Infrared Spectroscopic Identification of β-Thalassemia
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to investigate the potential of infrared (IR) spectroscopy as a fast and reagent-free adjunct tool in the diagnosis and screening of beta-thalassemia. METHODS: Blood was obtained from 56 patients with beta-thalassemia major, 1 patient with hemoglobin H disease, and 35 age-matched controls. Hemolysates of blood samples were centrifuged to remove stroma. IR absorption spectra were recorded for duplicate films dried from 5 microL of hemolysate. Differentiation between the two groups of hemoglobin spectra was by two statistical methods: an unsupervised cluster analysis and a supervised linear discriminant analysis (LDA). RESULTS: The IR spectra revealed changes in the secondary structure of hemoglobin from beta-thalassemia patients compared with that from controls, in particular, a decreased alpha-helix content, an increased content of parallel and antiparallel beta-sheets, and changes in the tyrosine ring absorption band. The hemoglobin from beta-thalassemia patients also showed an increase in the intensity of the IR bands from the cysteine -SH groups. The unsupervised cluster analysis, statistically separating spectra into different groups according to subtle IR spectral differences, allowed separation of control hemoglobin from beta-thalassemia hemoglobin spectra, based mainly on differences in protein secondary structure. The supervised LDA method provided 100% classification accuracy for the training set and 98% accuracy for the validation set in partitioning control and beta-thalassemia samples. CONCLUSION: IR spectroscopy holds promise in the clinical diagnosis and screening of beta-thalassemia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".