Evaluation of Vocal and Pulmonary Health Through Acoustic Analysis of Voice and Respiratory Measurements in People With Long COVID-19 Symptoms.

Background/objectives: The coronavirus disease (COVID-19) pandemic has significantly impacted global health, with Malaysia reporting over 5 million cases as of May 2024. While symptoms like fatigue and breathlessness are commonly reported among COVID-19 patients, limited research exists on the vocal and pulmonary conditions of individuals with long COVID symptoms. This study aims to assess vocal impairments and pulmonary function differences between long COVID patients and healthy controls, addressing gaps in understanding how long COVID affects vocal and respiratory health.

Climate data dynamics: A high-volume real world structured weather dataset.

The dataset at hand is a unique resource, officially procured from the Bangladesh Meteorological Department, the sole government institution that diligently monitors weather through 35 strategically placed weather stations across the nation. This dataset is a treasure trove of actual data spanning several decades, from the inception of each weather station to the present. It has been meticulously restructured and processed into four (Rainfall, Temperature, Humidity, and Sunshine) key weather parameters, presented in a highly organized and accessible format.

Human milk improves the oral bioavailability of the poorly water-soluble drug clofazimine.

Clofazimine is an emerging drug for the treatment of cryptosporidiosis in infants. As a poorly water-soluble drug, the formulation of clofazimine in age-appropriate vehicles is challenging and often results in the use of off-label formulations. Milk-based vehicles such as human milk and bovine milk have been investigated as age-appropriate formulations and shown to increase the solubilisation of poorly water-soluble drugs via enhanced solubility in lipid digestion products in vitro.

Decoding the synergy: unveiling gradient boosting regression model for multivariate quantitation of pioglitazone, alogliptin and glimepiride in pure and tablet dosage forms.

This study represents a comparison among the performances of four multivariate procedures: partial least square (PLS) and artificial neural networks (ANN) in addition to support vector regression (SVR) and extreme gradient boosting (XG Boost) algorithm for the determination of the anti-diabetic mixture of pioglitazone (PIO), alogliptin (ALG) and glimepiride (GLM) in pharmaceutical formulations with aid of UV spectrometry. Key wavelengths were selected using knowledge-based variable selection and various preprocessing methods (e.g.