Alzheimer model chip with microglia BV2 cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Amyloid beta oligomers (AβO) are pivotal in Alzheimer's Disease (AD), cleared by microglia cells, as immune cells in the brain. Microglia cells exposed to AβO are involved with migration, apoptosis, phagocytosis, and activated microglial receptors through AβO, impacting cellular mechanobiological characteristics such as microglial adhesion strength to the underlying substrate. Herein, a label-free microfluidic device was used to detect advancing AD conditions with increasing AβO concentrations on microglia BV2 cells by quantitatively comparing the cell-substrate adhesion. The microfluidic device, acting as an AD model, comprises a single channel, which functions as a cell adhesion assay. To assess cell-substrate adhesion under different AβO concentrations of 1 µM, 2.5 µM, and 5 µM, the number of the cells attached to the substrate was counted by real-time microscopy when the cells were under the flow shear stress of 3 Pa and 7.5 Pa corresponding to Reynolds number (Re) of 10 and 25, respectively. The data showed that quantifying the cell-substrate adhesion using the microfluidic device could successfully identify conditions of advancing AβO concentrations. Our findings indicated that the increased incubation time with AβO caused reduced cell-substrate adhesion strength. Additionally, increased AβO concentration was another factor that weakened microglial interaction with the substrate. The quantification of cell-substrate adhesion using 3 Pa compared to 7.5 Pa clearly demonstrated advancing AβO in AD. This study using the chip provides an AD model for a deeper understanding mechanobiological behaviors of microglia exposed to AβO corresponding to diagnosed AD conditions under an in vitro microenvironment.
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.
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 it