Early Treatment Schedule Optimization of Trained-Immunity Mediated Immunotherapy of Non-Muscle Invasive Bladder Cancer (NMIBC): A Research Protocol
Bibliographic record
Abstract
Introduction: Bacillus Calmette-Guérin (BCG) is commonly used as an immunotherapeutic agent following tumour resection in high-risk non-muscle invasive bladder cancer patients. Past studies have shown that BCG confers non-specific innate immune reprogramming, resulting in altered innate immune responses following a secondary antigen challenge. This evidence for innate immune memory has resulted in the identification of a phenomenon known as trained immunity. Recent studies have described the beneficial effects of BCG immunotherapy in terms of trained immunity acquisition and have identified key pro-inflammatory cytokines as trained immunity markers. However, previous studies have not analyzed the impacts of alteration of the standard BCG immunotherapy schedule on acquisition of trained immunity. It is hypothesized that accelerating the BCG treatment schedule will result in greater acquisition of trained immunity, marked by increased levels of IFN-γ and IL-2. Methods: The proposed protocol will make use of a lab-generated murine NMIBC model. The model will be verified via ultrasonography and intravesical BCG instillations will follow both standard and accelerated immunotherapy schedules. A multiplex assay, targeting 40 pro-inflammatory cytokines, will be employed to measure the levels of cytokines in the plasma, and cytokines produced by PBMCs following secondary stimulation with antigen in vitro. Anticipated Results: It is anticipated that ultrasonography will confirm successful generation of the NMIBC murine model. Furthermore, it is expected that the acceleration of BCG instillations will cause upregulation of pro-inflammatory cytokines, specifically IL-2 and IFN-γ, after week 1 of treatment. Following the BCG treatment, greater induction of trained immunity, marked by the upregulation of IL-1β, IL-2, TNFα, and IFN-γ, is expected relative to untreated controls. Discussion: The quantitative results of the multiplex cytokine assay will be used to conduct 1-way ANOVA tests to compare several parameters; cytokine expression will be compared between time points, treatment groups, and sampling methodologies. Conclusion: Results of the proposed study will guide future research on BCG immunotherapy by identifying potential points of optimization for the BCG treatment schedule of bladder cancer. Assessment of non-classical cytokines along with classically defined trained immunity cytokines will aid future studies in tailoring their assays for determining acquisition of trained immunity.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".