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E-Learning for Rare Diseases: An Example Using Fabry Disease.

ABSTRACT: Rare diseases represent a challenge for physicians because patients are rarely seen, and they can manifest with symptoms similar to those of common diseases. In this work, genetic confirmation of diagnosis is derived from DNA sequencing. We present a tutorial for the molecular analysis of a rare disease using Fabry disease as an example.An exonic sequence derived from a hypothetical male patient was matched against human reference data using a genome browser. The missense mutation was identified by running BlastX, and information on the affected protein was retrieved from the database UniProt. The pathogenic nature of the mutation was assessed with PolyPhen-2. Disease-specific databases were used to assess whether the missense mutation led to a severe phenotype, and whether pharmacological therapy was an option.An inexpensive bioinformatics approach is presented to get the reader acquainted with the diagnosis of Fabry disease. The reader is introduced to the field of pharmacological chaperones, a therapeutic approach that can be applied only to certain Fabry genotypes.The principle underlying the analysis of exome sequencing can be explained in simple terms using web applications and databases which facilitate diagnosis and therapeutic choices.

SUBMITTER: Cimmaruta C

PROVIDER: S-EPMC5666731 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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E-Learning for Rare Diseases: An Example Using Fabry Disease.

Cimmaruta Chiara C Liguori Ludovica L Monticelli Maria M Andreotti Giuseppina G Citro Valentina V

International journal of molecular sciences 20170924 10

<h4>Background</h4>Rare diseases represent a challenge for physicians because patients are rarely seen, and they can manifest with symptoms similar to those of common diseases. In this work, genetic confirmation of diagnosis is derived from DNA sequencing. We present a tutorial for the molecular analysis of a rare disease using Fabry disease as an example.<h4>Methods</h4>An exonic sequence derived from a hypothetical male patient was matched against human reference data using a genome browser. T ...[more]

PMID: 28946642

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Project description:BackgroundThygeson's superficial punctate keratitis (TSPK) is a rare and still poorly understood disease of the ocular surface, responsible for recurrent episodes of photophobia and eye pain. While TSPK is considered as a benign condition, a subset of patients has frequent recurrences or even chronic disease, two situations in which there are currently no therapeutic guidelines. We used a preexisting Facebook TSPK patient support group to assess the clinical journey and the burden of disease of TSPK.ResultsAn online survey was sent to the patient support group. The first part of the questionnaire gathered information on demographics and the patient's clinical journey [diagnostic modalities, symptoms, duration and frequency of recurrent episodes (RE), efficacy and tolerance to treatments]. The second part focused on quality of life (QoL) using the Ocular Surface Disease-QoL (OSD-QoL) questionnaire. Seventy-two patients out of 595 members of the support group completed the questionnaire during the 3-months study period. Eighty percent of patients developed symptoms before 30 years old, and 47% reported a delay in the diagnosis above 1 year. Sixty percent of patients reported over 5 RE yearly, and 18% of RE lasted more than 3 months. Forty percent of all patients used cyclosporine eyedrops (50% of those with >?5 episodes/year) and it was perceived as effective by 72% of these patients. The impact on daily life activities was judged as severe by 22% of patients, while 38% reported reduced professional activity and 80% were deeply saddened by their eye condition.ConclusionTSPK patients may present with frequent recurrences and/or chronic disease, that result in a severe impact on QoL, and an off-label use of topical immunomodulatory eye drops, suggesting the urgent need for controlled studies. The utility of using social networks for rare ophthalmic disease research includes, faster data collection, data from patients across the globe, and also raises relevant questions about their real needs.

Project description:IntroductionDetermining the epidemiology of disease is critical for multiple reasons, including to perform risk assessment, compare disease rates in varying populations, support diagnostic decisions, evaluate health care needs and disease burden, and determine the economic benefit of treatment. However, establishing epidemiological measures for rare diseases can be difficult owing to small patient populations, variable diagnostic techniques, and potential disease and diagnostic heterogeneity. To determine the epidemiology of rare diseases, investigators often develop estimation models to account for missing or unobtainable data, and to ensure that their findings are representative of their desired patient population.MethodsA modeling methodology to estimate the prevalence of rare diseases in one such population-patients with long-chain fatty acid oxidation disorders (LC-FAOD)-as an illustrative example of its applicability.ResultsThe proposed model begins with reliable source data from newborn screening reports and applies to them key modifiers. These modifiers include changes in population growth over time and variable standardization rates of LC-FAOD screening that lead to (1) a confirmed diagnosis and (2) improvements in standards of care and survival estimates relative to the general population. The model also makes necessary assumptions to allow the broad applicability of the estimation of LC-FAOD prevalence, including rates of diagnosed versus undiagnosed patients in the USA over time.ConclusionsAlthough each rare disease is unique, the approach described here and demonstrated in the estimation of LC-FAOD prevalence provides the necessary tools to calculate key epidemiological estimates useful in performing risk assessment analyses; comparing disease rates between different subgroups of people; supporting diagnostic decisions; planning health care needs; comparing disease burden, including cost; and determining the economic benefit of treatment.

Project description:BACKGROUND:Emerging machine learning technologies are beginning to transform medicine and healthcare and could also improve the diagnosis and treatment of rare diseases. Currently, there are no systematic reviews that investigate, from a general perspective, how machine learning is used in a rare disease context. This scoping review aims to address this gap and explores the use of machine learning in rare diseases, investigating, for example, in which rare diseases machine learning is applied, which types of algorithms and input data are used or which medical applications (e.g., diagnosis, prognosis or treatment) are studied. METHODS:Using a complex search string including generic search terms and 381 individual disease names, studies from the past 10 years (2010-2019) that applied machine learning in a rare disease context were identified on PubMed. To systematically map the research activity, eligible studies were categorized along different dimensions (e.g., rare disease group, type of algorithm, input data), and the number of studies within these categories was analyzed. RESULTS:Two hundred eleven studies from 32 countries investigating 74 different rare diseases were identified. Diseases with a higher prevalence appeared more often in the studies than diseases with a lower prevalence. Moreover, some rare disease groups were investigated more frequently than to be expected (e.g., rare neurologic diseases and rare systemic or rheumatologic diseases), others less frequently (e.g., rare inborn errors of metabolism and rare skin diseases). Ensemble methods (36.0%), support vector machines (32.2%) and artificial neural networks (31.8%) were the algorithms most commonly applied in the studies. Only a small proportion of studies evaluated their algorithms on an external data set (11.8%) or against a human expert (2.4%). As input data, images (32.2%), demographic data (27.0%) and "omics" data (26.5%) were used most frequently. Most studies used machine learning for diagnosis (40.8%) or prognosis (38.4%) whereas studies aiming to improve treatment were relatively scarce (4.7%). Patient numbers in the studies were small, typically ranging from 20 to 99 (35.5%). CONCLUSION:Our review provides an overview of the use of machine learning in rare diseases. Mapping the current research activity, it can guide future work and help to facilitate the successful application of machine learning in rare diseases.

Project description:BackgroundRare diseases may be defined as occurring in less than 1 in 2000 patients. Such conditions are, however, so numerous that up to 5.9% of the population is afflicted by a rare disease. The gambling industry attests that few people have native skill evaluating probabilities. We believe that both students and academics, under-estimate the likelihood of encountering rare diseases. This combines with pressure on curriculum time, to reduce both student interest in studying rare diseases, and academic content preparing students for clinical practice. Underestimation of rare diseases, may also contribute to unhelpful blindness to considering such conditions in the clinic.MethodsWe first developed a computer simulation, modelling the number of cases of increasingly rare conditions encountered by a cohort of clinicians. The simulation captured results for each year of practice, and for each clinician throughout the entirety of their careers. Four hundred sixty-two theoretical conditions were considered, with prevalence ranging from 1 per million people through to 64.1% of the population. We then delivered a class with two in-class on-line surveys evaluating student perception of the importance of learning about rare diseases, one before and the other after an in-class real-time computer simulation. Key simulation variables were drawn from the student group, to help students project themselves into the simulation.ResultsThe in-class computer simulation revealed that all graduating clinicians from that class would frequently encounter rare conditions. Comparison of results of the in-class survey conducted before and after the computer simulation, revealed a significant increase in the perceived importance of learning about rare diseases (p < 0.005).ConclusionsThe computer career simulation appeared to affect student perception. Because the computer simulation demonstrated clinicians frequently encounter patients with rare diseases, we further suggest this should be considered by academics during curriculum review and design.

Dataset Information

E-Learning for Rare Diseases: An Example Using Fabry Disease.

Publications

E-Learning for Rare Diseases: An Example Using Fabry Disease.

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