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.

Objective To explore the effects of physical activity(PA)on carotid intima-media thickness(CIMT)thickening in patients with type 2 diabetes mellitus(T2DM)with poor HbA1c control.Methods A total of 2505 T2DM inpatients with poor HbA1c control who visited the Endocrinology Department of the Integrated Traditional Chinese and Western Medicine Hospital Affiliated with Nanjing University of Traditional Chinese Medicine and Changzhou Second People's Hospital Affiliated with Nanjing Medical University from January 2019 to February 2022 were enrolled in this study.The patients were categorized into the mild-intensity PA(1～599 MET-min/w,n=795),moderate-intensity PA(600～1499 MET-min/w,n=1107)and high-intensity PA(≥1500 MET-min/w,n=603)based on the metabolic equivalence level(MET).Logistic regression was performed to analyze the relationship between PA and CIMT thickening.Statified analysis was used to investigate the effects of blood pressure(BP)and low-density lipoprotein cholesterol(LDL-C)target status on CIMT thickness in different PA groups.Results Restricted cubic spline plots showed a nonlinear relationship between PA levels and the risk of CIMT thickening(P<0.05).Logistic regression showed that with moderate-intensity PA as the control,while high-intensity PA was the influence factor of CIMT thickening(OR 1.581,95%CI 1.244～2.010,P<0.05).Statified analyses showed that under mild-intensity PA,using neither BP nor LDL-C on target as reference,achievement of either index and both were the influence factor of CIMT thickening.Using BP on target as reference,achievements of both indexes,achievements of neither of both indexes,and achievement of LDL-C only were the influence factor of CIMT thickening.Using LDL-C on target as reference,achievement of neither of them and achievement of BP only were the influence factor of CIMT thickening.Under high-intensity PA,achievement of both indexes was negatively associated with CIMT thickening.Conclusions Moderate-intensity PA is the recommended scheme to prevent CIMT thickening in T2DM patients whose HbA1c is not up to standard.Mild-intensity PA is not related to the risk of CIMT thickening,while high-intensity PA is positively related to the risk of CIMT thickening,and focusing on LDL-C compliance is particularly important to avoid CIMT thickening.

Recent data suggest that vascular endothelial growth factor receptor inhibitor (VEGFRi) can enhance the anti-tumor activity of the anti-programmed cell death-1 (anti-PD-1) antibody in colorectal cancer (CRC) with microsatellite stability (MSS). However, the comparison between this combination and standard third-line VEGFRi treatment is not performed, and reliable biomarkers are still lacking. We retrospectively enrolled MSS CRC patients receiving anti-PD-1 antibody plus VEGFRi (combination group, n=54) or VEGFRi alone (VEGFRi group, n=32), and their efficacy and safety were evaluated. We additionally examined the immune characteristics of the MSS CRC tumor microenvironment (TME) through single-cell and spatial transcriptomic data, and an MSS CRC immune cell-related signature (MCICRS) that can be used to predict the clinical outcomes of MSS CRC patients receiving immunotherapy was developed and validated in our in-house cohort. Compared with VEGFRi alone, the combination of anti-PD-1 antibody and VEGFRi exhibited a prolonged survival benefit (median progression-free survival: 4.4 vs. 2.0 months, P=0.0024; median overall survival: 10.2 vs. 5.2 months, P=0.0038) and a similar adverse event incidence. Through single-cell and spatial transcriptomic analysis, we determined ten MSS CRC-enriched immune cell types and their spatial distribution, including naive CD4⁺ T, regulatory CD4⁺ T, CD4⁺ Th17, exhausted CD8⁺ T, cytotoxic CD8⁺ T, proliferated CD8⁺ T, natural killer (NK) cells, plasma, and classical and intermediate monocytes. Based on a systemic meta-analysis and ten machine learning algorithms, we obtained MCICRS, an independent risk factor for the prognosis of MSS CRC patients. Further analyses demonstrated that the low-MCICRS group presented a higher immune cell infiltration and immune-related pathway activation, and hence a significant relation with the superior efficacy of pan-cancer immunotherapy. More importantly, the predictive value of MCICRS in MSS CRC patients receiving immunotherapy was also validated with an in-house cohort. Anti-PD-1 antibody combined with VEGFRi presented an improved clinical benefit in MSS CRC with manageable toxicity. MCICRS could serve as a robust and promising tool to predict clinical outcomes for individual MSS CRC patients receiving immunotherapy.
Humans ; Colorectal Neoplasms/drug therapy* ; Male ; Female ; Immunotherapy ; Middle Aged ; Aged ; Tumor Microenvironment/immunology* ; Retrospective Studies ; Microsatellite Instability ; Transcriptome ; Single-Cell Analysis ; Programmed Cell Death 1 Receptor/immunology* ; Gene Expression Profiling ; Immune Checkpoint Inhibitors/therapeutic use* ; Adult ; Receptors, Vascular Endothelial Growth Factor/antagonists & inhibitors*

The elevated polyamines, amine-rich molecules with diverse functions in pathophysiology processes, are implicated in contributing to tumorigenesis and progression. Whether and how they affect the efficacy of chemotherapy is incompletely understood. Our screening assays reveal that the supplement with a low dose of spermidine (Spd), one of the polyamines, enhances ferroptosis in prostate cancer cells as evidenced by increased lipid peroxidation and intracellular Fe²⁺ levels in vitro. Combination treatment with Spd and a low dose of ferroptosis inducer erastin synergistically augments anti-tumor efficacy with undetectable toxicity in mice. Analysis of RNA-seq data indicates that heme oxygenase 1 (HMOX1), an enzyme that catalyzes the cleavage of heme to release Fe²⁺, is significantly upregulated in response to Spd and erastin cotreatment. Spd mediated the hypusine modification of the eukaryotic initiation factor 5A (EIF5A) promotes the translation of the nuclear factor erythroid 2-related factor 2 (NRF2), subsequently leading to elevation of HMOX1. Moreover, Spd and erastin significantly inhibit proteasome activity which results in a decrease in proteasomal degradation of NRF2, although many proteasome-related genes are induced either by Spd or Spd plus erastin. Thus, in addition to its pro-oncogenic activity, the supplement of Spd improves antitumor activity in combination with ferroptosis inducers and offers an optional approach to cancer treatment.

Uveitis is an intraocular inflammation involving the iris,ciliary body,and choroid,with various etiologies.Accurate diagnosis of uveitis and identification of its related systemic diseases was a great challenge in the past as it requires a detailed analysis of the clinical history,physical examination results,and diagnostic tests.The continuous emergence and increasing application of artificial intelligence(AI)in diagnostic decision support systems in recent years provide new meth-ods for the assessment and diagnosis of uveitis.The Ophthalmic Imaging and Intelligent Medicine Branch of the China Medi-cal Education Association and the Ophthalmology Professional Committee of the International Translational Medicine Asso-ciation has organized experts to summarize the current application status of AI in uveitis diseases,and to provide guidance on the application of AI in the diagnosis,classification,and evaluation of uveitis etiologies.This guideline aims to help clin-ical doctors diagnose,classify,and manage uveitis patients.

Metabolic dysfunction-associated fatty liver disease （MAFLD） is a common chronic liver disease with the pathological feature of lipid accumulation in the liver， and it is closely associated with liver metabolic disorders. The latest research has shown that the pathogenesis of MAFLD is associated with the abnormal expression of specific genes， especially the fat mass and obesity-associated （FTO） gene. The abnormal activity of the FTO gene may lead to an imbalance in liver lipid metabolism， which manifests as the increase in fatty acid synthesis and the reduction in fatty acid oxidation， thereby promoting liver fat deposition and inflammatory response. Therefore， regulating the expression or activity of the FTO gene is considered one of the potential strategies for the treatment of MAFLD. At present， drug research targeting the function of the FTO gene has achieved preliminary results， and inhibition of the activity of the FTO gene can help to regulate liver lipid metabolism and alleviate liver inflammatory injury. This article reviews the mechanism of action of the FTO gene in the development and progression of MAFLD， summarizes the advances in drug research on the FTO gene and related metabolic pathways in recent years， and analyzes their application prospect in research and treatment.

BACKGROUND

Recovery is essential for high-intensity intermittent sports athletes to achieve optimal performance. Heart rate variability (HRV) serves as a marker of the autonomic nervous system, which also measures the parasympathetic regulation that facilitates recovery. Neurofeedback (NBF) intervention, combined with deep breathing and mental imagery, presented positive results in facilitating parasympathetic reactivation. However, limited studies exist in investigating the influence of the NBF intervention on HRV parameters and recovery, specifically in high-intensity intermittent sports athletes.

This pilot study aims to investigate the effects and influence of neurobiofeedback intervention on recovery via the use of HRV of UAAP Collegiate Basketball and Football Athletes.

The research will be done with a Quasi-experimental onegroup pretest-posttest study design.

Participants will undergo a neurobiofeedback intervention following neuromuscular and metabolic training. Data is collected with a Polar H10 HRM Chest Strap connected to an Elite HRV monitoring application and will be analyzed by Kubios HRV software.

Descriptive statistics will be computed for participant characteristics. Kolmogorov-Smirnov test (p >0.05) will assess normality. Two-way repeated-measures ANOVAs will examine NBF effects across exercise types, with Bonferroni-corrected pairwise comparisons and trend analysis for the main effects and non-significant but clinically relevant patterns. All analyses will be done using SPSS v25.

It is expected that the neurobiofeedback intervention will have an effect and influence by eliciting a lower LF/HF ratio and SD1/SD2, suggesting a facilitated reactivation of the parasympathetic nervous system, promoting recovery after undergoing neuromuscular or metabolic training.

Cerebral small vessel disease represents a group of common vascular disorders involving pathological changes in arterioles,capillaries and venules,with microvascular investigation remaining a key challenge in stroke.With high signal-to-noise ratio and high contrast enabled by enhanced field strength,7T MRI can surpass the resolution limits of 3T MRI,revealing structural and functional abnormalities in cerebral small vessels below 400 μm,as well as detecting subtle lesions in brain tissue.This paper reviews the research progress of multimodal high-resolution imaging techniques based on 7T MRI,such as time-of-flight angiography,phase contrast imaging and susceptibility imaging,in the study of cerebral small vessel disease.Utilizing these technologies,7T MRI can clearly display the structure of cerebral small vessels,such as the lenticulostriate arteries and deep medullary veins,and measure functional parameters like flow velocity and susceptibility.Additionally,it can sensitively detect cerebral microbleeds and cortical cerebral microinfarct.These imaging data provide valuable information for detecting early features of cerebral small vessel disease and assessing its progression,offering new insights into its pathogenesis.Combined with artificial intelligence-based image analysis methods,7T MRI holds great promise for early diagnosis and progression evaluation in cerebral small vessel disease.

Objective To explore the mediating effect of empowerment ability between type D personality and self-management behavior of patients with diabetes mellitus(DM).Methods A total of 738 patients with type 2 diabetes mellitus(T2DM)hospitalized in the Department of Endocrinology of three tertiary hospitals in Hainan Province from December 2022 to May 2023 were selected and divided into Type D personality(Type D,n=104)group and T2DM group(n=634).The general data,biochemical indexes,scores of negative emotion(NA),social inhibition(SI),empowerment ability,and scale of DM self-management activities(SDSCA)were compared between the two groups,and the correlation between type D personality,empowerment ability and self-management ability was analyzed.The mediating effect model was used to analyze the mediating effect of empowerment ability on the four self-management behaviors of patients with type D personality,and the chain mediating effect model was used to analyze the relationship between type D personality,empowerment ability,self-management behaviors and HbA1c.Results Compared with the T2DM group,HbA1c,proportion of rural residence,proportion of complications≥3,proportion of education level of junior high school or above,proportion of monthly income<3000 yuan,and NA and SI scores were significantly higher in the Type D group(P<0.05).The empowerment ability and scores of healthy diet,regular exercise,blood glucose monitoring and medication compliance were lower in the Type D group than in the T2DM group(P<0.05).Spearman correlation analysis showed that the empowerment ability score was positively correlated with the scores of healthy diet,regular exercise,blood glucose monitoring and medication compliance(P<0.05).NA and SI scores were negatively correlated with empowerment ability score,healthy diet,regular exercise,blood glucose monitoring and medication compliance(P<0.05).The results of model analysis with empowerment ability as the mediating variable showed that type D personality had direct,indirect and total effects on regular exercise,blood glucose monitoring,medication compliance and SDSCA total score(P<0.05),and indirect and total effects on regular diet score(P<0.05).The mediating effect of empowerment ability was significant(Bootstrap CI did not include 0).The chain mediating effect analysis showed that type D personality could indirectly affect HbA1c through empowerment ability,healthy diet(γ=0.389,95%CI 0.206～0.591),and medication compliance(γ=0.149,95%CI 0.040～0.265),and the effect proportion was 39.4%and 14.1%,respectively.Conclusions Type D personality can indirectly influence self-management behavior through the mediating effect of empowerment,and simultaneously affecting HbA1c through the chain effect of empowerment,diet,and medication behavior.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.