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Enhancement of microalgae growth using magnetic artificial cilia.

ABSTRACT: Microalgae have shown great potential as a source of biofuels, food, and other bioproducts. More recently, microfluidic devices have been employed in microalgae-related studies. However, at small fluid volumes, the options for controlling flow conditions are more limited and mixing becomes largely reliant on diffusion. In this study, we fabricated magnetic artificial cilia (MAC) and implemented them in millimeter scale culture wells and conducted growth experiments with Scenedesmus subspicatus while actuating the MAC in a rotating magnetic field to create flow and mixing. In addition, surface of MAC was made hydrophilic using plasma treatment and its effect on growth was compared with untreated, hydrophobic MAC. The experiments showed that the growth was enhanced by ten and two times with hydrophobic and hydrophilic MAC, respectively, compared with control groups which contain no MAC. This technique can be used to investigate mixing and flow in small sample volumes, and the enhancement in growth can be beneficial for the throughput of screening studies. Moreover, the methods used for creating and controlling MAC can be easily adopted in labs without microfabrication infrastructures, and they can be mastered by people with little prior experience in microfluidics.

SUBMITTER: Verburg T

PROVIDER: S-EPMC8251745 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundLate gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging is the gold standard for noninvasive myocardial tissue characterization but requires intravenous contrast agent administration. It is highly desired to develop a contrast agent-free technology to replace LGE for faster and cheaper CMR scans.MethodsA CMR virtual native enhancement (VNE) imaging technology was developed using artificial intelligence. The deep learning model for generating VNE uses multiple streams of convolutional neural networks to exploit and enhance the existing signals in native T1 maps (pixel-wise maps of tissue T1 relaxation times) and cine imaging of cardiac structure and function, presenting them as LGE-equivalent images. The VNE generator was trained using generative adversarial networks. This technology was first developed on CMR datasets from the multicenter Hypertrophic Cardiomyopathy Registry, using hypertrophic cardiomyopathy as an exemplar. The datasets were randomized into 2 independent groups for deep learning training and testing. The test data of VNE and LGE were scored and contoured by experienced human operators to assess image quality, visuospatial agreement, and myocardial lesion burden quantification. Image quality was compared using a nonparametric Wilcoxon test. Intra- and interobserver agreement was analyzed using intraclass correlation coefficients (ICC). Lesion quantification by VNE and LGE were compared using linear regression and ICC.ResultsA total of 1348 hypertrophic cardiomyopathy patients provided 4093 triplets of matched T1 maps, cines, and LGE datasets. After randomization and data quality control, 2695 datasets were used for VNE method development and 345 were used for independent testing. VNE had significantly better image quality than LGE, as assessed by 4 operators (n=345 datasets; P<0.001 [Wilcoxon test]). VNE revealed lesions characteristic of hypertrophic cardiomyopathy in high visuospatial agreement with LGE. In 121 patients (n=326 datasets), VNE correlated with LGE in detecting and quantifying both hyperintensity myocardial lesions (r=0.77-0.79; ICC=0.77-0.87; P<0.001) and intermediate-intensity lesions (r=0.70-0.76; ICC=0.82-0.85; P<0.001). The native CMR images (cine plus T1 map) required for VNE can be acquired within 15 minutes and producing a VNE image takes less than 1 second.ConclusionsVNE is a new CMR technology that resembles conventional LGE but without the need for contrast administration. VNE achieved high agreement with LGE in the distribution and quantification of lesions, with significantly better image quality.

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Enhancement of microalgae growth using magnetic artificial cilia.

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