A comprehensive review on potential drug-drug interactions of proton pump inhibitors with antidiabetic drugs metformin and DPP-4 inhibitors.

A drug interaction is a condition in which two or more drugs are taken at the same time. Type 2 diabetes mellitus is a significant contributor to polypharmacy. Proton pump inhibitors (PPIs) are often prescribed in combination with metformin or DPP-4 inhibitors (sitagliptin, saxagliptin, linagliptin, and alogliptin) or a combined dose of metformin and DPP-4 inhibitor to treat gastritis in diabetic patients. This review article mainly focused on evaluating the potential drug-drug interactions (DDIs) between PPIs (i.e. esomeprazole, lansoprazole, omeprazole, pantoprazole, and rabeprazole) with metformin and PPIs with DPP-4 inhibitors. The findings demonstrated the existence of pharmacokinetic and pharmacodynamic DDIs between the aforementioned PPIs with metformin and DPP-4 inhibitors, which could impact the biological activities (i.e., hypoglycemia) of these drugs. Moreover, this review suggested that esomeprazole could be the best drug in the PPI group to be prescribed simultaneously with metformin and DPP-4 inhibitors, as most of the antidiabetic drugs of this study did not show any interaction with esomeprazole. The findings of this study also revealed that both antidiabetic drugs and PPIs could have positive interactions as PPIs have the potential to lessen the gastrointestinal side effects of metformin and DPP-4 inhibitors. To achieve the greatest therapeutic impact with the fewest side effects, careful dose control of these drugs is required. So, more extensive research on both human and animal subjects are needed to ascertain the veracity of this hypothesis.

Spatiotemporal distribution of microplastic debris in the surface beach sediment of the southeastern coast of Bangladesh.

This study undertakes a spatiotemporal analysis of microplastic pollution in surface beach sediments, covering 7 coastal beaches in Bangladesh and two seasons-monsoon and winter. The concentration of microplastics extracted from the surface beach sediment is 242.86 particles/kg dw. The results showed both significant seasonal (p value = 0.001) and spatial (p value = 0.004) variation. The abundance and polymer types were significantly higher (57 %) in winter than in the monsoon season (43 %). Touristic and commercial beaches showed higher levels of microplastic pollution than the non-touristic beaches. Polyethylene (28.8 %) and Polypropylene (27.6 %) were the most abundant polymer. The most dominant coloration of microplastics was white (42.6 %). The majority of the microplastics were fibers (33.5 %). Smallest particles measuring <1 mm constituted nearly half of the total microplastics load (48.5 %). This baseline data can be useful in terms of coastal zone management for the southeastern coastal beaches of Bangladesh.

CAM-QUS guided self-tuning modular CNNs with multi-loss functions for fully automated breast lesion classification in ultrasound images.

Objective.Breast cancer is the major cause of cancer death among women worldwide. Deep learning-based computer-aided diagnosis (CAD) systems for classifying lesions in breast ultrasound images can help materialise the early detection of breast cancer and enhance survival chances.Approach.This paper presents a completely automated BUS diagnosis system with modular convolutional neural networks tuned with novel loss functions. The proposed network comprises a dynamic channel input enhancement network, an attention-guided InceptionV3-based feature extraction network, a classification network, and a parallel feature transformation network to map deep features into quantitative ultrasound (QUS) feature space. These networks function together to improve classification accuracy by increasing the separation of benign and malignant class-specific features and enriching them simultaneously. Unlike the categorical crossentropy (CCE) loss-based traditional approaches, our method uses two additional novel losses: class activation mapping (CAM)-based and QUS feature-based losses, to capacitate the overall network learn the extraction of clinically valued lesion shape and texture-related properties focusing primarily the lesion area for explainable AI (XAI).Main results.Experiments on four public, one private, and a combined breast ultrasound dataset are used to validate our strategy. The suggested technique obtains an accuracy of 97.28%, sensitivity of 93.87%, F1-score of 95.42% on dataset 1 (BUSI), and an accuracy of 91.50%, sensitivity of 89.38%, and F1-score of 89.31% on the combined dataset, consisting of 1494 images collected from hospitals in five demographic locations using four ultrasound systems of different manufacturers. These results outperform techniques reported in the literature by a considerable margin.Significance.The proposed CAD system provides diagnosis from the auto-focused lesion area of B-mode BUS images, avoiding the explicit requirement of any segmentation or region of interest extraction, and thus can be a handy tool for making accurate and reliable diagnoses even in unspecialized healthcare centers.