Dexmedetomidine ameliorates acute kidney injury by regulating mitochondrial dynamics via the α2-AR/SIRT1/PGC-1α pathway activation in rats.

BACKGROUND: Sepsis-associated acute kidney injury (AKI) is a serious complication of systemic infection with high morbidity and mortality in patients. However, no effective drugs are available for AKI treatment. Dexmedetomidine (DEX) is an alpha 2 adrenal receptor agonist with antioxidant and anti-apoptotic effects. This study aimed to investigate the therapeutic effects of DEX on sepsis-associated AKI and to elucidate the role of mitochondrial dynamics during this process. METHODS: A lipopolysaccharide (LPS)-induced AKI rat model and an NRK-52E cell model were used in the study. This study investigated the effects of DEX on sepsis-associated AKI and the molecular mechanisms using histologic assessment, biochemical analyses, ultrastructural observation, western blotting, immunofluorescence, immunohistochemistry, qRT-PCR, flow cytometry, and si-mRNA transfection. RESULTS: In rats, the results showed that administration of DEX protected kidney structure and function from LPS-induced septic AKI. In addition, we found that DEX upregulated the α2-AR/SIRT1/PGC-1α pathway, protected mitochondrial structure and function, and decreased oxidative stress and apoptosis compared to the LPS group. In NRK-52E cells, DEX regulated the mitochondrial dynamic balance by preventing intracellular Ca2+ overloading and activating CaMKII. CONCLUSIONS: DEX ameliorated septic AKI by reducing oxidative stress and apoptosis in addition to modulating mitochondrial dynamics via upregulation of the α2-AR/SIRT1/PGC-1α pathway. This is a confirmatory study about DEX pre-treatment to ameliorate septic AKI. Our research reveals a novel mechanistic molecular pathway by which DEX provides nephroprotection.

Responses of Crop Yield, Soil Fertility, and Heavy Metals to Spent Mushroom Residues Application.

Waste mushroom residues are often returned to fields as organic amendments. Here, we estimated the effects of the continuous applications of different spent mushroom substrates for 2 years on crop yields, soil nutrients, and heavy metals in paddy fields. The study comprised seven treatments: no fertilization (CK) and mineral NPK fertilizer (CF), as well as NPK fertilizer combined with Enoki mushroom residue (EMR50), Oyster mushroom residue (OMR50), Auricularia polytricha mushroom residue (APR50), Shiitake mushroom residue (SMR50), and Agaricus bisporus residue (ABR50). The grain yield was highest under the APR50 treatment. The short-term application of waste mushroom residue significantly increased SOC, TN, TP, and TK content relative to the CK treatment. The SOC, TP, and TK were highest under ABR50. Both total Cr and Cd contents were highest under CF treatment. The highest cumulative ecological risk was observed under OMR50 treatment. In addition, crop yield was positively correlated with SOC, TN, TP, and TP. Our results highlight that further research and innovation are needed to optimize the benefits and overcome the challenges of mushroom residue application.

Students learning performance prediction based on feature extraction algorithm and attention-based bidirectional gated recurrent unit network.

With the development of information technology construction in schools, predicting student grades has become a hot area of application in current educational research. Using data mining to analyze the influencing factors of students' performance and predict their grades can help students identify their shortcomings, optimize teachers' teaching methods and enable parents to guide their children's progress. However, there are no models that can achieve satisfactory predictions for education-related public datasets, and most of these weakly correlated factors in the datasets can still adversely affect the predictive effect of the model. To solve this issue and provide effective policy recommendations for the modernization of education, this paper seeks to find the best grade prediction model based on data mining. Firstly, the study uses the Factor Analyze (FA) model to extract features from the original data and achieve dimension reduction. Then, the Bidirectional Gate Recurrent Unit (BiGRU) model and attention mechanism are utilized to predict grades. Lastly, Comparing the prediction results of ablation experiments and other single models, such as linear regression (LR), back propagation neural network (BP), random forest (RF), and Gate Recurrent Unit (GRU), the FA-BiGRU-attention model achieves the best prediction effect and performs equally well in different multi-step predictions. Previously, problems with students' grades were only detected when they had already appeared. However, the methods presented in this paper enable the prediction of students' learning in advance and the identification of factors affecting their grades. Therefore, this study has great potential to provide data support for the improvement of educational programs, transform the traditional education industry, and ensure the sustainable development of national talents.

An Active Repetitive Learning Control Method for Lateral Suspension Systems of High-Speed Trains.

This article presents a novel perspective to improve the ride quality of high-speed trains (HSTs), namely, by virtue of the periodicity of lateral dynamics to suppress the lateral vibration of HST. To resolve the contradiction between the complex HST model and the effective controller design, a simplified three-degrees-of-freedom (3-DOF) quarter-vehicle model is first employed for controller design, while a 17-DOF full-vehicle model is built for efficiency verification, where periodic and random track irregularities are considered, respectively. An active repetitive learning control (RLC) method is proposed to achieve the periodic tracking control, where the learning convergence is proved rigorously in a Lyapunov way. The configuration of RLC-based lateral suspensions is economical in the sense that only four actuators and six sensors are needed. It is verified by simulation that, compared with the dynamic matrix controller, the proposed RLC controller has greatly reduced the lateral vibration of a vehicle body, especially the lateral acceleration in the frequency range of (0, 3] Hz to which human body is strongly sensitive.

Isoflurane anesthesia promotes cognitive impairment by inducing expression of ß-amyloid protein-related factors in the hippocampus of aged rats.

Isoflurane anesthesia has been shown to be responsible for cognitive impairment in Alzheimer's disease (AD) and development of AD in the older age groups. However, the pathogenesis of AD-related cognitive impairments induced by isoflurane anesthesia remains elusive. Thus, this study was designed to investigate the mechanism by which isoflurane anesthesia caused AD-related cognitive impairments. Aged Wistar rats were randomly divided into 6 groups (n = 12), 1 control group (CONT) and 5 isoflurane treated (ISO) groups (ISO 0, ISO 0.5D, ISO 1D, ISO 3D and ISO 7D). The CONT group inhaled 30% O2 for 2 h without any anesthesia. ISO groups were placed under anesthesia with 3% isoflurane and then exposed to 1.5% isoflurane delivered in 30% O2 for 2 h. Rats in each ISO group were then analyzed immediately (ISO 0) or at various time points (0.5, 1, 3 or 7 day) after this exposure. Cognitive function was assessed using the Morris water maze test. Protein levels of amyloid precursor protein (APP), ß-site APP cleavage enzyme-1 (BACE-1) and Aß42 peptide were analyzed in hippocampal samples by Western blot. ß-Amyloid (Abeta) plaques were detected in hippocampal sections by Congo red staining. Compared with controls, all ISO groups showed increased escape latency and impaired spatial memory. Isoflurane increased APP mRNA expression and APP protein depletion, promoting Aß42 overproduction, oligomerization and accumulation. However, isoflurane did not affect BACE-1 expression. Abeta plaques were observed only in those ISO groups sacrificed at 3 or 7 d. Our data indicate that aged rats exposed to isoflurane had increased APP mRNA expression and APP protein depletion, with Aß42 peptide overproduction and oligomerization, resulting in formation of Abeta plaques in the hippocampus. Such effects might have contributed to cognitive impairments, including in spatial memory, observed in these rats after isoflurane anesthesia.