Residential exposure to magnetic fields from transformer stations and risk of childhood leukemia.

BACKGROUND: Several studies have documented an increased risk of leukemia among children exposed to magnetic fields from high-voltage power lines, with some evidence of dose-response relation. However, findings in some studies have been inconsistent, and data on the effects of different sources of exposure are lacking. In this study, we evaluated the relation of childhood leukemia risk to exposure to magnetic fields from transformer stations. METHODS: We conducted a population-based case-control study in a pediatric population of two Northern Italian provinces of Modena and Reggio Emilia. We included 182 registry-identified childhood leukemia cases diagnosed during 1998-2019 and 726 population controls matched on sex, year of birth, and province of residence. We assessed exposure by calculating distance from childhood residence to the nearest transformer station within a geographical information system, computing disease odds ratios (ORs) and 95% confidence intervals (CIs) using conditional logistic regression, adjusting for potential confounders. We evaluated exposure using two buffers (15 m and 25 m radius) and assessed two case groups: leukemia (all subtypes) and acute lymphoblastic leukemia (ALL). RESULTS: Residing within 15 m of a transformer station (vs. ≥15 m) was not appreciably associated with risk of leukemia (all subtypes) or ALL. We found similar results using a less stringent exposure buffer (25 m). Among children aged ≥5 years, the adjusted ORs were 1.3 (95% CI 0.1-12.8) for leukemia and 1.3 (95% CI 0.1-12.4) for ALL using the 15 m buffer, while they were 1.7 (95% CI 0.4-7.0) for leukemia and 0.6 (95% CI 0.1-4.8) for ALL using the 25 m buffer. CONCLUSIONS: While we found no overall association between residential proximity to transformer stations and childhood leukemia, there was some evidence for elevated risk of childhood leukemia among children aged ≥5 years. Precision was limited by the low numbers of exposed children.

The influence of meteorological factors on COVID-19 spread in Italy during the first and second wave.

The relation between meteorological factors and COVID-19 spread remains uncertain, particularly with regard to the role of temperature, relative humidity and solar ultraviolet (UV) radiation. To assess this relation, we investigated disease spread within Italy during 2020. The pandemic had a large and early impact in Italy, and during 2020 the effects of vaccination and viral variants had not yet complicated the dynamics. We used non-linear, spline-based Poisson regression of modeled temperature, UV and relative humidity, adjusting for mobility patterns and additional confounders, to estimate daily rates of COVID-19 new cases, hospital and intensive care unit admissions, and deaths during the two waves of the pandemic in Italy during 2020. We found little association between relative humidity and COVID-19 endpoints in both waves, whereas UV radiation above 40 kJ/m2 showed a weak inverse association with hospital and ICU admissions in the first wave, and a stronger relation with all COVID-19 endpoints in the second wave. Temperature above 283 K (10 °C/50 °F) showed a strong non-linear negative relation with COVID-19 endpoints, with inconsistent relations below this cutpoint in the two waves. Given the biological plausibility of a relation between temperature and COVID-19, these data add support to the proposition that temperature above 283 K, and possibly high levels of solar UV radiation, reduced COVID-19 spread.

Residential exposure to magnetic fields from high-voltage power lines and risk of childhood leukemia.

BACKGROUND: Several studies have suggested an excess risk of leukemia among children living close to high-voltage power lines and exposed to magnetic fields. However, not all studies have yielded consistent results, and many studies may have been susceptible to confounding and exposure misclassification. METHODS: We conducted a case-control study to investigate the risk of leukemia associated with magnetic field exposure from high-voltage power lines. Eligible participants were children aged 0-15 years residing in the Northern Italian provinces of Modena and Reggio Emilia. We included all 182 registry-identified childhood leukemia cases diagnosed in 1998-2019, and 726 age-, sex- and province-matched population controls. We assessed exposure by calculating distance from house to nearest power line and magnetic field intensity modelling at the subjects' residence. We used conditional logistic regression models to estimate odds ratios (ORs) and 95% confidence intervals (CIs), with adjustment for potential confounders (distance from nearest petrol station and fuel supply within the 1000 m-buffer, traffic-related particulate and benzene concentrations, presence of indoor transformers, percentage of urban area and arable crops). RESULTS: In multivariable analyses, the OR comparing children living <100 m from high-voltage power-lines with children living ≥400 m from power-lines was 2.0 (95% CI 0.8-5.0). Results did not differ substantially by age at disease diagnosis, disease subtype, or when exposure was based on modeled magnetic field intensity, though estimates were imprecise. Spline regression analysis showed an excess risk for both overall leukemia and acute lymphoblastic leukemia among children with residential distances <100 m from power lines, with a monotonic inverse association below this cutpoint. CONCLUSIONS: In this Italian population, close proximity to high-voltage power lines was associated with an excess risk of childhood leukemia.

Neuroanatomical Correlates of Cognitive Tests in Young-onset MCI.

BACKGROUND: Mild Cognitive Impairment (MCI) is a heterogeneous condition characterised by cognitive changes that do not affect everyday functioning and may represent a predementia phase. Research on the neuroanatomical correlates of cognitive tests used to diagnose MCI is heterogeneous and has mainly focused on elderly populations of patients with MCI, usually well above the age of 65. However, the effect of ageing on brain structure is known to be substantial and to affect brain-behaviour associations in older people. We explored the brain correlates of different cognitive tests in a group of young-onset MCI (i.e., with symptoms onset before the age of 65) to minimise the effect of ageing on brain-behaviour associations. METHODS: Patients with a clinical diagnosis of young-onset MCI underwent extensive cognitive assessment and multimodal Magnetic Resonance Imaging (MRI) including high-resolution T1-weighted and Diffusion Tensor Imaging (DTI) sequences. Their scores on cognitive tests were related to measures of grey matter (GM) density and white matter (WM) integrity using, respectively, Voxel Based Morphometry (VBM) and Tract-Based Spatial Statistics (TBSS). RESULTS: 104 young-onset MCI were recruited. VBM and TBSS whole-brain correlational analyses showed that between-subject variability in cognitive performance was significantly associated with regional variability in GM density and WM integrity. While associations between cognitive scores and focal GM density in our young-onset MCI group reflected the well-known lateralization of verbal and visuo-spatial abilities on the left and right hemispheres respectively, the associations between cognitive scores and WM microstructural integrity were widespread and diffusely involved most of the WM tracts in both hemispheres. CONCLUSIONS: We investigated the structural neuroanatomical correlates of cognitive tests in young-onset MCI in order to minimise the effect of ageing on brain-behaviour associations.

The impact of transfer learning on 3D deep learning convolutional neural network segmentation of the hippocampus in mild cognitive impairment and Alzheimer disease subjects.

Research on segmentation of the hippocampus in magnetic resonance images through deep learning convolutional neural networks (CNNs) shows promising results, suggesting that these methods can identify small structural abnormalities of the hippocampus, which are among the earliest and most frequent brain changes associated with Alzheimer disease (AD). However, CNNs typically achieve the highest accuracy on datasets acquired from the same domain as the training dataset. Transfer learning allows domain adaptation through further training on a limited dataset. In this study, we applied transfer learning on a network called spatial warping network segmentation (SWANS), developed and trained in a previous study. We used MR images of patients with clinical diagnoses of mild cognitive impairment (MCI) and AD, segmented by two different raters. By using transfer learning techniques, we developed four new models, using different training methods. Testing was performed using 26% of the original dataset, which was excluded from training as a hold-out test set. In addition, 10% of the overall training dataset was used as a hold-out validation set. Results showed that all the new models achieved better hippocampal segmentation quality than the baseline SWANS model (ps < .001), with high similarity to the manual segmentations (mean dice [best model] = 0.878 ± 0.003). The best model was chosen based on visual assessment and volume percentage error (VPE). The increased precision in estimating hippocampal volumes allows the detection of small hippocampal abnormalities already present in the MCI phase (SD = [3.9 ± 0.6]%), which may be crucial for early diagnosis.

The association between air pollutants and hippocampal volume from magnetic resonance imaging: A systematic review and meta-analysis.

Growing epidemiological evidence suggests that air pollution may increase the risk of cognitive decline and neurodegenerative disease. A hallmark of neurodegeneration and an important diagnostic biomarker is volume reduction of a key brain structure, the hippocampus. We aimed to investigate the possibility that outdoor air nitrogen dioxide (NO2) and particulate matter with diameter ≤2.5 µm (PM2.5) and ≤10 µm (PM10) adversely affect hippocampal volume, through a meta-analysis. We considered studies that assessed the relation between outdoor air pollution and hippocampal volume by structural magnetic resonance imaging in adults and children, searching in Pubmed and Scopus databases from inception through July 13, 2021. For inclusion, studies had to report the correlation coefficient along with its standard error or 95% confidence interval (CI) between air pollutant exposure and hippocampal volume, to use standard space for neuroimages, and to consider at least age, sex and intracranial volume as covariates or effect modifiers. We meta-analyzed the data with a random-effects model, considering separately adult and child populations. We retrieved four eligible studies in adults and two in children. In adults, the pooled summary ß regression coefficients of the association of PM2.5, PM10 and NO2 with hippocampal volume showed respectively a stronger association (summary ß -7.59, 95% CI -14.08 to -1.11), a weaker association (summary ß -2.02, 95% CI -4.50 to 0.47), and no association (summary ß -0.44, 95% CI -1.27 to 0.40). The two studies available for children, both carried out in preadolescents, did not show an association between PM2.5 and hippocampal volume. The inverse association between PM2.5 and hippocampal volume in adults appeared to be stronger at higher mean PM2.5 levels. Our results suggest that outdoor PM2.5 and less strongly PM10 could adversely affect hippocampal volume in adults, a phenomenon that may explain why air pollution has been related to memory loss, cognitive decline, and dementia.

DCE-MRI Radiomic analysis in triple negative ductal invasive breast cancer. Comparison between BRCA and not BRCA mutated patients: Preliminary results.

OBJECTIVE: The research aimed to determine whether and which radiomic features from breast dynamic contrast enhanced (DCE) MRI could predict the presence of BRCA1 mutation in patients with triple-negative breast cancer (TNBC). MATERIAL AND METHODS: This retrospective study included consecutive patients histologically diagnosed with TNBC who underwent breast DCE-MRI in 2010-2021. Baseline DCE-MRIs were retrospectively reviewed; percentage maps of wash-in and wash-out were computed and breast lesions were manually segmented, drawing a 5 mm-Region of Interest (ROI) inside the tumor and another 5 mm-ROI inside the contralateral healthy gland. Features for each map and each ROI were extracted with Pyradiomics-3D Slicer and considered first separately (tumor and contralateral gland) and then together. In each analysis the more important features for BRCA1 status classification were selected with Maximum Relevance Minimum Redundancy algorithm and used to fit four classifiers. RESULTS: The population included 67 patients and 86 lesions (21 in BRCA1-mutated, 65 in non BRCA-carriers). The best classifiers for BRCA mutation were Support Vector Classifier and Logistic Regression in models fitted with both gland and tumor features, reaching an Area Under ROC Curve (AUC) of 0.80 (SD 0.21) and of 0.79 (SD 0.20), respectively. Three features were higher in BRCA1-mutated compared to non BRCA-mutated: Total Energy and Correlation from gray level cooccurrence matrix, both measured in contralateral gland in wash-out maps, and Root Mean Squared, selected from the wash-out map of the tumor. CONCLUSIONS: This study showed the feasibility of a radiomic study with breast DCE-MRI and the potential of radiomics in predicting BRCA1 mutational status.

Zinc and selenium supplementation in COVID-19 prevention and treatment: a systematic review of the experimental studies.

BACKGROUND AND AIM: The COVID-19 pandemic has severely affected the world's population in the last two years. Along with non-pharmacological public health interventions, major efforts have also been made to identify effective drugs or active substances for COVID-19 prevention and treatment. These include, among many others, the trace elements zinc and selenium, based on laboratory studies and some observational human studies. However, both of these study designs are not adequate to identify and approve treatments in human medicine, and experimental studies in the form of randomized controlled trials are needed to demonstrate the effectiveness and the safety of any interventions. METHODS: We undertook a systematic review in which we searched for published and unpublished clinical trials using zinc or selenium supplementation to treat or prevent COVID-19 in the Pubmed, Scopus and ClinicalTrials databases up to 10 January 2022. RESULTS: Amongst the published studies, we did not find any trial with selenium, whereas we retrieved four eligible randomized clinical trials using zinc supplementation, only one of which was double-blind. One of these trials looked at the effect of the intervention on the rate of new SARS-CoV-2 infections, and three at the COVID-19 clinical outcome in already infected individuals. The study populations of the four trials were very heterogeneous, ranging from uninfected individuals to those hospitalized for COVID-19. Only two studies investigated zinc alone in the intervention arm with no differences in the endpoints. The other two studies examined zinc in association with one or more drugs and supplements in the intervention arm, therefore making it impossible to disentangle any specific effects of the element. In addition, we identified 22 unpublished ongoing clinical trials, 19 on zinc, one on selenium and two on both elements. CONCLUSION: No trials investigated the effect of selenium supplementation on COVID-19, while the very few studies on the effects of zinc supplementation did not confirm efficacy. Therefore, preventive or therapeutic interventions against COVID-19 based on zinc or selenium supplementation are currently unjustified, although when the results of the on-going studies are published, this may change our conclusion.

Substantial impact of mobility restrictions on reducing COVID-19 incidence in Italy in 2020.

BACKGROUND: Italy was the first country after China to be severely affected by the COVID-19 pandemic, in early 2020. The country responded swiftly to the outbreak with a nationwide two-step lockdown, the first one light and the second one tight. By analyzing 2020 national mobile phone movements, we assessed how lockdown compliance influenced its efficacy. METHODS: We measured individual mobility during the first epidemic wave with mobile phone movements tracked through carrier networks, and related this mobility to daily new SARS-CoV-2 infections, hospital admissions, intensive care admissions and deaths attributed to COVID-19, taking into account reason for travel (work-related or not) and the means of transport. RESULTS: The tight lockdown resulted in an 82% reduction in mobility for the entire country and was effective in swiftly curbing the outbreak as indicated by a shorter time-to-peak of all health outcomes, particularly for provinces with the highest mobility reductions and the most intense COVID-19 spread. Reduction of work-related mobility was accompanied by a nearly linear benefit in outbreak containment; work-unrelated movements had a similar effect only for restrictions exceeding 50%. Reduction in mobility by car and by airplane was nearly linearly associated with a decrease in most COVID-19 health outcomes, while for train travel reductions exceeding 55% had no additional beneficial effects. The absence of viral variants and vaccine availability during the study period eliminated confounding from these two sources. CONCLUSIONS: Adherence to the COVID-19 tight lockdown during the first wave in Italy was high and effective in curtailing the outbreak. Any work-related mobility reduction was effective, but only high reductions in work-unrelated mobility restrictions were effective. For train travel, there was a threshold above which no further benefit occurred. These findings could be particular to the spread of SARS-CoV-2, but might also apply to other communicable infections with comparable transmission dynamics.

Why can spontaneous intracranial hypotension cause behavioral changes? A case report and multimodality neuroimaging comparison with frontotemporal dementia.

Frontotemporal Brain Sagging Syndrome (FBSS) is a rare condition characterized by the presence of spontaneous intracranial hypotension associated with behavioural disturbances mimicking the behavioural variant of Frontotemporal dementia (bvFTD). It has been suggested that behavioural symptoms are caused by damage to the connectivity of the frontal lobes due to the brain sagging. However, no studies have directly explored brain connectivity in patients with FBSS. Here, we report a new case of FBSS with persistent behavioural disturbances, whom we compared to 20 patients with bvFTD and to 13 cognitively healthy controls using Magnetic Resonance Imaging (MRI). We explored differences related to grey matter (GM) volume with voxel-based morphometry, functional connectivity with seed-based analysis, and white matter (WM) microstructural integrity with tract-based spatial statistics. We found that the FBSS patient, like the controls, had greater GM volume relative to the bvFTD patients. Moreover, the FBSS patient had greater functional connectivity from a left inferior frontal gyrus seed than both the bvFTD patients and healthy controls groups in dorsolateral frontal areas. Like the bvFTD group the FBSS patient had decreased WM integrity relative to the controls, especially in the posterior part of the corpus callosum, and the magnitude of these abnormalities correlated with measures of apathy across the FBSS and bvFTD patients. Our results suggest that behavioural changes associated with SIH are mainly due to altered WM connectivity.