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Record W2100969293 · doi:10.1093/humrep/dep021

Distribution of MLH1 foci and inter-focal distances in spermatocytes of infertile men

2009· article· en· W2100969293 on OpenAlexafffund
Kristi J. Ferguson, Samuel Leung, D.M. Jiang, Sai Ma

Bibliographic record

VenueHuman Reproduction · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health ResearchUniversity of British Columbia
KeywordsAndrologyMeiosisInfertilityBiologyMale infertilityPosition (finance)MLH1GynecologyMedicineGeneticsPregnancy

Abstract

fetched live from OpenAlex

BACKGROUND: In a previous study on severely infertile men, we observed alterations in the number of meiotic crossovers; however, it is unknown if these men also show alterations in the position of crossovers. METHODS: Spermatocytes from 15 men (5 control men and 10 infertile men) were immunostained to observe the synaptonemal complex and MLH1 foci, which localize to sites of crossovers. Fluorescent in situ hybridization was performed to identify chromosomes 13, 18 and 21. Chromosome bivalents were separated into those with single and double crossover configurations, and the distribution of MLH1 foci along each chromosome arm was calculated. The inter-focal distances on chromosome 13 and 18 bivalents with double crossovers were also calculated. RESULTS: Four of the infertile men displayed an altered MLH1 distribution on at least one of the chromosome arms studied. Of these four men, two displayed reduced rates of meiotic recombination. Only one man displayed an abnormality in crossover interference, with inter-focal distances reduced on chromosome 13 bivalents. CONCLUSIONS: Recombination defects in infertile men may include alterations in the number of crossovers, the position of crossovers or both. Alterations in both the number and position of crossovers may increase the risk of aneuploid sperm in infertile men.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.258

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.010
GPT teacher head0.257
Teacher spread0.247 · 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 designBench or experimental
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

Citations20
Published2009
Admission routes2
Has abstractyes

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