Bibliographic record
Abstract
A new technique for micropatterning surfaces for cell growth support is described and characterized. This technique allows covering of large three-dimensional surfaces at low cost with controllable micropatterns. This method takes advantage of the random properties of aerosols and the principles of liquid atomization. Parameters of interest were the pressure of atomization air, the flow rate and volume of the atomised liquid, and the distance between the spray nozzle and the surface of the sample. The experimental setup permitted to obtain mean diameters of spots between 10 and 20 microns with a maximum surface coverage of 20%. In an initial step, polytetrafluoroethylene (PTFE) films were treated with ammonia plasma to insert amino groups on the surface. The ammonia plasma treated films were immersed in a solution containing sulfosuccinimidyl 4-(N-maleidomethyl)cyclohexane-1-carboxy-late (SSMCC) to permit the introduction of maleimido groups on the PTFE surface to subsequently conjugate peptides through a sulfhydryl containing N-terminal cystein residue. Plasma/S-SMCC pretreated surfaces were then sprayed with peptide sequences CGRGDS and CWQPPRARI. Our data showed that spots of CGRGDS peptides over a background of CWQPPRARI peptides were the most effective combination to enhance endothelialization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".