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.

ObjectiveTo systematically and objectively characterize the pharmacological effects of Fufang Biejia Ruangan pills （FBRP） in the intervention of alcoholic liver disease （ALD） using acute and chronic ALD mouse models and to elucidate its molecular mechanisms. MethodFifty SPF-grade male BALB/c mice were randomly divided into the normal group， model group， and FBRP low-， medium-， and high-dose groups （9.6， 19.2， 38.4 mg·kg^-1）. Except for the normal group， the remaining groups were given 56° white wine by gavage to establish the acute ALD model， with samples collected after 4 weeks. Thirty SPF-grade male C57BL/6N mice were randomly divided into the normal group， model group， and FBRP medium-dose group （19.2 mg·kg^-1）. The chronic ALD mouse model was established using the Lieber-DeCarli method over a 10-week period. Inflammatory markers in liver tissues were assessed using hematoxylin-eosin （HE）， Sirius Red， oil red O staining， and enzyme-linked immunosorbent assay （ELISA）. Intoxication behaviors of each group were objectively evaluated through sobering-up time， net-catching， and pole-climbing tests. Further bioinformatics analyses based on clinical transcriptomic data were conducted to identify key targets and molecular mechanisms of FBRP in alleviating ALD through liver-brain dialogue， with experimental validation by ELISA， Western blot， and immunohistochemical staining. ResultCompared with the normal group, the levels of aspartate aminotransferase （AST） and alanine aminotransferase （ALT） in liver tissues of mice in the acute and chronic ALD model groups were significantly increased （P<0.05）. Compared with the model group， the levels of AST and ALT in liver tissue of mice in FBRP groups were significantly decreased （P<0.05）. Compared with the normal group， the time of grasping the net and climbing the pole in the acute ALD model group was significantly decreased within 4 weeks （P<0.01）. Compared with the model group， the grasping and climbing time of FBRP high dose groups increased significantly within 4 weeks （P<0.05）. Compared with the normal group， the expression of high mobility group protein B1 （HMGB1） protein in liver tissue and prefrontal lobe tissue of mice in the chronic ALD model group was significantly increased （P<0.01）. Compared with the model group， the expression of HMGB1 protein in FBRP medium dose group was significantly decreased （P<0.05，P<0.01）. Compared with the normal group， the expression of brain-derived neurotrophic factor （BDNF） protein and the release of γ-aminobutyric acid （GABA） in the prefrontal cortex of the model group were significantly decreased （P<0.01）. Compared with the model group, the expression of BDNF protein and the release of GABA in the FBRP medium dose group were significantly increased （P<0.05）. ConclusionThis study revealed that FBRP improved key pathological changes in ALD by modulating liver-brain dialogue mediated by the HMGB1-BDNF axis. These findings provide experimental evidence for the clinical use of FBRP in treating ALD and offer new insights for the development of ALD therapeutic agents.

ObjectiveTo identify the pharmacodynamic material basis of Ruyi Zhenbaowan in relieving neuropathic pain by integrating the calculation of biological network proximity and pharmacokinetic characterization. MethodThe interaction network of "drug candidate target-related gene of disease" was constructed by Cytoscape 3.8.2， and the average shortest path value of each drug putative target acting on neuropathic pain-related genes in this network was calculated by Pesca 3.8.0 tool so as to evaluate the network proximity between them， and screen prescription candidate targets with strong intervention efficiency and their corresponding potential effect components. After that， plasma and cerebrospinal fluid samples were collected from rats after administration of Ruyi Zhenbaowan at set time points， and the contents of potential effect components in samples was quantified by ultra performance liquid chromatography-quadrupole-ion trap mass spectrometry（UPLC-Q-TRAP/MS）， and drug concentration-time curves were plotted， then the pharmacokinetic parameters were calculated by DAS 2.1.1. ResultBy evaluating the network proximity between candidate targets and neuropathic pain-related genes in the interaction network， a total of 40 putative targets of Ruyi Zhenbaowan with strong intervention effects on neuropathic pain-related genes， such as estrogen receptor 1（ESR1）， cyclic adenosine monophosphate（cAMP）-dependent protein kinase catalytic subunit alpha（PRKACA） and protein kinase B1 （Akt1）， and 10 corresponding potential effect components， such as glycyrrhizic acid and betulinic acid， were obtained. Pharmacokinetic characterization showed that among the 10 potential effect components， gallic acid， apigenin-7-O-glucuronide， glycyrrhizic acid and apigenin were well absorbed and metabolized in plasma and cerebrospinal fluid， with long onset time and good bioavailability. ConclusionFrom the perspective of efficacy-target-constituent-pharmacokinetic， this study analyzes the main effective materials of Ruyi Zhenbaowan， such as glycyrrhizic acid， gallic acid， apigenin-7-O-glucuronide and apigenin， which have a high exposure in plasma or cerebrospinal fluid and have a strong intervention effect on neuropathic pain. The related results provide reliable experimental evidences for clarifying the material basis and developing quality standards of Ruyi Zhenbaowan.

ObjectiveTo identify the chemical constituents of Ruyi Zhenbaowan in vitro and in vivo. MethodThe chemical constituents of Ruyi Zhenbaowan were identified based on UHPLC-Q Exactive Orbitrap HRMS. A total of 12 male SD rats were randomized into two groups： control （pure water） and Ruyi Zhenbaowan （1.8 g·kg^-1）. The rats were administrated with the suspension of Ruyi Zhenbaowan or pure water by gavage. After 1.5 h， the plasma and cerebrospinal fluid were collected. Chromatographic separation was performed on a Waters ACQUITY UPLC BEH C₁₈ column （2.1 mm × 150 mm， 1.7 μm） with a mixture of 0.1% formic acid aqueous solution （A） and acetonitrile （B） as the mobile phase. Gradient elution was carried out according to the procedure of 0~15 min，97%~80%A；15~30 min ，80%~60%A；30~40 min，60%~30%A；40~45 min，30%~5%A. The ion source was electrospray ionization， and scan range was m/z 100-1 500. The prototype components and the components in the plasma and cerebrospinal fluid were analyzed qualitatively by scanning in positive and negative ion modes and identified by comparison with the data in published literature and the information of standard substances. ResultA total of 126 chemical constituents were identified from the 80% methanol solution of Ruyi Zhenbaowan， and 14 and 7 prototype constituents were detected in the plasma and the cerebrospinal fluid， respectively. In addition， the fragmentation rules of apigenin， apigenin-7-O-glucuronide， galangin， liquiritin， piperine， glycyrrhizic acid， eugenol， gallic acid， and cholic acid were deduced. ConclusionThis study achieved rapid multicomponent characterization and identification of Ruyi Zhenbaowan in vitro and in vivo， providing theoretical support for exploring active substances and performing quality control.l.

Based on the reality that the Health Management Alliance(HMA)has been initially established nationwide as an important component of the integrated health care service system,on the basis of summarizing the evolution process of transaction cost theory,discriminating the essential characteristics of HMA,deconstructing HMA transaction cost and putting forward a quantitative measurement model,the coping strategies of HMA transaction cost are summarized into three levels:"knowing","reducing"and"increasing".It aims to solve the high transaction cost effectively and explore the economic system supply path;correct the deviation of the field of vision which only focuses on the"output orientation"of HMA in the early stage,and arouse the attention and thinking of all parties on the transaction cost of HMA;provide a new position and a new idea for HMA policy research and reform practice in China.