Application of the Mass Spectrometry–High‐Throughput Technique Over the Immunohistochemical Analysis for Human Brain Tumor Diagnosis and Prognosis: Insights Into Biomarkers' Identification for the Case Study of Grade IV Astrocytomas and Meningiomas
Bibliographic record
Abstract
Human brain tumors were commonly monitored in hospital/clinical laboratories by immunohistochemistry (IHC) technique, which provides major insights into their classification. However, this technique remains laborious and still shows pitfalls. Therefore, the current study was endeavored to reveal the assets of the application of high-throughput mass spectrometry (MS) for medical diagnosis. In this study, we focused on the Grade IV astrocytoma and meningioma brain tumors. The collected specimens were first monitored for histopathological diagnosis, followed by IHC staining for the characterization of stemness gene marker, then analyzed by a shotgun proteomic-based approach with high-resolution tandem MS. The IHC analysis only confirmed the histopathological diagnosis, whereas the proteomic analysis unraveled several differently expressed proteins. By bioinformatics, the major enriched pathways and the significance of each protein with its meaningful relationships were identified. The key hub genes were allied for prognostic biomarkers of malignant, metastatic, and invasive forms of cancer with poor prognosis. Overall, the high-throughput MS technique is the most powerful tool to achieve medical analysis at high sensitivity and accuracy and in a very straightforward and timely manner. Hence, its medical implementation in the hospital management system is imperative to counteract the caveats of traditional diagnostic methods and improve the quality of healthcare performance and therapeutic targets.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".