Due to the complex interplay among different urban-related exposures, a comprehensive approach is advisable to estimate the health effects. We simultaneously assessed the effect of “green”, “grey” and air pollution exposure on respiratory/allergic conditions and general symptoms in schoolchildren. This study involved 219 schoolchildren (8–10 years) of the Municipality of Palermo, Italy. Data were collected through questionnaires self-administered by parents and children. Exposures to greenness and greyness at the home addresses were measured using the normalized difference vegetation index (NDVI), residential surrounding greyness (RSG) and the CORINE land-cover classes (CLC). RSG was defined as the percentage of buffer covered by either industrial, commercial and transport units, or dump and construction sites, or urban fabric related features. Two specific categories of CLC, namely “discontinuous urban fabric - DUF” - and “continuous urban fabric - CUF” - areas were found. Exposure to traffic-related nitrogen dioxide (NO2) was assessed using a Land-Use Regression model. A symptom score ranging from 0 to 22 was built by summing affirmative answers to twenty-two questions on symptoms. To avoid multicollinearity, multiple Logistic and Poisson ridge regression models were applied to assess the relationships between environmental factors and self-reported symptoms. A very low exposure to NDVI ≤0.15 (1st quartile) had a higher odds of nasal symptoms (OR = 1.47, 95% CI [1.07–2.03]). Children living in CUF areas had higher odds of ocular symptoms (OR = 1.49, 95% CI [1.10–2.03]) and general symptoms (OR = 1.18, 95% CI [1.00–1.48]) than children living in DUF areas. Children living in proximity (≤200 m) to High Traffic Roads (HTRs) had increased odds of ocular (OR = 1.68, 95% CI [1.31–2.17]) and nasal symptoms (OR = 1.49, 95% CI [1.12–1.98]). A very high exposure to NO2 ≥ 60 μg/m3 (4th quartile) was associated with a higher odds of general symptoms (OR = 1.28, 95% CI [1.10–1.48]). No associations were found with RGS. A Poisson ridge regression model on the symptom score showed that children living in proximity to HTRs (≤200 m) had a higher symptoms score (RR = 1.09, 95% CI [1.02–1.17]) than children living > 200 m from HTRs. Children living in CUF areas had a higher symptoms score (RR = 1.11, 95% CI [1.03–1.19]) than children living in DUF areas. Multiple exposures related to greenness, greyness (measured by CORINE) and air pollution within the urban environment are associated with respiratory/allergic and general symptoms in schoolchildren. No associations were found when considering the individual exposure to greyness measured using the RSG indicator.

Knowledge on multiple interdependences between quality of life (QoL) and behavioural problems in relation to asthma severity and control is undetermined. The aims of the study were: (i) to assess the relationship of QoL and behavioural problems with asthma severity and control (ii) to predict children’s “abnormal/borderline” status with variation in QoL. For these purposes a multicenter case-control study on 47 Severe Asthma (SA) and 94 Moderate Asthma (MA) children was performed. The MIMIC approach was applied to investigate the effect of SA and non-controlled asthma (NC) on QoL and behavioural disorders. Logistic regression was used to estimate probabilities of having an “abnormal/borderline” status with variation in QoL. The MIMIC model showed that the magnitude of the effect of SA and NC was larger on QoL (β = −0.37 and β = −0.30, respectively) than on behavioural problems (β = 0.27). With regards to the probability of having a borderline status, in MA a QoL of 1 returned a probability of 0.81, whereas in SA a QoL of 1 returned a probability of 0.89. In conclusion, SA children are highly affected by impaired QoL and behavioural problems. The MIMIC model allowed us to obtain a comprehensive assessment of QoL and behavioural problems with asthma severity and control.

Asthma is a common chronic multifactorial disease that affects >300 million people worldwide. Outdoor and indoor pollution exposure has been associated with respiratory health effects in adults and children. Smoking still represents a huge public health problem and millions of children suffer the detrimental effects of passive smoke exposure. This study was designed to review the current evidences on exposure to passive smoke as a risk factor for asthma onset in childhood. A review of the most recent studies on this topic was undertaken to provide evidence about the magnitude of the effect of passive smoking on the risk of incidence of asthma in children. The effects of passive smoking are different depending on individual and environmental factors. Environmental tobacco smoke (ETS) is one of the most important indoor air pollutants and can interact with other air pollutants in eliciting respiratory outcomes during childhood. The increased risk of respiratory outcomes in children exposed to prenatal and early postnatal passive smoke might be caused by an adverse effect on both the immune system and the structural and functional development of the lung; this may explain the subsequent increased risk of incident asthma. The magnitude of the exposure is quite difficult to precisely quantify because it is significantly influenced by the child's daily activities. Because exposure to ETS is a likely cause for asthma onset in childhood, there is a strong need to prevent infants and children from breathing air contaminated with tobacco smoke.

This narrative review aims to provide an overview of the main Machine Learning (ML) techniques and their applications in pharmacogenetics (such as antidepressant, anti-cancer and warfarin drugs) over the past 10 years. ML deals with the study, the design and the development of algorithms that give computers capability to learn without being explicitly programmed. ML is a sub-field of artificial intelligence, and to date, it has demonstrated satisfactory performance on a wide range of tasks in biomedicine. According to the final goal, ML can be defined as Supervised (SML) or as Unsupervised (UML). SML techniques are applied when prediction is the focus of the research. On the other hand, UML techniques are used when the outcome is not known, and the goal of the research is unveiling the underlying structure of the data. The increasing use of sophisticated ML algorithms will likely be instrumental in improving knowledge in pharmacogenetics.

Achieving and maintaining asthma control (AC) is the main goal of asthma management. Indoor and outdoor environmental factors may play an important role on AC. The aim of this longitudinal study was to evaluate the association between AC and exposure to greenness and other outdoor or indoor environmental factors in a cohort of asthmatic children. This study involved 179 asthmatic children (5–16 years). Parents were interviewed through a modified version of the SIDRIA questionnaire. AC was assessed at each visit. Exposure to greenness was measured using the normalized difference vegetation index (NDVI). A logistic regression model was applied for assessing risk factors for uncontrolled asthma (UA). Low NDVI exposure was a risk factor for UA (OR: 2.662, 95% CI (1.043–6.799)); children exposed to passive smoke during pregnancy had a higher risk of UA than those non-exposed to passive smoke during pregnancy (OR: 3.816, 95% CI (1.114–13.064)); and a unit increase in the crowding index was associated with an increased risk of UA (OR: 3.376, 95% CI (1.294–8.808)). In conclusion, the current study provided a comprehensive assessment of urban-related environmental exposures on asthma control in children, using multiple indicators of greenness and other outdoor or indoor environmental factors.