Effects of Electronic Cigarette E-Liquids on the Adhesion and Growth of Osteoblast Cells
Bibliographic record
Abstract
Electronic cigarettes (E-cigarettes) were designed to replace traditional cigarettes to decrease the harmful effects of smoking. The liquid in e-cigarettes contains different chemicals, such as Propylene Glycol (PG) and Vegetable Glycerin (VG), and may contain nicotine. To be more attractive, e-liquids are supplemented with flavorings. Because vaped e-liquids are shown to be damaging to oral tissues, similar effects could occur in the oral cavity with non-vaped e-liquids. We evaluated the effects of non-vaped e-liquid constituents on osteoblast behavior. For this purpose, human osteoblast cells (MG-63) were exposed to e-liquid containing 70% PG -30% VG with and without tobacco flavor and with or without nicotine at 12, 18 mg/mL, or no nicotine content. The e-liquids were used at various concentrations (0, 1, and 5%). To evaluate the effect of the e-liquids on osteoblast morphology and adhesion, optical microscope observations and viable cell counting using trypan blue exclusion were used. Cell growth was also analyzed using the Methyl Thiazol Tetrazolium (MTT) assay, while cytotoxicity was tested by measuring Lactate Dehydrogenase (LDH) levels after cell exposure for 24 h to the e-liquids. Our results show that e-liquids induced significant morphological changes evidenced by round cell forms, with no contact between cells. Adhesion was significantly reduced, particularly in the presence of nicotine. E-liquids at a concentration of 5% significantly reduced osteoblast growth. This effect was observed with both flavored and non-flavored e-liquid with or without nicotine. The decreased osteoblast adhesion and growth after exposure to e-liquid was confirmed by increased levels of LDH. Overall results indicate the potentially harmful effects of e-liquid non-vaped chemicals on bone cells, which could lead to the impairment of bone regeneration and tissue remodeling processes.
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) | 0.001 | 0.001 |
| 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) | 0.002 | 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".