Publications by authors named "P Tondo"
Article Synopsis
- Interstitial lung diseases (ILDs), especially idiopathic pulmonary fibrosis (IPF), lead to severe lung scarring, increased lung cancer risk, and poor patient outcomes, with a high incidence of mortality.
- A study analyzed a cohort of 73 ILD patients with lung cancer, primarily focusing on 55 with comprehensive data, discovering significant predictors such as age at diagnosis, comorbidities, and gender differences in survival rates.
- Key findings indicate that most tumors were adenocarcinomas, early detection is common, and factors like surgical treatment and autoantibody presence are important for predicting patient survival outcomes in those with IPF-associated lung cancer.
Background: Obstructive sleep apnea (OSA) is a heterogeneous sleep disorder for which the identification of phenotypes might help for risk stratification for long-term mortality. Thus, the aim of the study was to identify distinct phenotypes of OSA and to study the association of phenotypes features with long-term mortality by using machine learning.
Methods: This retrospective study included patients diagnosed with OSA who completed a 15-year follow-up and were adherent to continuous positive airway pressure (CPAP) therapy.
Study Objectives: Obstructive sleep apnea (OSA) is considered a risk factor for sleepiness at the wheel (SW) and near-miss accidents (NMA). To date, there are subjective and objective methods such as the Maintenance of Wakefulness Test (MWT) to investigate sleepiness. However, these methods have limitations.
View Article and Find Full Text PDFThis study investigates volatile organic compound (VOC) profiles in the exhaled breath of normal subjects under different oxygenation conditions-normoxia (FiO2 21%), hypoxia (FiO2 11%), and hyperoxia (FiO2 35%)-using an electronic nose (e-nose). We aim to identify significant differences in VOC profiles among the three conditions utilizing principal component analysis (PCA) and canonical discriminant analysis (CDA). Our results indicate distinct VOC patterns corresponding to each oxygenation state, demonstrating the potential of e-nose technology in detecting physiological changes in breath composition (cross-validated accuracy values: FiO2 21% vs.
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