Effect of mitochondrial potassium channel on the renal protection mediated by sodium thiosulfate against ethylene glycol induced nephrolithiasis in rat model

Purpose: Sodium thiosulfate (STS) is clinically reported to be a promising drug in preventing nephrolithiasis. However, its mechanism of action remains unclear. In the present study, we investigated the role of mitochondrial KATP channel in the renal protection mediated by STS. Materials and Methods: Nephrolithiasis was induced in Wistar rats by administrating 0.4% ethylene glycol (EG) along with 1% ammonium chloride for one week in drinking water followed by only 0.75% EG for two weeks. Treatment groups received STS, mitochondrial KATP channel opener and closer exclusively or in combination with STS for two weeks. Results: Animals treated with STS showed normal renal tissue architecture, supported by near normal serum creatinine, urea and ALP activity. Diazoxide (mitochondria KATP channel opening) treatment to the animal also showed normal renal tissue histology and improved serum chemistry. However, an opposite result was shown by glibenclamide (mitochondria KATP channel closer) treated rats. STS administered along with diazoxide negated the renal protection rendered by diazoxide alone, while it imparted protection to the glibenclamide treated rats, formulating a mitochondria modulated STS action. Conclusion: The present study confirmed that STS render renal protection not only through chelation and antioxidant effect but also by modulating the mitochondrial KATP channel for preventing urolithiasis

YOLO deep learning algorithm for object detection in agriculture: a review

YOLO represents the one-stage object detection also called regression-based object detection. Object in the given input is directly classified and located instead of using the candidate region. The accuracy from two-stage detection is higher than one-stage detection where one-stage object detection speed is higher than two-stage object detection. YOLO has become popular because of its Detection accuracy, good generalization, open-source, and speed. YOLO boasts exceptional speed due to its approach of using regression problems for frame detection, eliminating the need for a complex pipeline.  In agriculture, using remote sensing and drone technologies YOLO classifies and detects crops, diseases, and pests, and is also used for land use mapping, environmental monitoring, urban planning, and wildlife. Recent research highlights YOLO's impressive performance in various agricultural applications. For instance, YOLOv4 demonstrated high accuracy in counting and locating small objects in UAV-captured images of bean plants, achieving an AP of 84.8% and a recall of 89%. Similarly, YOLOv5 showed significant precision in identifying rice leaf diseases, with a precision rate of 90%. In this review, we discuss the basic principles behind YOLO, different versions of YOLO, limitations, and YOLO application in agriculture and farming