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Record W2770751512 · doi:10.13034/jsst.v10i2.136

Influence of major histocompatibility complex (MHC) on human mating preferences

2017· article· en· W2770751512 on OpenAlexvenueno aff
Anvita Kulshrestha

Bibliographic record

VenueJournal of Student Science and Technology · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicT-cell and B-cell Immunology
Canadian institutionsnot available
Fundersnot available
KeywordsMajor histocompatibility complexBiologyGeneticsOffspringMate choiceMatingMating preferencesLoss of heterozygosityAlleleGeneKin recognitionEvolutionary biologyPregnancy

Abstract

fetched live from OpenAlex

Several studies related to vertebrates have revealed that highly polymorphic genes within the Major Histocompatibility Complex (MHC) may play a role in mate choice. Females gain direct or indirect fitness benefits by choosing between males with traits that are expressed from good genes, as females can obtain good genes for their offspring by mating with males whose genes are compatible or complementary to their own. There is a tendency for humans to prefer MHC-dissimilar mates, as it would favour the production of heterozygous offspring who would be more resistant to pathogens. This phenomenon has been reviewed on the similar concepts of the influence of MHC genes on human mating preferences, with potential but largely unknown in offspring fitness. The qualitative method can include surveying and interviewing people about their mate choices i.e. females select males with heterozygosity MHC genes over males with homozygous MHC genes. Chi-square test can be performed for statistical analysis. Mating with a MHC dissimilar individual can produce MHC heterozygous offspring that has strong immunocompetence against several parasite types. A heterozygous MHC gene combination has more capability to identify rapidly evolving parasites, which can escape recognition by immune systems containing common alleles.Plusieurs études reliées aux vertébrés ont révélé que les gènes extrêmement polymorphes au sein du complexe majeur d’histocompatibilité (MHC) peuvent jouer un rôle dans le choix d’un partenaire. Les femmes tirent des avantages directs ou indirects de valeur sélective en choisissant entre les hommes dont les traits sont exprimés avec les bons gènes, comme les femmes peuvent obtenir de bons gènes pour leur progéniture par accouplement avec des mâles dont les gènes sont compatibles ou complémentaires à leurs propres. Il y a une tendance pour les humains de préférer les camarades MHC-dissemblables, car ceci favoriserait la production de descendants hétérozygotes qui seraient plus résistants aux pathogènes. Ce phénomène était étudié sur les concepts similairesde l’influence des gènes du MHC sur les préférences d’accouplement humaines, avec un potentiel largement inconnu dans la valeur sélective de la progéniture. La méthode qualitative peut comprendre une enquête et l’interrogation des gens sur leur choix de partenaire, par ex. les femelles choisissent les mâles avec des gènes du CMH hétérozygote sur les hommes ayant des gènes du CMH homozygotes. Le test du chi carré peut être effectué pour faire l’analyse statistique. L’accouplement avec une personne avec un différent CMH peut produire la progéniture CMH hétérozygote qui a une forte immunocompétence contre plusieurs types de parasites. Une combinaison de gènes CMH hétérozygotes a plus de capacité d’identifier les parasites qui évoluent rapidement, qui peuvent échapper à la reconnaissance par le système immunitaire contenant des allèles communs.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0040.000

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 head0.046
GPT teacher head0.334
Teacher spread0.288 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2017
Admission routes1
Has abstractyes

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