Dipyridamole attenuates ischemia reperfusion induced acute kidney injury through adenosinergic A1 and A2A receptor agonism in rats

Caffeine and neonatal acute kidney injury

Acute kidney injury is one of the most threatening diseases in neonates, with complex pathogenesis and limited treatment options. Caffeine is a commonly used central nervous system stimulant for treating apnea in preterm infants. There is compelling evidence that caffeine may have potential benefits for preventing neonatal acute kidney injury, but comprehensive reports are lacking in this area. Hence, this review aims to provide a summary of clinical data on the potential benefits of caffeine in improving neonatal acute kidney injury. Additionally, it delves into the molecular mechanisms underlying caffeine's effects on acute kidney injury, with a focus on various aspects such as oxidative stress, adenosine receptors, mitochondrial dysfunction, endoplasmic reticulum stress, inflammasome, autophagy, p53, and gut microbiota. The ultimate goal of this review is to provide information for healthcare professionals regarding the link between caffeine and neonatal acute kidney injury and to identify gaps in our current understanding.

The Impact of Dipyridamole on Disease-Associated Microglia Phenotypic Transformation in White Matter Lesions Induced by Chronic Cerebral Hypoperfusion

White matter lesions (WMLs) resulting from chronic cerebral hypoperfusion (CCH) are the leading cause of vascular dementia (VaD). This study aimed to investigate whether dipyridamole could alleviate WMLs by regulating the phenotype of disease-associated microglia (DAM) through equilibrative nucleoside transporter 2 (ENT2) and adenosine A2A receptor (Adora2a) and to clarify the underlying molecular mechanisms. CCH rat models were constructed to mimic VaD. Morris water maze and Luxol Fast Blue staining were employed to assess cognitive function and quantify the severity of WMLs, respectively. Immunofluorescent staining was performed to analyze the activation of glial cells and the phenotypic transformation of DAM. Additionally, levels of ENT2, proteins in the NF-κB and ERK1/2 pathways and inflammatory cytokines were detected. The results indicated that dipyridamole diminished the activation and proliferation of microglia and astrocytes, increased the expression of myelin basic protein and ameliorated WMLs and cognitive decline in CCH rats. Further study revealed that dipyridamole decreased the expression of ENT2 and inhibited the activation of ERK1/2 and NF-κB signaling pathways, which ultimately converted DAM to anti-inflammatory phenotype and suppressed the levels of TNF-α, IL-1β, IL-6 in WMLs. However, Adora2a inhibitor (SCH58261) attenuated above effects. Our study demonstrates that dipyridamole facilitates the conversion of DAM to the anti-inflammatory phenotype through ENT2/Adora2a pathway and inhibits the activation of ERK1/2 and NF-κB signaling pathways, thereby alleviating neuroinflammation in WMLs. The current findings establish the basis for using dipyridamole to treat VaD.

Possible role of purinergic signaling in COVID-19

The coronavirus disease (COVID-19), caused by SARS-CoV-2 infection, accounts for more than 2.4 million deaths worldwide, making it the main public health problem in 2020. Purinergic signaling is involved in the pathophysiology of several viral infections which makes the purinergic system a potential target of investigation in COVID-19. During viral infections, the ATP release initiates a cascade that activates purinergic receptors. This receptor activation enhances the secretion of pro-inflammatory cytokines and performs the chemotaxis of macrophages and neutrophils, generating an association between the immune and the purinergic systems. This review was designed to cover the possible functions of purinergic signaling in COVID-19, focusing on the possible role of purinergic receptors such as P2X7 which contributes to cytokine storm and inflammasome NLRP3 activation and P2Y1 that activates the blood coagulation pathway. The possible role of ectonucleotidases, such as CD39 and CD73, which have the function of dephosphorylating ATP in an immunosuppressive component, adenosine, are also covered in detail. Moreover, therapeutic combination or association possibilities targeting purinergic system components are also suggested as a possible useful tool to be tested in future researches, aiming to unveil a novel option to treat COVID-19 patients.

Machine Learning for Identifying Medication-Associated Acute Kidney Injury

One of the prominent problems in clinical medicine is medication-induced acute kidney injury (AKI). Avoiding this problem can prevent patient harm and reduce healthcare expenditures. Several researches have been conducted to identify AKI-associated medications using statistical, data mining, and machine learning techniques. However, these studies are limited to assessing the impact of known nephrotoxic medications and do not comprehensively explore the relationship between medication combinations and AKI. In this paper, we present a population-based retrospective cohort study that employs automated data analysis techniques to identify medications and medication combinations that are associated with a higher risk of AKI. By integrating multivariable logistic regression, frequent itemset mining, and stratified analysis, this study is designed to explore the complex relationships between medications and AKI in such a way that has never been attempted before. Through an analysis of prescription records of one million older patients stored in the healthcare administrative dataset at ICES (an independent, non-profit, world-leading research organization that uses population-based health and social data to produce knowledge on a broad range of healthcare issues), we identified 55 AKI-associated medications among 595 distinct medications and 78 AKI-associated medication combinations among 7748 frequent medication combinations. In addition, through a stratified analysis, we identified 37 cases where a particular medication was associated with increasing the risk of AKI when used with another medication. We have shown that our results are consistent with previous studies through consultation with a nephrologist and an electronic literature search. This research demonstrates how automated analysis techniques can be used to accomplish data-driven tasks using massive clinical datasets.