PAMAM-Functionalized Paper as a New Substrate for the Paper Spray Mass Spectrometry Measurement of Proteins
Bibliographic record
Abstract
Paper surface functionalization with polyamidoamine (PAMAM) dendrimers has been developed for increased sensitivity analysis of proteins by paper spray mass spectrometry (PS-MS). PAMAM is a branched polymeric compound with an ethylenediamine core linked to repeating PAMAM units that generates an outer surface rich in primary amines. These positively charged amine groups can interact electrostatically with negatively charged residues (e.g., aspartate, glutamate) on the protein surface. PAMAM inner amide moieties can also promote hydrogen bonding with protein surface oxygens, making PAMAM a useful material for protein extraction. PAMAM-functionalized PS-MS paper strips were used to extract proteins from biofluids, dipped in acetonitrile to remove unbound constituents, dried, and then measured with PS-MS. The use of this strategy was optimized and compared with unmodified paper strips. PAMAM-functionalized paper substrates provided sixfold greater sensitivity for albumin, 11-fold for hemoglobin, sevenfold for insulin, and twofold for lysozyme. The analytical performance of the functionalized paper substrate was evaluated through the analysis of albumin in urine, achieving linearity with R 2 > 0.99, LOD of 1.1 μg mL –1, LOQ of 3.8 μg mL –1, precision better than 10%, and relative recovery 70–83%. The method was applied to quantify urinary albumin from nine anonymous patient samples (concentrations ranged from 6.5 to 77.4 μg mL –1 ), illustrating its potential for the diagnosis of microalbuminuria. These data demonstrate the utility of paper modification with the PAMAM dendrimer for sensitive PS-MS analysis of proteins, opening a path for further applications in clinical diagnosis through the analysis of disease-related proteins.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".