Prolonged in-vivo tracking of vitreous fluid and its early diagnostic imaging biomarkers for cancer growth
Goswami, M.; Shakya, S.; Zhang, P.
Show abstract
PurposeEstimation of a correlation between cells in vitreous humour and growth in glioblastoma xenografts. MethodsStreams of cells in vitreous humor are observed in optical coherence tomography (OCT) imaging data of animal (NSGS and Athymic Nude-Foxn1nu) eyes (34 in total) subjected to xenograft growth study, in-vivo. The cancer disease model is studied with and without nanodrug-based treatment protocols. ResultsThe presence of CD8+ and CD4+ is reported inside the tumor using the same data earlier. The transition of these cells is shown to take place from the optic nerve via the vitreous into the nerve fiber layer (NFL) at tumor locations and xenograft -related injuries. Functional analysis of dense temporal imaging series (varying from 28 to more than 100 days) reveals a mild correlation between the volumetric growth of the tumor with the density of these cells, quantitatively and qualitatively. The cross-correlation analysis indicates imaging assisted photodynamic treatment protocol perform relatively better if started with certain delay. Doxorubicin treatment to Nu/Nu Male and NSGS female transforms mild weak negative correlation into mild weak positive correlation. ConclusionsThe plots indicate that the mix of the cells in vitreous humor are effectively dominated by immunosuppressor cytotoxic component. Translational RelevanceWe propose that the vitreous cell density can be used as imaging biomarker helpful for clinicians in early diagnosis and treatment planning of similar disease models. The limitation of this work is that high resolution OCT systems and data-dependent image segmentation methods are required.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Label-free visualization and quantification of the drug-type-dependent response of tumor spheroids by dynamic optical coherence tomography 96%
- Method for in vivo assessment of cancer tissue inhomogeneity and accurate histology-like morphological segmentation based on Optical Coherence Elastography 95%
- Investigating the role of molecular coating in human corneal endothelial cell primary culture using artificial intelligence-driven image analysis 94%
Similar papers in this journal
- Diagnosis of central serous chorioretinopathy by deep learning analysis of en face images of choroidal vasculature 94%
- Glaucoma Detection and Staging from Visual Field Images using Machine Learning Techniques 94%
- Neuroprotective effects of exogenous erythropoietin in Wistar rats by downregulating apoptotic factors to attenuate N-methyl-D-aspartate-mediated retinal ganglion cells death 94%
Similar papers in this journal
- TissueGrinder, a novel technology for rapid generation of patient-derived single cell suspensions from solid tumors by mechanical tissue dissociation 93%
- Modified Preparation Method of Ideal Platelet-Rich Fibrin Matrix (PRFM) from Whole Blood 91%
- Development And Characterization Of A New Endoscopic Drug Eluting Platform With Proven Efficacy In Acute And Chronic Experimental Colitis 90%
Similar papers in this journal
- Ultrasound-mediated drug diffusion, uptake, and cytotoxicity in glioblastoma 3D tumour sphere model 93%
- Modulation of Extracellular Matrix Composition and Chronic Inflammation by Pirfenidone Promotes Scar Reduction in Retinal Wound Repair 93%
- Characterizing stored red blood cells using ultra-high throughput holographic cytometry 92%
Similar papers in this journal
- The mathematics of erythema: Development of machine learning models for artificial intelligence assisted measurement and severity scoring of radiation induced dermatitis 92%
- Risk assessment of cancer patients based on HLA-I alleles, neobinders and expression of cytokines 91%
- A method for predicting linear and conformational B-cell epitopes in an antigen from its primary sequence 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.