Publications by authors named "N S Praveen"

Inflammatory myofibroblastic tumor (IMFT) is a rare tumor of unknown etiology. It can involve any part of the body. The IMFT involving the base of skull is rare with only 36 cases reported in the literature.

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The high prevalence of oral potentially-malignant disorders exhibits diverse severity and risk of malignant transformation, which mandates a Point-of-Care diagnostic tool. Low patient compliance for biopsies underscores the need for minimally-invasive diagnosis. Oral cytology, an apt method, is not clinically applicable due to a lack of definitive diagnostic criteria and subjective interpretation.

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Evidence suggests a limited contribution to the total research output in leading obstetrics and gynaecology journals by researchers from the developing world. Editorial bias, quality of scientific research produced and language barriers have been attributed as possible causes for this phenomenon. The aim of this study was to understand the prevalence of editorial board members based out of low and lower-middle income countries in leading journals in the field of obstetrics and gynaecology.

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Background And Objectives: Transient thyroid hormone alterations are common during critical illness and are termed non-thyroidal illness syndrome (NTIS). We studied the prevalence of NTIS in the ICU setting and its impact on predicting mortality and other outcomes and compared it to the Acute Physiology and Chronic Health Evaluation II (APACHE II) score.

Materials And Methods: The study included 119 consecutive patients admitted with a critical illness.

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Article Synopsis
  • Cytology is a minimally invasive technique for cancer screening, particularly oral cancer, which is prevalent worldwide, and the study aims to create an automated tool for analyzing cytology images to identify abnormal cells.
  • The researchers used a large dataset of 2730 multi-channel, fluorescent microscopy images to train different segmentation and classification models, including U-Net and its modifications, to effectively separate and classify single epithelial cells from background noise like artifacts and blood cells.
  • Results showed that both U-Net and modified U-Net models performed well in segmenting the images, indicating their potential use in enhancing diagnostic accuracy in oral cancer screening.
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