Clinical prediction model for prognosis in kidney transplant recipients (KIDMO): study protocol.

BACKGROUND: Many potential prognostic factors for predicting kidney transplantation outcomes have been identified. However, in Switzerland, no widely accepted prognostic model or risk score for transplantation outcomes is being routinely used in clinical practice yet. We aim to develop three prediction models for the prognosis of graft survival, quality of life, and graft function following transplantation in Switzerland. METHODS: The clinical kidney prediction models (KIDMO) are developed with data from a national multi-center cohort study (Swiss Transplant Cohort Study; STCS) and the Swiss Organ Allocation System (SOAS). The primary outcome is the kidney graft survival (with death of recipient as competing risk); the secondary outcomes are the quality of life (patient-reported health status) at 12 months and estimated glomerular filtration rate (eGFR) slope. Organ donor, transplantation, and recipient-related clinical information will be used as predictors at the time of organ allocation. We will use a Fine & Gray subdistribution model and linear mixed-effects models for the primary and the two secondary outcomes, respectively. Model optimism, calibration, discrimination, and heterogeneity between transplant centres will be assessed using bootstrapping, internal-external cross-validation, and methods from meta-analysis. DISCUSSION: Thorough evaluation of the existing risk scores for the kidney graft survival or patient-reported outcomes has been lacking in the Swiss transplant setting. In order to be useful in clinical practice, a prognostic score needs to be valid, reliable, clinically relevant, and preferably integrated into the decision-making process to improve long-term patient outcomes and support informed decisions for clinicians and their patients. The state-of-the-art methodology by taking into account competing risks and variable selection using expert knowledge is applied to data from a nationwide prospective multi-center cohort study. Ideally, healthcare providers together with patients can predetermine the risk they are willing to accept from a deceased-donor kidney, with graft survival, quality of life, and graft function estimates available for their consideration. STUDY REGISTRATION: Open Science Framework ID: z6mvj.

ARPEGES: A Bayesian Belief Network to Assess the Risk of Pesticide Contamination for the River Network of France.

Pesticides are priority concerns in aquatic risk assessment due to their widespread use, ongoing development of new molecules, and potential effects from short- and long-term exposures to aquatic life. Water quality assessments are also challenged by contrasting pesticide behaviors (e.g., mobility, half-life time, solubility) in different environmental contexts. Furthermore, monitoring networks are not well adapted to the pesticide media transfer dynamics and therefore fail at providing a reliable assessment of pesticides. We present here a Bayesian belief network that was developed in a cooperative process between researchers specializing in Bayesian modeling, soil sciences, agronomy, and diffuse pollutants to provide a tool for stakeholders to assess surface water contamination by pesticides. It integrates knowledge on dominant transfer pathways according to basin physical context and climate for different pesticides properties, such as half-life duration and affinity to organic C, to develop an assessment of risks of contamination for every watershed in France. The resulting model, ARPEGES (Analyse de Risque PEsticide pour la Gestion des Eaux de Surface; trans. Risk analysis of contamination by pesticides for surface water management), was developed in R. A user-friendly R interface was built to enable stakeholders to not only obtain ARPEGES' results, but also freely use it to test management scenarios. Though it is applicable to any chemical, its results are illustrated for S-Metolachlor, a pesticide that was widely used on cereals crops worldwide. In addition to providing contamination potential, ARPEGES also provides a way to diagnose its main explaining factors, enabling stakeholders to focus efforts in the most potentially affected basins, but also on the most probable cause of contamination. In this context, the Bayesian belief network allowed us to use information at different scales (i.e., regional contexts for climate, pedology at the basin scale, pesticide use at the municipality scale) to provide an expert assessment of the processes driving pesticide contamination of streams and the associated uncertainties. Integr Environ Assess Manag 2021;17:188-201. © 2020 SETAC.

Circadian organization of the rodent retina involves strongly coupled, layer-specific oscillators.

Rhythmic physiology is central to retinal function and survival and adapts vision to daily light intensity changes. Mammalian retina rhythmically releases melatonin when cultured under constant conditions, and the occurrence of clock gene [e.g., Period (Per)] expression has been shown for most cellular layers. However, contribution of the distinct layers to genesis of circadian rhythms within the retina is still debated. To characterize their endogenous oscillatory capacity and their communication at the whole-tissue level, we used a vibratome-based method to isolate individual or paired retina cellular layers from the mPer2(Luc) mouse and Per1-luciferase (Per1-Luc) rat, and real-time recorded bioluminescence. We report that each layer of the mouse retina harbors a self-sustained oscillator whose period is significantly longer (∼ 26 hours) than in whole-retina explants (∼ 22.9 hours), indicating that the period is correlated with the degree of coupling. Accordingly, the maximal period (∼ 29 hours) is reached upon complete enzymatic dissociation of the retina. By using pharmacological approaches, we demonstrate that connection between retina oscillators involves gap junctions but only minor contribution from the main retina neurochemicals. Taken together with results from Per1-Luc rats, these data show that mammalian retina consists of a network of layer-specific oscillators whose period is determined by their connectivity.