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Record W3004200942 · doi:10.1038/s41598-020-58079-3

Human Genetic Susceptibility of Leprosy Recurrence

2020· article· en· W3004200942 on OpenAlexaff
Priscila Verchai Uaska Sartori, Gerson Oliveira Penna, Samira Bührer-Sékula, Maria Araci de Andrade Pontes, Heitor S. Gonçalves, Rossilene Conceição da Silva Cruz, Marcos Virmond, Ida Maria Foschiani Dias‐Baptista, Patrícia Sammarco Rosa, Maria Lúcia Fernandes Penna, Vinicius M. Fava, Mariane M. A. Stefani, Marcelo Távora Mira

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldMedicine
TopicLeprosy Research and Treatment
Canadian institutionsMcGill University Health Centre
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoMedical Research CouncilMinistério da Ciência, Tecnologia e Inovação
KeywordsLeprosyMycobacterium lepraeAlleleDiseaseGenetic predispositionBiologyGeneticsGeneMedicineImmunologyPathology

Abstract

fetched live from OpenAlex

Host genetic susceptibility to leprosy has been intensively investigated over the last decades; however, there are no studies on the role of genetic variants in disease recurrence. A previous initiative identified three recurrent cases of leprosy for which none of the M. leprae strains, as obtained in the first and the second diagnosis, had any known genomic variants associated to resistance to Multidrug therapy; in addition, whole genome sequencing indicated that the same M. leprae was causing two out of the three recurrences. Thus, these individuals were suspected of being particularly susceptible to M. leprae infection, either as relapse or reinfection. To verify this hypothesis, 19 genetic markers distributed across 11 loci (14 genes) classically associated with leprosy were genotyped in the recurrent and in three matching non-recurrent leprosy cases. An enrichment of risk alleles was observed in the recurrent cases, suggesting the existence of a particularly high susceptibility genetic profile among leprosy patients predisposing to disease recurrence.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.153
Threshold uncertainty score0.555

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.055
GPT teacher head0.339
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations30
Published2020
Admission routes1
Has abstractyes

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