Biological attributes required for epidermal regeneration: Evaluation of the next-generation autologous cell harvesting device.

Early wound intervention and closure is critical for reducing infection and improving aesthetic and functional outcomes for patients with acute burn wounds and nonthermal full-thickness skin defects. Treatment of partial-thickness burns or full-thickness injuries with autologous skin cell suspension (ASCS) achieves robust wound closure while limiting the amount of donor skin compared with standard autografting. A Next Generation Autologous Cell Harvesting Device (NG-ACHD) was developed to standardize the preparation process for ASCS to ensure biological attributes are obtained known to correlate with well-established safety and performance data. This study compared ASCS prepared using the NG-ACHD and ACHD following the manufacturer's guidance, evaluating cellular yields, viability, apoptotic activity, aggregates, phenotypes and functional capacity. Non-inferiority was established for all biological attributes tested and comparable healing trajectories were demonstrated using an in vitro skin regeneration model. In addition to standardization, the NG-ACHD also provides workflow efficiencies with the potential to decrease training requirements and increase the ease of incorporation and utilization of ASCS in clinical practice.

Analysis of HEp-2 images using MD-LBP and MAD-bagging.

Indirect immunofluorescence imaging is employed to identify antinuclear antibodies in HEp-2 cells which founds the basis for diagnosing autoimmune diseases and other important pathological conditions involving the immune system. Six categories of HEp-2 cells are generally considered, namely homogeneous, fine speckled, coarse speckled, nucleolar, cyto-plasmic, and centromere cells. Typically, this categorisation is performed manually by an expert and is hence both time consuming and subjective. In this paper, we present a method for automatically classifiying HEp-2 cells using texture information in conjunction with a suitable classification system. In particular, we extract multidimensional local binary pattern (MD-LBP) texture features to characterise the cell area. These then form the input for a classification stage, for which we employ a margin distribution based bagging pruning (MAD-Bagging) classifier ensemble. We evaluate our algorithm on the ICPR 2012 HEp-2 contest benchmark dataset, and demonstrate it to give excellent performance, superior to all algorithms that were entered in the competition.

Scleroderma capillary pattern identification using texture descriptors and ensemble classification.

Various connective tissue diseases lead to morphological alternations of blood capillaries. Consequently, observation of the capillaries at the finger nailfold - nailfold capillaroscopy (NC) - is a standard method for diagnosing diseases such as scleroderma or Raynaud's phenomenon. This is typically performed through manual inspection by an expert to lead to a determination of one of the established NC scleroderma patterns (early, active, and late). In this paper, we present an automated method of analysing nailfold capillaroscopy images and categorising them into NC patterns. For this purpose, we extract a carefully chosen set of texture features from the images and employ an ensemble classification approach to arrive at decisions for each captured finger which are then aggregated to form a diagnosis for the patient. Experimental results on a set of 60 NC images from 16 subjects demonstrate the accuracy and usefulness of our presented approach.