Pulmonary Arteriovenous Malformation: Not all Hypoxemia During Covid-19 is the Same.

Unlabelled: Hypoxemia in coronavirus disease 2019 (COVID-19) patients is not solely due to alveolar damage but can also involve factors like vascular shunts, such as pulmonary arteriovenous malformation, contributing to persistent hypoxemia. We report a case of a 59-year-old male, with COVID-19 pneumonia, requiring high-flow respiratory support, who later was also diagnosed with a large pulmonary arteriovenous fistula, which probably worsened because of COVID-19 infection.

Learning Points: Hypoxemia in coronavirus disease 2019 (COVID-19) patients is multifaceted not only with alveolar damage but also with other factors such as vascular shunts, like pulmonary arteriovenous malformation.

An approach to reduce inhaler errors using Donabedian's triad.

Inhaler errors inversely affect the outcome of respiratory diseases. Inhaler devices, such as the metered-dose inhalers (MDI) and dry powder inhalers (DPI), are commonly used in treating respiratory diseases like asthma and chronic obstructive pulmonary disease (COPD), and incorrect use of these devices can result in suboptimal treatment outcomes, increased probabilities of hospitalizations or admissions, and poorer quality of life. Patient related factors to inhaler errors include age, cognitive and physical abilities, education, language barriers, and preferences.

Applications of artificial intelligence in emergency and critical care diagnostics: a systematic review and meta-analysis.

Introduction: Artificial intelligence has come to be the highlight in almost all fields of science. It uses various models and algorithms to detect patterns and specific findings to diagnose a disease with utmost accuracy. With the increasing need for accurate and precise diagnosis of disease, employing artificial intelligence models and concepts in healthcare setup can be beneficial.

Metaheuristic integrated machine learning classification of colon cancer using STFT LASSO and EHO feature extraction from microarray gene expressions.

The microarray gene expression data poses a tremendous challenge due to their curse of dimensionality problem. The sheer volume of features far surpasses available samples, leading to overfitting and reduced classification accuracy. Thus the dimensionality of microarray gene expression data must be reduced with efficient feature extraction methods to reduce the volume of data and extract meaningful information to enhance the classification accuracy and interpretability.