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Record W4408026230 · doi:10.1111/cts.70161

Application of Human Plasma/Serum to Cell Culture In Vitro: A Translational Research Approach to Better Define Disease Mechanisms

2025· review· en· W4408026230 on OpenAlexafffund
Eric K. Patterson, Sean E. Gill, Gediminas Cepinskas, Douglas D. Fraser

Bibliographic record

VenueClinical and Translational Science · 2025
Typereview
Languageen
FieldMedicine
TopicXenotransplantation and immune response
Canadian institutionsChildren’s Health Research InstituteLondon Health Sciences CentreWestern University
FundersCanadian Institutes of Health ResearchAcademic Medical Organization of Southwestern OntarioNatural Sciences and Engineering Research Council of CanadaHeart and Stroke Foundation of Canada
KeywordsTranslational researchIn vitroDiseaseMedicineCell cultureComputational biologyImmunologyBiologyBioinformaticsCell biologyInternal medicinePathologyBiochemistryGenetics

Abstract

fetched live from OpenAlex

In vitro cell culture experiments play an important role in medical research. Various cellular mechanisms and signaling pathways have been identified with in vitro experimental techniques. Unfortunately, the clinical and translational impact of these studies is often limited due to their inability to closely resemble physiological or pathophysiological milieus in cell culture and the use of unrealistic experimental conditions. Thus, further developments must be made to improve the translation of in vitro cell culture work. The application of human plasma or serum as a stimulus for cells, human or otherwise, is a relatively new approach that ultimately overcomes many of the in vitro limitations and provides a more physiologically relevant model. While this technique has been used for the investigation of various diseases and pharmacological mechanisms, discrepancies remain regarding the appropriate methodologies. This review provides insight into recent findings through the application of human plasma or serum as stimuli, as well as an analysis of methodological considerations and suggestions for future directions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.952
Threshold uncertainty score0.729

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.137
GPT teacher head0.478
Teacher spread0.341 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations6
Published2025
Admission routes2
Has abstractyes

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