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Rare Diseases

Rare Diseases are a diverse group of disorders that affect a small percentage of the population.
These conditions are often challenging to diagnose and treat due to their complexity and the limited availability of research and resources.
Leveraging the power of artificial intelligence, PubCompare.ai offers a unique platform to enhance rare disease research reproducibility and accuracy.
By locating protocols from literature, pre-prints, and patents, and utilizing AI-driven comparisons, researchers can identify the best protocols and products for their rare disease investigations.
This optimizes the research process and drives breakthroughs in rare disease treatment, leading to improved outcomes for individuals affected by these rare and often neglected conditions.

Most cited protocols related to «Rare Diseases»

1
Instrumental Variable Analysis in Case-Control Studies
When case–control data are available, it is usual to make inferences on the IV-exposure association using only the controls.37 (link) This makes the assumption that the association of the IV with the exposure in the controls is similar to that in the general population, which is true for a rare disease.38 (link) This is necessary for two reasons. The first is reverse causation, as measurements of the exposure in a retrospective setting may be distorted by a disease event. Second, over-recruitment of cases into the study means that the distribution of confounders in the case–control sample is different to that in the general population. An association may be induced between the IV and the confounders, leading to possible bias in the IV estimate.9 This affects not only the ratio method but all IV methods. The IV–outcome association estimate should not be affected provided that logistic regression is used, as logistic regression estimates are invariant to outcome-dependent sampling. It is also necessary to assume that the probability of recruitment into the study is not dependent on the genetic variants used as IVs. For example, a genetic variant associated with increased mortality after an outcome event would appear to be protective rather than deleterious if the individuals who died were excluded from the study.
If the outcome is common and its prevalence in the population from which the case–control sample was taken is known, such as in a nested case–control study, then inferences on the IV–exposure association can be obtained using both cases and controls, provided that measurements of the exposure in cases were taken prior to the outcome event. This analysis can be performed by weighting the sample so that the proportions of cases and controls in the reweighted sample match those in the underlying population.38 (link)
Burgess S., Small D.S, & Thompson S.G. (2015). A review of instrumental variable estimators for Mendelian randomization. Statistical Methods in Medical Research, 26(5), 2333-2355.
Publication 2015
Genetic Diversity Rare Diseases
2
Web-based Genomic Variant Annotation
The web server is composed of a web interface and a background program for executing annotation tasks. Our tests indicated that the server performed well under a light load for user queries. For example, annotating an exome with ~20 000 SNPs and indels takes merely a few minutes in the server. The subroutines for handling user query were written in Perl and were facilitated by the Common Gateway Interface module (CGI.pm). The static and dynamic HTML pages have been tested in different versions of Internet Explorer, Firefox and Google Chrome browsers.
Input fields for the wANNOVAR server include a sample identifier, an email address, a variant file, the reference genome build, the gene definition system and optionally a disease model for running the ‘variants reduction’ pipeline. The default input format for the variant file is variant call format (VCF),3 which is a text file that contains meta-information lines, a header line, and data lines containing information about a position in the genome. The server can also handle other input formats, including the ANNOVAR input format, the Complete Genomics ASM.tsv format and the GFF3-SOLiD format. Currently, the input file size is restricted to less than 200 MB, and the input file can be compressed in .gz or .zip format. If all input fields are correctly set, the server will return a webpage with a URL for the results page.
The results page contains a collection of functional annotations for variant calls. Users can download the ‘exome summary results’ or the ‘genome summary results’ as Excel-compatible files or tab-delimited files, or choose to view the annotation results in a table on the webpage. The annotations on all variants were grouped into several broad categories including gene annotation, variation databases, functional prediction and region annotations (table 1). Several functional prediction scores for exonic variants from the dbNSFP Database4 (link) including SIFT,5 (link) PolyPhen,6 (link) LRT,7 (link) MutationTaster8 (link) and PhyloP,9 are also provided in the wANNOVAR server to help users judge the functionality of variants using multiple sources of information. As previously described, wANNOVAR can perform a ‘variants reduction’ procedure to identify a subset of the most likely causal variants/genes for Mendelian diseases, from a large list of variants on personal genomes.2 (link) For example, users can remove variants observed in public databases such as the 1000 Genomes Project,10 (link) NHLBI-ESP 5400 exomes11 and dbSNP12 (link) with specific minor allele frequency cut-off. The server uses modified versions of dbSNP that excluded all SNPs flagged as ‘clinically associated’ by dbSNP. We provide several default pipelines for different disease models such as ‘rare recessive Mendelian disease’ and ‘rare dominant Mendelian disease’, but users can also use ‘advanced options’ to specify a custom filtering strategy (table 2).
Chang X, & Wang K. (2012). wANNOVAR: annotating genetic variants for personal genomes via the web. Journal of medical genetics, 49(7), 433-436.
Publication 2012
Exome Exons Gene Annotation Genes, vif Genitalia Genome INDEL Mutation Light Rare Diseases Single Nucleotide Polymorphism Strains
3
Practical Guide to Random Forests
Random forests have only recently been included in standard textbooks on statistical learning, such as Hastie et al. (2009) (while the previous edition, Hastie et al. 2001 , did not cover this topic yet). In addition to a short introduction of random forests, this reference gives a thorough background on classification trees and related concepts of resampling and model validation, and is therefore highly recommended for further reading. For the social sciences audience a first instructive review on ensemble methods, including random forests and the related method bagging, was given by Berk (2006) . We suggest this reference for the treatment of unbalanced data (for example in the case of a rare disease or mental condition), that can be treated either by means of asymmetric misclassification costs or equivalently by means of weighting with different prior probabilities in classification trees and related methods (see also Chen, Liaw, and Breiman 2004 , for the alternative strategy of “down sampling”, i.e., sampling from the majority class as few observations as there are of the minority class), even though the interpretation of interaction effects in Berk (2006) is not coherent, as demonstrated above. The original works of Breiman (1996a ,b , 1998a,b , 2001a ,b ), to name a few, are also well suited and not too technical for further reading.
For practical applications of the methods introduced here, several up-to-date tools for data analysis are freely available in the R system for statistical computing (R Development Core Team 2008 ). Regarding this choice of software, we believe that the supposed disadvantage of command line data analysis criticized by Berk (2006) is easily outweighed by the advanced functionality of the R language and its add-on packages at the state of the art of statistical research. However, in statistical computing the textbooks also lag behind the latest scientific developments: The standard reference Venables and Ripley (2002) does not (yet) cover random forests either, while the handbook of Everitt and Hothorn (2006) gives a short introduction to the use of both classification trees and random forests. This handbook, together with the instructive examples in the following section and the R-code provided in a supplement to this work, can offer a good starting point for applying random forests to your data. Interactive means of visual data exploration in R, that can support further interpretation, are described in Cook and Swayne (2007) .
Strobl C., Malley J, & Tutz G. (2009). An Introduction to Recursive Partitioning: Rationale, Application and Characteristics of Classification and Regression Trees, Bagging and Random Forests. Psychological methods, 14(4), 323-348.
Publication 2009
Dietary Supplements Minority Groups Rare Diseases Teaching Teaching Methods Trees
4
Governance and Access Control for Clinical Research Database
The CRIS Oversight Committee (which evolved from the Stakeholder Committee, after CRIS received research ethics approval as a de-identified database) comprises the central governance entity overseeing security. Access to CRIS is application-based. Potential users submit an application to the CRIS Oversight Committee, in which they are asked to describe their project and the variables of interest. The committee, chaired by a mental health service user, also includes a child and adolescent mental health clinical representative, a representative of the Trust’s Caldicott Guardian, a Research Ethics representative, the CRIS academic project lead and the CRIS project manager. Potential applications looking to conduct audit of clinical services using CRIS need to gain approval from the relevant audit committee (within SLaM) before applying to use CRIS. Likewise, research project applicants need a senior university or NHS affiliated supervisor attached to and taking responsibility for the project and applicant before applying to use CRIS. Each applicant must have a formal affiliation in the form of an honorary or substantive contract with the hospital or the university before applying to access CRIS. These formally bind the applicant to the NHS duty of confidentiality when dealing with patient data (including de-identified patient data) [27 ].
Upon submission, the Oversight Committee determines whether a project is deemed suitable to access the CRIS database. “Suitability” is ascertained by verifying the need for the project, the scientific robustness of the application, and any patient confidentiality concerns to which the project may give rise. Any projects with the potential to identify patients, such as those investigating rare disorders or outcomes, are carefully discussed with the researcher and their supervisor and, where possible, alternatives provided (for example, the applicant is encouraged to obtain patient consent).
If researchers receive approval to use the CRIS system for the submitted project, they are permitted to access CRIS only within the SLaM security firewall and must follow a set of rules which facilitate responsible handling of data and uphold duties of confidentiality. All projects are audited weekly to ensure searches are being carried out within the remit of the submitted and approved project. Approval to use CRIS can be withdrawn in cases where inappropriate searches have been made in violation of the terms of the approved project. These procedures focus on close regulation of access to CRIS, as well as close monitoring of use of CRIS (Figure 3). The researcher must commit to ensuring that s/he will uphold the NHS duty of confidentiality when handling the data and adhere to the guidelines set out by CRIS (including not carrying data out of the Trust firewall for any purpose). In this way, the security model endeavours significantly to mould the researcher’s intentions – and hence behaviour – when encountering the data, so as to minimize any threats posed to patient anonymity identified above.
Fernandes A.C., Cloete D., Broadbent M.T., Hayes R.D., Chang C.K., Jackson R.G., Roberts A., Tsang J., Soncul M., Liebscher J., Stewart R, & Callard F. (2013). Development and evaluation of a de-identification procedure for a case register sourced from mental health electronic records. BMC Medical Informatics and Decision Making, 13, 71.
Publication 2013
Adolescent Child Clinical Audit Fungus, Filamentous Legal Guardians Mental Health Mental Health Services Patients Rare Diseases Secure resin cement
5
Comprehensive Immune Disorder Categorization
We included in our figures all disorders indexed in the 2019 update of the IUIS IEI classification [1 ]. A phenotypic algorithm was assigned to each of the ten main groups of the classification and the same color was used for each group of similar conditions. Given the high number of diseases, several categories have been split since last update [3 (link)] in two sub-figures to be more informative.
Disease names are presented in red and genes in bold italic. An asterisk is added to highlight extremely rare disorders (less than 10 reported cases to date). However, the reader should keep in mind that some genes have been very recently described and that true prevalence is unknown. A double asterisk is added when only one case or one kindred has been reported to date. In these cases, it is difficult to confirm than observed phenotype would be reproducible in other patients carrying the same defect, or if it is an exception.
Bousfiha A., Jeddane L., Picard C., Al-Herz W., Ailal F., Chatila T., Cunningham-Rundles C., Etzioni A., Franco J.L., Holland S.M., Klein C., Morio T., Ochs H.D., Oksenhendler E., Puck J., Torgerson T.R., Casanova J.L., Sullivan K.E, & Tangye S.G. (2020). Human Inborn Errors of Immunity: 2019 Update of the IUIS Phenotypical Classification. Journal of Clinical Immunology, 40(1), 66-81.
Publication 2020
Genes Patients Phenotype Rare Diseases

Most recents protocols related to «Rare Diseases»

1
Isolation and Treatment of Striatal Neurons
Primary striatal neurons were isolated from ICR mice at embryonic day 17 as previously described (Fitting et al., 2014 (link); Zou et al., 2011 (link)). Briefly, striatal tissues were dissected, minced, and incubated for 30 min at 37oC in neurobasal medium (NBM) containing 0.015 mg/mL DNase and 2.5 mg/ml trypsin. NBM was supplemented with 25 mM glutamate, 0.5 mM glutamine, B27 (Invitrogen), and an antibiotic-antimycotic solution (Sigma). Cells were then triturated, filtered (2×) through a 70 μm-diameter pore membrane, and seeded into six-well plates (15   ×   105 neurons/well) pre-coated with poly-L-lysine. Cells were maintained in NBM supplemented at decreasing concentrations of glutamate (days 1–4, 25 mM; days 5–6, 12.5 mM; days 7–11, 0 mM) at 37oC in a 5% CO2 environment until they matured (at 11 days). Based on our findings that TDP-43 phosphorylation was greatest following co-exposure to morphine and Tat in murine brain tissues, and to optimize detecting alterations in TDP-43 phosphorylation, we co-exposed cells to Tat and morphine to verify the involvement of CK2 in the pathologic TDP-43 phosphorylation. Cells were either treated with vehicle (DNase/RNase-free non-pyrogenic Ultrapure water), co-treated with Tat (100 nM) and morphine (500 nM), or a combination of Tat, morphine, and 0.5, 1, or 2 µM concentrations of the highly selective CK2 antagonist CX-4945 (#A11060, AdooQ Bioscience, Irvine, CA). CX-4945 (Silmitasertib) was granted Orphan Drug Designation by the US FDA in December 2021 for the treatment of medulloblastoma and potentially other rare diseases targeting CK2. Vehicle-treated cells served as controls. The selected concentrations of Tat and morphine do not affect cell viability within 24 h (Zou et al., 2011 (link)). The doses of CX-4945 selected for testing are those previously determined to reduce pro-inflammatory mediators to baseline levels in immune cells (Jang et al., 2017 (link)). Treated neurons were incubated at 37oC in a 5% CO2 environment for 24 h. Afterward, the cells were harvested, and the cytoplasmic and nuclear fractions were extracted using a NE-PER nuclear and cytoplasmic extraction kit (Thermo Fisher). BCA was used to determine protein concentrations and samples were stored in aliquots at −80oC until use.
Ohene-Nyako M., Nass S.R., Richard H.T., Lukande R., Nicol M.R., McRae M., Knapp P.E, & Hauser K.F. (2023). Casein Kinase 2 Mediates HIV- and Opioid-Induced Pathologic Phosphorylation of TAR DNA Binding Protein 43 in the Basal Ganglia. ASN NEURO, 15, 17590914231158218.
Publication 2023
Antibiotics Brain Cells Cell Survival CX 4945 Cytoplasm Deoxyribonucleases Drugs, Orphan Embryo Glutamate Glutamine Inflammation Mediators Lysine Medulloblastoma Mice, Inbred ICR Morphine Mus Neurons Phosphorylation Poly A Proteins protein TDP-43, human Rare Diseases Ribonucleases silmitasertib Striatum, Corpus Tissue, Membrane Tissues Trypsin
2
Genetic Sequencing and Plasma Analysis of Rare Kidney Disease
Human data for the current study were provided by the Wake Forest Rare Inherited Kidney Disease Registry, as approved by the Wake Forest School of Medicine Institutional Review Board, in adherence with the Declaration of Helsinki (15 (link)). MUC1 sequencing was performed either at the Broad Institute using mass spectrometry–based probe extension (16 (link)) or at the Charles University, using Illumina and SMRT methods (17 (link)). Plasma creatinine and total Ca levels were measured using standard clinical methods at the Wake Forest Baptist Health clinical pathology laboratory. For each individual, an average Ca and eGFR value was calculated based on the first three available measurements. Individuals were excluded if they had mean eGFR less than 60 ml/min or had nonphysiologic eGFR (greater than 200 ml/min) or Ca2+ (greater than 12 mg/dl or less than 8 mg/dl).
Al-bataineh M.M., Kinlough C.L., Marciszyn A., Lam T., Ye L., Kidd K., Maggiore J.C., Poland P.A., Kmoch S., Bleyer A., Bain D.J., Montalbetti N., Kleyman T.R., Hughey R.P, & Ray E.C. (2023). Influence of glycoprotein MUC1 on trafficking of the Ca2+-selective ion channels, TRPV5 and TRPV6, and on in vivo calcium homeostasis. The Journal of Biological Chemistry, 299(3), 102925.
Publication 2023
Alport Syndrome Clinical Laboratory Techniques Creatinine EGFR protein, human Ethics Committees, Research Forests Homo sapiens Mass Spectrometry MUC1 protein, human NCOR2 protein, human Pharmaceutical Preparations Plasma Rare Diseases
3
Down Syndrome Regression Disorder Cases
Between December 2021 and December 2022, we included four patients with DS who presented criteria for DSRD and were seen at the reference center for rare diseases with psychiatric expression (CRMR, PEPIT department, GHU Paris Psychiatry and Neurosciences). Diagnosis of DSRD was done by two expert psychiatrists (PE and BC) according to the Expert Consensus (Santoro et al., 2022b (link)) and after reasonable exclusion of alternative causes of regression, including primary psychiatric disorders. All patients were referred from the Jérôme Lejeune Institute in Paris, where patients were regularly followed. The Jérôme Lejeune institute is an expert center specialized in healthcare and research for individuals with DS. All patients and their families gave their consent for publication of this case series. Clinical presentations, the main workup, and the therapeutic challenges of the four cases are summarized in the Table 1 below.
Bonne S., Iftimovici A., Mircher C., Conte M., Louveau C., Legrand A., Danset-Alexandre C., Cannarsa C., Debril A., Consoli A., Krebs M.O., Ellul P, & Chaumette B. (2023). Down syndrome regression disorder, a case series: Clinical characterization and therapeutic approaches. Frontiers in Neuroscience, 17, 1126973.
Publication 2023
Diagnosis Mental Disorders Patients Psychiatrist Rare Diseases Therapeutics Vision
4
PCR Product Sequencing for Genetic Diagnosis
PCR products were purified and sequenced by ‘Intergen Genetics and Rare Diseases Diagnosis and Research Centre’ in Turkey using standard operating procedures.
Lydia N., Basamba T.A., Nyakoojo C., Mustafa A.S., Saidi N., Mutumba G.M, & Ssenku J.E. (2023). Ecological distribution and genetic diversity of Azolla in Uganda. BMC Plant Biology, 23, 131.
Publication 2023
Diagnosis Rare Diseases
5
Ex Vivo Fluorescence Imaging of Thymoma and Thymic Carcinoma
22 patients with thymoma or thymic carcinoma who underwent surgery between February 2013 and January 2021 at the University of Tokyo were included in the study. These are relatively rare diseases and only a few cases are operated on each year. In addition, only tumors larger than 2 cm were selected in this study to avoid compromising pathological assessment. Twenty patients were enrolled for the ex vivo fluorescence imaging study (Figs. 1, 2, 3, 4 and Supplementary Table S1), one patient was enrolled for the experiment involving application to serial sections of tumor and normal tissue (Fig. 5) and one patient was enrolled for the chemical inhibition experiment (Fig. 6). Some patients were included in plural analyses. Written informed consent was obtained from all patients, and this study was approved by the ethics committee of The University of Tokyo and the local ethics committees. All experiments were performed in accordance with guidelines and regulations approved by the ethics committees. All specimens were taken intraoperatively. Fluorescence images, except for the inhibition experiment and the experiment comparing HMRG only and gGlu-HMRG, were collected within a day after resection. For the inhibition experiment, frozen specimens were thawed at room temperature and used within 6 months. With regard to the experiment comparing HMRG only and gGlu-HMRG, data from frozen and raw specimens are mixed.
Yoshida D., Kamiya M., Kawashima S., Yoshioka T., Hino H., Abe A., Fujita K., Kojima R., Shinozaki-Ushiku A., Urano Y, & Nakajima J. (2023). Rapid imaging of thymoma and thymic carcinoma with a fluorogenic probe targeting γ-glutamyltranspeptidase. Scientific Reports, 13, 3757.
Publication 2023
Ethics Committees Fluorescence Freezing Neoplasms Operative Surgical Procedures Patients Psychological Inhibition Rare Diseases Regional Ethics Committees Thymic Carcinoma Thymoma Tissues

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1
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More about "Rare Diseases"

Rare disorders, uncommon conditions, infrequent maladies, and orphan diseases are terms used to describe a diverse group of medical issues that affect a small percentage of the population.
These complex and often challenging-to-diagnose conditions can be difficult to research and treat due to the limited availability of resources and research data.
Leveraging artificial intelligence (AI) and machine learning (ML) technologies, platforms like PubCompare.ai offer innovative solutions to enhance the reproducibility and accuracy of rare disease investigations.
By locating relevant protocols from literature, pre-prints, and patents, and utilizing AI-driven comparative analyses, researchers can identify the most effective protocols and products for their rare disease studies.
This optimization of the research process can lead to breakthroughs in rare disease treatment, ultimately improving outcomes for individuals affected by these often overlooked and underserved conditions.
Key subtopics within the realm of rare diseases include genetic disorders, autoimmune diseases, neurological conditions, and metabolic disorders, among others.
Advancements in high-throughput sequencing technologies, such as HiSeq 2500 and HiSeq 2000, as well as statistical software like Stata version 14, SAS version 9.4, SPSS version 20 and 22.0, have contributed to the growing understanding and management of these rare and complex conditions.
Additionally, the use of tools like the Nextera Rapid Capture panel and the incorporation of Penicillin/streptomycin in research protocols have further enhanced the ability to study and treat rare diseases.
By harnessing the power of AI and leveraging the latest advancements in technology and research methods, the rare disease community can work towards improved outcomes and a brighter future for those affected by these often overlooked and underserved conditions.