Are We Learning as Much as Possible from Spaceflight to Better Understand Health and Risks to Health on Earth, as Well as in Space?
Notice bibliographique
Résumé
The objective of this review is to discuss the changes in human biology and physiology that occur when humans, who evolved on Earth for millions of years, now are subjected to space flight for extended periods of time, and how detailing such changes associated with space flight could help better understand risks for loss of health on Earth. Space programs invest heavily in the selection and training of astronauts. They also are investing in maintaining the health of astronauts, both for extensive stays in low earth orbit on ISS, and in preparation for deep space missions in the future. This effort is critical for the success of such missions as the N is small and the tasks needed to be performed in a hostile environment are complex and demanding. However, space is a unique environment, devoid of many of the “boundary conditions” that shaped human evolution (e.g. 1 g environment, magnetic fields, background radiation, oxygen, water, etc). Therefore, for humans to be successful in space, we need to learn to adapt and minimize the impact of an altered environment on human health. Conversely, we can also learn considerably from this altered environment for life on earth. The question is, are we getting the maximal information from life in space to learn about like on earth? The answer is likely No, and as such, our “Return on Investment” is not as great as it could be. Even though the number of astronauts is not large, what we can learn from them could help shape new questions for research focused on health for those on earth, as well is contribute to “precision health” from the study of astronaut diversity. This latter effort would contribute to both the health of astronauts identifying risks, as well as contribute to health on earth via better understanding of the human genome and epigenome, as well as factors contributing to risk for diseases on earth, particularly as individuals age and regulatory systems become altered. Better use of the International Space Station, and similar platforms in the future, could provide critical insights in aging-associated risks for loss of health on Earth, as well as promote new approaches to using precision medicine to overcome threats to health while in space. To achieve this goal will likely require advanced approaches to collecting such information and use of more systems biology, systems physiology approaches to integrate the information.
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 | 0,004 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,005 |
| Communication savante | 0,004 | 0,011 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,005 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,002 |
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 ».