Biophysical evaluation of treating adipose tissue-derived stem cells using non-thermal atmospheric pressure plasma
Bibliographic record
Abstract
Non-thermal atmospheric pressure plasma (NTAPP) is a partially ionized gas containing fast electrons and relatively slow ions. This study aims to investigate the influences of NTAPP on human adipose tissue-derived stem cells (ADSCs) and examine the feasibility of using optical spectroscopy as a non-destructive method for cell analysis. A plasma jet is used as the source of low-temperature plasma in which pure helium gas is ionized by a high voltage (8 kV) and frequency (6 kHz). ADSCs were exposed to the NTAPP for 30 s, 60 s, 90 s, and 120 s. The efficiency of the plasma treatment was investigated using flow cytometry and optical spectroscopy methods. This study compared surface markers of NTAPP treated and untreated ADSCs using CD90 and CD105 as positive markers. The result proved that NTAPP-exposed ADSCs maintain their stemming. Measuring ADSCS apoptosis by labeling Annexin V-Propidium Iodide showed that the plasma at short exposure time is relatively non-toxic. However, a longer exposure time can lead to apoptosis and necrosis. Moreover, Cell cycle analysis revealed that NTAPP accelerates the cell cycle in very low doses and can cause proliferation. In this experiment, flow cytometry measurements have been used to determine oxidative stress. The results showed that with increasing plasma dose, intracellular ROS levels reduced. This data also suggests that intracellular ROS are not responsible for the cells' viability. Furthermore, we used reflectance spectroscopy as a non-destructive method for evaluating treatment response and comparing this method with cell analysis techniques. The results indicate spectroscopy's efficiency as a method of cell analysis. This study suggests that NTAPP would be an efficient tool to improve ADSCs culture's efficiency in vitro; thus, we support the potential applications of NTAPP in the field of stem cell therapy and regenerative medicine.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".