Treatment adherence can be impacted by several factors, including route of administration, dosing schedule, and pt perceptions. This study assessed pt-reported adherence and associated barriers in pts with melanoma treated with adjuvant therapy. Adult pts with resected stage 3/4 melanoma with no evidence of disease were recruited by a pt panel and the Melanoma Research Foundation to participate in an online survey. Pts were required to have received adjuvant therapy with nivolumab or pembrolizumab (intravenous [IV] cohort) or dabrafenib+trametinib (oral cohort). Adherence was estimated using self-reported overall number of infusions missed for the IV cohort and self-reported adherence level in the past 2 months for the oral cohort. Of 184 eligible pts (127 IV, 57 oral), mean age was 45 years, 44% were female, and 78% were white. Most pts were engaged in treatment decision-making (86%) and considered their well-being to be "good" (46%), "very good" (26%), or "excellent" (9%). Compared with the oral cohort, more pts in the IV cohort were employed full time (67% vs 47%; P¼0.012) and had commercial insurance (76% vs 47%; P<0.001). Mean time on the current adjuvant treatment was similar between the IV (8.3 months) and oral (7.7 months) cohorts. Adherence was relatively high, with pts following their regimens always (81% IV, 58% oral; P¼0.002) or almost all of the time (17% IV, 33% oral; P¼0.002). Nonadherence behavior was lower in the IV cohort than in the oral cohort (19% vs 42%; P<0.001), with forgetfulness (54% vs 46%), affordability (0% vs 46%; P<0.001), and safety concerns (29% vs 42%) listed as common reasons for nonadherence. Many pts did not expect to follow future regimens as instructed (37% IV, 46% oral), primarily due to affordability and safety concerns, which were similar in both cohorts. This study found that pt-reported adherence to adjuvant therapy was relatively high among those with advanced resectable melanoma but also presented potential areas for further improvement.

The mainstream examination for Parkinsons disease is still to determine the Unified Parkinsons Disease Rating Scale (UPDRS), it can be inaccurate due to doctors or patients subjectivity. But recent studies have shown that patients of Parkinsons disease will represent a certain extent of dysgraphia in the early stage. Based on this feature, we proposed a method to realize the preliminary diagnosis for Parkinsons disease based on patients hand-drawings. In this paper, after the images that we used are pre-processed based on Threshold Segmentation, we set a 4-layer network for Convolutional Neural Networks (CNNs). First, we design a convolutional layer to learn the local features of hand-drawing, then the image is passed to then Maxpooling to go through the maximum pooling operation to preserve the contour features and to remove extraneous information. We set up two fully connected layers to capture more nonlinear relationships between images and labels. In the last, an accuracy calculation formula is adopted to diagnose Parkinsons disease. Overall, this diagnosis scheme that only requires patients hand-drawing can be completely automatic and more convenient than the traditional examination, the accuracy of result can be further improved if more details in the hand-drawing can be gathered.