Efficacy of Co-administration of Liuwei Dihuang Pills and Ginkgo Biloba Tablets on Albuminuria in Type 2 Diabetes: A 24-Month, Multicenter, Double-Blind, Placebo-Controlled, Randomized Clinical Trial

Purpose: We investigated the effects of Traditional Chinese Medicine (TCM) on the occurrence and progression of albuminuria in patients with type 2 diabetes.Methods: In this randomized, double-blind, multicenter, controlled trial, we enrolled 600 type 2 diabetes without diabetic nephropathy (DN) or with early-stage DN. Patients were randomly assigned (1:1) to receive Liuwei Dihuang Pills (LWDH) (1.5 g daily) and Ginkgo biloba Tablets (24 mg daily) orally or matching placebos for 24 months. The primary endpoint was the change in urinary albumin/creatinine ratio (UACR) from baseline to 24 months.Results: There were 431 patients having UACR data at baseline and 24 months following-up in both groups. Changes of UACR from baseline to follow-up were not affected in both groups: −1.61(−10.24, 7.17) mg/g in the TCM group and −0.73(−7.47, 6.75) mg/g in the control group. For patients with UACR ≥30 mg/g at baseline, LWDH and Ginkgo biloba significantly reduced the UACR value at 24 months [46.21(34.96, 58.96) vs. 20.78(9.62, 38.85), P < 0.05]. Moreover, the change of UACR from baseline to follow-up in the TCM group was significant higher than that in the control group [−25.50(−42.30, −9.56] vs. −20.61(−36.79, 4.31), P < 0.05].Conclusion: LWDH and Ginkgo biloba may attenuate deterioration of albuminuria in type 2 diabetes patients. These results suggest that TCM is a promising option of renoprotective agents for early stage of DN.Trial registration: The study was registered in the Chinese Clinical Trial Registry. (no. ChiCTR-TRC-07000037, chictr.org

Investigation of the association between lens autofluorescence ratio and diabetes: a cross-sectional study.

Lens autofluorescence ratio (LFR) is a novel approach to detect advanced glycation end products in a time-saving and non-invasive manner. However, its associations with glycemia and diabetes remain unclear. We conducted this study to address this issue in Chinese adults. We enrolled a total of 4,705 participants aged 20-70 years in China between May 2020 and January 2021 in a cross-sectional study. LFR was determined by biomicroscopy (ClearPath DS-120). Diabetes was ascertained by oral glucose tolerance test, self-reported history, and/or antidiabetic medication use. Correlation and logistic regression analyses were performed. LFR was higher in participants with diabetes than those without (23.27 ± 6.51 vs. 19.45 ± 5.08, p < 0.001). LFR correlated with fasting plasma glucose and hemoglobin A1c in the overall and diabetes-stratified populations. The odds of diabetes was increased by 6% per one percent higher of LFR after multivariable-adjustment (odds ratio (OR) 1.06, 95% CI 1.04-1.08, p < 0.001). Participants in the highest quartile of LFR had higher odds of diabetes compared with those in the lowest quartile (OR 1.83, 95% CI 1.33-2.52, p < 0.001). Mediation analysis showed that, insulin resistance, as assessed by triglyceride-glucose index, may underline the relationship between high LFR and increased odds of diabetes. LFR, a non-invasive indirect measure of advanced glycation end products, appears to be associated with glycemia and the risk of developing diabetes in Chinese adults

Enhanced open biomass burning detection: The BranTNet approach using UAV aerial imagery and deep learning for environmental protection and health preservation

Open biomass burning (OBB) in agriculture presents a significant and well-documented challenge, posing severe consequences for both environmental and human health. OBB releases air pollutants that degrade air quality and contribute to climate change, leading to premature deaths in regions with high concentrations of open crop straw burning (OCSB) emissions. Although policies aimed at prohibiting OBB are in place, the efficacy of these regulations in mitigating OCSB emissions remains ambiguous. Consequently, early prevention and monitoring of open biomass combustion are imperative for environmental preservation. Traditional monitoring techniques, reliant on fixed-position cameras, are constrained by their location and monitoring intensity, making concealed fire recognition a complex problem. To address this limitation and monitor the human living environment more flexibly and accurately, we propose a new method to identify straw fires in UAV Aerial Image Using CNN Branch Reinforce Transformer which named BranTNet, enabling early detection and rapid response to crop straw fires. By integrating computer vision technology and deep learning algorithms, straw fires in UAV-acquired aerial survey images can be detected and categorized. In the realm of artificial intelligence algorithms, we skillfully merge convolution and attention mechanisms, harnessing the full potential of both methodologies. Moreover, we seamlessly incorporate transfer learning, skillfully unifying self-training convolution modules with pre-trained transformer modules. This strategic amalgamation not only minimizes time costs but also ensures optimal experimental outcomes. Regarding data, we meticulously collected a substantial number of authentic samples, ensuring the sufficiency of our experimental dataset. The experimental results demonstrate that our proposed method exhibits exceptional accuracy and robustness in detecting and identifying straw fires in UAV aerial survey images. Our approach outperforms the use of convolution or attention mechanisms alone. By integrating this approach with drone technology, we unlock the potential for developing more versatile and precise monitoring solutions, expanding the application of drones to diverse domains. This progress contributes significantly to the early detection and prevention of crop straw fires, fundamentally reducing environmental pollution, curbing carbon emissions, and advancing the cause of carbon neutrality. This innovative technique for monitoring and preventing OBB holds substantial promise in mitigating the adverse effects of OBB on the environment and human health

Double Soft-Template Synthesis of Nitrogen/Sulfur-Codoped Hierarchically Porous Carbon Materials Derived from Protic Ionic Liquid for Supercapacitor

Heteroatom-doped hierarchical porous carbon materials derived from the potential precursors and prepared by a facile, effective, and low-pollution strategy have recently been particularly concerned in different research fields. In this study, the interconnected nitrogen/sulfur-codoped hierarchically porous carbon materials have been successfully obtained via one-step carbonization of the self-assembly of [Phne]­[HSO₄] (a protic ionic liquid originated from dilute sulfuric acid and phenothiazine by a straightforward acid–base neutralization) and the double soft-template of OP-10 and F-127. During carbonization process, OP-10 as macroporous template and F-127 as mesoporous template were removed, while [Phne]­[HSO₄] not only could be used as carbon, nitrogen, and sulfur source, but also as a pore forming agent to create micropores. The acquired carbon materials for supercapacitor not only hold a large specific capacitance of 302 F g^–1 even at 1.0 A g^–1, but also fine rate property with 169 F g^–1 at 10 A g^–1 and excellent capacitance retention of nearly 100% over 5000 circulations in 6 M KOH electrolyte. Furthermore, carbon materials also present eximious rate performance with 70% in 1 M Na₂SO₄ electrolyte

Clinical Factors Associated with Progression and Prolonged Viral Shedding in COVID-19 Patients: A Multicenter Study

Coronavirus disease 2019 (COVID-19) is a global pandemic associated with a high mortality. Our study aimed to determine the clinical risk factors associated with disease progression and prolonged viral shedding in patients with COVID-19. Consecutive 564 hospitalized patients with confirmed COVID-19 between January 17, 2020 and February 28, 2020 were included in this multicenter, retrospective study. The effects of clinical factors on disease progression and prolonged viral shedding were analyzed using logistic regression and Cox regression analyses. 69 patients (12.2%) developed severe or critical pneumonia, with a higher incidence in the elderly and in individuals with underlying comorbidities, fever, dyspnea, and laboratory and imaging abnormalities at admission. Multivariate logistic regression analysis indicated that older age (odds ratio [OR], 1.04; 95% confidence interval [CI], 1.02-1.06), hypertension without receiving angiotensinogen converting enzyme inhibitors or angiotensin receptor blockers (ACEI/ARB) therapy (OR, 2.29; 95% CI, 1.14-4.59), and chronic obstructive pulmonary disease (OR, 7.55; 95% CI, 2.44-23.39) were independent risk factors for progression to severe or critical pneumonia. Hypertensive patients without receiving ACEI/ARB therapy showed higher lactate dehydrogenase levels and computed tomography (CT) lung scores at about 3 days after admission than those on ACEI/ARB therapy. Multivariate Cox regression analysis revealed that male gender (hazard ratio [HR], 1.22; 95% CI, 1.02-1.46), receiving lopinavir/ritonavir treatment within 7 days from illness onset (HR, 0.75; 95% CI, 0.63-0.90), and receiving systemic glucocorticoid therapy (HR, 1.79; 95% CI, 1.46-2.21) were independent factors associated with prolonged viral shedding. Our findings presented several potential clinical factors associated with developing severe or critical pneumonia and prolonged viral shedding, which may provide a rationale for clinicians in medical resource allocation and early intervention

Smad2/3/4 complex could undergo liquid liquid phase separation and induce apoptosis through TAT in hepatocellular carcinoma

Abstract Background Hepatocellular carcinoma (HCC) represents one of the most significant causes of mortality due to cancer-related deaths. It has been previously reported that the TGF-β signaling pathway may be associated with tumor progression. However, the relationship between TGF-β signaling pathway and HCC remains to be further elucidated. The objective of our research was to investigate the impact of TGF-β signaling pathway on HCC progression as well as the potential regulatory mechanism involved. Methods We conducted a series of bioinformatics analyses to screen and filter the most relevant hub genes associated with HCC. E. coli was utilized to express recombinant protein, and the Ni–NTA column was employed for purification of the target protein. Liquid liquid phase separation (LLPS) of protein in vitro, and fluorescent recovery after photobleaching (FRAP) were utilized to verify whether the target proteins had the ability to drive force LLPS. Western blot and quantitative real-time polymerase chain reaction (qPCR) were utilized to assess gene expression levels. Transcription factor binding sites of DNA were identified by chromatin immunoprecipitation (CHIP) qPCR. Flow cytometry was employed to examine cell apoptosis. Knockdown of target genes was achieved through shRNA. Cell Counting Kit-8 (CCK-8), colony formation assays, and nude mice tumor transplantation were utilized to test cell proliferation ability in vitro and in vivo. Results We found that Smad2/3/4 complex could regulate tyrosine aminotransferase (TAT) expression, and this regulation could relate to LLPS. CHIP qPCR results showed that the key targeted DNA binding site of Smad2/3/4 complex in TAT promoter region is −1032 to −1182. In addition. CCK-8, colony formation, and nude mice tumor transplantation assays showed that Smad2/3/4 complex could repress cell proliferation through TAT. Flow cytometry assay results showed that Smad2/3/4 complex could increase the apoptosis of hepatoma cells. Western blot results showed that Smad2/3/4 complex would active caspase-9 through TAT, which uncovered the mechanism of Smad2/3/4 complex inducing hepatoma cell apoptosis. Conclusion This study proved that Smad2/3/4 complex could undergo LLPS to active TAT transcription, then active caspase-9 to induce hepatoma cell apoptosis in inhibiting HCC progress. The research further elucidate the relationship between TGF-β signaling pathway and HCC, which contributes to discover the mechanism of HCC development

Machine learning based on clinical characteristics and chest CT quantitative measurements for prediction of adverse clinical outcomes in hospitalized patients with COVID-19

ObjectivesTo develop and validate a machine learning model for the prediction of adverse outcomes in hospitalized patients with COVID-19.MethodsWe included 424 patients with non-severe COVID-19 on admission from January 17, 2020, to February 17, 2020, in the primary cohort of this retrospective multicenter study. The extent of lung involvement was quantified on chest CT images by a deep learning–based framework. The composite endpoint was the occurrence of severe or critical COVID-19 or death during hospitalization. The optimal machine learning classifier and feature subset were selected for model construction. The performance was further tested in an external validation cohort consisting of 98 patients.ResultsThere was no significant difference in the prevalence of adverse outcomes (8.7% vs. 8.2%, p = 0.858) between the primary and validation cohorts. The machine learning method extreme gradient boosting (XGBoost) and optimal feature subset including lactic dehydrogenase (LDH), presence of comorbidity, CT lesion ratio (lesion%), and hypersensitive cardiac troponin I (hs-cTnI) were selected for model construction. The XGBoost classifier based on the optimal feature subset performed well for the prediction of developing adverse outcomes in the primary and validation cohorts, with AUCs of 0.959 (95% confidence interval [CI]: 0.936–0.976) and 0.953 (95% CI: 0.891–0.986), respectively. Furthermore, the XGBoost classifier also showed clinical usefulness.ConclusionsWe presented a machine learning model that could be effectively used as a predictor of adverse outcomes in hospitalized patients with COVID-19, opening up the possibility for patient stratification and treatment allocation.Key Points• Developing an individually prognostic model for COVID-19 has the potential to allow efficient allocation of medical resources.• We proposed a deep learning–based framework for accurate lung involvement quantification on chest CT images.• Machine learning based on clinical and CT variables can facilitate the prediction of adverse outcomes of COVID-19