A Cross-sectional Study on the Aetiology and Predisposing Factors of Hoarseness of Voice.

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. After obtaining a written informed consent, history was taken using a structured proforma and a proper clinical examination was done including indirect laryngoscopy. Nasopharyngolarygoscopy was done where indirect laryngoscopy was difficult. In a sample of 178 patients with hoarseness, 159 patients had structural lesions and 19 patients had movement disorders. Of the structural lesions, 86 patients were due to malignancy, 64 due to non-neoplastic causes, and 9 were due to premalignant causes. The most common malignant cause for hoarseness was Malignancy glottis, which had a male predilection. Smoking and alcoholism were found to be the main predisposing factors. The most common non neoplastic cause were vocal cord nodule and vocal cord polyp. The main predisposing factor was vocal abuse and was seen mostly in females. Vocal cord palsy was found to be the most common movement disorder. Hoarseness as a symptom if taken lightly can lead to serious consequences. Therefore it is important to avoid predisposing factors like smoking, alcoholism and also to educate the people regarding the proper use of voice.

Combining Machine Learning with Metabolomic and Embryologic Data Improves Embryo Implantation Prediction.

This study investigated whether combining metabolomic and embryologic data with machine learning (ML) models improve the prediction of embryo implantation potential. In this prospective cohort study, infertile couples (n=56) undergoing day-5 single blastocyst transfer between February 2019 and August 2021 were included. After day-5 single blastocyst transfer, spent culture medium (SCM) was subjected to metabolite analysis using nuclear magnetic resonance (NMR) spectroscopy. Derived metabolite levels and embryologic parameters between successfully implanted and failed groups were incorporated into ML models to explore their predictive potential regarding embryo implantation. The SCM of blastocysts that resulted in successful embryo implantation had significantly lower pyruvate (p<0.05) and threonine (p<0.05) levels compared to medium control but not compared to SCM related to embryos that failed to implant. Notably, the prediction accuracy increased when classical ML algorithms were combined with metabolomic and embryologic data. Specifically, the custom artificial neural network (ANN) model with regularized parameters for metabolomic data provided 100% accuracy, indicating the efficiency in predicting implantation potential. Hence, combining ML models (specifically, custom ANN) with metabolomic and embryologic data improves the prediction of embryo implantation potential. The approach could potentially be used to derive clinical benefits for patients in real-time.