Notice bibliographique
Résumé
Fifteen years ago, human immunology was stagnating compared with the rapid pace of work with inbred mice.There was a desperate need for new strategies and methods that would allow us to leverage the advantages of immunology research in humans: the genetic and environmental diversity, the thousands of infectious diseases, and responses to clinically deployed treatments and vaccines.Development of the much-needed approaches led to a new field, systems immunology, with its emphasis on gathering as much information as possible from human blood samples, focusing on the cells and cytokines of the immune system, and organizing studies of vaccine responses in different human cohorts, including twins, the elderly, and children in high versus low pathogen environments.These types of studies have grown exponentially over the years, with great advances in technology and analysis, and have become an important way to understand the vast differences in human responses and what they can tell us about our own immune systems.What follows is a personal account of the role that I and my colleagues played in the early days of systems immunology.For a more extensive treatment of the current state of the field, I recommend some recent reviews (1, 2).For my part, almost two decades ago, I became alarmed that human immunology seemed to be almost at a standstill while murine work was racing ahead.This struck me as unsustainable because if all we do is improve the health of mice, even if the science is wonderful, we will lose public support and become another obscure academic curiosity.Although our work on imaging T cells and understanding the intricacies of cell-cell interactions was going well, I thought this problem was so compelling that I decided to shift my laboratory's focus to human immunology and to help develop more effective technologies and strategies.Both were clearly needed better technologies, because most of what we do in mouse immunology is impossible or very limited in humans, forcing immunologists to use a relatively small set of tools.Moreover, we needed distinct strategies for human immunology, especially because, as far as I could tell, the main strategy in mice was to create or find a model of a disease with the hope of uncovering the key to the human equivalent.But this wasn't working in most cases: lots of interesting data to be sure, but typically falling short of something "translatable" or failing in clinical trials.Some years before we made the switch, my then colleague at Stanford, Alan Krensky, told me, "Mark, we've cured cancer and autoimmunity in mice many times."This suggested to me that we were not facing just a medical problem; there was important immunology we knew very little about.But what should a new strategy look like?I thought it needed to be independent of mouse immunology, not because I think the immune systems of these two species are very different, but because if we wanted to understand why these models of disease or new HHS Public Access
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,010 | 0,004 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,459 | 0,201 |
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 ».