MEATERIALS AND METHODS: A cross-sectional diagnostic study was performed on patients with edematous mucosa of the middle turbinate head. Under traditional white light endoscopy, areas of edematous mucosa were identified. Using NBI, these areas were compared to areas of normal mucosa on the middle turbinate head. NBI images of these same areas were then converted to grey scale and a vascularity index was created by pixel analysis and brightness in Fiji Image J software (Wisconsin, US).
RESULTS: Thirty-three middle turbinates were assessed (age 42.4 ± 12.5, 42.4% female). NBI discriminated between areas identified under white light endoscopy as edematous and normal (158.2 ± 48.4 v 96.9 ± 32.7 p
MATERIALS AND METHODS: A retrospective study was carried out on 318 subjects with hypochromic anaemia, which comprised 162 IDA and 156 thalassaemia trait subjects with α-thalassemia, β-thalassemia and HbE trait. Optimal cut-off value, sensitivity and specificity of M/H ratio for thalassaemia trait discrimination was determined using Receiver Operating Characteristic (ROC) analysis.
RESULTS: Subjects with thalassaemia trait showed higher MicroR compared to IDA ( p< 0.001) while subjects with IDA demonstrated higher Hypo-He than thalassaemia trait (p < 0.001). M/H ratio was significantly higher in thalassaemia trait compared to IDA, with medians of 3.77 (interquartile range: 2.57 - 6.52) and 1.73 (interquartile range: 1.27 - 2.38), respectively (p < 0.001). M/H ratio ≥ 2.25 was the optimal cut-off value for discriminating thalassaemia trait from IDA in hypochromic anaemia, with the area under ROC curve (AUC) of 0.83, sensitivity of 80.8% and specificity of 71.6%.
CONCLUSIONS: M/H ratio is a useful discriminant index to distinguish thalassaemia trait from IDA in hypochromic anaemia prior to diagnostic analysis for thalassaemia confirmation. High M/H ratio is suggestive of thalassaemia trait than of IDA. However, more studies are required to establish the role of M/H ratio as a screening tool for thalassaemia discrimination in hypochromic anaemia.
METHODS: The POCT was used to test 170 serum specimens collected through measles surveillance or vaccination programmes in Ethiopia, Malaysia and the Russian Federation: 69 were positive for measles immunoglobulin M (IgM) antibodies, 74 were positive for rubella IgM antibodies and 7 were positive for both. Also tested were 282 oral fluid specimens from the measles, mumps and rubella (MMR) surveillance programme of the United Kingdom of Great Britain and Northern Ireland. The Microimmune measles IgM capture enzyme immunoassay was the gold standard for comparison. A panel of 24 oral fluids was used to investigate if measles virus haemagglutinin (H) and nucleocapsid (N) genes could be amplified by polymerase chain reaction directly from used POCT strips.
FINDINGS: With serum POCT showed a sensitivity and specificity of 90.8% (69/76) and 93.6% (88/94), respectively; with oral fluids, sensitivity and specificity were 90.0% (63/70) and 96.2% (200/208), respectively. Both H and N genes were reliably detected in POCT strips and the N genes could be sequenced for genotyping. Measles virus genes could be recovered from POCT strips after storage for 5 weeks at 20-25 °C.
CONCLUSION: The POCT has the sensitivity and specificity required of a field-based test for measles diagnosis. However, its role in global measles control programmes requires further evaluation.
METHODS: The pterygium screening system was tested on two normal eye databases (UBIRIS and MILES) and two pterygium databases (Australia Pterygium and Brazil Pterygium). This system comprises four modules: (i) a preprocessing module to enhance the pterygium tissue using HSV-Sigmoid; (ii) a segmentation module to differentiate the corneal region and the pterygium tissue; (iii) a feature extraction module to extract corneal features using circularity ratio, Haralick's circularity, eccentricity, and solidity; and (iv) a classification module to identify the presence or absence of pterygium. System performance was evaluated using support vector machine (SVM) and artificial neural network.
RESULTS: The three-step frame differencing technique was introduced in the corneal segmentation module. The output image successfully covered the region of interest with an average accuracy of 0.9127. The performance of the proposed system using SVM provided the most promising results of 88.7%, 88.3%, and 95.6% for sensitivity, specificity, and area under the curve, respectively.
CONCLUSION: A basic platform for computer-aided pterygium screening was successfully developed using the proposed modules. The proposed system can classify pterygium and non-pterygium cases reasonably well. In our future work, a standard grading system will be developed to identify the severity of pterygium cases. This system is expected to increase the awareness of communities in rural areas on pterygium.