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Record W4396699826 · doi:10.30699/ijmm.18.1.25

Investigation of Parvovirus B19 Infection Among Iranian Patients with Behcet’s Disease

2024· article· en· W4396699826 on OpenAlexaff
Saied Ghorbani, Hassan Saadati, Ahmad Tavakoli, Seyed Jalal Kiani, Kimia Ghasemi, Seyed Hamidreza Monavari

Bibliographic record

VenueIranian Journal of Medical Microbiology · 2024
Typearticle
Languageen
FieldMedicine
TopicParvovirus B19 Infection Studies
Canadian institutionsYork University
Fundersnot available
KeywordsBehcet's diseaseParvovirusMedicineVirologyDiseaseBehcet diseaseInternal medicineVirus

Abstract

fetched live from OpenAlex

Background and Aim: Behcet's disease is rare and can cause inflammation in blood vessels throughout the body.Although various studies have been conducted on the possible association between BD and various pathogens such as viruses, the major cause of this disease is still unknown.Our study aimed to evaluate the presence of B19 in Behcet patients and healthy carriers. Materials and Methods:For the current case-control study, we examined 103 samples including 54 males and 49 females, and 40 healthy control samples.At first, all samples were checked by ELISA technique and then, the level of B19 DNA was confirmed by Realtime PCR.Finally, the results of patients were compared to healthy control samples.Results: A wide range of clinical manifestations was observed in the BD patient group.We found statistical differences in the prevalence of B19 IgG between patients and healthy populations (84.46% vs. 55%, respectively).However, the prevalence of B19 IgM was similar between patients and healthy control groups (4.58% vs. 2.5%, respectively).We couldn't observe any detectable levels of B19 DNA in the patient and healthy carrier groups. Conclusion:Our results failed to establish a relationship between B19 infection and BD development, but such a correlation has been reported.However, there may be an indirect association between genetically susceptible people after a viral infection.

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)0.001
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 score0.010
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.270
Teacher spread0.252 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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