A5023000784- Nanoplatforms for cancer theranostics
- Nanoparticle-Based Drug Delivery
- Spectroscopy Techniques in Biomedical and Chemical Research
- AI in cancer detection
- Photoacoustic and Ultrasonic Imaging
- Graphene and Nanomaterials Applications
- Optical Imaging and Spectroscopy Techniques
- Radiomics and Machine Learning in Medical Imaging
- Cell Image Analysis Techniques
- Spectroscopy and Chemometric Analyses
- Ultrasound and Hyperthermia Applications
- Radiation Therapy and Dosimetry
- Complex Systems and Time Series Analysis
- Angiogenesis and VEGF in Cancer
- Optical Polarization and Ellipsometry
- 3D Printing in Biomedical Research
- Medical Imaging and Analysis
- Brain Tumor Detection and Classification
- Cancer Cells and Metastasis
- Tendon Structure and Treatment
- Artificial Intelligence in Healthcare and Education
- Clusterin in disease pathology
- Spondyloarthritis Studies and Treatments
- Systemic Lupus Erythematosus Research
- Cancer Research and Treatments
Mayo Clinic
2021-2024
WinnMed
2022-2024
Mayo Clinic in Arizona
2022-2024
Medical College of Wisconsin
2017-2022
Indian Institute of Technology Kanpur
2010-2017
Indian Institute of Technology Indore
2013
We report sub-100 nm optical/magnetic resonance (MR)/X-ray contrast-bearing theranostic nanoparticles (TNPs) for interventional image-guided photothermal therapy (PTT) of solid tumors. TNPs were composed Au@Gd2O3:Ln (Ln = Yb/Er) with X-ray contrast (∼486 HU; 1014 NPs/mL, 0.167 nM) and MR (∼1.1 × 108 mM-1 S-1 at 9.4 T field strength). Although are deposited in tumors following systemic administration via enhanced permeation retention effect, the delivered dose to is typically low; this can...
The increasing use of machine learning (ML) algorithms in clinical settings raises concerns about bias ML models. Bias can arise at any step creation, including data handling, model development, and performance evaluation. Potential biases the be minimized by implementing these steps correctly. This report focuses on evaluation discusses fitness, as well a set toolboxes: namely, metrics, interpretation maps, uncertainty quantification. By discussing strengths limitations each toolbox, our...
There are increasing concerns about the bias and fairness of artificial intelligence (AI) models as they put into clinical practice. Among steps for implementing machine learning tools workflow, model development is an important stage where different types biases can occur. This report focuses on four aspects such may arise: data augmentation, loss function, optimizers, transfer learning. emphasizes appropriate considerations practices that mitigate in radiology AI studies.
Quantitative fluorescence spectroscopic Mueller matrix measurements from the connective tissue regions of human cervical reveal intriguing diattenuation and polarizance effects. Interestingly, estimated linear parameters were considerably reduced in precancerous tissues as compared to normal ones. These polarimetry effects autofluorescence found originate anisotropically organized collagen molecular structures present tissues. Consequently, reduction magnitude these polarimetric at higher...
Abstract Purpose Total kidney volume (TKV) is the most important imaging biomarker for quantifying severity of autosomal-dominant polycystic disease (ADPKD). 3D ultrasound (US) can accurately measure compared to 2D US; however, manual segmentation tedious and requires expert annotators. We investigated a deep learning-based approach automated TKV from US in ADPKD patients. Method used axially acquired US-kidney images 22 patients where each patient were scanned three times, resulting 132...
Multiresolution analysis on the spatial refractive index inhomogeneities in epithelium and connective tissue regions of a human cervix reveals clear signature multifractality. Importantly, derived multifractal parameters, namely, generalized Hurst exponent width singularity spectrum, via detrended fluctuation analysis, shows interesting differences between tissues having different grades precancers. The refractive-index fluctuations are found to be more anticorrelated, strength...
An optical quantitative histological method in human tissues using spatial frequencies is demonstrated. Optical frequency spectra from different stages of Cervical Intraepithelial Neoplasia (CIN) tissue are evaluated as a potential pathological tool. The degree randomness structures normal to CIN can be recognized by analysis. standard deviation, σ and tissue, obtained assuming the Gaussian distribution. A support vector machine classifier (SVM) trained subspace σ. Twenty‐eight samples...
<h3>ABSTRACT</h3> <h3>BACKGROUND AND PURPOSE:</h3> Recent advances in deep learning have shown promising results medical image analysis and segmentation. However, most brain MRI segmentation models are limited by the size of their datasets and/or number structures they can identify. This study evaluates performance six advanced segmenting 122 from T1-weighted scans, aiming to identify effective model for clinical research applications. <h3>MATERIALS METHODS:</h3> 1,510 MRIs were used compare...
Vascular supply is a critical component of the tumor microenvironment (TME) and essential for growth metastasis, yet endogenous genetic modifiers that impact vascular function in TME are largely unknown. To identify host function, we combined novel mapping strategy [Consomic Xenograft Model] with near-infrared (NIR) fluorescence imaging multiparametric analysis pharmacokinetic modeling. detect flow, an intensified cooled camera based dynamic NIR system 785 nm laser diode excitation was used...
Abstract Background Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease that can lead to cirrhosis and hepatic decompensation. However, predicting future outcomes in patients with PSC challenging. Our aim was extract magnetic resonance imaging (MRI) features predict the development of decompensation by applying algebraic topology-based machine learning (ML). Methods We conducted retrospective multicenter study among adults large duct who underwent MRI. A topological...
To develop a dynamic in vivo near-infrared (NIR) fluorescence imaging assay to quantify sequential changes lung vascular permeability-surface area product (PS) rodents. Dynamic NIR methods for determining were developed and tested on non-irradiated 13 Gy irradiated rats with/without treatment with lisinopril, radiation mitigator. A physiologically-based pharmacokinetic (PBPK) model of indocyanine green (ICG) pulmonary disposition was applied data PS estimated. In results validated by five...
We report the impact of notch-DLL4-based hereditary vascular heterogeneities on enhanced permeation and retention (EPR) effect plasmonic photothermal therapy response in tumors.Methods: generated two consomic rat strains with differing DLL4 expression 3 rd chromosome.These were based immunocompromised Salt-sensitive or SS IL2Rγ- (DLL4-high) SS.BN3 IL2Rγ-(DLL4-low) rats 3rd chromosome substituted from Brown Norway rat.We further constructed three novel IL2Rγ-congenic by introgressing varying...
The fluctuations in the elastic light scattering spectra of normal and dysplastic human cervical tissues analyzed through wavelet transform based techniques reveal clear signatures self-similar behavior spectral fluctuations. Significant differences power law ascertained scaling exponent was observed these tissues. strong dependence on size distribution scatterers manifests angular variation exponent. Interestingly, both showed multi-fractality (non-stationarity fluctuations), degree being...
Purpose: The aim of this study was to develop and evaluate a liposome formulation that deliver oxaliplatin under magnetic field stimulus in high concentration alleviate the off-target effects rat model colorectal liver metastases (CRLM). Materials Methods: Hybrid liposome-magnetic nanoparticles loaded with Cy5.5 dye (L-NIR- Fe3O4/OX) were synthesized by using thermal decomposition method. CRLM (CC-531) cell viability assessed rats orthotopically implanted CC-531 cells treated L-NIR-Fe3O4/OX...
Delta like canonical notch ligand 4 (Dll4) expression levels in tumors are known to affect the efficacy of cancer therapies. This study aimed develop a model predict Dll4 using dynamic enhanced near-infrared (NIR) imaging with indocyanine green (ICG). Two rat-based consomic xenograft (CXM) strains breast different and eight congenic were studied. Principal component analysis (PCA) was used visualize segment tumors, modified PCA techniques identified analyzed tumor normal regions interest...
Introduction Radiation therapy for head and neck squamous cell carcinoma is constrained by radiotoxicity to normal tissue. We demonstrate 100 nm theranostic nanoparticles image-guided radiation planning enhancement in rat models. Methods PEG conjugated comprising of Au nanorods coated with Gadolinium oxide layers were tested 2D cultures OSC-19-GFP-luc cells, orthotopic tongue xenografts male immunocompromised Salt sensitive or SS rats via both intratumoral intravenous delivery. The mechanism...
We demonstrate a Bayesian statistics-based outlier separation algorithm, which clearly distinguishes microscope captured images of unstained human cervical tissue sections normal and different grades precancerous tissues. The semi-automated global adaptive method implements based on the statistical characterization image histogram distribution. This multi-level thresholding achieves an effective quantization high cell density domain, most affected in progression disease, yields precise...
Left panel: Pseudocolor map of 3 principle components from NIR-II kinetic imaging, Right panel (top to bottom): <italic>In vivo</italic> Ag<sub>2</sub>S QD fluorescence, <italic>ex iodine micro-CT, FITC dextran perfusion, and H&E staining in control <italic>vs</italic> CCM1+/− mice brain.
Microscope images of biopsy samples cervical precancers conventionally discriminated by histopathology, the current "gold standard" for cancer detection, showed that their correlation properties are segregated into different classes. The domains clearly indicate increasing cellular clustering in grades precancer compared with normal counterparts. This trend indicates probability pixel distribution corresponding tissue images. Because cell density is not uniform higher grades, skewness...