AnEEG: leveraging deep learning for effective artifact removal in EEG data.

In neuroscience and clinical diagnostics, electroencephalography (EEG) is a crucial instrument for capturing neural activity. However, this signal is polluted by different artifacts like muscle activity, eye blinks, environmental interference, etc., which makes it more difficult to retrieve important information from the signal.

Improved selectivity of electrochemical aniline sensing using one-dimensional silver nanorods with high aspect ratio synthesized by ascorbic acid assisted method.

Background: Aniline serves as a pivotal precursor in many industries such as pesticides, pharmaceuticals, and chemicals. However, its ingestion can lead to severe health consequences, including the potential to induce cancer, respiratory tract irritation, and adverse effects on the nervous and digestive systems in the human body. The widespread use of aniline in industrial processes, coupled with inadequate wastewater management that allows for the direct release of aniline into the environment, leads to surface and groundwater contamination.

Comparative study on costs incurred for treatment of patients with bacterial and fungal keratitis - A retrospective analysis.

Purpose: To compare the costs associated with medications and travel of patients with smear-proven bacterial keratitis and fungal keratitis in a tertiary care center in India.

Methods: Retrospective analysis of case records of a cohort of patients who presented between April 2017 and March 2018 to a tertiary care center in India, with infectious keratitis who were smear-positive for bacteria or fungi, and whose costs of treatment and travel were supported by a philanthropic program.

Results: In total, 672 case records of 177 smear-positive bacterial keratitis (BK) and 495 smear-positive fungal keratitis (FK) were included in the study.

Classification of childhood medulloblastoma into WHO-defined multiple subtypes based on textural analysis.

Childhood medulloblastoma is a case of a childhood brain tumour that requires close attention due to the low survival rate. Effective prognosis depends a lot on accurate detection of its subtype. The present study proposes a texture-based computer-aided categorization of childhood medulloblastoma samples.

Study on Contribution of Biological Interpretable and Computer-Aided Features Towards the Classification of Childhood Medulloblastoma Cells.

Diagnosis and Prognosis of brain tumour in children is always a critical case. Medulloblastoma is that subtype of brain tumour which occurs most frequently amongst children. Post-operation, the classification of its subtype is most vital for further clinical management.