Water immobilization by glass microspheres affects biological activity
Bibliographic record
Abstract
Abstract We recently reported that the water holding capacity of myofibrillar protein hydrogels could be increased upon addition of small amounts of microparticles, particularly glass microspheres. Glass microspheres were found to decrease the spin-spin relaxation time (T2) of water protons in the gels, which was interpreted as enhanced water binding by the glass. We were thus interested in determining whether the observed effects on water proton relaxation were a direct consequence of water-glass interactions. Here we show how glass microspheres reduce the mobility of pure water, reflected in large decreases in the T2 of water protons, decreases in the self-diffusion coefficient of water molecules, a lower water activity, and strengthening of O-H bonds. Even though glass is considered an inert material, glass microspheres were shown to inhibit the growth of human embryonic kidney cells, and stimulate or inhibit the growth of leukemia and monocytic lymphoma cells in vitro, depending on dose and time. The germination of alfalfa seeds and the growth of E.coli cells were also inhibited upon exposure to glass microspheres. This work indicates that the properties and behavior of materials, even ones considered inert, can be affected by their size. These observations suggest possible toxicological consequences of exposure to microparticles, but also open us possibilities to affect cellular/organism function via modulation of macromolecular hydration.
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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) | 0.001 | 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".