With the accelerating trend of population aging, the number of elderly patients with lung cancer continues to rise, and the disease burden is becoming increasingly heavy. The clinical management of these patients faces severe challenges due to their decreased physiological reserve, complex comorbidities, and significant individual heterogeneity. Consequently, under traditional diagnosis and treatment models, doctors often struggle to identify the individualized risks of elderly patients in a timely and comprehensive manner, which can easily lead to decision biases such as undertreatment or overtreatment. In view of this, this study advocates for the establishment of an umbrella decision-making model specifically tailored for elderly lung cancer patients. Grounded in a multidisciplinary team (MDT) platform, this model deeply integrates oncological indicators with the comprehensive geriatric assessment (CGA) system. By holistically considering multidimensional variables including tumor burden, organ function, frailty index, cognitive status, and social support, the model establishes an operational mechanism characterized by "single entry, precise stratification, and targeted selection". Accordingly, patients can be scientifically triaged into distinct intervention tiers, such as active surveillance, minimally invasive surgery, drug therapy, radiotherapy, and best supportive care, thereby achieving real-time alignment between treatment intensity and patient fitness. This article elaborates on the construction logic and key operational procedures of this novel decision-making framework, aiming to guide clinical practice beyond the limitations of a tumor-centric perspective toward a holistic, dynamic, whole-course management strategy. This transition seeks to ensure optimal quality of life and clinical net benefit for elderly patients alongside survival prolongation.

ObjectiveTo analyze the epidemiological characteristics of latent tuberculosis infection (LTBI) among newly detained populations in eastern China, to identify high-risk groups, and to provide a scientific basis for formulating tuberculosis prevention and control strategies in the prison system. MethodsA cross-sectional study was conducted among the newly admitted detainees in two prisons in eastern China in 2022. Data on demographic characteristics, behavioral risk factors and previous disease history of the research subjects were collected through a structured questionnaire survey. The LTBI status of the detainees was determined using the QuantiFERON-TB Gold In-Tube (QFT-GIT) method. Lasso regression was used to screen variables, followed by multivariate logistic regression analysis to investigate the influencing factors of LTBI. ResultsA total of 305 detainees were included in the study. The median age of detainees was 35 (31, 43) years. The study population was predominantly male (67.21%), of Han ethnicity (95.41%), had a junior or senior high school education (59.34%), and was unemployed (31.80%). A history of smoking was reported by 52.79% of participants, while 57.70% reported no alcohol consumption. The majority had no history of hypertension (85.90%), diabetes mellitus (93.77%), human immunodeficiency virus (HIV) infection (97.38%), familial genetic diseases (95.08%), surgery or trauma (73.77%), drug use (92.79%), or hepatitis (93.77%). The LTBI rate was 14.75%. After comparing the demographic characteristics of LTBI and non-infected groups, it was found that smoking history (χ²=7.40, P=0.025), drug use history (χ²=5.49, P=0.019), and HIV infection (χ²=8.12, P=0.004) were statistically correlated with LTBI infection. The results of multivariate logistic regression analyses showed that smoking [adjusted odds ratio (aOR)=4.08, 95%CI: 1.60‒10.42, P=0.003], HIV infection (aOR=11.57, 95%CI: 2.50‒53.51, P=0.002) and drug use (aOR=3.04, 95%CI: 1.02‒9.09, P=0.046) were risk factors for LTBI. ConclusionThe LTBI rate among newly detainees in two prisons in eastern China is slightly lower than that among long-term detainees. Early screening and intervention should be implemented for newly detainees, with particular attention focused on high-risk groups such as those with a history of smoking, HIV infection, or drug use.

Objective To evaluate the efficacy and safety of a combination therapy regimen comprising stereotactic body radiation therapy (SBRT) combined with liposomal irinotecan, camrelizumab, and anti-angiogenic therapy (CAP) in patients with advanced bone and soft tissue sarcomas. Methods This study is a prospective subgroup analysis of the multicenter, open-label, single-arm phase Ⅱ CAP trial (NCT04569916), which enrolled 60 patients with advanced solid tumors who had progressed after standard therapy between November 2020 and February 2023, including 11 patients with advanced bone and soft tissue sarcomas. All patients received sequential liposomal irinotecan within 48 hours after stereotactic body radiotherapy (SBRT), followed by maintenance therapy with camrelizumab and an anti-angiogenic agent until disease progression or intolerable toxicity. The primary efficacy endpoint was objective response rate (ORR). Secondary endpoints included disease control rate (DCR), progression-free survival (PFS), overall survival (OS), and safety. Results In the bone and soft tissue sarcoma subgroup (n=11), three patients achieved partial response (PR), five had stable disease (SD), and three experienced disease progression (PD). ORR was 27.3%, and DCR was 72.7%. mPFS was 5.8 months (95%CI: 2.2-10.3), and mOS not reached. Regarding safety, adverse reactions primarily included bone marrow suppression, fatigue, and gastrointestinal reactions, indicating overall manageable toxicity. Conclusion As a subgroup analysis of the CAP study, this research suggests that radiotherapy combined with liposomal irinotecan, camrelizumab, and anti-angiogenic therapy demonstrates certain efficacy in advanced bone and soft tissue sarcomas that have failed multiple lines of treatment, with overall manageable adverse reactions. Future studies should expand the sample size.

An ideal dermal filler should integrate filling, repair, and anti-aging effects, with immediate tissue augmentation, slow degradation, and progressive stimulation of collagen regeneration. However, commonly used hyaluronic acid (HA) hydrogels, while effective for rapid filling, suffer from limited duration of support, weak cell adhesion, and an inability to promote collagen regeneration. Silk fibroin (SF), a natural protein from silkworm cocoons, is known for its excellent cell adhesion and collagen-stimulating abilities. However, its limited gelation capability restricts its potential application as a standalone injectable hydrogel. Based on a complementary strategy, this study combines the rapid gelling properties of HA with the collagen regenerative properties of SF to create a co-crosslinked HA-SF hydrogel. The composite hydrogel merges HA's rapid filling effect with SF's strong tissue adhesion and collagen-stimulating abilities. The formulation, physicochemical properties, degradation, biocompatibility, and filling effects of the HA-SF hydrogel were systematically investigated. HA-SF hydrogel exhibits excellent mechanical properties and ensures long-term support while maintaining injectability. Interestingly, after intradermal injection in the UVB-induced photoaging model, HA-SF hydrogel not only enhances hydrogel-cell interaction but also continues to stimulate collagen regeneration, especially type III collagen. This dual action achieves the biological effects of repair and anti-aging while maintaining the filling effect. Proteomic analysis confirms that repair and anti-aging effects are enhanced by the regulation of skin fibroblasts and modulation of amino acid and lipid metabolism. This composite hydrogel holds strong promise for clinical applications, offering a safer, long-lasting, and more natural injectable filler that combines filling, repair, and anti-aging into one system.

Deactivation of the mitochondrial pyruvate dehydrogenase complex (PDC) is important for the metabolic switching of cancer cell from oxidative phosphorylation to aerobic glycolysis. Studies examining PDC activity regulation have mainly focused on the phosphorylation of pyruvate dehydrogenase (E1), leaving other post-translational modifications largely unexplored. Here, we demonstrate that the acetylation of Lys 488 of pyruvate dehydrogenase complex component X (PDHX) commonly occurs in hepatocellular carcinoma, disrupting PDC assembly and contributing to lactate-driven epigenetic control of gene expression. PDHX, an E3-binding protein in the PDC, is acetylated by the p300 at Lys 488, impeding the interaction between PDHX and dihydrolipoyl transacetylase (E2), thereby disrupting PDC assembly to inhibit its activation. PDC disruption results in the conversion of most glucose to lactate, contributing to the aerobic glycolysis and H3K56 lactylation-mediated gene expression, facilitating tumor progression. These findings highlight a previously unrecognized role of PDHX acetylation in regulating PDC assembly and activity, linking PDHX Lys 488 acetylation and histone lactylation during hepatocellular carcinoma progression and providing a potential biomarker and therapeutic target for further development.
Humans ; Acetylation ; Carcinoma, Hepatocellular/genetics* ; Liver Neoplasms/genetics* ; Pyruvate Dehydrogenase Complex/genetics* ; Gene Expression Regulation, Neoplastic ; Animals ; Mice ; Cell Line, Tumor ; Protein Processing, Post-Translational ; Histones/metabolism* ; Disease Progression

Champagne bottle neck sign (CBNS) is an important morphological feature of extracranial artery damage in patients with moyamoya disease (MMD), and more than half of them have this sign. Carotid ultrasound is the most convenient imaging examination method for diagnosing CBNS. CBNS can have a serious impact on cerebral hemodynamics and is closely associated with the staging of MMD, stroke risk, stroke characteristics, MMD-related headaches, and the risk of postoperative complications. This article comprehensively reviews the pathology and pathogenesis, imaging examination, correlation with clinical symptoms, and intervention of CBNS in patients with MMD, aiming to provide reference for the clinical diagnosis, treatment, and research of MMD combined with CBNS.

Knee osteoarthritis exhibits a high prevalence and significantly impacts patients′ quality of life. Although surgical interventions demonstrate definitive efficacy, their acceptance rate remains generally low among patients. While knee braces have been widely adopted internationally, domestic research in this field is still limited. International studies suggest that knee braces can be recommended as an early-stage intervention strategy for knee osteoarthritis. Knee braces encompass diverse designs, each with distinct advantages and limitations. Many researchers are actively refining existing brace technologies, while others are innovating novel biomechanical approaches to develop next-generation knee brace systems. Currently, no standardized treatment protocol exists for the use of knee braces in early-stage knee osteoarthritis. This study summarizes and analyzes the research on the application of knee braces in knee osteoarthritis.

The risk prediction of paroxysmal atrial fibrillation (PAF) is a challenge in the field of biomedical engineering. This study integrated the advantages of machine learning feature engineering and end-to-end modeling of deep learning to propose a PAF risk prediction method based on multimodal feature fusion. Additionally, the study utilized four different feature selection methods and Pearson correlation analysis to determine the optimal multimodal feature set, and employed random forest for PAF risk assessment. The proposed method achieved accuracy of (92.3 ± 2.1)% and F1 score of (91.6 ± 2.9)% in a public dataset. In a clinical dataset, it achieved accuracy of (91.4 ± 2.0)% and F1 score of (90.8 ± 2.4)%. The method demonstrates generalization across multi-center datasets and holds promising clinical application prospects.
Humans ; Atrial Fibrillation/diagnosis* ; Machine Learning ; Deep Learning ; Risk Assessment/methods*

Automatic classification of medical questions is of great significance in improving the quality and efficiency of online medical services, and belongs to the task of intent recognition. Joint entity recognition and intent recognition perform better than single task models. Currently, most publicly available medical text intent recognition datasets lack entity annotation, and manual annotation of these entities requires a lot of time and manpower. To solve this problem, this paper proposes a medical text classification model, bidirectional encoder representation based on transformer-recurrent convolutional neural network-entity-label-semantics (BRELS), which integrates medical entity label semantics. This model firstly utilizes an adaptive fusion mechanism to absorb prior knowledge of medical entity labels, achieving local feature enhancement. Then in global feature extraction, a lightweight recurrent convolutional neural network (LRCNN) is used to suppress parameter growth while preserving the original semantics of the text. The ablation and comparison experiments are conducted on three public medical text intent recognition datasets to validate the performance of the model. The results show that F1 score reaches 87.34%, 81.71%, and 77.74% on each dataset, respectively. The results show that the BRELS model can effectively identify and understand medical terminology, thereby effectively identifying users' intentions, which can improve the quality and efficiency of online medical services.
Semantics ; Neural Networks, Computer ; Humans ; Natural Language Processing

OBJECTIVE: To evaluate the early effectiveness of transosseous suture fixation in treating recurrent acute patellar dislocation with patellar osteochondral fractures (OCFs). METHODS: A retrospective analysis was conducted on 19 patients with recurrent acute patellar dislocation and patellar OCFs, who underwent transosseous suture fixation between January 2018 and December 2022 and were followed up 2 years. The cohort included 8 males and 11 females, aged 13-21 years (mean, 16.2 years). Patients experienced 2-5 times of patellar dislocation (mean, 3.2 times). The interval from the last dislocation to operation ranged from 3 to 15 days (mean, 9.6 days). Preoperative imaging revealed the intra-articular osteochondral fragments and medial patellofemoral ligament (MPFL) injury. Clinical outcomes were evaluated using the visual analogue scale (VAS) score for pain, the International Knee Documentation Committee (IKDC) score, the Hospital for Special Surgery (HSS) knee score, the Lysholm score, and the Tegner score. Postoperative complications were recorded. During follow-up, the knee X-ray films, CT, and MRI were taken to evaluate fragment healing, displacement, and the morphology and tension of the MPFL reconstruction graft. RESULTS: All incisions healed primarily, and no complication occurred such as infection, joint stiffness, patellofemoral arthritis, or redislocation. Patients were followed up 24-60 months (mean, 43.5 months). At 12 months postoperatively and the last follow-up, significant improvements ( P<0.05) were observed in VAS, Lysholm, IKDC, HSS, and Tegner scores compared to preoperative values. Further improvements were observed at last follow-up compared with the 12 months postoperatively, and the differences were significant ( P<0.05). Imaging studies demonstrated satisfactory osteochondral fragment positioning with stable fixation. At last follow-up, all fragments had healed, and MPFL reconstruction grafts exhibited optimal morphology and tension. No joint adhesion or fragment displacement occurred. CONCLUSION For recurrent acute patellar dislocation with patellar OCFs, transosseous suture fixation proves to be both safe and effective, achieving satisfactory early effectiveness.
Humans ; Male ; Female ; Patellar Dislocation/surgery* ; Adolescent ; Young Adult ; Retrospective Studies ; Patella/surgery* ; Suture Techniques ; Treatment Outcome ; Recurrence ; Fracture Fixation, Internal/methods* ; Fractures, Bone/surgery* ; Follow-Up Studies