Author

A. Ozcan

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioengineering· 5 Biophysics· 2

Signature Assays, Organisms & Methods

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Organism:human· 4 Assay:proteomics· 4 Approach:deep learning· 3 Approach:experimental assay· 3 Research Focus:cell type annotation· 2 Research Focus:drug response / drug discovery· 2 Assay:imaging· 2 Research Focus:differential expression· 1

Frequent Co-Authors

A. Goncharov· 3 shared D. Di Carlo· 3 shared H.-A. Joung· 2 shared O. B. Garner· 2 shared A. Akdemir· 1 shared A. Arnheim· 1 shared A. Kayabolen· 1 shared A. O' Kula· 1 shared

Activity by Year

2018-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 17 mutual co-author links

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3 shared papersBioengineering

A. Goncharov

Mutual co-authors in cluster

Author profile →

7 papers

Recent·Most cited

Continuous Monitoring of Glucose and Oxygen using an Insertable Biomaterial-based Multianalyte Barcode Sensor

R. Pradhan, D. Chimene, B. S. Ko, A. Goncharov, A. Ozcan, M. J. McShane

This paper develops a biocompatible barcode sensor for continuous glucose and oxygen monitoring, demonstrating stability, reversibility, and multianalyte sensing without crosstalk.

bioRxiv · Jul 21, 2024 · PreprintBioengineering

Single-tier point-of-care serodiagnosis of Lyme disease

R. Ghosh, H.-A. Joung, A. Goncharov, B. Palanisamy, K. Ngo, K. Pejcinovic, N. Krockenberger, E. J. Horn, O. B. Garner, E. Ghazal, A. O' Kula, P. M. Arnaboldi, R. J. Dattwyler, A. Ozcan, D. Di Carlo

This paper develops a single-tier point-of-care serodiagnostic test for Lyme disease using synthetic peptides and machine learning, achieving sensitivity and specificity comparable to traditional two-tier lab tests.

bioRxiv · Jun 14, 2023 · PreprintBioengineering

Amphiphilic particle-stabilized nanoliter droplet reactors with a multi-modal portable reader for distributive biomarker quantification

V. Shah, X. Yang, A. Arnheim, S. Udani, D. Tseng, Y. Luo, M. Ouyang, G. Destgeer, O. Garner, H. C. Koydemir, A. Ozcan, D. Di Carlo

This paper develops a portable droplet reactor system for biomarker quantification in resource-limited settings, achieving low detection limits and comparable accuracy to ELISA.

bioRxiv · Apr 24, 2023 · PreprintBioengineering

The neutralization effect of Montelukast on SARS-CoV-2 is shown by multiscale in silico simulations and combined in vitro studies

S. Durdagi, T. Avsar, M. D. Orhan, M. Serhatli, B. K. Balcioglu, H. U. Ozturk, A. Kayabolen, Y. Cetin, S. Aydinlik, T. Bagci-Onder, S. Tekin, H. Demirci, M. Guzel, A. Akdemir, S. Calis, , Y. Yumak

This paper shows Montelukast inhibits SARS-CoV-2 by targeting both Spike/ACE2 and main protease through in silico and in vitro studies.

bioRxiv · Dec 26, 2020Biophysics

Addressable Nanoantennas with Cleared Hotspots for Single-Molecule Detection on a Portable Smartphone Microscope

K. Trofymchuk, V. Glembockyte, L. Grabenhorst, F. Steiner, C. Vietz, C. Close, M. Pfeiffer, L. Richter, M. L. Schütte, F. Selbach, R. Yaadav, J. Zähringer, Q. Wei, A. Ozcan, B. Lalkens, , P. Tinnefeld

This paper introduces NACHOS, a DNA origami-based nanoantenna system that enables single-molecule detection using a smartphone microscope, reducing costs and complexity.

bioRxiv · Apr 9, 2020Biophysics

DEEP LEARNING-ENABLED POINT-OF-CARE SENSING USING MULTIPLEXED PAPER-BASED SENSORS

Z. Ballard, H.-A. Joung, A. Goncharov, J. Liang, K. Nugroho, D. Di Carlo, O. B. Garner, A. Ozcan

This paper presents a deep learning framework for designing and quantifying point-of-care sensors, demonstrating a paper-based VFA for high-sensitivity hsCRP testing with competitive accuracy and hook-effect mitigation.

bioRxiv · Jun 11, 2019Bioengineering

Deep learning achieves super-resolution in fluorescence microscopy

H. Wang, Y. Rivenson, Y. Jin, Z. Wei, R. Gao, H. Gunaydin, L. Bentolila, A. Ozcan

This paper introduces a deep learning method that achieves super-resolution in fluorescence microscopy without requiring numerical models or parameter tuning, successfully resolving images with low numerical aperture lenses and diffraction-limited confocal microscopy.

bioRxiv · Apr 27, 2018Bioengineering