Toilet-based continuous health monitoring using urine

Tasoğlu, Savaş

doi:10.1038/s41585-021-00558-x

Cited by 44 publications

(31 citation statements)

References 122 publications

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“…IC-Chip is gas-permeable to ensure the exchange of CO 2 and O 2 gases. The base of the chip is a thin microscope slide, allowing the direct observation of the biological sample with temporal and spatial resolution by using optical, fluorescent, and confocal microscopes [64][65][66][67][68]. The specifications of the IC-Chip are organized in Table 2.…”

Section: Methodsmentioning

confidence: 99%

Microfluidic Invasion Chemotaxis Platform for 3D Neurovascular Co-Culture

Sokullu

Cücük

Sarabi

et al. 2022

Fluids

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Advances in microfabrication and biomaterials have enabled the development of microfluidic chips for studying tissue and organ models. While these platforms have been developed primarily for modeling human diseases, they are also used to uncover cellular and molecular mechanisms through in vitro studies, especially in the neurovascular system, where physiological mechanisms and three-dimensional (3D) architecture are difficult to reconstruct via conventional assays. An extracellular matrix (ECM) model with a stable structure possessing the ability to mimic the natural extracellular environment of the cell efficiently is useful for tissue engineering applications. Conventionally used techniques for this purpose, for example, Matrigels, have drawbacks of owning complex fabrication procedures, in some cases not efficient enough in terms of functionality and expenses. Here, we proposed a fabrication protocol for a GelMA hydrogel, which has shown structural stability and the ability to imitate the natural environment of the cell accurately, inside a microfluidic chip utilizing co-culturing of two human cell lines. The chemical composition of the synthesized GelMA was identified by Fourier transform infrared spectrophotometry (FTIR), its surface morphology was observed by field emission electron microscopy (FESEM), and the structural properties were analyzed by atomic force microscopy (AFM). The swelling behavior of the hydrogel in the microfluidic chip was imaged, and its porosity was examined for 72 h by tracking cell localization using immunofluorescence. GelMA exhibited the desired biomechanical properties, and the viability of cells in both platforms was more than 80% for seven days. Furthermore, GelMA was a viable platform for 3D cell culture studies and was structurally stable over long periods, even when prepared by photopolymerization in a microfluidic platform. This work demonstrated a viable strategy to conduct co-culturing experiments as well as modeling invasion and migration events. This microfluidic assay may have application in drug delivery and dosage optimization studies.

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Section: Methodsmentioning

confidence: 99%

Microfluidic Invasion Chemotaxis Platform for 3D Neurovascular Co-Culture

Sokullu

Cücük

Sarabi

et al. 2022

Fluids

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“…Advancements in nanotechnology and biosensing sciences have facilitated utilizing POC biosensing platforms in various fields, including medicine, industry, and defense [ 8 , 9 ]. A biosensor is composed of two fundamental parts: a bio-recognition element and a signal transducer [ 10 ].…”

Section: Mna-based Point-of-care (Poc) Biosensingmentioning

confidence: 99%

3D-Printed Microneedles for Point-of-Care Biosensing Applications

Sarabi

Nakhjavani

2022

Micromachines

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Microneedles (MNs) are an emerging technology for user-friendly and minimally invasive injection, offering less pain and lower tissue damage in comparison to conventional needles. With their ability to extract body fluids, MNs are among the convenient candidates for developing biosensing setups, where target molecules/biomarkers are detected by the biosensor using the sample collected with the MNs. Herein, we discuss the 3D printing of microneedle arrays (MNAs) toward enabling point-of-care (POC) biosensing applications.

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“…Early detection and treatment of illnesses (e.g., cancer) can substantially increase the survival rate, life quality of patients, and, on the other hand, can reduce healthcarerelated costs [1,2]. Despite investing a tremendous amount of money in the research and development of diagnostic approaches, the outcome of clinical treatments is not ideal so far [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Enabled Technologies for Bioimage Analysis

et al. 2022

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Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases.

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Toilet-based continuous health monitoring using urine

Cited by 44 publications

References 122 publications

Microfluidic Invasion Chemotaxis Platform for 3D Neurovascular Co-Culture

Microfluidic Invasion Chemotaxis Platform for 3D Neurovascular Co-Culture

3D-Printed Microneedles for Point-of-Care Biosensing Applications

Deep Learning-Enabled Technologies for Bioimage Analysis

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