Objective: To evaluate the diagnostic performance of magnetic resonance spectroscopy (MRS) in discriminating fumarate hydratase-deficient (FH-d) uterine leiomyomas (ULs) from FH-preserved ULs. Materials and Methods: This study consisted of three stages, with independent cohorts recruited for each stage: 1) sample-size estimation was retrospectively performed on UL specimens (diameter ≥3 cm; age, 20–40 years) from our database with immunohistochemistry (IHC) for 2-succinocysteine (2-SC) as the reference, without genetic testing, 2) MRS sequence optimization in confirmed FH germline mutation participants with ultrasound-detected ULs (diameter ≥3 cm), without IHC analysis, and 3) prospective diagnostic test accuracy was evaluated in consecutive participants with ultrasound-detected ULs (diameter ≥3 cm;age, 20–40 years), using IHC for 2-SC for determining the FH status and subsequent genetic testing in those with positive 2-SC results to identify whether FH mutations were germline or somatic in origin. The choline and fumarate peaks in MRS were classified as positive, negative, or technical failure (TF). TFs were analyzed separately and excluded from the primary diagnostic accuracy calculations. T1-, T2-, and diffusion-weighted images were interpreted as hyperintense or hypointense. The enhancement rate and apparent diffusion coefficient were also acquired. Diagnostic performance was compared between MRS and various magnetic resonance imaging (MRI) features. Results: The optimal MRS parameters for the fumarate peak were echo time (TE) = 140 ms and an average of 256. Among the 360 prospective participants, 37 were confirmed to have FH-dULs. MRS showed positive fumarate peaks in 35 of 37 FH-dULs.After excluding six TFs, the positive fumarate peak on MRS showed 94.6% (35/37) sensitivity, 99.7% (316/317) specificity, and 99.2% (351/354) accuracy, all of which were significantly superior to those of other MRI features (P ≤ 0.002). Conclusion A positive fumarate peak on MRS may be a useful imaging biomarker for diagnosing FH-dULs.

Background/Aims: Liver transplantation (LT) following total hepatectomy is a life-saving treatment for hepatocellular carcinoma (HCC). The HCC recurrence after LT hinders the effectiveness of the procedure. The objective of this study is to develop a pre-operative risk stratification model based on a liquid biopsy. Methods: We conducted a comprehensive multi-omics study of 260 HCC patients from three centers, including clinical data, low-coverage whole-genome sequencing of cell-free DNA (cfDNA) from plasma, as well as whole-exome, single-nucleus RNA, and spatial transcriptomics from matched tumor and non-tumor tissues. Results: We identified cfDNA-derived copy number alteration (CNA) signatures associated with post-transplant recurrence. By integrating cfDNA-derived CNA profiles with single-cell transcriptomic data, we traced recurrence-associated cfDNA to a distinct subpopulation of malignant cells within the primary tumor. These cells were embedded in a pro-metastatic microenvironment of specialized endothelial subtypes and cancer-associated fibroblasts. Notably, most recurrence-associated lesions were detectable in cfDNA prior to liver transplantation (LT). Building on these insights, we developed the ZJU Criteria based on CNA fragments and tumor markers, a pre-LT risk prediction tool that integrates conventional clinical factors with cfDNA-derived CNA signatures, and validated it using internal and independent external cohorts. Conclusion Our findings suggest that post-transplant recurrence commonly originates from advanced subclones that emerge late during tumor evolution. The ZJU Criteria provides an accurate, non-invasive strategy that significantly improves pre-LT risk stratification and clinical decision-making for patients with HCC.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

OBJECTIVES: Older adults in rural areas with multiple chronic conditions (MCC) generally exhibit poorer medication adherence than the general elderly population. Considering individual heterogeneity helps to design precise subgroup-based interventions. This study aims to identify latent profile types of medication adherence mechanisms among rural older adults with MCC based on the capability-opportunity-motivation-behavior (COM-B) model, and to explore factors influencing medication adherence. METHODS: A multistage sampling method was used to recruit 349 rural older adults with MCC from 10 administrative villages in Jianghua County, Yongzhou City, Hunan Province, between July and September, 2024. Participants were surveyed using a general information questionnaire, the Health Literacy Scale for Chronic Patients, the Beliefs about Medicines Questionnaire-Specific, the Multidimensional Scale of Perceived Social Support, and the Morisky Medication Adherence Scale. Latent profile analysis based on the COM-B model was conducted to identify subgroups of medication adherence mechanisms. Univariate and Logistic regression analyses were used to identify influencing factors associated with different latent profiles and adherence levels. RESULTS: Among the participants, 33.5% demonstrated good medication adherence. The 5 most prevalent chronic diseases were hypertension (86.5%), diabetes (36.7%), arthritis or rheumatism (34.4%), stroke (21.8%), and heart disease (17.5%). Overall, rural older adults with MCC exhibited relatively good medication capability, opportunity, and motivation. Their medication adherence mechanisms were classified into 3 latent profiles: "family-support restrained type" (5.2%), "family-support driven type" (52.1%), and "comprehensive advantage type" (42.7%). Significant differences were observed among the three profiles in terms of education level, marital status, living arrangement, and per capita monthly household income (all P<0.05). Multivariate Logistic regression revealed that higher education level was a protective factor for belonging to the "comprehensive advantage type" rather than the "family-support driven type" [OR=0.277, 95% CI (PL) 0.126 to 0.614, P=0.002]. Furthermore, significant differences in education level, self-rated health status, and latent profile type were found between participants with good and poor adherence (P<0.05). Binary Logistic regression indicated that with each one-level increase in self-rated health status, the risk of poor adherence increased by 293.9% [OR=3.939, 95% CI (PL) 1.610 to 9.636, P=0.003]. Compared with the "family-support restrained type", individuals classified as the "comprehensive advantage type" had a 96.8% [OR=0.032, 95% CI (PL) 0.008 to 0.123, P<0.001] lower risk of poor medication adherence. CONCLUSIONS The mechanisms underlying medication adherence among rural older adults with MCC show clear heterogeneity. Primary healthcare providers should focus on the "family-support restrained type" subgroup, strengthen social support networks, and implement targeted interventions to improve medication adherence.
Humans ; Aged ; Rural Population ; Male ; Female ; China ; Medication Adherence/psychology* ; Surveys and Questionnaires ; Chronic Disease/drug therapy* ; Multiple Chronic Conditions/drug therapy* ; Social Support ; Motivation ; Middle Aged ; Health Literacy ; Aged, 80 and over

T-cell acute lymphoblastic leukemia (T-ALL) is a highly aggressive hematologic malignancy with a poor prognosis, despite advancements in treatment. Many patients struggle with relapse or refractory disease. Investigating the role of the super-enhancer (SE) regulated gene ubiquitin-specific protease 20 (USP20) in T-ALL could enhance targeted therapies and improve clinical outcomes. Analysis of histone H3 lysine 27 acetylation (H3K27ac) chromatin immunoprecipitation sequencing (ChIP-seq) data from six T-ALL cell lines and seven pediatric samples identified USP20 as an SE-regulated driver gene. Utilizing the Cancer Cell Line Encyclopedia (CCLE) and BloodSpot databases, it was found that USP20 is specifically highly expressed in T-ALL. Knocking down USP20 with short hairpin RNA (shRNA) increased apoptosis and inhibited proliferation in T-ALL cells. In vivo studies showed that USP20 knockdown reduced tumor growth and improved survival. The USP20 inhibitor GSK2643943A demonstrated similar anti-tumor effects. Mass spectrometry, RNA-Seq, and immunoprecipitation revealed that USP20 interacted with hypoxia-inducible factor 1 subunit alpha (HIF1A) and stabilized it by deubiquitination. Cleavage under targets and tagmentation (CUT&Tag) results indicated that USP20 co-localized with HIF1A, jointly modulating target genes in T-ALL. This study identifies USP20 as a therapeutic target in T-ALL and suggests GSK2643943A as a potential treatment strategy.

Protrusive facial deformities, characterized by the forward displacement of the teeth and/or jaws beyond the normal range, affect a considerable portion of the population. The manifestations and morphological mechanisms of protrusive facial deformities are complex and diverse, requiring orthodontists to possess a high level of theoretical knowledge and practical experience in the relevant orthodontic field. To further optimize the correction of protrusive facial deformities, this consensus proposes that the morphological mechanisms and diagnosis of protrusive facial deformities should be analyzed and judged from multiple dimensions and factors to accurately formulate treatment plans. It emphasizes the use of orthodontic strategies, including jaw growth modification, tooth extraction or non-extraction for anterior teeth retraction, and maxillofacial vertical control. These strategies aim to reduce anterior teeth and lip protrusion, increase chin prominence, harmonize nasolabial and chin-lip relationships, and improve the facial profile of patients with protrusive facial deformities. For severe skeletal protrusive facial deformities, orthodontic-orthognathic combined treatment may be suggested. This consensus summarizes the theoretical knowledge and clinical experience of numerous renowned oral experts nationwide, offering reference strategies for the correction of protrusive facial deformities.
Humans ; Orthodontics, Corrective/methods* ; Consensus ; Malocclusion/therapy* ; Patient Care Planning ; Cephalometry

Clear aligner treatment is a novel technique in current orthodontic practice. Distinct from traditional fixed orthodontic appliances, clear aligners have different material features and biomechanical characteristics and treatment efficiencies, presenting new clinical challenges. Therefore, a comprehensive and systematic description of the key clinical aspects of clear aligner treatment is essential to enhance treatment efficacy and facilitate the advancement and wide adoption of this new technique. This expert consensus discusses case selection and grading of treatment difficulty, principle of clear aligner therapy, clinical procedures and potential complications, which are crucial to the clinical success of clear aligner treatment.
Humans ; Consensus ; Orthodontic Appliance Design ; Orthodontic Appliances, Removable ; Tooth Movement Techniques/methods* ; Malocclusion/therapy* ; Orthodontics, Corrective/instrumentation*

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