N-acetylserotonin alleviates retinal ischemia-reperfusion injury via HMGB1/RAGE/NF-κB pathway in rats.

AIM: To observe the effects of N-acetylserotonin (NAS) administration on retinal ischemia-reperfusion (RIR) injury in rats and explore the underlying mechanisms involving the high mobility group box 1 (HMGB1)/receptor for advanced glycation end-products (RAGE)/nuclear factor-kappa B (NF-κB) signaling pathway. METHODS: A rat model of RIR was developed by increasing the pressure of the anterior chamber of the eye. Eighty male Sprague Dawley were randomly divided into five groups: sham group (n=8), RIR group (n=28), RIR+NAS group (n=28), RIR+FPS-ZM1 group (n=8) and RIR+NAS+ FPS-ZM1 group (n=8). The therapeutic effects of NAS were examined by hematoxylin-eosin (H&E) staining, and retinal ganglion cells (RGCs) counting. The expression of interleukin 1 beta (IL-1ß), HMGB1, RAGE, and nod-like receptor 3 (NLRP3) proteins and the phosphorylation of nuclear factor-kappa B (p-NF-κB) were analyzed by immunohistochemistry staining and Western blot analysis. The expression of HMGB1 protein was also detected by enzyme-linked immunosorbent assay (ELISA). RESULTS: H&E staining results showed that NAS significantly reduced retinal edema and increased the number of RGCs in RIR rats. With NAS therapy, the HMGB1 and RAGE expression decreased significantly, and the activation of the NF-κB/NLRP3 pathway was antagonized along with the inhibition of p-NF-κB and NLRP3 protein expression. Additionally, NAS exhibited an anti-inflammatory effect by reducing IL-1ß expression. The inhibitory of RAGE binding to HMGB1 by RAGE inhibitor FPS-ZM1 led to a significant decrease of p-NF-κB and NLRP3 expression, so as to the IL-1ß expression and retinal edema, accompanied by an increase of RGCs in RIR rats. CONCLUSION: NAS may exhibit a neuroprotective effect against RIR via the HMGB1/RAGE/NF-κB signaling pathway, which may be a useful therapeutic target for retinal disease.

Pre-intensive care unit use of selective serotonin reuptake inhibitors and mortality in critically ill adults with mental disorders: analysis from the MIMIC-IV database.

Selective serotonin reuptake inhibitors (SSRIs) are the most commonly prescribed drugs for mental disorders in critically ill patients. We performed a retrospective cohort study to investigate the association between pre-ICU use of SSRIs and mortality in critically ill adults with mental disorders. We identified critically ill adults with mental disorders based on the Medical Information Mart in Intensive Care-IV database. The exposure was the use of SSRIs during the period after hospital admission and before ICU admission. The outcome was in-hospital mortality. Time-dependent Cox proportional hazards regression models were used to estimate the adjusted hazard ratio (aHR) with 95% confidence interval (CI). To further test the robustness of the results, we performed propensity score matching and marginal structural Cox model estimated by inverse probability of treatment weighting. The original cohort identified 16601 patients. Of those, 2232 (13.4%) received pre-ICU SSRIs, and 14369 (86.6%) did not. Matched cohort obtained 4406 patients, with 2203 patients in each group (SSRIs users vs. non-users). In the original cohort, pre-ICU use of SSRIs was associated with a 24% increase in the hazard for in-hospital mortality (aHR, 1.24; 95% CI, 1.05-1.46; P = 0.010). The results were robust in the matched cohort (aHR, 1.26; 95% CI, 1.02-1.57; P = 0.032) and the weighted cohort (aHR, 1.43; 95% CI, 1.32-1.54; P < 0.001). Pre-ICU use of SSRIs is associated with an increase in the hazard for in-hospital mortality in critically ill adults with mental disorders.

Influence of systolic blood pressure trajectory on in-hospital mortality in patients with sepsis.

BACKGROUND: Numerous studies have investigated the mean arterial pressure in patients with sepsis, and many meaningful results have been obtained. However, few studies have measured the systolic blood pressure (SBP) multiple times and established trajectory models for patients with sepsis with different SBP trajectories. METHODS: Data from patients with sepsis were extracted from the Medical Information Mart for Intensive Care-III database for inclusion in a retrospective cohort study. Ten SBP values within 10 h after hospitalization were extracted, and the interval between each SBP value was 1 h. The SBP measured ten times after admission was analyzed using latent growth mixture modeling to construct a trajectory model. The outcome was in-hospital mortality. The survival probability of different trajectory groups was investigated using Kaplan-Meier (K-M) analysis, and the relationship between different SBP trajectories and in-hospital mortality risk was investigated using Cox proportional-hazards regression model. RESULTS: This study included 3034 patients with sepsis. The median survival time was 67 years (interquartile range: 56-77 years). Seven different SBP trajectories were identified based on model-fit criteria. The in-hospital mortality rates of the patients in trajectory classes 1-7 were 25.5%, 40.5%, 11.8%, 18.3%, 23.5%, 13.8%, and 10.5%, respectively. The K-M analysis indicated that patients in class 2 had the lowest probability of survival. Univariate and multivariate Cox regression analysis indicated that, with class 1 as a reference, patients in class 2 had the highest in-hospital mortality risk (P < 0.001). Subgroup analysis indicated that a nominal interaction occurred between age group and blood pressure trajectory in the in-hospital mortality (P < 0.05). CONCLUSION: Maintaining a systolic blood pressure of approximately 140 mmHg in patients with sepsis within 10 h of admission was associated with a lower risk of in-hospital mortality. Analyzing data from multiple measurements and identifying different categories of patient populations with sepsis will help identify the risks among these categories.

Pelvic Injury Discriminative Model Based on Data Mining Algorithm.

OBJECTIVES: To reduce the dimension of characteristic information extracted from pelvic CT images by using principal component analysis (PCA) and partial least squares (PLS) methods. To establish a support vector machine (SVM) classification and identification model to identify if there is pelvic injury by the reduced dimension data and evaluate the feasibility of its application. METHODS: Eighty percent of 146 normal and injured pelvic CT images were randomly selected as training set for model fitting, and the remaining 20% was used as testing set to verify the accuracy of the test, respectively. Through CT image input, preprocessing, feature extraction, feature information dimension reduction, feature selection, parameter selection, model establishment and model comparison, a discriminative model of pelvic injury was established. RESULTS: The PLS dimension reduction method was better than the PCA method and the SVM model was better than the naive Bayesian classifier (NBC) model. The accuracy of the modeling set, leave-one-out cross validation and testing set of the SVM classification model based on 12 PLS factors was 100%, 100% and 93.33%, respectively. CONCLUSIONS: In the evaluation of pelvic injury, the pelvic injury data mining model based on CT images reaches high accuracy, which lays a foundation for automatic and rapid identification of pelvic injuries.

Neighboring Algorithm for Visual Semantic Analysis toward GAN-Generated Pictures.

Generative adversarial network (GAN)-guided visual quality evaluation means scoring GAN-propagated portraits to quantify the degree of visual distortions. In general, there are very few image- and character-evaluation algorithms generated by GAN, and the algorithm's athletic ability is not capable. In this article, we proposed a novel image ranking algorithm based on the nearest neighbor algorithm. It can obtain automatic and extrinsic evaluation of GAN procreate images using an efficient evaluation technique. First, with the support of the artificial neural network, the boundaries of the variety images are extracted to form a homogeneous portrait candidate pool, based on which the comparison of product copies is restricted. Subsequently, with the support of the K-nearest neighbors algorithm, from the unified similarity candidate pool, we extract the most similar concept of K-Emperor to the generated portrait and calculate the portrait quality score accordingly. Finally, the property of generative similarity that produced by the GAN models are trained on a variety of classical datasets. Comprehensive experimental results have shown that our algorithm substantially improves the efficiency and accuracy of the natural evaluation of pictures generated by GAN. The calculated metric is only 1/9-1/28 compared to the other methods. Meanwhile, the objective evaluation of the GAN and human consistency has increased by more than 80% in line with human visual perception.

Machine-Vision-Based Enhanced Deep Genetic Algorithm for Robot Action Analysis.

The machine-driven products processed by CNC machine tools are divided into multiple parts, which are often transmitted to unqualified processing crystals due to the error of the part's posture, thus affecting the projection effect. Aiming at handling this problem, we, in this work, propose a method for locating and correcting machine capabilities based on bicycle visual recognition. The robot obtains coordinates and offset points through the vision system. In order to satisfy the requirement of fast sorting of the robot parts, a robot species method (BAS-GA) that supports machine vision and improved genetic algorithm rules is proposed. The rank method first preprocesses the part copies, then uses the Sift feature twin similarity notification algorithm to filter the part idols, and finally uses in-law deformation to place the target parts. Afterward, an particular model is built for the maintenance of the nurtural skill, and the mathematical mold is solved using the BAS-GA algorithmic program authority. The close trail of the robot is maintained to manifest the marijuana of the robot. Experimental inference have shown that the BAS-GA algorithm rule achieves the optimal conclusion similar to the pseudo-annealing algorithmic program plant. Meanwhile, the genetic algorithm rule is modified ant settlement algorithm program. Knife sharpening was also reduced by 7%, inferring that this process can effectively improve the robot's action success rate.

Landscape Fusion Method Based on Augmented Reality and Multiview Reconstruction.

This paper proposes a fused landscape augmented reality method based on 3D model multiview reconstruction. Based on the principles related to augmented reality technology, the proposed method uses natural features of images for training and extraction, which solves the problems of convenience and aesthetics caused by artificial signs. By extracting and training natural features at different scales of the acquired images, Harris and FREAK algorithms are used to extract features and create binary descriptors for real-time acquired images. Feature matching is performed on the above two features to estimate the location where the reconstructed model will appear. At the same time, for the difficulties of 3D model reconstruction requiring relevant expertise and the defects of poor reconstruction effect, the SFM algorithm is used for multiview reconstruction of landscape models to realize the augmented reality fusion method of natural scenes and landscape models. After the experiments, the fusion achieved by this method works well, which proves that the method is feasible and has potential.