Objective To summarize and analyze the efficacy and influencing factors of second allogeneic hematopoietic stem cell transplantation (allo-HSCT) for acute leukemia relapsing after the first allo-HSCT. Methods Clinical data of 17 patients with acute leukemia who underwent second allo-HSCT at Peking University First Hospital from January 2005 to December 2024 were retrospectively analyzed. Results Among the 17 patients, 7 achieved long-term disease-free survival after second transplantation. The median progression-free survival after successful second transplantation was 7 months (range 8 days to 69 months). The relapse fatality was 24%, and the transplant-related fatality was 35%. Conclusions Second transplantation is an effective treatment for relapsed and refractory acute leukemia, but the relapse fatality and transplant-related fatality remain high. Patient age, time of relapse after the first transplantation and disease status before second transplantation are all factors that affect the efficacy of second transplantation. Younger age, late relapse and complete remission of disease before second transplantation are all beneficial for long-term disease-free survival after second transplantation.

BACKGROUND: The global rise in diabetes prevalence is a pressing concern. Despite initiatives like "The Healthy Beijing Action 2020-2030" advocating for increased awareness, treatment, and control, the specific situation in Beijing remains unexplored. This study aimed to analyze the trends in diabetes prevalence, awareness, treatment, and control among Beijing adults. METHODS: Through a stratified multistage probability cluster sampling method, a series of representative cross-sectional surveys were conducted in Beijing from 2005 to 2022, targeting adults aged 18-79 years. A face-to-face questionnaire, along with body measurements and laboratory tests, were administered to 111,943 participants. Data from all survey were age- and/or gender-standardized based on the 2020 Beijing census population. Annual percentage rate change (APC) or average annual percentage rate change (AAPC) was calculated to determine prevalence trends over time. Complex sampling logistic regression models were employed to explore the relationship between various characteristics and diabetes. RESULTS: From 2005 to 2022, the total prevalence of diabetes among Beijing adults aged 18-79 years increased from 9.6% (95% CI: 8.8-10.4%) to 13.9% (95% CI: 13.1-14.7%), with an APC/AAPC of 2.1% (95% CI: 1.1-3.2%, P <0.05). Significant increases were observed among adults aged 18-39 years and rural residents. Undiagnosed diabetes rose from 3.5% (95% CI: 3.2-4.0%) to 7.2% (95% CI: 6.6-7.9%) with an APC/AAPC of 4.1% (95% CI: 0.5-7.3%, P <0.05). However, diabetes awareness and treatment rates showed annual declines of 1.4% (95% CI: -3.0% to -0.2%, P <0.05) and 1.3% (95% CI: -2.6% to -0.2%, P <0.05), respectively. The diabetes control rate decreased from 21.5% to 19.1%, although not statistically significant (APC/AAPC = -1.5%, 95% CI: -5.6% to 1.9%). Overweight and obesity were identified as risk factors for diabetes, with ORs of 1.65 (95% CI: 1.38-1.98) and 2.48 (95% CI: 2.07-2.99), respectively. CONCLUSIONS The prevalence of diabetes in Beijing has significantly increased between 2005 and 2022, particularly among young adults and rural residents. Meanwhile, there has been a concerning decrease in diabetes awareness and treatment rates, while control rates have remained stagnant. Regular blood glucose testing, especially among adults aged 18-59 years, should be warranted. Furthermore, being male, elderly, overweight, or obese was associated with higher diabetes risk, suggesting the needs for targeted management strategies.
Humans ; Adult ; Middle Aged ; Male ; Female ; Aged ; Adolescent ; Young Adult ; Cross-Sectional Studies ; Diabetes Mellitus/epidemiology* ; Beijing/epidemiology* ; Prevalence ; China/epidemiology* ; Surveys and Questionnaires

Recent advances in large models demonstrate significant prospects for transforming the field of medical imaging. These models, including large language models, large visual models, and multimodal large models, offer unprecedented capabilities in processing and interpreting complex medical data across various imaging modalities. By leveraging self-supervised pretraining on vast unlabeled datasets, cross-modal representation learning, and domain-specific medical knowledge adaptation through fine-tuning, large models can achieve higher diagnostic accuracy and more efficient workflows for key clinical tasks. This review summarizes the concepts, methods, and progress of large models in medical imaging, highlighting their potential in precision medicine. The article first outlines the integration of multimodal data under large model technologies, approaches for training large models with medical datasets, and the need for robust evaluation metrics. It then explores how large models can revolutionize applications in critical tasks such as image segmentation, disease diagnosis, personalized treatment strategies, and real-time interactive systems, thus pushing the boundaries of traditional imaging analysis. Despite their potential, the practical implementation of large models in medical imaging faces notable challenges, including the scarcity of high-quality medical data, the need for optimized perception of imaging phenotypes, safety considerations, and seamless integration with existing clinical workflows and equipment. As research progresses, the development of more efficient, interpretable, and generalizable models will be critical to ensuring their reliable deployment across diverse clinical environments. This review aims to provide insights into the current state of the field and provide directions for future research to facilitate the broader adoption of large models in clinical practice.
Humans ; Diagnostic Imaging/methods* ; Precision Medicine/methods* ; Image Processing, Computer-Assisted/methods*

OBJECTIVE: To screen novel diagnostic marker or therapeutic target for multiple myeloma (MM). METHODS: Sel1L, SPAG4, KCNN3 and PARM1 were identified by bioinformatics method based on GEO database as high expression genes in MM. Their RNA and protein expression levels in bone marrow mononuclear cells from myeloma cell lines U266, NCI-H929, MM.1s, RPMI8226 and leukemia cell line THP1, as well as 31 MM patients were evaluated by RT-PCR and Western blot, respectively. Meanwhile, 5 samples of bone marrow from healthy donors for allogeneic hematopoietic stem cell transplantation were employed as controls. RESULTS: Compared with leukemia cell line THP1, the expression levels of KCNN3, PARM1 and Sel1L mRNA were significantly increased in myeloma cell lines U266, NCI-H929 and MM.1s, while PARM1 was further increased in myeloma cell lines 8226. Western blot showed that the 4 genes were all expressed in the 4 myeloma cell lines. Compared with healthy controls, the expression levels of Sel1L, SPAG4, KCNN3 and PARM1 mRNA were significantly higher in MM patients (all P < 0.05). Western blot showed that the 4 genes were all expressed in MM patients, and the protein expression level of Sel1L and KCNN3 were significantly different compared with healthy donors (all P < 0.01). CONCLUSION Sel1L, SPAG4, KCNN3 and PARM1 may be potential diagnostic markers and therapeutic targets for MM.
Humans ; Multiple Myeloma/genetics* ; Cell Line, Tumor ; Proteins/metabolism* ; Computational Biology ; RNA, Messenger/genetics*

OBJECTIVE: To screen anti-tumor drugs that improve antigen processing and presentation in acute myeloid leukemia (AML) cells. METHODS: A TCR-like or TCR mimic antibody that can specifically recognize HLA-A*0201:WT1_126-134 ( RMFPNAPYL) complex (hereafter referred to as HLA-A2:WT1) was synthesized to evaluate the function of antigen processing and presentation machinery (APM) in AML cells. AML cell line THP1 was incubated with increasing concentrations of IFN-γ, hypomethylating agents (HMA), immunomodulatory drugs (IMiD), proteasome inhibitors (PI) and γ-secretase inhibitors (GSI), followed by measuring of HLA-ABC, HLA-A2 and HLA-A2:WT1 levels by flow cytometry at consecutive time points. RESULTS: The TCR-like antibody we generated only binds to HLA-A*0201⁺WT1⁺ cells, indicating the specificity of the antibody. HLA-A2:WT1 level of THP-1 cells detected with the TCR-like antibody was increased significantly after co-incubation with IFN-γ, showing that the HLA-A2:WT1 TCR like antibody could evaluate the function of APM. Among the anti-tumor agents screened in this study, GSI (LY-411575) and HMA (decitabine and azacitidine) could significantly increase the HLA-A2:WT1 level. The IMiD lenalidomide and pomalidomide could aslo upregulate the expression of HLA-A2:WT1 complex under certain concentrations of the drugs and incubation time. As proteasome inhibitors, carfilzomib could significantly decreased the expression of HLA-A2:WT1, while bortezomib had no significant effect on HLA-A2:WT1 expression. CONCLUSION HLA-A2:WT1 TCR-like antibody can effectively reflect the APM function. Some of the anti-tumor drugs can affect the APM function and immunogenicity of tumor cells.
Humans ; Leukemia, Myeloid, Acute/immunology* ; Antineoplastic Agents/pharmacology* ; Antigen Presentation/drug effects* ; HLA-A2 Antigen/immunology* ; Receptors, Antigen, T-Cell/immunology* ; Cell Line, Tumor ; Interferon-gamma

Extensive evidence has demonstrated that glucagon-like peptide-1 receptor agonists (GLP-1RAs) can ameliorate obesity. Our previous studies revealed that (Ex-4)₂-Fc, a long-acting GLP-1RA we developed, depends on the leptin pathway to treat obesity. However, the mechanisms linking (Ex-4)₂-Fc and leptin resistance remain largely unclear. To address this question, we explored the mechanism of GLP-1RAs from the perspective of the gut microbiota, as increasing evidence indicates an important link between the gut microbiota and obesity. This study aimed to explore the potential role of the gut microbiota in the treatment of GLP-1RAs. We found that (Ex-4)₂-Fc treatment reshaped obesity-induced gut microbiota disturbances and substantially increased the abundance of Akkermansia muciniphila (Am). In addition, (Ex-4)₂-Fc did not respond well in antibiotic-treated (ATB) Obese mice. Subsequent studies have shown that this defect can be overcome by gavage with Am. In addition, we found that Am enhanced (Ex-4)₂-Fc therapy by producing the metabolite inosine. Inosine regulates the macrophage adenosine A2A receptor (A2A) pathway to indirectly reduce leptin levels in adipocytes Thus, elucidating the role of metabolites in regulating the leptin pathway will provide new insights into GLP-1RAs therapy and may lead to more effective strategies for guiding the clinical use of antidiabetic agents.

Cemental tear is a rare and indetectable condition unless obvious clinical signs present with the involvement of surrounding periodontal and periapical tissues. Due to its clinical manifestations similar to common dental issues, such as vertical root fracture, primary endodontic diseases, and periodontal diseases, as well as the low awareness of cemental tear for clinicians, misdiagnosis often occurs. The critical principle for cemental tear treatment is to remove torn fragments, and overlooking fragments leads to futile therapy, which could deteriorate the conditions of the affected teeth. Therefore, accurate diagnosis and subsequent appropriate interventions are vital for managing cemental tear. Novel diagnostic tools, including cone-beam computed tomography (CBCT), microscopes, and enamel matrix derivatives, have improved early detection and management, enhancing tooth retention. The implementation of standardized diagnostic criteria and treatment protocols, combined with improved clinical awareness among dental professionals, serves to mitigate risks of diagnostic errors and suboptimal therapeutic interventions. This expert consensus reviewed the epidemiology, pathogenesis, potential predisposing factors, clinical manifestations, diagnosis, differential diagnosis, treatment, and prognosis of cemental tear, aiming to provide a clinical guideline and facilitate clinicians to have a better understanding of cemental tear.
Humans ; Dental Cementum/injuries* ; Consensus ; Diagnosis, Differential ; Cone-Beam Computed Tomography ; Tooth Fractures/therapy*

Quantitative structure-retention relationship (QSRR) is an important tool in chromatography. QSRR examines the correlation between molecular structures and their retention behaviors during chromatographic separation. This approach involves developing models for predicting the retention time (RT) of analytes, thereby accelerating method development and facilitating compound identification. In addition, QSRR can be used to study compound retention mechanisms and support drug screening efforts. This review provides a comprehensive analysis of QSRR workflows and applications, with a special focus on the role of artificial intelligence-an area not thoroughly explored in previous reviews. Moreover, we discuss current limitations in RT prediction and propose promising solutions. Overall, this review offers a fresh perspective on future QSRR research, encouraging the development of innovative strategies that enable the diverse applications of QSRR models in chromatographic analysis.

Functional dyspepsia (FD), characterized by persistent or recurrent dyspeptic symptoms without identifiable organic, systemic or metabolic causes, is an increasingly recognized global health issue. The objective of this guideline is to equip clinicians and nursing professionals with evidence-based strategies for the management and treatment of adult patients with FD using traditional Chinese medicine (TCM). The Guideline Development Group consulted existing TCM consensus documents on FD and convened a panel of 35 clinicians to generate initial clinical queries. To address these queries, a systematic literature search was conducted across PubMed, EMBASE, the Cochrane Library, China National Knowledge Infrastructure (CNKI), VIP Database, China Biology Medicine (SinoMed) Database, Wanfang Database, Traditional Medicine Research Data Expanded (TMRDE), and the Traditional Chinese Medical Literature Analysis and Retrieval System (TCMLARS). The evidence from the literature was critically appraised using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. The strength of the recommendations was ascertained through a consensus-building process involving TCM and allopathic medicine experts, methodologists, pharmacologists, nursing specialists, and health economists, leveraging their collective expertise and empirical knowledge. The guideline comprises a total of 43 evidence-informed recommendations that span a range of clinical aspects, including the pathogenesis according to TCM, diagnostic approaches, therapeutic interventions, efficacy assessments, and prognostic considerations. Please cite this article as: Zhang SS, Zhao LQ, Hou XH, Bian ZX, Zheng JH, Tian HH, Yang GH, Hong WS, He YY, Liu L, Shen H, Li YP, Xie S, Shu J, Zeng BF, Li JX, Liu Z, Xiao ZH, Xiao JD, Zheng PY, Huang SG, Chen SL, Fei GJ. International clinical practice guideline on the use of traditional Chinese medicine for functional dyspepsia (2025). J Integr Med. 2025; 23(5):502-518.
Dyspepsia/drug therapy* ; Humans ; Medicine, Chinese Traditional/methods* ; Practice Guidelines as Topic ; Drugs, Chinese Herbal/therapeutic use*