Notice bibliographique
Résumé
Two aspects caught my attention in this month's issue: the changing perspective regarding the role of alcohol and cardiovascular diseases, and the complex interaction between biomarkers and mechanisms of heart failure (HF). Larsson et al. present a meta-analysis of eight prospective studies of HF risk associated with drinking alcohol.1 The analysis shows a non-linear relationship between alcohol consumption and risk of HF, with a reduced risk with moderate alcohol consumption corresponding to a 17% lower risk of HF with seven drinks per week. Although the authors state that it is ‘unwise that physicians recommend light to moderate drinking to their patients’, Arthur L. Klatsky concludes his editorial stating that although ‘all persons should avoid heavy drinking and many persons should avoid all alcohol. Many middle-aged and older persons at risk of CAD or HF should be told that he or she is better off as a light to moderate drinker’.2 Wong et al.3 and Watson et al.4 studied the role of microRNAs (miRNAs) for the diagnosis of HF and to differentiate between HF with preserved (HFpEF) and reduced ejection fraction (HFrEF). Wong et al. identified 12 mRNAs that were different in patients with HF, compared with normal subjects, and 4 miRNAs which differentiated patients with HFpEF and HFrEF.3 Five miRNAs differentiated patients with HFpEF and HFrEF in the second study.4 These are among the first studies showing differences in miRNAs levels between patients with HFpEF and HFrEF.5 They support the hypothesis that HFpEF and HFrEF are two distinct diseases and indicate mechanisms which can be targeted by specific treatments.6 An even tighter interaction between plasma assays and mechanisms of disease is shown by the study regarding Chagas cardiomyopathy.7 The relative roles of autoimmunity and of parasite persistence are unsettled in this disease. In the study of Sabino et al., Trypanosoma cruzi DNA was measured in a large cohort of seropositive subjects identified more than a decade ago, compared with a control group and with a group of patients with clinical Chagas cardiomyopathy. Trypanosoma cruzi parasitaemia persisted for more than a decade in two-thirds of the seropositive subjects, and parasite concentrations were associated with the presence and severity of Chagas cardiomyopathy, suggesting a direct role for parasite persistence in disease pathogenesis.7, 8 A further study suggests caution with respect of the use of one biomarker, namely galectin-3, as specific for the measurement of fibrosis and the diagnosis of HFpEF, two conditions with which it has been tightly associated.9 Galectin-3 was related to fibrosis in tissue samples from endomyocardial biopsies in 39 HF patients, and plasma levels of galectin-3 were related to serum levels of markers of collagen production, C-terminal propeptide of procollagen type I (PICP) and N-terminal propeptide of procollagen type III (PIIINP), in 220 HF patients.10 All these measurements were significantly increased in patients with HF, compared with controls. However, neither myocardial nor plasma levels of galectin-3 were correlated with myocardial fibrosis or with plasma levels of the other markers of collagen production, and no difference in galectin-3 was found between patients with HFrEF and HFpEF.10
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,008 | 0,005 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,007 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,511 | 0,406 |
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 source (Gemma direct ou Codex distillé), 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 ».