Quantitative Screening of Cervical Cancers for Low-Resource Settings: Pilot Study of Smartphone-Based Endoscopic Visual Inspection After Acetic Acid Using Machine Learning Techniques

Background: Approximately 90% of global cervical cancer (CC) is mostly found in low- and middle-income countries. In most cases, CC can be detected early through routine screening programs, including a cytology-based test. However, it is logistically difficult to offer this program in low-resource settings due to limited resources and infrastructure, and few trained experts. A visual inspection following the application of acetic acid (VIA) has been widely promoted and is routinely recommended as a viable form of CC screening in resource-constrained countries. Digital images of the cervix have been acquired during VIA procedure with better quality assurance and visualization, leading to higher diagnostic accuracy and reduction of the variability of detection rate. However, a colposcope is bulky, expensive, electricity-dependent, and needs routine maintenance, and to confirm the grade of abnormality through its images, a specialist must be present. Recently, smartphone-based imaging systems have made a significant impact on the practice of medicine by offering a cost-effective, rapid, and noninvasive method of evaluation. Furthermore, computer-aided analyses, including image processing-based methods and machine learning techniques, have also shown great potential for a high impact on medicinal evaluations

Genome sequence of the chromate-resistant bacterium Leucobacter salsicius type strain M1-8T

Leucobacter salsicius M1-8(T) is a member of the Microbacteriaceae family within the class Actinomycetales. This strain is a Gram-positive, rod-shaped bacterium and was previously isolated from a Korean fermented food. Most members of the genus Leucobacter are chromate-resistant and this feature could be exploited in biotechnological applications. However, the genus Leucobacter is poorly characterized at the genome level, despite its potential importance. Thus, the present study determined the features of Leucobacter salsicius M1-8(T), as well as its genome sequence and annotation. The genome comprised 3,185,418 bp with a G+C content of 64.5%, which included 2,865 protein-coding genes and 68 RNA genes. This strain possessed two predicted genes associated with chromate resistance, which might facilitate its growth in heavy metal-rich environments.