Abstract In recent years, the prevalence of overweight and obesity among children and adolescents has risen rapidly, posing a serious threat to their physical and mental health. To provide scientific, systematic, and standardized weight management guidance for overweight and obese children and adolescents, the study focuses on the core concept of healthy lifestyle intervention, integrates multidisciplinary expert opinions and research findings,and proposes a comprehensive multidisciplinary intervention framework covering scientific exercise intervention, precise nutrition and diet, optimized sleep management, and standardized psychological support. It calls for the establishment of a multi agent collaborative management mechanism led by the government, implemented by families, fostered by schools, initiated by individuals, optimized by communities, reinforced by healthcare, and coordinated by multiple stakeholders. Emphasizing a child and adolescent centered approach, the consensus advocates for comprehensive, multi level, and personalized guidance strategies to promote the internalization and maintenance of a healthy lifestyle. It serves as a reference and provides recommendations for the effective prevention and control of overweight and obesity, and enhancing the health level of children and adolescents.

Objective To analyze dynamic contrast-enhanced MRI (DCE-MRI) radiomic features using machine learning algorithms, and to develop and validate a predictive model for HER-2 status in breast cancer. Methods The DCE-MRI images of 272 treatment-naive female patients with breast cancer between 2020 and 2022 were included in this study. Regions of interest (ROIs) were manually segmented using 3d-Slicer software, and radiomic features were extracted. All patients were randomly divided into training sets or validation sets at a ratio of 4∶1. The least absolute shrinkage and selection operator (LASSO) algorithm was used for feature screening on the training set, followed by the development of predictive models using six machine learning algorithms. Internal cross-validation was performed to compare the performance differences between the models. The best-performing model was selected, trained on the training set, and evaluated on the validation set. Evaluation metrics included area under the curve (AUC), sensitivity, specificity, precision, and recall rate. Results The clinical data of patients in the training set and validation set showed no significant differences. Five features were identified by the LASSO algorithm. With these features, six machine learning models were developed on the training set, and their predictive performance was internally cross-validated using the bagging method. XGBoost model had the highest mean AUC (0.696), followed by RF model (0.690); XGBoost model had the highest mean precision (0.756), followed by LR and RF models. Therefore, XGBoost was the optimal model. An HER-2 predictive model was built using the XGBoost algorithm on the training set and applied to the validation set. The AUC, precision, sensitivity, and specificity of the predictive model on the validation set were calculated, and ROC curves, precision-recall curves, calibration curves, and decision-making curves were plotted. Conclusion This study constructed and evaluated different DCE-MRI radiomics-based machine learning models for predicting HER-2 status in breast cancer. Among them, XGBoost algorithm performed the best and has the potential to become a new non-invasive method for preoperative prediction of HER-2 status, providing reliable evidence for personalized clinical diagnosis and treatment.

Objective:This study aims to analyze the gamma band effective connectivity characteristics of the prefrontal-striatal circuitry in bipolar disorder patients with and without a history of manic episodes, as well as in major depressive disorder patients, during the recognition of positive emotional faces, this study aims to identify unique neurophysiological features that may aid in the early detection of bipolar disorder.Methods:This retrospective study collected clinical data and magnetoencephalography (MEG) imaging data from patients performing a positive emotional face recognition task at the Affiliated Brain Hospital of Nanjing Medical University from May 2009 to December 2019. The study included 75 patients with major depressive disorder and 29 patients with bipolar disorder in a depressive episode (rBD group). Concurrently, 39 age-and gender-matched healthy controls (HC group) were recruited. After a follow-up period of at least 5 years, 23 out of the 75 patients with major depressive disorder converted to bipolar disorder (ctBD group), while the remaining 52 who did not convert maintained a diagnosis of major depressive disorder.Results:There were statistically significant differences in gamma-band effective connectivity in the prefrontal-striatal circuit when recognizing positive emotional faces among the converted to bipolar disorder (ctBD), raw bipolar disorder, major depressive disorder, and HC groups ( H=9.04, 10.30, 8.30, 13.43, 14.38, 12.62, 9.82, 8.94, 24.62, 7.89, 18.53, 9.97, 9.58, 12.79, P<0.05). The ctBD group, rBD group, and major depressive group all showed reduction in effective connectivity from the right orbital inferior frontal gyrus (ORBinf.R) to the left orbital inferior frontal gyrus (ORBinf.L) [ Z=-1.98, -3.38, -2.88], from the right orbital inferior frontal gyrus to the right ventral striatum (VS.R) ( Z=-2.05, -2.76, -2.11; P<0.05) and from the left ventral striatum (VS.L) to the left orbital middle frontal gyrus (ORBmid.L) ( Z=-2.76, -1.98, -2.43; P<0.05). Among the disease groups, the ctBD group showed significantly enhanced effective connectivity strength compared to the major depressive group from the right amygdala (AMYG.R) to the left orbital inferior frontal gyrus(0.04(0.03, 0.08)), from the right amygdala to the left ventral striatum(0.05(0.03, 0.09)), and from the right ventral striatum to the right anterior cingulate and paracingulate gyri (ACG.R) (0.04(0.02, 0.08)) ( Z=4.17, 3.70, 3.35; P<0.001).The ctBD group also exhibited enhanced effective connectivity compared to the rBD group from ORBinf.R to the ACG.R, fron the AMYG.R to the ORBinf.L, from the AMYG.R to the VS.L, and from the VS.R to the ACG.R ( Z=2.05, 4.61, 3.60, 3.04; P<0.05).The rBD group demonstrated reduced effective connectivity compared to the major depressive disorder group from the right orbital middle frontal gyrus(ORBmid.R) to the left anterior cingulate and paracingulate gyri (ACG.L), ORBinf.R to the ACG.R and from the ORBinf.R to the AMYG.R ( Z=-2.12, -2.40, -2.22; P<0.05). Conclusion:There are significant differences in the gamma-band effective connectivity characteristics of the prefrontal-striatal pathway when recognizing positive emotional faces between patients with bipolar disorder in depressive episodes and those with depression, as well as differences between bipolar depressed patients with and without a history of manic episodes.

Objective:To explore the effect of mesoporous silica (SiO 2) chitosan (CS) hydrogel loaded with neurotrophin-3 (NT-3) on repair of spinal cord injury. Methods:After 2%, 4%, and 6% NT-3/SiO 2 were dissolved in CS solution, they were added into β-glycerophosphate sodium solution for chemical cross-linking to obtain hydrogel patches of different NT-3 loadings. The specific surface area, pore size and pore volume of mesoporous SiO 2 nanoparticles were detected by specific surface area analyzer. The morphology of mesoporous SiO 2 nanoparticles and the pore structure of freeze-dried hydrogel were detected by scanning electron microscopy. Adhesion of the hydrogel was verified by spinal cord tissue. After the NT-3/SiO 2@CS hydrogel was placed in the medium, the concentrations of NT-3 were measured for 1 to 20 days. Neural stem cells (NSCs) were isolated from fetal rats and identified. Cell counting kit 8 (CCK-8) assay was used to detect the proliferation of NSCs treated with different concentrations of hydrogel. Immunofluorescence staining was used to detect the effects of NT-3/SiO 2@CS hydrogel on differentiation of NSCs. Twenty-four 8-week-old C57BL/6JNifdc female mice were randomly divided into a sham operation group (sham), a spinal cord injury group (SCI), a chitosan hydrogel group (CS) and a mesoporous SiO 2-loaded NT-3 hydrogel group (NT-3/SiO 2@CS). In the sham group, the muscle and skin were sutured immediately after laminectomy without spinal cord injury. The CS hydrogel and NT-3/SiO 2@CS hydrogel patches were implanted without treatment after spinal cord injury in the other 3 groups, respectively. Basso Mouse Scale (BMS) was used to evaluate the mice every 7 days for 8 weeks after modeling. The hot and cold board test and Catwalk gait analysis were performed at 8 weeks after surgery. Results:The mesoporous SiO 2 nanoparticles showed typical spherical morphology and a uniform particle size (about 160 nm). The specific surface area, pore volume and pore size of the mesoporous SiO 2 nanoparticles loaded with NT-3 changed from 1,039 m 2/g, 0.726 cm 3/g and 2.754 nm to 779 m 2/g, 0.403 cm 3/g and 1.903 nm, respectively. The hydrogel had a uniform internal microporous structure and good porosity so that it easily adhered to the spinal cord and achieved long-term stable release of NT-3 for at least 20 days. CCK-8 results showed that at 24 hours after the neural stem cells were laid, the cell proliferative activities in the 4%NT-3/SiO 2@CS and 6% NT-3/SiO 2@CS groups were significantly lower than that in the group untreated ( P<0.05). The immunofluorescence staining showed that the fluorescence intensity of glial fibrillary acidic protein (GFAP), the NSC marker in NT-3/SiO 2@CS, insignificantly decreased in the groups with different concentrations of NT-3/SiO 2@CS compared with the group untreated ( P>0.05). The fluorescence intensities of GFAP, MAP2 and GFAP/MAP2 in the NT-3/SiO 2@CS group were significantly higher than those in the other groups ( P<0.05). The BMS scoring for mice showed sham group>NT-3/SiO 2@CS group>SCI group and CS group for 8 weeks of modeling, and the response time of mice to cold and hot stimulations in the NT-3/SiO 2@CS group was significantly shorter than that in the SCI and CS groups. The differences above were statistically significant ( P<0.05). The Catwalk gait analysis showed that the hindlimb footprints in the NT-3/SiO 2@CS group were significantly clearer and more coherent than those in the SCI and CS groups. Conclusions:The sustained-release gel patch based on CS, SiO 2 and NT-3 has a uniform pore structure, good biocompatibility and excellent drug sustained-release effect. It can promote the differentiation of NSCs into neurons, thus contributing to recovery of motor and sensory functions after spinal cord injury.

Cancer incidence in China has been rising steadily,with a particularly heavy burden from several high-prevalence malignancies.Medical examination for cancer plays a critical role in the early detection of cancer,precancerous lesions,and precursor conditions,thereby facilitating timely diagnosis and intervention.Such examination also addresses the growing demand for person-alized cancer screening services among diverse population groups.The development of evidence-based,context-specific cancer screening guidelines is essential to enhance the standardization,quality,and equity of preventive screening practices across the country,ultimately improving out-comes in early cancer detection and treatment.Guided by the Department of Medical Emergency Response of the National Health Commission,the Guidelines for Medical Examination for Cancer in Health Examination Agency(2025 Edition)were developed under the leadership of the National Cancer Center.A multidisciplinary panel of experts formulated the guidelines in accordance with the principles and methodology of the World Health Organization Handbook for Guideline Deve-lopment.The guidelines provide evidence-based recommendations on key clinical domains:target cancers and populations,overall screening workflow,screening protocols,diagnostic technolo-gies,result interpretation,follow-up procedures,and quality control.The primary objective is to standardize cancer screening practices in health examination agency and strengthen China's ca-pacity for prevention and control of high-burden cancers.

Hyperuricemia(HUA)and gout became typical metabolic disorders characterized by multiple pathogenic factors.Their incidence increased annually,affecting younger populations.Given that uric acid(UA)and inflammation were the primary disease mechanisms,the search for effective and low-side-effect UA-lowering and anti-inflammatory drugs became a pressing scientific priority.Traditional Chinese medi-cine(TCM)encompassed a rich array of theoretical and practical experience,along with a diverse range of chemical substances,making herbs or their components potential sources for therapeutic drugs.Despite the significant role that modern herbal medicines played in treating HUA and gout,the existing research literature remained fragmented,lacking comprehensive and systematic reviews.In this review,we focused on the regulation of UA and summarized the discovery of UA-lowering pharmacodynamic components or ingredients derived from herbs and formulas,as well as their multi-targeted mechanisms of action.Emphasizing this focus,we proposed that,compared to acute inflammation,low-grade inflammation may play a relatively"unnoticed"role in the disease process.In contrast to Western medicine,we discussed the risks and benefits of herbal medicines and their ingredients for treatment,drawing from theoretical insights and clinical practice.This review offered comprehensive perspectives on the research into anti-HUA and gout treatments using herbal medicines and their natural products.Additionally,it provided a forward-looking view on natural product discovery,the exploration of ther-apeutic strategies,and new drug research in this field.

The level of urinary albumin is a critical indicator for the early diagnosis and management of chronic kidney disease(CKD).However,existing methods for detecting albumin are not conducive to point-of-care testing due to the complexity of reagent addition and incubation processes.This study presents a smartphone-integrated handheld automated biochemical analyzer(sHABA)designed for point-of-care testing of urinary albumin.The sHABA features a pre-loaded,disposable reagent cassette with re-agents for the albumin assay arranged in the order of their addition within a hose.The smartphone-integrated analyzer can drive the reagents following a preset program,to enable automatic sequential addition.The sHABA has a detection limit for albumin of 5.9 mg/L and a linear detection range from 7 to 450 mg/L.The consistency of albumin level detection in 931 urine samples using sHABA with clinical tests indicates good sensitivity(95.78％)and specificity(90.16％).This research advances the field by providing an automated detection method for albumin in a portable device,allowing even untrained individuals to monitor CKD in real time at the patient's bedside.In the context of promoting tiered diagnosis and treatment,the sHABA has the potential to become an essential tool for the early diagnosis and comprehensive management of CKD and other chronic conditions.

The level of urinary albumin is a critical indicator for the early diagnosis and management of chronic kidney disease (CKD). However, existing methods for detecting albumin are not conducive to point-of-care testing due to the complexity of reagent addition and incubation processes. This study presents a smartphone-integrated handheld automated biochemical analyzer (sHABA) designed for point-of-care testing of urinary albumin. The sHABA features a pre-loaded, disposable reagent cassette with reagents for the albumin assay arranged in the order of their addition within a hose. The smartphone-integrated analyzer can drive the reagents following a preset program, to enable automatic sequential addition. The sHABA has a detection limit for albumin of 5.9 mg/L and a linear detection range from 7 to 450 mg/L. The consistency of albumin level detection in 931 urine samples using sHABA with clinical tests indicates good sensitivity (95.78%) and specificity (90.16%). This research advances the field by providing an automated detection method for albumin in a portable device, allowing even untrained individuals to monitor CKD in real time at the patient's bedside. In the context of promoting tiered diagnosis and treatment, the sHABA has the potential to become an essential tool for the early diagnosis and comprehensive management of CKD and other chronic conditions.

Hyperuricemia (HUA) and gout became typical metabolic disorders characterized by multiple pathogenic factors. Their incidence increased annually, affecting younger populations. Given that uric acid (UA) and inflammation were the primary disease mechanisms, the search for effective and low-side-effect UA-lowering and anti-inflammatory drugs became a pressing scientific priority. Traditional Chinese medicine (TCM) encompassed a rich array of theoretical and practical experience, along with a diverse range of chemical substances, making herbs or their components potential sources for therapeutic drugs. Despite the significant role that modern herbal medicines played in treating HUA and gout, the existing research literature remained fragmented, lacking comprehensive and systematic reviews. In this review, we focused on the regulation of UA and summarized the discovery of UA-lowering pharmacodynamic components or ingredients derived from herbs and formulas, as well as their multi-targeted mechanisms of action. Emphasizing this focus, we proposed that, compared to acute inflammation, low-grade inflammation may play a relatively "unnoticed" role in the disease process. In contrast to Western medicine, we discussed the risks and benefits of herbal medicines and their ingredients for treatment, drawing from theoretical insights and clinical practice. This review offered comprehensive perspectives on the research into anti-HUA and gout treatments using herbal medicines and their natural products. Additionally, it provided a forward-looking view on natural product discovery, the exploration of therapeutic strategies, and new drug research in this field.

Objective:To explore the neurophysiological features of the prefrontal-limbic-striatal circuit in patients with early-stage bipolar disorder without manic or hypomanic episodes, and its role in identifying early-stage bipolar disorder.Methods:From 2009 to 2019, a total of 155 hospitalized patients with major depressive disorder (MDD) from Nanjing Brain Hospital were selected after at least 5 years of follow-up, 31 patients with depression transitioned to bipolar disorder(ctBD group) and 76 patients remained the diagnosis of MDD(MDD group) were recruited.Sixty-two healthy controls matched for age, gender, and education years were selected as control group(HC group). Resting-state magnetoencephalography (MEG) data in eyes-open state of all subjects were collected.Data were analyzed based on the fieldtrip toolkit on the MATLAB platform. The key brain area of the prefrontal-limbic-striatal circuit were selected. Inter-group statistical analysis were conducted on the spectral energy and power-correlated functional connectivity at the theta, alpha, beta, and gamma frequency bands in the brain area of interest. In addition, the prediction model was constructed to early recognize bipolar disorder.Results:(1)There were statistically significant differences in the spectral energy of theta and alpha frequency bands in the prefrontal-limbic-striatal circuit among the 3 groups (cluster- F=120.50, 112.39, both P<0.05). The spectral energy of theta and alpha frequency bands in interest brain regions of prefrontal-limbic-striatal circuit in MDD group was lower than that in HC group (cluster- t=89.52, P<0.05). The spectral energy of theta band in prefrontal-limbic-striatal circuit in ctBD group was lower than that in HC group(cluster- t=105.82, P<0.05), and the spectral energy of alpha band in inferior frontal gyrus, orbitofrontal gyrus and caudate nucleus was lower than that in HC group (cluster- t=75.78, P<0.05), while there was no significant difference between the MDD group and the ctBD group ( P>0.05).(2)After FDR correction, there were statistically significant differences in functional connectivity between the left orbitofrontal gyrus and the right ventral striatum among the three groups (0.26 (0.13, 0.34), 0.12 (0.09, 0.24), 0.27 (0.20, 0.37), H=13.51, P<0.05, FDR correction). The strength of functional connectivity between the left orbitofrontal gyrus and the right ventral striatum in the MDD group was weaker than that in the HC group and the ctBD group (all P<0.05).(3)Binary Logistic regression analysis showed that the functional connectivity of beta frequency band between the left orbitofrontal gyrus and the right ventral striatum ( B=1.50, OR=4.50, 95% CI=1.73-11.70), the functional connectivity between the right orbitofrontal gyrus and the right amygdala( B=0.98, OR=2.68, 95% CI=1.18-6.13), the total HAMD score ( B=0.80, OR=2.28, 95% CI=1.36-3.67), the body weight factor score ( B=-1.99, OR=0.14, 95% CI=0.04-0.45), the anxiety factor score ( B=-0.99, OR=0.37, 95% CI=0.19-0.71), and sleep factor score( B=-1.14, OR=0.32, 95% CI=0.16-0.65)were the influencing factors for depression transitioned to bipolar disorder. Conclusion:The decreased resting low-frequency energy in the prefrontal-limbic-striatal circuit may be the common neural basis for the onset of unipolar and bipolar depression, and enhanced functional connectivity may be a potential neural circuit mechanism for depression transitioned to bipolar disorder. Functional connectivity combined with clinical manifestations is helpful for early recognition of bipolar disorder.