Immunosupportive therapies in aging
Bibliographic record
Abstract
Tamas Fülöp1, Anis Larbi2, Katsuiku Hirokawa3, Eugenio Mocchegiani4, Bruno Lesourd5, Stephen Castle6, Anders Wikby7, Claudio Franceschi8, Graham Pawelec21Research Center on Aging, Immunology Program, Geriatric Division, Faculty of Medicine, University of Sherbrooke, Quebec, Canada; 2Tübingen Ageing and Tumour Immunology Group, Center for Medical Research, University of Tübingen Medical School, Tübingen, Germany; 3Department of Pathology and Immunology Ageing and Developmental Sciences, Tokyo-Medical and Dental University Graduate School, Tokyo, Japan; 4Immunology Center, Section Nutrition, Immunity and Ageing, Research Department, Italian National Research Centres on Ageing, INRCA, Ancona, Italy; 5Hopital Universitaire de Clermont-Ferrand, Service Soins de Suite, Route de Châteaugay BP56, F-63118 Cebazat, France; 6Geriatric Research, Education and Clinical Center (GRECC), VA Greater Los Angeles Healthcare System and Multicampus Division of Geriatric and Gerontology, Department of Medicine, UCLA, Los Angeles, CA 90073, USA; 7School of Health Sciences, Jönköping University, Department of Natural Science and Biomedicine, Jönköping, Sweden; 8Italian National Research Center on Aging, Department of Experimental pathology, University of Bologna, Bologna, ItalyAbstract: The primary role of the immune system is to protect the organism against pathogens, but age-associated alterations to immunity increase the susceptibility of the elderly to infectious disease. The exact nature of these changes is still controversial, but the use of screening procedures, such as the SENIEUR protocol to exclude underlying illness, helped to better characterize the changes actually related to physiological aging rather than pathology. It is generally agreed that the most marked changes occur in the cellular immune response reflecting profound alterations in T cells. Much of this is due to thymic involution as well as changes in the proportions of T cell subpopulations resulting from antigen exposure, and altered T cell activation pathways. However, a body of data indicates that innate immune responses, including the critical bridge between innate and adaptive immunity, and antigen presenting capacity are not completely resistant to senescence processes. The consequences of all these alterations are an increased incidence of infections, as well as possibly cancers, autoimmune disorders, and chronic inflammatory diseases. The leading question is what, if anything, can we do to prevent these deleterious changes without dangerously dysregulating the precarious balance of productive immunity versus immunopathology? There are many potential new therapeutic means now available to modulate immunosenescence and many others are expected to be available shortly. One main problem in applying these experimental therapies is ethical: there is a common feeling that as ageing is not a disease; the elderly are not sick and therefore do not require adventurous therapies with unpredictable side-effects in mostly frail individuals. Animal models are not helpful in this context. In this chapter we will first briefly review what we think we know about human immunosenescence and its consequences for the health status of elderly individuals. We will then discuss possible interventions that might one day become applicable in an appropriate ethical environment.Keywords: immunosenescence, T cells, phagocytic cells, nutrition, vaccination, exercise, CMV, inflammaging, IRP, immunorestorative therapies
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".