A Bayesian network approach to modelling rip-current drownings and shore-break wave injuries

A Bayesian network (BN) approach is used to model and predict shore-break-related injuries and rip-current drowning incidents based on detailed environmental conditions (wave, tide, weather, beach morphology) on the high-energy Gironde coast, southwest France. Six years (2011–2017) of boreal summer (15 June–15 September) surf zone injuries (SZIs) were analysed, comprising 442 (fatal and non-fatal) drownings caused by rip currents and 715 injuries caused by shore-break waves. Environmental conditions at the time of the SZIs were used to train two separate Bayesian networks (BNs), one for rip-current drownings and the other one for shore-break wave injuries. Each BN included two so-called “hidden” exposure and hazard variables, which are not observed yet interact with several of the observed (environmental) variables, which in turn limit the number of BN edges. Both BNs were tested for varying complexity using K-fold cross-validation based on multiple performance metrics. Results show a poor to fair predictive ability of the models according to the different metrics. Shore-break-related injuries appear more predictable than rip-current drowning incidents using the selected predictors within a BN, as the shore-break BN systematically performed better than the rip-current BN. Sensitivity and scenario analyses were performed to address the influence of environmental data variables and their interactions on exposure, hazard and resulting life risk. Most of our findings are in line with earlier SZI and physical hazard-based work; that is, more SZIs are observed for warm sunny days with light winds; long-period waves, with specifically more shore-break-related injuries at high tide and for steep beach profiles; and more rip-current drownings near low tide with near-shore-normal wave incidence and strongly alongshore non-uniform surf zone morphology. The BNs also provided fresh insight, showing that rip-current drowning risk is approximately equally distributed between exposure (variance reduction Vr=14.4 %) and hazard (Vr=17.4 %), while exposure of water user to shore-break waves is much more important (Vr=23.5 %) than the hazard (Vr=10.9 %). Large surf is found to decrease beachgoer exposure to shore-break hazard, while this is not observed for rip currents. Rapid change in tide elevation during days with large tidal range was also found to result in more drowning incidents. We advocate that such BNs, providing a better understanding of hazard, exposure and life risk, can be developed to improve public safety awareness campaigns, in parallel with the development of more skilful risk predictors to anticipate high-life-risk days.Marier les objectifs de défense côtière avec ceux de la protection du milieu naturel grâce aux dunes sableuse

Trends in reasons for emergency calls during the COVID-19 crisis in the department of Gironde, France using artificial neural network for natural language classification

Abstract Objectives During periods such as the COVID-19 crisis, there is a need for responsive public health surveillance indicators in order to monitor both the epidemic growth and potential public health consequences of preventative measures such as lockdown. We assessed whether the automatic classification of the content of calls to emergency medical communication centers could provide relevant and responsive indicators. Methods We retrieved all 796,209 free-text call reports from the emergency medical communication center of the Gironde department, France, between 2018 and 2020. We trained a natural language processing neural network model with a mixed unsupervised/supervised method to classify all reasons for calls in 2020. Validation and parameter adjustment were performed using a sample of 39,907 manually-coded free-text reports. Results The number of daily calls for flu-like symptoms began to increase from February 21, 2020 and reached an unprecedented level by February 28, 2020 and peaked on March 14, 2020, 3 days before lockdown. It was strongly correlated with daily emergency room admissions, with a delay of 14 days. Calls for chest pain and stress and anxiety, peaked 12 days later. Calls for malaises with loss of consciousness, non-voluntary injuries and alcohol intoxications sharply decreased, starting one month before lockdown. No noticeable trends in relation to lockdown was found for other groups of reasons including gastroenteritis and abdominal pain, stroke, suicide and self-harm, pregnancy and delivery problems. Discussion The first wave of the COVID-19 crisis came along with increased levels of stress and anxiety but no increase in alcohol intoxication and violence. As expected, call related to road traffic crashes sharply decreased. The sharp decrease in the number of calls for malaise was more surprising. Conclusion The content of calls to emergency medical communication centers is an efficient epidemiological surveillance data source that provides insights into the societal upheavals induced by a health crisis. The use of an automatic classification system using artificial intelligence makes it possible to free itself from the context that could influence a human coder, especially in a crisis situation. The COVID-19 crisis and/or lockdown induced deep modifications in the population health profile.Surveillance épidémiologique de la période pandémique covid-19 par classification automatique en temps réel des notes cliniques des centres d'appels d'urgence du 15 à l'aide de réseaux de neurones artificiels de type Transformer

Predicting drowning from sea and weather forecasts: development and validation of a model on surf beaches of southwestern France

OBJECTIVE: To predict the coast-wide risk of drowning along the surf beaches of Gironde, southwestern France. METHODS: Data on rescues and drownings were collected from the Medical Emergency Center of Gironde (SAMU 33). Seasonality, holidays, weekends, weather and metocean conditions were considered potentially predictive. Logistic regression models were fitted with data from 2011 to 2013 and used to predict 2015-2017 events employing weather and ocean forecasts. RESULTS: Air temperature, wave parameters, seasonality and holidays were associated with drownings. Prospective validation was performed on 617 days, covering 232 events (rescues and drownings) reported on 104 different days. The area under the curve (AUC) of the daily risk prediction model (combined with 3-day forecasts) was 0.82 (95% CI 0.79 to 0.86). The AUC of the 3-hour step model was 0.85 (95% CI 0.81 to 0.88). CONCLUSIONS: Drowning events along the Gironde surf coast can be anticipated up to 3 days in advance. Preventative messages and rescue preparations could be increased as the forecast risk increased, especially during the off-peak season, when the number of available rescuers is low

A higher-order entity formed by the flexible assembly of RAP1 with TRF2

Essonne committee of the Ligue contre le cancer M18756 M22897 Foundation ARC pour la Recherche sur le Cancer SFI20121205503International audienceTelomere integrity is essential to maintain genome stability, and telomeric dysfunctions are associated with cancer and aging pathologies. In human, the shelterin complex binds TTAGGG DNA repeats and provides capping to chromosome ends. Within shel-terin, RAP1 is recruited through its interaction with TRF2, and TRF2 is required for telomere protection through a network of nucleic acid and protein interactions. RAP1 is one of the most conserved shelterin proteins although one unresolved question is how its interaction may influence TRF2 properties and regulate its capacity to bind multiple proteins. Through a combination of biochemical, biophysical and structural approaches, we unveiled a unique mode of assembly between RAP1 and TRF2. The complete interaction scheme between the full-length proteins involves a complex biphasic interaction of RAP1 that directly affects the binding properties of the assembly. These results reveal how a non-DNA binding protein can influence the properties of a DNA-binding partner by mutual conformational adjustments

Nat. Hazards Earth Syst. Sci.

International audienceThe two primary causes of surf zone injuries (SZIs) worldwide, including fatal drowning and severe spinal injuries, are rip currents (rips) and shore-break waves. SZIs also result from surfing and bodyboarding activity. In this paper we address the primary environmental controls on SZIs along the high-energy meso-macro-tidal surf beach coast of southwestern France. A total of 2523 SZIs recorded by lifeguards over 186 sample days during the summers of 2007, 2009 and 2015 were combined with measured and/or hindcast weather, wave, tide, and beach morphology data. All SZIs occurred disproportionately on warm sunny days with low wind, likely because of increased beachgoer numbers and hazard exposure. Relationships were strongest for shore-break- and rip-related SZIs and weakest for surfingrelated SZIs, the latter being also unaffected by tidal stage or range. Therefore, the analysis focused on bathers. More shore-break-related SZIs occur during shore-normal incident waves with average to below-average wave height (significant wave height, Hs = 0.75-1.5 m) and around higher water levels and large tide ranges when waves break on the steepest section of the beach. In contrast, more rip-related drownings occur near neap low tide, coinciding with maximised channel rip flow activity, under shore-normal incident waves with Hs > 1.25 m and mean wave periods longer than 5 s. Addi- tional drowning incidents occurred at spring high tide, presumably due to small-scale swash rips. The composite wave and tide parameters proposed by Scott et al. (2014) are key controlling factors determining SZI occurrence, although the risk ranges are not necessarily transferable to all sites. Summer beach and surf zone morphology is interannually highly variable, which is critical to SZI patterns. The upper beach slope can vary from 0.06 to 0.18 between summers, resulting in low and high shore-break-related SZIs, respectively. Summers with coast-wide highly (weakly) developed rip channels also result in widespread (scarce) rip-related drowning incidents. With life risk defined in terms of the number of people exposed to life threatening hazards at a beach, the ability of morphodynamic models to simulate primary beach morphology characteristics a few weeks or months in advance is therefore of paramount importance for predicting the primary surf zone life risks along this coast

Development and Validation of Deep Learning Transformer Models for Building a Comprehensive and Real-time Trauma Observatory

BACKGROUND In order to study the feasibility of setting up a national trauma observatory in France, OBJECTIVE we compared the performance of several automatic language processing methods on a multi-class classification task of unstructured clinical notes. METHODS A total of 69,110 free-text clinical notes related to visits to the emergency departments of the University Hospital of Bordeaux, France, between 2012 and 2019 were manually annotated. Among those clinical notes 22,481 were traumas. We trained 4 transformer models (deep learning models that encompass attention mechanism) and compared them with the TF-IDF (Term- Frequency - Inverse Document Frequency) associated with SVM (Support Vector Machine) method. RESULTS The transformer models consistently performed better than TF-IDF/SVM. Among the transformers, the GPTanam model pre-trained with a French corpus with an additional auto-supervised learning step on 306,368 unlabeled clinical notes showed the best performance with a micro F1-score of 0.969. CONCLUSIONS The transformers proved efficient multi-class classification task on narrative and medical data. Further steps for improvement should focus on abbreviations expansion and multiple outputs multi-class classification

Cough aerosol in healthy participants: fundamental knowledge to optimize droplet-spread infectious respiratory disease management

<p>Abstract</p> <p>Background</p> <p>The Influenza A H1N1 virus can be transmitted via direct, indirect, and airborne route to non-infected subjects when an infected patient coughs, which expels a number of different sized droplets to the surrounding environment as an aerosol. The objective of the current study was to characterize the human cough aerosol pattern with the aim of developing a standard human cough bioaerosol model for Influenza Pandemic control.</p> <p>Method</p> <p>45 healthy non-smokers participated in the open bench study by giving their best effort cough. A laser diffraction system was used to obtain accurate, time-dependent, quantitative measurements of the size and number of droplets expelled by the cough aerosol.</p> <p>Results</p> <p>Voluntary coughs generated droplets ranging from 0.1 - 900 microns in size. Droplets of less than one-micron size represent 97% of the total number of measured droplets contained in the cough aerosol. Age, sex, weight, height and corporal mass have no statistically significant effect on the aerosol composition in terms of size and number of droplets.</p> <p>Conclusions</p> <p>We have developed a standard human cough aerosol model. We have quantitatively characterized the pattern, size, and number of droplets present in the most important mode of person-to-person transmission of IRD: the cough bioaerosol. Small size droplets (< 1 μm) predominated the total number of droplets expelled when coughing. The cough aerosol is the single source of direct, indirect and/or airborne transmission of respiratory infections like the Influenza A H1N1 virus.</p> <p>Study design</p> <p>Open bench, Observational, Cough, Aerosol study</p

Vaccine breakthrough hypoxemic COVID-19 pneumonia in patients with auto-Abs neutralizing type I IFNs

Life-threatening `breakthrough' cases of critical COVID-19 are attributed to poor or waning antibody response to the SARS- CoV-2 vaccine in individuals already at risk. Pre-existing autoantibodies (auto-Abs) neutralizing type I IFNs underlie at least 15% of critical COVID-19 pneumonia cases in unvaccinated individuals; however, their contribution to hypoxemic breakthrough cases in vaccinated people remains unknown. Here, we studied a cohort of 48 individuals ( age 20-86 years) who received 2 doses of an mRNA vaccine and developed a breakthrough infection with hypoxemic COVID-19 pneumonia 2 weeks to 4 months later. Antibody levels to the vaccine, neutralization of the virus, and auto- Abs to type I IFNs were measured in the plasma. Forty-two individuals had no known deficiency of B cell immunity and a normal antibody response to the vaccine. Among them, ten (24%) had auto-Abs neutralizing type I IFNs (aged 43-86 years). Eight of these ten patients had auto-Abs neutralizing both IFN-a2 and IFN-., while two neutralized IFN-omega only. No patient neutralized IFN-ss. Seven neutralized 10 ng/mL of type I IFNs, and three 100 pg/mL only. Seven patients neutralized SARS-CoV-2 D614G and the Delta variant (B.1.617.2) efficiently, while one patient neutralized Delta slightly less efficiently. Two of the three patients neutralizing only 100 pg/mL of type I IFNs neutralized both D61G and Delta less efficiently. Despite two mRNA vaccine inoculations and the presence of circulating antibodies capable of neutralizing SARS-CoV-2, auto-Abs neutralizing type I IFNs may underlie a significant proportion of hypoxemic COVID-19 pneumonia cases, highlighting the importance of this particularly vulnerable population

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

Relatório de estágio em farmácia comunitária

Relatório de estágio realizado no âmbito do Mestrado Integrado em Ciências Farmacêuticas, apresentado à Faculdade de Farmácia da Universidade de Coimbr