MildInt: Deep Learning-Based Multimodal Longitudinal Data Integration Framework

As large amounts of heterogeneous biomedical data become available, numerous methods for integrating such datasets have been developed to extract complementary knowledge from multiple domains of sources. Recently, a deep learning approach has shown promising results in a variety of research areas. However, applying the deep learning approach requires expertise for constructing a deep architecture that can take multimodal longitudinal data. Thus, in this paper, a deep learning-based python package for data integration is developed. The python package deep learning-based multimodal longitudinal data integration framework (MildInt) provides the preconstructed deep learning architecture for a classification task. MildInt contains two learning phases: learning feature representation from each modality of data and training a classifier for the final decision. Adopting deep architecture in the first phase leads to learning more task-relevant feature representation than a linear model. In the second phase, linear regression classifier is used for detecting and investigating biomarkers from multimodal data. Thus, by combining the linear model and the deep learning model, higher accuracy and better interpretability can be achieved. We validated the performance of our package using simulation data and real data. For the real data, as a pilot study, we used clinical and multimodal neuroimaging datasets in Alzheimer's disease to predict the disease progression. MildInt is capable of integrating multiple forms of numerical data including time series and non-time series data for extracting complementary features from the multimodal dataset

Repurposed Agents in the Alzheimer\u27s Disease Drug Development Pipeline

Background: Treatments are needed to address the growing prevalence of Alzheimer’s disease (AD). Clinical trials have failed to produce any AD drugs for Food and Drug Administration (FDA) approval since 2003, and the pharmaceutical development process is both time-consuming and costly. Drug repurposing provides an opportunity to accelerate this process by investigating the AD-related effects of agents approved for other indications. These drugs have known safety profiles, pharmacokinetic characterization, formulations, doses, and manufacturing processes. Methods: We assessed repurposed AD therapies represented in Phase I, Phase II, and Phase III of the current AD pipeline as registered on ClinicalTrials.gov as of February 27, 2020. Results: We identified 53 clinical trials involving 58 FDA-approved agents. Seventy-eight percent of the agents in trials had putative disease-modifying mechanisms of action. Of the repurposed drugs in the pipeline 20% are hematologic-oncologic agents, 18% are drugs derived from cardiovascular indications, 14% are agents with psychiatric uses, 12% are drug used to treat diabetes, 10% are neurologic agents, and the remaining 26% of drugs fall under other conditions. Intellectual property strategies utilized in these programs included using the same drug but altering doses, routes of administration, or formulations. Most repurposing trials were supported by Academic Medical Centers and were not funded through the biopharmaceutical industry. We compared our results to a European trial registry and found results similar to those derived from ClinicalTrials.gov. Conclusions: Drug repurposing is a common approach to AD drug development and represents 39% of trials in the current AD pipeline. Therapies from many disease areas provide agents potentially useful in AD. Most of the repurposed agents are generic and a variety of intellectual property strategies have been adopted to enhance their economic value

Predicting Alzheimer’s disease progression using multi-modal deep learning approach

Alzheimer’s disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. It is critical for timely treatment to detect AD in its earlier stage before clinical manifestation. Mild cognitive impairment (MCI) is an intermediate stage between cognitively normal older adults and AD. To predict conversion from MCI to probable AD, we applied a deep learning approach, multimodal recurrent neural network. We developed an integrative framework that combines not only cross-sectional neuroimaging biomarkers at baseline but also longitudinal cerebrospinal fluid (CSF) and cognitive performance biomarkers obtained from the Alzheimer’s Disease Neuroimaging Initiative cohort (ADNI). The proposed framework integrated longitudinal multi-domain data. Our results showed that 1) our prediction model for MCI conversion to AD yielded up to 75% accuracy (area under the curve (AUC) = 0.83) when using only single modality of data separately; and 2) our prediction model achieved the best performance with 81% accuracy (AUC = 0.86) when incorporating longitudinal multi-domain data. A multi-modal deep learning approach has potential to identify persons at risk of developing AD who might benefit most from a clinical trial or as a stratification approach within clinical trials

Alzheimer\u27s Disease Drug Development Pipeline: 2019

Introduction Alzheimer\u27s disease (AD) has few available treatments, and there is a high rate of failure in AD drug development programs. Study of the AD drug development pipeline can provide insight into the evolution of drug development and how best to optimize development practices. Methods We reviewed clinicaltrials.gov and identified all pharmacologic AD trials of all agents currently being developed for treatment of AD. Results There are 132 agents in clinical trials for the treatment of AD. Twenty-eight agents are in 42 phase 3 trials; 74 agents are in 83 phase 2 trials; and 30 agents are in 31 phase 1 trials. There is an increase in the number of agents in each phase compared with that in the 2018 pipeline. Nineteen agents in trials target cognitive enhancement, and 14 are intended to treat neuropsychiatric and behavioral symptoms. There are 96 agents in disease modification trials; of these, 38 (40%) have amyloid as the primary target or as one of several effects. Eighteen of the antiamyloid agents are small molecules, and 20 are monoclonal antibodies or biological therapies. Seven small molecules and ten biologics have tau as a primary or combination target (18%). Amyloid is the most common specific target in phase 3 and phase 2 disease modification trials. Novel biomarkers (e.g., neurofilament light), new outcomes (e.g., AD Composite Score [ADCOMS]), enrollment of earlier populations, and innovative trial designs (e.g., Bayesian adaptive designs) are new features in recent clinical trials. Discussion Drug development continues robustly at all phases despite setbacks in several programs in the recent past. Continuing unmet needs require a commitment to growing and accelerating the pipeline

Korean Emotional Laborers' Job Stressors and Relievers: Focus on Work Conditions and Emotional Labor Properties

Background: The present study aims to investigate job stressors and stress relievers for Korean emotional laborers, specifically focusing on the effects of work conditions and emotional labor properties. Emotional laborers are asked to hide or distort their real emotions in their interaction with clients. They are exposed to high levels of stress in the emotional labor process, which leads to serious mental health risks including burnout, depression, and even suicide impulse. Exploring job stressors and relieving factors would be the first step in seeking alternatives to protect emotional laborers from those mental health risks. Methods: Using the third wave data of Korean Working Conditions Survey, logistic regression analysis was conducted for two purposes: to examine the relations of emotional labor and stress, and to find out job stressors and relievers for emotional laborers. Results: The chances of stress arousal are 3.5 times higher for emotional laborers; emotional laborers experience double risk-burden for stress arousal. In addition to general job stressors, emotional laborers need to bear burdens related to emotional labor properties. The effect of social support at the workplace is not significant for stress relief, unlike common assumptions, whereas subjective satisfaction (wage satisfaction and work-life balance) is proven to have relieving effects on emotional laborers' job stress. Conclusion: From the results, the importance of a balanced understanding of emotional labor for establishing effective policies for emotional laborer protection is stressed

Image quality comparison between LCD and OLED display

The image qualities of LCD and OLED monitors set to their own default settings were compared using forced-choice experiment method. Both displays' peak white luminance was around 1,000 cd/m2 and color gamut setting was fixed as DCI-P3 for LCD while OLED's gamut was set as DCI-P3 or BT.2020. The twelve image quality evaluation keywords were collected through the focus group Interview and eleven HDR video clips were selected as test stimuli. During the experiment, the test video was shown on two displays placed side-by-side and thirteen na??ve participants were asked to select the display having the better image quality. The experimental results showed that OLED has higher image quality than LCD does because of higher colorfulness in general. Black luminance level affected the image quality for the dark images but the images having large bright area, colorfulness affected the overall image quality. This result shows that not display technology but the color characteristics affects the image quality

High On-Current Ge-Channel Heterojunction Tunnel Field-Effect Transistor Using Direct Band-to-Band Tunneling

The main challenge for tunnel field-effect transistors (TFETs) is achieving high on-current (Ion) and low subthreshold swing (SS) with reasonable ambipolar characteristics. In order to address these challenges, Ge-channel heterostructure TFET with Si source and drain region is proposed, and its electrical characteristics are compared to other TFET structures. From two-dimensional (2-D) device simulation results, it is confirmed that the Si/Ge heterostructure source junction improves Ion and SS characteristics by using the direct band-to-band tunneling current. Furthermore, the proposed structure shows suppressed ambipolar behavior since the Ge/Si heterostructure is used at the drain junction