PRPH2 mutation c.582-1G>A causing adult-onset macular dystrophy with a benign concentric annular macular dystrophy phenotype in a family / Mutação c.582-1G>A do gene PRPH2 causando distrofia macular de início adulto com fenótipo de distrofia macular anular concêntrica benigna em uma família

ABSTRACT The peripherin gene (PRPH2) mutation is associated with photoreceptor cell dysfunction as well as in several inherited retinal dystrophies. The PRPH2 mutation c.582-1G>A is a rare variant reported in retinitis pigmentosa and pattern dystrophy. Here Case 1 was of a 54-year-old woman with bilateral atrophy of the perifoveal retinal pigmentary epithelium and choriocapillaris with central foveolar respect. Autofluorescence and fluorescein angiography revealed perifoveal atrophy of the retinal pigmentary epithelium with an annular window effect without the "dark choroid" sign. Case 2 (mother of Case 1) presented with extensive atrophy of the retinal pigmentary epithelium and choriocapillaris. PRPH2 was evaluated and the c.582-1G>A mutation was identified in heterozygosity. An advanced adult-onset benign concentric annular macular dystrophy diagnosis was thereby proposed. The c.582-1G>A mutation is poorly known and not present in all common genomic databases. This case report is the first one to report a c.582-1G>A mutation associated with benign concentric annular macular dystrophy.

RESUMO Mutações do gene da periferina (PRPH2) estão associadas à disfunção das células fotorreceptoras e estão envolvidas em várias distrofias retinianas hereditárias. A mutação c.582-1G>A do gene PRPH2 é uma variante rara, relatada na retinite pigmentosa e nas distrofias em padrão. O caso 1 foi de uma mulher de 54 anos com atrofia bilateral do epitélio pigmentar da retina perifoveal e da coriocapilar, com acometimento foveolar central. A autofluorescência e a angiofluoresceinografia revelaram atrofia perifoveal do epitélio pigmentar da retina, com efeito de janela anular, sem o sinal da "coroide escura". O caso 2 (mãe) apresentava extensa atrofia do epitélio pigmentar da retina e da coriocapilar. Foi feito um estudo do gene PRPH2, que identificou a mutação c.582-1G>A em heterozigose. Foi proposto um diagnóstico de distrofia macular anular concêntrica benigna de início adulto em estágio avançado. A mutação c.582-1G>A é pouco conhecida e não está presente em todos os bancos de dados genômicos usuais. Este é o primeiro relato de caso publicado de uma mutação c.582-1G>A associada à distrofia macular anular concêntrica benigna.

PRPH2 mutation c.582-1G>A causing adult-onset macular dystrophy with a benign concentric annular macular dystrophy phenotype in a family.

The peripherin gene (PRPH2) mutation is associated with photoreceptor cell dysfunction as well as in several inherited retinal dystrophies. The PRPH2 mutation c.582-1G>A is a rare variant reported in retinitis pigmentosa and pattern dystrophy. Here Case 1 was of a 54-year-old woman with bilateral atrophy of the perifoveal retinal pigmentary epithelium and choriocapillaris with central foveolar respect. Autofluorescence and fluorescein angiography revealed perifoveal atrophy of the retinal pigmentary epithelium with an annular window effect without the "dark choroid" sign. Case 2 (mother of Case 1) presented with extensive atrophy of the retinal pigmentary epithelium and choriocapillaris. PRPH2 was evaluated and the c.582-1G>A mutation was identified in heterozygosity. An advanced adult-onset benign concentric annular macular dystrophy diagnosis was thereby proposed. The c.582-1G>A mutation is poorly known and not present in all common genomic databases. This case report is the first one to report a c.582-1G>A mutation associated with benign concentric annular macular dystrophy.

Characteristics and outcomes of an international cohort of 600 000 hospitalized patients with COVID-19.

BACKGROUND: We describe demographic features, treatments and clinical outcomes in the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) COVID-19 cohort, one of the world's largest international, standardized data sets concerning hospitalized patients. METHODS: The data set analysed includes COVID-19 patients hospitalized between January 2020 and January 2022 in 52 countries. We investigated how symptoms on admission, co-morbidities, risk factors and treatments varied by age, sex and other characteristics. We used Cox regression models to investigate associations between demographics, symptoms, co-morbidities and other factors with risk of death, admission to an intensive care unit (ICU) and invasive mechanical ventilation (IMV). RESULTS: Data were available for 689 572 patients with laboratory-confirmed (91.1%) or clinically diagnosed (8.9%) SARS-CoV-2 infection from 52 countries. Age [adjusted hazard ratio per 10 years 1.49 (95% CI 1.48, 1.49)] and male sex [1.23 (1.21, 1.24)] were associated with a higher risk of death. Rates of admission to an ICU and use of IMV increased with age up to age 60 years then dropped. Symptoms, co-morbidities and treatments varied by age and had varied associations with clinical outcomes. The case-fatality ratio varied by country partly due to differences in the clinical characteristics of recruited patients and was on average 21.5%. CONCLUSIONS: Age was the strongest determinant of risk of death, with a ∼30-fold difference between the oldest and youngest groups; each of the co-morbidities included was associated with up to an almost 2-fold increase in risk. Smoking and obesity were also associated with a higher risk of death. The size of our international database and the standardized data collection method make this study a comprehensive international description of COVID-19 clinical features. Our findings may inform strategies that involve prioritization of patients hospitalized with COVID-19 who have a higher risk of death.

Variation in SARS-CoV-2 seroprevalence in children in the region of Asturias, Northern Spain.

BACKGROUND: Updated seroprevalence estimates are important to describe the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) landscape and to guide public health decisions. The aims are to describe longitudinal changes in seroprevalence in children in a region in Northern Spain and to analyze factors associated with SARS-CoV-2 seropositivity. METHODS: Prospective multicenter longitudinal study with subjects recruited from July to September 2020. Children (up to 14 years old) were included and followed up until September 2021. Venous blood samples were collected every six months during three testing rounds and were analyzed for SARS-CoV-2 antibodies. The data regarding epidemiological features, contact tracing, symptoms, and virological tests were collected. The evolution of SARS-CoV-2 seroprevalence during the study and the differences between children with positive and negative SARS-CoV-2 antibody tests were analyzed. RESULTS: Two hundred children were recruited (50.5% girls, median age 9.7 years). The overall seroprevalence increased from round 1 [1.5%, 95% confidence interval (CI) 0.3%-4.3%] to round 2 (9.1%, 95% CI 4.6%-12.7%) and round 3 (16.6%, 95% CI 9.5%-19.6%) (P < 0.001). Main changes occurred in children aged zero to four years (P = 0.001) who lived in urban areas (P < 0.001). None of the children who were previously positive became seronegative. Following multivariable analysis, three variables independently associated with SARS-CoV-2 seropositivity were identified: close contact with coronavirus disease 2019 (COVID-19) confirmed or suspected cases [odds ratio (OR) = 3.9, 95% CI 1.2-12.5], previous positive virological test (OR = 17.1, 95% CI 3.7-78.3) and fatigue (OR = 18.1, 95% CI 1.7-193.4). CONCLUSIONS: SARS-CoV-2 seroprevalence in children has remarkably increased during the time of our study. Fatigue was the only COVID-19-compatible symptom that was more frequent in seropositive than in seronegative children.

International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality.

Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach.

SurvMaximin: Robust Federated Approach to Transporting Survival Risk Prediction Models

ObjectiveFor multi-center heterogeneous Real-World Data (RWD) with time-to-event outcomes and high-dimensional features, we propose the SurvMaximin algorithm to estimate Cox model feature coefficients for a target population by borrowing summary information from a set of health care centers without sharing patient-level information. Materials and MethodsFor each of the centers from which we want to borrow information to improve the prediction performance for the target population, a penalized Cox model is fitted to estimate feature coefficients for the center. Using estimated feature coefficients and the covariance matrix of the target population, we then obtain a SurvMaximin estimated set of feature coefficients for the target population. The target population can be an entire cohort comprised of all centers, corresponding to federated learning, or can be a single center, corresponding to transfer learning. ResultsSimulation studies and a real-world international electronic health records application study, with 15 participating health care centers across three countries (France, Germany, and the U.S.), show that the proposed SurvMaximin algorithm achieves comparable or higher accuracy compared with the estimator using only the information of the target site and other existing methods. The SurvMaximin estimator is robust to variations in sample sizes and estimated feature coefficients between centers, which amounts to significantly improved estimates for target sites with fewer observations. ConclusionsThe SurvMaximin method is well suited for both federated and transfer learning in the high-dimensional survival analysis setting. SurvMaximin only requires a one-time summary information exchange from participating centers. Estimated regression vectors can be very heterogeneous. SurvMaximin provides robust Cox feature coefficient estimates without outcome information in the target population and is privacy-preserving.

Predicting the Evolution of COVID-19 Mortality Risk: a Recurrent Neural Network Approach

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSThe propagation of COVID-19 in Spain prompted the declaration of the state of alarm on March 14, 2020. On 2 December 2020, the infection had been confirmed in 1,665,775 patients and caused 45,784 deaths. This unprecedented health crisis challenged the ingenuity of all professionals involved. Decision support systems in clinical care and health services management were identified as crucial in the fight against the pandemic. MethodsThis study applies Deep Learning techniques for mortality prediction of COVID-19 patients. Two datasets with clinical information (medication, laboratory tests, vital signs etc.) were used. They are comprised of 2,307 and 3,870 COVID-19 infected patients admitted to two Spanish hospital chains. Firstly, we built a sequence of temporal events gathering all the clinical information for each patient. Next, we used the temporal sequences to train a Recurrent Neural Network (RNN) model with an attention mechanism exploring interpretability. We conducted extensive experiments and trained the RNNs in different settings, performing hyperparameter search and cross-validation. We ensembled resulting RNNs to reduce variability and enhance sensitivity. ResultsWe assessed the performance of our models using global metrics, by averaging the performance across all the days in the sequences. We also measured day-by-day metrics starting from the day of hospital admission and the outcome day and evaluated the daily predictions. Regarding sensitivity, when compared to more traditional models, our best two RNN ensemble models outperform a Support Vector Classifier in 6 and 16 percentage points, and Random Forest in 23 and 18 points. For the day-by-day predictions from the outcome date, the models also achieved better results than baselines showing its ability towards early predictions. ConclusionsWe have shown the feasibility of our approach to predict the clinical outcome of patients infected with SARS-CoV-2. The result is a time series model that can support decision-making in healthcare systems and aims at interpretability. The system is robust enough to deal with real world data and it is able to overcome the problems derived from the sparsity and heterogeneity of the data. In addition, the approach was validated using two datasets showing substantial differences. This not only validates the robustness of the proposal but also meets the requirements of a real scenario where the interoperability between hospitals datasets is difficult to achieve.

International Comparisons of Harmonized Laboratory Value Trajectories to Predict Severe COVID-19: Leveraging the 4CE Collaborative Across 342 Hospitals and 6 Countries: A Retrospective Cohort Study

ObjectivesTo perform an international comparison of the trajectory of laboratory values among hospitalized patients with COVID-19 who develop severe disease and identify optimal timing of laboratory value collection to predict severity across hospitals and regions. DesignRetrospective cohort study. SettingThe Consortium for Clinical Characterization of COVID-19 by EHR (4CE), an international multi-site data-sharing collaborative of 342 hospitals in the US and in Europe. ParticipantsPatients hospitalized with COVID-19, admitted before or after PCR-confirmed result for SARS-CoV-2. Primary and secondary outcome measuresPatients were categorized as "ever-severe" or "never-severe" using the validated 4CE severity criteria. Eighteen laboratory tests associated with poor COVID-19-related outcomes were evaluated for predictive accuracy by area under the curve (AUC), compared between the severity categories. Subgroup analysis was performed to validate a subset of laboratory values as predictive of severity against a published algorithm. A subset of laboratory values (CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin) was compared between North American and European sites for severity prediction. ResultsOf 36,447 patients with COVID-19, 19,953 (43.7%) were categorized as ever-severe. Most patients (78.7%) were 50 years of age or older and male (60.5%). Longitudinal trajectories of CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin showed association with disease severity. Significant differences of laboratory values at admission were found between the two groups. With the exception of D-dimer, predictive discrimination of laboratory values did not improve after admission. Sub-group analysis using age, D-dimer, CRP, and lymphocyte count as predictive of severity at admission showed similar discrimination to a published algorithm (AUC=0.88 and 0.91, respectively). Both models deteriorated in predictive accuracy as the disease progressed. On average, no difference in severity prediction was found between North American and European sites. ConclusionsLaboratory test values at admission can be used to predict severity in patients with COVID-19. Prediction models show consistency across international sites highlighting the potential generalizability of these models.

ISARIC COVID-19 Clinical Data Report: 8 June 2020

ISARIC (International Severe Acute Respiratory and emerging Infections Consortium) partnerships and outbreak preparedness initiatives enabled the rapid launch of standardised clinical data collection on COVID-19 in Jan 2020. Extensive global participation has resulted in a large, standardised collection of comprehensive clinical data from hundreds of sites across dozens of countries. Data are analysed regularly and reported publicly to inform patient care and public health response. This report, our 17th report, is a part of a series published over the past 2 years. Data have been entered for 800,459 individuals from 1701 partner institutions and networks across 60 countries. The comprehensive analyses detailed in this report includes hospitalised individuals of all ages for whom data collection occurred between 30 January 2020 and up to and including 5 January 2022, AND who have laboratory-confirmed SARS-COV-2 infection or clinically diagnosed COVID-19. For the 699,014 cases who meet eligibility criteria for this report, selected findings include: O_LImedian age of 58 years, with an approximately equal (50/50) male:female sex distribution C_LIO_LI29% of the cohort are at least 70 years of age, whereas 4% are 0-19 years of age C_LIO_LIthe most common symptom combination in this hospitalised cohort is shortness of breath, cough, and history of fever, which has remained constant over time C_LIO_LIthe five most common symptoms at admission were shortness of breath, cough, history of fever, fatigue/malaise, and altered consciousness/confusion, which is unchanged from the previous reports C_LIO_LIage-associated differences in symptoms are evident, including the frequency of altered consciousness increasing with age, and fever, respiratory and constitutional symptoms being present mostly in those 40 years and above C_LIO_LI16% of patients with relevant data available were admitted at some point during their illness into an intensive care unit (ICU), which is slightly lower than previously reported (19%) C_LIO_LIantibiotic agents were used in 35% of patients for whom relevant data are available (669,630), a significant reduction from our previous reports (80%) which reflects a shifting proportion of data contributed by different institutions; in ICU/HDU admitted patients with data available (50,560), 91% received antibiotics C_LIO_LIuse of corticosteroids was reported in 24% of all patients for whom data were available (677,012); in ICU/HDU admitted patients with data available (50,646), 69% received corticosteroids C_LIO_LIoutcomes are known for 632,518 patients and the overall estimated case fatality ratio (CFR) is 23.9% (95%CI 23.8-24.1), rising to 37.1% (95%CI 36.8-37.4) for patients who were admitted to ICU/HDU, demonstrating worse outcomes in those with the most severe disease C_LI To access previous versions of ISARIC COVID-19 Clinical Data Report please use the link below: https://isaric.org/research/covid-19-clinical-research-resources/evidence-reports/

Further delineation of Malan syndrome.

Malan syndrome is an overgrowth disorder described in a limited number of individuals. We aim to delineate the entity by studying a large group of affected individuals. We gathered data on 45 affected individuals with a molecularly confirmed diagnosis through an international collaboration and compared data to the 35 previously reported individuals. Results indicate that height is > 2 SDS in infancy and childhood but in only half of affected adults. Cardinal facial characteristics include long, triangular face, macrocephaly, prominent forehead, everted lower lip, and prominent chin. Intellectual disability is universally present, behaviorally anxiety is characteristic. Malan syndrome is caused by deletions or point mutations of NFIX clustered mostly in exon 2. There is no genotype-phenotype correlation except for an increased risk for epilepsy with 19p13.2 microdeletions. Variants arose de novo, except in one family in which mother was mosaic. Variants causing Malan and Marshall-Smith syndrome can be discerned by differences in the site of stop codon formation. We conclude that Malan syndrome has a well recognizable phenotype that usually can be discerned easily from Marshall-Smith syndrome but rarely there is some overlap. Differentiation from Sotos and Weaver syndrome can be made by clinical evaluation only.

LRH-1 agonism favours an immune-islet dialogue which protects against diabetes mellitus.

Type 1 diabetes mellitus (T1DM) is due to the selective destruction of islet beta cells by immune cells. Current therapies focused on repressing the immune attack or stimulating beta cell regeneration still have limited clinical efficacy. Therefore, it is timely to identify innovative targets to dampen the immune process, while promoting beta cell survival and function. Liver receptor homologue-1 (LRH-1) is a nuclear receptor that represses inflammation in digestive organs, and protects pancreatic islets against apoptosis. Here, we show that BL001, a small LRH-1 agonist, impedes hyperglycemia progression and the immune-dependent inflammation of pancreas in murine models of T1DM, and beta cell apoptosis in islets of type 2 diabetic patients, while increasing beta cell mass and insulin secretion. Thus, we suggest that LRH-1 agonism favors a dialogue between immune and islet cells, which could be druggable to protect against diabetes mellitus.

Functional and regulatory profiling of energy metabolism in fission yeast.

BACKGROUND: The control of energy metabolism is fundamental for cell growth and function and anomalies in it are implicated in complex diseases and ageing. Metabolism in yeast cells can be manipulated by supplying different carbon sources: yeast grown on glucose rapidly proliferates by fermentation, analogous to tumour cells growing by aerobic glycolysis, whereas on non-fermentable carbon sources metabolism shifts towards respiration. RESULTS: We screened deletion libraries of fission yeast to identify over 200 genes required for respiratory growth. Growth media and auxotrophic mutants strongly influenced respiratory metabolism. Most genes uncovered in the mutant screens have not been implicated in respiration in budding yeast. We applied gene-expression profiling approaches to compare steady-state fermentative and respiratory growth and to analyse the dynamic adaptation to respiratory growth. The transcript levels of most genes functioning in energy metabolism pathways are coherently tuned, reflecting anticipated differences in metabolic flows between fermenting and respiring cells. We show that acetyl-CoA synthase, rather than citrate lyase, is essential for acetyl-CoA synthesis in fission yeast. We also investigated the transcriptional response to mitochondrial damage by genetic or chemical perturbations, defining a retrograde response that involves the concerted regulation of distinct groups of nuclear genes that may avert harm from mitochondrial malfunction. CONCLUSIONS: This study provides a rich framework of the genetic and regulatory basis of energy metabolism in fission yeast and beyond, and it pinpoints weaknesses of commonly used auxotroph mutants for investigating metabolism. As a model for cellular energy regulation, fission yeast provides an attractive and complementary system to budding yeast.