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Record W7057399810

Investigating the Importance of Physiological Cell Culture Conditions in Modeling Cancer Metabolism and Metabolic Reprogramming

2024· other· en· W7057399810 on OpenAlexaff

Bibliographic record

VenueBrock University Digital Repository (Brock University) · 2024
Typeother
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsBrock University
Fundersnot available
KeywordsCell cultureMetaboliteCancer cellCellCancerMechanism (biology)
DOInot available

Abstract

fetched live from OpenAlex

Standard cell culture conditions do not mimic the physiological environment of cancer cells. Traditional culture media contain metabolites at concentrations that far exceed conditions measured in vivo, and oxygen is often unregulated, exposing cells to atmospheric oxygen concentrations (~18%), rather than the 0-3% O2 measured in solid tumours in vivo. Recently, plasma-like media have been developed to address these limitations, aiming to improve culture conditions and maintain biologically relevant cancer phenotypes in vitro. However, these conditions remain unrepresentative of interstitial fluid in solid tumours. The goal of this thesis was to investigate how physiological culture conditions affect cancer cell behaviours, specifically cell metabolism and adaptive metabolic responses. In the first data chapter of this thesis, I explored nutrient exhaustion in physiological plasma-like medium (Plasmax) at 18% and 5% O2, and the adaptive mechanisms by which cancer cells can maintain survival under metabolic stress conditions. Here, I found that glucose and amino acid depletion from Plasmax over 48 hours is associated with several adaptive mechanisms consistent with metabolic reprogramming in vivo. Given these responses, I hypothesized that a media formulation designed using metabolite concentrations from tumour interstitial fluid may modulate metabolic phenotypes in a similar manner, providing a more physiologically relevant culture model for cancerous cells. Data chapter 2 addresses this hypothesis, whereby I formulated a novel cell culture medium using quantitative metabolite data from murine pancreatic ductal adenocarcinoma (PDAC) tumour interstitial fluid, named Tumour Microenvironment Medium (TMEM), and investigated the effects of TMEM and 1.5% O2 on an adapted murine PDAC cell line (KPCY). Importantly, I found that tumour-like conditions elicited a substantial transcriptional and functional response in cultured cells, modulating cell proliferation, migration, glucose utilization, and mitochondrial bioenergetics in ways relevant to in vivo cancer biology. Overall, the results of this thesis highlight the responsiveness of cultured cells to their environment, and the importance of representative culture conditions in the acquisition of biologically accurate experimental data.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.218
Teacher spread0.202 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations0
Published2024
Admission routes1
Has abstractyes

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