Age estimation is crucial in forensic and anthropological fields. Teeth, are valued for their resilience to environmental factors and their preservation over time, making them essential for age estimation when other skeletal remains deteriorate. Recently, Machine Learning algorithms have been used in age estimation, demonstrating high levels of accuracy. However, their precision with respect to the trend of age estimation error, typical in some traditional methods like linear regression, has not been thoroughly investigated. To evaluate and compare the performance of frequently used Machine Learning-assisted methods against two traditional age estimation methods, linear regression and the Segmented Normal Bayesian Calibration model. Overall, 1.949 orthopantomographs from black and white South African children aged 5-14 years, with 49 % males, were evaluated. The performance of Random Forest, Support Vector Regression, K-Nearest Neighbors and the Gradient Boosting Method were compared against traditional linear regression and the Segmented Normal Bayesian Calibration model. The comparison was based on accuracy measures, including Mean Absolute Error and Root Mean Squared Error, and precision measures, including the Inter-Quartile Range of the error distribution and the slope of the estimated age error relative to chronological age. The Machine Learning methods outperformed linear regression and the Segmented Normal Bayesian Calibration models in terms of accuracy, although the differences were small. Gradient Boosting Method and Support Vector Regression achieved the highest levels of accuracy (Mean Absolute Error: 0.69 years, Root Mean Squared Error: 0.85 years). All Machine Learning methods and linear regression exhibited significant bias in residuals, whereas the Segmented Normal Bayesian Calibration model showed no significant bias. Gender-stratified analyses revealed similar results in terms of the accuracy and precision of all considered models. Although Machine Learning methods demonstrate high levels of accuracy, they may be prone to trends in error distribution when estimating dental age. Evaluating this error is crucial and should be an integral part of model performance evaluation. Future research should aim to improve accuracy while rigorously addressing systematic biases.

To analyze metabolic outcomes, diabetes impact and device satisfaction in children and adolescents with type 1 diabetes in Italy who used different treatment modalities for diabetes care in a real-life context. In this multicenter, nationwide, cross-sectional study, 1464 participants were enrolled at a routine visit. The following treatment modalities were considered MDI + SMBG; MDI + CGM; Sensor Augmented Pump Therapy; predictive management of low glucose; Hybrid Closed Loop (HCL); Advanced Hybrid Closed Loop (AHCL). Health related quality of life was evaluated by the Italian version of the Diabetes Impact and Device Satisfaction Scale (DIDS) questionnaire. Patients treated with AID systems were more likely to have HbA1c ≤ 6.5 %, higher percentage of time with glucose levels between 70 and 180 mg/dL, lower percentage of time with glucose levels above 180 mg/dL, higher device satisfaction, and reduced impact of diabetes. All the therapeutic modalities with respect to MDI + CGM, except for MDI + SMBG, contributed to increase the device satisfaction. HCL and AHCL respect to MDI + CGM were associated with lower diabetes impact. Real-life use of automated insulin delivery systems is associated with reduced type 1 diabetes impact, increased device satisfaction, and achievement of glycemic goals.

Background: Pirfenidone and Nintedanib have significantly improved the prognosis of patients with idiopathic pulmonary fibrosis (IPF), reducing mortality risk and exacerbations. This study aimed to analyze antifibrotic treatment utilization and its association with clinical outcomes (i.e., acute exacerbation or death) during 2014–2021 in newly diagnosed IPF patients, using Healthcare Utilization Databases of the Marche Region, Italy. Methods: The first 12-month adherence to antifibrotic was estimated using the Proportion of Days Covered (PDC), defining adherence as PDC ≥ 75%. State Sequence Analysis over the initial 52 weeks of treatment was used to identify adherence patterns. The role of adherence patterns on acute exacerbations/death, adjusted by demographic, clinical features, and monthly adherence after the 52-week period (time-dependent variable), was assessed with Cox regression. Results: Among 667 new IPF cases, 296 received antifibrotic prescriptions, with 62.8% being adherent in the first year. Three antifibrotic utilization patterns emerged—high adherence (37.2%), medium adherence (42.5%), and low adherence (20.3%)—with median PDCs of 95.3%, 79.5%, and 18.6%, respectively. These patterns did not directly influence three-year mortality/exacerbation probability, but sustained adherence reduced risk over time. Conclusions: Good adherence was observed in in this population-based study, emphasizing the importance of continuous antifibrotics therapy over time to mitigate adverse outcomes.

The aim was to evaluate the determinants of acute exacerbation (AE) and death in new cases of idiopathic pulmonary fibrosis (IPF) using administrative databases in the Marche Region. Adults at their first prescription of antifibrotics or hospitalization with a diagnosis of IPF occurring in 2014–2019 were considered as new cases. Multiple Cox regression was used to estimate the risk of AE and of all-cause mortality adjusted by demographic and clinical characteristics, stratifying patients according to antifibrotic treatment. Overall, 676 new cases of IPF were identified and 276 deaths and 248 AE events occurred. In never-treated patients, the risk of AE was higher in patients with poor health conditions at diagnosis; the risk of death was higher in males, in patients aged ≥75 and in those with poor health conditions at baseline. The increasing number of AEs increased the risk of death in treated and never-treated patients. Within the limits of an observational study based on secondary data, the combined use of healthcare administrative databases allows the accurate analysis of progression and survival of IPF from the beginning of the antifibrotic therapy era, suggesting that timely and early diagnosis is critical to prescribing the most suitable treatment to increase survival and maintain a healthy life expectancy.

Cold-exposure promotes energy expenditure by inducing brown adipose tissue (BAT) thermogenesis, which over time, is also sustained by browning, the appearance, or increase, of brown-like cells into white fat depots. Identification of circulating markers reflecting BAT activity and browning is crucial to study this phenomenon and its triggers, also holding possible implications for the therapy of obesity and metabolic diseases. Using RT-qPCR, we evaluated the peripheral blood mononuclear cells (PBMC) expression profile of regulators of BAT activity (CIDEA, PRDM16), white adipocytes browning (HOXC9 and SLC27A1), and fatty acid β-oxidation (CPT1A) in 150 Siberian healthy miners living at extremely cold temperatures compared to 29 healthy subjects living in thermoneutral conditions. Anthropometric parameters, glucose, and lipid profiles were also assessed. The cold-exposed group showed significantly lower weight, BMI, hip circumference, and PBMC expression of CIDEA, but higher expression of HOXC9 and higher circulating glucose compared to controls. Within the cold-exposed group, BMI, total cholesterol, and the atherogenic coefficient were lower in individuals exposed to low temperatures for a longer time. In conclusion, human PBMC expresses the brown adipocytes marker CIDEA and the browning marker HOXC9, which, varying according to cold-exposure, possibly reflect changes in BAT activation and white fat browning.

The aim of the study was to evaluate the trend in the incidence of idiopathic pulmonary fibrosis (IPF) in a real-world setting of the Marche region, a region of Central Italy, between 2014 and 2019. This observational prospective study was based on administrative databases of hospital discharges and drug prescriptions. All adult residents in the Marche Region with a first prescription of antifibrotic drugs, or a first hospitalization with a diagnosis of IPF during the study period, were identified as incident cases of IPF. A multiple Poisson regression analysis was used to estimate the IPF incidence trend, adjusted for age, sex, and health conditions. The mean incidence rate was 9.8 cases per 100,000 person-years. A significant increasing trend of 6% per year was observed. The incidence rates were significantly higher in males than females, older subjects, and those with poorer health conditions. To our knowledge, this is the first study evaluating incidences of IPF over a 6-year period in Italy, combining hospital discharge and drug prescription databases. The study highlights that the combined use of two secondary sources is a reliable strategy to accurately identify new cases of IPF when the appropriate disease registry is lacking.