Rethabile Khutlang

BACKGROUND: Statins are cholesterol-lowering drugs, targeting HMG-CoA reductase, thereby reducing the risk of coronary disorders and hypercholesterolemia. However, they also can influence immunologic responses. METHODS: Peripheral blood mononuclear cells (PBMCs) and monocyte-derived macrophages (MDMs) were isolated from patients with familial hypercholesterolemia (FH) during statin therapy. After infection of cells with Mycobacterium tuberculosis, bacterial burden was determined. In vivo, mice were treated with statins before aerosol-based infection with M. tuberculosis and were monitored for disease progression. RESULTS: PBMCs and MDMs from patients with FH receiving statin therapy were more resistant to M. tuberculosis infection, with reduced bacterial burdens, compared with those of healthy donors. Moreover, statin treatment in experimental murine M. tuberculosis infection studies increased host protection, with reduced lung burdens and improved histopathologic findings. Mechanistically, metabolic rescue experiments demonstrated that statins reduce membrane cholesterol levels, particularly by the mevalonate-isoprenoid arm of the sterol pathway. This promoted phagosomal maturation (EEA-1/Lamp-3) and autophagy (LC3-II), as shown by confocal microscopy and Western blot in macrophages. In addition, inhibitors of phagosome and autophagosome maturation reversed the beneficial effect of statins on bacterial growth. CONCLUSION: These results suggest that statin-mediated reduction in cholesterol levels within phagosomal membranes counteract M. tuberculosis-induced inhibition of phagosomal maturation and promote host-induced autophagy, thereby augmenting host protection against tuberculosis.

Screening for tuberculosis (TB) in low- and middle-income countries is centered on the microscope. We present methods for the automated identification of Mycobacterium tuberculosis in images of Ziehl-Neelsen (ZN) stained sputum smears obtained using a bright-field microscope. We segment candidate bacillus objects using a combination of two-class pixel classifiers. The algorithm produces results that agree well with manual segmentations, as judged by the Hausdorff distance and the modified Williams index. The extraction of geometric-transformation-invariant features and optimization of the feature set by feature subset selection and Fisher transformation follow. Finally, different two-class object classifiers are compared. The sensitivity and specificity of all tested classifiers is above 95% for the identification of bacillus objects represented by Fisher-transformed features. Our results may be used to reduce technician involvement in screening for TB, and would be particularly useful in laboratories in countries with a high burden of TB, where, typically, ZN rather than auramine staining of sputum smears is the method of choice.

Screening for tuberculosis in high-prevalence countries relies on sputum smear microscopy. We present a method for the automated identification of Mycobacterium tuberculosis in images of Ziehl-Neelsen-stained sputum smears obtained using a bright-field microscope. We use two stages of classification. The first comprises a one-class pixel classifier for object segmentation. Geometric transformation invariant features are extracted for implementation of the second stage, namely one-class object classification. Different classifiers are compared; the sensitivity of all tested classifiers is above 90% for the identification of a single bacillus object using all extracted features. The mixture of Gaussians classifier performed well in both stages of classification. This method may be used as a step in the automation of tuberculosis screening, in order to reduce technician involvement in the process.

Biometric fingerprint scanners scan the external skin features onto a 2-D image. The performance of the automatic fingerprint identification system suffers if the finger skin is wet, worn out, fake fingerprint is used et cetera. In this paper, we present an automatic segmentation of the papillary layer method, in 3-D swept source optical coherence tomography (SS-OCT) images. The papillary contour represents the internal fingerprint, which does not suffer external skin problems. The slices composing the 3-D image are filtered by the regularized Perona and Malik partial differential equations filter to minimize the effect of speckle noise. Then the corneum stratum is detected, which in turn leads to the extraction of the epidermis using prior knowledge of the epidermis depth. The epidermis is used as the target of the novelty detection that is applied to the image slices. The contour of the papillary layer is segmented as the boundary between the target and rejection classes resulting from novelty detection. The papillary contours are consistent with those segmented manually, with the modified Williams index above 0.9400 on average. The 3-D papillary contour represents an internal fingerprint.

We present a method for the identification of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Mycobacterium</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tuberculosis</i> in images of Ziehl-Neelsen stained sputum smears obtained using a bright field microscope. We use two stages of classification; the first is a one-class pixel classifier, after which geometric transformation invariant features are extracted. The second stage is a one-class object classifier. Different classifiers are compared; the sensitivity of all tested classifiers is above 90% for the identification of a single bacillus object using all extracted features. Our results may be used to reduce technician involvement in screening for tuberculosis, and will be particularly useful in laboratories in countries with a high burden of tuberculosis.