Non-targeted LC-MS metabolomics reveal shifts from wound-induced enzymatic browning to lignification during extended storage of fresh-cut lettuce in modified atmosphere packaging.

Modified Atmosphere Packaging (MAP) is a conventional method used to prolong the shelf-life of fresh-cut vegetables, including lettuce. However, MAP-stored lettuce remains perishable, and its deterioration mechanism is not fully understood. Here, we utilized non-targeted LC-MS metabolomics to evaluate the effects of cutting and extended storage time on metabolite profiles of lettuce stored in MAP.

Improving performance in colorectal cancer histology decomposition using deep and ensemble machine learning.

In routine colorectal cancer management, histologic samples stained with hematoxylin and eosin are commonly used. Nonetheless, their potential for defining objective biomarkers for patient stratification and treatment selection is still being explored. The current gold standard relies on expensive and time-consuming genetic tests.

An Automatic Method for Assessing Spiking of Tibial Tubercles Associated with Knee Osteoarthritis.

Efficient and scalable early diagnostic methods for knee osteoarthritis are desired due to the disease's prevalence. The current automatic methods for detecting osteoarthritis using plain radiographs struggle to identify the subjects with early-stage disease. Tibial spiking has been hypothesized as a feature of early knee osteoarthritis.

FPI Based Hyperspectral Imager for the Complex Surfaces-Calibration, Illumination and Applications.

Hyperspectral imaging (HSI) applications for biomedical imaging and dermatological applications have been recently under research interest. Medical HSI applications are non-invasive methods with high spatial and spectral resolution. HS imaging can be used to delineate malignant tumours, detect invasions, and classify lesion types.

Differentiating Malignant from Benign Pigmented or Non-Pigmented Skin Tumours-A Pilot Study on 3D Hyperspectral Imaging of Complex Skin Surfaces and Convolutional Neural Networks.

Several optical imaging techniques have been developed to ease the burden of skin cancer disease on our health care system. Hyperspectral images can be used to identify biological tissues by their diffuse reflected spectra. In this second part of a three-phase pilot study, we used a novel hand-held SICSURFIS Spectral Imager with an adaptable field of view and target-wise selectable wavelength channels to provide detailed spectral and spatial data for lesions on complex surfaces.

Generating Hyperspectral Skin Cancer Imagery using Generative Adversarial Neural Network.

In this study we develop a proof of concept of using generative adversarial neural networks in hyperspectral skin cancer imagery production. Generative adversarial neural network is a neural network, where two neural networks compete. The generator tries to produce data that is similar to the measured data, and the discriminator tries to correctly classify the data as fake or real.