Biology subjects

Ozcan, A.

Publications and source records attributed to Ozcan, A..

8 recordsLinked to original sources

bioRxiv2026

Lab-on-a-3D-Printer for Democratized Reconfigurable Digital Microfluidics

Automation in the life sciences remains dominated by expensive, centralized robotic systems, leaving most laboratories unable to access precision robotic liquid handling. Here we present the Lab-on-a-3D-Printer (Lo3DP), a reconfigurable robotic microfluidic platform that repurposes the motion control, thermal regulation, and open-source programmability of consumer 3D printers to deliver laboratory-grade automation at a hardware cost below $500. By replacing the extruder with a multifunctional magnetic toolhead, Lo3DP actuates ferrofluid droplets with sub-100 {micro}m positioning accuracy and programmable volume control spanning 0.5-25 {micro}L, enabling the full repertoire of digital microfluidic operations within inexpensive laser-cut chips (<$3). We quantitatively validate system performance through long-term droplet transport exceeding 40,000 s without degradation, automated serial dilutions with high linearity (R{superscript 2} [≥] 0.99), and fully automated colorimetric nucleic acid amplification assays whose results match conventional benchtop workflows under tightly regulated isothermal conditions (CV {approx} 0.3%). By harnessing the global economies of scale of consumer 3D printers, Lo3DP provides accurate, reproducible, and programmable assay automation at a fraction of the cost and footprint of conventional systems, establishing a practical path toward desktop-scale, democratized laboratory robotics.

bioengineering↗

bioRxiv2024

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

Chronic diseases including diabetes, cardiovascular diseases, and microvascular complications contribute significantly to global morbidity and mortality. Multiplexing technologies offer a promising approach for the simultaneous detection and management of comorbidities, providing comprehensive disease insights. In this work, we describe a miniaturized optical "barcode" sensor with high biocompatibility for continuous monitoring of glucose and oxygen. This enzymatic sensor relies on oxygen consumption in proportion to local glucose levels and the phosphorescence reporting of tissue oxygen with a lifetime-based probe. The sensor was designed to operate in a tissue environment with low levels of dissolved oxygen. The barcode sensor consists of a poly(ethylene) glycol diacrylate (PEGDA) hydrogel with four discrete compartments separately filled with glucose or oxygen-sensing phosphorescent microparticles. We evaluated the response of the barcode hydrogels to fluctuating glucose levels over the physiological range under low oxygen conditions, demonstrating controlled tuning of dynamic range and sensitivity. Moreover, the barcode sensor exhibited remarkable storage stability over 12 weeks, along with full reversibility and excellent reproducibility ([~]6% variability in phosphorescence lifetime). Electron beam sterilization had a negligible impact on the glucose response of the barcode sensors. Furthermore, our investigation revealed minimal phosphorescence lifetime changes in oxygen compartments while exhibiting increased lifetime in glucose-responsive compartments when subjected to alternating glucose concentrations (0 and 200 mg/dL), showcasing the sensors multianalyte sensing capabilities without crosstalk between compartments. Additionally, evaluation of tissue response to sensors inserted in pigs revealed appropriate biocompatibility of the barcodes.

bioengineering↗

bioRxiv2023

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

Point-of-care (POC) serological testing provides actionable information for several difficult to diagnose illnesses, empowering distributed health systems. Accessible and adaptable diagnostic platforms that can assay the repertoire of antibodies formed against pathogens are essential to drive early detection and improve patient outcomes. Here, we report a POC serologic test for Lyme disease (LD), leveraging synthetic peptides tuned to be highly specific to the LD antibody repertoire across patients and compatible with a paper-based platform for rapid, reliable, and cost-effective diagnosis. A subset of antigenic epitopes conserved across Borrelia burgdorferi genospecies and targeted by IgG and IgM antibodies, were selected based on their seroreactivity to develop a multiplexed panel for a single-step measurement of combined IgM and IgG antibodies from LD patient sera. Multiple peptide epitopes, when combined synergistically using a machine learning-based diagnostic model, yielded a high sensitivity without any loss in specificity. We blindly tested the platform with samples from the U.S. Centers for Disease Control & Prevention (CDC) LD repository and achieved a sensitivity and specificity matching the lab-based two-tier results with a single POC test, correctly discriminating cross-reactive look-alike diseases. This computational LD diagnostic test can potentially replace the cumbersome two-tier testing paradigm, improving diagnosis and enabling earlier effective treatment of LD patients while also facilitating immune monitoring and surveillance of the disease in the community.

bioengineering↗

bioRxiv2023

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

Compartmentalization, leveraging microfluidics, enables highly sensitive assays; but the requirement for significant infrastructure for their design, build, and operation limits access. Newer multi-material particle-based technologies thermodynamically stabilize monodisperse droplets as individual reaction compartments with simple liquid handling steps, precluding the need for expensive microfluidic equipment. Here, we further improve the accessibility of this lab on a particle technology to resource-limited settings by combining this assay system with a portable multi-modal reader, thus enabling nanoliter droplet assays in an accessible platform. We show the utility of this platform in measuring N-terminal propeptide B-type natriuretic peptide (NT-proBNP), a heart failure biomarker, in complex medium and patient samples. We report a limit of detection of [~]0.05 ng/ml and a linear response between 0.2 - 2 ng/ml in spiked plasma samples. We also show that, owing to the plurality of measurements per sample, "swarm" sensing acquires better statistical quantitation with a portable reader. Monte Carlo simulations show the increasing capability of this platform to differentiate between negative and positive samples, i.e. below or above the clinical cut-off for acute heart failure ([~]0.1ng/ml), as a function of the number of particles measured. Our platform measurements correlate with gold standard ELISA measurement in cardiac patient samples, and achieve lower variation in measurement across samples compared to the standard well plate-based ELISA. Thus, we show the capabilities of a cost-effective droplet-reader system in accurately measuring biomarkers in nanoliter droplets for diseases that disproportionally affect underserved communities in resource-limited settings.

bioengineering↗

bioRxiv2020

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

Small molecule inhibitors have previously been investigated in different studies as possible therapeutics in the treatment of SARS-CoV-2. In the current drug repurposing study, we identified the leukotriene (D4) receptor antagonist Montelukast as a novel agent that simultaneously targets two important drug targets of SARS-CoV-2. We initially demonstrated the dual inhibition profile of Montelukast through multiscale molecular modeling studies. Next, we characterized its effect on both targets by different in vitro experiments including the Fluorescent Resonance Energy Transfer (FRET)-based main protease enzyme inhibition assay, surface plasmon resonance (SPR) spectroscopy, pseudovirus neutralization on HEK293T / hACE2, and virus neutralization assay using xCELLigence MP real time cell analyzer. Our integrated in silico and in vitro results confirmed the dual potential effect of the Montelukast both on virus entry into the host cell (Spike/ACE2) and on the main protease enzyme inhibition. The virus neutralization assay results showed that while no cytotoxicity of the Montelukast was observed at 12 M concentration, the cell index time 50 (CIT50) value was delayed for 12 hours. Moreover, it was also shown that Favipiravir, a well-known antiviral used in COVID-19 therapy, should be used by 16-fold higher concentrations than Montelukast in order to have the same effect of Montelukast. The rapid use of new small molecules in the pandemic is very important today. Montelukast, whose pharmacokinetic and pharmacodynamic properties are very well characterized and has been widely used in the treatment of asthma since 1998, should urgently be completed in clinical phase studies and if its effect is proven in clinical phase studies, it should be used against COVID-19.

biophysics↗

bioRxiv2020

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

The advent of highly sensitive photodetectors1,2 and the development of photostabilization strategies3 made detecting the fluorescence of a single molecule a routine task in many labs around the world. However, to this day, this process requires cost-intensive optical instruments due to the truly nanoscopic signal of a single emitter. Simplifying single-molecule detection would enable many exciting applications, e.g. in point-of-care diagnostic settings, where costly equipment would be prohibitive.4 Here, we introduce addressable NanoAntennas with Cleared HOtSpots (NACHOS) that are scaffolded by DNA origami nanostructures and can be specifically tailored for the incorporation of bioassays. Single emitters placed in the NACHOS emit up to 461-fold brighter enabling their detection with a customary smartphone camera and an 8-US-dollar objective lens. To prove the applicability of our system, we built a portable, battery-powered smartphone microscope and successfully carried out an exemplary single-molecule detection assay for DNA specific to antibiotic-resistant Klebsiella pneumonia "on the road ".

biophysics↗

bioRxiv2019

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

We present a deep learning-based framework to design and quantify point-of-care sensors. As its proof-of-concept and use-case, we demonstrated a low-cost and rapid paper-based vertical flow assay (VFA) for high sensitivity C-Reactive Protein (hsCRP) testing, a common medical test used for quantifying the degree of inflammation in patients at risk of cardio-vascular disease (CVD). A machine learning-based sensor design framework was developed for two key tasks: (1) to determine an optimal configuration of immunoreaction spots and conditions, spatially-multiplexed on a paper-based sensing membrane, and (2) to accurately infer the target analyte concentration based on the signals of the optimal VFA configuration. Using a custom-designed mobile-phone based VFA reader, a clinical study was performed with 85 human serum samples to characterize the quantification accuracy around the clinically defined cutoffs for CVD risk stratification. Results from blindly-tested VFAs indicate a competitive coefficient of variation of 11.2% with a linearity of R2 = 0.95; in addition to the success in the high-sensitivity CRP range (i.e., 0-10 mg/L), our results further demonstrate a mitigation of the hook-effect at higher CRP concentrations due to the incorporation of antigen capture spots within the multiplexed sensing membrane of the VFA. This paper-based computational VFA that is powered by deep learning could expand access to CVD health screening, and the presented machine learning-enabled sensing framework can be broadly used to design cost-effective and mobile sensors for various point-of-care diagnostics applications.

bioengineering↗

bioRxiv2018

Deep learning achieves super-resolution in fluorescence microscopy

AbtsractWe present a deep learning-based method for achieving super-resolution in fluorescence microscopy. This data-driven approach does not require any numerical models of the imaging process or the estimation of a point spread function, and is solely based on training a generative adversarial network, which statistically learns to transform low resolution input images into super-resolved ones. Using this method, we super-resolve wide-field images acquired with low numerical aperture objective lenses, matching the resolution that is acquired using high numerical aperture objectives. We also demonstrate that diffraction-limited confocal microscopy images can be transformed by the same framework into super-resolved fluorescence images, matching the image resolution acquired with a stimulated emission depletion (STED) microscope. The deep network rapidly outputs these super-resolution images, without any iterations or parameter search, and even works for types of samples that it was not trained for.

bioengineering↗