PB1048 Implementation of Electronic Medical Records in a Resource-Limited Haematology Department in North-West Nigeria
Bibliographic record
Abstract
Background: C1 esterase inhibitor (C1INH) is a serine proteinase inhibitor (serpin) that regulates plasma kallikrein (PK).C1INH deficiency results in Hereditary Angioedema (HAE), manifesting as acute swelling that can be lifethreatening.Another serpin variant, alpha-1-antitrypsin (AAT) M358R, inhibits multiple proteinases including PK.Previously, our laboratory employed phage display and mutagenesis of the AAT M358R reactive center loop (RCL) to increase the selectivity of AAT M358R for coagulation factor XIa (fXIa) (Hamada M et al.Front Cardiovasc Med.2021).Herein we applied a similar approach using PK.Aims: To increase the selectivity of AAT M358R for PK.Methods: Two AAT M358R T7Select bacteriophage libraries separately randomized at RCL positions P7-P3 and P2-P3′ (with P1 fixed as M358R) were biopanned with PK.After five rounds, the most abundant sequences were expressed as hexahistidine-tagged proteins in E. coli and second order rate constants of inhibition (k2) vs. PK or fXIa were determined (all values n = 5 ± SD).Selectivity was calculated as k2 (PK)/k2 (fXIa).Results: PK biopanning selected sequences QLIPS at P7-P3 (vs.native FLEAI) and VRRAY at P2-P3′ (vs PRSIP).QLIPS variant inhibited PK 2.2-fold faster than AAT M358R (k2 0.39 ± 0.02 vs 0.87 ± 0.08, x 10 EXP 5 M-1S-1) and inhibited fXIa 3.1-fold slower, increasing PK selectivity from 0.2 to 1.3.VRRAY variant inhibited both PK and fXIa less rapidly (k2 values 0.18 ± 0.01 and 0.12 ± 0.01 × 10 EXP 5 M-1S-1, respectively), also increasing selectivity to 1.4.Conclusion(s): Both variants selected by biopanning phage display libraries with PK were more selective inhibitors of PK than AAT M358R, although variant QLIPS was a more rapid inhibitor than variant VRRAY.Combining and
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".