Simple Silica Column–Based Method to Quantify Inorganic Polyphosphates in Cartilage and Other Tissues
Bibliographic record
Abstract
Objective. Inorganic polyphosphates (polyP) play a multitude of roles in mammalian biology. PolyP research is hindered by the lack of a simple and sensitive quantification method. The aim of this study was to develop a robust method for quantifying the low levels of polyP in mammalian tissue such as cartilage, which is rich in macromolecules that interfere with its determination. Design. Native and in vitro formed tissues were digested with proteinase K to release sequestrated polyP. The tissue digest was loaded on to silica spin columns, followed by elution of bound polyP and various treatments were assessed to minimize non-polyP fluorescence. The eluent was then quantified for polyP content using fluorometry based on DAPI (4′,6-diamidino-2-phenylindole) fluorescence shift occurring with polyP. Results. Proteinase K pretreatment reduced the inhibitory effect of proteins on polyP recovery. The eluent was contaminated with nucleic acids and glycosaminoglycans, which cause extraneous fluorescence signals. These were then effectively eliminated by nucleases treatment and addition of concentrated Tris buffer. PolyP levels were quantified and recovery ratio determined using samples spiked with a known amount of polyP. This silica spin column method was able to recover at least 80% of initially loaded polyP, and detect as little as 10 −10 mol. Conclusions. This sensitive, reproducible, easy to do method of quantifying polyP will be a useful tool for investigation of polyP biology in mammalian cells and tissues. Although the protocol was developed for mammalian tissues, this method should be able to quantify polyP in most biological sources, including fluid samples such as blood and serum.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".