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Enregistrement W7161978143 · doi:10.82308/49773

Understanding the determinants and improving detection of bone fragility in female chickens

2025· dissertation· en· W7161978143 sur OpenAlexaboutno aff
Isabela Vitienes

Notice bibliographique

Revuenon disponible
Typedissertation
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueAnimal Nutrition and Physiology
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésContext (archaeology)Strain (injury)Bone healthSustainabilityIn vivoWelfareFragilityAnimal welfareBone remodeling

Résumé

récupéré en direct d'OpenAlex

Each year, in Canada, about 25 million commercial laying hens produce more than nine billion eggs, providing affordable, nutrient-dense food for consumers. However, studies have reported up to 97% fracture prevalence in these commercial flocks by the end of their lives, which poses a major welfare concern and has a detrimental impact on the sustainability of the egg- farming industry. Providing increased opportunities for physical activity by housing design is currently the main strategy used to improve welfare and bone health in commercial hens. My research investigates how genetics and experience of physical activity during youth influence bone structure, bone mechanical behaviour, and bone mechanoadaptation, and validates common methods of fracture detection and severity assessment. All studies examine chickens of two commercially relevant genetic strains, who were raised in several different styles of housing that allow for varying types and amounts of physical activity.First, I characterized the in vivo mechanical behaviour of the tibiotarsi of young chickens during habitual activities, using strain gauge sensors to measure strains engendered in vivo. I found that the tibiotarsus undergoes a complex strain environment and that torsion is the predominant source of mechanical strain. Genetic strain and loading history both influenced the in vivo mechanical behaviour of the bone, with sedentary chickens exhibiting higher in vivo mechanical strain levels compared to those with a more active loading history. These findings provide important context to interpreting bone structural and material properties as determinants of these in vivo strains, and directly informed my third study investigating the bone’s mechanoresponse to controlled loading.Next, I sought to characterize whole bone mechanical behaviour due to axial compressive loading and assess its correlation to bone material and structural properties. I created finite element models mimicking our axial compressive loading model, with either heterogeneous or homogeneous tissue mineral density-derived elastic moduli, and correlated the simulated engendered stresses to measures of bone structure along the length of the bone. I found that mineral density heterogeneity did not influence stress magnitudes or patterns along the bone length, and I identified a set of structural parameters with a strong negative correlation to engendered stress levels. These findings inform future interpretations of the effect of interventions that aim toxviimprove tissue mineral density as a means to improve bone stiffness and provide a set of candidate stiffening structural parameters that can be targeted by genetic selection.In a third study, I performed in vivo controlled loading over a 2-week period to study the bone mechanoresponse, using a load waveform protocol that has been shown to successfully elicit bone formation in murine models. Applied load levels were set to engender mechanical strains above the measured habitual levels from my first study. I found that loaded limbs had impaired bone structure and decreased bone surface undergoing formation, compared to non-loaded limbs. These results indicate that the mechanoresponse is different in chickens compared to murine models; future studies are warranted to investigate the osteogenic components of load stimuli in chickens.My last study focuses on adult chickens during the laying phase and sought to cross- validate commonly used methods of keel bone damage detection and severity assessment. Chickens underwent in vivo palpation to categorize them as either having or lacking fracture(s). Then, bones were radiographed, given a fracture severity score based on this image, and scored again based on visual inspection of the keel. Then, palpation and radiograph scores were correlated against the dissected keel scores. I found that although palpation lacks precision and accuracy, it was still more highly correlated to dissected fracture scores than the radiograph scores.Overall, the findings from this thesis inform about the determinants of bone health in young female chickens, and what detection and outcome measures of bone health are meaningful and informative in this group. This research also contributes to the general pool of knowledge on bone biomechanics and mechanobiology in birds, and towards understanding bone’s form-function relationship

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)0,001
score de la tête « metaresearch » (Gemma)0,001
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: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,023
Score d'incertitude au seuil0,046

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,056
Tête enseignante GPT0,258
Écart entre enseignants0,202 · 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
GenreEmpirique

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
Publié2025
Routes d'admission1
Résumé présentoui

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