Exposure to ambient air pollution and the incidence of lung cancer and breast cancer in the Ontario Population Health and Environment Cohort

Lung and female breast cancers are highly prevalent worldwide. Although the association between exposure to ambient fine particulate matter (PM_2.5) and lung cancer has been recognized, there is less evidence for associations with other common air pollutants such as nitrogen dioxide (NO₂) and ozone (O₃). Even less is known about potential associations between these pollutants and breast cancer. We conducted a population-based cohort study to investigate the associations of chronic exposure to PM_2.5, NO₂, O₃ and redox-weighted average of NO₂ and O₃ (O_x) with incident lung and breast cancer, using the Ontario Population Health and Environment Cohort (ONPHEC), which includes all long-term residents aged 35–85 years who lived in Ontario, Canada, 2001–2015. Incident lung and breast cancers were ascertained using the Ontario Cancer Registry. Annual estimates of exposures were assigned to the residential postal codes of subjects for each year during follow-up. We used Cox proportional-hazards models adjusting for personal- and neighborhood-level covariates. Our cohorts for lung and breast cancer analyses included ~4.9 million individuals and ~2.5 million women, respectively. During follow-up, 100,146 incident cases of lung cancer and 91,146 incident cases of breast cancer were diagnosed. The fully adjusted analyses showed positive associations of lung cancer incidence with PM_2.5 (hazard ratio [HR] = 1.02 [95% CI: 1.01–1.05] per 5.3 μg/m³) and NO₂ (HR = 1.05 [95% CI: 1.03–1.07] per 14 ppb). No associations with lung cancer were observed for O₃ or O_x. Relationships between PM_2.5 and NO₂ with lung cancer exhibited a sublinear shape. We did not find compelling evidence linking air pollution to breast cancer.     

Does Oil Rich in Alpha-Linolenic Fatty Acid Cause the Same Immune Modulation as Fish Oil in Walker 256 Tumor-Bearing Rats?

Objective: Polyunsaturated fatty acids n-3 (PUFA n-3) have shown effects in reducing tumor growth, in particular eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) abundantly present in fish oil (FO). When these fatty acids are provided in the diet, they alter the functions of the cells, particularly in tumor and immune cells. However, the effects of α-linolenic fatty acid (ALA), which is the precursor of EPA and DHA, are controversial. Thus, our objective was to test the effect of this parental fatty acid. Methods: Non-tumor-bearing and tumor-bearing Wistar rats (70 days) were supplemented with 1 g/kg body weight of FO or Oro Inca® (OI) oil (rich in ALA). Immune cells function, proliferation, cytokine production, and subpopulation profile were evaluated. Results: We have shown that innate immune cells enhanced phagocytosis capacity, and increased processing and elimination of antigens. Moreover, there was a decrease in production of pro-inflammatory cytokines (tumor necrosis factor-alpha (TNF-α) and interleukin 6 (IL-6)) by macrophages. Lymphocytes showed decreased proliferation capacity, increased cluster of differentiation 8 (CD8⁺) subpopulation, and increased TNF-α production. Conclusions: Oil rich in ALA caused similar immune modulation in cancer when compared with FO.     

Collecting and evaluating convalescent plasma for COVID-19 treatment: why and how?

Plasma provided by COVID-19 convalescent patients may provide therapeutic relief as the number of COVID-19 cases escalates steeply worldwide. Prior findings in various viral respiratory diseases including SARS-CoV-related pneumonia suggest that convalescent plasma can reduce mortality, although formal proof of efficacy is still lacking. By reducing viral spread early on, such an approach may possibly downplay subsequent immunopathology. Identifying, collecting, qualifying and preparing plasma from convalescent patients with adequate SARS-CoV-2-neutralizing Ab titres in an acute crisis setting may be challenging, although well within the remit of most blood establishments. Careful clinical evaluation should allow to quickly establish whether such passive immunotherapy, administered at early phases of the disease in patients at high risk of deleterious evolution, may reduce the frequency of patient deterioration, and thereby COVID-19 mortality.     

Automated classification of depression from structural brain measures across two independent community‐based cohorts

Major depressive disorder (MDD) has been the subject of many neuroimaging case–control classification studies. Although some studies report accuracies ≥80%, most have investigated relatively small samples of clinically‐ascertained, currently symptomatic cases, and did not attempt replication in larger samples. We here first aimed to replicate previously reported classification accuracies in a small, well‐phenotyped community‐based group of current MDD cases with clinical interview‐based diagnoses (from STratifying Resilience and Depression Longitudinally cohort, ‘STRADL’). We performed a set of exploratory predictive classification analyses with measures related to brain morphometry and white matter integrity. We applied three classifier types—SVM, penalised logistic regression or decision tree—either with or without optimisation, and with or without feature selection. We then determined whether similar accuracies could be replicated in a larger independent population‐based sample with self‐reported current depression (UK Biobank cohort). Additional analyses extended to lifetime MDD diagnoses—remitted MDD in STRADL, and lifetime‐experienced MDD in UK Biobank. The highest cross‐validation accuracy (75%) was achieved in the initial current MDD sample with a decision tree classifier and cortical surface area features. The most frequently selected decision tree split variables included surface areas of bilateral caudal anterior cingulate, left lingual gyrus, left superior frontal, right precentral and paracentral regions. High accuracy was not achieved in the larger samples with self‐reported current depression (53.73%), with remitted MDD (57.48%), or with lifetime‐experienced MDD (52.68–60.29%). Our results indicate that high predictive classification accuracies may not immediately translate to larger samples with broader criteria for depression, and may not be robust across different classification approaches.