Artificial Neural Networks Indicate Cardiac TIMP1, JNK, and Collagen I & III Predict Sex Hormone Status in Female Aortic‐Banded Yucatan Mini‐Swine in a Chamber‐Specific Manner
Notice bibliographique
Résumé
The prevalence of heart failure (HF) with preserved ejection fraction (HFpEF) is increased in older, postmenopausal women and often associated with an increase in cardiac fibrosis. Therefore, the goal of this study was to assess the role of female sex hormones on chamber‐dependent differences i.e., left ventricle (LV) vs. right ventricle (RV), in extracellular matrix (ECM) remodeling and regulation in a mini‐swine model of pressure overload‐induced heart failure. We hypothesized molecular markers involved in the bioregulation of the cardiac ECM can predict experimental intervention combinations in a chamber‐specific manner. An ovariectomy (OVX) model of surgical menopause was used in aortic‐banded (AB) female Yucatan mini‐swine (sex hormones X pressure‐overload) divided into 4 groups: 1) Control, intact (CON‐INT; n=6); 2) CON‐OVX (n=5); 3) AB‐INT (n=7;) and 4) AB‐OVX (n=6). Seventy‐seven input variables from both the LV and RV included: 1) mRNA levels of estrogen (isoforms 1, 2) and progesterone receptors; ERK/JNK signaling and regulation including MAPK isoforms 1, 3, 8, and 9, MAPKK isoforms 1, 2, 4, and 7, and dual specificity phosphatases (DUSP) isoforms 1, 4, 6, 9, and 10; matrix metalloproteinase (MMP) isoforms 1, 2, 3, 9, 13, and 14; tissue inhibitors of MMP (TIMP) isoforms 1, 2, and 4; the ECM components collagen (isoforms 1 and 3) and fibronectin; and 2) protein levels of ERK/JNK (total and phosphorylated), MMP14, TIMP2, and fibronectin. Missing data were mean imputed and the min‐max normalization method was used for all measures. One‐way ANOVA models were used to identify mRNA or protein targets associated with group status. Resulting molecular predictors were then used in an artificial neural network (ANN) model, with logistic activation function, composed of 1 hidden layer and 5 nodes. 5‐fold cross‐validation conditioned on group i.e., at least one observation from each of the four groups, and confusion matrices were used to test the developed ANN model. One observation from each group (n=4 total) was retained for later model testing with the remaining observations used for ANN development i.e., 84% training and 16% testing. One‐way ANOVA models indicated TIMP1 mRNA and total JNK protein levels in the LV, and Collagen I and III mRNA levels in the RV, were associated with group status (p<0.05). These 4 molecular markers were then used to develop the ANN model (Figure 1). Cross‐validation and confusion matrices indicate all 4 targets formed a linear relationship predictive of group with an accuracy of 70.8%. In conclusion, molecular mechanisms involved in the bioregulation of the ECM have analytical power to extrapolate sex hormone and aortic‐banding status in a pre‐clinical model of pressure overload‐induced HF. Ongoing work will compare multiple activation functions to improve prediction accuracy and delineate non‐linear relationships amongst these molecular ECM targets.
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 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».