The fabrication of fluorescence polymer/InGaN hybrid light-emitting diodes (LEDs) that emit highly bright broadband red light is presented in this Letter. The absorption peak of the fluorescence polymer was 455 nm, and the emission peak was 640 nm. The light output power and external quantum efficiency of hybrid LEDs at a driving current of 100 mA were 46.6 mW and 24.1%, respectively. The emission spectrum of hybrid LEDs was located at a wavelength of 641 nm, with a broadband FWHM of 106 nm. Thus this study offers potential methods for enhancing the output power of commercial white-light-emitting devices.

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ObjectivesTo identify the neuroimaging predictors for the responsiveness of patients to sumatriptan and use an independent cohort for external validation.MethodsStructuralized headache questionnaire and 3-Tesla brain magnetic resonance imaging were performed in migraine patients. Regional brain volumes were automatically calculated using FreeSurfer version 6.0, including bilateral amygdala, anterior cingulated cortex, caudate, putamen, precuneus, orbitofrontal cortex, superior frontal gyri, middle frontal gyri, hippocampus, and parahippocampus. A sumatriptan-responder was defined as headache relief within 2 h after the intake of sumatriptan in at least two out of three treated attacks. We constructed a prediction model for sumatriptan response using the regional brain volume and validated it with an independent cohort of migraine patients.ResultsA total of 105 migraine patients were recruited, including 73 sumatriptan responders (69.5%) and 32 (30.5%) non-responders. We divided the migraine patients into derivation (n = 73) and validation cohorts (n = 32). In the derivation cohort, left hippocampal volume was larger in sumatriptan responders (responders vs. non-responders: 3,929.5 ± 403.1 vs. 3,611.0 ± 389.9 mm3, p = 0.002), and patients with a larger left hippocampal volume had a higher response rate to sumatriptan (>4,036.2 vs. ≤4,036.2 mm3: 92.0 vs. 56.3%, p = 0.001). Based on the findings, we constructed a prediction model using the cutoff value of 4,036.2 mm3, and we found that patients with a left hippocampal volume >4,032.6 mm3 had a higher response rate to sumatriptan than those with a left hippocampal volume ≤4,032.6 mm3 (84.6 vs. 42.1%, odds ratio [OR] = 7.6 [95% confidence interval = 1.3–44.0], p = 0.013) in the validation cohort.ConclusionOur study showed that left hippocampal volume is helpful to identify sumatriptan non-responders. This proof-of-concept study shows that left hippocampal volume could be used to predict the treatment response to sumatriptan in migraine patients.

Magnetic resonance fingerprinting (MRF) based on echo-planar imaging (EPI) enables whole-brain imaging to rapidly obtain T1 and T2* relaxation time maps. Reconstructing parametric maps from the MRF scanned baselines by the inner-product method is computationally expensive. We aimed to accelerate the reconstruction of parametric maps for MRF-EPI by using a deep learning model. The proposed approach uses a two-stage model that first eliminates noise and then regresses the parametric maps. Parametric maps obtained by dictionary matching were used as a reference and compared with the prediction results of the two-stage model. MRF-EPI scans were collected from 32 subjects. The signal-to-noise ratio increased significantly after the noise removal by the denoising model. For prediction with scans in the testing dataset, the mean absolute percentage errors between the standard and the final two-stage model were 3.1%, 3.2%, and 1.9% for T1, and 2.6%, 2.3%, and 2.8% for T2* in gray matter, white matter, and lesion locations, respectively. Our proposed two-stage deep learning model can effectively remove noise and accurately reconstruct MRF-EPI parametric maps, increasing the speed of reconstruction and reducing the storage space required by dictionaries.