Plasmonic Schirmer Strip for Human Tear-Based Gouty Arthritis Diagnosis Using Surface-Enhanced Raman Scattering

Biomarkers in tear fluid have attracted much interest in daily healthcare sensing and monitoring. Recently, surface-enhanced Raman scattering (SERS) has enabled highly sensitive label-free detection of small molecules. However, a highly stable straightforward tear assay with superior sensitivity is still under development in tear collection and analysis. Here we report a plasmonic Schirmer strip for on-demand, rapid, and simple identification of biomarkers in human tears. The diagnostic strip features gold nanoislands directly and evenly formed on the top surface of cellulose fibers, which maintain a hygroscopic nature for an efficient collection of tear production as well as provide plasmonic enhancement in SERS signals for identification of tear molecules. The uric acid in human tears was quantitatively detected at physiological levels (25–150 μM) by using SERS. The experimental results also clearly reveal a strong linear correlation between uric acid level in both human tears and blood for gouty arthritis diagnosis. This functional paper strip enables noninvasive diagnosis of disease-related biomarkers and healthcare monitoring using human tears

Skeleton of nested cross-validation.

<p>The skeleton of the nested cross-validation for measuring the performance of the proposed method.</p

An Example-Based Multi-Atlas Approach to Automatic Labeling of White Matter Tracts

<div><p>We present an example-based multi-atlas approach for classifying white matter (WM) tracts into anatomic bundles. Our approach exploits expert-provided example data to automatically classify the WM tracts of a subject. Multiple atlases are constructed to model the example data from multiple subjects in order to reflect the individual variability of bundle shapes and trajectories over subjects. For each example subject, an atlas is maintained to allow the example data of a subject to be added or deleted flexibly. A voting scheme is proposed to facilitate the multi-atlas exploitation of example data. For conceptual simplicity, we adopt the same metrics in both example data construction and WM tract labeling. Due to the huge number of WM tracts in a subject, it is time-consuming to label each WM tract individually. Thus, the WM tracts are grouped according to their shape similarity, and WM tracts within each group are labeled simultaneously. To further enhance the computational efficiency, we implemented our approach on the graphics processing unit (GPU). Through nested cross-validation we demonstrated that our approach yielded high classification performance. The average sensitivities for bundles in the left and right hemispheres were 89.5% and 91.0%, respectively, and their average false discovery rates were 14.9% and 14.2%, respectively.</p></div

Seven anatomic bundles of twelve example subjects.

<p>Seven anatomic bundles which were obtained through manual labeling for left hemispheres of twelve example subjects.</p

Skeleton of nested cross-validation.

<p>The skeleton of the nested cross-validation for measuring the performance of the proposed method.</p

Sensitivity histogram for tract group and direct tract labeling.

<p>The x-axis and y-axis represents sensitivity ranges and percentage of bundles that are included in the corresponding sensitivity ranges, respectively.</p

Overview of the proposed approach to automatic classification.

<p>The method consists of two parts: Example data construction and automatic tract classification.</p

Rhythmical Photic Stimulation at Alpha Frequencies Produces Antidepressant-Like Effects in a Mouse Model of Depression

<div><p>Current therapies for depression consist primarily of pharmacological agents, including antidepressants, and/or psychiatric counseling, such as psychotherapy. However, light therapy has recently begun to be considered as an effective tool for the treatment of the neuropsychiatric behaviors and symptoms of a variety of brain disorders or diseases, including depression. One methodology employed in light therapy involves flickering photic stimulation within a specific frequency range. The present study investigated whether flickering and flashing photic stimulation with light emitting diodes (LEDs) could improve depression-like behaviors in a corticosterone (CORT)-induced mouse model of depression. Additionally, the effects of the flickering and flashing lights on depressive behavior were compared with those of fluoxetine. Rhythmical flickering photic stimulation at alpha frequencies from 9–11 Hz clearly improved performance on behavioral tasks assessing anxiety, locomotor activity, social interaction, and despair. In contrast, fluoxetine treatment did not strongly improve behavioral performance during the same period compared with flickering photic stimulation. The present findings demonstrated that LED-derived flickering photic stimulation more rapidly improved behavioral outcomes in a CORT-induced mouse model of depression compared with fluoxetine. Thus, the present study suggests that rhythmical photic stimulation at alpha frequencies may aid in the improvement of the quality of life of patients with depression.</p></div

Major anatomic bundle list.

<p>Major anatomic bundle list.</p

Sensitivity and FDR histograms for tract group labeling, direct tract labeling, and Guevara’s method.

<p>Top left: sensitivities of the three methods for each anatomic bundle, Top right: sensitivities of the three methods for each example subject. Bottom left: FDRs of the three methods for each anatomic bundle, Bottom right: FDRs of the three methods for each example subject.</p