Total-body PET/CT, with its long axial field of view and high sensitivity detector, has shown potential for reducing the dose of radiopharmaceuticals. However, pediatric patients are significantly more sensitive to radiation and have a higher long-term cancer risk than adults, posing fundamental challenges for dose management in PET/CT examinations for these patients. In this article, the technical characteristics of total-body PET/CT and its radiation exposure status in children were systematically analyzed. The radiation exposure could be controlled by the following optimization strategies: adjusting the CT exposure parameters, optimizing the scanning mode, adding reconstruction algorithm, and reducing the injected dose of radioactive tracer. By addressing both external and internal radiation during the PET/CT scanning process, the overall radiation dose received by pediatric patients can be reduced within a certain range. In addition, this article also discusses the technical differences between “total-body” and “whole-body” concepts, and emphasizes that the future optimization of radiation dose in pediatric PET/CT should be realized by integration of personalized scanning protocols. Through reasonable management of scanning protocols and processes, low-dose and high-quality PET/CT imaging can be achieved in clinical environments, thus maximizing protection of pediatric patient health while minimizing the risks associated with ionizing radiation exposure.

Objective: To explore the correlation between traditional Chinese medicine (TCM) constitution and depressive symptom among school aged children and adolescents, so as to provide evidences for informing constitution based regulation and prevention of depressive symptom. Methods: From June to December 2024, a total of 4 729 students aged 6-14 were recruited by cluster random sampling from 10 primary schools in Baoding (Hebei Province), Heze and Liaocheng (Shandong Province). General information, TCM constitution and depressive symptom were collected. Restricted cubic spline (RCS) models were used to analyze related factors and threshold effects of depressive symptom. Binary Logistic regression was applied to examine the association between depressive symptom and TCM constitution, with subgroup analyses conducted. Results: The detection rate of depressive symptom among the included children and adolescents was 25.82%. RCS analyses indicated non linear associations between depressive symptom and age (inflection point at 10 years old), bedtime (inflection point at 22:00), and wake up time (inflection point at 6:30 ) (all P non linearity <0.01). Linear associations were observed with body mass index (BMI) and sleep duration (all P non linearity > 0.05 ). After adjusting for covariates such as age, BMI and sleep status, binary Logistic regression analyses showed that Yin deficient constitution ( OR =1.26, 95% CI =1.09-1.45) and Phlegm-dampness constitution ( OR =1.42, 95% CI =1.11-1.82) were significantly associated with depressive symptom among children and adolescents (all P <0.05). Conclusions Depressive symptom among school aged children and adolescents is primarily associated with Yin deficiency and Phlegm dampness constitutions in TCM constitution. Active attention should be paid to susceptible TCM constitution among children and adolescents. Targeted health guidance and interventions should be implemented to improve TCM constitution health status for preventing the occurrence of depressive symptom.

With the rapid development of artificial intelligence, computational pathology has been seamlessly integrated into the entire clinical workflow, which encompasses diagnosis, treatment, prognosis, and biomarker discovery. This integration has significantly enhanced clinical accuracy and efficiency while reducing the workload for clinicians. Traditionally, research in this field has depended on the collection and labeling of large datasets for specific tasks, followed by the development of task-specific computational pathology models. However, this approach is labor intensive and does not scale efficiently for open-set identification or rare diseases. Given the diversity of clinical tasks, training individual models from scratch to address the whole spectrum of clinical tasks in the pathology workflow is impractical, which highlights the urgent need to transition from task-specific models to foundation models (FMs). In recent years, pathological FMs have proliferated. These FMs can be classified into three categories, namely, pathology image FMs, pathology image-text FMs, and pathology image-gene FMs, each of which results in distinct functionalities and application scenarios. This review provides an overview of the latest research advancements in pathological FMs, with a particular emphasis on their applications in oncology. The key challenges and opportunities presented by pathological FMs in precision oncology are also explored.
Humans ; Precision Medicine/methods* ; Medical Oncology/methods* ; Artificial Intelligence ; Neoplasms/pathology* ; Computational Biology/methods*

A strategy combining a tailored database and high-throughput activity screening that discover bioactive metabolites derived from Magnoliae Officinalis Cortex (MOC) was developed and implemented to rapidly profile and discover bioactive metabolites in vivo derived from traditional Chinese medicine (TCM). The strategy possessed four characteristics: 1) The tailored database consisted of metabolites derived from big data-originated reference compound, metabolites predicted in silico, and MOC chemical profile-based pseudomolecular ions. 2) When profiling MOC-derived metabolites in vivo, attentions were paid not only to prototypes of MOC compounds and metabolites directly derived from MOC compounds, as reported by most papers, but also to isomerized metabolites and the degradation products of MOC compounds as well as their derived metabolites. 3) Metabolite traceability was performed, especially to distinguish isomeric prototypes-derived metabolites, prototypes of MOC compounds as well as phase I metabolites derived from other MOC compounds. 4) Molecular docking was utilized for high-throughput activity screening and molecular dynamic simulation as well as zebrafish model were used for verification. Using this strategy, 134 metabolites were swiftly characterized after the oral administration of MOC to rats, and several metabolites were reported for the first time. Furthermore, 17 potential active metabolites were discovered by targeting the motilin, dopamine D2, and the serotonin type 4 (5-HT₄) receptors, and part bioactivities were verified using molecular dynamic simulation and a zebrafish constipation model. This study extends the application of mass spectrometry (MS) to rapidly profile TCM-derived metabolites in vivo, which will help pharmacologists rapidly discover potent metabolites from a complex matrix.

Drug repurposing offers a promising alternative to traditional drug development and significantly reduces costs and timelines by identifying new therapeutic uses for existing drugs. However, the current approaches often rely on limited data sources and simplistic hypotheses, which restrict their ability to capture the multi-faceted nature of biological systems. This study introduces adaptive multi-view learning (AMVL), a novel methodology that integrates chemical-induced transcriptional profiles (CTPs), knowledge graph (KG) embeddings, and large language model (LLM) representations, to enhance drug repurposing predictions. AMVL incorporates an innovative similarity matrix expansion strategy and leverages multi-view learning (MVL), matrix factorization, and ensemble optimization techniques to integrate heterogeneous multi-source data. Comprehensive evaluations on benchmark datasets (Fdataset, Cdataset, and Ydataset) and the large-scale iDrug dataset demonstrate that AMVL outperforms state-of-the-art (SOTA) methods, achieving superior accuracy in predicting drug-disease associations across multiple metrics. Literature-based validation further confirmed the model's predictive capabilities, with seven out of the top ten predictions corroborated by post-2011 evidence. To promote transparency and reproducibility, all data and codes used in this study were open-sourced, providing resources for processing CTPs, KG, and LLM-based similarity calculations, along with the complete AMVL algorithm and benchmarking procedures. By unifying diverse data modalities, AMVL offers a robust and scalable solution for accelerating drug discovery, fostering advancements in translational medicine and integrating multi-omics data. We aim to inspire further innovations in multi-source data integration and support the development of more precise and efficient strategies for advancing drug discovery and translational medicine.

Chronic coronary syndrome(CCS)is one of the most prevalent cardiovascular disease(CVD)in clinical practice.With the accelerating aging of the population and the increasing prevalence of cardiovascular risk factors,CVD has become the leading cause of death among Chinese residents.To optimize diagnosis and treatment strategies through integrated traditional Chinese and Western medicine approaches,and to further improve the clinical management of CCS,the expert group reviewed recent domestic and international guidelines on CCS diagnosis and treatment.Incorporating advances in traditional Chinese medicine(TCM)and leveraging the unique characteristics of Yunnan's local herbal medicine(Yunyao),this consensus document was developed.It aims to guide clinical practice and enhance the overall management of CCS patients in the province.

A strategy combining a tailored database and high-throughput activity screening that discover bioactive metabolites derived from Magnoliae Officinalis Cortex(MOC)was developed and implemented to rapidly profile and discover bioactive metabolites in vivo derived from traditional Chinese medicine(TCM).The strategy possessed four characteristics:1)The tailored database consisted of metabolites derived from big data-originated reference compound,metabolites predicted in silico,and MOC chemical profile-based pseudomolecular ions.2)When profiling MOC-derived metabolites in vivo,attentions were paid not only to prototypes of MOC compounds and metabolites directly derived from MOC compounds,as reported by most papers,but also to isomerized metabolites and the degradation products of MOC compounds as well as their derived metabolites.3)Metabolite traceability was performed,especially to distinguish isomeric prototypes-derived metabolites,prototypes of MOC compounds as well as phase Ⅰ metabolites derived from other MOC compounds.4)Molecular docking was utilized for high-throughput activity screening and molecular dynamic simulation as well as zebrafish model were used for verification.Using this strategy,134 metabolites were swiftly characterized after the oral administration of MOC to rats,and several metabolites were reported for the first time.Furthermore,17 potential active metabolites were discovered by targeting the motilin,dopamine D2,and the serotonin type 4(5-HT4)receptors,and part bioactivities were verified using molecular dynamic simulation and a zebrafish constipation model.This study extends the application of mass spectrometry(MS)to rapidly profile TCM-derived metabolites in vivo,which will help pharmacologists rapidly discover potent metabolites from a complex matrix.

Drug repurposing offers a promising alternative to traditional drug development and significantly re-duces costs and timelines by identifying new therapeutic uses for existing drugs.However,the current approaches often rely on limited data sources and simplistic hypotheses,which restrict their ability to capture the multi-faceted nature of biological systems.This study introduces adaptive multi-view learning(AMVL),a novel methodology that integrates chemical-induced transcriptional profiles(CTPs),knowledge graph(KG)embeddings,and large language model(LLM)representations,to enhance drug repurposing predictions.AMVL incorporates an innovative similarity matrix expansion strategy and leverages multi-view learning(MVL),matrix factorization,and ensemble optimization techniques to integrate heterogeneous multi-source data.Comprehensive evaluations on benchmark datasets(Fdata-set,Cdataset,and Ydataset)and the large-scale iDrug dataset demonstrate that AMVL outperforms state-of-the-art(SOTA)methods,achieving superior accuracy in predicting drug-disease associations across multiple metrics.Literature-based validation further confirmed the model's predictive capabilities,with seven out of the top ten predictions corroborated by post-2011 evidence.To promote transparency and reproducibility,all data and codes used in this study were open-sourced,providing resources for pro-cessing CTPs,KG,and LLM-based similarity calculations,along with the complete AMVL algorithm and benchmarking procedures.By unifying diverse data modalities,AMVL offers a robust and scalable so-lution for accelerating drug discovery,fostering advancements in translational medicine and integrating multi-omics data.We aim to inspire further innovations in multi-source data integration and support the development of more precise and efficient strategies for advancing drug discovery and translational medicine.

Objective:To explore the impact of sleep duration, physical exercise, and their interactions on the risk of dyslipidemia in older adults aged ≥80 (the oldest old) in China.Methods:The study subjects were the oldest old from four rounds of Healthy Aging and Biomarkers Cohort Study (2008-2009, 2011-2012, 2014 and 2017-2018). The information about their demographic characteristics, lifestyles, physical examination results and others were collected, and fasting venous blood samples were collected from them for blood lipid testing. Competing risk model was used to analyze the causal associations of sleep duration and physical exercise with the risk for dyslipidemia. Restricted cubic spline (RCS) function was used to explore the dose-response relationship between sleep duration and the risk for dyslipidemia. Additive and multiplicative interaction model were used to explore the interaction of sleep duration and physical exercise on the risk for dyslipidemia.Results:The average age of 1 809 subjects was (93.1±7.7) years, 65.1% of them were women. The average sleep duration of the subjects was (8.0±2.5) hours/day, 28.1% of them had sleep duration for less than 7 hours/day, and 27.2% had sleep for duration more than 9 hours/day at baseline survey. During the 9-year cumulative follow-up of 6 150.6 person years (follow-up of average 3.4 years for one person), there were 304 new cases of dyslipidemia, with an incidence density of 4 942.6/100 000 person years. The results of competitive risk model analysis showed that compared with those who slept for 7-9 hours/day, the risk for dyslipidemia in oldest old with sleep duration >9 hours/day increased by 22% ( HR=1.22, 95% CI: 1.07-1.39). Compared with the oldest old having no physical exercise, the risk for dyslipidemia in the oldest old having physical exercise decreased by 33% ( HR=0.67, 95% CI: 0.57-0.78). The RCS function showed a linear positive dose-response relationship between sleep duration and the risk for hyperlipidemia. The interaction analysis showed that physical exercise and sleep duration had an antagonistic effect on the risk for hyperlipidemia. Conclusion:Physical exercise could reduce the adverse effects of prolonged sleep on blood lipids in the oldest old.