A background correction method to compensate illumination variation in hyperspectral imaging.

Hyperspectral imaging (HSI) can measure both spatial (morphological) and spectral (biochemical) information from biological tissues. While HSI appears promising for biomedical applications, interpretation of hyperspectral images can be challenging when data is acquired in complex biological environments. Variations in surface topology or optical power distribution at the sample, encountered for example during endoscopy, can lead to errors in post-processing of the HSI data, compromising disease diagnostic capabilities. Here, we propose a background correction method to compensate for such variations, which estimates the optical properties of illumination at the target based on the normalised spectral profile of the light source and the measured HSI intensity values at a fixed wavelength where the absorption characteristics of the sample are relatively low (in this case, 800 nm). We demonstrate the feasibility of the proposed method by imaging blood samples, tissue-mimicking phantoms, and ex vivo chicken tissue. Moreover, using synthetic HSI data composed from experimentally measured spectra, we show the proposed method would improve statistical analysis of HSI data. The proposed method could help the implementation of HSI techniques in practical clinical applications, where controlling the illumination pattern and power is difficult

Deep learning applied to hyperspectral endoscopy for online spectral classification

Abstract: Hyperspectral imaging (HSI) is being explored in endoscopy as a tool to extract biochemical information that may improve contrast for early cancer detection in the gastrointestinal tract. Motion artefacts during medical endoscopy have traditionally limited HSI application, however, recent developments in the field have led to real-time HSI deployments. Unfortunately, traditional HSI analysis methods remain unable to rapidly process the volume of hyperspectral data in order to provide real-time feedback to the operator. Here, a convolutional neural network (CNN) is proposed to enable online classification of data obtained during HSI endoscopy. A five-layered CNN was trained and fine-tuned on a dataset of 300 hyperspectral endoscopy images acquired from a planar Macbeth ColorChecker chart and was able to distinguish between its 18 constituent colors with an average accuracy of 94.3% achieved at 8.8 fps. Performance was then tested on a set of images simulating an endoscopy environment, consisting of color charts warped inside a rigid tube mimicking a lumen. The algorithm proved robust to such variations, with classification accuracies over 90% being obtained despite the variations, with an average drop in accuracy of 2.4% being registered at the points of longest working distance and most inclination. For further validation of the color-based classification system, ex vivo videos of a methylene blue dyed pig esophagus and images of different disease stages in the human esophagus were analyzed, showing spatially distinct color classifications. These results suggest that the CNN has potential to provide color-based classification during real-time HSI in endoscopy

Hyperspectral Colour Classification (HCC)

Dataset utilised for the training and testing of the spectral classification system for a line-scanning hyperspectral endoscope and the associated core algorithms. See the Readme file for a detailed description

Facile Electrodeposition-Based Chemosensors Using PANI and C-Hybrid Nanomaterials for the Selective Detection of Ammonia and Nitrogen Dioxide at Room Temperature

Sensor systems for monitoring indoor air quality are vital for the precise quantification of the mechanisms which lead to the deterioration of human health, with a typical person spending an average of 20 h a day in an enclosed space. Thus, a series of layered chemoresistive sensors, obtained by the facile electrodeposition of carbon nanomaterial-enhanced PANI composites, have been tested for the selective detection of two core indoor pollutants: ammonia and nitrogen dioxide. The sensors were tested with respect to sensitivity and selectivity to the target gasses, with performance being assessed based on response linearity and repeatability at room temperature. Of the tested sensors, two have been identified as having an adequate performance on ammonia, with sensitivities of up to 96.99% and resolutions of up to 0.85 ppm being observed, while on nitrogen dioxide, despite the successful sensor having a lower sensitivity, 10.71%, it has shown high resolution, 1.25 ppm, and linearity over a large concentration domain. These high performances highlight the viability of multi-layers chemosensors based on the electrodeposition of nanomaterial-enhanced conductive polymers for the detection of pollutant gasses, with finetuning of the detection layer allowing the accurate monitoring of a wide range of gasses

The Physicochemical and Antimicrobial Properties of Silver/Gold Nanoparticles Obtained by “Green Synthesis” from Willow Bark and Their Formulations as Potential Innovative Pharmaceutical Substances

Green chemistry is a pharmaceutical industry tool, which, when implemented correctly, can lead to a minimization in resource consumption and waste. An aqueous extract of Salix alba L. was employed for the efficient and rapid synthesis of silver/gold particle nanostructures via an inexpensive, nontoxic and eco-friendly procedure. The nanoparticles were physicochemically characterized using ultraviolet–visible spectroscopy (UV–Vis), Fourier transform infrared spectroscopy (FT-IR), dynamic light scattering (DLS), X-ray diffraction (XRD) and scanning electron microscopy (SEM), with the best stability of up to one year in the solution obtained for silver nanoparticles without any chemical additives. A comparison of the antimicrobial effect of silver/gold nanoparticles and their formulations (hydrogels, ointments, aqueous solutions) showed that both metallic nanoparticles have antibacterial and antibiofilm effects, with silver-based hydrogels having particularly high antibiofilm efficiency. The highest antibacterial and antibiofilm efficacies were obtained against Pseudomonas aeruginosa when using silver nanoparticle hydrogels, with antibiofilm efficacies of over 75% registered. The hydrogels incorporating green nanoparticles displayed a 200% increased bacterial efficiency when compared to the controls and their components. All silver nanoparticle formulations were ecologically obtained by “green synthesis” and were shown to have an antimicrobial effect or potential as keratinocyte-acting pharmaceutical substances for ameliorating infectious psoriasis wounds