Periodic Table of Immunomodulatory Elements and Derived Two‐Dimensional Biomaterials
Bibliographic record
Abstract
Periodic table of chemical elements serves as the foundation of material chemistry, impacting human health in many different ways. It contributes to the creation, growth, and manipulation of functional metallic, ceramic, metalloid, polymeric, and carbon-based materials on and near an atomic scale. Recent nanotechnology advancements have revolutionized the field of biomedical engineering to tackle longstanding clinical challenges. The use of nano-biomaterials has gained traction in medicine, specifically in the areas of nano-immunoengineering to treat inflammatory and infectious diseases. Two-dimensional (2D) nanomaterials have been found to possess high bioactive surface area and compatibility with human and mammalian cells at controlled doses. Furthermore, these biomaterials have intrinsic immunomodulatory properties, which is crucial for their application in immuno-nanomedicine. While significant progress has been made in understanding their bioactivity and biocompatibility, the exact immunomodulatory responses and mechanisms of these materials are still being explored. Current work outlines an innovative "immunomodulatory periodic table of elements" beyond the periodic table of life, medicine, and microbial genomics and comprehensively reviews the role of each element in designing immunoengineered 2D biomaterials in a group-wise manner. It recapitulates the most recent advances in immunomodulatory nanomaterials, paving the way for the development of new mono, hybrid, composite, and hetero-structured biomaterials.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".