Bibliographic record
Abstract
Selective IgA deficiency (IgAD) is the most common primary immunodeficiency disorder in Caucasians and is defined as serum IgA concentrations below or equal to 0.07 g/l, with normal serum concentrations of IgM and IgG, in individuals 4 years of age or older. The prevalence of IgAD is approximately 1:600 in the general population and recent results have shown that patients with IgAD have significantly poorer physical health and an increased risk of early death. The domestic dog is more than just a companion and working animal. The almost 400 distinct modern dog breeds represent great phenotypical diversity and there are more than 350 naturally occurring genetic diseases in dogs which clinically resemble the corresponding human diseases. As a result of domestication, the dog’s genome is characterised by long haplotype blocks and linkage disequilibrium (LD) making the dog an appealing model for genetic studies of human diseases. Low serum IgA concentrations in dogs have been reported to clinically resemble human IgAD. However, even though the literature on IgA concentrations in dogs is extensive, the normal range of serum IgA as well as a generally accepted cut-off value for IgAD deficiency has not yet been established. We performed an extensive screen of serum IgA concentrations in more than 1,500 dogs from 22 breeds. Dog breed-specific differences in the prevalence of IgAD indicate the involvement of genetic factors in the development of the disease. Furthermore, certain dog breeds were found to stand out as high-risk breeds. Genome-wide association studies (GWAS) in selected high-risk breeds show that IgAD is associated with genes involved in B cell development and haematopoiesis. Additionally, serum IgA concentrations were found to play an important role in the aetiology of canine atopic dermatitis (CAD), an autoimmune disease in dogs. Molecular identity allowed the quantification of IgA in serum samples from Canadian and Scandinavian wolves with the same antibodies as those used in dogs. Interestingly, wolves from Scandinavia show significantly lower IgA concentrations as compared to Canadian wolves. Due to its size, the Scandinavian wolf population is prone to inbreeding and therefore, it is known to suffer from a decrease in genetic variation. Further analyses are needed to investigate whether Scandinavian wolves and dogs with a high-risk profile for IgAD share certain genetic factors List of scientific papers I. Olsson M, Frankowiack M, Tengvall K, Roosje P, Fall T, Ivansson E et al. The dog as a genetic model for immunoglobulin A (IgA) deficiency: Identification of several breeds with low serum IgA concentrations. Vet Immunol Immunopathol. 2014;255–259. https://doi.org/10.1016/j.vetimm.2014.05.010 II. Tengvall K, Kierczak M, Bergvall K, Olsson M, Frankowiack M, Farias FHG et al. Genome-wide analysis in German shepherd dogs reveals association of a locus on CFA 27 with atopic dermatitis. PLoS Genet. 2013;9:e1003475. https://doi.org/10.1371/journal.pgen.1003475 III. Olsson M, Tengvall K, Frankowiack M, Kierczak M, Bergvall K, Axelsson E et al. Genome-wide Analyses Suggest Mechanisms Involving Early B-cell Development in Canine IgA Deficiency. [Manuscript] IV. Frankowiack M, Hellman L, Zhao Y, Arnemo JM, Lin M, Tengvall K et al. IgA deficiency in wolves. Dev Comp Immunol. 2013;40:180–184. https://doi.org/10.1016/j.dci.2013.01.005 V. Frankowiack M, Olsson M, Cluff HD, Evans AL, Hellman L, Månsson J et al. IgA deficiency in wolves from Canada and Scandinavia. Dev Comp Immunol. 2015;50:26–28. https://doi.org/10.1016/j.dci.2014.12.009
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".