Publications by authors named "M Rainio"

Non-ferrous smelters emit toxic metals into the environment, posing a threat to wildlife health. Despite the acknowledged role of microbes in host health, the impact of such emissions on host-associated microbiota, especially in wild birds, remains largely unexplored. This study investigates the associations of metal pollution, fitness, and nest microbiota (serving as a proxy for early-life microbial environment) which may influence the nestling health and development.

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Sensitivity of bird species to environmental metal pollution varies but there is currently no general framework to predict species-specific sensitivity. Such information would be valuable from a conservation point-of-view. Calcium (Ca) has antagonistic effects on metal toxicity and studies with some common model species show that low dietary and circulating calcium (Ca) levels indicate higher sensitivity to harmful effects of toxic metals.

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Glyphosate-based herbicides (GBHs) are the most frequently used herbicides worldwide. The use of GBHs is intended to tackle weeds, but GBHs have been shown to affect the life-history traits and antioxidant defense system of invertebrates found in agroecosystems. Thus far, the effects of GBHs on detoxification pathways among invertebrates have not been sufficiently investigated.

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Endoscopic retrograde cholangiopancreatography (ERCP) procedures may result in remarkable radiation doses to patients and staff. The aim of this prospective study was to determine occupational exposures in gastrointestinal endoscopy procedures, with a special emphasis on eye lens dose in ERCP. Altogether 604 fluoroscopy-guided procedures, of which 560 were ERCPs belonging to four American Society for Gastrointestinal Endoscopy procedural complexity levels, were performed using two fluoroscopy systems.

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Background: Predicting Post-Endoscopic Retrograde Cholangiopancreatography (ERCP) pancreatitis (PEP) risk can be determinant in reducing its incidence and managing patients appropriately, however studies conducted thus far have identified single-risk factors with standard statistical approaches and limited accuracy.

Aim: To build and evaluate performances of machine learning (ML) models to predict PEP probability and identify relevant features.

Methods: A proof-of-concept study was performed on ML application on an international, multicenter, prospective cohort of ERCP patients.

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