Mapping the metabolic responses to oxaliplatin-based chemotherapy with in vivo spatiotemporal metabolomics
Bibliographic record
Abstract
Adjuvant chemotherapy improves the survival outlook for patients undergoing operations for lung metastases caused by colorectal cancer (CRC). However, a multidisciplinary approach that evaluates several factors related to patient and tumor characteristics is necessary for managing chemotherapy treatment in metastatic CRC patients with lung disease, as such factors dictate the timing and drug regimen, which may affect treatment response and prognosis. In this study, we explore the potential of spatial metabolomics for evaluating metabolic phenotypes and therapy outcomes during the local delivery of the anticancer drug, oxaliplatin, to the lung. 12 male Yorkshire pigs underwent a 3 h left lung in vivo lung perfusion (IVLP) with various doses of oxaliplatin (7.5, 10, 20, 40, and 80 mg/L), which were administered to the perfusion circuit reservoir as a bolus. Biocompatible solid-phase microextraction (SPME) microprobes were combined with global metabolite profiling to obtain spatiotemporal information about the activity of the drug, determine toxic doses that exceed therapeutic efficacy, and conduct a mechanistic exploration of associated lung injury. Mild and subclinical lung injury was observed at 40 mg/L of oxaliplatin, and significant compromise of the hemodynamic lung function was found at 80 mg/L. This result was associated with massive alterations in metabolic patterns of lung tissue and perfusate, resulting in a total of 139 discriminant compounds. Uncontrolled inflammatory response, abnormalities in energy metabolism, and mitochondrial dysfunction next to accelerated kynurenine and aldosterone production were recognized as distinct features of dysregulated metabolipidome. Spatial pharmacometabolomics may be a promising tool for identifying pathological responses to chemotherapy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".