Natural products target glycolysis in liver disease

Mitochondrial dysfunction plays an important role in the occurrence and development of different liver diseases. Oxidative phosphorylation (OXPHOS) dysfunction and production of reactive oxygen species are closely related to mitochondrial dysfunction, forcing glycolysis to become the main source of energy metabolism of liver cells. Moreover, glycolysis is also enhanced to varying degrees in different liver diseases, especially in liver cancer. Therefore, targeting the glycolytic signaling pathway provides a new strategy for the treatment of non-alcoholic fatty liver disease (NAFLD) and liver fibrosis associated with liver cancer. Natural products regulate many steps of glycolysis, and targeting glycolysis with natural products is a promising cancer treatment. In this review, we have mainly illustrated the relationship between glycolysis and liver disease, natural products can work by targeting key enzymes in glycolysis and their associated proteins, so understanding how natural products regulate glycolysis can help clarify the therapeutic mechanisms these drugs use to inhibit liver disease

LDP: Language-driven Dual-Pixel Image Defocus Deblurring Network

Recovering sharp images from dual-pixel (DP) pairs with disparity-dependent blur is a challenging task.~Existing blur map-based deblurring methods have demonstrated promising results. In this paper, we propose, to the best of our knowledge, the first framework to introduce the contrastive language-image pre-training framework (CLIP) to achieve accurate blur map estimation from DP pairs unsupervisedly. To this end, we first carefully design text prompts to enable CLIP to understand blur-related geometric prior knowledge from the DP pair. Then, we propose a format to input stereo DP pair to the CLIP without any fine-tuning, where the CLIP is pre-trained on monocular images. Given the estimated blur map, we introduce a blur-prior attention block, a blur-weighting loss and a blur-aware loss to recover the all-in-focus image. Our method achieves state-of-the-art performance in extensive experiments

Using hyperhomocysteinemia and body composition to predict the risk of non-alcoholic fatty liver disease in healthcare workers

PurposeThis study investigated associations between serum homocysteine levels, body composition, and the probability of having nonalcoholic fatty liver disease (NAFLD) in Chinese healthcare workers.Patients and MethodsA total of 4028 healthcare workers were enrolled in this study, and all underwent a physical examination. Body composition was measured using multifrequency bioelectrical impedance analysis.ResultsThere were 1507 NAFLD patients (72.26% male, 27.74% female) and 2521 controls (39.83% male, 60.17% female). Body mass index (BMI), waistline, neck-circumference (NC), abdominal visceral fat area (AVFA), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), glucose (Glu), homocysteinemia (hcy) were higher in the NAFLD group than controls. Additionally, the skeletal-muscle was associated with a lower risk of NAFLD, whereas BMI, waistline, NC, hyperhomocysteinemia (HHcy) were associated with a higher risk of NAFLD. The best NC cut-off point for NAFLD was 34.45 cm (sensitivity 83.3% and specificity 83.9%) in women with HHcy, and the best skeletal-muscle content cut-off point for NAFLD was 41.335% (sensitivity 74.2% and specificity 65.6%) in men with HHcy.ConclusionInteractions between skeletal-muscle content, NC, and HHcy may affect the incidence of NAFLD in healthcare workers. This may provide a novel approach for diagnosing NAFLD

RITA: Boost Autonomous Driving Simulators with Realistic Interactive Traffic Flow

High-quality traffic flow generation is the core module in building simulators for autonomous driving. However, the majority of available simulators are incapable of replicating traffic patterns that accurately reflect the various features of real-world data while also simulating human-like reactive responses to the tested autopilot driving strategies. Taking one step forward to addressing such a problem, we propose Realistic Interactive TrAffic flow (RITA) as an integrated component of existing driving simulators to provide high-quality traffic flow for the evaluation and optimization of the tested driving strategies. RITA is developed with consideration of three key features, i.e., fidelity, diversity, and controllability, and consists of two core modules called RITABackend and RITAKit. RITABackend is built to support vehicle-wise control and provide traffic generation models from real-world datasets, while RITAKit is developed with easy-to-use interfaces for controllable traffic generation via RITABackend. We demonstrate RITA's capacity to create diversified and high-fidelity traffic simulations in several highly interactive highway scenarios. The experimental findings demonstrate that our produced RITA traffic flows exhibit all three key features, hence enhancing the completeness of driving strategy evaluation. Moreover, we showcase the possibility for further improvement of baseline strategies through online fine-tuning with RITA traffic flows.Comment: 8 pages, 5 figures, 3 table

Perpendicular in-plane negative magnetoresistance in ZrTe5

The unique band structure in topological materials frequently results in unusual magneto-transport phenomena, one of which is in-plane longitudinal negative magnetoresistance (NMR) with the magnetic field aligned parallel to the electrical current direction. This NMR is widely considered as a hallmark of chiral anomaly in topological materials. Here we report the observation of in-plane NMR in the topological material ZrTe5 when the in-plane magnetic field is both parallel and perpendicular to the current direction, revealing an unusual case of quantum transport beyond the chiral anomaly. We find that a general theoretical model, which considers the combined effect of Berry curvature and orbital moment, can quantitatively explain this in-plane NMR. Our results provide new insights into the understanding of in-plane NMR in topological materials

Integrative Analysis of Identifying Methylation-Driven Genes Signature Predicts Prognosis in Colorectal Carcinoma

BackgroundAberrant DNA methylation is a critical regulator of gene expression and plays a crucial role in the occurrence, progression, and prognosis of colorectal cancer (CRC). We aimed to identify methylation-driven genes by integrative epigenetic and transcriptomic analysis to predict the prognosis of CRC patients.MethodsMethylation-driven genes were selected for CRC using a MethylMix algorithm and LASSO regression screening strategy, and were further used to construct a prognostic risk-assessment model. The Cancer Genome Atlas (TCGA) database was obtained as the training set for both the screening of methylation-driven genes and the effect of genes signature on CRC prognosis. Then, the prognostic genes signature was validated in three independent expression arrays of CRC data from Gene Expression Omnibus (GEO).ResultsWe identified 143 methylation-driven genes, of which the combination of BATF, PHYHIPL, RBP1, and PNPLA4 expression levels was screened as a better prognostic model with the best area under the curve (AUC) (AUC = 0.876). Compared with patients in the low-risk group, CRC patients in the high-risk group had significantly poorer overall survival in the training set (HR = 2.184, 95% CI: 1.404–3.396, P < 0.001). Similar results were observed in the validation set. Moreover, VanderWeele’s mediation analysis indicated that the effect of methylation on prognosis was mediated by the levels of their expression (HRindirect = 1.473, P = 0.001, Proportion mediated, 69.10%).ConclusionsWe identified a four-gene prognostic signature by integrative analysis and developed a risk-assessment model that is significantly associated with patients’ survival. Methylation-driven genes might be a potential prognostic signature for CRC patients