Biology subjects

Wen, J. C.

Publications and source records attributed to Wen, J. C..

3 recordsLinked to original sources

bioRxiv2022

Floating Sphere Assay: A rapid qualitative method for microvolume analysis of gelation

1A huge, unprecedented demand for gelatin coupled with its implications on global sustainability has resulted in the need to discover novel proteins with gelling attributes for applications in the food industry. Currently used gelation assays require large sample volumes and thus the screening for novel gelling proteins is a formidable technical challenge. In this paper, we report the Floating Sphere Assay which is a simple, economical, and miniaturized assay to detect minimum gelling concentration with volumes as low as 50 l. Results from the Floating Sphere Assay are consistent with currently used methods for gelation tests and accurately estimate the Minimum Gelling Concentrations (MGCs) of gelatin, {kappa}-carrageenan and gellan gum. The assay was also able to differentiate the strengths of strong and weak gellan gum gels prepared at pH 3.5 and pH 7.0 respectively. The Floating Sphere Assay can be utilized in high-throughput screens for gelling proteins and can accelerate the discovery of gelatin substitutes. 2 HighlightsO_LIWe report the Floating Sphere Assay that can be used to assesses minimum gelling concentration of solutions with volumes as low as 50 l. C_LIO_LIObserving whether a glass sphere placed on the surface of a test solution floats or sinks is diagnostic of gel formation C_LIO_LIFloating Sphere Assay can distinguish a strong gel from a weak gel C_LIO_LIFloating Sphere Assay is a rapid and cost-effective approach to screen for novel plant-based gelatin alternatives. C_LI

biochemistry↗

bioRxiv2020

Automated detection of glaucoma with interpretable machine learning using clinical data and multi-modal retinal images

Glaucoma, the leading cause of irreversible blindness worldwide, is a disease that damages the optic nerve. Current machine learning (ML) approaches for glaucoma detection rely on features such as retinal thickness maps; however, the high rate of segmentation errors when creating these maps increase the likelihood of faulty diagnoses. This paper proposes a new, comprehensive, and more accurate ML-based approach for population-level glaucoma screening. Our contributions include: (1) a multi-modal model built upon a large data set that includes demographic, systemic and ocular data as well as raw image data taken from color fundus photos (CFPs) and macular Optical Coherence Tomography (OCT) scans, (2) model interpretation to identify and explain data features that lead to accurate model performance, and (3) model validation via comparison of model output with clinician interpretation of CFPs. We also validated the model on a cohort that was not diagnosed with glaucoma at the time of imaging but eventually received a glaucoma diagnosis. Results show that our model is highly accurate (AUC 0.97) and interpretable. It validated biological features known to be related to the disease, such as age, intraocular pressure and optic disc morphology. Our model also points to previously unknown or disputed features, such as pulmonary capacity and retinal outer layers.

bioengineering↗

bioRxiv2018

Forecasting Future Humphrey Visual Fields Using Deep Learning

PurposeTo determine if deep learning networks could be trained to forecast a future 24-2 Humphrey Visual Field (HVF).\n\nDesignRetrospective database study.\n\nParticipantsAll patients who obtained a HVF 24-2 at the University of Washington.\n\nMethodsAll datapoints from consecutive 24-2 HVFs from 1998 to 2018 were extracted from a University of Washington database. Ten-fold cross validation with a held out test set was used to develop the three main phases of model development: model architecture selection, dataset combination selection, and time-interval model training with transfer learning, to train a deep learning artificial neural network capable of generating a point-wise visual field prediction.\n\nMain outcome measuresMean absolute error (MAE) and difference in Mean Deviation (MD) between predicted and actual future HVF.\n\nResultsMore than 1.7 million perimetry points were extracted to the hundredth decibel from 32,443 24-2 HVFs. The best performing model with 20 million trainable parameters, CascadeNet-5, was selected. The overall MAE for the test set was 2.47 dB (95% CI: 2.45 dB to 2.48 dB). The 100 fully trained models were able to successfully predict progressive field loss in glaucomatous eyes up to 5.5 years in the future with a correlation of 0.92 between the MD of predicted and actual future HVF (p < 2.2 = 10-16) and an average difference of 0.41 dB.\n\nConclusionsUsing unfiltered real-world datasets, deep learning networks show an impressive ability to not only learn spatio-temporal HVF changes but also to generate predictions for future HVFs up to 5.5 years, given only a single HVF.

bioinformatics↗