The effects of freeze/thawing on human synovial fluid observed by 500 MHz 1H magnetic resonance spectroscopy.
Bibliographic record
Abstract
OBJECTIVE: To investigate the effect of freeze/thaw and low temperature storage on the biomolecular profile of human synovial fluid (SF) using high resolution (500 MHz 1H) magnetic resonance spectroscopy (MRS). METHODS: SF was collected from 12 patients undergoing arthroscopic debridement for treatment of moderate osteoarthritis (OA). Six of the larger samples were divided into 5 parts and treated as follows: the first was analyzed with spin-echo MRS soon after arthroscopy (< 24 h); the 2nd, 3rd, and 4th parts were frozen (-75 degrees C) and thawed for a total of one, 5, and 10 freeze/thaw cycles, respectively, followed by MRS analysis; the 5th part was kept in -75 degrees C storage for > or = 1 year before MRS processing. The 6 smaller samples were divided into 2 parts, the first analyzed shortly after extraction (< or = 24 h), while the 2nd was processed after storage at -75 degrees C for > or = 1 year. Changes in measured metabolite levels were tested for significance using paired t tests. RESULTS: Freeze-thaw cycling had no statistically significant effect on the relative concentrations of endogenous metabolites measured by MRS, though it did alter individual sample results. Prolonged low temperature storage resulted in a significant drop (p < 0.05) in the signal intensities of glucose (45%), N-acetyl glycoproteins (39%), CH2-chain and CH3-terminal and resonances of lipoproteins (46 and 37%, respectively), valine (43%), leucine (35%), and isoleucine (43%). CONCLUSION: This study raises questions about routine procedures that may inadvertently affect the outcomes of quantitative SF analyses. Extended low temperature storage should be avoided as it permanently alters the biochemical profile of SF, possibly leading to erroneous conclusions about the nature of OA related changes in metabolite levels with disease progression.
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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) | 0.001 | 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) | 0.001 | 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".