Quantum monitoring of cellular metabolic activities in single mitochondria.

Free radicals play a vital role in all kinds of biological processes including immune responses. However, free radicals have short lifetimes and are highly reactive, making them difficult to measure using current methods. Here, we demonstrate that relaxometry measurement, or T1, inherited from the field of diamond magnetometry can be used to detect free radicals in living cells with subcellular resolution. This quantum sensing technique is based on defects in diamond, which convert a magnetic signal into an optical signal, allowing nanoscale magnetic resonance measurements. We functionalized fluorescent nanodiamonds (FNDs) to target single mitochondria within macrophage cells to detect the metabolic activity. In addition, we performed measurements on single isolated mitochondria. We were able to detect free radicals generated by individual mitochondria in either living cells or isolated mitochondria after stimulation or inhibition.

Co-circulation of dengue virus type 3-genotype I and type 2-Cosmopolitan genotype in 2018 outbreak in Dhaka, Bangladesh.

Dengue virus (DENV) that caused an outbreak in Dhaka, Bangladesh during 2018 was analysed phylogenetically. DENV samples were classified into type 2-Cosmopolitan genotype (54%) and type 3-genotype I (46%), indicating co-circulation of two DENV types and resurgence of type 3 associated with genotype replacement.

EMD-DWT based transform domain feature reduction approach for quantitative multi-class classification of breast lesions.

Using a large set of ultrasound features does not necessarily ensure improved quantitative classification of breast tumors; rather, it often degrades the performance of a classifier. In this paper, we propose an effective feature reduction approach in the transform domain for improved multi-class classification of breast tumors. Feature transformation methods, such as empirical mode decomposition (EMD) and discrete wavelet transform (DWT), followed by a filter- or wrapper-based subset selection scheme are used to extract a set of non-redundant and more potential transform domain features through decorrelation of an optimally ordered sequence of N ultrasonic bi-modal (i.e., quantitative ultrasound and elastography) features. The proposed transform domain bi-modal reduced feature set with different conventional classifiers will classify 201 breast tumors into benign-malignant as well as BI-RADS⩽3, 4, and 5 categories. For the latter case, an inadmissible error probability is defined for the subset selection using a wrapper/filter. The classifiers use train truth from histopathology/cytology for binary (i.e., benign-malignant) separation of tumors and then bi-modal BI-RADS scores from the radiologists for separating malignant tumors into BI-RADS category 4 and 5. A comparative performance analysis of several widely used conventional classifiers is also presented to assess their efficacy for the proposed transform domain reduced feature set for classification of breast tumors. The results show that our transform domain bimodal reduced feature set achieves improvement of 5.35%, 3.45%, and 3.98%, respectively, in sensitivity, specificity, and accuracy as compared to that of the original domain optimal feature set for benign-malignant classification of breast tumors. In quantitative classification of breast tumors into BI-RADS categories⩽3, 4, and 5, the proposed transform domain reduced feature set attains improvement of 3.49%, 9.07%, and 3.06%, respectively, in likelihood of malignancy and 4.48% in inadmissible error probability compared to that of the original domain optimal subset. In summary, the construction of a transform domain reduced feature set by extracting complementary information from a large set of available bi-modal features and use of qualitative bi-modal BI-RADS can contribute to improved quantitative classification of breast tumors and thereby help reduce the number of unnecessary biopsies, securing a nearly minimum chance of a life-endangering diagnosis.

Bimodal Multiparameter-Based Approach for Benign-Malignant Classification of Breast Tumors.

Proposed here is a breast tumor classification technique using conventional ultrasound B-mode imaging and a new elasticity imaging-based bimodal multiparameter index. A set of conventional ultrasound (US) and ultrasound elastography (UE) parameters are studied, and among those, the effective ones whose independent as well as combined performance is found satisfactory are selected. To improve the combined US performance, two new US parameters are proposed: edge diffusivity, which assesses edge blurriness to differentiate malignant from benign lesions, and the shape asymmetry factor, which quantifies tumor shape irregularity by comparing the tumor boundary with an ellipse fitted to the lesion. Then a new bimodal multiparameter characterization index is defined to discriminate 201 pathologically confirmed breast tumors of which 56 are malignant lesions, 79 are fibroadenomas, 42 are cysts and 24 are inflammatory lesions. The weights of the multiparameter bimodal index are optimally computed using a genetic algorithm (GA). To evaluate the performance variation of the index on different data sets, the tumors are categorized into three classes: malignant lesion versus fibroadenoma, malignant lesion versus fibroadenoma and cyst and malignant lesion versus fibroadenoma, cyst and inflammation. The test results reveal that the proposed bimodal index achieves satisfactory quality metrics (e.g., 94.64%-98.21% sensitivity, 97.24%-100.00% specificity and 96.52%-99.44% accuracy) for classification of the aforementioned three classes of breast tumors. Its performance is also observed to be better in totality of the quality metrics sensitivity, specificity, accuracy, positive predictive value and negative predictive value as compared with that of a conventional bimodal index as well as unimodal multiparameter indices based on US or UE. It is suggested that the proposed simple bimodal linear classifier may assist radiologists in better diagnosis of breast tumors and help reduce the number of unnecessary biopsies.

Dengue infection in Dhaka City, Bangladesh.

Dengue fever (DF) and dengue haemorrhagic fever (DHF) are now endemic in Bangladesh with outbreaks being reported quite frequently. This cross sectional study was done clinically suspected dengue patients were selected from different hospitals of Dhaka city, Bangladesh, from January 2008 to December 2008. The clinical features, risk factors and laboratory findings associated with dengue infection were investigated among 201 clinically suspected patients. Antibodies were detected in 137(68.2%) cases. Of these, 80(58.4%) were primary and 57(41.6%) as secondary dengue cases according to presence of dengue-specific IgM and/or IgG antibodies. Among primary cases, 70(87.5%) were DF and 10(12.5%) were DHF cases, in contrast to secondary cases, where 10(18.1%) were DF and 47(81.9%) were DHF cases. Majority (57.9%) of patients presented with Grade I and 42.1% had Grade II disease. Patients between 16-30 years were the most vulnerable age group (81.3% DF and 71.9% DHF patients). Males out-numbered females with 72.5% male vs. 27.5% female patients having DF and 68.4% male vs. 31.6% female patients having DHF. The monsoon period was the peak season for dengue infection. Headache and arthralgia were the most frequent symptoms in both DF and DHF, but vomiting was more common in DHF. The Tourniquet test was significantly positive among DHF than DF cases (p = 0.001). Platelet count was the only laboratory parameter that showed significantly higher values among DHF than DF cases (p = 0.001).

Phase-based direct average strain estimation for elastography.

In this paper, a phase-based direct average strain estimation method is developed. A mathematical model is presented to calculate axial strain directly from the phase of the zero-lag cross-correlation function between the windowed precompression and stretched post-compression analytic signals. Unlike phase-based conventional strain estimators, for which strain is computed from the displacement field, strain in this paper is computed in one step using the secant algorithm by exploiting the direct phase-strain relationship. To maintain strain continuity, instead of using the instantaneous phase of the interrogative window alone, an average phase function is defined using the phases of the neighboring windows with the assumption that the strain is essentially similar in a close physical proximity to the interrogative window. This method accounts for the effect of lateral shift but without requiring a prior estimate of the applied strain. Moreover, the strain can be computed both in the compression and relaxation phases of the applied pressure. The performance of the proposed strain estimator is analyzed in terms of the quality metrics elastographic signal-to-noise ratio (SNRe), elastographic contrast-to-noise ratio (CNRe), and mean structural similarity (MSSIM), using a finite element modeling simulation phantom. The results reveal that the proposed method performs satisfactorily in terms of all the three indices for up to 2.5% applied strain. Comparative results using simulation and experimental phantom data, and in vivo breast data of benign and malignant masses also demonstrate that the strain image quality of our method is better than the other reported techniques.

Using nearest neighbors for accurate estimation of ultrasonic attenuation in the spectral domain.

Attenuation is a key diagnostic parameter of tissue pathology change and thus may play a vital role in the quantitative discrimination of malignant and benign tumors in soft tissue. In this paper, two novel techniques are proposed for estimating the average ultrasonic attenuation in soft tissue using the spectral domain weighted nearest neighbor method. Because the attenuation coefficient of soft tissues can be considered to be a continuous function in a small neighborhood, we directly estimate an average value of it from the slope of the regression line fitted to the 1) modified average midband fit value and 2) the average center frequency shift along the depth. To calculate the average midband fit value, an average regression line computed from the exponentially weighted short-time Fourier transform (STFT) of the neighboring 1-D signal blocks, in the axial and lateral directions, is fitted over the usable bandwidth of the normalized power spectrum. The average center frequency downshift is computed from the maximization of a cost function defined from the normalized spectral cross-correlation (NSCC) of exponentially weighted nearest neighbors in both directions. Different from the large spatial signal-block-based spectral stability approach, a costfunction- based approach incorporating NSCC functions of neighboring 1-D signal blocks is introduced. This paves the way for using comparatively smaller spatial area along the lateral direction, a necessity for producing more realistic attenuation estimates for heterogeneous tissue. For accurate estimation of the attenuation coefficient, we also adopt a reference-phantombased diffraction-correction technique for both methods. The proposed attenuation estimation algorithm demonstrates better performance than other reported techniques in the tissue-mimicking phantom and the in vivo breast data analysis.