Bibliographic record
Abstract
Introduction: Glioblastoma, also known as glioblastoma multiforme (GBM), is a highly aggressive and incurable form of brain tumor that grows rapidly. Intracellular cell adhesion molecule 1 (ICAM1) is a glycoprotein and adhesion receptor that plays a multifaceted role in immune response and can serve as a therapeutic target. Due to the severity and poor prognosis associated with GBM, the interaction between ICAM1 and GBM has been a topic of interest in the hope of providing therapeutic value. Methods: This review synthesizes existing literature and studies through searches in the PubMed, Google Scholar, Web of Science, Ovid-Medline, and EBSCO databases to explore the role of ICAM1 on immune cells in the context of GBM. Search terms such as “GBM,” “ICAM1,” “tumor microenvironment,” as well as “adhesion molecules” and “GBM treatment” were employed. Literature was selected based on its applicability to key aspects of the research topic. Results: This comprehensive review anticipated uncovering the complex role of ICAM1 in immune cell adhesion in GBM. By promoting the adhesion of immune cells, such as T lymphocytes and natural killer cells, ICAM1 can potentially enhance the body's natural defense mechanisms against GBM. However, the tumor microenvironment (and interaction with other molecules) can also manipulate and alter ICAM1 in ways that inhibits an effective immune response, potentially resulting in tumor progression. Discussion: Understanding the role of ICAM1 in GBM can present new strategies for immune-based therapies in GBM, potentially leading to improved treatment outcomes. Moreover, gaining insight into how adhesion molecules, such as ICAM1, respond to the tumor microenvironment can advance GBM therapy and also provide insights into treatment options for various cancers. Conclusion: ICAM1 exerts both pro-tumorigenic and anti-tumorigenic effects, shaping tumor progression and immune evasion. Understanding these dual roles can guide the development of targeted therapies for GBM.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".