In Vitro High Throughput Phage Display Selection of Ovarian Cancer Avid Phage Clones for Near-Infrared Optical Imaging

2015 ◽  
Vol 17 (10) ◽  
pp. 859-867 ◽  
Author(s):  
Mette Soendergaard ◽  
Jessica Newton-Northup ◽  
Susan Deutscher
Keyword(s):  
Ovarian Cancer ◽  
Phage Display ◽  
Optical Imaging ◽  
High Throughput ◽  
Near Infrared ◽  
Selection Of

scholarly journals Molecular imaging and deep learning analysis of uMUC1 expression in response to chemotherapy in an orthotopic model of ovarian cancer

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Hongwei Zhao ◽  
Hasaan Hayat ◽  
Xiaohong Ma ◽  
Daguang Fan ◽  
Ping Wang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Ovarian Cancer ◽  
Deep Learning ◽  
Cancer Progression ◽  
In Vivo Imaging ◽  
Near Infrared ◽  
Response To Therapy ◽  
Response To Chemotherapy

Abstract Artificial Intelligence (AI) algorithms including deep learning have recently demonstrated remarkable progress in image-recognition tasks. Here, we utilized AI for monitoring the expression of underglycosylated mucin 1 (uMUC1) tumor antigen, a biomarker for ovarian cancer progression and response to therapy, using contrast-enhanced in vivo imaging. This was done using a dual-modal (magnetic resonance and near infrared optical imaging) uMUC1-specific probe (termed MN-EPPT) consisted of iron-oxide magnetic nanoparticles (MN) conjugated to a uMUC1-specific peptide (EPPT) and labeled with a near-infrared fluorescent dye, Cy5.5. In vitro studies performed in uMUC1-expressing human ovarian cancer cell line SKOV3/Luc and control uMUC1low ES-2 cells showed preferential uptake on the probe by the high expressor (n = 3, p < .05). A decrease in MN-EPPT uptake by SKOV3/Luc cells in vitro due to uMUC1 downregulation after docetaxel therapy was paralleled by in vivo imaging studies that showed a reduction in probe accumulation in the docetaxel treated group (n = 5, p < .05). The imaging data were analyzed using deep learning-enabled segmentation and quantification of the tumor region of interest (ROI) from raw input MRI sequences by applying AI algorithms including a blend of Convolutional Neural Networks (CNN) and Fully Connected Neural Networks. We believe that the algorithms used in this study have the potential to improve studying and monitoring cancer progression, amongst other diseases.

scholarly journals Correction: Optical imaging of ovarian cancer using a matrix metalloproteinase-3-sensitive near-infrared fluorescent probe

PLoS ONE ◽  
2018 ◽  
Vol 13 (8) ◽  
pp. e0202610
Author(s):  
Kuo-Hwa Wang ◽  
Yun-Ming Wang ◽  
Li-Hsuan Chiu ◽  
Tze-Chien Chen ◽  
Yu-Hui Tsai ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Matrix Metalloproteinase ◽  
Optical Imaging ◽  
Fluorescent Probe ◽  
Near Infrared ◽  
Matrix Metalloproteinase 3

Prediction of Stilbene Content from Heartwood Increment Cores of Scots Pine Using near Infrared Spectroscopy Methodology

2016 ◽  
Vol 24 (6) ◽  
pp. 517-528 ◽  
Author(s):  
Susanna Pulkka ◽  
Vincent Segura ◽  
Anni Harju ◽  
Tarja Tapanila ◽  
Johanna Tanner ◽  
...  
Keyword(s):  
Scots Pine ◽  
High Throughput ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Tree Breeding ◽  
Decay Resistance ◽  
Increment Core ◽  
Automatic Selection ◽  
Non Destructive ◽  
Selection Of

High-throughput and non-destructive methods for quantifying the content of the stilbene compounds of Scots pine ( Pinus sylvestris L.) heartwood are needed in the breeding for decay resistance of heartwood timber. In this study, near infrared (NIR) spectroscopy calibrations were developed for a large collection of solid heartwood increment core samples in order to predict the amount of the stilbene pinosylvin (PS), its monomethyl ether (PSM) and their sum (STB). The resulting models presented quite accurate predictions in an independent validation set with R2V values ranging between 0.79 and 0.91. The accuracy of the models strongly depended on the chemical being calibrated, with the lowest accuracy for PS, intermediate accuracy for PSM and highest accuracy for STB. The effect of collecting one, two or more (up to five) spectra per sample on the calibration models was studied and it was found that averaging multiple spectra yielded better accuracy as it may account for the heterogeneity of wood along the increment core within and between rings. Several statistical pretreatments of the spectra were tested and an automatic selection of wavenumbers prior to calibration. Without the automatic selection of wavenumbers, a first derivative of normalised spectra yielded the best accuracies, whereas after the automatic selection of wavenumbers, no particular statistical pretreatment appeared to yield better results than any other. Finally, the automatic selection of wavenumbers slightly improved the accuracy of the models for all traits. These results demonstrate the potential of NIR spectroscopy as a high-throughput and non-destructive phenotyping technique in tree breeding for the improvement of decay resistance in heartwood timber.

scholarly journals Recent Advances in In-Vitro Assays for Type 2 Diabetes Mellitus: An Overview

2020 ◽  
Vol 16 (1) ◽  
pp. 13-23
Author(s):  
Nazmina Vhora ◽  
Ujjal Naskar ◽  
Aishwarya Hiray ◽  
Abhijeet S. Kate ◽  
Alok Jain
Keyword(s):  
Type 2 Diabetes ◽  
Drug Discovery ◽  
High Throughput ◽  
High Throughput Screening ◽  
Antidiabetic Activity ◽  
Screening Methods ◽  
Selection Of

BACKGROUND: A higher rate of attenuation of molecules in drug discovery has enabled pharmaceutical companies to enhance the efficiency of their hit identification and lead optimization. Selection and development of appropriate in-vitro and in-vivo strategies may improve this process as primary and secondary screening utilize both strategies. In-vivo approaches are too relentless and expensive for assessing hits. Therefore, it has become indispensable to develop and implement suitable in-vitro screening methods to execute the required activities and meet the respective targets. However, the selection of an appropriate in-vitro assay for specific evaluation of cellular activity is no trivial task. It requires thorough investigation of the various parameters involved. AIM: In this review, we aim to discuss in-vitro assays for type 2 diabetes (T2D), which have been utilized extensively by researchers over the last five years, including target-based, non-target based, low-throughput, and high-throughput screening assays. METHODS: The literature search was conducted using databases including Scifinder, PubMed, ScienceDirect, and Google Scholar to find the significant published articles. DISCUSSION and CONCLUSION: The accuracy and relevance of in-vitro assays have a significant impact on the drug discovery process for T2D, especially in assessing the antidiabetic activity of compounds and identifying the site of effect in high-throughput screening. The report reviews the advantages, limitations, quality parameters, and applications of the probed invitro assays, and compares them with one another to enable the selection of the optimal method for any purpose. The information on these assays will accelerate numerous procedures in the drug development process with consistent quality and accuracy.

scholarly journals Ovarian Cancer Targeting Phage Clones for In Vivo Near‐Infrared Optical Imaging

2018 ◽  
Vol 32 (S1) ◽  
Author(s):  
Mallika Asar ◽  
Jessica Newton‐Northup ◽  
Susan Deutscher ◽  
Mette Soendergaard
Keyword(s):  
Ovarian Cancer ◽  
Optical Imaging ◽  
Near Infrared ◽  
Cancer Targeting

scholarly journals Ovarian Cancer Targeting Phage for In Vivo Near-Infrared Optical Imaging

Diagnostics ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 183 ◽  
Author(s):  
Mallika Asar ◽  
Jessica Newton-Northup ◽  
Susan Deutscher ◽  
Mette Soendergaard
Keyword(s):  
Ovarian Cancer ◽  
Optical Imaging ◽  
Near Infrared ◽  
Fluorescent Microscopy ◽  
Reticuloendothelial System ◽  
Detection Methods ◽  
Biodistribution Studies ◽  
Late Stages ◽  
Background Contrast

Ovarian cancer is often diagnosed at late stages due to current inadequate detection. Therefore, the development of new detection methods of ovarian cancer is needed. This may be achieved by phage nanoparticles that display targeting peptides for optical imaging. Here, two such phage clones are reported. Ovarian cancer binding and specificity of phage clones (pJ18, pJ24) and peptides (J18, J24) were investigated using fluorescent microscopy and modified ELISA. Further, AF680-labeled phage particles were subjected to biodistribution and optical imaging studies in SKOV-3 xenografted mice. Fluorescent microscopy and ELISA of phage and peptides showed significantly increased binding to SKOV-3 cells compared to controls. Additionally, these studies revealed that J18 exhibits specificity for ovarian cancer SKOV-3 and OVCAR-3 cell lines. Further, peptides displayed increased SKOV-3 binding compared to N35 (non-relevant peptide) with EC50 values of 22.2 ± 10.6 μM and 29.0 ± 6.9 (mean ± SE), respectively. Biodistribution studies of AF680-labeled phage particles showed tumor uptake after 4 h and excretion through the reticuloendothelial system. Importantly, SKOV-3 tumors were easily localized by optical imaging after 2 h and 4 h and displayed good tumor-to-background contrast. The fluorescent tumor signal intensity was significantly higher for pJ18 compared to wild type (WT) after 2 h.

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