Identification Of Rare Diseases Research Articles

Artificial Intelligence, Big Data, and Regulation of Immunity: Challenges and Opportunities.

The immune system is regulated by a complex set of genetic, molecular, and cellular interactions. Rapid advances in the study of immunity and its network of interactions have been boosted by a spectrum of "omics" technologies that have generated huge amounts of data that have reached the status of big data (BD). With recent developments in artificial intelligence (AI), theoretical and clinical breakthroughs could emerge. Analyses of large data sets with AI tools will allow the formulation of new testable hypotheses open new research avenues and provide innovative strategies for regulating immunity and treating immunological diseases. This includes diagnosis and identification of rare diseases, prevention and treatment of autoimmune diseases, allergic disorders, infectious diseases, metabolomic disorders, cancer, and organ transplantation. However, ethical and regulatory challenges remain as to how these studies will be used to advance our understanding of basic immunology and how immunity might be regulated in health and disease. This will be particularly important for entities in which the complexity of interactions occurring at the same time and multiple cellular pathways have eluded conventional approaches to understanding and treatment. The analyses of BD by AI are likely to be complicated as both positive and negative outcomes of regulating immunity may have important ethical ramifications that need to be considered. We suggest there is an immediate need to develop guidelines as to how the analyses of immunological BD by AI tools should guide immune-based interventions to treat various diseases, prevent infections, and maintain health within an ethical framework.

Cannabis for medicinal use in patients with rare diseases

BACKGROUND: Patients with Rare Diseases (RDs) present with chronic and debilitating symptoms such as pain, anxiety and epileptic seizures. Symptoms can be unresponsive to conventional treatment and may lead to a decreased Quality of Life for patients. Cannabinoids have been reported to be efficacious against chronic pain refractory to conventional analgesics, anxiety and seizures. OBJECTIVE: Identification of RDs for which Medicinal Cannabis (MC) can be used and identification of issues related to RDs and perceptions on the use of MC in patients with RDs. METHODS: Study was divided into 2 phases. Phase 1: Literature Review to identify RDs in which cannabis or cannabinoids are used Phase 2: Development, validation and dissemination of 2 questionnaires for: (i) Health Care Professionals (HCPs) and (ii) RD patients. RESULTS: Cannabinoids were described as possible therapeutic agents in 20 RDs. The questionnaires were completed by 101 HCPs and 38 RD patients. Thirty-three HCPs had no experience on use of MC but would consider using it in their practice for management of RDs. Most patients (n = 29) did not have experience with use of MC and 20 patients would consider using MC to treat their condition or relieve symptoms of their disease. CONCLUSION: The study helps identify the potential of MC use in RDs associated with chronic symptoms such as pain, muscle spasticity, seizures and anxiety.

Rare Metabolic and Endocrine Diseases with Cardiovascular Involvement: Insights from Cardiovascular Magnetic Resonance - A Review.

The identification of rare diseases with cardiovascular involvement poses significant diagnostic challenges due to the rarity of the diseases, but also due to the lack of knowledge and expertise. Most of them remain underrecognized and undiagnosed, leading to clinical mismanagement and affecting the patients' prognosis, as these diseases are per definition life-threatening or chronic debilitating. This article reviews the cardiovascular involvement of the most well-known rare metabolic and endocrine diseases and their diagnostic approach through the lens of cardiovascular magnetic resonance (CMR) imaging and its prognostic role, highlighting its fundamental value compared to other imaging modalities.

Rare Disease Identification from Clinical Notes with Ontologies and Weak Supervision.

The identification of rare diseases from clinical notes with Natural Language Processing (NLP) is challenging due to the few cases available for machine learning and the need of data annotation from clinical experts. We propose a method using ontologies and weak supervision. The approach includes two steps: (i) Text-to-UMLS, linking text mentions to concepts in Unified Medical Language System (UMLS), with a named entity linking tool (e.g. SemEHR) and weak supervision based on customised rules and Bidirectional Encoder Representations from Transformers (BERT) based contextual representations, and (ii) UMLS-to-ORDO, matching UMLS concepts to rare diseases in Orphanet Rare Disease Ontology (ORDO). Using MIMIC-III US intensive care discharge summaries as a case study, we show that the Text-to-UMLS process can be greatly improved with weak supervision, without any annotated data from domain experts. Our analysis shows that the overall pipeline processing discharge summaries can surface rare disease cases, which are mostly uncaptured in manual ICD codes of the hospital admissions.

Diagnostic precision and identification of rare diseases is dependent on distance of residence relative to tertiary medical facilities

BackgroundDiagnostic precision and the identification of rare diseases is a daily challenge, which needs specialized expertise. We hypothesized, that there is a correlation between the distance of residence to the next tertiary medical facility with highly specialized care and the diagnostic precision, especially for rare diseases.ResultsUsing a nation-wide hospitalization database, we found a negative association between diagnostic diversity and travel time to the next tertiary referral hospital when including all cases throughout the overall International Classification of Diseases version 10 German Modification (ICD-10-GM) diagnosis codes. This was paralleled with a negative association of standardized incidence rates in all groups of rare diseases defined by the Orphanet rare disease nomenclature, except for rare teratologic and rare allergic diseases.ConclusionOur findings indicate a higher risk of being mis-, under- or late diagnosed especially in rare diseases when living more distant to a tertiary medical facility. Greater distance to the next tertiary medical facility basically increases the chance for hospitalization in a non-comprehensive regional hospital with less diagnostic capacity, and, thus, impacts on adapted health care access. Therefore, solutions for overcoming the distance to specialized care as an indicator of health care access are a major goal in the future.

The interoperability between the Spanish version of the International Classification of Diseases and ORPHAcodes: towards better identification of rare diseases

BackgroundRare diseases present a wide spectrum of clinical manifestations and severity levels and are often poorly known and underrepresented, making them difficult to classify. Diagnoses are usually coded using the International Classification of Diseases (ICD), with its different versions. In Spain, the ICD-10-ES (stem from the ICD-10-CM–Clinical Modification) is used throughout the National Healthcare System since 2016, indistinctively including rare diseases that often lack a specific code. Orphanet aims to provide high-quality resources on rare diseases. The goal was to interrelate the Orphanet classification with the ICD-10-ES in order to engage a tool to track rare diseases diagnosis and characterize the improvement space for the identification of rare diseases patients in the Spanish Healthcare System.Methods5775 disorder level ORPHAcodes were mapped to ICD-10-ES codes by comparing the descriptors associated in both classifications. ORPHAcodes were then clustered based on their assigned ICD-10-ES chapter and the redundancy of each individual ICD-10-ES code was calculated by counting the ORPHAcodes they mapped to. Three groups were established: Group 1 (1 ORPHAcode per ICD-10-ES), Group 2 (between 2–49 ORPHAcodes per ICD-10-ES) and Group 3 (≥ 50 ORPHAcodes per ICD-10-ES).ResultsEquivalences to 1700 ICD-10-ES codes were established for 5664 ORPHAcodes. The ORPHAcodes distribution within the ICD-10-ES showed an aggregation in the “Q” (> 40%), “G” (> 14%), and “E” (12%) chapters. The availability of ICD-10-ES codes to map ORPHAcodes reached its lowest at the “G” and “Q” chapters with less than 0.2 ICD-10-ES codes available per ORPHAcode. Global ICD-10-ES codes redundancy analysis revealed that only 1055 of the equivalences pertain to group 1. Group 2 contained 3358 equivalences with 634 ICD-10-ES codes while 1322 equivalences were group 3 (11 ICD-10-ES). Within ICD-10-ES chapters, “G” and “Q” contained over 30% and 45% of their own equivalences in the highest redundancy level (group 3) respectively, but under 10% one to one equivalences each (group 1).ConclusionsICD-10-ES codes have not enough specificity to identify rare diseases. Direct mapping between ICD and ORPHAcodes or the integration of ORPHAcodes at the healthcare system for diagnoses codification would enable better detection and epidemiological analysis of rare diseases.

Identification of rare diseases by screening a population selected on the basis of routine pathology results—the PATHFINDER project: lysosomal acid lipase/cholesteryl ester storage disease substudy

AimsLysosomal acid lipase deficiency (LALD) is an autosomal recessive disorder of cholesterol ester storage associated with hepatic disease, cirrhosis and accelerated atherosclerosis. Its prevalence in the general population, patients with...

Facial analysis technology aids diagnoses of genetic disorders: applications narrow down potential genetic syndromes by matching facial phenotypes to distinct set of possible genetic conditions.

Facial analysis technology aids diagnoses of genetic disorders: applications narrow down potential genetic syndromes by matching facial phenotypes to distinct set of possible genetic conditions.

Prevalence of Cholesteryl Ester Storage Disease

Cholesteryl ester storage disease (CESD) is an autosomal recessive chronic liver disease caused by lysosomal acid lipase (LAL) deficiency. The gene is located on chromosome 10q23.2-q23.3, and the enzyme is essential for triglycerides and cholesteryl ester hydrolysis in lysosomes. CESD is characterized by hypercholesterolemia, hypertriglyceridemia, HDL deficiency, and abnormal lipid deposition in many organs. In the liver this results in hepatomegaly caused by hepatic steatosis and fibrosis that can lead to micronodular cirrhosis.1 Disease onset takes place during childhood or adolescence. Males and females are affected in about equal numbers. Patients rarely reach the age of 30. Biochemically, the disorder is recognized by largely reduced lysosomal acid lipase activity.2,3 Complete absence of LAL activity causes Wolman Disease, which is normally fatal within the first 6 months of life.1,4 Several groups have identified mutations in the LAL gene underlying CESD and Wolman disease.5–9 Mutations causing Wolman disease produce an enzyme with no residual activity or no enzyme at all, whereas CESD-causing mutations encode for LAL which retains some enzyme activity.4,10 A G-to-A transition at position −1 of the exon 8 splice donor (E8SJM, E xon 8 S plice J unction M utation) leads to an in-frame deletion of exon 8. The resulting protein is 24 amino acids shorter and has no residual LAL activity, however E8SJM does not cause Wolman Disease because 2% to 4% of normally spliced LAL is present in homozygote carriers.11,12 The vast majority of CESD …