KUB-UNet: Segmentation of Organs of Urinary System from a KUB X-ray Image.

PURPOSE: The alarming increase in diseases of urinary system is a cause of concern for the populace and health experts. The traditional techniques used for the diagnosis of these diseases are inconvenient for patients, require high cost, and additional waiting time for generating the reports. The objective of this research is to utilize the proven potential of Artificial Intelligence for organ segmentation. Correct identification and segmentation of the region of interest in a medical image are important to enhance the accuracy of disease diagnosis. Also, it improves the reliability of the system by ensuring the extraction of features only from the region of interest. METHOD: A lot of research works are proposed in the literature for the segmentation of organs using MRI, CT scans, and ultrasound images. But, the segmentation of kidneys, ureters, and bladder from KUB X-ray images is found under explored. Also, there is a lack of validated datasets comprising KUB X-ray images. These challenges motivated the authors to tie up with the team of radiologists and gather the anonymous and validated dataset that can be used to automate the diagnosis of diseases of the urinary system. Further, they proposed a KUB-UNet model for semantic segmentation of the urinary system. RESULTS: The proposed KUB-UNet model reported the highest accuracy of 99.18% for segmentation of organs of urinary system. CONCLUSION: The comparative analysis of its performance with state-of-the-art models and validation of results by radiology experts prove its reliability, robustness, and supremacy. This segmentation phase may prove useful in extracting the features only from the region of interest and improve the accuracy diagnosis.

Identification and Genome Analysis of an Arsenic-Metabolizing Strain of Citrobacter youngae IITK SM2 in Middle Indo-Gangetic Plain Groundwater.

Whole-genome sequencing (WGS) data of a bacterial strain IITK SM2 isolated from an aquifer located in the middle Indo-Gangetic plain is reported here, along with its physiological, morphological, biochemical, and redox-transformation characteristics in the presence of dissolved arsenic (As). The aquifer exhibits oxidizing conditions relative to As speciation. Analyses based on 16S rRNA and recN sequences indicate that IITK SM2 was clustered with C. youngae NCTC 13708T and C. pasteuri NCTC UMH17T. However, WGS analyses using the digital DNA-DNA hybridization and Rapid Annotations using Subsystems Technology suggest that IITK SM2 belongs to a strain of C. youngae. This strain can effectively reduce As(V) to As(III) but cannot oxidize As(III) to As(V). It exhibited high resistance to As(V) [32,000 mg L-1] and As(III) [1,100 mg L-1], along with certain other heavy metals typically found in contaminated groundwater. WGS analysis also indicates the presence of As-metabolizing genes such as arsC, arsB, arsA, arsD, arsR, and arsH in this strain. Although these genes have been identified in several As(V)-reducers, the clustering of these genes in the forms of arsACBADR, arsCBRH, and an independent arsC gene has not been observed in any other Citrobacter species or other selected As(V)-reducing strains of Enterobacteriaceae family. Moreover, there were differences in the number of genes corresponding to membrane transporters, virulence and defense, motility, protein metabolism, phages, prophages, and transposable elements in IITK SM2 when compared to other strains. This genomic dataset will facilitate subsequent molecular and biochemical analyses of strain IITK SM2 to identify the reasons for high arsenic resistance in Citrobacter youngae and understand its role in As mobilization in middle Indo-Gangetic plain aquifers.