Development of rapid nucleic acid testing techniques for common respiratory infectious diseases in the Chinese population

Background: Most respiratory viruses can cause serious lower respiratory diseases at any age. Therefore, timely and accurate identification of respiratory viruses has become even more important. This study focused on the development of rapid nucleic acid testing techniques for common respiratory infectious diseases in the Chinese population.Methods: Multiplex fluorescent quantitative polymerase chain reaction (PCR) assays were developed and validated for the detection of respiratory pathogens including the novel coronavirus (SARS-CoV-2), influenza A virus (FluA), parainfluenza virus (PIV), and respiratory syncytial virus (RSV).Results: The assays demonstrated high specificity and sensitivity, allowing for the simultaneous detection of multiple pathogens in a single reaction. These techniques offer a rapid and reliable method for screening, diagnosis, and monitoring of respiratory pathogens.Conclusion: The implementation of these techniques might contribute to effective control and prevention measures, leading to improved patient care and public health outcomes in China. Further research and validation are needed to optimize and expand the application of these techniques to a wider range of respiratory pathogens and to enhance their utility in clinical and public health settings

Trs20, Trs23, Trs31 and Bet5 participate in autophagy through GTPase Ypt1 in Saccharomyces cerevisiae

TRAPP (transport protein particle) is a large, highly conserved, multi-subunit complex. Four types of TRAPP complexes (I, II, III and most recently IV) have been identified in Saccharomyces cerevisiae. Studies on the roles of TRAPP II, TRAPP III and TRAPP IV specific subunits (Trs130, Trs85 and Trs33) have demonstrated that TRAPP II, TRAPP III and TRAPP IV activate the small GTPases that regulate autophagy. Up to now, the roles of the common TRAPP subunits have been well studied in vesicle transport. However, the roles of the common TRAPP subunits and their relationship to Ypt/Rab GTPases in autophagy are not clear. In this paper, we examined Trs20, Trs23, Trs31, and Bet5 (the common TRAPP subunits), which are required for starvation-induced autophagy and the cytoplasm-to-vacuole targeting (Cvt) pathway. During autophagy, GFP-Atg8 accumulates as single or multiple dots and is not recruited into the pre-autophagosomal structures (PAS) in trs20ts, trs23ts, trs31ts and bet5ts mutant cells. Furthermore, these dots are linked to the endoplasmic reticulum in mutant cells. Additionally, overexpression of Ypt1, but not Ypt31, suppresses the autophagy defect in trs20ts, trs23ts, trs31ts and bet5ts mutant cells. Based on these results, we concluded that Trs20, Trs23, Trs31, and Bet5 are required for autophagy, and that these common TRAPP subunits regulate autophagy partially through GTPase Ypt1, but not Ypt31

Intelligent Tennis Robot Based on a Deep Neural Network

In this paper, an improved you only look once (YOLOv3) algorithm is proposed to make the detection effect better and improve the performance of a tennis ball detection robot. The depth-separable convolution network is combined with the original YOLOv3 and the residual block is added to extract the features of the object. The feature map output by the residual block is merged with the target detection layer through the shortcut layer to improve the network structure of YOLOv3. Both the original model and the improved model are trained by the same tennis ball data set. The results show that the recall is improved from 67.70% to 75.41% and the precision is 88.33%, which outperforms the original 77.18%. The recognition speed of the model is increased by half and the weight is reduced by half after training. All these features provide a great convenience for the application of the deep neural network in embedded devices. Our goal is that the robot is capable of picking up more tennis balls as soon as possible. Inspired by the maximum clique problem (MCP), the pointer network (Ptr-Net) and backtracking algorithm (BA) are utilized to make the robot find the place with the highest concentration of tennis balls. According to the training results, when the number of tennis balls is less than 45, the accuracy of determining the concentration of tennis balls can be as high as 80%

Sex-Dependent Depression-Like Behavior Induced by Respiratory Administration of Aluminum Oxide Nanoparticles

Ultrafine aluminum oxide, which are abundant in ambient and involved occupational environments, are associated with neurobehavioral alterations. However, few studies have focused on the effect of sex differences following exposure to environmental Al2O3 ultrafine particles. In the present study, male and female mice were exposed to Al2O3 nanoparticles (NPs) through a respiratory route. Only the female mice showed depression-like behavior. Although no obvious pathological changes were observed in mice brain tissues, the neurotransmitter and voltage-gated ion channel related gene expression, as well as the small molecule metabolites in the cerebral cortex, were differentially modulated between male and female mice. Both mental disorder-involved gene expression levels and metabolomics analysis results strongly suggested that glutamate pathways were implicated in sex differentiation induced by Al2O3 NPs. Results demonstrated the potential mechanism of environmental ultrafine particle-induced depression-like behavior and the importance of sex dimorphism in the toxic research of environmental chemicals

Data_Sheet_1_An exploration on the machine-learning-based stroke prediction model.ZIP

IntroductionWith the rapid development of artificial intelligence technology, machine learning algorithms have been widely applied at various stages of stroke diagnosis, treatment, and prognosis, demonstrating significant potential. A correlation between stroke and cytokine levels in the human body has recently been reported. Our study aimed to establish machine-learning models based on cytokine features to enhance the decision-making capabilities of clinical physicians.MethodsThis study recruited 2346 stroke patients and 2128 healthy control subjects from Chongqing University Central Hospital. A predictive model was established through clinical experiments and collection of clinical laboratory tests and demographic variables at admission. Three classification algorithms, namely Random Forest, Gradient Boosting, and Support Vector Machine, were employed. The models were evaluated using methods such as ROC curves, AUC values, and calibration curves.ResultsThrough univariate feature selection, we selected 14 features and constructed three machine-learning models: Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Machine (GBM). Our results indicated that in the training set, the RF model outperformed the GBM and SVM models in terms of both the AUC value and sensitivity. We ranked the features using the RF algorithm, and the results showed that IL-6, IL-5, IL-10, and IL-2 had high importance scores and ranked at the top. In the test set, the stroke model demonstrated a good generalization ability, as evidenced by the ROC curve, confusion matrix, and calibration curve, confirming its reliability as a predictive model for stroke.DiscussionWe focused on utilizing cytokines as features to establish stroke prediction models. Analyses of the ROC curve, confusion matrix, and calibration curve of the test set demonstrated that our models exhibited a strong generalization ability, which could be applied in stroke prediction.</p