Millions of experimental animals are widely used in the assessment of toxicological or biological effects of manufactured nanomaterials in medical technology. However, the animal consciousness has increased and become an issue for debate in recent years. Currently, the principle of the 3Rs (i.e., reduction, refinement, and replacement) is applied to ensure the more ethical application of humane animal research. In order to avoid unethical procedures, the strategy of alternatives to animal testing has been employed to overcome the drawbacks of animal experiments. This article provides current alternative strategies to replace or reduce the use of experimental animals in the assessment of nanotoxicity. The currently available alternative methods include in vitro and in silico approaches, which can be used as cost-effective approaches to meet the principle of the 3Rs. These methods are regarded as non-animal approaches and have been implemented in many countries for scientific purposes. The in vitro experiments related to nanotoxicity assays involve cell culture testing and tissue engineering, while the in silico methods refer to prediction using molecular docking, molecular dynamics simulations, and quantitative structure–activity relationship (QSAR) modeling. The commonly used novel cell-based methods and computational approaches have the potential to help minimize the use of experimental animals for nanomaterial toxicity assessments.

Invasive candidiasis, mainly caused by Candida albicans, is a serious healthcare problem with high mortality rates, particularly in immunocompromised patients. Innate immune cells express pathogen recognition receptors (PRRs) including C-type lectin-like receptors (CLRs) that bind C. albicans to initiate an immune response. Multiple CLRs including Dectin-1, Dectin-2 and Mincle have been proposed individually to contribute to the immune response to C. albicans. However how these receptors collaborate to clear a fungal infection is unknown. Herein, we used novel multi-CLR knockout (KO) mice to decipher the individual, collaborative and collective roles of Dectin-1, Dectin-2 and Mincle during systemic C. albicans infection. These studies revealed an unappreciated and profound role for CLR co-operation in anti-fungal immunity. The protective effect of multiple CLRs was markedly greater than any single receptor, and was mediated through inflammatory monocytes via recognition and phagocytosis of C. albicans, and production of C. albicans-induced cytokines and chemokines. These CLRs were dispensable for mediating similar responses from neutrophils, likely due to lower expression of these CLRs on neutrophils compared to inflammatory monocytes. Concurrent deletion of Dectin-1 and Dectin-2, or all three CLRs, resulted in dramatically increased susceptibility to systemic C. albicans infection compared to mice lacking a single CLR. Multi-CLR KO mice were unable to control fungal growth due to an inadequate early inflammatory monocyte-mediated response. In response to excessive fungal growth, the multi-CLR KO mice mounted a hyper-inflammatory response, likely leading to multiple organ failure. Thus, these data reveal a critical role for CLR co-operation in the effective control of C. albicans and maintenance of organ function during infection. Fungal infections including invasive candidiasis are a serious healthcare problem particularly for immunocompromised patients. Mortality rates for invasive candidiasis are very high and complex anti-fungal immune responses are poorly understood, hindering the development of novel immunotherapies. Dectin-1, Dectin-2 and Mincle are three cell surface receptors that are proposed to be involved in the immune response to fungal pathogens. However, if or how these receptors work together during infection is currently unknown. Here we demonstrate that these receptors, in particular Dectin-1 and Dectin-2, work together to promote fungal clearance by a group of innate immune cells called inflammatory monocytes. Furthermore, we found that mice lacking these three receptors are dramatically susceptible to systemic Candida albicans infection due to defective early innate immune responses. These mice develop hyper-inflammation to try to control excessive fungal growth likely resulting in multi-organ failure. Our work helps explain how these receptors work together to clear/control invasive candidiasis. Our improved knowledge of the interactions between these receptors could be used to help design novel anti-fungal immunotherapies.

Aim: To investigate the factors affecting hemodialysis patients’ self-management ability at a dialysis center in Taiwan. Background: Taiwan has the highest incidence and prevalence of end-stage kidney disease (ESKD) in the world. Over 90% of patients with ESKD receiving hemodialysis (HD) and self-management behaviors are critical among these patients. Failure to adhere to self-managed care increases the cost of medical care and the risk of morbidity and mortality. Methods: In this cross-sectional study, a total of 150 HD patients were observed for their self-management behaviors and the factors influencing these behaviors including education level, comorbid conditions, biochemical analysis, depression, and social support, etc., were analyzed. Results: Self-management behaviors in HD patients were significantly impaired in the presence of diabetes mellitus, hypertension, anemia, hypoalbuminemia, and depression. The major predictor of patients’ self-management was depression, explaining 14.8% of the total variance. Further addition of social support, hypertension, and diabetes mellitus into the regression model increased the total explained variance to 28.6%. Of the various domains of self-management, the partnership domain received the highest score, whereas emotional processing received the lowest score. Conclusions: This study found the important factors influencing self-management behaviors; through this acknowledgement and early correction of these factors, we hope to improve HD patients’ individual life quality and further decrease their morbidity and mortality.

Acute kidney injury (AKI) is a heterogeneous syndrome, comprising diverse etiologies of kidney insults that result in high mortality and morbidity if not well managed. Although great efforts have been made to investigate underlying pathogenic mechanisms of AKI, there are limited therapeutic strategies available. Extracellular vesicles (EV) are membrane-bound vesicles secreted by various cell types, which can serve as cell-free therapy through transfer of bioactive molecules. In this review, we first overview the AKI syndrome and EV biology, with a particular focus on the technical aspects and therapeutic application of cell culture-derived EVs. Second, we illustrate how multi-omic approaches to EV miRNA, protein, and genomic cargo analysis can yield new insights into their mechanisms of action and address unresolved questions in the field. We then summarize major experimental evidence regarding the therapeutic potential of EVs in AKI, which we subdivide into stem cell and non-stem cell-derived EVs. Finally, we highlight the challenges and opportunities related to the clinical translation of animal studies into human patients.

Background Optimal timing for initiating maintenance dialysis in patients with chronic kidney disease (CKD) stages 3–5 is challenging. This study aimed to develop and validate a machine learning (ML) model for early personalised prediction of maintenance dialysis initiation within 1-year and 3-year timeframes among patients with CKD stages 3–5. Methods Retrospective electronic health record data from the Taipei Medical University clinical research database were used. Newly diagnosed patients with CKD stages 3–5 between 2008 and 2017 were identified. The observation period spanned from the diagnosis of CKD stages 3–5 until the maintenance dialysis initiation or a maximum follow-up of 3 years. Predictive models were developed using patient demographics, comorbidities, laboratory data and medications. The dataset was divided into training and testing sets to ensure robust model performance. Model evaluation metrics, including area under the curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value and F1 score, were employed. Results A total of 6123 and 5279 patients were included for 1 year and 3 years of the model development. The artificial neural network demonstrated better performance in predicting maintenance dialysis initiation within 1 year and 3 years, with AUC values of 0.96 and 0.92, respectively. Important features such as baseline estimated glomerular filtration rate and albuminuria significantly contributed to the predictive model. Conclusion This study demonstrates the efficacy of an ML approach in developing a highly predictive model for estimating the timing of maintenance dialysis initiation in patients with CKD stages 3–5. These findings have important implications for personalised treatment strategies, enabling improved clinical decision-making and potentially enhancing patient outcomes.