Notice bibliographique
Résumé
To the Editor: The article by Nichols et al1 has addressed an important and complex issue. Because increasing birth order and younger maternal age are likely to be associated with lower levels of exposure to potentially carcinogenic environmental pollutants in breastmilk, the authors postulated that adult breast cancer risk would be lower in breastfed women with higher birth order, and in those breastfed by younger mothers. Their results showed that higher birth order is associated with reduced risk of breast cancer among those who were breastfed in infancy, but younger maternal age was not. Overall, breast cancer risk was substantially lower in breastfed women than formula-fed women, although the reduced risk was not apparent in the first-born women. The authors cite breastmilk contaminated with persistent organic pollutants (POPs) as a plausible mechanism to explain their findings. However, an equally plausible and not mutually exclusive hypothesis is that exposure to infant formula (or lack of human milk) is associated with increased risk of breast cancer. Toxic effects of POPs are orchestrated by arylhydrocarbon receptor (AhR) in the cell. AhR activation is the initial event of biologically significant exposures to POPs, which causes, among other things, cytochrome P4501A (CYP1A) induction, a biomarker of AhR activation. Therefore, we were surprised when we unexpectedly discovered that AhR is strongly activated, and CYP1A induced, in vitro by infant formula, but not by human milk.2 A recent study by Blake et al3 showed that CYP1A activity is significantly higher in formula-fed infants, consistent with our data.2 POPs are surely present in human milk, but the amount found in women may not be biologically meaningful. Breastmilk is still far better than infant formula under most circumstances for many other reasons. Because the findings by Nichols et al1 are important, it is even more important to be interpreted in a fair and balanced manner. Many studies including ours and Nichols’ have delved into the big black box. At least for now, the answers remain unclear. Shinya Ito Division of Clinical Pharmacology and Toxicology Hospital for Sick Children University of Toronto Toronto, Canada
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».