Exploring Urban Green Spaces' Effect against Traffic Exposure on Childhood Leukaemia Incidence

Background: Several environmental factors seem to be involved in childhood leukaemia incidence. Traffic exposure could increase the risk while urban green spaces (UGS) exposure could reduce it. However, there is no evidence how these two factors interact on this infant pathology. Objectives: to evaluate how residential proximity to UGS could be an environmental protective factor against traffic exposure on childhood leukaemia incidence. Methods: A population-based case control study was conducted across thirty Spanish regions during the period 2000-2018. It included 2526 incident cases and 15,156, individually matched by sex, year-of-birth, and place-of-residence. Using the geographical coordinates of the participants' home residences, a 500 m proxy for exposure to UGS was built. Annual average daily traffic (AADT) was estimated for all types of roads 100 m near the children's residence. Odds ratios (ORs) and 95% confidence intervals (95% CIs), UGS, traffic exposure, and their possible interactions were calculated for overall childhood leukaemia, and the acute lymphoblastic (ALL) and acute myeloblastic leukaemia (AML) subtypes, with adjustment for socio-demographic covariates. Results: We found an increment of childhood leukaemia incidence related to traffic exposure, for every 100 AADT increase the incidence raised 1.1% (95% CI: 0.58-1.61%). UGS exposure showed an incidence reduction for the highest exposure level, Q5 (OR = 0.63; 95% CI = 0.54-0.72). Regression models with both traffic exposure and UGS exposure variables showed similar results but the interaction was not significant. Conclusions: Despite their opposite effects on childhood leukaemia incidence individually, our results do not suggest a possible interaction between both exposures. This is the first study about the interaction of these two environmental factors; consequently, it is necessary to continue taking into account more individualized data and other possible environmental risk factors involved.This study was funded by Carlos III Institute of Health, Spain (grant numbers PI19CIII/00025, PI16CIII/00009 and EPY-505/19-PFIS), and Spain’s Health Research Fund (Fondo de Investigación Sanitaria-FIS grant number 12/01416). The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.S

Connectivity strength of the EEG functional network in schizophrenia and bipolar disorder.

peer reviewedThe application of graph theory measures in the study of functional brain networks allows for the description of their general properties and their alterations in mental illness. Among these measures, connectivity strength (CS) estimates the degree of functional connectivity of the whole network. Previous studies in schizophrenia patients have reported higher baseline CS values and modulation deficits in EEG spectral properties during cognitive activity. The specificity of these alterations and their relationships with pharmacological treatments remain unknown. Therefore, in the present study, we assessed functional CS on EEG-based brain networks in 79 schizophrenia and 29 bipolar patients in addition to 63 healthy controls. The subjects performed a P300 task during the EEG recordings from which the pre-stimulus and the task-related modulation CS values were computed in the global and theta bands. These values were compared between the groups and between the patients who had and had not received different treatments. The global band pre-stimulus CS was significantly higher in the schizophrenia group compared with the bipolar and control groups. Theta band CS modulation was decreased in schizophrenia and bipolar patients. Treatment with antipsychotics, lithium, benzodiazepines, and anticonvulsants did not significantly alter these CS values. The first-episode and chronic schizophrenia patients did not show significant differences in CS values. Higher global band pre-stimulus CS values were associated with worse general cognition in schizophrenia patients. These data support increased connectivity in the whole-brain network that is specific to schizophrenia and suggest a general hyper-synchronized basal state that might hamper cognition in this syndrome

Fasciola hepatica reinfection potentiates a mixed Th1/Th2/Th17/Treg response and correlates with the clinical phenotypes of anemia

Background: Fascioliasis is a severe zoonotic disease of worldwide extension caused by liver flukes. In human fascioliasis hyperendemic areas, reinfection and chronicity are the norm and anemia is the main sign. Herein, the profile of the Th1/Th2/Th17/Treg expression levels is analyzed after reinfection, correlating them with their corresponding hematological biomarkers of morbidity. Methodology/Principal findings: The experimental design reproduces the usual reinfection/chronicity conditions in human fascioliasis endemic areas and included Fasciola hepatica primo-infected Wistar rats (PI) and rats reinfected at 8 weeks (R8), and at 12 weeks (R12), and negative control rats. In a cross-sectional study, the expression of the genes associated with Th1 (Ifng, Il12a, Il12b, Nos2), Th2 (Il4, Arg1), Treg (Foxp3, Il10, Tgfb, Ebi3), and Th17 (Il17) in the spleen and thymus was analyzed. After 20 weeks of primary infection, PI did not present significant changes in the expression of those genes when compared to non-infected rats (NI), but an increase of Il4, Arg1 and Ifng mRNA in the spleen was observed in R12, suggesting the existence of an active mixed Th1/Th2 systemic immune response in reinfection. Foxp3, Il10, Tgfb and Ebi3 levels increased in the spleen in R12 when compared to NI and PI, indicating that the Treg gene expression levels are potentiated in chronic phase reinfection. Il17 gene expression levels in R12 in the spleen increased when compared to NI, PI and R8. Gene expression levels of Il10 in the thymus increased when compared to NI and PI in R12. Ifng expression levels in the thymus increased in all reinfected rats, but not in PI. The clinical phenotype was determined by the fluke burden, the rat body weight and the hemogram. Multivariate mathematical models were built to describe the Th1/Th2/Th17/Treg expression levels and the clinical phenotype. In reinfection, two phenotypic patterns were detected: i) one which includes only increased splenic Ifng expression levels but no Treg expression, correlating with severe anemia; ii) another which includes increased splenic Ifng and Treg expression levels, correlating with a less severe anemia. Conclusions/Significance: In animals with established F. hepatica infection a huge increase in the immune response occurs, being a mixed Th2/Treg associated gene expression together with an expression of Ifng. Interestingly, a Th17 associated gene expression is also observed. Reinfection in the chronic phase is able to activate a mixed immune response (Th1/Th2/Th17/Treg) against F. hepatica but T and B proliferation to mitogens is strongly suppressed in all infected rats vs control in the advanced chronic phase independently of reinfection The systemic immune response is different in each group, suggesting that suppression is mediated by different mechanisms in each case. Immune suppression could be due to the parasite in PI and R8 rats and the induction of suppressive cells such as Treg in R12. This is the first study to provide fundamental insight into the immune profile in fascioliasis reinfection and its relation with the clinical phenotypes of anemia.Red de InvestigacioÂn Cooperativa en Enfermedades Tropicales (RICET, Instituto de Salud Carlos III RD16/0027/0023); Proyectos de InvestigacioÂn en Salud (Instituto de Salud Carlos III, MINECO, Madrid, Spain PI16/00520); No. SAF2006-09278 and No. SAF2010-20805 of the Ministry of Economy and Competitiveness, Madrid, Spain. MF is supported by the following funding sources: Ministerio Ciencia y Tecnologia (SAF2005-02220, SAF2007-61716 and SAF2010-18733)Peer Reviewe

Identificacion of MRI-based psychosis subtypes: Replication and refinement.

peer reviewedThe identification of the cerebral substrates of psychoses such as schizophrenia and bipolar disorder is likely hampered by its biological heterogeneity, which may contribute to the low replication of results in the field. In this study we aimed to replicate in a completely new sample and supplement the results of a previous study with additional data on this topic. In the aforementioned study we identified a schizophrenia cluster characterized by high mean cortical curvature and low cortical thickness, subcortical hypometabolism and progressive negative symptoms. Here, we have used magnetic resonance images from 61 schizophrenia and 28 bipolar patients, as well as 51 healthy controls and a cluster analysis to search for possible subgroups primarily characterized by cerebral structural data. Diffusion tensor imaging (fractional anisotropy, FA), cognition, clinical data and electroencephalographic (EEG) modulation during a P300 task were used to validate the possible clusters. Two clusters of patients were identified. The first cluster (29 schizophrenia and 18 bipolar patients) showed decreased cortical thickness and area values, as well as lower subcortical volumes and higher cortical curvature in some regions, as compared to the second cluster. This first cluster also showed decreased FA in frontal lobe connections and worse cognitive performance. Although this cluster also showed longer illness duration, there were first episode patients in both clusters and treatment doses and types were not different between clusters. Both clusters of patients showed decreased EEG task-related modulation. In conclusion, our data give additional support to a distinct biologically based cluster encompassing schizophrenia and bipolar disorder patients with cortical and subcortical alterations, hampered cortical connectivity and lower cognitive performance

Search for schizophrenia and bipolar biotypes using functional network properties

Abstract Introduction Recent studies support the identification of valid subtypes within schizophrenia and bipolar disorder using cluster analysis. Our aim was to identify meaningful biotypes of psychosis based on network properties of the electroencephalogram. We hypothesized that these parameters would be more altered in a subgroup of patients also characterized by more severe deficits in other clinical, cognitive, and biological measurements. Methods A clustering analysis was performed using the electroencephalogram‐based network parameters derived from graph‐theory obtained during a P300 task of 137 schizophrenia (of them, 35 first episodes) and 46 bipolar patients. Both prestimulus and modulation of the electroencephalogram were included in the analysis. Demographic, clinical, cognitive, structural cerebral data, and the modulation of the spectral entropy of the electroencephalogram were compared between clusters. Data from 158 healthy controls were included for further comparisons. Results We identified two clusters of patients. One cluster presented higher prestimulus connectivity strength, clustering coefficient, path‐length, and lower small‐world index compared to controls. The modulation of clustering coefficient and path‐length parameters was smaller in the former cluster, which also showed an altered structural connectivity network and a widespread cortical thinning. The other cluster of patients did not show significant differences with controls in the functional network properties. No significant differences were found between patients´ clusters in first episodes and bipolar proportions, symptoms scores, cognitive performance, or spectral entropy modulation. Conclusion These data support the existence of a subgroup within psychosis with altered global properties of functional and structural connectivity

Effect of mistimed eating patterns on breast and prostate cancer risk (MCC-Spain Study)

Modern life involves mistimed sleeping and eating patterns that in experimental studies are associated with adverse health effects. We assessed whether timing of meals is associated with breast and prostate cancer risk taking into account lifestyle and chronotype, a characteristic correlating with preference for morning or evening activity. We conducted a population-based case-control study in Spain, 2008-2013. In this analysis we included 621 cases of prostate and 1,205 of breast cancer and 872 male and 1,321 female population controls who had never worked night shift. Subjects were interviewed on timing of meals, sleep and chronotype and completed a Food Frequency Questionaire. Adherence to the World Cancer Research Fund/American Institute of Cancer Research recommendations for cancer prevention was examined. Compared with subjects sleeping immediately after supper, those sleeping two or more hours after supper had a 20% reduction in cancer risk for breast and prostate cancer combined (adjusted Odds Ratio [OR] = 0.80, 95%CI 0.67-0.96) and in each cancer individually (prostate cancer OR = 0.74, 0.55-0.99; breast cancer OR = 0.84, 0.67-1.06). A similar protection was observed in subjects having supper before 9 pm compared with supper after 10 pm. The effect of longer supper-sleep interval was more pronounced among subjects adhering to cancer prevention recommendations (OR both cancers= 0.65, 0.44-0.97) and in morning types (OR both cancers = 0.66, 0.49-0.90). Adherence to diurnal eating patterns and specifically a long interval between last meal and sleep are associated with a lower cancer risk, stressing the importance of evaluating timing in studies on diet and cancer

Mid-trimester uterine artery Doppler for aspirin discontinuation in pregnancies at high risk for preterm pre-eclampsia : Post-hoc analysis of StopPRE trial

Altres ajuts: acords transformatius de la UABObjective: To assess whether aspirin treatment can be discontinued in pregnancies with normal uterine artery pulsatility index (≤90th percentile) at 24-28 weeks. Design: Post-hoc analysis of a clinical trial. Setting: Nine maternity hospitals in Spain. Population or Sample: Pregnant individuals at high risk of pre-eclampsia at 11-13 weeks and normal uterine artery Doppler at 24-28 weeks. Methods: All participants received treatment with daily aspirin at a dose of 150 mg. Participants were randomly assigned, in a 1:1 ratio, either to continue aspirin treatment until 36 weeks (control group) or to discontinue aspirin treatment (intervention group), between September 2019 and September 2021. In this secondary analysis, women with a UtAPI >90th percentile at 24-28 weeks were excluded. The non-inferiority margin was set at a difference of 1.9% for the incidence of preterm pre-eclampsia. Main outcome measures: Incidence of preterm pre-eclampsia. Results: Of the 1611 eligible women, 139 were excluded for UtAPI >90th percentile or if UtAPI was not available. Finally, 804 were included in this post-hoc analysis. Preterm pre-eclampsia occurred in three of 409 (0.7%) women in the aspirin discontinuation group and five of 395 (1.3%) women in the continuation group (−0.53; 95% CI −1.91 to 0.85), indicating non-inferiority of aspirin discontinuation. Conclusions: Discontinuing aspirin treatment at 24-28 weeks in women with a UtAPI ≤90th percentile was non-inferior to continuing aspirin treatment until 36 weeks for preventing preterm pre-eclampsia

Development of a severity of disease score and classification model by machine learning for hospitalized COVID-19 patients.

BackgroundEfficient and early triage of hospitalized Covid-19 patients to detect those with higher risk of severe disease is essential for appropriate case management.MethodsWe trained, validated, and externally tested a machine-learning model to early identify patients who will die or require mechanical ventilation during hospitalization from clinical and laboratory features obtained at admission. A development cohort with 918 Covid-19 patients was used for training and internal validation, and 352 patients from another hospital were used for external testing. Performance of the model was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity and specificity.ResultsA total of 363 of 918 (39.5%) and 128 of 352 (36.4%) Covid-19 patients from the development and external testing cohort, respectively, required mechanical ventilation or died during hospitalization. In the development cohort, the model obtained an AUC of 0.85 (95% confidence interval [CI], 0.82 to 0.87) for predicting severity of disease progression. Variables ranked according to their contribution to the model were the peripheral blood oxygen saturation (SpO2)/fraction of inspired oxygen (FiO2) ratio, age, estimated glomerular filtration rate, procalcitonin, C-reactive protein, updated Charlson comorbidity index and lymphocytes. In the external testing cohort, the model performed an AUC of 0.83 (95% CI, 0.81 to 0.85). This model is deployed in an open source calculator, in which Covid-19 patients at admission are individually stratified as being at high or non-high risk for severe disease progression.ConclusionsThis machine-learning model, applied at hospital admission, predicts risk of severe disease progression in Covid-19 patients