Total iridoid glycoside extract of Lamiophlomis rotata (Benth) Kudo accelerates diabetic wound healing by the NRF2/COX2 axis.

BACKGROUND: Lamiophlomis rotata (Benth.) Kudo (L. rotata), the oral Traditional Tibetan herbal medicine, is adopted for treating knife and gun wounds for a long time. As previously demonstrated, total iridoid glycoside extract of L. rotata (IGLR) induced polarization of M2 macrophage to speed up wound healing. In diabetic wounds, high levels inflammatory and chemotactic factors are usually related to high reactive oxygen species (ROS) levels. As a ROS target gene, nuclear factor erythroid 2-related factor 2 (NRF2), influences the differentiation of monocytes to M1/M2 macrophages. Fortunately, iridoid glycosides are naturally occurring active compounds that can be used as the oxygen radical scavenger. Nevertheless, the influence of IGLR in diabetic wound healing and its associated mechanism is largely unclear. MATERIALS AND METHODS: With macrophages and dermal fibroblasts in vitro, as well as a thickness excision model of db/db mouse in vivo, the role of IGLR in diabetic wound healing and the probable mechanism of the action were investigated. RESULTS: Our results showed that IGLR suppressed oxidative distress and inflammation partly through the NRF2/cyclooxygenase2 (COX2) signaling pathway in vitro. The intercellular communication between macrophages and dermal fibroblasts was investigated by the conditioned medium (CM) of IGLR treatment cells. The CM increased the transcription and translation of collagen I (COL1A1) and alpha smooth muscle actin (α-SMA) within fibroblasts. With diabetic wound mice, the data demonstrated IGLR activated the NRF2/KEAP1 signaling and the downstream targets of the pathway, inhibited COX2/PEG2 signaling and decreased the interaction inflammatory targets of the axis, like interleukin-1beta (IL-1ß), interleukin 6 (IL-6), apoptosis-associated speck-like protein (ASC), cysteinyl aspartate specific proteinase1 (caspase1) and NOD-like receptor-containing protein 3 (NLRP3).In addition, the deposition of COL1A1, and the level of α-SMA, and Transforming growth factor-ß1 (TGF-ß1) obviously elevated, whereas that of pro-inflammatory factors reduced in the diabetic wound tissue with IGLR treatment. CONCLUSION: IGLR suppressed oxidative distress and inflammation mainly through NRF2/COX2 axis, thus promoting paracrine and accelerating wound healing in diabetes mice.

The causal relationship between serum metabolites and the risk of psoriasis: a Mendelian randomization and meta-analysis study.

Objective: To explore the influence of serum metabolites on the risk of psoriasis. Methods: In the initial stage, we applied Mendelian randomization to evaluate the association between 1,400 serum metabolites and the risk of psoriasis. Causal effects were primarily assessed through the Inverse-Variance Weighted method and Wald Ratio's odds ratios, and 95% confidence intervals. False Discovery Rate was used for multiple comparison corrections. Sensitivity analyses were conducted using Cochran's Q Test, MR-PRESSO. MR-Steiger Test was employed to check for reverse causality. In the validation stage, we sought other sources of psoriasis GWAS data to verify the initial results and used meta-analysis to combine the effect sizes to obtain robust causal relationships. In addition, we also conducted metabolic pathway enrichment analysis on known metabolites that have a causal relationship with the risk of psoriasis in both stages. Results: In the initial stage, we identified 112 metabolites causally associated with psoriasis, including 32 metabolite ratios and 80 metabolites (69 known and 11 unknown). In the validation stage, 24 metabolites (16 known, 1 unknown, and 7 metabolite ratios) were confirmed to have a causal relationship with psoriasis onset. Meta-analysis results showed that the overall effect of combined metabolites was consistent with the main analysis in direction and robust in the causal relationship with psoriasis onset. Of the 16 known metabolites, most were attributed to lipid metabolism, with 5 as risk factors and 8 as protective factors for psoriasis. Peptidic metabolite Gamma-glutamylvaline levels had a negative causal relationship with psoriasis, while exogenous metabolite Catechol sulfate levels and amino acid 3-methylglutaconate levels had a positive causal relationship with the disease onset. The metabolites associated with psoriasis risk in the two stages are mainly enriched in the following metabolic pathways: Glutathione metabolism, Alpha Linolenic Acid and Linoleic Acid Metabolism, Biosynthesis of unsaturated fatty acids, Arachidonic acid metabolism, Glycerophospholipid metabolism. Conclusion: Circulating metabolites may have a potential causal relationship with psoriasis risk, and targeting specific metabolites may benefit psoriasis diagnosis, disease assessment, and treatment.

A visual-degradation-inspired model with HSV color-encoding for contour detection.

BACKGROUND: Given energy metabolism, visual information degradation plays an essential role in the retina- lateral geniculate nucleus (LGN)-primary visual cortex (V1)-secondary visual cortex (V2) pathway, and is a pivotal issue for visual information processing. Degradation helps the visual nervous system conserve brain energy and efficiently perceive the real world even though a small fraction of visual information reaches the early visual areas. The coding of contour features (edge and corner) is achieved in the retina-LGN-V1-V2 pathway. Based on the above, we proposed a contour detection model based on degradation (CDMD). NEW METHOD: Inspired by pupillary light reflex regulation, we took into consideration the novel approach of the hue-saturation-value (HSV) module for color encoding to meet the subtle chromaticity change rather than using the traditional red-green-blue (RGB) module, following the mechanisms of dark (DA) and light (LA) adaptation processes in photoreceptors. Meanwhile, the degradation mechanism was introduced as a novel strategy focusing only on the essential information to detect contour features, mimicking contour detection by visual perception under the restriction of axons in each optic nerve biologically. Ultimately, we employed the feedback mechanism achieving the optimal HSV value for each pixel of the experimental datasets. RESULTS: We used the publicly available Berkeley Segmentation Data Set 500 (BSDS500) to assess the effectiveness of our CDMD model, introduced the F-measure to evaluate the results. The F-measure score was 0.65, achieved by our model. Moreover, CDMD with HSV has a better sensitivity for subtle chromaticity changes than CDMD with RGB. COMPARISON WITH EXISTING METHODS: Experimental results demonstrated that our CDMD model, which functions close to the real visual system, achieved a more competitive performance with low computational cost than some state-of-the-art non-deep-learning and biologically inspired models. Compared with deep-learning-based algorithms, our model contains fewer parameters and computation time, does not require additional visual features, as well as an extra training process. CONCLUSIONS: Our proposed CDMD model is a novel approach for contour detection, which mimics the cognitive function of contour detection in early visual areas, and realizes a competitive performance in image processing. It contributes to bridging the gap between the biological visual system and computer vision.

Neural mechanism of visual information degradation from retina to V1 area.

The information processing mechanism of the visual nervous system is an unresolved scientific problem that has long puzzled neuroscientists. The amount of visual information is significantly degraded when it reaches the V1 after entering the retina; nevertheless, this does not affect our visual perception of the outside world. Currently, the mechanisms of visual information degradation from retina to V1 are still unclear. For this purpose, the current study used the experimental data summarized by Marcus E. Raichle to investigate the neural mechanisms underlying the degradation of the large amount of data from topological mapping from retina to V1, drawing on the photoreceptor model first. The obtained results showed that the image edge features of visual information were extracted by the convolution algorithm with respect to the function of synaptic plasticity when visual signals were hierarchically processed from low-level to high-level. The visual processing was characterized by the visual information degradation, and this compensatory mechanism embodied the principles of energy minimization and transmission efficiency maximization of brain activity, which matched the experimental data summarized by Marcus E. Raichle. Our results further the understanding of the information processing mechanism of the visual nervous system.