Epigallocatechin gallate alleviates non-alcoholic fatty liver disease through the inhibition of the expression and activity of Dipeptide kinase 4.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) has emerged as the most prevalent glocal cause of chronic hepatic disease, with incidence rates that continue to rise steadily. Treatment options for affected patients are currently limited to dietary changes and exercise interventions, with no drugs having been licensed for the treatment of this disease. There is thus a pressing need for the development of novel therapeutic strategies. Work from our group suggests that the primary bioactive ingredient in green tea, epigallocatechin gallate (EGCG), may help reduce liver fat content and protect against hepatic injury through the inhibition of dipeptidyl peptidase 4 (DPP4) expression and activity. The study investigated the potential pathways by which EGCG may improve NAFLD, identified the sites of interaction between EGCG and DPP4, and proposed novel clinical treatment strategies. METHODS: A clinical randomized controlled trial was conducted to investigate the potential efficacy of EGCG in NAFLD patients. The study compared relevant indices before and after EGCG administration. Animal models of NAFLD were constructed using male C57BL/6J mice fed a high-fat diet to observe the ameliorative effects of EGCG on the livers of the model mice and to investigate the potential pathways by which EGCG alleviates NAFLD. The interaction mechanism between EGCG and DPP4 was investigated using oleic acid and palmitic acid-treated HepG2 cell lines. Plasmids in which different sites had been disrupted were used to identify the effective interaction sites. RESULTS: ECGC was found to suppress the accumulation of lipids, inhibit inflammation, remediate dysregulated lipid metabolism, and improve the pathogenesis of NAFLD via the inhibition of the expression and activity of DPP4. CONCLUSIONS: The study results indicate that EGCG has a positive impact on improving NAFLD. These results highlight promising new opportunities to safely and effectively treat NAFLD in the clinic. STUDY ID NUMBER: ChiCTR2300076741; https://www.chictr.org.cn/.

The effect of different treatment strategies on glycolipid metabolism disorders and cardiovascular events in primary aldosteronism.

Recent studies have explored the association between primary aldosteronism and cardiovascular disease incidence. The association between specific primary aldosteronism treatments and differential improvement in cardiovascular event rates is yet to be established. This study was designed to compare the relative effects of spironolactone therapy and surgical intervention on cardiovascular outcomes among primary aldosteronism patients. This retrospective observational study included 853 primary aldosteronism patients from the First Affiliated Hospital of China Medical University between 2014 and 2022. Patients who had completed abdominal computed tomography (CT) examinations with similar metabolic characteristics and 6-month follow-up analyses were included in this study. These patients were separated into a surgical treatment group (n = 33) and a spironolactone treatment group (n = 51). Demographic data, biochemical analysis results, liver/spleen (L/S) X-ray attenuation ratio, hospitalization frequency, and cardiovascular events were compared between the two groups. The spironolactone group demonstrated significantly improved metabolic characteristics compared to the surgical group, shown by lower BMI, blood pressure, total cholesterol (TC), insulin resistance index (IRI), and reduced non-alcoholic fatty liver disease prevalence. Metabolic parameters did not differ significantly within the surgical treatment group when comparing pre- and postoperative values. The incidence of cardiovascular events was lower in the spironolactone group compared to the surgery group (23/33 vs. 20/51, P < 0.001) despite higher hospitalization rates(37/31 vs. 61/53, P < 0.001). In patients with primary aldosteronism, spironolactone treatment is more effective than surgical intervention in remediating abnormal lipid and glucose metabolism while improving cardiovascular outcomes. Chinese clinical trial registry registration number: ChiCTR2300074574.

Development and Validation of a Feature-Based Broad-Learning System for Opportunistic Osteoporosis Screening Using Lumbar Spine Radiographs.

RATIONALE AND OBJECTIVES: Osteoporosis is primarily diagnosed using dual-energy X-ray absorptiometry (DXA); yet, DXA is significantly underutilized, causing osteoporosis, an underdiagnosed condition. We aimed to provide an opportunistic approach to screen for osteoporosis using artificial intelligence based on lumbar spine X-ray radiographs. MATERIALS AND METHODS: In this institutional review board-approved retrospective study, female patients aged ≥50 years who received both X-ray scans and DXA of the lumbar vertebrae, in three centers, were included. A total of 1180 cases were used for training and 145 cases were used for testing. We proposed a novel broad-learning system (BLS) and then compared the performance of BLS models using radiomic features and deep features as a source of input. The deep features were extracted using ResNet18 and VGG11, respectively. The diagnostic performances of these BLS models were evaluated with the area under the curve (AUC), sensitivity, and specificity. RESULTS: The incidence rate of osteoporosis in the training and test sets was 35.9% and 37.9%, respectively. The radiomic feature-based BLS model achieved higher testing AUC (0.802 vs. 0.654 vs. 0.632, both P = .002), sensitivity (78.2% vs. 56.4% vs. 50.9%), and specificity (82.2% vs. 74,4% vs. 75.6%) than the two deep feature-based BLS models. CONCLUSION: Our proposed radiomic feature-based BLS model has the potential to expand osteoporosis screening to a broader population by identifying osteoporosis on lumbar spine X-ray radiographs.

CT-based radiomics for predicting the rapid progression of coronavirus disease 2019 (COVID-19) pneumonia lesions.

OBJECTIVES: To develop and validate a radiomic model to predict the rapid progression (defined as volume growth of pneumonia lesions > 50% within seven days) in patients with coronavirus disease 2019 (COVID-19). METHODS: Patients with laboratory-confirmed COVID-19 who underwent longitudinal chest CT between January 01 and February 18, 2020 were included. A total of 1316 radiomic features were extracted from the lung parenchyma window for each CT. The least absolute shrinkage and selection operator (LASSO), Relief, Las Vegas Wrapper (LVW), L1-norm-Support Vector Machine (L1-norm-SVM), and recursive feature elimination (RFE) were applied to select the features that associated with rapid progression. Four machine learning classifiers were used for modeling, including Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and Decision Tree (DT). Accordingly, 20 radiomic models were developed on the basis of 296 CT scans and validated in 74 CT scans. Model performance was determined by the receiver operating characteristic curve. RESULTS: A total of 107 patients (median age, 49.0 years, interquartile range, 35-54) were evaluated. The patients underwent a total of 370 chest CT scans with a median interval of 4 days (interquartile range, 3-5 days). The combination methods of L1-norm SVM and SVM with 17 radiomic features yielded the highest performance in predicting the likelihood of rapid progression of pneumonia lesions on next CT scan, with an AUC of 0.857 (95% CI: 0.766-0.947), sensitivity of 87.5%, and specificity of 70.7%. CONCLUSIONS: Our radiomic model based on longitudinal chest CT data could predict the rapid progression of pneumonia lesions, which may facilitate the CT follow-up intervals and reduce the radiation. ADVANCES IN KNOWLEDGE: Radiomic features extracted from the current chest CT have potential in predicting the likelihood of rapid progression of pneumonia lesions on the next chest CT, which would improve clinical decision-making regarding timely treatment.