Purpose: The diagnosis of fungal keratitis using potassium hydroxide (KOH) smears of corneal scrapings enables initiation of the correct antimicrobial therapy at the point-of-care but requires time-consuming manual examination and expertise. This study evaluates the efficacy of a deep learning framework, dual stream multiple instance learning (DSMIL), in automating the analysis of whole slide imaging (WSI) of KOH smears for rapid and accurate detection of fungal infections.
Design: Retrospective observational study.
Importance: Microbial keratitis (MK) is a common cause of unilateral visual impairment, blindness, and eye loss in low-income and middle-income countries. There is an urgent need to develop and implement rapid and simple point-of-care diagnostics for MK to increase the likelihood of good outcomes.
Objective: To evaluate the diagnostic performance of the Aspergillus-specific lateral-flow device (AspLFD) to identify Aspergillus species causing MK in corneal scrape and corneal swab samples of patients presenting with microbial keratitis.
Ultrasensitive measurements of intracellular ATP (intATP) based on the firefly luciferase reactions are frequently used to enumerate bacterial or mammalian cells. During clinical applications, extracellular ATP (extATP) should be depleted in biological samples since it interferes with intATP and affects the quantification of bacteria. The extATP can be eliminated by ATP-degrading enzymes but complete hydrolysis of extATP remains a challenge for today's commercial enzymes.
View Article and Find Full Text PDFObjective: To develop an improved protocol for micropropagation of ethnomedicinally important Scoparia dulcis (S. dulcis) L.
Methods: Explants were inoculated on MS basal medium supplemented with kinetin and 6-benzylaminopurine for shoot bud induction.