name: 'hemoglobinopathy-analysis-agent'
description: 'AI-powered analysis of hemoglobin disorders including sickle cell disease, thalassemias, and variant hemoglobins using HPLC, electrophoresis, and molecular data.'
measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.
allowed-tools:
- read_file
- run_shell_command
Hemoglobinopathy Analysis Agent
The Hemoglobinopathy Analysis Agent provides comprehensive AI-driven analysis of hemoglobin disorders. It integrates HPLC chromatograms, electrophoresis patterns, CBC parameters, and molecular genetics for diagnosis and management of sickle cell disease, thalassemias, and variant hemoglobins.
When to Use This Skill
- When interpreting HPLC hemoglobin chromatograms for variant identification.
- To diagnose and classify thalassemia syndromes (α, β, δβ).
- For comprehensive sickle cell disease phenotype assessment.
- When correlating genotype with clinical phenotype severity.
- To guide hydroxyurea dosing and transfusion management.
Core Capabilities
HPLC Interpretation: AI pattern recognition for hemoglobin variant identification from HPLC chromatograms.
Thalassemia Classification: Distinguish α-thalassemia (silent carrier to Hb Bart's) and β-thalassemia (minor to major).
Sickle Cell Phenotyping: Integrate HbS%, HbF%, α-globin status for phenotype prediction.
Variant Identification: Database matching for >1,500 known hemoglobin variants.
Molecular Correlation: Link genetic variants (HBB, HBA1/2) to protein phenotypes.
Management Guidance: Treatment recommendations based on disease severity.
Hemoglobin Pattern Analysis
| Condition |
HbA |
HbA2 |
HbF |
Variants |
RBC Indices |
| Normal adult |
96-98% |
2-3% |
<1% |
- |
Normal |
| β-thal trait |
92-95% |
3.5-7% |
1-3% |
- |
Microcytic |
| β-thal major |
0-10% |
Variable |
90-95% |
- |
Severe anemia |
| α-thal trait |
97-98% |
2-3% |
<1% |
- |
Microcytic |
| HbH disease |
70-90% |
1-2% |
<1% |
HbH 5-30% |
Moderate anemia |
| Sickle trait |
55-60% |
2-3% |
<1% |
HbS 38-45% |
Normal |
| Sickle cell |
0% |
2-3% |
2-20% |
HbS 80-95% |
Sickle cells |
Workflow
Input: HPLC chromatogram, CBC with indices, peripheral smear findings, molecular data (if available).
Pattern Recognition: AI analysis of HPLC retention times and peak areas.
Variant Matching: Compare against hemoglobin variant database.
RBC Correlation: Integrate MCV, MCH, RDW, reticulocyte count.
Phenotype Classification: Assign clinical phenotype category.
Management: Generate treatment and monitoring recommendations.
Output: Diagnosis, variant identification, clinical classification, management plan.
Example Usage
User: "Interpret this HPLC chromatogram showing an abnormal peak and correlate with the CBC findings."
Agent Action:
python3 Skills/Hematology/Hemoglobinopathy_Analysis_Agent/hb_analyzer.py \
--hplc_data chromatogram.csv \
--retention_times peak_times.json \
--cbc cbc_results.json \
--peripheral_smear smear_findings.txt \
--molecular hbb_sequencing.vcf \
--output hb_report.json
Key Hemoglobin Variants
| Variant |
Mutation |
HPLC Window |
Clinical Significance |
| HbS |
β6 Glu→Val |
S window |
Sickling disorders |
| HbC |
β6 Glu→Lys |
C window |
HbC disease, HbSC |
| HbE |
β26 Glu→Lys |
A2/E window |
Common in SE Asia |
| HbD-Punjab |
β121 Glu→Gln |
D window |
HbSD-Punjab |
| Hb Lepore |
δβ fusion |
S window |
Thalassemia |
| HbH |
β4 tetramer |
Fast band |
α-thalassemia |
| Hb Bart's |
γ4 tetramer |
Very fast |
Hydrops fetalis |
AI/ML Components
HPLC Pattern Recognition:
- CNN trained on 50,000+ chromatograms
- Identifies peaks by retention time and shape
- Quantifies hemoglobin fractions
- Flags unusual patterns for review
Phenotype Prediction:
- Gradient boosting model
- Features: Hb%, HbF%, α-globin genotype, F-cell distribution
- Predicts clinical severity (mild/moderate/severe)
- VOC risk, stroke risk, TCD velocity correlation
Genotype-Phenotype Correlation:
- Database of published correlations
- Modifier genes (BCL11A, HBS1L-MYB, α-globin)
- Pharmacogenomics (HU response prediction)
Clinical Decision Support
Hydroxyurea Candidacy:
- Severe phenotype
- ≥3 pain crises/year
- ACS history
- Stroke prevention
Transfusion Protocols:
- Simple vs exchange transfusion
- Target HbS% thresholds
- Iron chelation monitoring
Monitoring Schedule:
- LDH, reticulocytes, bilirubin
- Ferritin for transfused patients
- TCD for children with SCD
Prerequisites
- Python 3.10+
- PyTorch for image/signal analysis
- Hemoglobin variant databases
- Clinical lab interface
Related Skills
- Blood_Smear_Analysis - For morphology assessment
- Variant_Interpretation - For molecular findings
- Flow_Cytometry_AI - For F-cell quantification
Newborn Screening Integration
- Interpret newborn screening HPLC patterns
- Distinguish FAS (sickle trait) from FS (sickle disease)
- Flag FAE (HbE), FAC (HbC), F-only (β-thal major)
- Generate confirmatory testing recommendations
Author
AI Group - Biomedical AI Platform
1---2name: hemoglobinopathy-analysis-agent3description: <!--4---5<!--6# COPYRIGHT NOTICE7# This file is part of the "Universal Biomedical Skills" project.8# Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>9# All Rights Reserved.10#11# This code is proprietary and confidential.12# Unauthorized copying of this file, via any medium is strictly prohibited.13#14# Provenance: Authenticated by MD BABU MIA1516-->1718---19name: 'hemoglobinopathy-analysis-agent'20description: 'AI-powered analysis of hemoglobin disorders including sickle cell disease, thalassemias, and variant hemoglobins using HPLC, electrophoresis, and molecular data.'21measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.22allowed-tools:23 - read_file24 - run_shell_command25---262728# Hemoglobinopathy Analysis Agent2930The **Hemoglobinopathy Analysis Agent** provides comprehensive AI-driven analysis of hemoglobin disorders. It integrates HPLC chromatograms, electrophoresis patterns, CBC parameters, and molecular genetics for diagnosis and management of sickle cell disease, thalassemias, and variant hemoglobins.3132## When to Use This Skill3334* When interpreting HPLC hemoglobin chromatograms for variant identification.35* To diagnose and classify thalassemia syndromes (α, β, δβ).36* For comprehensive sickle cell disease phenotype assessment.37* When correlating genotype with clinical phenotype severity.38* To guide hydroxyurea dosing and transfusion management.3940## Core Capabilities41421. **HPLC Interpretation**: AI pattern recognition for hemoglobin variant identification from HPLC chromatograms.43442. **Thalassemia Classification**: Distinguish α-thalassemia (silent carrier to Hb Bart's) and β-thalassemia (minor to major).45463. **Sickle Cell Phenotyping**: Integrate HbS%, HbF%, α-globin status for phenotype prediction.47484. **Variant Identification**: Database matching for >1,500 known hemoglobin variants.49505. **Molecular Correlation**: Link genetic variants (HBB, HBA1/2) to protein phenotypes.51526. **Management Guidance**: Treatment recommendations based on disease severity.5354## Hemoglobin Pattern Analysis5556| Condition | HbA | HbA2 | HbF | Variants | RBC Indices |57|-----------|-----|------|-----|----------|-------------|58| Normal adult | 96-98% | 2-3% | <1% | - | Normal |59| β-thal trait | 92-95% | 3.5-7% | 1-3% | - | Microcytic |60| β-thal major | 0-10% | Variable | 90-95% | - | Severe anemia |61| α-thal trait | 97-98% | 2-3% | <1% | - | Microcytic |62| HbH disease | 70-90% | 1-2% | <1% | HbH 5-30% | Moderate anemia |63| Sickle trait | 55-60% | 2-3% | <1% | HbS 38-45% | Normal |64| Sickle cell | 0% | 2-3% | 2-20% | HbS 80-95% | Sickle cells |6566## Workflow67681. **Input**: HPLC chromatogram, CBC with indices, peripheral smear findings, molecular data (if available).69702. **Pattern Recognition**: AI analysis of HPLC retention times and peak areas.71723. **Variant Matching**: Compare against hemoglobin variant database.73744. **RBC Correlation**: Integrate MCV, MCH, RDW, reticulocyte count.75765. **Phenotype Classification**: Assign clinical phenotype category.77786. **Management**: Generate treatment and monitoring recommendations.79807. **Output**: Diagnosis, variant identification, clinical classification, management plan.8182## Example Usage8384**User**: "Interpret this HPLC chromatogram showing an abnormal peak and correlate with the CBC findings."8586**Agent Action**:87```bash88python3 Skills/Hematology/Hemoglobinopathy_Analysis_Agent/hb_analyzer.py \89 --hplc_data chromatogram.csv \90 --retention_times peak_times.json \91 --cbc cbc_results.json \92 --peripheral_smear smear_findings.txt \93 --molecular hbb_sequencing.vcf \94 --output hb_report.json95```9697## Key Hemoglobin Variants9899| Variant | Mutation | HPLC Window | Clinical Significance |100|---------|----------|-------------|----------------------|101| HbS | β6 Glu→Val | S window | Sickling disorders |102| HbC | β6 Glu→Lys | C window | HbC disease, HbSC |103| HbE | β26 Glu→Lys | A2/E window | Common in SE Asia |104| HbD-Punjab | β121 Glu→Gln | D window | HbSD-Punjab |105| Hb Lepore | δβ fusion | S window | Thalassemia |106| HbH | β4 tetramer | Fast band | α-thalassemia |107| Hb Bart's | γ4 tetramer | Very fast | Hydrops fetalis |108109## AI/ML Components110111**HPLC Pattern Recognition**:112- CNN trained on 50,000+ chromatograms113- Identifies peaks by retention time and shape114- Quantifies hemoglobin fractions115- Flags unusual patterns for review116117**Phenotype Prediction**:118- Gradient boosting model119- Features: Hb%, HbF%, α-globin genotype, F-cell distribution120- Predicts clinical severity (mild/moderate/severe)121- VOC risk, stroke risk, TCD velocity correlation122123**Genotype-Phenotype Correlation**:124- Database of published correlations125- Modifier genes (BCL11A, HBS1L-MYB, α-globin)126- Pharmacogenomics (HU response prediction)127128## Clinical Decision Support129130**Hydroxyurea Candidacy**:131- Severe phenotype132- ≥3 pain crises/year133- ACS history134- Stroke prevention135136**Transfusion Protocols**:137- Simple vs exchange transfusion138- Target HbS% thresholds139- Iron chelation monitoring140141**Monitoring Schedule**:142- LDH, reticulocytes, bilirubin143- Ferritin for transfused patients144- TCD for children with SCD145146## Prerequisites147148* Python 3.10+149* PyTorch for image/signal analysis150* Hemoglobin variant databases151* Clinical lab interface152153## Related Skills154155* Blood_Smear_Analysis - For morphology assessment156* Variant_Interpretation - For molecular findings157* Flow_Cytometry_AI - For F-cell quantification158159## Newborn Screening Integration160161- Interpret newborn screening HPLC patterns162- Distinguish FAS (sickle trait) from FS (sickle disease)163- Flag FAE (HbE), FAC (HbC), F-only (β-thal major)164- Generate confirmatory testing recommendations165166## Author167168AI Group - Biomedical AI Platform169170171<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->