This study aimed to investigate the protective effect of metformin on trabecular meshwork (TM) and explore its molecular mechanisms in vivo and in vitro. Ocular hypertension (OHT) mouse models were induced with dexamethasone and further treated with metformin to determine the intraocular pressure (IOP)-lowering effect. Cultured human TM cells (HTMCs) were pre-stimulated with tert-butyl hydroperoxide (tBHP) to induce oxidative damage and then supplemented with metformin for another 24 hr. The expression of fibrotic markers and integrin/Rho-associated kinase (ROCK) signals, including α-smooth muscle actin (α-SMA), transforming growth factor-β (TGF-β), fibronectin, integrin beta 1, ROCK 1/2, AMP-activated protein kinase, myosin light chain 1, and F-actin were determined by western blotting and immunofluorescence. Reactive oxygen species (ROS) content was analysed using flow cytometry. Transmission electron microscopy was performed to observe microfilaments in HTMCs. It showed that metformin administration reduced the elevated IOP and alleviated the fibrotic activity of aqueous humour outflow in OHT models. Additionally, metformin rearranged the disordered cytoskeleton in the TM both in vivo and in vitro and significantly inhibited ROS production and activated integrin/ROCK signalling induced by tBHP in HTMCs. These results indicated that metformin reduced the elevated IOP in steroid-induced OHT mouse models and exerted its protective effects against oxidative injury by regulating cytoskeleton remodelling through the integrin/ROCK pathway. This study provides new insights into metformin use and preclinical evidence for the potential treatment of primary open-angle glaucoma.

Energy saving in palletizing robot is a fundamental problem in the field of industrial robots. However, the palletizing robot often suffers from the problems of high energy consumption and lacking flexibility. In this work, we introduce a novel differential evolution algorithm to address the adverse effects caused by the instability of the initial trajectory parameters while reducing the energy. Specially, a simplified analytical model of the palletizing robot is firstly developed. Then, the simplified analytical model and the differential evolutionary algorithm are combined to form a planner with the goal of reducing energy consumption. The energy saving planner optimizes the initial parameters of the trajectories collected by the bionic demonstration system, which in turn enables a reduction in the operating power consumption of the palletizing robot. The major novelty of this article is the use of a differential evolutionary algorithm that can save the energy consumption as well as boosting its flexibility. Comparing with the traditional algorithms, the proposed method can achieve the state-of-the-art performance. Simulated and actual experimental results illustrate that the optimized trajectory parameters can effectively reduce the energy consumption of palletizing robot by 16%.

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Pulmonary Hypertension (PH) is a global health problem that affects about 1% of the global population. Animal models of PH play a vital role in unraveling the pathophysiological mechanisms of the disease. The present study proposes a Kernel Extreme Learning Machine (KELM) model based on an improved Whale Optimization Algorithm (WOA) for predicting PH mouse models. The experimental results showed that the selected blood indicators, including Haemoglobin (HGB), Hematocrit (HCT), Mean, Platelet Volume (MPV), Platelet distribution width (PDW), and Platelet–Large Cell Ratio (P-LCR), were essential for identifying PH mouse models using the feature selection method proposed in this paper. Remarkably, the method achieved 100.0% accuracy and 100.0% specificity in classification, demonstrating that our method has great potential to be used for evaluating and identifying mouse PH models.

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