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Enregistrement W3135528641 · doi:10.1113/jp281198

Oestrous cycle in female mice is ‘immune’ to variability

2021· letter· en· W3135528641 sur OpenAlexaboutno aff
Joohyung Lee

Notice bibliographique

RevueThe Journal of Physiology · 2021
Typeletter
Langueen
DomaineMedicine
ThématiqueSex and Gender in Healthcare
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPhysiologyEstrous cycleDiseaseHuman biologyPreclinical researchPsychologyBiologyDemographyMedicineDevelopmental psychologyInternal medicineNeuroscience

Résumé

récupéré en direct d'OpenAlex

Biological sex is being increasingly recognized as a significant variable in biomedical research. Numerous animal and human studies demonstrate that males and females can differ significantly in their genetics, anatomy, physiology and behaviour through various stages of development and ageing. Moreover, these inherent sex differences in male and female biology directly contribute to sex differences in disease incidence, severity, onset and treatment response (Miller et al. 2017). Despite these striking sex differences in health and disease, females have been traditionally underrepresented in biomedical research. In response, guidelines implemented by funding agencies such as the National Institutes of Health have led to significant improvement in the recruitment of women in clinical research. However, traditional sex biases still significantly influence how scientists approach basic and preclinical animal research. Male animals still predominate and neglect of females is widespread in varying disciplines of preclinical research, including physiology, pharmacology, neuroscience, endocrinology and immunology (Beery & Zucker, 2011). The underrepresentation of females in preclinical research is based on assumptions that data from males can predict results in females or that sex differences are mainly associated with reproductive function. In particular, female animals are commonly excluded in the belief that they are intrinsically more variable than males due to the fluctuation of hormones across their oestrus cycle (Beery & Zucker, 2011). Surprisingly, there are few studies to support this widely held belief. The article by Breznik and colleagues in this issue of the Journal of Physiology tests this widely held belief by assessing whether the oestrous cycle or biological sex significantly influence peripheral blood immune cell population and variability in the commonly used C57BL/6J mice. The authors utilized a method known as flow cytometry to precisely identify and quantify various types of immune cells in peripheral blood, otherwise known as immunophenotyping. Using vaginal cytology to identify the various oestrous stages, the authors found that the peripheral immune cell prevalence or variability was not altered across the four stages of the oestrous cycle in female mice. They did find significant sex differences in immune cell prevalence, with a greater number of monocytes, neutrophils and natural killer cells in male mice, whilst female mice had a higher proportion of lymphocytes. Interestingly, they found that male mice exhibit greater variability in peripheral immunophenotype than their female counterparts, which was influenced by body weight. In summary, the study by Breznik and colleagues contradicts the popular belief that fluctuation of the hormones during the oestrous cycle affects physiological traits and data variability in female animals. Rather, the authors found that it was the male, and not female, mice which showed greater variability in peripheral immunophenotype. Indeed, previous meta-analyses of behavioural, physiological, anatomical and molecular traits from mouse (Prendergast et al. 2014) and rat studies (Dayton et al. 2016) revealed that male animals may be just as variable, or even more so, than females. Whilst female animals have higher levels of oestrogen than male animals, males have higher levels and fluctuations of testosterone – a hormone which can affect body weight and consequently data variability in males. Given that data variability may differ between sexes with certain physiological traits, such as immunophenotype, the authors note that the common approach of using equal sample numbers for both sexes may not be suitable for all study designs. Hence, future studies to better understand data variability in male and female traits across a broad range of biological disciplines will be critical in guiding optimal study design and statistical analysis for each sex. Overall, the study by (Breznik et al. 2021) provides compelling experimental proof that that hormonal variability no longer justifies the neglect of female animals in basic and preclinical studies. Considering the undeniable sex differences in the incidence of autoimmune disorders (e.g. multiple sclerosis) or susceptibility to viral infections (e.g. COVID-19), the current findings will hopefully renew the enthusiasm for considering sex as a variable in the field of immunology – which ranks one of the lowest in biological disciplines for reporting the sex of animal or human subjects in published papers (Beery & Zucker, 2011). On the other hand, the increased research cost and time of including both sexes will still be a major obstacle for the research community. However, one cannot ignore the fact that overreliance on the male sex in preclinical research has, in part, contributed to unforeseen sex-biased adverse effects in women during clinical research. Thus, analysing sex as a variable in preclinical research has the potential to save much greater cost and time by minimizing health risks and costly failures during the clinical trials. In support, prominent research agencies such as the Canadian Institutes of Health Research have taken steps to ensure that sex and gender are an integral component of the whole research process, including project rationale, experimental design, methods, analysis and translation (Duchesne et al. 2017). Taken together, considering sex as a significant biological variable in basic, preclinical and clinical studies in will not only produce precise and reproducible results applicable for both males and females, but also optimize health outcomes for both men and women – who account for more than half the world's population. The author does not have any conflicts of interest. Sole author. No funding was received for the preparation of the manuscript.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Éditorial · Signal consensuel: aucune
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,021

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,003
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0010,002
Communication savante0,0020,001
Science ouverte0,0010,001
Intégrité de la recherche0,0020,004
Charge utile insuffisante (le modèle a refusé de juger)0,0060,002

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,049
Tête enseignante GPT0,334
Écart entre enseignants0,285 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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