects. Additionally, as the frequency of the time-varying field increases, induced currents or potentials can be generated within the bone, leading to electrical effects. When the frequency and power exceed a certain threshold, electromagnetic energy can be converted into thermal energy, producing thermal effects. In summary, external electromagnetic fields with different characteristics can generate multiple physical quantities within biological tissues, such as magnetic, electric, mechanical, acoustic, and thermal effects. These physical quantities may also interact and couple with each other, stimulating the biological tissues in a combined or composite manner, thereby producing biological effects. This understanding is key to elucidating the electromagnetic mechanisms of how electromagnetic fields influence biological tissues. In the study of electromagnetic fields for bone remodeling diseases, attention should be paid to the biological effects of bone remodeling under different electromagnetic wave characteristics. This includes exploring innovative electromagnetic source technologies applicable to bone remodeling, identifying safe and effective electromagnetic field parameters, and combining basic research with technological invention to develop scientifically grounded, advanced key technologies for innovative electromagnetic treatment devices targeting bone remodeling diseases. In conclusion, electromagnetic fields and multiple physical factors have the potential to prevent and treat bone remodeling diseases, and have significant application prospects.

Background: Optimizing cultivation techniques for traditional Chinese medicine has become a crucial means to improve the quality of medicinal materials. Microbial agents, as environmentally friendly and efficient plant growth promoters and soil conditioners, have increasingly attracted attention in eco-agriculture research. Objective: Our understanding remains limited regarding how the application of microbial agents, alone or in combination, affects changes in the rhizosphere microbiome and its association with the bioactive components of medicinal materials. Methods: In this study, Epimedium pubescens Maxim. was employed as a model plant to examine the effects of 2 microbial agents(Paenibacillus mucilaginosus and Bacillus subtilis) applied individually and in combination on plant growth and the accumulation of bioactive components. Additionally, this study explored the relationship between the rhizosphere microbiome and plant development. Results: The application of microbial agents increased the yield of E. pubescens leaves by 20.30% to 33.66% and enhanced the total flavonol glycosides content by 11.40% to 29.94%. Meanwhile, microbial treatments reshaped the rhizosphere microbiome, promoted the enrichment of beneficial microorganisms (e.g., Frankia and Paenibacillus), suppressed phytopathogenic fungi such as Didymella and Scytalidium, and enhanced the stability of the soil microbial co-occurrence network. The partial least squares path model suggested that microbial agents not only directly impact the quality of medicinal herbs but also indirectly alter the accumulation of bioactive components by modulating the soil microbiome. Conclusion: These findings deepen our understanding of the relationship between medicinal plant quality and rhizosphere microbiomes as mediated by microbial agents. They also provide a basis for designing and manipulating synthetic microbial communities to promote sustainable development in eco-agriculture.

Objective To investigate the associations between unilateral or bilateral hearing loss and 12 chronic diseases as well as multimorbidity among the oldest old in China,and to identify disparities in these associations of left-and right-side hearing loss with chronic diseases.Methods Totally 7 437 people aged ≥80 years old were selected from the Chinese Longitudinal Health and Longevity Survey(CLHLS)2018 cross-sectional data.With 12 chronic diseases and multimorbidity as outcome variables,the hearing loss as explanatory variable,socio-demographic characteristics,family factors,and lifestyle as covariates,the correlations of unilateral(left-or right-side)and bilateral hearing loss with chronic diseases and multimorbidity were analyzed using multivariate logistic regression model,and the trend analyses were carried out.Results There were 205(2.76％),227(3.05％)and 3 598(48.38％)old people with left-side,right-side and bilateral hearing loss,respectively.After adjusting for confounders,the oldest old with left-sided or bilateral hearing loss had a greater risk of multimorbidity compared with those with normal hearing function,with odds ratio(95％confidence interval)of 2.14(1.58-2.90)and 1.27(1.13-1.43),respectively,while no association between right-sided hearing loss and multimorbidity was observed(P＞0.05).Trend analysis showed that the risk of multimorbidity increased with hearing loss from none to unilateral and then to bilateral(P＜0.001).Conclusion Hearing loss may be related to the increased risk of multimorbidity in the oldest old,and the risk of those with bilateral hearing loss is higher.More attention should be paid to the prevention and treatment of hearing loss in the oldest old.

Forensic medicine has been designated as a first-level discipline,presenting new opportunities and challenges for the development of forensic medicine.Since the 1980s,the establishment of foren-sic medicine discipline and the cultivation of high-level forensic talents have become hot topics in the development of forensic medicine in China.Since the 13th Five-Year Plan,the forensic team of Shanxi Medical University has been aiming at the forefront,proposing the development goals of"Five First-class"and the discipline development path"Six Major Achievements".It has selected benchmark disci-plines,identified gaps in disciplinary development,unified thoughts,formulated completion timelines,concentrated superior resources,assigned tasks to individuals,and created an"Organized Fill-in-the-Blank Format"forensic medicine discipline construction model with the characteristics of the new era.The construction model of forensic medicine has achieved good results in the goals,discipline frame-work,scientific research,talent cultivation,discipline team and platform construction,forming a rela-tively complete discipline construction and management system,and accumulating valuable experience for the construction of first-level discipline and high-level talent cultivation of forensic medicine.

Objective To explore a deep learning-based automatic bone age estimation model for elbow joint X-ray images of Chinese Han adolescents and children and evaluate its performance.Methods A total of 943(517 males and 426 females)elbow joint frontal view X-ray images of Chinese Han ado-lescents and children aged 6.00 to<16.00 years were collected from East,South,Central and North-west China.Three experimental schemes were adopted for bone age estimation.Scheme 1:Directly in-put preprocessed images into the regression model;Scheme 2:Train a segmentation network using"key elbow joint bone annotations"as labels,then input segmented images into the regression model;Scheme 3:Train a segmentation network using"full elbow joint bone annotations"as labels,then in-put segmented images into the regression model.For segmentation,the optimal model was selected from U-Net,UNet++and TransUNet.For regression,VGG16,VGG19,InceptionV2,InceptionV3,ResNet34,ResNet50,ResNet101 and DenseNet121 models were selected for bone age estimation.The dataset was randomly split into 80%(754 samples)for training and validation for model fitting and hyperparameter tuning,and 20%(189 samples)as an internal test set to test the performance of the trained model.An additional 104 elbow joint X-ray images from the same demographic and age group were col-lected and used as an external test set.Model performance was evaluated by comparing the mean ab-solute error(MAE),root mean square error(RMSE),accuracies within±0.7 years(P±0.7 years)and±1.0 years(P±1.0 years)between the estimated age and the actual age,and by drawing radar charts,scat-ter plots,and heatmaps.Results When segmented with Scheme 3,the UNet++model achieved good segmentation performance with a segmentation loss of 0.000 4 and an accuracy of 93.8%at a learning rate of 0.000 1.In the internal test set,the DenseNet121 model with Scheme 3 yielded the best results with MAE,P±0.7 years and P±1.0 years being 0.83 years,70.03%,and 84.30%,respectively.In the external test set,the DenseNet121 model with Scheme 3 also performed best,with an average MAE of 0.89 years and an average RMSE of 1.00 years.Conclusion When performing automatic bone age estima-tion using elbow joint X-ray images in Chinese Han adolescents and children,it is recommended to use the UNet++model for segmentation.The DenseNet121 model with Scheme 3 achieves optimal per-formance.Using segmentation networks,especially that trained with annotation areas encompassing the full elbow joint including the distal humerus,proximal radius,and proximal ulna,can improve the ac-curacy of bone age estimation based on elbow joint X-ray images.

Objective To explore a deep learning network model suitable for bone age estimation using shoulder joint X-ray images in Chinese Han adolescents.Methods A retrospective collection of 1 286 shoulder joint X-ray images of Chinese Han adolescents aged 12.0 to<18.0 years(708 males and 578 females)was conducted.Using random sampling,approximately 80%of the samples(1 032 cases)were selected as the training and validation sets for model learning,selection and optimization,and the other 20%samples(254 cases)were used as the test set to evaluate the model's generalization ability.The original single-channel shoulder joint X-ray images and dual-channel inputs combining original images with segmentation labels(manually annotated shoulder joint regions multiplied pixel-by-pixel with original images,followed by segmentation via the U-Net++network to retain only key shoulder joint region information)were respectively input into four network models,namely VGG16,ResNet18,ResNet50 and DenseNet121 for bone age estimation.Additionally,manual bone age estimation was con-ducted on the test set data,and the results were compared with the four network models.The mean absolute error(MAE),root mean square error(RMSE),coefficient of determination(R2),and Pear-son correlation coefficient(PCC)were used as main evaluation indicators.Results In the test set,the bone age estimation results of the four models with dual-channel input of shoulder joint X-ray images outperformed those with single-channel input in all four evaluation indicators.Among them,DenseNet121 with dual-channel input achieved best results with MAE of 0.54 years,RMSE of 0.82 years,R2 of 0.76,and PCC(r)of 0.88.Manual estimation yielded an MAE of 0.82 years,ranking second only to dual-channel DenseNet121.Conclusion The DenseNet121 model with dual-channel input combined with original images and segmentation labels is superior to manual evaluation results,and can effectively estimate the bone age of Chinese Han adolescents.

Objective To construct an aptamer-functionalized carboxylated graphene oxide(CGO)fluo-rescent sensor to achieve highly sensitive and specific detection of ketamine(KET)and its metabolite norketamine(NK)using an aptamer capable of simultaneously recognizing KET and NK.Methods A specific aptamer for simultaneous recognition of KET and NK was screened using graphene oxide-sys-tematic evolution of ligand by exponential enrichment(GO-SELEX)and molecular docking tech-niques.The aptamer,labeled with Cy5 fluorescence,was chemically conjugated to CGO to construct an aptamer-functionalized CGO fluorescent sensor.By optimizing detection conditions,including the mass concentration of CGO,aptamer concentration,reaction temperature,and incubation time,quantita-tive analysis of the target analytes was achieved using the ratio of fluorescence intensity changes be-fore and after target addition.The stability of the sensor in biological matrices was evaluated by moni-toring fluorescence intensity changes over incubation time in blank blood and urine,in comparison with the traditional physical adsorption-based CGO fluorescent sensor.Spiked recovery experiments in blank blood and urine were conducted to compare performance with that of HPLC-MS/MS.Results A specific aptamer A5 was selected and chemically conjugated with CGO to construct the aptamer-functionalized CGO fluorescent sensor.Under optimized conditions,the proposed fluorescent sensor ex-hibited a linear detection range of 1.0-5.0 ng/mL for KET,with a limit of detection(LOD)of 0.86 ng/mL;while for NK,the linear detection range was 1.0-5.0 ng/mL,with an LOD of 0.70 ng/mL.Com-pared with the CGO fluorescent sensor constructed via physical adsorption,this sensor demonstrated greater stability in blood and urine.The spiked recovery rates of KET and NK in blank blood and urine ranged from 81.50%to 110.03%,exhibiting detection performance comparable to that of HPLC-MS/MS.Conclusion The aptamer screening method offers a novel approach for selecting aptamers tar-geting drugs and their metabolites.The constructed aptamer-functionalized CGO fluorescent sensor pro-vides an efficient and reliable strategy for the high-performance detection of KET and NK.

Objective To investigate the relationship between the ratio of C-reactive protein(CRP)/preal-bumin(PAB)and severity of illness and onset in patients with primary knee osteoarthritis(KOA).Methods A total of 102 patients with primary KOA admitted to a hospital from October 2018 to October 2023 were selected as the study group,and 100 healthy people in a hospital during the same period were select-ed as the control group.According to the Kelgren-Lawrence(K-L)grading criteria,102 patients with primary KOA were divided into K-L grade Ⅰ group(n=21),K-L grade Ⅱ group(n=27),K-L grade Ⅲ group(n=34),and K-L grade Ⅳ group(n=20).The levels of CRP and PAB were measured by automatic biochemical analyzer,and the ratio of CRP/PAB was calculated.The predictive value of CRP,PAB and CRP/PAB ratio to the incidence of primary KOA was analyzed by receiver operating characteristic curve,and the influencing fac-tors of the incidence of primary KOA patients were analyzed by multivariate Logistic regression.Results There were significant differences in CRP,PAB and CRP/PAB ratio among primary KOA patients with different K-L grades(P＜0.05).The total score of Western Ontario and McMaster Universities Osteoar-thritis Index(WOMAC)for primary KOA patients from K-L group Ⅰ to K-L group Ⅳ were(40.91±5.18),(46.08±9.21),(51.24±7.46),(65.83±12.93)points,respectively.The difference was statistically signifi-cant(P＜0.05).In patients with primary KOA,CRP and CRP/PAB ratio were positively correlated with K-L grade and WOMAC total score,while PAB was negatively correlated with K-L grade and WOMAC total score(P＜0.05).CRP and CRP/PAB ratio in study group were higher than those in control group,and PAB level was lower than those in control group,the differences were statistically significant(P＜0.05).The area under the curve(AUC)and 95％CI of CRP and PAB levels were 0.781(0.736-0.831)and 0.853(0.781-0.876),respectively,and the AUC of CRP/PAB ratio was 0.937(0.892-0.987).Conclusion CRP and CRP/PAB ratio are highly expressed in patients with primary KOA,and PAB is low expressed in patients with pri-mary KOA,which is related to the severity of illness and onset in patients,and can be used as a potential marker to predict the severity of illness and onset in patients with primary KOA.