Demonstration of cross reaction in hybrid graphene oxide/tantalum dioxide guided mode resonance sensor for selective volatile organic compound.

This paper experimentally demonstrates a crossed reaction of pure and hybrid graphene oxide (GO)/tantalum dioxide (TaO2) as a volatile organic compound (VOC) absorber in a guided mode resonance (GMR) sensing platform. The proposed GMR platform has a porous TaO2 film as the main guiding layer, allowing for more molecular adsorption and enhanced sensitivity. GO is applied on top as an additional VOC absorber to increase the selectivity. The hybrid sensing mechanism is introduced by varying the concentration of the GO aqueous solution. The experimental results show that the pure TaO2-GMR has a high tendency to adsorb most of the tested VOC molecules, with the resonance wavelength shifting accordingly to the physical properties of the VOCs (molecular weight, vapor pressure, etc). The largest signal appears in the large molecule such as toluene, and its sensitivity is gradually reduced in the hybrid sensors. At the optimum GO concentration of 3 mg/mL, the hybrid GO/TaO2 -GMR is more sensitive to methanol, while the pure GO sensor coated with GO at 5 mg/mL is highly selective to ammonia. The sensing mechanisms are verified using the distribution function theory (DFT) to simulate the molecular absorption, along with the measured functional groups measured on the sensor surface by the Fourier transform infrared spectroscopy (FTIR). The crossed reaction of these sensors is further analyzed by means of machine learning, specifically the principal component analysis (PCA) method and decision tree algorithm. The results show that this sensor is a promising candidate for quantitative and qualitative VOCs detection in sensor array platform.

Single-step 3D-printed integrated optical system and its implementation for a sensing application using digital light processing technology.

This study proposes a single-step integrated optical fabrication scheme utilizing a 3D printer using digital light processing technology. Strong light confinement in the fabricated structure is realized through the introduction of an elevated (tower-shaped) waveguide in a transparent photosensitive resin (PX-8880). The fabrication is optimized to maximize light confinement through varying the dimensions of the guiding region and the tower structure. Benefiting from the surface roughness produced by the slicing process in the 3D printing (50 µm resolution), the fabricated structure was tested for vapor sensing. Obvious intensity dynamics have been reported due to the change of the optical scattering due to the presence of vapor as well as polymer vapor interaction. Though the reported response time is long, further optimization can lead to practical operation time.

Novel p16 binding peptide development for p16-overexpressing cancer cell detection using phage display.

Protein p(16INK4a) (p16) is a well-known biomarker for diagnosis of human papillomavirus (HPV) related cancers. In this work, we identify novel p16 binding peptides by using phage display selection method. A random heptamer phage display library was screened on purified recombinant p16 protein-coated plates to elute only the bound phages from p16 surfaces. Binding affinity of the bound phages was compared with each other by enzyme-linked immunosorbent assay (ELISA), fluorescence imaging technique, and bioinformatic computations. Binding specificity and binding selectivity of the best candidate phage-displayed p16 binding peptide were evaluated by peptide blocking experiment in competition with p16 monoclonal antibody and fluorescence imaging technique, respectively. Five candidate phage-displayed peptides were isolated from the phage display selection method. All candidate p16 binding phages show better binding affinity than wild-type phage in ELISA test, but only three of them can discriminate p16-overexpressing cancer cell, CaSki, from normal uterine fibroblast cell, HUF, with relative fluorescence intensities from 2.6 to 4.2-fold greater than those of wild-type phage. Bioinformatic results indicate that peptide 'Ser-His-Ser-Leu-Leu-Ser-Ser' binds to p16 molecule with the best binding score and does not interfere with the common protein functions of p16. Peptide blocking experiment shows that the phage-displayed peptide 'Ser-His-Ser-Leu-Leu-Ser-Ser' can conceal p16 from monoclonal antibody interaction. This phage clone also selectively interacts with the p16 positive cell lines, and thus, it can be applied for p16-overexpressing cell detection.

Advances in imaging probes and optical microendoscopic imaging techniques for early in vivo cancer assessment.

A new chapter in the history of medical diagnosis happened when the first X-ray technology was invented in the late 1800s. Since then, many non-invasive and minimally invasive imaging techniques have been invented for clinical diagnosis to research in cellular biology, drug discovery, and disease monitoring. These imaging modalities have leveraged the benefits of significant advances in computer, electronics, and information technology and, more recently, targeted molecular imaging. The development of targeted contrast agents such as fluorescent and nanoparticle probes coupled with optical imaging techniques has made it possible to selectively view specific biological events and processes in both in vivo and ex vivo systems with great sensitivity and selectivity. Thus, the combination of targeted molecular imaging probes and optical imaging techniques have become a mainstay in modern medicinal and biological research. Many promising results have demonstrated great potentials to translate to clinical applications. In this review, we describe a discussion of employing imaging probes and optical microendoscopic imaging techniques for cancer diagnosis.

Skin parameter map retrieval from a dedicated multispectral imaging system applied to dermatology/cosmetology.

In vivo quantitative assessment of skin lesions is an important step in the evaluation of skin condition. An objective measurement device can help as a valuable tool for skin analysis. We propose an explorative new multispectral camera specifically developed for dermatology/cosmetology applications. The multispectral imaging system provides images of skin reflectance at different wavebands covering visible and near-infrared domain. It is coupled with a neural network-based algorithm for the reconstruction of reflectance cube of cutaneous data. This cube contains only skin optical reflectance spectrum in each pixel of the bidimensional spatial information. The reflectance cube is analyzed by an algorithm based on a Kubelka-Munk model combined with evolutionary algorithm. The technique allows quantitative measure of cutaneous tissue and retrieves five skin parameter maps: melanin concentration, epidermis/dermis thickness, haemoglobin concentration, and the oxygenated hemoglobin. The results retrieved on healthy participants by the algorithm are in good accordance with the data from the literature. The usefulness of the developed technique was proved during two experiments: a clinical study based on vitiligo and melasma skin lesions and a skin oxygenation experiment (induced ischemia) with healthy participant where normal tissues are recorded at normal state and when temporary ischemia is induced.

Reconstruction of hyperspectral cutaneous data from an artificial neural network-based multispectral imaging system.

The development of an integrated MultiSpectral Imaging (MSI) system yielding hyperspectral cubes by means of artificial neural networks is described. The MSI system is based on a CCD camera, a rotating wheel bearing a set of seven interference filters, a light source and a computer. The resulting device has been elaborated for in vivo imaging of skin lesions. It provides multispectral images and is coupled with a software reconstructing hyperspectral cubes from multispectral images. Reconstruction is performed by a neural network-based algorithm using heteroassociative memories. The resulting hyperspectral cube provides skin optical reflectance spectral data combined with bidimensional spatial information. This combined information will hopefully improve diagnosis and follow-up in a range of skin disorders from skin cancer to inflammatory diseases.