Genetic and phenotypic landscape of pediatric-onset epilepsy in 142 Indian families: Counseling and therapeutic implications.

The application of genomic technologies has led to unraveling of the complex genetic landscape of disorders of epilepsy, gaining insights into their underlying disease mechanisms, aiding precision medicine, and providing informed genetic counseling. We herein present the phenotypic and genotypic insights from 142 Indian families with epilepsy with or without comorbidities. Based on the electroclinical findings, epilepsy syndrome diagnosis could be made in 44% (63/142) of the families adopting the latest proposal for the classification by the ILAE task force (2022). Of these, 95% (60/63) of the families exhibited syndromes with developmental epileptic encephalopathy or progressive neurological deterioration. A definitive molecular diagnosis was achieved in 74 of 142 (52%) families. Infantile-onset epilepsy was noted in 81% of these families (61/74). Fifty-five monogenic, four chromosomal, and one imprinting disorder were identified in 74 families. The genetic variants included 65 (96%) single-nucleotide variants/small insertion-deletions, 1 (2%) copy-number variant, and 1 (2%) triplet-repeat expansion in 53 epilepsy-associated genes causing monogenic disorders. Of these, 35 (52%) variants were novel. Therapeutic implications were noted in 51% of families (38/74) with definitive diagnosis. Forty-one out of 66 families with monogenic disorders exhibited autosomal recessive and inherited autosomal dominant disorders with high risk of recurrence.

Further validation of craniosynostosis as a part of phenotypic spectrum of BCL11B-related BAFopathy.

Heterozygous disease-causing variants in BCL11B are the basis of a rare neurodevelopmental syndrome with craniofacial and immunological involvement. Isolated craniosynostosis, without systemic or immunological findings, has been reported in one of the 17 individuals reported with this disorder till date. We report three additional individuals harboring de novo heterozygous frameshift variants, all lying in the exon 4 of BCL11B. All three individuals presented with the common findings of this disorder i.e. developmental delay, recurrent infections with immunologic abnormalities and facial dysmorphism. Notably, craniosynostosis of variable degree was seen in all three individuals. We, thus add to the evolving genotypes and phenotypes of BCL11B-related BAFopathy and also review the clinical, genomic spectrum along with the underlying disease mechanisms of this disorder.

Further delineation of KIF21B-related neurodevelopmental disorders.

Kinesin Family Member 21B (KIF21B) encoded by KIF21B (MIM*608322), belongs to the Kinesin superfamily proteins, which play a key role in microtubule organisation in neuronal dendrites and axons. Recently, heterozygous variants in KIF21B were implicated as the cause of intellectual disability and brain malformations in four unrelated individuals. We report a 9-year-old male with delayed speech, hyperactivity, poor social interaction, and autistic features. A parent-offspring trio exome sequencing identified a novel de novo rare heterozygous variant, NM_001252102.2: c.1513A>C, p.(Ser505Arg) in exon 11 of KIF21B. In vivo functional analysis using in utero electroporation in mouse embryonic cortex revealed that the expression of Ser505Arg KIF21B protein in the cerebral cortex impaired the radial migration of projection neurons, thus confirming the pathogenicity of the variant. Our report further validates pathogenic variants in KIF21B as a cause of neurodevelopmental disorder.

Osteogenesis Imperfecta type 1: like mother, like daughter - Challenges in the perinatal management.

A third gravida with osteogenesis imperfecta (OI) type 1, in her 20s, was referred from the Medical Genetics department at 12+ weeks with a prenatal diagnosis of OI type 1 in this fetus for further management. She was wheelchair-bound and keen to continue this pregnancy. She had medical termination in her two previous pregnancies for OI in the fetuses. Ultrasound at 12+ weeks revealed a short-bent femur with sparing of the long bones of the upper limb. Serial ultrasound revealed progressive affliction of the long bones with falling growth profile and polyhydramnios. She was delivered at 36 weeks by caesarean for breech in labour under regional anaesthesia.A multidisciplinary approach, patient determination, and good partner support helped in the successful management of this pregnancy.The neonate had blue sclera, dentigerous imperfecta, bowing of the femur and relatively spared upper limbs. Growth was on the third centile. The mother says she brings the girl for follow-up every 3-6 months to give injection zoledronate. The mother confirms her girl can stand with support, crawl, and speak two-syllable words. Her daughter had to undergo femur corrective osteotomy rush nailing and hip spice application for a closed fracture of the left femur.

Artificial intelligence for diagnosis of mild-moderate COVID-19 using haematological markers.

OBJECTIVE: The persistent spread of SARS-CoV-2 makes diagnosis challenging because COVID-19 symptoms are hard to differentiate from those of other respiratory illnesses. The reverse transcription-polymerase chain reaction test is the current golden standard for diagnosing various respiratory diseases, including COVID-19. However, this standard diagnostic method is prone to erroneous and false negative results (10% -15%). Therefore, finding an alternative technique to validate the RT-PCR test is paramount. Artificial intelligence (AI) and machine learning (ML) applications are extensively used in medical research. Hence, this study focused on developing a decision support system using AI to diagnose mild-moderate COVID-19 from other similar diseases using demographic and clinical markers. Severe COVID-19 cases were not considered in this study since fatality rates have dropped considerably after introducing COVID-19 vaccines. METHODS: A custom stacked ensemble model consisting of various heterogeneous algorithms has been utilized for prediction. Four deep learning algorithms have also been tested and compared, such as one-dimensional convolutional neural networks, long short-term memory networks, deep neural networks and Residual Multi-Layer Perceptron. Five explainers, namely, Shapley Additive Values, Eli5, QLattice, Anchor and Local Interpretable Model-agnostic Explanations, have been utilized to interpret the predictions made by the classifiers. RESULTS: After using Pearson's correlation and particle swarm optimization feature selection, the final stack obtained a maximum accuracy of 89%. The most important markers which were useful in COVID-19 diagnosis are Eosinophil, Albumin, T. Bilirubin, ALP, ALT, AST, HbA1c and TWBC. CONCLUSION: The promising results suggest using this decision support system to diagnose COVID-19 from other similar respiratory illnesses.

A Decision Support System for Diagnosis of COVID-19 from Non-COVID-19 Influenza-like Illness Using Explainable Artificial Intelligence.

The coronavirus pandemic emerged in early 2020 and turned out to be deadly, killing a vast number of people all around the world. Fortunately, vaccines have been discovered, and they seem effectual in controlling the severe prognosis induced by the virus. The reverse transcription-polymerase chain reaction (RT-PCR) test is the current golden standard for diagnosing different infectious diseases, including COVID-19; however, it is not always accurate. Therefore, it is extremely crucial to find an alternative diagnosis method which can support the results of the standard RT-PCR test. Hence, a decision support system has been proposed in this study that uses machine learning and deep learning techniques to predict the COVID-19 diagnosis of a patient using clinical, demographic and blood markers. The patient data used in this research were collected from two Manipal hospitals in India and a custom-made, stacked, multi-level ensemble classifier has been used to predict the COVID-19 diagnosis. Deep learning techniques such as deep neural networks (DNN) and one-dimensional convolutional networks (1D-CNN) have also been utilized. Further, explainable artificial techniques (XAI) such as Shapley additive values (SHAP), ELI5, local interpretable model explainer (LIME), and QLattice have been used to make the models more precise and understandable. Among all of the algorithms, the multi-level stacked model obtained an excellent accuracy of 96%. The precision, recall, f1-score and AUC obtained were 94%, 95%, 94% and 98% respectively. The models can be used as a decision support system for the initial screening of coronavirus patients and can also help ease the existing burden on medical infrastructure.

COVID-19 diagnosis using clinical markers and multiple explainable artificial intelligence approaches: A case study from Ecuador.

The COVID-19 pandemic erupted at the beginning of 2020 and proved fatal, causing many casualties worldwide. Immediate and precise screening of affected patients is critical for disease control. COVID-19 is often confused with various other respiratory disorders since the symptoms are similar. As of today, the reverse transcription-polymerase chain reaction (RT-PCR) test is utilized for diagnosing COVID-19. However, this approach is sometimes prone to producing erroneous and false negative results. Hence, finding a reliable diagnostic method that can validate the RT-PCR test results is crucial. Artificial intelligence (AI) and machine learning (ML) applications in COVID-19 diagnosis has proven to be beneficial. Hence, clinical markers have been utilized for COVID-19 diagnosis with the help of several classifiers in this study. Further, five different explainable artificial intelligence techniques have been utilized to interpret the predictions. Among all the algorithms, the k-nearest neighbor obtained the best performance with an accuracy, precision, recall and f1-score of 84%, 85%, 84% and 84%. According to this study, the combination of clinical markers such as eosinophils, lymphocytes, red blood cells and leukocytes was significant in differentiating COVID-19. The classifiers can be utilized synchronously with the standard RT-PCR procedure making diagnosis more reliable and efficient.

De novo variants underlying monogenic syndromes with intellectual disability in a neurodevelopmental cohort from India.

The contribution of de novo variants as a cause of intellectual disability (ID) is well established in several cohorts reported from the developed world. However, the genetic landscape as well as the appropriate testing strategies for identification of de novo variants of these disorders remain largely unknown in low-and middle-income countries like India. In this study, we delineate the clinical and genotypic spectrum of 54 families (55 individuals) with syndromic ID harboring rare de novo variants. We also emphasize on the effectiveness of singleton exome sequencing as a valuable tool for diagnosing these disorders in resource limited settings. Overall, 46 distinct disorders were identified encompassing 46 genes with 51 single-nucleotide variants and/or indels and two copy-number variants. Pathogenic variants were identified in CREBBP, TSC2, KMT2D, MECP2, IDS, NIPBL, NSD1, RIT1, SOX10, BRWD3, FOXG1, BCL11A, KDM6B, KDM5C, SETD5, QRICH1, DCX, SMARCD1, ASXL1, ASXL3, AKT3, FBN2, TCF12, WASF1, BRAF, SMARCA4, SMARCA2, TUBG1, KMT2A, CTNNB1, DLG4, MEIS2, GATAD2B, FBXW7, ANKRD11, ARID1B, DYNC1H1, HIVEP2, NEXMIF, ZBTB18, SETD1B, DYRK1A, SRCAP, CASK, L1CAM, and KRAS. Twenty-four of these monogenic disorders have not been previously reported in the Indian population. Notably, 39 out of 53 (74%) disease-causing variants are novel. These variants were identified in the genes mainly encoding transcriptional and chromatin regulators, serine threonine kinases, lysosomal enzymes, molecular motors, synaptic proteins, neuronal migration machinery, adhesion molecules, structural proteins and signaling molecules.

Second report of SHMT2 related neurodevelopmental disorder with cardiomyopathy, spasticity, and brain abnormalities.

Neurodevelopmental disorder with cardiomyopathy, spasticity, and brain abnormalities (NEDCASB; MIM# 619121) is a recently described metabolic disorder with characteristic features of mild dysmorphism, intellectual disability, spasticity, peripheral neuropathy, cardiomyopathy, and thin corpus callosum. Biallelic variants in SHMT2 (MIM 138450), encoding mitochondrial serine hydroxymethyltransferase enzyme, have been recently linked to this disorder. Till now, a total of seven variants including six missense and one deletion-insertion has been reported in SHMT2. We hereby report an additional individual with novel homozygous missense variant c.1133A > G in SHMT2 (NM_005412.6) identified by exome sequencing and review the phenotype and genotype of the previously reported individuals with NEDCASB.

Clinical and Laboratory Approach to Diagnose COVID-19 Using Machine Learning.

Coronavirus 2 (SARS-CoV-2), often known by the name COVID-19, is a type of acute respiratory syndrome that has had a significant influence on both economy and health infrastructure worldwide. This novel virus is diagnosed utilising a conventional method known as the RT-PCR (Reverse Transcription Polymerase Chain Reaction) test. This approach, however, produces a lot of false-negative and erroneous outcomes. According to recent studies, COVID-19 can also be diagnosed using X-rays, CT scans, blood tests and cough sounds. In this article, we use blood tests and machine learning to predict the diagnosis of this deadly virus. We also present an extensive review of various existing machine-learning applications that diagnose COVID-19 from clinical and laboratory markers. Four different classifiers along with a technique called Synthetic Minority Oversampling Technique (SMOTE) were used for classification. Shapley Additive Explanations (SHAP) method was utilized to calculate the gravity of each feature and it was found that eosinophils, monocytes, leukocytes and platelets were the most critical blood parameters that distinguished COVID-19 infection for our dataset. These classifiers can be utilized in conjunction with RT-PCR tests to improve sensitivity and in emergency situations such as a pandemic outbreak that might happen due to new strains of the virus. The positive results indicate the prospective use of an automated framework that could help clinicians and medical personnel diagnose and screen patients.

Report of rapid diagnosis and precise management of MMADHC-related intracellular cobalamin defect.

Disorders of intracellular cobalamin metabolism are a group of metabolic disorders that lead to varied clinical presentation from intrauterine life to adulthood. We report a male infant with developmental regression, macrocytic anaemia and hyperpigmentation. Exome sequencing identified a homozygous pathogenic variant in the MMADHC gene, known to cause homocystinuria, cblD type (MIM #277410). We describe significant clinical improvement with targeted therapy and emphasise the relevance of genomic testing in accurate management of inherited metabolic disorders.