Modeling and insights into the structural characteristics of endocrine-disrupting chemicals.

Endocrine-disrupting chemicals (EDCs) can cause serious harm to human health and the environment; therefore, it is important to rapidly and correctly identify EDCs. Different computational models have been proposed for the prediction of EDCs over the past few decades, but the reported models are not always easily available, and few studies have investigated the structural characteristics of EDCs. In the present study, we have developed a series of artificial intelligence models targeting EDC receptors: the androgen receptor (AR); estrogen receptor (ER); and pregnane X receptor (PXR). The consensus models achieved good predictive results for validation sets with balanced accuracy values of 87.37%, 90.13%, and 79.21% for AR, ER, and PXR binding assays, respectively. Analysis of the physical-chemical properties suggested that several chemical properties were significantly (p < 0.05) different between EDCs and non-EDCs. We also identified structural alerts that can indicate an EDC, which were integrated into the web server SApredictor. These models and structural characteristics can provide useful tools and information in the discrimination and mechanistic understanding of EDCs in drug discovery and environmental risk assessment.

Association of proton pump inhibitors with gastric and colorectal cancer risk: A systematic review and meta-analysis.

Background: Proton pump inhibitors (PPI) are generally considered to be one of the well-established prescription drug classes and are commonly used to treat most acid-related diseases. However, a growing body of literature showing an association between gastric and colorectal cancer risk and PPI use continues to raise concerns about the safety of PPI use. Therefore, we aimed to investigate the association between proton pump inhibitor use and risk of gastric and colorectal cancer. Methods: We collected relevant articles using PubMed, Embase, Web of Science and Cochrane library from 1 January 1990 to 21 March 2022. The pooled effect sizes were calculated based on the random-effects model. The study was registered with PROSPERO (CRD42022351332). Results: A total of 24 studies (n = 8,066,349) were included in the final analysis in the screening articles. Compared with non-PPI users, PPI users had a significantly higher risk of gastric cancer (RR = 1.82, 95% CI: 1.46-2.29), but not colorectal cancer (RR = 1.22, 95% CI: 0.95-1.55). Subgroup analysis showed that there was a significant positive correlation between the use of PPI and the risk of non-cardiac cancer (RR = 2.75, 95% CI: 2.09-3.62). There was a significant trend between the duration dependent effect of PPI use and the risk of gastric cancer (<1 year RR = 1.56, 95% CI: 1.30-1.86; 1-3 years RR = 1.75, 95% CI: 1.28-2.37; >3 years RR = 2.32, 95% CI: 1.15-4.66), but not colorectal cancer (≤1 year RR = 1.00, 95% CI: 0.78-1.28; >1 year RR = 1.18, 95% CI: 0.91-1.54; ≥5 years RR = 1.06, 95% CI: 0.95-1.17). Conclusion: We found that PPI use increased gastric cancer risk, but not colorectal cancer risk. This result may be biased due to confounding factors. More prospective studies are needed to further validate and support our findings. Systematic Review Registration: [https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022351332], identifier [CRD42022351332].

The incidence and risk factors analysis of acute kidney injury in hospitalized patients received diuretics: A single-center retrospective study.

Diuretics have been one of the well-known nephrotoxic drugs which can lead to acute kidney injury (AKI). However, there are few real-world studies on the incidence of AKI in hospitalized patients received diuretics. In the present study, a single-center retrospective study was conducted in our center. The clinical data of hospitalized patients received diuretics from January 2018 to December 2020 was retrospectively analyzed. Among the 18,148 hospitalized patients included in the study, 2,589 patients (14.26%) were judged as incidence with AKI, while only 252 patients were diagnosed with AKI in the medical record. Among diuretics drugs in the study, the incidence rate of AKI with torasemide was the highest with 21.62%, and hydrochlorothiazide had the lowest incidence rate (6.80%). The multiple logistic regression analysis suggested that complicated with hypertension, anemia, pneumonia, shock, sepsis, heart failure, neoplastic diseases, combined use of proton pump inhibitors (PPI) were independent risk factors for AKI related to diuretics. The logic regression models for diuretics related AKI were developed based on the included data. The model for diuretics-AKI achieved the area under the receiver operating characteristic curves (AUC) with 0.79 on 10-fold cross validation. It is urgent to improve the understanding and attention of AKI in patients received diuretics for medical workers, and the assessment of risk factors before the use of diuretics should be contributed to the early prevention, diagnosis and treatment of AKI, and ultimately reducing morbidity and improving prognosis.