Quantitative assessment of cancer cell morphology and motility using telecentric digital holographic microscopy and machine learning.

The noninvasive, fast acquisition of quantitative phase maps using digital holographic microscopy (DHM) allows tracking of rapid cellular motility on transparent substrates. On two-dimensional surfaces in vitro, MDA-MB-231 cancer cells assume several morphologies related to the mode of migration and substrate stiffness, relevant to mechanisms of cancer invasiveness in vivo. The quantitative phase information from DHM may accurately classify adhesive cancer cell subpopulations with clinical relevance. To test this, cells from the invasive breast cancer MDA-MB-231 cell line were cultured on glass, tissue-culture treated polystyrene, and collagen hydrogels, and imaged with DHM followed by epifluorescence microscopy after staining F-actin and nuclei. Trends in cell phase parameters were tracked on the different substrates, during cell division, and during matrix adhesion, relating them to F-actin features. Support vector machine learning algorithms were trained and tested using parameters from holographic phase reconstructions and cell geometric features from conventional phase images, and used to distinguish between elongated and rounded cell morphologies. DHM was able to distinguish between elongated and rounded morphologies of MDA-MB-231 cells with 94% accuracy, compared to 83% accuracy using cell geometric features from conventional brightfield microscopy. This finding indicates the potential of DHM to detect and monitor cancer cell morphologies relevant to cell cycle phase status, substrate adhesion, and motility. © 2017 International Society for Advancement of Cytometry.

Lab on a chip sensor for rapid detection and antibiotic resistance determination of Staphylococcus aureus.

The Gram-positive bacterium, Staphylococcus aureus (S. aureus), is a major pathogen responsible for a variety of infectious diseases ranging from cellulitis to more serious conditions such as septic arthritis and septicaemia. Timely treatment with appropriate antibiotic therapy is essential to ensure clinical defervescence and to prevent further complications such as infective endocarditis or organ impairment due to septic shock. To date, initial antibiotic choice is empirical, using a "best guess" of likely organism and sensitivity- an approach adopted due to the lack of rapid identification methods for bacteria. Current culture based methods take up to 5 days to identify the causative bacterial pathogen and its antibiotic sensitivity. This paper provides proof of concept for a biosensor, based on interdigitated electrodes, to detect the presence of S. aureus and ascertain its sensitivity to flucloxacillin rapidly (within 2 hours) in a cost effective manner. The proposed method is label-free and uses non-faradic measurements. This is the first study to successfully employ interdigitated electrodes for the rapid detection of antibiotic resistance. The method described has important potential outcomes of faster definitive antibiotic treatment and more rapid clinical response to treatment.

Machine Learning Assisted Screening of Two-Dimensional Materials for Water Desalination.

There exists a vast expanse of data in the literature which can be harnessed for accelerated design and discovery of advanced materials for various applications of importance ─ for example, desalination of seawater. Here, we develop a machine learning (ML) model, training it with ∼260 molecular dynamics (MD) computation results, to predict the desalination performance of 2D membranes that exist in the literature. The desalination performance variables of water flux and salt rejection rates are correlated to 49 material features related to the chemistry of the pores and the membranes along with applied pressure, salt concentration, partial charges on the atoms, geometry of the pore, the mechanical properties of the membranes, and the properties of water for the water model used. We used the ML model to screen 3814 structurally optimized 2D materials for maximum water flux and salt rejection rates from the literature. We found some candidates that perform ∼4 times better than the more popularly known 2D materials such as graphene and MoS2. This result is verified using data obtained from MD simulations performed on several representative 2D membranes for different classes. Such validated statistical frameworks using literature data can be very useful in guiding experiments in the field of functional materials for varied applications.

An interdigitated electrode biosensor platform for rapid HLA-B*15:02 genotyping for prevention of drug hypersensitivity.

Prevention of life threatening hypersensitivity reactions to carbamazepine is possible through pre-treatment screening of the associated HLA-B*15:02 risk allele. However, clinical implementation of screening is hindered by the high cost and slow turnaround of conventional HLA typing methods. We have developed an interdigitated electrode (IDE) biosensor platform utilizing loop mediated isothermal amplification (LAMP) that can rapidly detect the HLA-B*15:02 allele. DNA amplification is followed by solid-phase hybridization of LAMP amplicons to a DNA probe immobilized on the IDE sensor surface, resulting in a change in sensor impedance. The testing platform does not require DNA extraction or post-amplification staining, achieving sample-to-answer in 1 h and 20 min. The platform was tested on 27 whole blood samples (14 HLA-B*15:02 positive and 13 negative) with sensitivity of 92.9% and specificity of 84.6% when applying a cutoff of impedance change. Based on these characters the LAMP-IDE platform has potential to be further developed into point-of-care use to help overcome barriers in HLA-B*15:02 screening.

A Label-Free, Quantitative Fecal Hemoglobin Detection Platform for Colorectal Cancer Screening.

The early detection of colorectal cancer is vital for disease management and patient survival. Fecal hemoglobin detection is a widely-adopted method for screening and early diagnosis. Fecal Immunochemical Test (FIT) is favored over the older generation chemical based Fecal Occult Blood Test (FOBT) as it does not require dietary or drug restrictions, and is specific to human blood from the lower digestive tract. To date, no quantitative FIT platforms are available for use in the point-of-care setting. Here, we report proof of principle data of a novel low cost quantitative fecal immunochemical-based biosensor platform that may be further developed into a point-of-care test in low-resource settings. The label-free prototype has a lower limit of detection (LOD) of 10 µg hemoglobin per gram (Hb/g) of feces, comparable to that of conventional laboratory based quantitative FIT diagnostic systems.