The Qingchangligan formula, a traditional Chinese medicine comprising five herbs, is useful for treatment of patients with liver failure; however, its protective and regulatory mechanisms remain elusive. To test the hypothesis that the Qingchangligan formula protects mice against acute liver failure by inhibiting liver inflammation. Acute liver failure (ALF) was induced by intraperitoneal injection of D-GalN (700mg/kg) plus LPS (10μg/kg). The Qingchangligan formula was administered to mice in three doses of 50mg/kg (on day 1, day 2, and day 3) prior to D-GalN/LPS injection by intragastric administration. The mice in different groups were sacrificed at 6h after D-GalN/LPS injection, and liver samples and blood were collected for analysis. Administration of the Qingchangligan formula not only ameliorated liver injury, as evidenced by reduced transaminase levels and well-preserved liver architecture, but also decreased the lethality in ALF mice. Moreover, in the ALF model, pretreatment with the Qingchangligan formula alleviated liver inflammation and decreased hepatocyte apoptosis. Further demonstrating the protective effects of the Qingchangligan formula, we found that pretreatment with the Qingchangligan formula reduced the expression of inflammatory cytokines by decreasing the expression of components of the mitogen-activated protein kinase (MAPK) pathway and promoting autophagy in vitro and in vivo. Our findings demonstrated that the Qingchangligan formula exerts a protective effect against the pathophysiology of ALF, especially in regulating liver inflammation, and provide a rationale for using the Qingchangligan formula as a potential therapeutic strategy to ameliorate ALF.

Objectives: To identify the value of radiomics method derived from CT images to predict prognosis in patients with COVID-19. Methods: A total of 40 patients with COVID-19 were enrolled in the study. Baseline clinical data, CT images, and laboratory testing results were collected from all patients. We defined that ROIs in the absorption group decreased in the density and scope in GGO, and ROIs in the progress group progressed to consolidation. A total of 180 ROIs from absorption group (n = 118) and consolidation group (n = 62) were randomly divided into a training set (n = 145) and a validation set (n = 35) (8:2). Radiomics features were extracted from CT images, and the radiomics-based models were built with three classifiers. A radiomics score (Rad-score) was calculated by a linear combination of selected features. The Rad-score and clinical factors were incorporated into the radiomics nomogram construction. The prediction performance of the clinical factors model and the radiomics nomogram for prognosis was estimated. Results: A total of 15 radiomics features with respective coefficients were calculated. The AUC values of radiomics models (kNN, SVM, and LR) were 0.88, 0.88, and 0.84, respectively, showing a good performance. The C-index of the clinical factors model was 0.82 [95% CI (0.75–0.88)] in the training set and 0.77 [95% CI (0.59–0.90)] in the validation set. The radiomics nomogram showed optimal prediction performance. In the training set, the C-index was 0.91 [95% CI (0.85–0.95)], and in the validation set, the C-index was 0.85 [95% CI (0.69–0.95)]. For the training set, the C-index of the radiomics nomogram was significantly higher than the clinical factors model (p = 0.0021). Decision curve analysis showed that radiomics nomogram outperformed the clinical model in terms of clinical usefulness. Conclusions: The radiomics nomogram based on CT images showed favorable prediction performance in the prognosis of COVID-19. The radiomics nomogram could be used as a potential biomarker for more accurate categorization of patients into different stages for clinical decision-making process. Advances in knowledge: Radiomics features based on chest CT images help clinicians to categorize the patients of COVID-19 into different stages. Radiomics nomogram based on CT images has favorable predictive performance in the prognosis of COVID-19. Radiomics act as a potential modality to supplement conventional medical examinations.

Pyroptosis, an inflammatory regulated cell death (RCD) mechanism, is characterized by cellular swelling, membrane rupture, and subsequent discharge of cellular contents, exerting robust proinflammatory effects. Recent studies have significantly advanced our understanding of pyroptosis, revealing that it can be triggered through inflammasome- and caspase-independent pathways, and interacts intricately with other RCD pathways (e.g., pyroptosis, necroptosis, ferroptosis, and cuproptosis). The pathogenesis of pulmonary fibrosis (PF), including idiopathic pulmonary fibrosis (IPF) and other interstitial lung diseases, involves a multifaceted interplay of factors such as pathogen infections, environmental pollutants, genetic variations, and immune dysfunction. This chronic and progressive interstitial lung disease is characterized by persistent inflammation, extracellular matrix (ECM) accumulation, and fibrotic alveolar wall thickening, which potentially contribute to deteriorated lung function. Despite recent advances in understanding pyroptosis, the mechanisms by which it regulates PF are not entirely elucidated, and effective strategies to improve clinical outcomes remain unclear. This review strives to deliver a comprehensive overview of the biological functions and molecular mechanisms of pyroptosis, exploring its roles in the pathogenesis of PF. Furthermore, it examines potential biomarkers and therapeutic agents for anti-fibrotic treatments.

The adipose tissue surrounding blood vessels is known as perivascular adipose tissue (PVAT), which represents a distinct ectopic fat depot that adheres to the majority of the vasculature. In recent years, owing to its unique location and function, PVAT has been regarded as a new type of adipose tissue distinct from traditional visceral fat. It releases adipokines with vasoconstrictive functions, which regulate vascular function through paracrine and endocrine mechanisms. Interestingly, PVAT can be categorized as white, brown or a mixture of both depending on its anatomical location. Brown adipose tissue (BAT) is located adjacent to the thoracic aorta in rodents, while a mix of brown and white tissue surrounds the abdominal aorta. PVAT exhibits regional phenotypic differences in different parts of the vasculature bed, which may lead to heterogeneity in the secretion profiles and norepinephrine (NE) content in regional PVAT and subsequently affect the regulation of specific adipokine signaling pathways in regional PVAT, resulting in differences in the regulation of vascular function. The aim of this review was to explore the potential factors that influence the anticontractile function of regional PVAT in the vasculature, including the heterogeneity of regional PVAT, the anticontractile function mediated by endothelial nitric oxide synthase (eNOS) in regional PVAT, the activity of the adiponectin-eNOS pathway in regional PVAT adipocytes, and the concentration of the sympathetic neurotransmitter NE in regional PVAT.

Extracellular vesicles play an indispensable role in a variety of physiological and pathological processes, including embryonic development, cellular migration and malignant transformation of tumors. Tumor-derived exosomes (TDEs) have emerged as a key component of the tumor microenvironment (TME), intricately involved in tumor growth, vascular permeability and pre-metastatic niche (PMN) formation. Consequently, TDEs encapsulate bioactive molecules in their lumen, facilitating their delivery to distant microenvironments and inducing the reprogramming of specific cell types to establish PMNs. This, in turn, endows cancer cells with robust capabilities for growth, metastasis and chemoresistance. In this review, we consolidate current research elucidating the role of small EVs in cancer progression, with a particular focus on cancer metastasis, and their therapeutic potential. In addition, we review novel exosome-based methods, such as single EV profiling, which aim to open new avenues for cancer research, diagnosis and treatment.

Objectives: To develop and validate the performance of CT-based radiomics models for predicting the prognosis of acute pancreatitis. Methods: All 344 patients (51 ± 15 years, 171 men) in a first episode of acute pancreatitis (AP) were retrospectively enrolled and randomly divided into training (n = 206), validation (n = 69), and test (n = 69) sets with the ratio of 6:2:2. The patients were dichotomized into good and poor prognosis subgroups based on follow-up CT and clinical data. The radiomics features were extracted from contrast-enhanced CT. Logistic regression analysis was applied to analyze clinical-radiological features for developing clinical and radiomics-derived models. The predictive performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). Results: Eight pancreatic and six peripancreatic radiomics features were identified after reduction and selection. In the training set, the AUCs of clinical, pancreatic, peripancreatic, radiomics, and combined models were 0.859, 0.800, 0.823, 0.852, and 0.899, respectively. In the validation set, the AUCs were 0.848, 0.720, 0.746, 0.773, and 0.877, respectively. The combined model exhibited the highest AUC among radiomics-based models (pancreatic, peripancreatic, and radiomics models) in both the training (0.899) and validation (0.877) sets (all p < 0.05). Further, the AUC of the combined model was 0.735 in the test set. The calibration curve and DCA indicated the combined model had favorable predictive performance. Conclusions: CT-based radiomics incorporating clinical features was superior to other models in predicting AP prognosis, which may offer additional information for AP patients at higher risk of developing poor prognosis. Critical relevance statement: Integrating CT radiomics-based analysis of pancreatic and peripancreatic features with clinical risk factors enhances the assessment of AP prognosis, allowing for optimal clinical decision-making in individuals at risk of severe AP. Key Points: Radiomics analysis provides help to accurately assess acute pancreatitis (AP). CT radiomics-based models are superior to the clinical model in the prediction of AP prognosis. A CT radiomics-based nomogram integrated with clinical features allows a more comprehensive assessment of AP prognosis. Graphical