ATP, ADP and AMP profiling for diagnostic applications: Recent advances in analytical strategies
Bibliographic record
Abstract
Adenylate compounds – adenosine triphosphate (ATP), diphosphate (ADP), and monophosphate (AMP) – are central regulators of cellular energy metabolism and biomarkers of physiological and pathological states. Their rapid interconversion and enzymatic lability make accurate quantification challenging, especially in complex matrices. This review summarizes strategies for the analysis of ATP, ADP, and AMP, focusing on sample preparation and analytical methodologies for biological systems. Analytical techniques – including bioluminescent assays, biosensors, and chromatographic methods – have improved sensitivity and throughput, but most remain limited to endpoint measurements and cannot capture the dynamic nature of adenylate metabolism in vivo. Emerging trends emphasize integrative, miniaturized, and minimally invasive strategies for near-real-time monitoring. In particular, in vivo solid-phase microextraction (SPME) has gained attention as a minimally invasive sampling technique capable of extracting labile metabolites from tissues under physiological conditions. By assessing advancements and challenges, this review highlights the evolution of adenylate determination and the need for tools compatible with dynamic, spatially resolved sampling. • ATP, ADP, AMP analysis provides insight into physiological and pathological states. • Chromatographic strategies for the analysis of phosphate-rich compounds. • Sample preparation approaches for labile ATP, ADP, and AMP under native conditions. • Methodological challenges for ATP, ADP, and AMP determination in biological samples. • In vivo SPME as a minimally invasive tool for spatiotemporal profiling of adenylates.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".