The role of miR-216a-mediated Nrf2 pathway in muscle oxidative stress of Siniperca chuatsi induced by cadmium.

Cadmium (Cd) is a toxic heavy metal pollutant in the environment. Excessive Cd in water has toxic effects on fish, endangering their healthy growth and ultimately affecting the quality and safety of aquatic products. To evaluate the toxicity of excessive Cd to fish through potential oxidative damage, Siniperca chuatsi was exposed to Cd in water for 15 days. It was found that Cd exposure significantly decreased the survival rate of S. chuatsi and Cd was detected in their muscle. Meanwhile, Cd disrupts the redox balance by reducing antioxidant enzyme activities, increasing reactive oxygen species (ROS) and malondialdehyde (MDA) levels in muscle, and promoting oxidative damage. Histomorphology showed that enlargement of muscle fiber gaps, cell swelling and vacuolar degeneration after Cd exposure. In addition, Cd toxicity induced up-regulating the expression of miR-216a, while down-regulation of Nrf2 protein and its downstream antioxidant enzyme genes expression. Further analysis revealed that miR-216a was significantly negatively correlated with the expression of Nrf2, and injection of miR-216a antagomir significantly enhanced the expression of Nrf2 and antioxidant enzyme genes, as well as the activity of antioxidant enzymes, thereby reducing the damage of Cd to fish. These results suggested that miR-216a-mediated Nrf2 signaling pathway plays an important role in Cd-induced oxidative stress of S. chuatsi muscle.

Traffic Feature Selection and Distributed Denial of Service Attack Detection in Software-Defined Networks Based on Machine Learning.

As 5G technology becomes more widespread, the significant improvement in network speed and connection density has introduced more challenges to network security. In particular, distributed denial of service (DDoS) attacks have become more frequent and complex in software-defined network (SDN) environments. The complexity and diversity of 5G networks result in a great deal of unnecessary features, which may introduce noise into the detection process of an intrusion detection system (IDS) and reduce the generalization ability of the model. This paper aims to improve the performance of the IDS in 5G networks, especially in terms of detection speed and accuracy. It proposes an innovative feature selection (FS) method to filter out the most representative and distinguishing features from network traffic data to improve the robustness and detection efficiency of the IDS. To confirm the suggested method's efficacy, this paper uses four common machine learning (ML) models to evaluate the InSDN, CICIDS2017, and CICIDS2018 datasets and conducts real-time DDoS attack detection on the simulation platform. According to experimental results, the suggested FS technique may match 5G network requirements for high speed and high reliability of the IDS while also drastically cutting down on detection time and preserving or improving DDoS detection accuracy.

Novel pretreatment nomograms based on pan-immune-inflammation value for predicting clinical outcome in patients with head and neck squamous cell carcinoma.

Background: The prognostic value of an effective biomarker, pan-immune-inflammation value (PIV), for head and neck squamous cell carcinoma (HNSCC) patients after radical surgery or chemoradiotherapy has not been well explored. This study aimed to construct and validate nomograms based on PIV to predict survival outcomes of HNSCC patients. Methods: A total of 161 HNSCC patients who underwent radical surgery were enrolled retrospectively for development cohort. The cutoff of PIV was determined using the maximally selected rank statistics method. Multivariable Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to develop two nomograms (Model A and Model B) that predict disease-free survival (DFS). The concordance index, receiver operating characteristic curves, calibration curves, and decision curve analysis were used to evaluate the nomograms. A cohort composed of 50 patients who received radiotherapy or chemoradiotherapy (RT/CRT) alone was applied for generality testing of PIV and nomograms. Results: Patients with higher PIV (≥123.3) experienced a worse DFS (HR, 5.01; 95% CI, 3.25-7.72; p<0.0001) and overall survival (OS) (HR, 5.23; 95% CI, 3.34-8.18; p<0.0001) compared to patients with lower PIV (<123.3) in the development cohort. Predictors of Model A included age, TNM stage, neutrophil-to-lymphocyte ratio (NLR), and PIV, and that of Model B included TNM stage, lymphocyte-to-monocyte ratio (LMR), and PIV. In comparison with TNM stage alone, the two nomograms demonstrated good calibration and discrimination and showed satisfactory clinical utility in internal validation. The generality testing results showed that higher PIV was also associated with worse survival outcomes in the RT/CRT cohort and the possibility that the two nomograms may have a universal applicability for patients with different treatments. Conclusions: The nomograms based on PIV, a simple but useful indicator, can provide prognosis prediction of individual HNSCC patients after radical surgery and may be broadly applicated for patients after RT/CRT alone.

A multi-feature spatial-temporal fusion network for traffic flow prediction.

The traffic flow prediction is the key to alleviate traffic congestion, yet very challenging due to the complex influence factors. Currently, the most of deep learning models are designed to dig out the intricate dependency in continuous standardized sequences, which are dependent to high requirements for data continuity and regularized distribution. However, the data discontinuity and irregular distribution are inevitable in the real-world practical application, then we need find a way to utilize the powerful effect of the multi-feature fusion rather than continuous relation in standardized sequences. To this end, we conduct the prediction based on the multiple traffic features reflecting the complex influence factors. Firstly, we propose the ATFEM, an adaptive traffic features extraction mechanism, which can select important influence factors to construct joint temporal features matrix and global spatial features matrix according to the traffic condition. In this way, the feature's representation ability can be improved. Secondly, we propose the MFSTN, a multi-feature spatial-temporal fusion network, which include the temporal transformer encoder and graph attention network to obtain the latent representation of spatial-temporal features. Especially, we design the scaled spatial-temporal fusion module, which can automatically learn optimal fusion weights, further adapt to inconsistent spatial-temporal dimensions. Finally, the multi-layer perceptron gets the mapping function between these comprehensive features and traffic flow. This method helps to improve the interpretability of the prediction. Experimental results show that the proposed model outperforms a variety of baselines, and it can accurately predict the traffic flow when the data missing rate is high.

GSDMD protects intestinal epithelial cells against bacterial infections through its N-terminal activity impacting intestinal immune homeostasis.

The intestinal mucosal barrier serves as a vital guardian for gut health, maintaining a delicate equilibrium between gut microbiota and host immune homeostasis. Recent studies have found the intricate roles of Gasdermin D (GSDMD), a key executioner of pyroptosis downstream of the inflammasome, within the intestine, including controlling colitis in intestinal macrophage and the regulatory function in goblet cell mucus secretion. Thus, the exact role and nature of GSDMD's regulatory function in maintaining intestinal immune homeostasis and defending against pathogens remain elucidation. Here, we uncover that GSDMD plays a key role in defending against intestinal Citrobacter rodentium infection, with high expression in intestinal epithelial and lamina propria myeloid cells. Our results show that GSDMD specifically acts in intestinal epithelial cells to fight the infection, independently of its effects on antimicrobial peptides or mucin secretion. Instead, the resistance is mediated through GSDMD's N-terminal fragments, highlighting its importance in intestinal immunity. However, the specific underlying mechanism of GSDMD N-terminal activity in protection against intestinal bacterial infections still needs further study to clarify in the future.

Circadian Rhythm Alteration of the Core Clock Genes and the Lipid Metabolism Genes Induced by High-Fat Diet (HFD) in the Liver Tissue of the Chinese Soft-Shelled Turtle (Trionyx sinensis).

Physiology disorders of the liver, as it is an important tissue in lipid metabolism, can cause fatty liver disease. The mechanism might be regulated by 17 circadian clock genes and 18 fat metabolism genes, together with a high-fat diet (HFD). Due to their rich nutritional and medicinal value, Chinese soft-shelled turtles (Trionyx sinensis) are very popular among the Chinese people. In the study, we aimed to investigate the influence of an HFD on the daily expression of both the core clock genes and the lipid metabolism genes in the liver tissue of the turtles. The two diets were formulated with 7.98% lipid (the CON group) and 13.86% lipid (the HFD group) to feed 180 juvenile turtles, which were randomly divided into two groups with three replicates per group and 30 turtles in each replicate for six weeks, and the diet experiment was administrated with a photophase regimen of a 24 h light/dark (12L:12D) cycle. At the end of the experiment, the liver tissue samples were collected from nine turtles per group every 3 h (zeitgeber time: ZT 0, 3, 6, 9, 12, 15, 18, 21 and 24) for 24 h to investigate the daily expression and correlation analysis of these genes. The results showed that 11 core clock genes [i.e., circadian locomotor output cycles kaput (Clock), brain and muscle arnt-like protein 1 and 2 (Bmal1/2), timeless (Tim), cryptochrome 1 (Cry2), period2 (Per2), nuclear factor IL-3 gene (Nfil3), nuclear receptor subfamily 1, treatment D, member 1 and 2 (Nr1d1/2) and retinoic acid related orphan receptor α/ß/γ ß and γ (Rorß/γ)] exhibited circadian oscillation, but 6 genes did not, including neuronal PAS domain protein 2 (Npas2), Per1, Cry1, basic helix-loop-helix family, member E40 (Bhlhe40), Rorα and D-binding protein (Dbp), and 16 lipid metabolism genes including fatty acid synthase (Fas), diacylglycerol acyltransferase 1 (Dgat1), 3-hydroxy-3-methylglutaryl-CoA reductase (Hmgcr), Low-density lipoprotein receptor-related protein 1-like (Ldlr1), Lipin 1 (Lipin1), Carnitine palmitoyltransferase 1A (Cpt1a), Peroxisome proliferator activation receptor α, ß and γ (Pparα/ß/γ), Sirtuin 1 (Sirt1), Apoa (Apoa1), Apolipoprotein B (Apob), Pyruvate Dehydrogenase kinase 4 (Pdk4), Acyl-CoA synthase long-chain1 (Acsl1), Liver X receptors α (Lxrα) and Retinoid X receptor, α (Rxra) also demonstrated circadian oscillations, but 2 genes did not, Scd and Acaca, in the liver tissues of the CON group. However, in the HFD group, the circadian rhythms' expressional patterns were disrupted for the eight core clock genes, Clock, Cry2, Per2, Nfil3, Nr1d1/2 and Rorß/γ, and the peak expression of Bmal1/2 and Tim showed delayed or advanced phases. Furthermore, four genes (Cry1, Per1, Dbp and Rorα) displayed no diurnal rhythm in the CON group; instead, significant circadian rhythms appeared in the HFD group. Meanwhile, the HFD disrupted the circadian rhythm expressions of seven fat metabolism genes (Fas, Cpt1a, Sirt1, Apoa1, Apob, Pdk4 and Acsl1). Meanwhile, the other nine genes in the HFD group also showed advanced or delayed expression peaks compared to the CON group. Most importantly of all, there were remarkably positive or negative correlations between the core clock genes and the lipid metabolism genes, and their correlation relationships were altered by the HFD. To sum up, circadian rhythm alterations of the core clock genes and the lipid metabolism genes were induced by the high-fat diet (HFD) in the liver tissues of T. sinensis. This result provides experimental and theoretical data for the mass breeding and production of T. sinensis in our country.

Gasdermin D in macrophages drives orchitis by regulating inflammation and antigen presentation processes.

Inflammation in the testes induced by infection and autoimmunity contributes significantly to male infertility, a public health issue. Current therapies using antibiotics and broad-spectrum anti-inflammatory drugs are ineffective against non-bacterial orchitis and induce side effects. This highlights the need to explore the pathogenesis of orchitis and develop alternative therapeutic strategies. In this study, we demonstrated that Gasdermin D (GSDMD) was activated in the testes during uropathogenic Escherichia coli (UPEC)-induced acute orchitis, and that GSDMD in macrophages induced inflammation and affected spermatogenesis during acute and chronic orchitis. In testicular macrophages, GSDMD promoted inflammation and antigen presentation, thereby enhancing the T-cell response after orchitis. Furthermore, the pharmacological inhibition of GSDMD alleviated the symptoms of UPEC-induced acute orchitis. Collectively, these findings provide the first demonstration of GSDMD's role in driving orchitis and suggest that GSDMD may be a potential therapeutic target for treating orchitis.

A method based on improved ant colony algorithm feature selection combined with GWO-SVR model for predicting chlorophyll-a concentration in Wuliangsu Lake.

Chlorophyll-a (Chl-a) is an important parameter in water bodies. Due to the complexity of optics in water bodies, it is difficult to accurately predict Chl-a concentrations in water bodies by current traditional methods. In this paper, using Sentinel-2 remote sensing images as the data source combined with measured data, taking Wuliangsu Lake as the study area, a new intelligent algorithm is proposed for prediction of Chl-a concentration, which uses the adaptive ant colony exhaustive optimization algorithm (A-ACEO) for feature selection and the gray wolf optimization algorithm (GWO) to optimize support vector regression (SVR) to achieve Chl-a concentration prediction. The ant colony optimization algorithm is improved to select remote sensing feature bands for Chl-a concentration by introducing relevant optimization strategies. The GWO-SVR model is built by optimizing SVR using GWO with the selected feature bands as input and comparing it with the traditional SVR model. The results show that the usage of feature bands selected by the presented A-ACEO algorithm as inputs can effectively reduce complexity and improve the prediction performance of the model, under the condition of the same model, which can provide valuable references for monitoring the Chl-a concentration in Wuliangsu Lake.