Panyu Hospital of Chinese Medicine

Lengthy acquisition time remains a key bottleneck for the widespread use of MRI in clinics. While accelerated MRI can reduce scan duration, it often introduces increased noise, compromising image quality and diagnostic reliability. In this study, we present a unified deep learning-based denoising model for multi-organ accelerated MRI, designed to operate directly on reconstructed images from commercial MRI systems. Our model was trained on a prospectively collected, large-scale real-world dataset comprising 148,930 noisy-clean image pairs from six clinical centers and four major MRI vendors, spanning six organs and 96 MRI protocols. On a test set of 20,143 real-world image pairs, our model consistently outperforms state-of-the-art denoising methods. Importantly, downstream evaluation using tissue segmentation demonstrates a 7.05% improvement in Dice score across multiple organs compared to noisy images. The model further generalizes effectively to 46,870 external clinical images from four independent cohorts, highlighting its robustness across various scanners and acquisition protocols. To assess clinical utility, two experienced radiologists conducted blinded evaluations across multiple organs, focusing on overall image quality, diagnostic confidence, and disease diagnosis. The denoised images retained high visual fidelity and yielded diagnostic performance equivalent to clean images even with acceleration factor of 3× compared to clinical scanning setup, such that many acquisitions can be completed within one minute. This unified MRI denoising model holds great potential for various clinical applications.

Acute cerebral infarction (ACI) is a disease which seriously affects the people's health. To investigate the efficacy of combined atorvastatin and argatroban therapy in patients with ACI and its effects on cerebral hemodynamics, coagulation, endothelial function and inflammation. Eighty ACI patients were assigned into the atorvastatin group (n = 40) and atorvastatin + argatroban group (n = 40), which received the treatment using atorvastatin and atorvastatin combined with argatroban for one month, respectively. After treatment, the overall efficacy was evaluated. Before and after treatment, the cerebral blood flow indexes and blood indexes were determined. Compared to the atorvastatin group, the atorvastatin + argatroban group showed the increased overall effective rate (95.00% vs. 80.00%, p < 0.05). In the atorvastatin + argatroban group the significant improvements were also observed in cerebral hemodynamics (increased mean blood flow quantity and velocity, decreased pulsatility index and resistance), coagulation function (prolonged prothrombin time, thrombin time and activated partial thromboplastin time, reduced fibrinogen), endothelial function (increased nitric oxide and vascular endothelial growth factor, decreased endothelin-1) and inflammation (reduced hypersensitive C-reactive protein, tumor necrosis factor α and interleukin 6) compared to the atorvastatin group (all p < 0.05). Atorvastatin combined with argatroban can effectively improve the cerebral hemodynamics, coagulation and endothelial function and reduce the inflammation in ACI patients, thus exerting a good therapeutic efficacy.

PurposeChronic obstructive pulmonary disease (COPD) with hypoxemia is associated with high morbidity and mortality. Fire dragon jar moxibustion, a Traditional Chinese Medicine(TCM) therapy, may complement non-invasive ventilation (NIV).MethodsIn this single-center retrospective study, 100 hospitalized COPD patients with hypoxemia (PaO2 <60 mmHg) received NIV alone (control, n=50) or NIV plus moxibustion (treatment, n=50). Outcomes included FEV1, PaO2, inflammatory markers (PCT, CRP), dyspnea (mMRC), exacerbation frequency, TCM tongue-pulse scores, 30-day readmission, oxygen dependency, and glucocorticoid use. Primary endpoints were FEV1 improvement and PaO2 change at 12 weeks.ResultsAt 12 weeks, the treatment group showed greater improvement in FEV1 (17.4% vs 8.7%, P <0.01) and PaO2 (+6.4 mmHg vs −1.2 mmHg, P <0.001). PCT and CRP decreased more markedly in the treatment group (P <0.05). Dyspnea (mMRC) decreased by 1.3 points vs 0.4 points in controls, and annual exacerbation frequency reduced by 1.8 vs 0.6 (P <0.001). TCM tongue and pulse scores improved, with a trend from phlegm-dampness to lung qi deficiency (P <0.05). 30-day readmission (8% vs 12%), oxygen therapy dependency (22% vs 30%), and glucocorticoid dosage (850 mg vs 1200 mg) were lower in the treatment group (P <0.05). No serious adverse events attributable to the procedure were observed.ConclusionCombining fire dragon jar moxibustion with NIV significantly improves lung function, oxygenation, dyspnea, inflammatory markers, and TCM syndrome in COPD patients with hypoxemia, with a favorable safety profile.

Retroperitoneal dedifferentiated liposarcoma (RP DDLS) is considered highly malignant, with a high recurrence rate and poor prognosis, and is usually asymptomatic until the advanced stages. Retroperitoneal liposarcoma with colon invasion is extremely rare, with only a few cases reported in the literature. This study reported the case of a 75-year-old male patient with a retroperitoneal space-occupying lesion without any clinical symptoms. Abdominal computed tomography (CT) and magnetic resonance imaging (MRI) scans suggested a neoplastic lesion. After adequate preoperative preparation, the patient underwent laparoscopic tumor resection and right hemicolectomy. Pathological and immunohistochemical examinations of the resected retroperitoneal mass and right hemicolon revealed dedifferentiated liposarcoma (DDLPS). Dedifferentiated liposarcomas are characterized by local invasiveness and a high recurrence rate. The association between dedifferentiated liposarcoma and invasive behavior observed in this patient underscores the importance of multidisciplinary collaboration as the primary treatment strategy. Nevertheless, complete tumor resection remains crucial for improving the prognosis and reducing the high local recurrence rate.

BackgroundWhile dual-energy X-ray absorptiometry (DXA) remains the gold standard for osteoporosis diagnosis, its clinical utility is constrained by cost and accessibility challenges. This study aims to develop a predictive model for osteoporosis using routinely available clinical blood biomarkers, thereby providing an innovative and accessible approach for early detection.MethodsWe retrospectively analyzed 8,144 orthopedic inpatients who underwent DXA scans at Panyu Hospital of Guangzhou University of Chinese Medicine between January 2022 and December 2023. Demographic characteristics and first 24-hour admission blood parameters were collected. Potential predictors were identified through univariate analysis, least absolute shrinkage and selection operator (LASSO) regression, and Boruta algorithm. Ten supervised machine learning algorithms were employed to construct predictive models. Model performance was evaluated using area under the curve (AUC), calibration plots, decision curve analysis (DCA), accuracy, sensitivity, and specificity in the test cohort. SHapley Additive exPlanations (SHAP) analysis provided interpretable visualization of feature contributions.ResultsThe cohort was randomly divided into training (n = 5,702) and testing sets (n = 2,442). Feature selection convergence across three methods identified 11 key predictors. The logistic regression model demonstrated superior performance in the testing set (AUC = 0.800), outperforming other algorithms in calibration and clinical utility assessments. SHAP analysis revealed age, gender, uric acid concentration, alkaline phosphatase levels, hemoglobin levels, and neutrophil count as the six most influential predictors. An accessible web-based risk calculator has been deployed at:https://op-lm.shinyapps.io/osteoporosis/.ConclusionWe developed an easy-to-use online calculator based on machine learning, which outperforms traditional models, enabling patients to preliminarily screen for osteoporosis using routine blood test results from their health check-ups. This interpretable machine learning model demonstrated promising performance and may assist in improving osteoporosis screening and risk stratification in clinical settings.Clinical trial numberNot applicable.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12911-025-03292-1.

IntroductionThe COVID-19 pandemic has had a profound impact on the mental health of medical workers worldwide. While extensive research has investigated pandemic-related mental health challenges, longitudinal analyses of temporal trends remain scarce. This study employs a cross-sectional design to compare mental health outcomes among medical workers in Beijing’s Mobile Cabin Hospitals during the Early-Pandemic Era (2020) and Post-Pandemic Era (2022), with a focus on occupational disparities. MethodsThis study utilized a cross-sectional design to compare mental health outcomes among medical workers in Beijing’s Mobile Cabin Hospitals during two distinct pandemic phases. Data were collected through anonymized online surveys administered via Wenjuanxing. The questionnaire comprised three domains: demographic characteristics, professional roles within departments and the 12-item General Health Questionnaire (GHQ-12). Emotional distress was operationally defined as a GHQ-12 total score ≥ 4. ResultsComparative analysis revealed a significant deterioration in mental health outcomes among medical workers during the Post-Pandemic Era compared to the Early-Pandemic Era. The prevalence of emotional distress (GHQ-12 ≥ 4) remained elevated across specific subgroups: medical workers aged 30–39 years, married, working as doctors and other professionals. Statistically significant interphase differences emerged in vulnerable populations, including female (P < 0.001), unmarried (P = 0.004), worked with nurses (P = 0.003) and other professionals (P = 0.021), and aged less than 40 years (< 30 years old, P = 0.009; 30–39 years old, P = 0.021). Qualitative symptom profiling indicated that more people reported clinically meaningful manifestations of psychological distress, characterized by depressive symptoms, anxiety, diminished self-worth, and impaired coping capacity during adversity. ConclusionThe cumulative burden of prolonged pandemic has demonstrably exacerbated mental health deterioration among medical workers. These findings underscore the critical need to sustainably safeguard the mental health of medical workers in future public health crises.Supplementary InformationThe online version contains supplementary material available at 10.1007/s44192-025-00210-9.

BackgroundHandgrip strength (HGS) and functional disability may reflect interconnected biological processes contributing to functional decline. However, the extent to which their co-occurrence predicts cognitive impairment in aging populations remains unclear. This study investigated the joint association of low HGS and functional disability with cognitive impairment in a nationally representative cohort of older Chinese adults.MethodsWe analyzed data from the China Health and Retirement Longitudinal Study (2011–2020), including participants aged ≥ 60 years. Based on baseline HGS and functional disability status, participants were categorized into four groups. Logistic regression and Cox proportional hazards models were used to examine cross-sectional and longitudinal associations with cognitive impairment, respectively. Cognitive trajectories were assessed using linear mixed-effects models, and subgroup analyses evaluated the consistency of associations across demographic and clinical strata.ResultsA total of 2423 participants (mean age 65.4 ± 4.7 years; 44.1% female) were included. In cross-sectional analyses, compared with participants without low HGS or functional disability, the adjusted odds ratios (OR) for cognitive impairment were 1.62 (95% confidence interval [CI]: 1.23–2.13; P < 0.001) for functional disability alone, 1.81 (95% CI 1.35–2.41; P < 0.001) for low HGS alone, and 2.81 (95% CI 2.07–3.83; P < 0.001) for both conditions. Over nine years of follow-up, the corresponding adjusted hazard ratios (HR) for incident cognitive impairment were 1.52 (95% CI 1.16–1.99), 1.63 (95% CI 1.21–2.20), and 1.73 (95% CI 1.16–2.59), respectively. Linear mixed-effects models indicated significant differences in cognitive trajectories among groups, with participants having both conditions consistently maintaining the lowest standardized cognitive scores. Subgroup analyses demonstrated consistent associations across stratified analyses.ConclusionThe joint presence of low HGS and functional disability was strongly associated with an elevated risk of cognitive impairment. Incorporating combined assessments of muscle strength and functional status into routine geriatric evaluations may enable earlier risk stratification and inform comprehensive interventions to preserve cognitive health.Supplementary InformationThe online version contains supplementary material available at 10.1186/s40001-025-03353-1.

Cardiovascular disease (CVD) is the world’s leading cause of death and frequently comorbid with depression. Numerous clinical studies reveal a bidirectional interaction between CVD and depression, mutually exacerbating the pathological progression of both conditions. This comorbidity has emerged as a significant global health challenge, yet targeted therapeutic drugs and strategies remain underdeveloped. This article summarizes the relationship between depression and various CVD types, including coronary heart disease, atherosclerosis, myocardial infarction, and heart failure. We further analyze the underlying mechanisms through autonomic nervous system dysfunction, hypothalamic-pituitary-adrenal axis dysregulation, inflammatory responses, endothelial dysfunction, and platelet activation pathways. By integrating these insights, this review aims to provide references for the development of novel therapeutics for CVD combined with depression.