Acoustically accelerated neural differentiation of human embryonic stem cells
Bibliographic record
Abstract
Human embryonic stem cells (hESCs) and their derived products offer great promise for targeted therapies and drug screening, however, the hESC differentiation process of mature neurons is a lengthy process. To accelerate the neuron production, an acoustic stimulator producing surface acoustic waves (SAWs) is proposed and realized by clamping a flexible printed circuit board (PCB) directly onto a piezoelectric substrate. Neural differentiation of the hESCs is greatly accelerated after application of the acoustic stimulations. Acceleration mechanisms for neural differentiation have been explored by bulk RNA sequencing, quantitative polymerase chain reaction (qPCR) and immunostaining. The RNA sequencing results show changes of extracellular matrix-related and physiological activity-related gene expression in the low or medium SAW dose group and the high SAW dose group, respectively. The neural progenitor cell markers, including Pax6, Sox1, Sox2, Sox10 and Nkx2-1, are less expressed in the SAW dose groups compared with the control group by the qPCR. Other genes including Alk, Cenpf, Pcdh17, and Actn3 are also found to be regulated by the acoustic stimulation. Moreover, the immunostaining confirmed that more mature neuron marker Tuj1-positive cells, while less stem cell marker Sox2-positive cells, are presented in the SAW dose groups. These results indicate that the SAW stimulation accelerated neural differentiation process. The acoustic stimulator fabricated by using the PCB is a promising tool in regulation of stem cell differentiation process applied in cell therapy. STATEMENT OF SIGNIFICANCE: Human embryonic stem cells (hESC) are used for investigating the complex mechanisms involved in the development of specialized biological cells and organs. Different types of hESCs derived cell products can be used for cell therapy procedures aiming to regenerate functional tissues in patients who suffer from various degenerative diseases. Accelerating the hESCs' differentiation process can considerably benefit the clinical utilization of these cells. This study develops a highly effective acoustic stimulator working at ∼20 MHz to investigate what roles do acousto-mechanical stimuli play in the differentiation of hESCs. Our results show that acoustic dose alters the extracellular matrix and physiological activity-related gene expression, which indicates that the acoustic stimulation is an important tool for regulating the stem cells' differentiation processes in cell therapy.
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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 | 0.000 | 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 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".