Adam Wax

Overview:

Dr. Wax's research interests include optical spectroscopy for early cancer detection, novel microscopy and interferometry techniques.

The study of intact, living cells with optical spectroscopy offers the opportunity to observe cellular structure, organization and dynamics in a way that is not possible with traditional methods. We have developed a set of novel spectroscopic techniques for measuring spatial, temporal and refractive structure on sub-hertz and sub-wavelength scales based on using low-coherence interferometry (LCI) to detect scattered light. We have applied these techniques in different types of cell biology experiments. In one experiment, LCI measurements of the angular pattern of backscattered light are used to determine non-invasively the structure of sub-cellular organelles in cell monolayers, and the components of epithelial tissue from freshly excised rat esophagus. This work has potential as a diagnostic method for early cancer detection. In another experiment, LCI phase measurements are used to examine volume changes of epithelial cells in a monolayer in response to environmental osmolarity changes. Although cell volume changes have been measured previously, this work demonstrates for the first time the volume of just a few cells (2 or 3) tracked continuously and in situ.

Positions:

Professor of Biomedical Engineering

Biomedical Engineering
Pratt School of Engineering

Faculty Network Member of the Duke Institute for Brain Sciences

Duke Institute for Brain Sciences
Institutes and Provost's Academic Units

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Bass Fellow

Biomedical Engineering
Pratt School of Engineering

Education:

B.S. 1993

Rensselaer Polytechnic Institute

M.A. 1996

Duke University

Ph.D. 1999

Duke University

Grants:

InCh Microscope: Compact and Portable Quantitative Phase Microscope for Label-Free Morphological Diagnosis of Blood Samp

Administered By
Biomedical Engineering
Awarded By
M2 Photonics Innovations
Role
Principal Investigator
Start Date
End Date

Molecular Imaging Using Hyperspectral Darkfield Microscope of Nanoparticles

Administered By
Biomedical Engineering
Awarded By
National Science Foundation
Role
Principal Investigator
Start Date
End Date

Coherence Imaging for Assessing Colorectal Neoplasia

Administered By
Surgical Oncology
Awarded By
National Institutes of Health
Role
Principal Investigator
Start Date
End Date

Analysis of mechanical induction of bioelectric activity in cells

Administered By
Biomedical Engineering
Awarded By
Army Research Office
Role
Principal Investigator
Start Date
End Date

Advanced a/LCI systems for improved clinical utility

Administered By
Biomedical Engineering
Awarded By
National Institutes of Health
Role
Principal Investigator
Start Date
End Date

Publications:

Single Cell Analysis of Stored Red Blood Cells Using Ultra-High Throughput Holographic Cytometry.

Holographic cytometry is introduced as an ultra-high throughput implementation of quantitative phase imaging of single cells flowing through parallel microfluidic channels. Here, the approach was applied for characterizing the morphology of individual red blood cells during storage under regular blood bank conditions. Samples from five blood donors were examined, over 100,000 cells examined for each, at three time points. The approach allows high-throughput phase imaging of a large number of cells, greatly extending our ability to study cellular phenotypes using individual cell images. Holographic cytology images can provide measurements of multiple physical traits of the cells, including optical volume and area, which are observed to consistently change over the storage time. In addition, the large volume of cell imaging data can serve as training data for machine-learning algorithms. For the study here, logistic regression was used to classify the cells according to the storage time points. The analysis showed that at least 5000 cells are needed to ensure accuracy of the classifiers. Overall, results showed the potential of holographic cytometry as a diagnostic tool.
Authors
Park, H-S; Price, H; Ceballos, S; Chi, J-T; Wax, A
MLA Citation
Park, Han-Sang, et al. “Single Cell Analysis of Stored Red Blood Cells Using Ultra-High Throughput Holographic Cytometry.Cells, vol. 10, no. 9, Sept. 2021. Pubmed, doi:10.3390/cells10092455.
URI
https://scholars.duke.edu/individual/pub1497079
PMID
34572104
Source
pubmed
Published In
Cells
Volume
10
Published Date
DOI
10.3390/cells10092455

Optical coherence tomography of small intestine allograft biopsies using a handheld surgical probe.

SIGNIFICANCE: The current gold standard for monitoring small intestinal transplant (IT) rejection is endoscopic visual assessment and biopsy of suspicious lesions; however, these lesions are only superficially visualized by endoscopy. Invasive biopsies provide a coarse sampling of tissue health without depicting the true presence and extent of any pathology. Optical coherence tomography (OCT) presents a potential alternative approach with significant advantages over traditional white-light endoscopy. AIM: The aim of our investigation was to evaluate OCT performance in distinguishing clinically relevant morphological features associated with IT graft failure. APPROACH: OCT was applied to evaluate the small bowel tissues of two rhesus macaques that had undergone IT of the ileum. The traditional assessment from routine histological observation was compared with OCT captured using a handheld surgical probe during the days post-transplant and subsequently was compared with histophaology. RESULTS: The reported OCT system was capable of identifying major biological landmarks in healthy intestinal tissue. Following IT, one nonhuman primate (NHP) model suffered a severe graft ischemia, and the second NHP graft failed due to acute cellular rejection. OCT images show visual evidence of correspondence with histological signs of IT rejection. CONCLUSIONS: Results suggest that OCT imaging has significant potential to reveal morphological changes associated with IT rejection and to improve patient outcomes overall.
Authors
Jelly, ET; Kwun, J; Schmitz, R; Farris, AB; Steelman, ZA; Sudan, DL; Knechtle, SJ; Wax, A
MLA Citation
Jelly, Evan T., et al. “Optical coherence tomography of small intestine allograft biopsies using a handheld surgical probe.J Biomed Opt, vol. 26, no. 9, Sept. 2021. Pubmed, doi:10.1117/1.JBO.26.9.096008.
URI
https://scholars.duke.edu/individual/pub1497309
PMID
34561973
Source
pubmed
Published In
Journal of Biomedical Optics
Volume
26
Published Date
DOI
10.1117/1.JBO.26.9.096008

Deep learning classification of cervical dysplasia using depth-resolved angular light scattering profiles.

We present a machine learning method for detecting and staging cervical dysplastic tissue using light scattering data based on a convolutional neural network (CNN) architecture. Depth-resolved angular scattering measurements from two clinical trials were used to generate independent training and validation sets as input of our model. We report 90.3% sensitivity, 85.7% specificity, and 87.5% accuracy in classifying cervical dysplasia, showing the uniformity of classification of a/LCI scans across different instruments. Further, our deep learning approach significantly improved processing speeds over the traditional Mie theory inverse light scattering analysis (ILSA) method, with a hundredfold reduction in processing time, offering a promising approach for a/LCI in the clinic for assessing cervical dysplasia.
Authors
Zhang, H; Kendall, WY; Jelly, ET; Wax, A
MLA Citation
Zhang, Haoran, et al. “Deep learning classification of cervical dysplasia using depth-resolved angular light scattering profiles.Biomedical Optics Express, vol. 12, no. 8, Aug. 2021, pp. 4997–5007. Epmc, doi:10.1364/boe.430467.
URI
https://scholars.duke.edu/individual/pub1489833
PMID
34513238
Source
epmc
Published In
Biomedical Optics Express
Volume
12
Published Date
Start Page
4997
End Page
5007
DOI
10.1364/boe.430467

A review of low-cost and portable optical coherence tomography

Authors
Song, G; Jelly, ET; Chu, K; Kendall, WY; Wax, A
MLA Citation
Song, Ge, et al. “A review of low-cost and portable optical coherence tomography.” Progress in Biomedical Engineering, IOP Publishing, 2021.
URI
https://scholars.duke.edu/individual/pub1485484
Source
manual
Published In
Progress in Biomedical Engineering
Published Date

Connectivity-based deep learning approach for segmentation of the epithelium in in vivo human esophageal OCT images.

Optical coherence tomography (OCT) is used for diagnosis of esophageal diseases such as Barrett's esophagus. Given the large volume of OCT data acquired, automated analysis is needed. Here we propose a bilateral connectivity-based neural network for in vivo human esophageal OCT layer segmentation. Our method, connectivity-based CE-Net (Bicon-CE), defines layer segmentation as a combination of pixel connectivity modeling and pixel-wise tissue classification. Bicon-CE outperformed other widely used neural networks and reduced common topological prediction issues in tissues from healthy patients and from patients with Barrett's esophagus. This is the first end-to-end learning method developed for automatic segmentation of the epithelium in in vivo human esophageal OCT images.
Authors
Yang, Z; Soltanian-Zadeh, S; Chu, KK; Zhang, H; Moussa, L; Watts, AE; Shaheen, NJ; Wax, A; Farsiu, S
MLA Citation
Yang, Ziyun, et al. “Connectivity-based deep learning approach for segmentation of the epithelium in in vivo human esophageal OCT images.Biomed Opt Express, vol. 12, no. 10, Oct. 2021, pp. 6326–40. Pubmed, doi:10.1364/BOE.434775.
URI
https://scholars.duke.edu/individual/pub1497102
PMID
34745740
Source
pubmed
Published In
Biomedical Optics Express
Volume
12
Published Date
Start Page
6326
End Page
6340
DOI
10.1364/BOE.434775

Research Areas:

Anemia, Sickle Cell
Biomechanical Phenomena
Biomechanics
Biomedical Engineering
Biophysics
Biopsy
Biosensing Techniques
Burns
Carcinogens
Cartilage, Articular
Cell Differentiation
Cell Nucleus
Cell Nucleus Size
Cell Size
Cell Transformation, Neoplastic
Cells, Cultured
Chromatin
Computer Simulation
Diagnostic Imaging
Disease Models, Animal
Early Detection of Cancer
Emulsions
Epithelial Cells
Equipment Design
Erythrocytes
Erythrocytes, Abnormal
Esophageal Neoplasms
Esophagus
Fiber Optic Technology
Fourier Analysis
Fractals
Gels
Image Interpretation, Computer-Assisted
Interferometry
Intestines
Light
Metal Nanoparticles
Mice
Microscopy
Microscopy, Electron, Transmission
Microscopy, Fluorescence
Microscopy, Interference
Microspheres
Molecular Imaging
Mucous Membrane
Muscle, Skeletal
Nanoparticles
Nanotechnology
Neoplasms, Experimental
Nonlinear Dynamics
Optical Imaging
Optical Phenomena
Optical coherence tomography
Particle Size
Phantoms, Imaging
Phospholipids
Photons
Polymers
Precancerous Conditions
Receptor, Epidermal Growth Factor
Receptors, Cell Surface
Refractometry
Scattering, Radiation
Signal Processing, Computer-Assisted
Skin
Spectrum Analysis
Staining and Labeling
Stem Cells
Surface Plasmon Resonance
Surface Properties
Tissue Culture Techniques
Tomography, Optical Coherence
Tumor Cells, Cultured
Vaginal Creams, Foams, and Jellies