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Background: Second-generation antipsychotics (SGAs) frequently cause metabolic syndrome (MetS), which raises the risk of heart disease, type 2 diabetes, morbid obesity, atherosclerosis, and hypertension. MetS also impairs cognitive function in patients with schizophrenia. However, the fundamental reasons of MetS caused by SGAs are not yet fully understood. Thus, we aimed to identify potential therapeutic targets for MetS induced by SGAs. Methods: The serum biochemical parameters and the RNA-sequencing of peripheral blood mononuclear cells were measured in three groups (healthy controls and patients with schizophrenia with and without MetS taking SGAs). The study of the weighted gene co-expression network was utilized to pinpoint modules that were significantly connected to clinical markers. Results: Statistical analysis showed significant differences in triglyceride and high-density lipoprotein among the three groups. The TNF signaling pathway, TGF-β signaling pathway, fatty acid metabolism, NF-kappa B signaling pathway, MAPK signaling pathway, and Toll-like receptor signaling pathway were the pathways that were primarily enriched in the two unique co-expression network modules that were found. Finally, five specific genes (TNF, CXCL8, IL1B, TIMP1, and ESR1) associated with metabolism and immunity pathways were identified. Conclusions: This study indicated that SGAs differentially induced MetS of patients with schizophrenia through metabolic and inflammation-related pathways. Therefore, the potential side effects of drugs on inflammatory processes need to be considered when using SGAs for the treatment of schizophrenia.

Summary: Super killer (SKI) complex is a well-known cytoplasmic 3′–5′ mRNA decay complex that functions with the exosome to degrade excessive and aberrant mRNAs, is implicated with the extraction of mRNA at stalled ribosomes, tackling aberrant translation. Here, we show that SKIV2L and TTC37 of the hSKI complex are present within the nucleus, localize on chromatin and at some telomeres during the G2 cell cycle phase. In cells, SKIV2L prevents telomere replication stress, independently of its helicase domain, and increases the stability of telomere DNA-RNA hybrids in G2. We further demonstrate that purified hSKI complex binds telomeric DNA and RNA substrates in vitro and SKIV2L association with telomeres is dependent on DNA-RNA hybrids. Taken together, our results provide a nuclear function for SKIV2L of the hSKI complex in overcoming replication stress at telomeres mediated by its recruitment to DNA-RNA hybrid structures in G2 and thus maintaining telomere stability.

Objectives: Physical frailty has been found to increase the risk of multiple adverse outcomes including cardiovascular disease (CVD) in diabetic patients, but whether this could be modified by traditional risk factor control remains unknown. We aimed to explore the joint and interaction effects of frailty and traditional risk factor control on the risk of CVD. Design: A population-based cohort study. Setting and participants: We included 15,753 participants with type 2 diabetes at baseline from UK Biobank. Measurements: Physical frailty was assessed by Fried criteria’s frailty phenotype. The degree of risk factor control was determined by the numbers of the following factors controlled within the target range, including glycated hemoglobin, blood pressure, low-density lipoprotein cholesterol, smoking, and kidney condition. Incident CVD included coronary heart disease, stroke, or heart failure. Cox proportional hazard models were used to assess the individual and joint effects of frailty and risk factor control on the risk of CVD. Results: After a median follow-up of 13.5 years, 1129 incident CVD events were observed. Compared with non-frailty, both prefrailty and frailty were significantly associated with increased risk of CVD (HR 1.22, 95% CI [1.13, 1.31] for pre-frailty and 1.70 [1.53, 1.90] for frailty). For the joint effects, participants with frailty and a low degree of risk factor control (control of 0−1 risk factors) had the highest risk of CVD (2.92 [2.04, 4.17]) compared to those with non-frailty and optimal risk factor control (control of 4−5 risk factors). Moreover, a significant additive interaction between frailty and risk factor control was observed, with around 3.8% of CVD risk attributed to the interactive effects. Conclusions: Both prefrailty and frailty were associated with a higher risk of CVD in participants with type 2 diabetes. Moreover, physical frailty could interact with the degree of risk factor control in an additive manner to increase the CVD risk.

Accurate wind power prediction is critical for efficient grid management and the integration of renewable energy sources into the power grid. This study presents an effective deep-learning approach that improves short-term wind power forecasting accuracy. The method incorporates a Variational Autoencoder (VAE) with a self-attention mechanism applied in both the encoder and decoder. This empowers the model to leverage VAE's strengths in time-series modeling and nonlinear approximation while focusing on the most relevant features within the wind power data. The effectiveness of this approach is evaluated through a comprehensive comparison with eight established deep learning methods, including Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Bidirectional LSTMs (BiLSTMs), Convolutional LSTMs (ConvLSTMs), Gated Recurrent Units (GRUs), Stacked Autoencoders (SAEs), Restricted Boltzmann Machines (RBMs), and vanilla VAEs. Real-world data from five wind turbines in France and Turkey is used for the evaluation. Five statistical metrics are employed to quantitatively assess the prediction performance of each method. The results indicate that the SA-VAE model consistently outperformed other models, achieving the highest average R2 value of 0.992, demonstrating its superior predictive capability compared to existing techniques.

Precise traffic flow prediction is a central component of advancing intelligent transportation systems, providing essential insights for optimizing traffic management, reducing travel times, and alleviating congestion. This study introduces an efficient deep learning approach that synergistically integrates the benefits of wavelet-based denoising and Recurrent Neural Networks (RNNs). This integrated methodology is introduced to effectively capture the inherent nonlinearity and temporal dependencies in time series traffic data. Specifically, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are introduced to address the challenges associated with accurately forecasting traffic flow. To enhance prediction quality, traffic flow data is preprocessed using exponential smoothing and wavelet-based filtering as denoising filters, effectively eliminating outliers. The effectiveness of the proposed techniques is evaluated using traffic measurements collected from diverse highway locations across California, including the Old Bayshore highway, situated south of Interstate 880 (I880), and the Ashby Ave highway, positioned west of Interstate 80 (I80) in the San Francisco Bay Area. The results obtained through integrating both architectures, including LSTM and GRU, within the wavelet transform-based filter demonstrate an enhancement in forecasting performance. Specifically, LSTM with wavelet-based denoising using Symlet and Haar wavelets achieved high prediction performance with an average R2 of 0.982 and 0.9811, respectively.

Magnetic materials with non-collinear spin orderings provide an outstanding platform to probe spintronic phenomena owing to their strong spin-orbit coupling (SOC) and unique Berry phase. It is thus important to obtain a non-collinear antiferromagnetic (AFM) phase at room temperature (RT). Significantly, the discovery of novel materials with nearly zero thermal expansion (ZTE) property near RT is required and pursued for avoiding thermal stress and fracture in spintronic devices. Herein, the doping of Sn (Ge) at the Ag site in the triangular lattice Mn3Ag1−xSn(Ge)xN compounds increases effectively the Néel point and makes the interesting non-collinear Γ5g AFM phase exist above RT. The magnetic phase diagrams with Γ5g phase up to 498 K were built by the combined analysis of neutron powder diffraction (NPD), magnetic measurements, electronic transport, and differential scanning calorimetry (DSC). The thermal expansion behaviors of Mn3Ag1−xSn(Ge)xN were modulated, and the nearly ZTE above RT was achieved in Mn3Ag0.5Ge0.5N within Γ5g AFM ordering. Our findings offer an effective way to tailor the non-collinear AFM ordering and correlated thermal expansion behavior for potential use in the emerging field of thermal stress-free magnetic chip materials.

The combination of protein and polyphenol is an effective approach to improve the stability of protein emulsions. The lactoferrin (LF)-(−)-epigallocatechin-3-gallate (EGCG) covalent complex (LF-EGCG) was first prepared by alkali-induced reaction, then the structure and physicochemical properties between LF-EGCG and non-covalent complex (LF + EGCG) were compared, and finally the stability of complexes to fish oil high internal Pickering emulsions (HIPPEs) was tested. Results showed that LF-EGCG had stronger antioxidant activity, higher thermal stability, and better surface wettability than LF + EGCG. Meanwhile, the complexes showed no cytotoxicity within the tested concentration range (12.5–200 μg/mL). The HIPPEs stabilized with LF-EGCG possessed smaller droplet size, higher ζ-potential, and more uniform oil/water proton distribution. Covalent treatment also enhanced the storage, thermal, freeze-thaw and physical stability of LF HIPPEs. Furthermore, due to the higher antioxidant activity and denser microstructure, LF-EGCG HIPPE can more effectively inhibit the oxidation of fish oil.

Adhere to the concept of low-carbon environmental protection and turning waste into treasure, polysaccharides from Flammulina velutipes residue polysaccharide (FVRP) has been developed and possesses diverse bioactivities, comprising antioxidant, hypoglycemic, and relieving heavy metal damage, which still has the disadvantages of high molecular weight and low bioavailability. The current work is the first to prepare a degraded polysaccharide (FVRPV) from FVRP by ultrasonic assisted H2O2-Vc technique in order to reduce its molecular weight, thereby improving its activity and bioavailability. Our results found that the molecular weight and average particle size were declined, but the monosaccharide composition and characteristic functional group types of FVRPV had no impact. The structural changes of polysaccharides analyzed by XRD, Congo Red test, I2-KI, SEM, and methylation analysis indicated that the surface morphology and glycosidic bond composition of FVRPV possessed longer side chains and a greater number of branches with an amorphous crystal structure devoid of a triple helix configuration, and had experienced notable alterations after ultrasonic assisted H2O2-Vc treatment. Meanwhile, the in vitro antioxidant capacity of FVRPV had significantly increased compared to FVRP, implying ultrasonic assisted H2O2-Vc technique maybe a effective method to enhance the bioactivity of polysaccharides. In addition, the content of polysaccharide, reducing sugar, and uronic acid in FVRPV was significantly decreased, but antioxidant capacity of fermentation broth was stronger by in vitro human fecal fermentation. The 16S rDNA sequencing data displayed that FVRPV can enrich probiotics and reduce the abundance of pathogenic bacteria through different metabolic pathways mediated by gut microbiota, thereby exerting its potential probiotic effects. The interesting work provides a novel degraded polysaccharide by ultrasonic assisted H2O2-Vc technique, laying a foundation for developing FVRPV as a new antioxidant and prebiotic.

Cell isolation protocols from brain tissue include prolonged ex vivo processing durations, rendering them suboptimal for transcriptomic studies. Particularly for microglia and vascular cells, current isolation methods produce lower yields, necessitating addition of an enrichment step, and use of large tissue volumes - in most cases whole brain tissue - to obtain sufficient yields. Here, we developed a simple, rapid, and reproducible cell isolation method for generating single-cell suspensions from micro-dissected brain regions, enriched for microglia and vascular cells, without an enrichment step. Cells isolated using this method are suitable for molecular profiling studies using 10 × Genomics Chromium single-cell RNA sequencing with high reproducibility. Our method is valuable for longitudinal unbiased molecular profiling of microglia and vascular cells within different brain regions, spanning multiple time points across physiological development or disease progression.

Pregnane X receptor (PXR) is a heterologous biosensor that is involved in the metabolic pathway of environmental pollutants, regulating the transcription of genes involved in biotransformation. There are significant differences in the selectivity and specificity of organic pollutants (OPs) toward polar bear PXR (pbPXR) and human PXR (hPXR), but the detailed dynamical characteristics of their interactions are unclear. Homology Modeling, molecular docking, molecular dynamics simulation, and free energy calculation were used to analyze the recognition of pbPXR and hPXR by three OPs: BPA, chlordane and toxaphene. Comparing interaction patterns along with binding free energy of pbPXR and hPXR with these three OPs revealed that although pbPXR and hPXR interact similar with these three OPs, these OPs have different effects on the internal dynamics of pbPXR and hPXR. This results in significant alterations in the interaction of key residues near Leu209, Met243, Phe288, Met323, and His407 with OPs, thereby influencing their binding energy. Non-polar interactions, especially van der Waals interactions, were found to be the dominating factors in interacting of these OPs with PXRs. The region surrounding these key residues facilitates hydrophobic contacts with PXR, which are crucial for the selective activation of PXRs in different species by these three OPs. These findings are of significant guidance in understanding the impacts of environmental endocrine disruptors on different organisms.

Accurate mapping of irrigated croplands is crucial for a comprehensive understanding of agricultural practices and land management. Despite recent advancements, there remains room for further exploration of the effective fusion of temporal information from multitemporal remote sensing images, which is essential for capturing the dynamic nature of agricultural landscapes. Many existing irrigation mapping methods concatenate multitemporal images in a direct way and thus neglect the temporal relationships within the image time series, especially the sequence and interdependencies of the temporal dimension. To address this gap, a novel deep learning model, named the attention-enhanced spatial–temporal high-resolution network (AEST-HRNet), which incorporates parallel processing and a fusion mechanism of multiresolution information streams, three-dimensional (3D) spatial–temporal convolution, and temporal attention modules, was proposed. When applied to irrigated regions in Washington and California, USA, AEST-HRNet effectively extracted irrigated areas using multitemporal Sentinel-2 images obtained with the Google Earth Engine (GEE). To validate the results, 208 representative sample patches were selected, and the AEST-HRNet maps were compared against third-party ground reference data and statistics from the United States National Agricultural Statistics Survey (NASS). Quantitative assessment revealed an impressive F1-score of 0.956, an intersection over union (IoU) value of 0.867, and an overall accuracy (OA) value of 0.973 in Washington, outperforming publicly released maps. Comparative evaluations demonstrated that AEST-HRNet outperforms pixel-based classification using the random forest (RF) model and convolution-based semantic segmentation methods based on metrics such as F1-score, IoU, and Kappa. This study introduces a promising solution for precise irrigation mapping, offering increased accuracy and efficiency in producing reliable irrigation maps.

Background: Exposure to light at night (LAN) has been associated with multiple adverse health outcomes. However, evidence is limited regarding the impacts of LAN exposure on human inflammation. Objectives: To examine the association between real-ambient bedroom LAN exposure with systemic inflammation and circadian rhythm of inflammatory markers. Methods: Using data from a prospective cohort study of Chinese young adults. At baseline, bedroom LAN exposure was measured with a portable illuminance meter; fasting blood sample for high-sensitivity C-reactive protein (hs-CRP) assay was collected. At 3-year follow-up, 20 healthy young adults (10 LANavg < 5 lx, 10 LANavg ≥ 5 lx) were recruited from the same cohort; time-series venous blood samples were sampled every 4 h over a 24 h-cycle for the detection of 8 inflammatory markers. Circadian rhythm of inflammatory markers was assessed using cosinor analysis. Results: At baseline, the average age of the 276 participants was 18.7 years, and 33.3 % were male. Higher levels of bedroom LAN exposure were significantly associated with increased hs-CRP levels. The association between bedroom LAN exposure and systemic inflammation was only significant in the inactive group (MVPA < 2 h/d) but not in the physically active group (MVPA ≥ 2 h/d). In addition, exposure to higher levels of nighttime light (LANavg ≥ 5 lx) disrupted circadian rhythms (including rhythmic expression, circadian amplitude and circadian phase) of some inflammatory cytokines and inflammatory balance indicators. Conclusion: Exposure to bedroom nighttime light increases systemic inflammation and disrupts circadian rhythm of inflammatory markers. Keep bedroom darkness at night may represent important strategies for the prevention of chronic inflammation. Additionally, for people living a community with higher nighttime light pollution, regular physical activity may be a viable option to counteract the negative impacts of LAN exposure on chronic inflammation.

Depression is a prevalent psychiatric disorder with accumulating evidence implicating dysregulation of extracellular adenosine triphosphate (ATP) levels in the medial prefrontal cortex (mPFC). It remains unclear whether facilitating endogenous ATP production and subsequently increasing extracellular ATP level in the mPFC can exert a prophylactic effect against chronic social defeat stress (CSDS)-induced depressive-like behaviors and enhance stress resilience. Here, we found that nicotinamide mononucleotide (NMN) treatment effectively elevated nicotinamide adenine dinucleotide (NAD+) biosynthesis and extracellular ATP levels in the mPFC. Moreover, both the 2-week intraperitoneal (i.p.) injection and 3-week oral gavage of NMN prior to exposure to CSDS effectively prevented the development of depressive-like behavior in mice. These protective effects were accompanied with the preservation of both NAD+ biosynthesis and extracellular ATP level in the mPFC. Furthermore, catalyzing ATP hydrolysis by mPFC injection of the ATPase apyrase negated the prophylactic effects of NMN on CSDS-induced depressive-like behaviors. Prophylactic NMN treatment also prevented the reduction in GABAergic inhibition and the increase in excitability in mPFC neurons projecting to the lateral habenula (LHb). Collectively, these findings demonstrate that the prophylactic effects of NMN on depressive-like behaviors are mediated by preventing extracellular ATP loss in the mPFC, which highlights the potential of NMN supplementation as a novel approach for protecting and preventing stress-induced depression in susceptible individuals.

Ginsenoside Rd is a tetracyclic triterpenoid derivative, widely existing in Panax ginseng, Panax notoginseng and other traditional Chinese medicines. Many studies have proved that ginsenoside Rd have a variety of significant biological activities on certain types of cancer. However, the mechanism of ginsenoside Rd remains unclear in lung cancer. The findings of this study reveal that GS-Rd inhibits the proliferation of NSCLC cells, induces apoptosis, and suppresses migration and invasion. The results showed Ginsenoside Rd inhibited the cell proliferation (∼99.52 %) by S phase arrest in cell cycle and promoted the apoptosis (∼54.85 %) of NSCLC cells. It also inhibited the migration and invasion of cells (p

The NOD-like receptor protein 3 (NLRP3) inflammasome is a protein complex that regulates innate immune responses by activating caspase-1 and the inflammatory cytokines IL-1β and IL-18. Numerous studies have highlighted its crucial role in the pathogenesis and development of inflammatory bowel disease, rheumatoid arthritis, systemic lupus erythematosus, autoimmune thyroid diseases, and other autoimmune diseases. Therefore, investigating the underlying mechanisms of NLRP3 in disease and targeted drug therapies holds clinical significance. This review summarizes the structure, assembly, and activation mechanisms of the NLRP3 inflammasome, focusing on its role and involvement in various autoimmune diseases. This review also identifies studies where the involvement of the NLRP3 inflammasome in the disease mechanism within the same disease appears contradictory, as well as differences in NLRP3-related gene polymorphisms among different ethnic groups. Additionally, the latest therapeutic advances in targeting the NLRP3 inflammasome for autoimmune diseases are outlined, and novel clinical perspectives are discussed. Conclusively, this review provides a consolidated source of information on the NLRP3 inflammasome and may guide future research efforts that have the potential to positively impact patient outcomes.

Due to the scarcity of observational data in the early 20th century, very limited research has explored the impact of human activities on temperature extremes at the regional scale. Here we used a newly developed homogenized near-surface air temperature dataset from the beginning of the 20th century to estimate the frequency and intensity of extreme temperatures in eastern China and evaluate their anthropogenic influence based on models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We found clear increases in warm extremes and decreases in cold extremes since 1901 for both annual and seasonal mean temperatures, with more pronounced changes in recent decades. The most significant warming occurred in spring and winter, approximately double the smallest warming observed in autumn. The CMIP6 models generally replicated the century-scale warming in annual and seasonal temperature extremes, showing increases in the frequency and intensity of warm extremes and corresponding decreases in cold extremes. The optimal fingerprinting detections suggest that the century-scale warming can be clearly attributed to anthropogenic forcing, including changes in seasonal extreme temperatures. Most observed changes in extreme temperatures were attributable to anthropogenic greenhouse gas emissions, partially offset by a smaller negative impact from anthropogenic aerosol forcing, whereas natural forcing has played a minor role. These results provide important information for accurately projecting future changes in temperature extremes.

The active lithium ions loss during the initial charge and discharge process of lithium ion batteries seriously hampers its increasement of energy density. Pre-lithiation, involving the pre-storage of active lithium ions prior to cycling, emerges as a promising and effective strategy to offset this loss. Li6CoO4 has been identified as a candidate capable of releasing adequate lithium ions to compensate for such loss. However, its poor air stability renders it susceptible to side reactions in the atmosphere, leading to the formation of residual lithium and consequently affecting its electrochemical performance. In this study, we propose application of a lithium aluminate (LiAlO2) coated onto the surface of lithium cobalt oxide (Li6CoO4) to mitigate the presence of residual lithium. Meanwhile, with decreasing of residual lithium, the rate capability is also enhanced. The research results demonstrate that samples treated with this coating layer exhibit an enhanced energy density in the full cell, indicating the efficacy of this approach in optimizing the electrochemical performance of prelithiation additives.

Desertification detection is a crucial step to improve the management of affected areas and aid in mitigating the negative impacts of desertification. This study proposes a semi-supervised approach that uses Landsat imagery and radiometric data to detect desertification. The approach involves extracting radiometric data, which is used as an indicator to identify the thematic type and desertification evolution over time. Four anomaly detection techniques, including One-Class Support Vector Machine (OCSVM), Isolation Forest, Elliptic Envelope, and Local Outlier Factor, are trained on radiometric data from non-desertified regions to detect abnormal events related to desertification. These semi-supervised techniques use unlabeled data during training and only require desertification-free data, making them practical. The study was conducted in the arid region around Biskra, Algeria, which is a well-known area strongly affected by desertification. The OCSVM method achieved the highest detection accuracy of 95.40% in comparison to other methods and studies. Furthermore, to enhance result reliability, the Bootstrap technique was employed to generate 95% confidence intervals for five evaluation metrics.

Natural killer (NK) cells represent key player in immune surveillance to eliminate transformed or malignant cells. One of mechanisms of action of NK cells is antibody-dependent cell-mediated cytotoxicity (ADCC) by recognizing tumor antigens on the surface of cancer cells. However, the heterogeneity of tumor antigens and the scarcity of membrane surface targets significantly restrict this strategy. Recently, we constructed a new cargo by tethering a low pH insertion peptide (pHLIP) to the C terminus of the ectodomain of programed death ligand-1 (PD-L1) and demonstrated its ability to modulate immune responses. Herein, the potential application of PD-L1-pHLIP in cancer therapy was determined. pHLIP tethering had no effect on the binding capacity of PD-L1 protein to an anti-PD-L1 antibody (i.e. avelumab). Association of pHLIP rendered PD-L1 segment display on the surface of cellular membrane in the acidic buffer instead of the neutral solution. Importantly, plate-coated or beads-coupled PD-L1-pHLIP enable robust activation and expression of cytotoxic mediators of NK cells via engaging avelumab. Overall, this work provides proof of concept that recombinant PD-L1 protein decorated on the cellular membrane driven by pHLIP in combination with appropriate monoclonal antibody has potentials to elicit NK cytotoxicity, which may represent a novel and promising therapeutic avenue in cancer.

Summary: Diabetic kidney disease (DKD) is one of the most common complications of diabetes, and no specific drugs are clinically available. We have previously demonstrated that inhibiting microsomal prostaglandin E synthase-2 (mPGES-2) alleviated type 2 diabetes by enhancing β cell function and promoting insulin production. However, the involvement of mPGES-2 in DKD remains unclear. Here, we aimed to analyze the association of enhanced mPGES-2 expression with impaired metabolic homeostasis of renal lipids and subsequent renal damage. Notably, global knockout or pharmacological blockage of mPGES-2 attenuated diabetic podocyte injury and tubulointerstitial fibrosis, thereby ameliorating lipid accumulation and lipotoxicity. These findings were further confirmed in podocyte- or tubule-specific mPGES-2-deficient mice. Mechanistically, mPGES-2 and Rev-Erbα competed for heme binding to regulate fatty acid binding protein 5 expression and lipid metabolism in the diabetic kidney. Our findings suggest a potential strategy for treating DKD via mPGES-2 inhibition.

By analysing the factors influencing secondary vocational students' learning burnout in the context of social media, this study unearthed the underlying causes of learning burnout. It also determined the correlation paths among the factors influencing learning burnout, providing references for educational and pedagogical improvement. This contributes to preventing secondary vocational students' learning burnout and enhancing learning efficiency in secondary vocational schools. Combined with previous research results and a theoretical basis, this study identifies 10 influencing factors employing the Delphi method, and uses Interpretative Structural Modelling (ISM) and Matrice d' Impacts Croisés Multiplication Appliqués à un Classement (MICMAC) to elucidate the relationship between influencing factors of learning burnout among secondary vocational students in the context of social media. This study also constructs a corresponding mechanism model and subsequently proposes prevention and improvement strategies. The results show that the overdevelopment of social media, as driving factors, has the greatest impact on secondary vocational students' learning burnout. Simultaneously, it takes the lead in addressing cognitive bias among students, decreased self-control, and low learning efficiency, factors that contribute to learning burnout. This is particularly beneficial in alleviating the degree of learning burnout among secondary vocational students in the context of social media and improves overall learning outcomes for these students. The hierarchical structure and correlation paths identified in this study offer robust invaluable guidance for developing a scientific program to address the problem of learning burnout among this demographic. This includes implementing related educational practises, thereby reducing the unpredictability of the practical applications.

The Changbaishan Tianchi intraplate volcano is one of the most active and hazardous volcanoes of NE Asia, characterized by a summit caldera formed after the 946 CE ‘Millennium’ Plinian eruption. From December 2020 to June 2021, the frequency and magnitude of earthquakes at Tianchi were significantly higher than during background periods, with hundreds of earthquakes (46 events per month in average) and reaching a local magnitude of ML 3.1. This study reports a comprehensive deformation analysis and geophysical inversion scheme aimed to unveil the dynamics of this period. Multi-temporal InSAR analysis results of 32 ALOS-2 images from 2018 to 2022 show that the surface deformation is a combination of seasonal fluctuations (± 25 mm in average, with a maximum ± 45 mm) and a long-term positive component. The least squares linear regression of the deformation time series and temperature data, isolates the seasonal fluctuations, revealing a clear uplift-subsidence process from June 2020 to July 2021 in the caldera area. To constrain the Tianchi plumbing system dynamics, a combined inversion scheme consisting of three deformation sources is designed. The inversion results and the seismic records indicate that Tianchi volcano has experienced a low-level unrest episode from December 2020 to June 2021. The shallower plumbing system, located at about 5–9 km depth and modelled by pressurized spheroids, underwent a cumulative volume increase of 26 × 106 m3 from November 2018 to April 2021, followed by a volume decrease of 9 × 106 m3 from April to July 2021. This suggests magma uprising from the 14 km deep storage zone to the shallower plumbing system, followed by depressurization of the plumbing system due to the escape of fluids. This research provides a comprehensive understanding of the magma and fluid migration dynamics within the Tianchi multi-level plumbing system for the first time.

Accurately predicting key features in WWTPs is essential for optimizing plant performance and minimizing operational costs. This study assesses the potential of various machine learning models for predicting the inflow to anoxic sludge reactors. Firstly, it conducts a comprehensive evaluation of diverse machine learning models, including k-Nearest Neighbors (kNN), Random Forest (RF), XGBoost, CatBoost, LightGBM, and Decision Tree Regression (DTR), for predicting the flow into the Anoxic section under various weather conditions (dry, rainy, and stormy). Secondly, the study introduces parsimonious models guided by variable importance from the XGBoost algorithm. Furthermore, the study employs SHAP (SHapley Additive exPlanations) to elucidate model predictions, providing insights into the contribution of each feature. Data from the COST Benchmark Simulation Model (BSM1) is used to verify the investigated models' effectiveness. Each dataset consists of 14 days of influent data at 15-minute intervals, with 80% of the data used for model training. Results show that ensemble learning methods, particularly CatBoost and XGBoost, demonstrate satisfactory predictive results for Anoxic section flow in the presence of increased variability under rainy and stormy conditions. Notably, the CatBoost and XGBoost models achieve average Mean Absolute Percentage Error values of 1.33% and 1.59%, outperforming the other methods.

The accurate modeling and forecasting of Battery State of Health (SOH) is crucial for ensuring reliable performance and longevity of lithium-ion batteries. This article introduces a data-driven approach for SOH prediction using Gaussian Process Regression (GPR), selected for its ability to model complex data relationships and capture prediction uncertainty without relying on future load information. Recognizing the effect of reversible capacity recoveries on prediction accuracy, this work employs the ISEA-RWTH 48 Li-ion Cells dataset, deliberately devoid of such recoveries prior to training the GPR model. The GPR model was evaluated and compared with Support Vector Regression (SVR) using the publicly available dataset. First and second End of Life (EOL) scenarios were considered, relevant to primary and secondary battery applications. The results demonstrated the GPR model's superior performance. Particularly, mid-life and late-life predictions displayed better accuracy with GPR, showcasing higher R2 values and lower MAPE values (e.g., mid-life prediction: GPR's average R2 = 0.99, SVR's = 0.9789; GPR's average MAPE = 0.1916, SVR's = 1.3028). Moreover, GPR exhibited the ability to quantify uncertainty in capacity degradation and forecast first and second EOL instances effectively (e.g., mid-life predictions had 1.7 cycle error at 1st EOL and 8.9 cycle error at 2nd EOL). The research also offers valuable insights into the application of machine learning methods for predicting the health degradation of lithium-ion batteries.

One of the malignant tumors with a high occurrence rate worldwide is gastric carcinoma, which is an epithelial malignant tumor emerging from the stomach. Natural product polysaccharides are a kind of natural macromolecular polymers, which have the functions of regulating immunity, anti-oxidation, anti-fatigue, hypoglycemia, etc. Natural polysaccharides have remarkable effectiveness in preventing the onset, according to studies, and development of gastric cancer at both cellular and animal levels. This paper summarizes the inhibitory mechanisms and therapeutic significance of plant polysaccharides, fungi polysaccharides, and algal polysaccharides in natural product polysaccharides on the occurrence and development of gastric cancer in recent years, providing a theoretical basis for the research, development, and medicinal value of polysaccharides.

Solar photovoltaic (PV) systems have become a vital renewable energy source, witnessing rapid global demand. Nevertheless, these systems are susceptible to faults and anomalies that can deteriorate performance and yield significant consequences. Hence, this paper is dedicated to reviewing recent advancements in monitoring, modeling, and fault detection methods for PV systems. It encompasses diverse PV system types, including grid-connected, stand-alone, and hybrid configurations, and delves into the latest data acquisition and monitoring techniques. The review also discusses various performance modeling approaches, including empirical, analytical, and numerical models, highlighting the significance of model validation and calibration. Furthermore, it provides a comprehensive analysis of model-based fault detection techniques. Overall, this paper underscores the pivotal role of fault detection in PV systems and offers a thorough comprehension of available techniques vital for enhancing system management and maintenance.

In this paper, Fe−8.5Mn−1.5Al light−weight medium Mn steel is used as the research object, and the hot compression test is conducted by Gleeble−3500 thermal simulation tester. The dynamic reversion and dynamic recrystallization (DRX) processes are analyzed according to the true stress−strain curve; the θ−σ work−hardening rate curve is plotted by P−J double differential method to analyze the influence law between work−hardening and dynamic reversion and recrystallization softening effect of the material; the DRX critical strain model is established to analyze the influence law of deformation parameters on DRX; Based on the dynamic material model theory, the hot working diagram and three−dimensional power dissipation diagram are constructed, and the best hot working area is determined by combining the microstructure of the material. Finally, based on the above research content, a systematic overview of the dynamic softening effects of DRX and dynamic recovery (DRV) will be provided.

Mutations in GBA1, encoding the lysosomal acid β-glucosidase (GCase), cause neuronopathic Gaucher disease (nGD) and promote Parkinson disease (PD). The mutations on GBA1 include deletion and missense mutations that are pathological and lead to GCase deficiency in Gaucher disease. Both nGD and PD lack disease-modifying treatments and are critical unmet medical needs. In this study, we evaluated a cell therapy treatment using mouse iPSC-derived neural precursor cells (NPCs) engineered to overexpress GCase (termed hGBA1-NPCs). The hGBA1-NPCs secreted GCase that was taken up by adjacent mouse Gba−/− neurons and improved GCase activity, reduced GCase substrate accumulation, and improved mitochondrial function. Short-term in vivo effects were evaluated in 9H/PS-NA mice, an nGD mouse model exhibiting neuropathology and α-synuclein aggregation, the typical PD phenotypes. Intravenously administrated hGBA1-NPCs were engrafted throughout the brain and differentiated into neural lineages. GCase activity was increased in various brain regions of treated 9H/PS-NA mice. Compared with vehicle, hGBA1-NPC-transplanted mice showed ∼50% reduction of α-synuclein aggregates in the substantia nigra, significant reduction of neuroinflammation and neurodegeneration in the regions of NPC migration, and increased expression of neurotrophic factors that support neural cell function. Together, these results support the therapeutic benefit of intravenous delivery of iPSC-derived NPCs overexpressing GCase in mitigating nGD and PD phenotypes and establish the feasibility of combined cell and gene therapy for GBA1-associated PD.

Urban green spaces have long been cherished as the “lungs of the city”, it can not only improve air quality and enhance residents' health. The impact of urban green spaces on air pollution and residents' health has attracted great attention. In this paper, we select 31 provinces in China as research samples and use ArcGIS software to draw spatial distribution maps of urban green spaces, air pollution, and residents' health in China, characterizing their spatiotemporal changes in order to explore whether there is a correlation among the three. On this basis, this paper utilizes panel data of Chinese provincial-level from 2010 to 2020 and conducts empirical tests through a two-way fixed effect model and a threshold regression model. The research findings are as follows: (1) Urban green spaces not only bring environmental benefits, but also health benefits, and the health benefits are greater than the environmental benefits. (2) There is a health risk of air pollution in northern China, and the health benefits of urban green spaces are not significant. Conversely, there is no health risk of air pollution in southern China, and the health benefits of urban green spaces are significant. (3) Although environmental regulations can enhance the health benefits of urban green spaces, the role of environmental regulations in enhancing the health benefits of urban green spaces may be further weakened as the economy develops.

Summary: In 2016, the Yangtze River Protection Strategy was proposed and a series of measures were applied to restore the health and function of the Yangtze River ecosystem. However, the impact of these measures on the carbon (C) sink capacity of the Yangtze River estuary wetlands has not been exhaustively studied. In this work, the effects of these measures on the C sink capacity of Yangtze River estuary wetlands were examined through the long-term monitoring of C fluxes, soil respiration, plant growth and water quality. The C flux of the Yangtze River estuary wetlands has become increasingly negative after the implementation of these measures, mainly owing to reduction in soil CO2 emission. The decrease in the chemical fertilizer release and returning farmland to wetland had led to the improvement of water quality in the estuary area, which further reduced soil heterotrophic microbial activity, and ultimately decreasing soil CO2 emissions of estuary wetlands.

Background & Aims: Association studies have greatly refined the important role of the major histocompatibility complex (MHC) region in autoimmune hepatitis (AIH). However, the effects of human leucocyte antigen (HLA) polymorphisms on AIH are not well established. The aim of this study is to systematically characterise the association of MHC variants with AIH in our well-defined cohort of patients. Methods: We performed an imputation-based analysis on the extensive association observed within the MHC region using the Han-MHC reference panel, and tested the comprehensive associations of HLA polymorphisms with AIH in 1622 Chinese AIH type 1 patients and 10,466 population controls. Results: A total of 588 HLA variants were significantly associated with AIH, with HLA-B∗35:01 (p = 8.17 × 10−304; odds ratio [OR] = 7.32) contributing the strongest signal. Stepwise conditional analysis revealed additional independent signals at HLA-B∗08:01 (p = 1.35 × 10−33; OR = 4.26) and rs7765379 (p = 5.08 × 10−18; OR = 1.66). A strong link between the lead HLA variant and clinical phenotypes of AIH was observed: patients with HLA-B∗35:01 were less frequently positive for ANA and tended to have higher serum AST and ALT levels at diagnosis, but lower serum IgG levels. Conclusions: Our study reveals three novel and independent variants at HLA-B∗35:01, HLA-B∗08:01, and rs7765379 associated with AIH across the whole MHC region in the Han Chinese population. The findings illustrate the value of the MHC region in AIH and provide a new perspective for the immunogenetics of AIH. Impact and implications: This study revealed three novel and independent variants associated with autoimmune hepatitis across the whole major histocompatibility complex region in the Han Chinese population. These findings are significant in identifying autoantigens, providing insights into the activation of the autoimmune processes, and further advancing our understanding of the immunogenetic basis underlying autoimmune hepatitis.

Summary: Background: Cerebral Cavernous Malformation (CCM) is a rare cerebrovascular disease, characterized by the presence of multiple vascular malformations that may result in intracerebral hemorrhages (ICHs), seizure(s), or focal neurological deficits (FND). Familial CCM (fCCM) is due to loss of function mutations in one of the three independent genes KRIT1 (CCM1), Malcavernin (CCM2), or Programmed Cell death 10 (PDCD10/CCM3). The aim of this study was to identify plasma protein biomarkers of fCCM to assess the severity of the disease and predict its progression. Methods: Here, we have investigated plasma samples derived from n = 71 symptomatic fCCM patients (40 female/31 male) and n = 17 healthy donors (HD) (9 female/8 male) of the Phase 1/2 Treat_CCM trial, using multiplexed protein profiling approaches. Findings: Biomarkers as sCD14 (p = 0.00409), LBP (p = 0.02911), CXCL4 (p = 0.038), ICAM-1 (p = 0.02013), ANG2 (p = 0.026), CCL5 (p = 0.00403), THBS1 (p = 0.0043), CRP (p = 0.0092), and HDL (p = 0.027), were significantly different in fCCM compared to HDs. Of note, sENG (p = 0.011), THBS1 (p = 0.011) and CXCL4 (p = 0.011), were correlated to CCM genotype. sROBO4 (p = 0.014), TM (p = 0.026) and CRP (p = 0.040) were able to predict incident adverse clinical events, such as ICH, FND or seizure. GDF-15, FLT3L, CXCL9, FGF-21 and CDCP1, were identified as predictors of the formation of new MRI-detectable lesions over 2-year follow-up. Furthermore, the functional relevance of ang2, thbs1, robo4 and cdcp1 markers was validated by zebrafish pre-clinical model of fCCM. Interpretation: Overall, our study identifies a set of biochemical parameters to predict CCM progression, suggesting biological interpretations and potential therapeutic approaches to CCM disease. Funding: Italian Medicines Agency, Associazione Italiana per la Ricerca sul Cancro (AIRC), ERC, Leducq Transatlantic Network of Excellence, Swedish Research Council.

The integrated gasification combined cycle (IGCC) has increasingly attracted attention as a promising high-efficiency clean coal technology. The oxygen-to-carbon ratio (O/C), nitrogen reinjection coefficient (Xgn), and integration air separation coefficient (Xas) affect system performance greatly. Based on the selected coal type, this paper establishes a 200 MW IGCC system model with coal–water slurry gasification, matches the three-pressure reheating heat recovery steam generator and syngas coolers, simulates and calculates the system performance of O/C, Xas and Xgn using Thermo-flex software. From the perspective of the whole system, the optimal O/C of the system is obtained as 0.91 considering the syngas composition and gasification temperature. From the perspective of system efficiency, the Xgn is obtained as 60 % with the Xas of 20 %. The overall IGCC system model is optimized using the optimized O/C, Xgn, and Xas to obtain higher system power and efficiency, the system power generation efficiency can improve up to 51.52 %. A thermal balance diagram of the IGCC system is drawn using the calculation results and provides a reference for the future design and operation of IGCC systems.

Wastewater treatment plants (WWTPs) are energy-intensive facilities that play a critical role in meeting stringent effluent quality regulations. Accurate prediction of energy consumption in WWTPs is essential for cost savings, process optimization, regulatory compliance, and reducing carbon footprint. This paper introduces an efficient approach for predicting energy consumption in WWTPs, leveraging deep learning models, data augmentation, and feature selection. Specifically, Spline Cubic interpolation enriches the dataset, while the Random Forest model identifies important features. The study investigates the impact of lagged data to capture temporal dependencies. Comparative analysis of five deep learning models on original and augmented datasets from Melbourne WWTP demonstrates substantial performance improvement with augmented data. Incorporating lagged energy consumption data further enhances accuracy, providing valuable insights for effective energy management. Notably, the Long Short-Term Memory (LSTM) and Bidirectional Gated Recurrent Unit (BiGRU) models achieve Mean Absolute Percentage Error (MAPE) values of 1.36% and 1.436%, outperforming state-of-the-art methods.

Summary: It is imperative to optimally utilize virtues and obviate defects of fully automated analysis and expert knowledge in new paradigms of healthcare. We present a deep learning-based semiautomated workflow (RAINMAN) with 12,809 follow-up scans among 2,172 patients with treated nasopharyngeal carcinoma from three centers (ChiCTR.org.cn, Chi-CTR2200056595). A boost of diagnostic performance and reduced workload was observed in RAINMAN compared with the original manual interpretations (internal vs. external: sensitivity, 2.5% [p = 0.500] vs. 3.2% [p = 0.031]; specificity, 2.9% [p

As a first attempt, the incremental differential quadrature element method (IDQEM) is used for the one-dimensional nonlinear transient heat transfer analysis of multilayered thermal protection systems (TPSs) under time-dependent aerodynamic heating. All thermophysical properties are considered to be temperature-dependent (TD). Based on the IDQEM, the multilayered TPS is divided into several spatial sub-domains along the layer interfaces, and the entire heating process is also divided into several temporal sub-domains. For each temporal sub-domain, the governing equations are discretized by the differential quadrature method and then solved by the Newton-Raphson method. The initial condition of each temporal sub-domain is defined by the temperature results at the end of the previous sub-domain. The temperature profile of the TPS during the entire heating process can be obtained by repeating the calculation procedure from the first temporal sub-domain to the last one. Convergence and accuracy of the method are confirmed. Numerical examples are carried out to show the effects of TD thermophysical properties, thermal boundary, and layer thickness on the temperature profile of a three-layer TPS. The results show that the temperature dependency of thermophysical properties is worthy of consideration in predicting the temperature profile of the TPSs.

Background: Whether or not there is targeted pharmacotherapy for dementia, an active and healthy lifestyle that includes physical activity (PA) may be a better option than medication for preventing dementia. We examined the association between leisure-time sedentary behavior (SB) and the risk of dementia incidence and mortality. We further quantified the effect on dementia risk of replacing sedentary time with an equal amount of time spent on different physical activities. Methods: In the UK Biobank, 484,169 participants (mean age = 56.5 years; 45.2% men) free of dementia were followed from baseline (2006–2010) through July 30, 2021. A standard questionnaire measured individual leisure-time SB (watching TV, computer use, and driving) and PA (walking for pleasure, light and heavy do-it-yourself activity, strenuous sports, and other exercise) frequency and duration in the 4 weeks prior to evaluation. Apolipoprotein E (APOE) genotype data were available for a subset of 397,519 (82.1%) individuals. A Cox proportional hazard model and an isotemporal substitution model were used in this study. Results: During a median 12.4 years of follow-up, 6904 all-cause dementia cases and 2115 deaths from dementia were recorded. In comparison to participants with leisure-time SB 8 h/day, and the HR of dementia mortality was 1.35 (1.12–1.61) for >8 h/day. A 1 standard deviation increment of sedentary time (2.33 h/day) was strongly associated with a higher incidence of dementia and mortality (HR = 1.06, 95%CI: 1.03–1.08 and HR = 1.07, 95%CI: 1.03–1.12, respectively). The association between sedentary time and the risk of developing dementia was more profound in subjects 8 h/day, p for interaction = 0.013). Replacing 30 min/day of leisure sedentary time with an equal time spent in total PA was associated with a 6% decreased risk and 9% decreased mortality from dementia, with exercise (e.g., swimming, cycling, aerobics, bowling) showing the strongest benefit (HR = 0.82, 95%CI: 0.78–0.86 and HR = 0.79, 95%CI: 0.72–0.86). Compared with APOE ε4 noncarriers, APOE ε4 carriers are more likely to see a decrease in Alzheimer's disease incidence and mortality when PA is substituted for SB. Conclusion: Leisure-time SB was positively associated with the risk of dementia incidence and mortality. Replacing sedentary time with equal time spent doing PA may be associated with a significant reduction in dementia incidence and mortality risk.

Considerable evidence indicates the important role of gut microbiota in human health. Through the interaction with the host and diet, it secretes a myriad of metabolites to modulate biological processes essential for health. Cognitive impairment is a common feature of psychiatric and neurological disorders, which may seriously damage the quality of patients' life. Studies have found that cognitive impairment has a close relationship with gut microbiota, and plant polysaccharides intervention to maintain intestinal micro-ecological balance has a great impact on ameliorating cognitive impairment. This review introduced the interaction between gut microbiota and plant polysaccharides, and focused on signaling pathogenesis of gut microbiota in cognitive impairment. The effect of plant polysaccharides intervention on regulation of gut microbiota was also discussed, so as to provide a promising strategy for ameliorating cognitive impairment.

Implants are commonly used to replace damaged bones in the human body. Fatigue failure is one of the most serious issues with implants. Understanding the underlying mechanisms that lead to fatigue failure is crucial in ensuring the longevity of biomaterial implants. In this study, the fatigue behavior of the porous biomaterial Ti6Al4V–6Cu, produced by selective laser melting (SLM) in air and in 0.9 wt% NaCl solution was experimentally investigated. The location of the fatigue crack (FC) initiation point on the cyclic strain curve was determined based on the fatigue cycle change. It indicates that crack initiation is accelerated in a state of high stress, and crack propagation is accelerated in a solution containing 0.9 wt% NaCl. Moreover, in an environment containing 0.9 wt% NaCl, the fatigue fracture is predominantly intergranular and partially transgranular, which is attributed to the brittle failure caused by corrosion fatigue. Additionally, in comparison to the Ti6Al4V sample, the porous Ti6Al4V–6Cu sample has a higher fatigue strength and longer fatigue life due to the effect of the Ti2Cu phase precipitated near the grain boundary and the zigzag propagation of FCs.

This paper introduces a feature extraction-based approach to ensure both robustness and reversibility of image. Low-order Zernike moments are utilized to embed a robust binary image as a watermark, which is used for information authentication. A reversible watermark is embedded outside the robust watermark regions and is employed for the purpose of restoring the cover image. It uses the combination of histogram shifting and prediction error, which can improve image restoration quality. Steady feature points are extracted in two ways, the speed-up robust features (SURF) algorithm and the oriented fast and rotated brief (ORB) algorithm. After extracting the feature points, the regions are obtained by extending the final selected feature points to embed the watermark. Consequently, the presented watermarking technique combines robust and reversible watermarking which has the ability to enhance the invisibility of the watermark and the clarity of image restoration. It is possible to extract the watermark even after an attack has been made on the watermarked image. Or we can recover the original image with no attacks. The results from the experiments indicate that the suggested method is resilient to geometric deformations, involving scaling and rotation, along with typical signal manipulation attacks, including noise-based attacks.

As a multifunctional DNA repair enzyme, human apurinic/apyrimidinic endonuclease 1 (APE1) plays a pivotal role in many cellular processes. In recent years, it has been recognized as a potential prognostic biomarker and a therapeutic target for many cancers and other diseases. In this work, we report a DNA/RNA hybrid-based fluorescent probe which allows rapid and high-throughput detection of APE1 in the nuclear, cytoplasmic, and mitochondrial extracts of different types of cultured cells without any purification steps. As a noncanonical substrate, the DNA/RNA hybrid probe possesses inherent specificity toward APE1 without the need of additional chemical modification. A substantial acceleration effect of the 3′-flanking mismatches enabled rapid digestion of the abasic sites in the DNA/RNA hybrid by APE1 at a comparable rate to those in natural double-stranded DNA. The hybrid probe showed adequately high sensitivity to the target enzyme (linear working range: 0.02–1.0 U/mL; detection limit: 0.01 U/mL) and superior resistance to interference from cellular proteins. The mechanism for the 3′-mismatch enhancement was also clarified via kinetic study and single-molecule fluorescence analysis. By using the probe, significantly varied subcellular distribution of APE1 in different cells were disclosed, showing the importance of quantitative measurement of APE1 activity in subcellular compartments for drug screening and evaluation of the treatment efficacy. The method was simple and robust, which offered an easily accessible molecular tool for high-throughput quantification of the subcellular APE1 activity. It may also facilitate the development of effective anticancer drugs.

We analyzed variations in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome during a flight-related cluster outbreak of coronavirus disease 2019 (COVID-19) in Shenzhen, China, to explore the characteristics of SARS-CoV-2 transmission and intra-host single nucleotide variations (iSNVs) in a confined space. Thirty-three patients with COVID-19 were sampled, and 14 were resampled 3–31 days later. All 47 nasopharyngeal swabs were deep-sequenced. iSNVs and similarities in the consensus genome sequence were analyzed. Three SARS-CoV-2 variants of concern, Delta (n = 31), Beta (n = 1), and C.1.2 (n = 1), were detected among the 33 patients. The viral genome sequences from 30 Delta-positive patients had similar SNVs; 14 of these patients provided two successive samples. Overall, the 47 sequenced genomes contained 164 iSNVs. Of the 14 paired (successive) samples, the second samples (T2) contained more iSNVs (median: 3; 95% confidence interval [95% CI]: 2.77–10.22) than did the first samples (T1; median: 2; 95% CI: 1.63–3.74; Wilcoxon test, P = 0.021). 38 iSNVs were detected in T1 samples, and only seven were also detectable in T2 samples. Notably, T2 samples from two of the 14 paired samples had additional mutations than the T1 samples. The iSNVs of the SARS-CoV-2 genome exhibited rapid dynamic changes during a flight-related cluster outbreak event. Intra-host diversity increased gradually with time, and new site mutations occurred in vivo without a population transmission bottleneck. Therefore, we could not determine the generational relationship from the mutation site changes alone.

Fatigue resistance is a key criterion for new material that would be applied as an implant. As a “smart” implant material with good biodegradability and high strength, Mg–2Zn–0.5Zr–0.5Nd alloy has been recognized as a promising candidate for bone implant. A series of tests on fatigue properties of extruded Mg–2Zn–0.5Zr–0.5Nd alloy are carried out in this study. Alternating cyclic dynamic loads at frequencies of 5 Hz and 10 Hz both in air and in 0.9 wt% NaCl solution were loaded on extruded Mg–2Zn–0.5Zr–0.5Nd alloy respectively. In air condition, the fatigue life variational trends of Mg–2Zn–0.5Zr–0.5Nd alloy at different frequencies are similar according to the stress-life (S–N) curves. However, under high fatigue cycles, the lower loading frequency prolongs the loading time of repetitive stresses and reduces the fatigue life of the samples. In 0.9 wt% NaCl solution, the S–N curves under different frequencies show different changing trends due to the synergistic effect of corrosion environment and loading frequency. It is also found that the fatigue resistance of the alloy in 0.9 wt% NaCl solution is poorer than that in air due to the significant promotion of the formation and growth of corrosion pits by the NaCl environment. In addition, according to the fatigue fracture morphology analysis, it can be found that the failed samples show different fracture patterns in air and 0.9 wt% NaCl solution.

Background: Treating neuropsychiatric symptoms (NPS) in Alzheimer's disease (AD) remains highly challenging. Noninvasive brain stimulation using repetitive transcranial magnetic stimulation (rTMS) or transcranial direct current stimulation (tDCS) is of considerable interest in this context. Objective: To investigate the efficacy and safety of a novel technique involving simultaneous application of rTMS and tDCS (rTMS–tDCS) over bilateral angular gyrus (AG, P5/P6 electrode site) for AD-related NPS. Methods: Eighty-four AD patients were randomized to receive rTMS–tDCS, single-rTMS, single-tDCS, or sham stimulation for 4 weeks, with evaluation at week-4 (W4, immediately after treatment) and week-12 (W12, follow-up period) after initial examination. Primary outcome comprising Neuropsychiatric Inventory (NPI) score and secondary outcomes comprising mini-mental state examination (MMSE), AD assessment scale-cognitive subscale (ADAS-cog), and Pittsburgh sleep quality index (PSQI) scores were collected and analyzed by a two-factor (time and treatment), mixed-design ANOVA. Results: rTMS–tDCS produced greater improvement in NPI scores than single-tDCS and sham at W4 and W12 (both P

Areca nut (Areca catechu L.) seeds are rich in polyphenols, while few studies focused on it. This study was designed to obtain the maximum extraction yield of areca nut seed polyphenol (ACP). An ultrasonic-assisted extraction method optimized by response surface methodology (RSM) was established to extract ACP. Under the optimal conditions (ultrasonic power of 87 W, ethanol concentration of 65%, extraction temperature of 62℃, and extraction time of 153 min), the actual extraction yield of ACP was 139.62 mg/g. Then we investigated the effects of ACP on the proliferation, differentiation and mineralization of MC3T3-E1 pre-osteoblasts. Results suggested that ACP notably promoted the proliferation of MC3T3-E1 cells without cytotoxicity, and the contents of collagen type Ⅰ (COL-Ⅰ) and osteocalcin (OCN) were rising. Meanwhile, the alkaline phosphatase (ALP) activity and mineralized nodules were enhanced. These findings demonstrated that ACP could induce the proliferation, differentiation and mineralization of osteoblasts in vitro. This work provided a certain experimental basis for the developing and utilization of polyphenols from Areca nut seeds.

Background: As a famous medicinal and edible spice, Green prickly ash fruit (GPAF) is widely used as anti-inflammatory agent in Asian countries. However, the key active components have not yet been identified. Methods: An animal model was established using a transgenic zebrafish line, Tg(llyz: DsRED2), with neutrophils labeled by red fluorescent protein to evaluate the anti-inflammatory activity. The main chemical components in GPAF were analyzed and identified by ultra-high performance liquid chromatography-quadrupole electrostatic field orbitrap-mass spectrometry technology (UHPLC-QE-Orbitrap-MS). The drug-likeness, absorption, distribution, metabolism, and elimination properties of these identified compounds were further screened using the SwissADME tool and dynamic molecular docking. Finally, the key components of GPAF were evaluated inflammation inhibition using the zebrafish model. Results: Using the established zebrafish model, we found that GPAF extract exhibited significant anti-inflammatory effects. 13 components were identified by UHPLC-QE-Orbitrap-MS in GPAF extract. 7 components screened by ADME screening exhibit good parameters of passive gastrointestinal absorption (HIA) and brain entry (BBB). The results of dynamic molecular docking showed that Toddaline, Hydroxy-α-sanshool, and Hydroxy-β-sanshool could dock stably with the three core targets of AKT1, VEGFA and MAPK3. Finally, We confirmed the anti-inflammation activities of Toddaline, Hydroxy-α-sanshool, and Hydroxy-β-sanshool in the zebrafish model. Conclusion: A new activity-directed strategy was applied to identify three new anti-inflammation components of GPAF as Toddaline, Hydroxy-α-sanshool, and Hydroxy-β-sanshool. The results may supply the strong scientific evidence for quality control and health product development of GPAF.

Summary: The immune-genetic changes that occur in cancer patients experiencing hyperprogressive disease (HPD) during combined immunotherapy are unclear. In this study, HPD patients with pre- and post-HPD samples and non-HPD patients with solid tumors were molecularly characterized by genetic and tumor immune microenvironment (TiME) analyses of paired samples by whole-exome sequencing, RNA sequencing, and multiplex immunofluorescence. The genetic analysis of paired samples showed that almost all the tumor driver gene mutations were preserved between pre- and post-HPD tumors. HPD patients had higher frequencies of mutations in TP53 and CNN2, and a significantly higher mutant-allele tumor heterogeneity than non-HPD patients. Tumor IL-6 mRNA was upregulated in post-HPD samples vs. pre-HPD, accompanied by a potential immune suppressive TiME with an elevated M2/M1 ratio. Salvage treatment with irinotecan plus bevacizumab was effective in one HPD patient, who experienced prolonged survival. These genetic features and TiME characteristics might help identify the features of HPD after immunotherapy.

In recent study, the pathological mechanism of liver fibrosis has been associated with hepatic stellate cell (HSC) senescence. Targeted induction of HSC senescence is considered as a new strategy to remove activated HSC. Nevertheless, little is known about the role of ferritinophagy in cell senescence. In this study, we reported that Oroxylin A from Scutellaria baicalensis Georgi can regulate HSC senescence induced by ferritinophagy through the cGAS-STING pathway to reduce liver fibrosis. We first found that Oroxylin A treatment alleviated the pathological changes of liver fibrosis, reduced collagen deposition, and significantly inhibited liver fibrosis. Interestingly, Oroxylin A treatment can activate HSC ferritinophagy and further induce HSC senescence. It is noteworthy that ferritinophagy is mediated by nuclear receptor coactivator 4 (NCOA4), an important selective mediator for ferritin degradation. NCOA4 siRNA causes Oroxylin A to reduce the degree of telomerase activity in HSCs and induce the expression of senescence markers, such as SA-β-Gal and related marker proteins. Importantly, the cGAS-STING pathway is crucial to the activation of HSC ferritinophagy by Oroxylin A. Specifically, Oroxylin A can promote the secretion of cytokines like IFN-β by the cGAS-STING pathway to regulate ferritinophagy. cGAS siRNA resulted in a dose-dependent decrease in the expression of NCOA4, a significant reduction in the expression level of autophagy-related phenotype, and a decrease in the content of ROS and iron ions in HSCs. In conclusion, we identified the new role of ferritinophagy and the GAS-STING pathway in Oroxylin A -mediated anti-hepatic fibrosis.