The construction of a training system for visiting physicians in the department of rare diseases in China is an important measure to improve the overall diagnosis and treatment capacity for rare diseases and address the critical challenge of insufficient knowledge and skills among clinicians in practice. This article systematically describes the visiting physician training system established by the Department of Rare Diseases at Peking Union Medical College Hospital. It summarizes the training objectives and positioning, design logic, and learning modules of the system, aiming to provide a reference for the construction of the specialized talent team for rare diseases in China.

OBJECTIVE: To explore the genetic etiology of 46 Chinese pedigrees affected with Hereditary multiple exostoses (HME) and provide genetic counseling and reproductive intervention. METHODS: Whole-exome sequencing and Sanger sequencing were carried out on 87 patients from the 46 pedigrees to analyze the variants of EXT1 and EXT2 genes. Pathogenicity of the variants was assessed based on the guidelines from the American College of Medical Genetics and Genomics and Association for Molecular Pathology (ACMG/AMP). Prenatal diagnosis and preimplantation genetic testing (PGT) were provided for couples with identified pathogenic mutations. This study was approved by the Medical Ethics Committee of the hospital (Ethics No.: LL-SC-SG-2014-010). RESULTS: In total 17 and 22 pathogenic variants were respectively identified in the EXT1 and EXT2 genes, among which 5 EXT1 and 12 EXT2 variants were unreported previously. Three patients with no family history were found to harbor de novo variants of the EXT1 gene. Twenty nine couples had opted for PGT or underwent prenatal diagnosis following natural conception, and 17 healthy babies were born. CONCLUSION This study has clarified the genetic etiology of 45 HME pedigrees and identified 17 novel variants, which has enriched the mutational spectrum of the EXT1 and EXT2 genes. Reproductive intervention through PGT and prenatal diagnosis have prevented the recurrence of HME in these families.
Humans ; Female ; Male ; Pedigree ; Exostoses, Multiple Hereditary/diagnosis* ; N-Acetylglucosaminyltransferases/genetics* ; Adult ; Exostosin 1 ; Asian People/genetics* ; Genetic Testing ; Exostosin 2 ; Mutation ; China ; Prenatal Diagnosis ; Pregnancy ; Genetic Counseling ; Preimplantation Diagnosis ; Exome Sequencing ; East Asian People

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective: Adolescent depression is a highly prevalent and disabling mental disorder with unclear pathophysiology and unfavorable treatment outcomes. Recent efforts have been focusing on searching for biomarkers as specific indicators of adolescent depression. We performed a systematic literature review and meta-analysis, specifically including studies with healthy control groups as an inclusion criterion. This approach helps to avoid confounding factors and provides more accurate results regarding the inflammatory and immune biomarkers associated with adolescent depression. Methods: Three electronic databases were searched for studies comparing the means and changes in the biomarkers between depressed adolescent patients and healthy controls published in English until February 2024. Two authors independently performed the screening, quality assessment, and data extraction of the studies. A meta-analysis was conducted on outcomes reported by two or more studies using a random-effects model and presented Forrest plots and test statistics (I2) for heterogeneity analysis. Results: Nine studies were included in the review, including seven case-control studies and two cross-sectional studies. These studies included 24 target biomarkers, 13 of which were quantified in 2 or more studies. Compared to the healthy controls, the depressed adolescents had significantly higher values in ten indicators. Additionally, the depressed adolescents had lower procalcitonin levels than the healthy controls. The two groups showed no significant differences in the remaining 13 biomarkers. Conclusion Our findings offer fresh insights into the pathophysiology of inflammatory and immune aspects of adolescent depression and provide helpful guidance in developing targeted and effective intervention and prevention strategies to address adolescent depression.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

The catalytic activity of 3-mercaptopyruvate (3MP) sulfurtransferase (MPST) converts 3MP to hydrogen sulfide (H₂S). However, the regulatory mechanisms governing MPST and its impact on the brain remain largely unexplored. Our study reveals the neuroprotective role of endothelial MPST-generated H₂S, regulated by protein phosphatase 2A (PP2A). Bioinformatics analysis and RNA sequencing demonstrated that endothelial PP2A is associated with neurodegenerative disease pathways. Cerebral ischemic mice exhibited significant inactivation of endothelial PP2A, evidenced by the reduction of PP2Acα in the brain endothelium. Mice with endothelium-specific null PP2A (PP2A^EC-cKO) exhibited neuronal loss, cognitive dysfunction, and long-term potentiation deficits. Postnatal inactivation of endothelial PP2A also contributes to cognitive dysfunction and neuronal loss. However, regaining endothelial PP2A activity by overexpressing Ppp2ca rescued neuronal dysfunction. Mechanistically, PP2A deficiency is intricately linked to the MPST-H₂S signaling pathway. A robust reduction in endothelial MPST-dependent H₂S production followed PP2A deficiency. Exogenous H₂S treatment and AAV-mediated overexpression of MPST in brain endothelial cells significantly mitigated neuronal dysfunction in PP2A^EC-cKO mice. Furthermore, PP2A deficiency promotes an increase in calcium influx and calpain2 phosphorylation, subsequently leading to MPST degradation. The PP2A activator (FTY720) and MPST activator (3MP sodium) both remarkably restored endothelial MPST-dependent H₂S production, subsequently rescuing ischemia-induced neurological deficits. In conclusion, our study demonstrates that endothelial PP2A deficiency leads to MPST degradation by activating calpain2, thus damaging neuronal function.

Addressing the enduring challenge of evaluating traditional Chinese medicines (TCMs), the integrated evidence chain-based effectiveness evaluation of TCMs (Eff-iEC) has emerged. This paper explored its capacity through a demonstration study that evaluated the effectiveness evidence of six commonly used anti-hepatic fibrosis Chinese patent medicines (CPMs), including Biejiajian Pill (BP), Dahuang Zhechong Pill (DZP), Biejia Ruangan Compound (BRC), Fuzheng Huayu Capsule (FHC), Anluo Huaxian Pill (AHP), and Heluo Shugan Capsule (HSC), using both Eff-iEC and the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) system. The recognition of these CPMs within the TCM academic community was also assessed through their inclusion in relevant medical documents. Results showed that the evidence of BRC and FHC received higher assessments in both Eff-iEC and GRADE system, while the assessments for others varied. Analysis of community recognition revealed that Eff-iEC more accurately reflects the clinical value of these CPMs, exhibiting superior evaluative capabilities. By breaking through the conventional pattern of TCMs effectiveness evaluation, Eff-iEC offers a novel epistemology that better aligns with the clinical realities and reasoning of TCMs, providing a coherent methodology for clinical decision-making, new drug evaluations, and health policy formulation.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.