Biology subjects

Sunar, U.

Publications and source records attributed to Sunar, U..

3 recordsLinked to original sources

bioRxiv2022

Chemophototherapy Overcomes Doxorubicin Resistance in Human Ovarian Cancer Cells

Porphyrin-phospholipid (PoP) liposomes loaded with Doxorubicin have been demonstrated to be an efficient vehicle for chemophototherapy (CPT). Multi-drug resistance (MDR) of cancer cells is a known resistance mechanism for cancer chemotherapies. We report a phototherapeutic measure to overcome Dox-resistance using Doxorubicin (Dox)-loaded PoP liposomes. In vitro studies using free Dox or Dox loaded into liposomes with 2 mol.% showed human ovarian carcinoma A2780 cells were more susceptible to these drugs compared to the corresponding Dox-resistant A2780-R cells. In contrast, when CPT was applied with LC-Dox-PoP liposomes, effective killing of both non-resistant and resistant A2780 cell lines was observed. An in vivo study to assess the efficiency of LC-Dox-PoP liposomes with phototreatment showed effective tumor shrinkage and prolonged survival of athymic nude mice bearing A2780-R tumor xenografts. Biodistribution analysis demonstrated enhanced tumoral drug uptake in Dox-resistant tumors with CPT, pointing to the likelihood that increased drug delivery overcame resistance mechanisms to provide for improved anti-cancer therapy.

bioengineering↗

bioRxiv2020

Quantification of Hypoperfusion in Autism Assessed by Diffuse Correlation Spectroscopy and Frequency Domain Near Infrared Spectroscopy

There is a need for quantitative biomarkers for early diagnosis of autism. We showed previously that hemoglobin concentrations show contrast between autistic group vs control group in humans. Cerebral blood flow can provide additional sensitivity for improved characterization. Diffuse correlation spectroscopy (DCS) has been shown to be reliable method to obtain blood flow contrast in animals and humans. Thus, in this study we evaluated DCS in an established autism model. Our results indicate that autistic group had significantly lower blood flow compared to control group. These results confirm the previous results obtained from humans by positron emission tomography and magnetic resonance imaging.

bioengineering↗

bioRxiv2020

Deep learning model for ultrafast quantification of blood flow in diffuse correlation spectroscopy

Diffuse correlation spectroscopy (DCS) is increasingly used in the optical imaging field to assess blood flow in humans due to its non-invasive, real-time characteristics and its ability to provide label-free, bedside monitoring of blood flow changes. Previous DCS studies have utilized a traditional curve fitting of the analytical or Monte Carlo models to extract the blood flow changes, which are computationally demanding and less accurate when the signal to noise ratio decreases. Here, we present a deep learning model that eliminates this bottleneck by solving the inverse problem more than 2300% faster, with equivalent or improved accuracy compared to the nonlinear fitting with an analytical method. The proposed deep learning inverse model will enable real-time and accurate tissue blood flow quantification with the DCS technique.

bioengineering↗