Polycystic Ovary Syndrome: Personality and Temperamental Characteristics.

Polycystic ovary syndrome (PCOS) is a common gynaecological/endocrine disorder that affects 5%-10% of women of reproductive age. Its association with psychiatric conditions is well known. This study aimed to evaluate personality, temperamental, and stress-related characteristics among PCOS patients by comparing them with a control group. We found that PCOS patients presented more pronounced features of type D personality and had higher NEO Five-Factor Inventory (NEO FFI) scores on neuroticism and lower NEO FFI scores on openness to experience and conscientiousness. On the Polish version of the Emotionality Activity Sociability Temperament Survey (EAS-D), PCOS patients reported higher emotionality-fear and lower activity than controls. The PCOS group also had higher scores on state anxiety and trait anxiety, using the State-Trait Anxiety Inventory (STAI) and Distressed Personality Scale (DS-14) questionnaires. These findings underline the importance of a multidisciplinary approach to the care of PCOS patients.

Associations of air pollution with acute coronary syndromes based on A/B/AB versus O blood types: case-crossover study.

Short-term exposure to air pollutants may contribute to an increased risk of acute coronary syndrome (ACS). This study assessed the role of short-term exposure to fine particulate matter (PM2.5) as well as fine and coarse PM (PM10) air pollution in ACS events and the effect of blood groups on this phenomenon. A retrospectively collected database of 9026 patients was evaluated. The study design was a case-crossover using a conditional logistic regression model. The main analysis focused on PM2.5 levels with a 1 day lag until the ACS event, using threshold-modelled predictor for all patients. Secondary analyses utilized separate threshold-modelled predictors for 2-7-days moving averages and for patients from specific ABO blood groups. Additional analysis was performed with the non-threshold models and for PM10 levels. Short-term exposure to increased PM2.5 and PM10 levels at a 1-day lag was associated with elevated risks of ACS (PM2.5: OR = 1.012 per + 10 µg/m3, 95% CI 1.003, 1.021; PM10: OR = 1.014 per + 10 µg/m3, CI 1.002, 1.025) for all patients. Analysis showed that exposure to PM2.5 was associated with increased risk of ACS at a 1-day lag for the A, B or AB group (OR = 1.012 per + 10 µg/m3, CI 1.001, 1.024), but not O group (OR = 1.011 per + 10 µg/m3, CI 0.994, 1.029). Additional analysis showed positive associations between exposure to PM10 and risk of ACS, with 7-days moving average models stratified by blood group revealing that exposures to PM2.5 and PM10 were associated with elevated risk of ACS for patients with group O. Short-term exposures to PM2.5 and PM10 were associated with elevated risk of ACS. Short-term exposure to PM2.5 was positively associated with the risk of ACS for patients with A, B, or AB blood groups for a 1-day lag, while risk in O group was delayed to 7 days.

Machine learning in the diagnosis of asthma phenotypes during coronavirus disease 2019 pandemic.

Background: During the coronavirus disease 2019 (COVID-19) pandemic, it has become a pressing need to be able to diagnose aspirin hypersensitivity in patients with asthma without the need to use oral aspirin challenge (OAC) testing. OAC is time consuming and is associated with the risk of severe hypersensitive reactions. In this study, we sought to investigate whether machine learning (ML) based on some clinical and laboratory procedures performed during the pandemic might be used for discriminating between patients with aspirin hypersensitivity and those with aspirin-tolerant asthma. Methods: We used a prospective database of 135 patients with non-steroidal anti-inflammatory drug (NSAID)-exacerbated respiratory disease (NERD) and 81 NSAID-tolerant (NTA) patients with asthma who underwent OAC. Clinical characteristics, inflammatory phenotypes based on sputum cells, as well as eicosanoid levels in induced sputum supernatant and urine were extracted for the purpose of applying ML techniques. Results: The overall best ML model, neural network (NN), trained on a set of best features, achieved a sensitivity of 95% and a specificity of 76% for diagnosing NERD. The 3 promising models (i.e., multiple logistic regression, support vector machine, and NN) trained on a set of easy-to-obtain features including only clinical characteristics and laboratory data achieved a sensitivity of 97% and a specificity of 67%. Conclusions: ML techniques are becoming a promising tool for discriminating between patients with NERD and NTA. The models are easy to use, safe, and achieve very good results, which is particularly important during the COVID-19 pandemic.