Immunohistochemistry (IHC) staining of in-vitro cancer cell-generated tumoroids
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
Targeting different pathways in combinational therapy may lead to synergistic effects with higher drug efficiency. Due to a large number of candidate drugs and the variability in the genomic landscape of the disease, conventional cell culture models have limited success. Three-dimensional (3D) cell culture platforms such as tumoroids not only provide a pathophysiological relevant condition but also allow for low-cost and high-throughput drug screening strategies. Immunostaining of targeted proteins within a tumoroid is challenging as the interior cells are difficult to access via a non-destructive method. Immunohistochemistry (IHC) is an important technique in clinical research to explore the expression of various biomarkers. IHC staining of tumoroids allows non-destructive detection of unstable proteins by direct fixation of cells at the state of tumor microenvironment (TME) context, providing two main advantages. First, the target protein can be fixed without dissociating cells and disintegration of tumoroids into a single-cell suspension. Second, staining the preserved structure of tumoroids helps identify the location of the target proteins as well as the spatial distribution throughout the tumoroid geometry. In this protocol, we describe the detailed methodology of a non-destructive IHC staining of cancer biomarkers which minimizes the manipulation of tumoroids prior to fixation by eliminating multiple centrifugations and shaking steps typically required for removing excess hydrogel and collecting tumoroids. The protocol can be used in studies involving prognostic and predictive biomarker investigations in new anti-tumor drug development strategies.
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