Chromosomal segregation analysis and HOST-based sperm selection in a complex reciprocal translocation carrier

International audienceIntroduction: Complex chromosomal rearrangements (CCRs) involve two or more chromosomes and at least three breakpoints. Due to their complexity, they are associated with a high number of unbalanced gametes, whose fertilization is often incompatible with viable fetal development. Preimplantation genetic diagnosis (PGD) is usually offered to those patients and typically shows modest results considering the high number of unbalanced embryos. We previously showed that a sperm selection process using the hypo-osmotic swelling test (HOST) allows for an 83% reduction in the proportion of unbalanced spermatozoa (US) in male rearrangements carriers. This is the first report of the use of this procedure in a CCR carrier.Case description: We report on the case of a 36-year-old male t(4;7;14)(q12;p21;q11.2) carrier who presented to our center for infertility. Sperm fluorescent in situ hybridization showed an 88% proportion of unbalanced spermatozoa. After hypo-osmotic incubation and selection of spermatozoa with a specific flagellar conformation, the proportion of unbalanced spermatozoa dropped to 15%.Discussion: In the present case, we show that it is possible to select chromosomally balanced prior to in vitro fertilization in male CCR carriers. This technique has the potential of increasing the proportion of euploid embryos and therefore the chances of healthy pregnancy and birth

Effectiveness of an electronic clinical decision support system in improving the management of childhood illness in primary care in rural Nigeria: an observational study

Objectives To evaluate the impact of ALgorithm for the MANAgement of CHildhood illness (‘ALMANACH’), a digital clinical decision support system (CDSS) based on the Integrated Management of Childhood Illness, on health and quality of care outcomes for sick children attending primary healthcare (PHC) facilities.Design Observational study, comparing outcomes of children attending facilities implementing ALMANACH with control facilities not yet implementing ALMANACH.Setting PHC facilities in Adamawa State, North-Eastern Nigeria.Participants Children 2–59 months presenting with an acute illness. Children attending for routine care or nutrition visits (eg, immunisation, growth monitoring), physical trauma or mental health problems were excluded.Interventions The ALMANACH intervention package (CDSS implementation with training, mentorship and data feedback) was rolled out across Adamawa’s PHC facilities by the Adamawa State Primary Health Care Development Agency, in partnership with the International Committee of the Red Cross and the Swiss Tropical and Public Health Institute. Tablets were donated, but no additional support or incentives were provided. Intervention and control facilities received supportive supervision based on the national supervision protocol.Primary and secondary outcome measures The primary outcome was caregiver-reported recovery at day 7, collected over the phone. Secondary outcomes were antibiotic and antimalarial prescription, referral, and communication of diagnosis and follow-up advice, assessed at day 0 exit interview.Results We recruited 1929 children, of which 1021 (53%) attended ALMANACH facilities, between March and September 2020. Caregiver-reported recovery was significantly higher among children attending ALMANACH facilities (adjusted OR=2·63, 95% CI 1·60 to 4·32). We observed higher parenteral and lower oral antimicrobial prescription rates (adjusted OR=2·42 (1·00 to 5·85) and adjusted OR=0·40 (0·22 to 0·73), respectively) in ALMANACH facilities as well as markedly higher rates for referral, communication of diagnosis, and follow-up advice.Conclusion Implementation of digital CDSS with training, mentorship and feedback in primary care can improve quality of care and recovery of sick children in resource-constrained settings, likely mediated by better guideline adherence. These findings support the use of CDSS for health systems strengthening to progress towards universal health coverage

Environnement et sociétés rurales en mutation

Les interactions entre sociétés humaines et environnement constituent un défi majeur pour l’avenir de la planète. Les conférences internationales (Rio, Kyoto, Johannesburg, etc.) montrent toute l’ambiguïté et tous les enjeux économiques et politiques nationaux qui s’y expriment. Dans ce contexte hautement politique, comment créer des convergences qui répondent aux besoins des populations et à une gestion environnementale appropriée ? C’est bien là toute la difficulté du développement durable. L’une des réponses qu’apporte ce livre passe par la nécessité de renouveler en profondeur les problématiques scientifiques et par l’importance de développer des études au niveau local ; car c’est là où se trouvent confrontées les stratégies des sociétés et les réponses qu’elles apportent aux multiples contraintes auxquelles elles ont à faire face. Connaître et faire connaître, dans les processus de prise de décision, les capacités d’adaptation et d’innovation des sociétés locales, cerner de nouveaux modes de régulation pour l’usage des ressources naturelles, proposer des stratégies alternatives de développement durable : tels sont les enjeux fondamentaux des études développées dans ce livre, à partir d’exemples contrastés pris dans la zone bioclimatique méditerranéenne

A new hybrid record linkage process to make epidemiological databases interoperable: application to the GEMO and GENEPSO studies involving BRCA1 and BRCA2 mutation carriers

International audienceBackground: Linking independent sources of data describing the same individuals enable innovative epidemiological and health studies but require a robust record linkage approach. We describe a hybrid record linkage process to link databases from two independent ongoing French national studies, GEMO (Genetic Modifiers of BRCA1 and BRCA2), which focuses on the identification of genetic factors modifying cancer risk of BRCA1 and BRCA2 mutation carriers, and GENEPSO (prospective cohort of BRCAx mutation carriers), which focuses on environmental and lifestyle risk factors.Methods: To identify as many as possible of the individuals participating in the two studies but not registered by a shared identifier, we combined probabilistic record linkage (PRL) and supervised machine learning (ML). This approach (named "PRL + ML") combined together the candidate matches identified by both approaches. We built the ML model using the gold standard on a first version of the two databases as a training dataset. This gold standard was obtained from PRL-derived matches verified by an exhaustive manual review. Results The Random Forest (RF) algorithm showed a highest recall (0.985) among six widely used ML algorithms: RF, Bagged trees, AdaBoost, Support Vector Machine, Neural Network. Therefore, RF was selected to build the ML model since our goal was to identify the maximum number of true matches. Our combined linkage PRL + ML showed a higher recall (range 0.988-0.992) than either PRL (range 0.916-0.991) or ML (0.981) alone. It identified 1995 individuals participating in both GEMO (6375 participants) and GENEPSO (4925 participants).Conclusions: Our hybrid linkage process represents an efficient tool for linking GEMO and GENEPSO. It may be generalizable to other epidemiological studies involving other databases and registries

The risk of COVID-19 death is much greater and age dependent with type I IFN autoantibodies

International audienceSignificance There is growing evidence that preexisting autoantibodies neutralizing type I interferons (IFNs) are strong determinants of life-threatening COVID-19 pneumonia. It is important to estimate their quantitative impact on COVID-19 mortality upon SARS-CoV-2 infection, by age and sex, as both the prevalence of these autoantibodies and the risk of COVID-19 death increase with age and are higher in men. Using an unvaccinated sample of 1,261 deceased patients and 34,159 individuals from the general population, we found that autoantibodies against type I IFNs strongly increased the SARS-CoV-2 infection fatality rate at all ages, in both men and women. Autoantibodies against type I IFNs are strong and common predictors of life-threatening COVID-19. Testing for these autoantibodies should be considered in the general population

The risk of COVID-19 death is much greater and age dependent with type I IFN autoantibodies

International audienceSignificance There is growing evidence that preexisting autoantibodies neutralizing type I interferons (IFNs) are strong determinants of life-threatening COVID-19 pneumonia. It is important to estimate their quantitative impact on COVID-19 mortality upon SARS-CoV-2 infection, by age and sex, as both the prevalence of these autoantibodies and the risk of COVID-19 death increase with age and are higher in men. Using an unvaccinated sample of 1,261 deceased patients and 34,159 individuals from the general population, we found that autoantibodies against type I IFNs strongly increased the SARS-CoV-2 infection fatality rate at all ages, in both men and women. Autoantibodies against type I IFNs are strong and common predictors of life-threatening COVID-19. Testing for these autoantibodies should be considered in the general population

Rare predicted loss-of-function variants of type I IFN immunity genes are associated with life-threatening COVID-19

BackgroundWe previously reported that impaired type I IFN activity, due to inborn errors of TLR3- and TLR7-dependent type I interferon (IFN) immunity or to autoantibodies against type I IFN, account for 15-20% of cases of life-threatening COVID-19 in unvaccinated patients. Therefore, the determinants of life-threatening COVID-19 remain to be identified in similar to 80% of cases.MethodsWe report here a genome-wide rare variant burden association analysis in 3269 unvaccinated patients with life-threatening COVID-19, and 1373 unvaccinated SARS-CoV-2-infected individuals without pneumonia. Among the 928 patients tested for autoantibodies against type I IFN, a quarter (234) were positive and were excluded.ResultsNo gene reached genome-wide significance. Under a recessive model, the most significant gene with at-risk variants was TLR7, with an OR of 27.68 (95%CI 1.5-528.7, P=1.1x10(-4)) for biochemically loss-of-function (bLOF) variants. We replicated the enrichment in rare predicted LOF (pLOF) variants at 13 influenza susceptibility loci involved in TLR3-dependent type I IFN immunity (OR=3.70[95%CI 1.3-8.2], P=2.1x10(-4)). This enrichment was further strengthened by (1) adding the recently reported TYK2 and TLR7 COVID-19 loci, particularly under a recessive model (OR=19.65[95%CI 2.1-2635.4], P=3.4x10(-3)), and (2) considering as pLOF branchpoint variants with potentially strong impacts on splicing among the 15 loci (OR=4.40[9%CI 2.3-8.4], P=7.7x10(-8)). Finally, the patients with pLOF/bLOF variants at these 15 loci were significantly younger (mean age [SD]=43.3 [20.3] years) than the other patients (56.0 [17.3] years; P=1.68x10(-5)).ConclusionsRare variants of TLR3- and TLR7-dependent type I IFN immunity genes can underlie life-threatening COVID-19, particularly with recessive inheritance, in patients under 60 years old

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients