Extended Kalman Filtering Projection Method to Reduce the 3σ Noise Value of Optical Biosensors.

Optical biosensors have experienced a rapid growth over the past decade because of their high sensitivity and the fact that they are label-free. Many optical biosensors rely on tracking the change in a resonance signal or an interference pattern caused by the change in refractive index that occurs upon binding to a target biomarker. The most commonly used method for tracking such a signal is based on fitting the data with an appropriate mathematical function, such as a harmonic function or a Fano, Gaussian, or Lorentz function. However, these functions have limited fitting efficiency because of the deformation of data from noise. Here, we introduce an extended Kalman filter projection (EKFP) method to address the problem of resonance tracking and demonstrate that it improves the tolerance to noise, reduces the 3σ noise value, and lowers the limit of detection (LOD). We utilize the method to process the data of experiments for detecting the binding of C-reactive protein in a urine matrix with a chirped guided mode resonance sensor and are able to improve the LOD from 10 to 1 pg/mL. Our method reduces the 3σ noise value of this measurement compared to a simple Fano fit from 1.303 to 0.015 pixels. These results demonstrate the significant advantage of the EKFP method to resolving noisy data of optical biosensors.

Label-free optical hemogram of granulocytes enhanced by artificial neural networks.

An outstanding challenge for immunology is the classification of immune cells in a label-free fashion with high speed. For this purpose, optical techniques such as Raman spectroscopy or digital holographic microscopy have been used successfully to identify immune cell subsets. To achieve high accuracy, these techniques require a post-processing step using linear methods of multivariate processing, such as principal component analysis. Here we demonstrate for the first time a comparison between artificial neural networks and principal component analysis (PCA) to classify the key granulocyte cell lineages of neutrophils and eosinophils using both digital holographic microscopy and Raman spectroscopy. Artificial neural networks can offer advantages in terms of classification accuracy and speed over a PCA approach. We conclude that digital holographic microscopy with convolutional neural networks based analysis provides a route to a robust, stand-alone and high-throughput hemogram with a classification accuracy of 91.3 % at a throughput rate of greater than 100 cells per second.

Quantitative & qualitative analysis of endothelial cells of donor cornea before & after penetrating keratoplasty in different pathological conditions.

BACKGROUND & OBJECTIVES: Endothelial cells of the donor cornea are known to be affected quantitatively and qualitatively in different pathological conditions after penetrating keratoplasty (PK) and this has direct effect on the clarity of vision obtained after PK. This study was undertaken to analyze the qualitative and quantitative changes in donor endothelial cells before and after PK in different pathological conditions. METHODS: A prospective investigational analysis of 100 consecutive donor corneas used for penetrating keratoplasty between June 2006 and June 2008, was conducted. The patients were evaluated on the first day, at the end of first week, first month, third and six months and one year. RESULTS: A decrease was observed in endothelial cell count in all pathological conditions. After one year of follow up the loss was 33.1 per cent in corneal opacity, 45.9 per cent in acute infective keratitis (AIK), 58.5 per cent in regrafts, 28.5 per cent in pseudophakic bullous keratopathy (PBK), 37 per cent in descemetocele, 27 per cent in keratoconus and 35.5 per cent in aphakic bullous keratopathy (ABK) cases. INTERPRETATION & CONCLUSIONS: The endothelial cell loss was highest in regraft cases which was significant (P<0.05), while the least endothelial cell loss was seen in keratoconus cases. The cell loss was associated with increase in coefficient of variation (CV), i.e. polymegathism and pleomorphism. Inspite of this polymegathism and pleomorphism, the clarity of the graft was maintained.