Whole genome sequencing as a first-tier diagnostic test for infants in neonatal intensive care units: A pilot study in Brazil.

In this pilot study, we aimed to evaluate the feasibility of whole genome sequencing (WGS) as a first-tier diagnostic test for infants hospitalized in neonatal intensive care units in the Brazilian healthcare system. The cohort presented here results from a joint collaboration between private and public hospitals in Brazil considering the initiative of a clinical laboratory to provide timely diagnosis for critically ill infants. We performed trio (proband and parents) WGS in 21 infants suspected of a genetic disease with an urgent need for diagnosis to guide medical care. Overall, the primary indication for genetic testing was dysmorphic syndromes (n = 14, 67%) followed by inborn errors of metabolism (n = 6, 29%) and skeletal dysplasias (n = 1, 5%). The diagnostic yield in our cohort was 57% (12/21) based on cases that received a definitive or likely definitive diagnostic result from WGS analysis. A total of 16 pathogenic/likely pathogenic variants and 10 variants of unknown significance were detected, and in most cases inherited from an unaffected parent. In addition, the reported variants were of different types, but mainly missense (58%) and associated with autosomal diseases (19/26); only three were associated with X-linked diseases, detected in hemizygosity in the proband an inherited from an unaffected mother. Notably, we identified 10 novel variants, absent from public genomic databases, in our cohort. Considering the entire diagnostic process, the average turnaround time from enrollment to medical report in our study was 53 days. Our findings demonstrate the remarkable utility of WGS as a diagnostic tool, elevating the potential of transformative impact since it outperforms conventional genetic tests. Here, we address the main challenges associated with implementing WGS in the medical care system in Brazil, as well as discuss the potential benefits and limitations of WGS as a diagnostic tool in the neonatal care setting.

Perspective on Schistosomiasis Drug Discovery: Highlights from a Schistosomiasis Drug Discovery Workshop at Wellcome Collection, London, September 2022.

In September 2022, the Drug Discovery Unit at the University of Dundee, UK, organised an international meeting at the Wellcome Collection in London to explore the current clinical situation and challenges associated with treating schistosomiasis. The aim of this meeting was to discuss the need for new treatments in view of the clinical situation and to ascertain what the key requirements would be for any potential new anti-schistosomals. This information will be essential to inform ongoing drug discovery efforts for schistosomiasis. We also discussed the potential drug discovery pathway and associated criteria for progressing compounds to the clinic. To date, praziquantel (PZQ) is the only drug available to treat all species causing schistosomiasis, but it is often unable to completely clear parasites from an infected patient, partially due to its inactivity against juvenile worms. PZQ-mediated mass drug administration campaigns conducted in endemic areas (e.g., sub-Saharan Africa, where schistosomiasis is primarily prevalent) have contributed to reducing the burden of disease but will not eliminate the disease as a public health problem. The potential for Schistosoma to develop resistance towards PZQ, as the sole treatment available, could become a concern. Consequently, new anthelmintic medications are urgently needed, and this Perspective aims to capture some of the learnings from our discussions on the key criteria for new treatments.

Automatically determining cause of death from verbal autopsy narratives.

BACKGROUND: A verbal autopsy (VA) is a post-hoc written interview report of the symptoms preceding a person's death in cases where no official cause of death (CoD) was determined by a physician. Current leading automated VA coding methods primarily use structured data from VAs to assign a CoD category. We present a method to automatically determine CoD categories from VA free-text narratives alone. METHODS: After preprocessing and spelling correction, our method extracts word frequency counts from the narratives and uses them as input to four different machine learning classifiers: naïve Bayes, random forest, support vector machines, and a neural network. RESULTS: For individual CoD classification, our best classifier achieves a sensitivity of.770 for adult deaths for 15 CoD categories (as compared to the current best reported sensitivity of.57), and.662 with 48 WHO categories. When predicting the CoD distribution at the population level, our best classifier achieves.962 cause-specific mortality fraction accuracy for 15 categories and.908 for 48 categories, which is on par with leading CoD distribution estimation methods. CONCLUSIONS: Our narrative-based machine learning classifier performs as well as classifiers based on structured data at the individual level. Moreover, our method demonstrates that VA narratives provide important information that can be used by a machine learning system for automated CoD classification. Unlike the structured questionnaire-based methods, this method can be applied to any verbal autopsy dataset, regardless of the collection process or country of origin.

Neurofibroma Plexiforme Vesical em portador de Neurofibromatose: relato de caso / Bladder Plexiform Neurofibroma in Neurofibromatosis carrier: a case report / Neurofibroma Plexiforme Vesical en portador de Neurofibromatosis: relato de caso

Introdução: A neurofibromatose do tipo 1 (NF1) é uma doença hereditária de caráter autossômico dominante, com penetrância completa e relacionada a mutações no gene NF1 (17q11.2). Apresenta expressão extremamente variável e predisposição à ocorrência de tumores. Complicações como neurofibromas viscerais estão presentes em apenas 1% dos casos de NF1. Neurofibromas vesicais são extremamente raros. Relato do caso: O presente caso faz referência a um paciente do sexo masculino com 4 anos de idade que apresentava sinais e sintomas de disfunção urinária e intestinal associados a desvio da coluna lombossacra. Ao exame, foram identificadas características típicas de NF1 e os exames complementares permitiram o diagnóstico de um neurofibroma vesical. Posteriormente, foi concluído o diagnóstico de NF1. Conclusão: O diagnóstico de síndromes predisponentes ao câncer e o rastreio de tumores associados a essas condições são essenciais aos portadores dessas doenças.

Introduction: Type 1 neurofibromatosis is an inherited autosomal dominant disease with complete penetrance and is related to mutations in the NF1 gene (17q11.2). It presents extremely variable expression and predisposition to the occurrence of tumors. Complications such as visceral neurofibromas occurs in only 1% of NF1 cases. Vesical neurofibromas are extremely rare. Case report: Here in, we expose a case of a 4 years old boy, who presented signs and symptoms of urinary and intestinal dysfunction associated with lumbosacral spine deviation. His physical exam had neurofibromatosis type 1 features and the complementary exams revealed a vesical neurofibroma. Subsequently, a neurofibromatosis type 1 diagnosis was performed. Conclusion: Diagnose tumor predisposing syndromes and associated complications is essential for these patients.

Introducción: La neurofibromatosis tipo 1 es una enfermedad hereditaria de carácter autosómico dominante, con penetración completa y relacionada con mutaciones en el gen NF1 (17q11.2). Se presenta una expresión extremadamente variable y predisposición a la ocurrencia de tumores. Las complicaciones como los neurofibromas viscerales están presentes en sólo el 1% de los casos de NF1. Los neurofibromas vesicales son extremadamente raros. Relato del caso: Exponemos el caso de un niño de 4 años que presentaba signos y síntomas de disfunción urinaria e intestinal asociados a la desviación de la columna lumbosacra. En el examen se identificaron características típicas de neurofibromatosis y los exámenes complementarios permitieron el diagnóstico de un neurofibroma vesical. Se ha concluido el diagnóstico de neurofibromatosis del tipo 1. Conclusión: Diagnosticar los síndromes predisponentes del tumor y las complicaciones asociadas son esenciais para estos pacientes.

Nationwide Mortality Studies To Quantify Causes Of Death: Relevant Lessons From India's Million Death Study.

Progress toward the United Nations 2030 Sustainable Development Goals requires improved information on mortality and causes of death. However, causes of many of the fifty million annual deaths in low- and middle-income countries remain unknown, as most of the deaths occur at home without medical attention. In 2001 India began the Million Death Study in 1.3 million nationally representative households. Nonmedical staff conduct verbal autopsies, which are structured interviews including a half-page narrative in local language of the family's story of the symptoms and events leading to death. Two physicians independently assess each death to arrive at an underlying cause of death. The study has thus far yielded information that substantially altered previous estimates of cause-specific mortality and risk factors in India. Similar robust studies are feasible at low cost in other low- and middle-income countries, particularly if they adopt electronic data management and ensure high quality of fieldwork and physician coding. Nationwide mortality studies enable the quantification of avoidable premature mortality and key risk factors for disease, and provide a practicable method to monitor progress toward the Sustainable Development Goals.

Molecular analysis of TSC1 and TSC2 genes and phenotypic correlations in Brazilian families with tuberous sclerosis.

Tuberous sclerosis complex (TSC) is an autosomal dominant multisystem disorder characterized by the development of multiple hamartomas in many organs and tissues. It occurs due to inactivating mutations in either of the two genes, TSC1 and TSC2, following a second hit in a tumor suppressor gene in most hamartomas. Comprehensive screening for mutations in both the TSC1 and TSC2 loci has been performed in several cohorts of patients and a broad spectrum of pathogenic mutations have been described. In Brazil, there is no data regarding incidence and prevalence of tuberous sclerosis and mutations in TSC1 and TSC2. We analyzed both genes in 53 patients with high suspicion of tuberous sclerosis using multiplex-ligation dependent probe amplification and a customized next generation sequencing panel. Confirmation of all variants was done by the Sanger method. We identified 50 distinct variants in 47 (89%) of the patients. Five were large rearrangements and 45 were point mutations. The symptoms presented by our series of patients were not different between male and female individuals, except for the more common occurrence of shagreen patch in women (p = 0.028). In our series, consistent with other studies, TSC2 mutations were associated with a more severe phenotypic spectrum than TSC1 mutations. This is the first study that sought to characterize the molecular spectrum of Brazilian individuals with tuberous sclerosis.

Naive Bayes classifiers for verbal autopsies: comparison to physician-based classification for 21,000 child and adult deaths.

BACKGROUND: Verbal autopsies (VA) are increasingly used in low- and middle-income countries where most causes of death (COD) occur at home without medical attention, and home deaths differ substantially from hospital deaths. Hence, there is no plausible "standard" against which VAs for home deaths may be validated. Previous studies have shown contradictory performance of automated methods compared to physician-based classification of CODs. We sought to compare the performance of the classic naive Bayes classifier (NBC) versus existing automated classifiers, using physician-based classification as the reference. METHODS: We compared the performance of NBC, an open-source Tariff Method (OTM), and InterVA-4 on three datasets covering about 21,000 child and adult deaths: the ongoing Million Death Study in India, and health and demographic surveillance sites in Agincourt, South Africa and Matlab, Bangladesh. We applied several training and testing splits of the data to quantify the sensitivity and specificity compared to physician coding for individual CODs and to test the cause-specific mortality fractions at the population level. RESULTS: The NBC achieved comparable sensitivity (median 0.51, range 0.48-0.58) to OTM (median 0.50, range 0.41-0.51), with InterVA-4 having lower sensitivity (median 0.43, range 0.36-0.47) in all three datasets, across all CODs. Consistency of CODs was comparable for NBC and InterVA-4 but lower for OTM. NBC and OTM achieved better performance when using a local rather than a non-local training dataset. At the population level, NBC scored the highest cause-specific mortality fraction accuracy across the datasets (median 0.88, range 0.87-0.93), followed by InterVA-4 (median 0.66, range 0.62-0.73) and OTM (median 0.57, range 0.42-0.58). CONCLUSIONS: NBC outperforms current similar COD classifiers at the population level. Nevertheless, no current automated classifier adequately replicates physician classification for individual CODs. There is a need for further research on automated classifiers using local training and test data in diverse settings prior to recommending any replacement of physician-based classification of verbal autopsies.

Mutual information and variants for protein domain-domain contact prediction.

BACKGROUND: Predicting protein contacts solely based on sequence information remains a challenging problem, despite the huge amount of sequence data at our disposal. Mutual Information (MI), an information theory measure, has been extensively employed and modified to identify residues within a protein (intra-protein) that are in contact. More recently MI and its variants have also been used in the prediction of contacts between proteins (inter-protein). METHODS: Here we assess the predictive power of MI and variants for domain-domain contact prediction. We test original MI and these variants, which are called MIp, MIc and ZNMI, on 40 domain-domain test cases containing 10,753 sequences. We also propose and evaluate two new versions of MI that consider triangles of residues and the physiochemical properties of the amino acids, respectively. RESULTS: We found that all versions of MI are skewed towards predicting surface residues. Since domain-domain contacts are on the surface of each domain, we considered only surface residues when attempting to predict contacts. Our analysis shows that MIc is the best current MI domain-domain contact predictor. At 20% recall MIc achieved a precision of 44.9% when only surface residues were considered. Our triangle and reduced alphabet variants of MI highlight the delicate trade-off between signal and noise in the use of MI for domain-domain contact prediction. We also examine a specific "successful" case study and demonstrate that here, when considering surface residues, even the most accurate domain-domain contact predictor, MIc, performs no better than random. CONCLUSIONS: All tested variants of MI are skewed towards predicting surface residues. When considering surface residues only, we find MIc to be the best current MI domain-domain contact predictor. Its performance, however, is not as good as a non-MI based contact predictor, i-Patch. Additionally, the intra-protein contact prediction capabilities of MIc outperform its domain-domain contact prediction abilities.