Оценка уровня готовности учащихся к проектной деятельности на уроках физики
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
Cell culture models of endothelial and epithelial barriers typically use porous membrane inserts (e.g., Transwell inserts) as a permeable substrate on which barrier cells are grown, often in coculture with other cell types on the opposite side of the membrane. Current methods to characterize barrier function in porous membrane inserts can disrupt the barrier or provide bulk measurements that cannot isolate barrier cell resistance alone. Electrical cell-substrate impedance sensing (ECIS) addresses these limitations, but its implementation on porous membrane inserts has been limited by costly manufacturing, low sensitivity, and lack of validation for barrier assessment. Here, we present porous membrane ECIS (PM-ECIS), a cost-effective method to adapt ECIS technology to porous substrate-based in vitro models. We demonstrate high fidelity patterning of electrodes on porous membranes that can be incorporated into well plates of a variety of sizes with excellent cell biocompatibility with mono- and coculture set ups. PM-ECIS provided sensitive, real-time measurement of isolated changes in endothelial cell barrier impedance with cell growth and barrier disruption. Barrier function characterized by PM-ECIS resistance correlated well with permeability coefficients obtained from simultaneous molecular tracer permeability assays performed on the same cultures, validating the device. Integration of ECIS into conventional porous cell culture inserts provides a versatile, sensitive, and automated alternative to current methods to measure barrier function in vitro, including molecular tracer assays and transepithelial/endothelial electrical resistance.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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