Peg Surface Modification to Control Biofouling in Microfluidic High Content Screening Devices
Bibliographic record
Abstract
High content screening (HCS) is a valuable research technique for biological assays and to identify drug candidates1. Miniaturization of the HCS platform has significantly reduced processing time and improved the productivity of early drug discovery1. Recently, our group developed a prototype micro optofluidic cell sensing device with localized dosing control for pathogen sensing2. The device is composed of glass wells for cell growth and PDMS microchannels with polycarbonate (PC) membrane valves which are electrically stimulated for controlled drug release2. However, experiment failure caused by cells adhering to and blocking the PDMS channels and PC membrane is of concern. Loss of any amount of sample is critical as it compromises the long term reliability of the device. Protein adsorption on materials is an essential prerequisite for cell adhesion, providing nutrients and anchorage for adherent cell lines3. This process, called biofouling, is mediated by surface hydrophobicity4. Implementation of surface modification strategies to further reduce the hydrophobicity of PDMS and PC membrane may be applied to decrease cell adhesion. Plasma treatment, UV treatment, metal coating, dynamic surface modification and polyethylene glycol (PEG) grafting are all commonly practiced antifouling techniques. Among these, PEG grafting is considered one of the most efficient and well documented technique for modifying PDMS surfaces5. Hence, PEG grafting procedures will be applied to modify antifouling PDMS and PC surfaces. The procedure for PEG grafting is presented in Fig. 1. A (3-Aminopropyl) triethoxysilane (APTES) layer is formed on PDMS and PC surfaces to tether the PEG-DA chains. Utilizing APTES and PEG-DA to modify both materials is advantageous in this field as most grafting procedures are specific to the material it was designed for. This method may also be applied to related silicon materials. Conversely, the hydrophilic nature of glass may reduce cell adhesion. Thus, APTES alone is used to increase glass biofouling and localize cell growth within the wells. Fig. 2 shows a 66% reduction in albumin adsorption on PDMS-PEG compared to unmodified PDMS. These preliminary results proves that the antifouling modification strategy for PDMS was successful. Quantification of protein adsorption on modified PC membrane and glass as well as cell adhesion experiments will be conducted in the near future. These modifications will then be applied to a new prototype device and tested for long term stability. References R. Kapur, K. A. Giuliano, M. Campana, T. Adams, K. Olson, D. Jung, ... D. L. Taylor. Biomedical Microdevices, vol. 2, pp. 99-109, 1999. S. Upadhyaya & P. R. Selvaganapathy. Lab Chip, vol. 10, pp. 341-348, 2010. M. Rabe, D. Verdes, & S. Seeger. Advanced Colloid and Interface Science, vol. 162, pp. 87-206, 2011. H.-C. Flemming. Applied Microbiology and Biotechnology, vol. 59, pp. 629–40, 2002. H. Chen, Z. Zhang, Y. Chen, M. Brook, & H. Sheardown. Biomaterials, vol. 26, pp. 2391-9, 2005.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".