Editorial: Women in lipids in cardiovascular disease
Notice bibliographique
Résumé
Daisy Sahoo, Dean of the School of Graduate Studies and Women in Science Endowed Professor focuses her research on the roles of scavenger receptors in lipoprotein metabolism, atherosclerosis, diabetes and obesity. In this issue, Daisy Sahoo and her team explore how dysfunctional HDL contributes to atherosclerosis. Epidemiological studies show that high HDLcholesterol levels are linked to a lower risk of cardiovascular disease (CVD). However, not all HDL particles are the same-for instance, those from patients with coronary artery disease have reduced anti-inflammatory and antioxidant functions. Given the lack of clinical benefits from therapies aimed at increasing HDL-cholesterol, assessing HDL function may offer a better measure of CVD risk. Building on their previous research, Sahoo group found that lipoproteins, in the presence of the ER stress inducer thapsigargin, synergistically increase ER stress markers. Both native and modified HDL had similar effects on ER stress as oxLDL.Mireille Ouimet, Associate Professor at the University of Ottawa, focuses on lipophagy, a novel pathway that degrades fat, and its role in atherosclerosis. In the current issue, Mireille Ouimet and her team emphasize the importance of sex-specific approaches in developing cardiovascular therapies. In this study, the team used trehalose, a transcription factor EB (TFEB) activator, to explore the role of autophagy in a mouse model of atherosclerosis regression. They report a sexspecific effect of trehalose, where it reduced lipid content, inflammation, and increased collagen in female mice but not in males. Additionally, the team identified intrinsic differences in autophagy flux between sexes, with female mice exhibiting lower plaque autophagy than males. The investigators suggest that these differences likely make female mice more responsive to atherosclerosis regression.Wann Jia Loh, a consultant endocrinologist working in Changi General Hospital, Singapore, where she leads the Lipid Service. In this issue, Wann Jia Loh and her team evaluated whether measuring the remnant cholesterol carried by triglyceride-rich lipoprotein fraction provides an efficient tool to access cardiovascular risk factor in patients with well controlled LDL levels. The motivation of the study is to develop criteria to assess the residual cardiovascular risk in patients who achieved the recommended levels of LDL cholesterol. In a 2-year retrospective study that included more than 20 thousand patients, the study finds that this is not an efficient strategy for evaluating residual cardiovascular risk except in patients with severe hypertriglyceridaemia. Further studies are needed to establish the appropriate guidelines to diminish the residual cardiovascular risk.Jingen Li, a cardiologist at Beijing University of Chinese Medicine and her team explore the role of Chinese herbal medicine in lowering blood cholesterol levels and improving cardiovascular outcomes. Analyzing 23 trials which included more than seven thousand participants, they discover that supplementing standard western medicine with Chinese herbal therapy resulted in a significant further decrease of plasma cholesterol and triglyceride levels, which was associated with a decreased cardiovascular risk.Taken together, these studies provide significant novel insights into lipoprotein research, both in terms of the basic science and clinical translational studies.Dr. Wann Jia Loh graduated from Imperial College London with Distinction and is a recipient of several awards, including Population Health New Investigator award Nurturing Clinician Researcher Award. Her research and clinical interests include dyslipidaemia, obesity, atypical diabetes, severe insulin resistant states, and women's health. Dr. Loh is a mother of 2 young children with a busy schedule and stress that includes clinical work, scientific pursuits and ensuring her family has good quality time and compensate for lack of time. She expresses gratitude for the support and understanding of her family, friends, kind neighbors, team members, and colleagues who helped her in various ways over the years. In her own words, "It takes a village to support a woman clinician scientist". Dr. Loh's colleague, Dr. Mon Tun is a Senior Analyst at Changi General Hospital, Singapore, trained in epidemiology and health technology assessment. With a background in population health, infectious disease epidemiology, and healthcare innovation, she focuses on improving healthcare delivery through research on disease burden, cost-effectiveness, and predictive analytics. In her own words: "While balancing a busy career, I am deeply inspired by the support from my colleagues and mentors, which fuels my passion for improving healthcare systems and outcomes."Irena Levitan is a professor at the University of Illinois, Chicago focusing her research on vascular biophysics, which includes studies of endothelial biomechanics and membrane properties. As an American-Israeli scientist, she believes in the importance of dialogues between people with different languages, cultures and identities. She also believes that women who are not only excellent scientists but also excel in emotional intelligence should play a major role in creating and supporting an inclusive and diverse scientific endeavors. This issue is our current contribution. Catherine Martel is an associate professor at the Université de Montréal, a French-speaking institution in Quebec, Canada. While Quebec takes pride in preserving the French language, science is primarily conducted in English. For non-English speakers, translating complex ideas into scientific research proposals or articles can be challenging, with concerns that culturally specific writing styles may hinder understanding. Though research proposals can be submitted in French in some funding agencies, this may limit access to expertise from English-speaking reviewers. A similar issue arises in publishing scientific articles. Non-English-speaking scientists need a platform to express their ideas clearly and promote their work, highlighting the need to democratize science across cultures and languages.
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 | 0,005 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,002 |
| Méta-épidémiologie (sens large) | 0,005 | 0,004 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,007 | 0,005 |
| Science ouverte | 0,005 | 0,002 |
| Intégrité de la recherche | 0,017 | 0,021 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,021 | 0,016 |
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 ».