LuQi Formula relieves ventricular remodeling through improvement of HIF-1α-mediated intestinal barrier integrity.

BACKGROUND: Ventricular remodeling is the adaptive process in which the heart undergoes changes due to stress, leading to heart failure (HF). The progressive decline in cardiac function is considered to contribute to intestinal barrier impairment. LuQi Formula (LQF) is a traditional Chinese medicine preparation widely used in the treatment of ventricular remodeling and HF. However, the role of LQF in the impairment of intestinal barrier function induced by ventricular remodeling remains unclear. MATERIALS AND METHODS: Ventricular remodeling was induced in rats by permanently ligating the left anterior descending branch coronary artery, and cardiac function indexes were assessed using echocardiography. Heart and colon tissue morphology were observed by hematoxylin-eosin, Masson's trichrome and Alcian Blue Periodic acid Schiff staining. Myocardial cell apoptosis was detected using TUNEL and immunohistochemistry. Circulatory levels of brain natriuretic peptide (BNP), intestinal permeability markers endotoxin, D-lactate and zonulin, as well as inflammatory cytokines tumor necrosis factor alpha and interleukin-1 beta were measured by Enzyme-linked immunosorbent assay. Expression levels of tight junction (TJ) proteins and hypoxia-inducible factor-1 alpha (HIF-1α) in colon tissue were detected by immunofluorescence, immunohistochemistry and western blotting. Cardiac function indexes and intestinal permeability markers of patients with HF were analyzed before and after 2-4 months of LQF treatment. RESULTS: LQF protected cardiac function and alleviated myocardial fibrosis and apoptosis in rats with ventricular remodeling. LQF protected the intestinal barrier integrity in ventricular remodeling rats, including maintaining colonic tissue morphology, preserving the number of goblet cells and normal expression of TJ proteins. Furthermore, LQF upregulated the expression of HIF-1α protein in colon tissue. Intervention with a HIF-1α inhibitor weakened the protective effect of LQF on intestinal barrier integrity. Moreover, a reduction of HIF-1α aggravated ventricular remodeling, which could be alleviated by LQF. Correspondingly, the circulating levels of intestinal permeability markers and BNP in HF patients were significantly decreased, and cardiac function markedly improved following LQF treatment. CONCLUSIONS: We demonstrated that LQF effectively protected cardiac function by preserving intestinal barrier integrity caused by ventricular remodeling, at least partially through upregulating HIF-1α expression.

Insights into Manganese Superoxide Dismutase and Human Diseases.

Redox equilibria and the modulation of redox signalling play crucial roles in physiological processes. Overproduction of reactive oxygen species (ROS) disrupts the body's antioxidant defence, compromising redox homeostasis and increasing oxidative stress, leading to the development of several diseases. Manganese superoxide dismutase (MnSOD) is a principal antioxidant enzyme that protects cells from oxidative damage by converting superoxide anion radicals to hydrogen peroxide and oxygen in mitochondria. Systematic studies have demonstrated that MnSOD plays an indispensable role in multiple diseases. This review focuses on preclinical evidence that describes the mechanisms of MnSOD in diseases accompanied with an imbalanced redox status, including fibrotic diseases, inflammation, diabetes, vascular diseases, neurodegenerative diseases, and cancer. The potential therapeutic effects of MnSOD activators and MnSOD mimetics are also discussed. Targeting this specific superoxide anion radical scavenger may be a clinically beneficial strategy, and understanding the therapeutic role of MnSOD may provide a positive insight into preventing and treating related diseases.

Logistic Regression Analysis of the Mechanism of Blunt Brain Injury Inference Based on CT Images.

OBJECTIVES: To study the correlation between CT imaging features of acceleration and deceleration brain injury and injury degree. METHODS: A total of 299 cases with acceleration and deceleration brain injury were collected and divided into acceleration brain injury group and deceleration brain injury group according to the injury mechanism. Subarachnoid hemorrhage (SAH) and Glasgow coma scale (GCS), combined with skull fracture, epidural hematoma (EDH), subdural hematoma (SDH) and brain contusion on the same and opposite sides of the stress point were selected as the screening indexes. χ2 test was used for primary screening, and binary logistic regression analysis was used for secondary screening. The indexes with the strongest correlation in acceleration and deceleration injury mechanism were selected. RESULTS: χ2 test showed that skull fracture and EDH on the same side of the stress point; EDH, SDH and brain contusion on the opposite of the stress point; SAH, GCS were correlated with acceleration and deceleration injury (P<0.05). According to binary logistic regression analysis, the odds ratio (OR) of EDH on the same side of the stress point was 2.697, the OR of brain contusion on the opposite of the stress point was 0.043 and the OR of GCS was 0.238, suggesting there was statistically significant (P<0.05). CONCLUSIONS: EDH on the same side of the stress point, brain contusion on the opposite of the stress point and GCS can be used as key indicators to distinguish acceleration and deceleration injury mechanism. In addition, skull fracture on the same side of the stress point, EDH and SDH on the opposite of the stress point and SAH were relatively weak indicators in distinguishing acceleration and deceleration injury mechanism.

Automatic Identification of Brain Injury Mechanism Based on Deep Learning.

OBJECTIVES: To apply the convolutional neural network (CNN) Inception_v3 model in automatic identification of acceleration and deceleration injury based on CT images of brain, and to explore the application prospect of deep learning technology in forensic brain injury mechanism inference. METHODS: CT images from 190 cases with acceleration and deceleration brain injury were selected as the experimental group, and CT images from 130 normal brain cases were used as the control group. The above-mentioned 320 imaging data were divided into training validation dataset and testing dataset according to random sampling method. The model classification performance was evaluated by the accuracy rate, precision rate, recall rate, F1-value and AUC value. RESULTS: In the training process and validation process, the accuracy rate of the model to classify acceleration injury, deceleration injury and normal brain was 99.00% and 87.21%, which met the requirements. The optimized model was used to test the data of the testing dataset, the result showed that the accuracy rate of the model in the test set was 87.18%, and the precision rate, recall rate, F1-score and AUC of the model to recognize acceleration injury were 84.38%, 90.00%, 87.10% and 0.98, respectively, to recognize deceleration injury were 86.67%, 72.22%, 78.79% and 0.92, respectively, to recognize normal brain were 88.57%, 89.86%, 89.21% and 0.93, respectively. CONCLUSIONS: Inception_v3 model has potential application value in distinguishing acceleration and deceleration injury based on brain CT images, and is expected to become an auxiliary tool to infer the mechanism of head injury.