Degradation of the electrospun silica nanofiber in a biological medium for primary hippocampal neuron – effect of surface modification
Bibliographic record
Abstract
Z Vivian Feng,1,* Wen Shuo Chen,2,* Khomson Keratithamkul,1 Michael Stoick,1 Brittany Kapala,3 Eryn Johnson,3 An-Chi Huang,2 Ting Yu Chin,4 Yui Whei Chen-Yang,2 Mong-Lin Yang3 1Chemistry Department, Augsburg College, Minneapolis, MN, USA; 2Department of Chemistry, Center for Nanotechnology, Center for Biomedical Technology, Chung Yuan Christian University, Chung Li, Taiwan, Republic of China; 3Department of Science, Concordia University Saint Paul, Saint Paul, MN, USA; 4Department of Bioscience Technology, Chung Yuan Christian University, Chung Li, Taiwan, Republic of China *These authors contributed equally to this work Abstract: In this work, silica nanofibers (SNFs) were prepared by an electrospinning method and modified with poly-D-lysine (PDL) or (3-aminopropyl) trimethoxysilane (APTS) making biocompatible and degradable substrates for neuronal growth. The as-prepared SNF, modified SNF-PDL, and SNF-APTS were evaluated using scanning electron microscopy, nitrogen adsorption/desorption isotherms, contact angle measurements, and inductively coupled plasma atomic emission spectroscopy. Herein, the scanning electron microscopic images revealed that dissolution occurred in a corrosion-like manner by enlarging porous structures, which led to loss of structural integrity. In addition, covalently modified SNF-APTS with more hydrophobic surfaces and smaller surface areas resulted in significantly slower dissolution compared to SNF and physically modified SNF-PDL, revealing that different surface modifications can be used to tune the dissolution rate. Growth of primary hippocampal neuron on all substrates led to a slower dissolution rate. The three-dimensional SNF with larger surface area and higher surface density of the amino group promoted better cell attachment and resulted in an increased neurite density. This is the first known work addressing the degradability of SNF substrate in physiological conditions with neuron growth in vitro, suggesting a strong potential for the applications of the material in controlled drug release. Keywords: silica nanofibers, electrospinning, dissolution, neurite density, surface modification
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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) | 0.000 | 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".