View clinical trials related to Genetic Diseases, Inborn.
Filter by:Pancreatic cancer is one of the diseases with the worst prognosis, which is mainly due to the initial asymptomatic prognosis. Unfortunately, the incidence of this disease in the Czech Republic is still increasing. In a certain proportion of patients, it is possible to predict the disease, e.g. due to family burdens. Regular follow-up of such individuals is the subject of the SCREPAN study: "Pancreatic Cancer Screening in High-Risk Persons".
This research project entails delivery of a personalized antisense oligonucleotide (ASO) drug designed for a single pediatric participant with SCN2A associated developmental epileptic encephalopathy
The goal of this clinical trial is to test a new method for newborn screening using whole genome sequencing, called BeginNGS. Parents will be approached to provide informed consent to enroll their newborns in prenatal, postnatal, and outpatient settings. The main questions this study aims to answer are: What is the utility of BeginNGS as compared to state newborn screening? What is the acceptability and feasibility of BeginNGS as compared to state newborn screening? What is the cost effectiveness of BeginNGS as compared to state newborn screening? Enrolled newborns will have a blood sample taken and will receive the BeginNGS test. Newborns will have also had the state newborn screening test.
Transcranial magnetic stimulation (TMS) uses electromagnetic induction as an efficient, painless, non-invasive method to generate a suprathreshold current at the level of the encephalon, and provide in vivo measurements of cortical excitability and reactivity at the level of the motor cortex (TMS-EMG) or the entire cortical mantle (TMS-EEG). This study proposes TMS measurements as a diagnostic tool in patients to understand mechanisms of epileptogenesis related to genetic mutations, and prognostic to guide and monitor precision treatments.
The goal of this clinical trial is to test a new method for newborn screening using whole genome sequencing, called BeginNGS. Newborns who are not suspected of having genetic diseases and who are admitted to the NICU at Rady Children's Hospital, San Diego, will be enrolled. The main questions this study aims to answer are: - What is the diagnostic yield of diagnostic whole genome sequencing (DWGS) in this population? - What is the diagnostic sensitivity and specificity of BeginNGS and whole exome sequencing (WES) as compared to DWGS? - What are the potential issues related to implementing DWGS in this population? Enrolled newborns will have a blood sample taken and will receive three tests: - DWGS - BeginNGS - WES
The DEVO-DECODE project aims to align our currently limited knowledge currently limited knowledge of the genetic architecture of developmental with our more advanced knowledge of their "phenome". To this end, we aim to establish a homogeneous cohort of patients with with developmental disorders to identify new genetic variants genetic variants, and thus study the association between developmental and genetic variants. Secondary objectives are:2 - Carry out WGS studies not only to refine exosomal sequencing data exome sequencing data, but above all to identify and validate non-coding non-coding DNA alterations, in both transcribed and non-transcribed transcribed or non-transcribed genomic domains - Develop precise preclinical models for functional studies of pathophysiological pathways
This cohort study aims to explore the trends and differences in multidimensional perceptual levels of patients after cochlear implants or gene therapy, as well as to comprehensively assess the efficacy of gene therapy for congenital deafness, thus providing a reference for making a well-rounded postoperative rehabilitation protocol for gene therapy patients.
Over the past twenty years, Prof. Yanick Crow and his team have developed internationally recognized expertise in genetic pathologies affecting the immune and neurological systems. The pathologies studied have a particularly severe impact on patients' quality of life, with a high mortality rate and a significant risk of occurrence in affected families. These pathologies are rare, and very often under-diagnosed. To date, there is virtually no effective curative treatment. Prof. Crow's team operates at the frontier between clinical and research work, and from experience, the team knows that patients and families affected by these serious pathologies are often highly motivated to help research into the pathology that affects them. Initially, Prof. Crow's research focused primarily on the study of the genetic disease Aicardi-Goutières Syndrome (AGS). However, there is an undeniable clinical and pathological overlap between AGS and other forms of disease such as autoimmune systemic lupus erythematosus and many other genetic pathologies - e.g. familial lupus engelure, spondyloenchondromatosis and COPA syndrome. This is why research is being extended to all genetic diseases with immune and neurological dysfunctions.
There are around 8,000 rare diseases and new ones are described every month in the scientific literature. They affect a limited number of patients. Nearly 80% of these diseases have a genetic origin and 30 to 40% of them are associated with dysmorphia. The latter can be suspected by evaluating the morphological characteristics of the patient. This medical skill, called dysmorphology, which allows a diagnosis to be made by evaluating the morphological characteristics of a patient, is based on experience. Diagnosis is often easy for relatively common diseases, but more difficult for rarer pathologies affecting few patients and often described in a single ethnicity and age of life. The study aims to create a dataset specific to the application of methods from artificial intelligence. Extending the methodologies described to profile and extremity photographs will allow better recognition and description of dysmorphia. This will allow to make diagnostic suggestions by comparison with the database. The Data Science team has already explored the notion of phenotypic similarity of patients. Jean Feydy is a mathematician expert in image analysis and will ensure the scientific robustness of the study methods. This project will conclude with the establishment of a diagnostic aid tool, integrating research results for doctors with a particular interest in developmental anomalies and intellectual disability.
object name: Multicenter analysis of genomic and metabolic data of neonatal genetic diseases. goal of study:(1) Gene sequencing data (138 genes related to 133 common genetic diseases) and tandem mass spectrometry metabolomics data (11 amino acids and 28 acylcarnitines) of about 40,000 newborns from the South China Neonatal Genetic Screening Alliance participating units were collected and collated to complete the database construction of genes and mass spectrometry. (2) Explore the use of genome and metabolome big data and machine learning algorithms such as Random forest, Support Vector Machine, Elastic net, Multilayer Perceptron to construct prediction models for common genetic diseases, and strive to achieve accurate diagnosis and prediction of common genetic diseases using simple tandem mass spectrometry metabolome data, and expand the application range of tandem mass spectrometry technology for disease detection. research design:retrospective observational study Research period:September 2022 to December 2025 Participating units:South China Neonatal genetic screening Alliance (including cooperation units of 123 hospitals) research object:Gene screening data of 40,000 newborns ( 138 genes related to 133 common genetic diseases ) and tandem mass spectrometry data ( 11 amino acids and 28 acylcarnitines ). Inclusion criteria:( 1 ) Newborns who underwent genetic screening and tandem mass spectrometry at the same time. ( 2 ) Age : 0-28 days, gestational age 37-42 weeks. Excluded criteria:Data that meets any of the following conditions need to be eliminated : ( 1 ) Neonatal data with unclear clinical basic information ; ( 2 ) Lack of traceability core information data ; ( 3 ) The data that the test results cannot be analyzed and interpreted. data collection:( 1 ) Basic information : gender, age, sample type, subject traceability number / ID number, etc. ( 2 ) Clinical symptoms, biochemical and imaging data of positive samples. ( 3 ) Gene detection results and tandem mass spectrometry results. ( 4 ) Date of test data, instrument model, reagent type, etc.