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Record W1965351429 · doi:10.1016/j.exger.2014.03.020

New advances in CMV and immunosenescence

2014· article· en· W1965351429 on OpenAlexaff
Paolo Sansoni, Rosanna Vescovini, Francesco Fagnoni, Arne N. Akbar, Ramon Arens, Yen‐Ling Chiu, Luka Čičin‐Šain, Julie Déchanet‐Merville, Evelyna Derhovanessian, Sara Ferrando-Martinez, Claudio Franceschi, Daniela Frasca, Tamàs Fülöp, David Furman, Effrossyni Gkrania‐Klotsas, Felicia Goodrum, Beatrix Grubeck‐Loebenstein, Mikko Hurme, Florian Kern, Daniele Lilleri, Miguel López‐Botet, Andrea B. Maier, Thomas F. Marandu, Arnaud Marchant, Catharina Matheï, Paul Moss, Aura Muntasell, Ester B. M. Remmerswaal, Natalie E. Riddell, Kathrin Rothe, Delphine Sauce, Eui‐Cheol Shin, Amanda M. Simanek, Megan J. Smithey, Cecilia Söderberg‐Nauclér, Rafael Solana, Paul G. Thomas, René A. W. van Lier, Graham Pawelec, Janko Nikolich‐Žugich

Bibliographic record

VenueExperimental Gerontology · 2014
Typearticle
Languageen
FieldMedicine
TopicCytomegalovirus and herpesvirus research
Canadian institutionsUniversité de Sherbrooke
FundersNational Institute of Allergy and Infectious DiseasesRegione Emilia-RomagnaUniversità degli Studi di ParmaFondazione Cariparma
KeywordsImmunosenescenceImmunologyCytomegalovirusImmune systemImmunityHuman cytomegalovirusImmune DysfunctionMedicineBiologyVirologyHerpesviridaeVirusViral disease

Abstract

fetched live from OpenAlex

Immunosenescence, defined as the age-associated dysregulation and dysfunction of the immune system, is characterized by impaired protective immunity and decreased efficacy of vaccines. An increasing number of immunological, clinical and epidemiological studies suggest that persistent Cytomegalovirus (CMV) infection is associated with accelerated aging of the immune system and with several age-related diseases. However, current evidence on whether and how human CMV (HCMV) infection is implicated in immunosenescence and in age-related diseases remains incomplete and many aspects of CMV involvement in immune aging remain controversial. The attendees of the 4th International Workshop on "CMV & Immunosenescence", held in Parma, Italy, 25-27th March, 2013, presented and discussed data related to these open questions, which are reported in this commentary.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0010.005
Scholarly communication0.0030.008
Open science0.0010.002
Research integrity0.0060.013
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.027
GPT teacher head0.361
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations150
Published2014
Admission routes1
Has abstractyes

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