Publications by authors named "Asha C S"

A systematic review of literature on treatment modalities available with respect to efficacy, complications and recurrences so as to guide the clinician to better treat this enigmatic condition. A systematic search of PubMed, Google Scholar, Ovid and Cochrane databases. Articles in English that described or compared treatment were included.

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Haze can significantly reduce visibility and contrast of images captured outdoors, necessitating the enhancement of images. This degradation in image quality can adversely affect various applications, including autonomous driving, object detection, and surveillance, where poor visibility may lead to navigation errors and obscure crucial details. Existing dehazing techniques face several challenges: spatial methods tend to be computationally heavy, transform methods often fall short in quality, hybrid methods can be intricate and demanding, and deep learning methods require extensive datasets and computational power.

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The primary objective of the study was to find out the aetiology of hoarseness and analyse people with hoarseness based on socio-demographic profile like age, gender, occupation and socio-economic status. Secondary objective was to find out the predisposing factors of hoarseness and to see their association between benign and malignant causes of hoarseness. The study was conducted for a period of one and half years in 178 patients who were above the age of 18 years of age and presented with hoarseness lasting for more than 2 weeks to the Department of Otorhinolaryngology.

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Article Synopsis
  • This study explored the integration of metabolomic and embryologic data with machine learning (ML) to enhance the prediction of embryo implantation success in infertile couples.
  • The research involved 56 couples undergoing single blastocyst transfer, analyzing spent culture medium (SCM) to measure metabolite levels using NMR spectroscopy.
  • Findings showed that lower levels of certain metabolites like pyruvate and threonine were linked to successful implantations, and a custom ML model achieved 100% accuracy in predicting embryo implantation potential, suggesting practical benefits for patient outcomes.
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