Explore the vast range of titles available.

Kidney is one of the most vulnerable organs in sepsis, resulting in sepsis-associated acute kidney injury (SA-AKI), which brings about not only morbidity but also mortality of sepsis. Ferroptosis is a new kind of death type of cells elicited by iron-dependent lipid peroxidation, which participates in pathogenesis of sepsis. The aim of this study was to verify the occurrence of ferroptosis in the SA-AKI pathogenesis and demonstrate that post-treatment with irisin could restrain ferroptosis and alleviate SA-AKI via activating the SIRT1/Nrf2 signaling pathway. We established a SA-AKI model by cecal ligation and puncture (CLP) operation and an in vitro model in LPS-induced HK2 cells, respectively. Our result exhibited that irisin inhibited the level of ferroptosis and ameliorated kidney injury in CLP mice, as evidenced by reducing the ROS production, iron content, and MDA level and increasing the GSH level, as well as the alteration of ferroptosis-related protein (GPX4 and ACSL4) expressions in renal, which was consistent with the ferroptosis inhibitor ferrostatin-1 (Fer-1). Additionally, we consistently observed that irisin inhibited ROS accumulation, iron production, and ameliorated mitochondrial dysfunction in LPS-stimulated HK-2 cells. Furthermore, our result also revealed that irisin could activate SIRT1/Nrf2 signaling pathways both in vivo and vitro. However, the beneficial effects of irisin were weakened by EX527 (an inhibitor of SIRT1) in vivo and by SIRT1 siRNA in vitro . In conclusion, irisin could protect against SA-AKI through ferroptotic resistance via activating the SIRT1/Nrf2 signaling pathway.

Proteolysis-targeting chimeras (PROTACs) are engineered techniques for targeted protein degradation. A bifunctional PROTAC molecule with two covalently-linked ligands recruits target protein and E3 ubiquitin ligase together to trigger proteasomal degradation of target protein by the ubiquitin-proteasome system. PROTAC has emerged as a promising approach for targeted therapy in various diseases, particularly in cancers. In this review, we introduce the principle and development of PROTAC technology, as well as the advantages of PROTACs over traditional anti-cancer therapies. Moreover, we summarize the application of PROTACs in targeting critical oncoproteins, provide the guidelines for the molecular design of PROTACs and discuss the challenges in the targeted degradation by PROTACs.

This study aims to report the trends and cross-national disparities in the burden of osteoarthritis (OA) by region, age, gender, and time from 1990 to 2021, and to further project changes through 2035. In this systematic analysis based on the Global Burden of Disease (GBD) study, population survey data on osteoarthritis from 21 countries/regions and U.S. insurance claims data were used to estimate the prevalence and incidence of OA in 204 countries and regions from 1990 to 2021. The reference case definition for OA was symptomatic and radiographically confirmed osteoarthritis. Studies using definitions other than the reference, such as self-reported OA, were adjusted through a regression model to align with the reference case. The distribution of OA severity was derived from a pooled meta-analysis using the Western Ontario and McMaster Universities Arthritis Index (WOMAC). Final prevalence estimates were multiplied by disability weights to calculate years lived with disability (YLD). An Autoregressive Integrated Moving Average (ARIMA) model was used to forecast the prevalence and incidence of OA through 2035. In 2021, approximately 607 million (95%UI 538-671) people worldwide were affected by osteoarthritis, accounting for 7.7% of the global population. Compared to 2020, the age-standardized prevalence of OA among males is projected to increase from 5,763 per 100,000-5,922 per 100,000 by 2036, while the age-standardized prevalence among females is expected to decline slightly from 8,034 per 100,000-7,925 per 100,000. In 2021, the global age-standardized YLD rate for osteoarthritis was 244.5 (95%UI 117.06-493.11), the global age-standardized prevalence rate was 6,967.29 (95%UI 6,180.7-7,686.06), and the global age-standardized incidence rate was 535 (95%UI 472.38-591.97). In 2021, the age-standardized prevalence rate exceeded 5.5% across all regions, ranging from 5,675.8 per 100,000 (95%UI 5,001.76-6,320.8) in Southeast Asia to 8,608.63 per 100,000 (95%UI 7,674.07-9,485.19) in high-income Asia Pacific regions. The knee was the most commonly affected joint. High BMI and metabolic risks are the only two GBD risk factors for osteoarthritis. From 1990 to 2021, the age-standardized prevalence, incidence, and YLD attributable to osteoarthritis have been on the rise, with substantial international variations across indicators. Countries with high socio-demographic index (SDI) bear a disproportionately high burden of OA, and inequalities in the burden of disease due to differences in SDI between countries have been increasing over time. As a major public health problem, the overall global burden of OA has shown an upward trend from 1990 to 2019, including an increase in the number of cases and inequalities in distribution across the globe, which has resulted in significant health losses and economic burdens. In addition, SDI-related inequalities between countries are increasing. In this regard, national public health authorities and the World Health Organization (WHO) should work together to improve diagnosis and early treatment rates by strengthening disease awareness and education, as well as strengthening international cooperation, providing necessary medical assistance to less developed regions, and actively exploring new strategies for the prevention and treatment of OA.

Reservoir computing is a highly efficient network for processing temporal signals due to its low training cost compared to standard recurrent neural networks, and generating rich reservoir states is critical in the hardware implementation. In this work, we report a parallel dynamic memristor-based reservoir computing system by applying a controllable mask process, in which the critical parameters, including state richness, feedback strength and input scaling, can be tuned by changing the mask length and the range of input signal. Our system achieves a low word error rate of 0.4% in the spoken-digit recognition and low normalized root mean square error of 0.046 in the time-series prediction of the Hénon map, which outperforms most existing hardware-based reservoir computing systems and also software-based one in the Hénon map prediction task. Our work could pave the road towards high-efficiency memristor-based reservoir computing systems to handle more complex temporal tasks in the future. Designing efficient neuromorphic systems for complex temporal tasks remains a challenge. Zhong et al. develop a parallel memristor-based reservoir computing system capable of tuning critical parameters, achieving classification accuracy of 99.6% in spoken-digit recognition and time-series prediction error of 0.046 in the Hénon map.

Background Prediction of drug–target interaction (DTI) is an essential step for drug discovery and drug reposition. Traditional methods are mostly time-consuming and labor-intensive, and deep learning-based methods address these limitations and are applied to engineering. Most of the current deep learning methods employ representation learning of unimodal information such as SMILES sequences, molecular graphs, or molecular images of drugs. In addition, most methods focus on feature extraction from drug and target alone without fusion learning from drug–target interacting parties, which may lead to insufficient feature representation. Motivation In order to capture more comprehensive drug features, we utilize both molecular image and chemical features of drugs. The image of the drug mainly has the structural information and spatial features of the drug, while the chemical information includes its functions and properties, which can complement each other, making drug representation more effective and complete. Meanwhile, to enhance the interactive feature learning of drug and target, we introduce a bidirectional multi-head attention mechanism to improve the performance of DTI. Results To enhance feature learning between drugs and targets, we propose a novel model based on deep learning for DTI task called MCL-DTI which uses multimodal information of drug and learn the representation of drug–target interaction for drug–target prediction. In order to further explore a more comprehensive representation of drug features, this paper first exploits two multimodal information of drugs, molecular image and chemical text, to represent the drug. We also introduce to use bi-rectional multi-head corss attention (MCA) method to learn the interrelationships between drugs and targets. Thus, we build two decoders, which include an multi-head self attention (MSA) block and an MCA block, for cross-information learning. We use a decoder for the drug and target separately to obtain the interaction feature maps. Finally, we feed these feature maps generated by decoders into a fusion block for feature extraction and output the prediction results. Conclusions MCL-DTI achieves the best results in all the three datasets: Human, C. elegans and Davis, including the balanced datasets and an unbalanced dataset. The results on the drug–drug interaction (DDI) task show that MCL-DTI has a strong generalization capability and can be easily applied to other tasks.

Intestinal microbiota dysbiosis is an established characteristic of ulcerative colitis (UC). Regulating the gut microbiota is an attractive alternative UC treatment strategy, considering the potential adverse effects of synthetic drugs used to treat UC. Kaempferol (Kae) is an anti-inflammatory and antioxidant flavonoid derived from a variety of medicinal plants. In this study, we determined the efficacy and mechanism of action of Kae as an anti-UC agent in dextran sulfate sodium (DSS)-induced colitis mice. DSS challenge in a mouse model of UC led to weight loss, diarrhea accompanied by mucous and blood, histological abnormalities, and shortening of the colon, all of which were significantly alleviated by pretreatment with Kae. In addition, intestinal permeability was shown to improve using fluorescein isothiocyanate (FITC)–dextran administration. DSS-induced destruction of the intestinal barrier was also significantly prevented by Kae administration via increases in the levels of ZO-1, occludin, and claudin-1. Furthermore, Kae pretreatment decreased the levels of IL-1β, IL-6 , and TNF-α and downregulated transcription of an array of inflammatory signaling molecules, while it increased IL-10 mRNA expression. Notably, Kae reshaped the intestinal microbiome by elevating the Firmicutes to Bacteroidetes ratio; increasing the linear discriminant analysis scores of beneficial bacteria, such as Prevotellaceae and Ruminococcaceae ; and reducing the richness of Proteobacteria in DSS-challenged mice. There was also an evident shift in the profile of fecal metabolites in the Kae treatment group. Serum LPS levels and downstream TLR4-NF-κB signaling were downregulated by Kae supplementation. Moreover, fecal microbiota transplantation from Kae-treated mice to the DSS-induced mice confirmed the effects of Kae on modulating the gut microbiota to alleviate UC. Therefore, Kae may exert protective effects against colitis mice through regulating the gut microbiota and TLR4-related signaling pathways. This study demonstrates the anti-UC effects of Kae and its potential therapeutic mechanisms, and offers novel insights into the prevention of inflammatory diseases using natural products.

Ferroptosis is a phospholipid peroxidation-mediated and iron-dependent cell death form, involved in sepsis-induced organ injury and other lung diseases. Yes-associated protein 1 (YAP1), a key regulator of the Hippo signaling pathway, could target multiple ferroptosis regulators. Herein, this study aimed to explore the involvement of ferroptosis in the etiopathogenesis of sepsis-induced acute lung injury (ALI) and demonstrate that YAP1 could disrupt ferritinophagy and moderate sepsis-induced ALI. Cecal ligation and puncture (CLP) models were constructed in wild-type (WT) and pulmonary epithelium-conditional knockout (YAP1 f/f ) mice to induce ALI, while MLE-12 cells with or without YAP1 overexpression were stimulated by lipopolysaccharide (LPS) in vitro . In-vivo modes showed that YAP1 knockout aggravated CLP-induced ALI and also accelerated pulmonary ferroptosis, as presented by the downregulated expression of GPX4, FTH1, and SLC7A11, along with the upregulated expression of SFXN1 and NCOA4. Transcriptome research identified these key genes and ferroptosis pathways involved in sepsis-induced ALI. In-vitro modes consistently verified that YAP1 deficiency boosted the ferrous iron accumulation and mitochondrial dysfunction in response to LPS. Furthermore, the co-IP assay revealed that YAP1 overexpression could prevent the degradation of ferritin to a mass of Fe 2+ (ferritinophagy) via disrupting the NCOA4–FTH1 interaction, which blocked the transport of cytoplasmic Fe 2+ into the mitochondria via the mitochondrial membrane protein (SFXN1), further reducing the generation of mitochondrial ROS. Therefore, these findings revealed that YAP1 could inhibit ferroptosis in a ferritinophagy-mediated manner, thus alleviating sepsis-induced ALI, which may provide a new approach to the therapeutic orientation for sepsis-induced ALI.

Despite increasing awareness of employee benefits, there are still relatively few studies that provide a thorough examination of how employee benefits affect various performance measures organizations. This paper provides an overview of the definition and categories of employee benefits, important employee benefits from the employee's perspective, and the influencing factors, then analyze reasons contributing to the mismatch of employees' expectations and actual benefits offered and the impacts of benefits on four aspects of business performance, namely retention, engagement, commitment, motivation, and productivity. This systemic and comprehensive understanding of employee benefits and their impacts on business performance contribute to the managerial implication of human resource management to redesign the compensation package to meet the expectations of the workforce and, in return, achieve desired performance.

Observational studies have indicated an inverse association between vitamin D levels and the risk of diabetes, yet evidence from population interventions remains inconsistent. PubMed, EMBASE, Cochrane Library and ClinicalTrials.gov were searched up to September 2017. Data from studies regarding serum 25(OH)D, fasting blood glucose (FBG), hemoglobin A1c (HbA1c), fasting insulin and homeostasis model assessment of insulin resistance (HOMA-IR) were pooled. Twenty studies (n = 2703) were included in the meta-analysis. Vitamin D supplementation resulted in a significant improvement in serum 25(OH)D levels (weighted mean difference (WMD) = 33.98; 95%CI: 24.60–43.37) and HOMA-IR (standardized mean difference (SMD) = −0.57; 95%CI: −1.09~−0.04), but not in other outcomes. However, preferred changes were observed in subgroups as follows: short-term (WMDFBG = −8.44; 95%CI: −12.72~−4.15), high dose (WMDFBG = −8.70; 95%CI: −12.96~−4.44), non-obese (SMDFasting insulin = −1.80; 95%CI: −2.66~−0.95), Middle Easterners (WMDFBG = −10.43; 95%CI: −14.80~−6.06), baseline vitamin D deficient individuals (WMDFBG = −5.77; 95%CI: −10.48~−1.05) and well-controlled HbA1c individuals (WMDFBG = −4.09; 95%CI: −15.44~7.27). Vitamin D supplementation was shown to increase serum 25(OH)D and reduce insulin resistance effectively. This effect was especially prominent when vitamin D was given in large doses and for a short period of time, and to patients who were non-obese, Middle Eastern, vitamin D deficient, or with optimal glycemic control at baseline.

To investigate whether diabetes contributes to mortality for major types of diseases. Six National Health and Nutrition Examination Survey data cycles (1999 to 2000, 2001 to 2002, 2003 to 2004, 2005 to 2006, 2007 to 2008, and 2009 to 2010) and their linked mortality files were used. A population of 15,513 participants was included according to the availability of diabetes and mortality status. Participants with diabetes tended to have higher all-cause mortality and mortality due to cardiovascular disease, cancer, chronic lower respiratory diseases, cerebrovascular disease, influenza and pneumonia, and kidney disease. Confounder-adjusted Cox proportional hazard models showed that both diagnosed diabetes category (yes or no) and diabetes status (diabetes, prediabetes, or no diabetes) were associated with all-cause mortality and with mortality due to cardiovascular disease, chronic lower respiratory diseases, influenza and pneumonia, and kidney disease. No associations were found for cancer-, accidents-, or Alzheimer's disease-related mortality. The current study's findings provide epidemiological evidence that diagnosed diabetes at the baseline is associated with increased mortality risk due to cardiovascular disease, chronic lower respiratory diseases, influenza and pneumonia, and kidney disease, but not with cancer or Alzheimer's disease.