Diffuse large B-cell lymphoma （DLBCL） is a highly heterogeneous disease. Although standard first-line regimens can cure ＞50% of patients， approximately one-third of them develop relapsed/refractory DLBCL （r/r DLBCL）. Consequently， immunotherapy targeting molecular abnormalities has become pivotal for managing r/r DLBCL. The results of this review show that with advances in understanding DLBCL pathogenesis and the tumor immune microenvironment， antibody-based therapies have evolved rapidly， progressing from monoclonal antibodies （e.g.， rituximab， tafasitamab） to bispecific antibodies（e.g.， odronextamab，glofitamab， epcoritamab） and antibody-drug conjugate （e.g.， polatuzumab vedotin， loncastuximab tesirine）. These engineered agents enhance immune cytotoxicity and tumor-specific targeting， providing novel therapeutic options for r/r DLBCL patients.

OBJECTIVE: To investigate the biological behavior, differentiation ability, and differential gene expression of lymph node mesenchymal stem cells (MSCs) in patients with diffuse large B-cell lymphoma (DLBCL) and reactive lymphoid hyperplasia (RLH), providing a theoretical basis for clinical chemotherapy resistance. METHODS: Lymph node MSCs from patients with DLBCL and RLH were separated, passaged and cultured. The cell morphology and growth status were observed. Flow cytometry was performed to detect the immune phenotype of MSCs. The in vitro directed differentiation ability of the two types of MSCs was observed. High-throughput sequencing was used to analyze the differential gene expression and enrichment of two groups of MSCs. RESULTS: The lymph node MSCs of patients with DLBCL and RLH had similar cell morphology and growth characteristics, and both groups of MSCs expressed CD90, CD105, and CD73 on the cell surface. Compared with lymph node MSCs derived from patients with RLH, lymph node MSCs derived from DLBCL patients showed stronger osteogenic and adipogenic differentiation abilities. High-throughput sequencing results displayed that lymph node MSCs derived from DLBCL patients significantly upregulated some genes such as TOP2A, LFNG, GRIA3, SEC14L2, SPON2, AURKA, LRRC15, FOXD1, HOXC9, CDC20 and remarkably downregulated some genes such as TBC1D8, LDLR, PCDHAC2, POLH, PKP2, ANKRD37, DMKN, HSD11B1, ARHGAP20, PTGS1,etc. CONCLUSION Lymph node MSCs in DLBCL patients exhibit unique biological behavior and gene expression profiles, which may be closely related to clinical chemotherapy resistance.
Humans ; Mesenchymal Stem Cells/cytology* ; Lymphoma, Large B-Cell, Diffuse/pathology* ; Cell Differentiation ; Lymph Nodes/pathology* ; Pseudolymphoma/pathology*

BACKGROUND: Non-invasive computed tomography angiography (CTA)-based fractional flow reserve (CT-FFR) could become a gatekeeper to invasive coronary angiography. Deep learning (DL)-based CT-FFR has shown promise when compared to invasive FFR. To evaluate the performance of a DL-based CT-FFR technique, DeepVessel FFR (DVFFR). METHODS: This retrospective study was designed for iScheMia Assessment based on a Retrospective, single-center Trial of CT-FFR (SMART). Patients suspected of stable coronary artery disease (CAD) and undergoing both CTA and invasive FFR examinations were consecutively selected from the Beijing Anzhen Hospital between January 1, 2016 to December 30, 2018. FFR obtained during invasive coronary angiography was used as the reference standard. DVFFR was calculated blindly using a DL-based CT-FFR approach that utilized the complete tree structure of the coronary arteries. RESULTS: Three hundred and thirty nine patients (60.5 ±10.0 years and 209 men) and 414 vessels with direct invasive FFR were included in the analysis. At per-vessel level, sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of DVFFR were 94.7%, 88.6%, 90.8%, 82.7%, and 96.7%, respectively. The area under the receiver operating characteristics curve (AUC) was 0.95 for DVFFR and 0.56 for CTA-based assessment with a significant difference (P < 0.0001). At patient level, sensitivity, specificity, accuracy, PPV and NPV of DVFFR were 93.8%, 88.0%, 90.3%, 83.0%, and 95.8%, respectively. The computation for DVFFR was fast with the average time of 22.5 ± 1.9 s. CONCLUSIONS The results demonstrate that DVFFR was able to evaluate lesion hemodynamic significance accurately and effectively with improved diagnostic performance over CTA alone. Coronary artery disease (CAD) is a critical disease in which coronary artery luminal narrowing may result in myocardial ischemia. Early and effective assessment of myocardial ischemia is essential for optimal treatment planning so as to improve the quality of life and reduce medical costs.

Colonic mucosal healing is the ultimate goal of ulcerative colitis (UC) treatment, but it remains difficult to realize. Given the higher incidence of UC in males and the beneficial effect of estrogen on UC, we conducted this study to examine the therapeutic potential of estrogen receptor β (ERβ), the primary ER subtype in colon, on mucosal healing in UC. Our study is the first to report that ERβ activation degree was positively correlated with mucosal healing in patients with UC. Furthermore, ERβ activation enhanced mucosal healing in mice with dextran sulfate sodium-induced and biopsy-induced colonic injuries. Mechanistically, ERβ activation promoted autophagy of colonic epithelial cells by inhibiting branched-chain amino acid transport, leading to focal adhesion kinase (FAK) activation. Activated FAK promoted focal adhesion turnover and colonic epithelial cell migration, ultimately facilitating mucosal healing. ERβ ^-/- colitis mice exhibited impaired mucosal healing compared to wild-type littermates, highlighting the crucial effect of ERβ. Importantly, combination with ERβ-agonist diarylpropionitrile enhanced the amelioration of 5-aminosalicylic acid, a standard UC treatment agent, against mouse colitis. These findings attest to the crucial role of ERβ activation in colonic mucosal healing and may further inform the development of novel strategies for UC treatment.

OBJECTIVES: We propose a YOLOv11-TDSP model for improving the accuracy of dental abnormality detection on panoramic oral X-ray images. METHODS: The SHSA single-head attention mechanism was integrated with C2PSA in the backbone layer to construct a new C2PSA_SHSA attention mechanism. The computational redundancy was reduced by applying single-head attention to some input channels to enhance the efficiency and detection accuracy of the model. A small object detection layer was then introduced into the head layer to correct the easily missed and false detections of small objects. Two rounds of structured pruning were implemented to reduce the number of model parameters, avoid overfitting, and improve the average precision. Before training, data augmentation techniques such as brightness enhancement and gamma contrast adjustment were employed to enhance the generalization ability of the model. RESULTS: The experiment results showed that the optimized YOLOv11-TDSP model achieved an accuracy of 94.5%, a recall rate of 92.3%, and an average precision of 95.8% for detecting dental abnormalities. Compared with the baseline model YOLOv11n, these metrics were improved by 6.9%, 7.4%, and 5.6%, respectively. The number of parameters and computational cost of the YOLOv11-TDSP model were only 12% and 13% of those of the high-precision YOLOv11x model, respectively. CONCLUSIONS The lightweight YOLOv11-TDSP model is capable of highly accurate identification of various dental diseases on panoramic oral X-ray images.
Radiography, Panoramic/methods* ; Humans ; Algorithms ; Tooth Abnormalities/diagnostic imaging*

Early correction of childhood malocclusion is timely managing morphological, structural, and functional abnormalities at different dentomaxillofacial developmental stages. The selection of appropriate imaging examination and comprehensive radiological diagnosis and analysis play an important role in early correction of childhood malocclusion. This expert consensus is a collaborative effort by multidisciplinary experts in dentistry across the nation based on the current clinical evidence, aiming to provide general guidance on appropriate imaging examination selection, comprehensive and accurate imaging assessment for early orthodontic treatment patients.
Humans ; Malocclusion/diagnostic imaging* ; Child ; Consensus

OBJECTIVE: This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study. METHODS: A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants. RESULTS: Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m ²) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m ²) and obesity (≥ 28.0 kg/m ²) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m ² ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses. CONCLUSION This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans ; Body Mass Index ; China/epidemiology* ; Male ; Female ; Middle Aged ; Prospective Studies ; Rural Population/statistics & numerical data* ; Aged ; Follow-Up Studies ; Adult ; Mortality ; Cause of Death ; Obesity/mortality* ; Overweight/mortality*

Wastewater-based epidemiology has emerged as a transformative surveillance tool for estimating substance consumption and monitoring disease prevalence, particularly during the COVID-19 pandemic. It enables the population-level monitoring of illicit drug use, pathogen prevalence, and environmental pollutant exposure. In this perspective, we summarize the key challenges specific to the Chinese context: (1) Sampling inconsistencies, necessitating standardized 24-hour composite protocols with high-frequency autosamplers (≤ 15 min/event) to improve the representativeness of samples; (2) Biomarker validation, requiring rigorous assessment of excretion profiles and in-sewer stability; (3) Analytical method disparities, demanding inter-laboratory proficiency testing and the development of automated pretreatment instruments; (4) Catchment population dynamics, reducing estimation uncertainties through mobile phone data, flow-based models, or hydrochemical parameters; and (5) Ethical and data management concerns, including privacy risks for small communities, mitigated through data de-identification and tiered reporting platforms. To address these challenges, we propose an integrated framework that features adaptive sampling networks, multi-scale wastewater sample banks, biomarker databases with multidimensional metadata, and intelligent data dashboards. In summary, wastewater-based epidemiology offers unparalleled scalability for equitable health surveillance and can improve the health of the entire population by providing timely and objective information to guide the development of targeted policies.
China/epidemiology* ; Humans ; Wastewater/analysis* ; COVID-19/epidemiology* ; Public Health ; Wastewater-Based Epidemiological Monitoring ; SARS-CoV-2