Cells, tissues, and organs on chips: challenges and opportunities for the cancer tumor microenvironment
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
The transition to increasingly sophisticated microfluidic systems has led to the emergence of "organ-on-chip" technology that can faithfully recapitulate organ-level function. Given the rapid progress at the interface between microfluidics and cell biology, there is need to provide a focused evaluation of the state-of-the-art in microfluidic systems for cancer research to advance development, accelerate discovery of novel insights, and facilitate cooperation between engineers, biologists and oncologists in the clinic. Here, we provide a focused review of microfluidics technology from cells- and tissues- to organs-on-chips with application toward studying the tumor microenvironment. Key aspects of the tumor microenvironment including angiogenesis, hypoxia, biochemical gradients, tumor-stromal interactions, and the extracellular matrix are summarized for both solid tumors and non-solid hematologic malignancies. An overview of microfluidic systems designed specifically to answer questions related to different aspects of the tumor microenvironment is provided, followed by an examination of how these systems offer new opportunities to study outstanding challenges related to the major cancer hallmarks. Challenges also remain for microfluidics engineers, but it is hoped that cooperation between engineers and biologists at the intersection of their respective fields will lead to significant impact on the utility of organs-on-chips in cancer research.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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