Machine learning-enhanced HRCT analysis for diagnosis and severity assessment in pediatric asthma.

OBJECTIVES: Chest high-resolution computed tomography (HRCT) is conditionally recommended to rule out conditions that mimic or coexist with severe asthma in children. However, it may provide valuable insights into identifying structural airway changes in pediatric patients. This study aims to develop a machine learning-based chest HRCT image analysis model to aid pediatric pulmonologists in identifying features of severe asthma. METHODS: This retrospective case-control study compared children with severe asthma (as defined by ERS/ATS guidelines) to age- and sex-matched controls without asthma, using chest HRCT scans for detailed imaging analysis. Statistical analysis included classification trees, random forests, and conventional ROC analysis to identify the most significant imaging features that mark severe asthma from controls. RESULTS: Chest HRCT scans differentiated children with severe asthma from controls. Compared to controls (n = 21, mean age 11.4 years), children with severe asthma (n = 20, mean age 10.4 years) showed significantly greater bronchial thickening (BT) scores (p < 0.001), airway wall thickness percentage (AWT%, p < 0.001), bronchiectasis grading (BG) and bronchiectasis severity (BS) scores (p = 0.016), mucus plugging, and centrilobular emphysema (p = 0.009). Using AWT% as the predictor in conventional ROC analysis, an AWT% ≥ 38.6 emerged as the optimal classifier for discriminating severe asthmatics from controls, with 95% sensitivity, specificity, and overall accuracy. CONCLUSION: Our study demonstrates the potential of machine learning-based analysis of chest HRCT scans to accurately identify features associated with severe asthma in children, enhancing diagnostic evaluation and contributing to the development of more targeted treatment approaches.

Assessing external exposome by implementing an Environmental Data Management System using Open Data.

Due to the increasing importance of exposome in environmental epidemiology, feasibility and usefulness of an Environmental Data Management System (EDMS) using Open Data was evaluated. The EDMS includes data from 10 European cities (Celje (Slovenia), Lódz (Poland), Manchester (UK), Palermo (Italy), Paris (France), Porto (Portugal), Regensburg (Germany), Reus (Spain), Rijeka (Croatia), Thessaloniki (Greece)) about external non-specific and specific exposome factors at the city or country level (2017-2020). Findings showed that the highest values of life expectancy were in Reus females (86 years) and Palermo males (81 years). UK had the highest obesity rate (28%), Croatia the highest prescribed drug consumption (62%), Greece and Portugal the highest smoking rates (37%, 42%) and daily alcohol consumption (21%), respectively. The most polluted cities were Thessaloniki for PM10 (38 µg/m3), Lódz for PM2.5 (25 µg/m3), Porto for NO2 (62 µg/m3) and Rijeka for O3 (92 µg/m3). Thessaloniki had the highest grey space (98%) and Lódz the highest cumulative amount of pollen (39,041 p/m3). The highest daily noise levels ≥ 55 dB was in Reus (81% to traffic) and Regensburg (21% to railway). In drinking water, arsenic had the highest value in Thessaloniki (6.4 µg/L), boron in Celje (24 mg/L) and lead in Paris (46.7 µg/L). Portugal and Greece showed the highest pesticide residues in food (7%). In conclusion, utilizing open-access databases enables the translation of research findings into actionable strategies for public health interventions.

Reference Equations for Within-Breath Respiratory Oscillometry in White Adults.

BACKGROUND: Within-breath analysis of oscillometry parameters is a growing research area since it increases sensitivity and specificity to respiratory pathologies and conditions. However, reference equations for these parameters in White adults are lacking and devices using multiple sinusoids or pseudorandom forcing stimuli have been underrepresented in previous studies deriving reference equations. The current study aimed to establish reference ranges for oscillometry parameters, including also the within-breath ones in White adults using multi-sinusoidal oscillations. METHODS: White adults with normal spirometry, BMI ≤30 kg/m2, without a smoking history, respiratory symptoms, pulmonary or cardiac disease, neurological or neuromuscular disorders, and respiratory tract infections in the previous 4 weeks were eligible for the study. Study subjects underwent oscillometry (multifrequency waveform at 5-11-19 Hz, Resmon PRO FULL, RESTECH Srl, Italy) in 5 centers in Europe and the USA according to international standards. The within-breath and total resistance (R) and reactance (X), the resonance frequency, the area under the X curve, the frequency dependence of R (R5-19), and within-breath changes of X (ΔX) were submitted to lambda-mu-sigma models for deriving reference equations. For each output parameter, an AIC-based stepwise input variable selection procedure was applied. RESULTS: A total of 144 subjects (age 20.8-86.3 years; height 146-193 cm; BMI 17.42-29.98 kg/m2; 56% females) were included. We derived reference equations for 29 oscillatory parameters. Predicted values for inspiratory and expiratory parameters were similar, while differences were observed for their limits of normality. CONCLUSIONS: We derived reference equations with narrow confidence intervals for within-breath and whole-breath oscillatory parameters for White adults.

Different greenness exposure in Europe and respiratory outcomes in youths. A systematic review and meta-analysis.

The existing evidence on the association between greenness and respiratory outcomes remains inconclusive. We aimed at systematically summarizing existing literature on greenness exposure and respiratory outcomes in European children and adolescents, with a preliminary attempt to qualify the distribution of dominant tree species across different geographical areas and bioclimatic regions. Overall, 4049 studies were firstly identified by searching PubMed/MEDLINE, EMBASE, Scopus, Web of Science, GreenFile and CAB direct, up to 29 August 2023. Eighteen primary studies were included in the systematic review and six were meta-analyzed. No overall significant association was observed between the Normalized Difference Vegetation Index, assessed within 500-m buffers (i.e. NDVI-500), and the odds of asthma for 0.3-increase in the exposure (OR: 0.97, 95% CI from 0.53 to 1.78). Similarly, an overall exposure to the NDVI-300 highest tertile, as compared to the lowest tertile, was not significantly associated with asthma (OR: 0.65, 95% CI from 0.22 to 1.91): heterogeneity among studies was significant (p = 0.021). We delineated some key elements that might have mostly contributed to the lack of scientific consensus on this topic, starting from the urgent need of harmonized approaches for the operational definition of greenness. Additionally, the complex interplay between greenness and respiratory health may vary across different geographical regions and climatic conditions. At last, the inconsistent findings may reflect the heterogeneity and complexity of this relationship, rather than a lack of scientific consensus itself. Future research should compare geographical areas with similar bioclimatic parameters and dominant or potentially present vegetation species, in order to achieve a higher inter-study comparability.

Robotic-Assisted Neck Dissection: Our Experience

Abstract Introduction Robotic neck dissection surgery allows less invasiveness to significantly improve the aesthetic impact even though it does not compromise the principles of radical cancer procedure. Objective The aim of our work is to describe our personal experience with robotic neck dissection surgery. Methods A retrospective study was conducted by analyzing 10 patients subjected to a robotic neck dissection surgery. In the period from August 2012 to December 2018, these patients have been treated exclusively with robotic lateral-cervical dissection. Five of them were subjected to robotic-assisted transaxillary neck dissection (RATAND) and the other 5 treated with robotic-assisted retroauricular neck dissection (RARAND), then the surgical results have been compared with 5 similar dissections performed by open neck dissection (OND). Results The average surgical time of RATAND was estimated in 166 minutes, the average surgical time of RARAND was estimated in 153 minutes and the average surgical time of OND was estimated in 48 minutes. Both robotic techniques are valid from the oncological and aesthetic point of view, but in terms of surgical time, they are much longer than the open technique. Conclusions In terms of the post-operative decree, in our opinion, the retroauricular technique is more rapid for the purposes of recovery.