Oral Dysaesthetic and Perceptual Disorder, a Distinct Subset of Chronic Orofacial Pain Without Burning Symptoms: A Case-Control Study.

Background: According to the ICOP 2020, burning mouth syndrome (BMS) is a chronic orofacial pain disorder characterised by an intraoral burning sensation, which represents the main diagnostic criterion. However, some patients experience other symptoms such as xerostomia, taste alterations and globus, without the burning sensation (non-BMS).

Objective: This study aims to explore non-BMS as a distinct subclinical entity by comparing the classical BMS with this new group of patients in a case-control study, addressing gaps in current diagnostic criteria.

Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis.

Background: Breast cancer (BC), as a leading cause of cancer mortality in women, demands robust prediction models for early diagnosis and personalized treatment. Artificial Intelligence (AI) and Machine Learning (ML) algorithms offer promising solutions for automated survival prediction, driving this study's systematic review and meta-analysis.

Methods: Three online databases (Web of Science, PubMed, and Scopus) were comprehensively searched (January 2016-August 2023) using key terms ("Breast Cancer", "Survival Prediction", and "Machine Learning") and their synonyms.

Predictive modeling of diazinon residual concentration in soils contaminated with potentially toxic elements: a comparative study of machine learning approaches.

The widespread use of pesticides, including diazinon, poses an increased risk of environmental pollution and detrimental effects on biodiversity, food security, and water resources. In this study, we investigated the impact of Potentially Toxic Elements (PTE) including Zn, Cd, V, and Mn on the degradation of diazinon in three different soils. We investigated the capability and performance of four machine learning models to predict residual pesticide concentration, including adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), radial basis function (RBF), and multi-layer perceptron (MLP).

Application of machine learning in breast cancer survival prediction using a multimethod approach.

Breast cancer is one of the most prevalent cancers with an increasing trend in both incidence and mortality rates in Iran. Survival analysis is a pivotal measure in setting appropriate care plans.  To the best of our knowledge, this study is pioneering in Iran, introducing a multi-method approach using a Deep Neural Network (DNN) and 11 conventional machine learning (ML) methods to predict the 5 year survival of women with breast cancer.

Cognitive profile in burning mouth syndrome versus mild cognitive impairment: A comparative study.

Objectives: This study aims to assess and contrast cognitive and psychological aspects of patients with burning mouth syndrome (BMS-MCI) and geriatric patients (G-MCI) with mild cognitive impairment, focusing on potential predictors like pain, mood disorders, blood biomarkers, and age-related white matter changes (ARWMCs).

Methods: The study enrolled 40 BMS-MCI and 40 geriatric G-MCI, matching them by age, gender, and educational background. Participants underwent psychological, sleepiness, and cognitive assessment including the Mini-Mental State Exam (MMSE), Trail Making Test (TMT), Corsi Block-Tapping Task, Rey Auditory Verbal Learning Test, Copying Geometric Drawings Test, Frontal Assessment Battery, and Digit Cancellation Test.