Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU- based disease: the Multi-Targeting Drug DREAM Challenge

A continuing challenge in modern medicine is the identification of safer and more efficacious drugs. Precision therapeutics, which have one molecular target, have been long promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets (‘polypharmacology’). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network-based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through rational polypharmacology

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts

Circulating tumor cells detected by lab-on-adisc: Role in early diagnosis of gastric cancer

[Background] The use of circulating tumor cells (CTCs) as an early diagnostic biomarker and prognostic indicator after surgery or chemotherapy has been suggested for various cancers. This study aimed to evaluate CTCs in patients who underwent gastrectomy for gastric cancer and to explore their clinical usefulness in the early diagnosis of gastric cancer. [Methods] A total of 116 patients with gastric cancer who underwent gastrectomy and 31 healthy volunteers were prospectively included between 2014 and 2015. Peripheral blood samples were collected before gastrectomy, and CTCs were examined using a centrifugal microfluidic system with a new fluid-assisted separation technique. [Results] After creating a receiver operating characteristic curve to identify the discriminative CTC value needed differentiate patients with gastric cancer from healthy volunteers, sensitivity and specificity were nearly optimized at a CTC threshold of 2 per 7.5 mL of blood. Of the 102 persons with a CTC level >= 2 per 7.5 mL of blood, 99 (97.1%) had gastric cancer, and of the 45 persons with a CTC level <2 per 7.5 mL of blood, 28 (62.2%) were healthy controls. Accordingly, the sensitivity and specificity for the differentiation of patients with gastric cancer from healthy controls were 85.3% and 90.3%, respectively. However, the presence of CTCs was not associated with any clinicopathologic features such as staging, histologic type, or mucin phenotype. [Conclusion] Although we could not prove the clinical feasibility of CTCs for gastric cancer staging, our results suggest a potential role of CTCs as an early diagnostic biomarker of gastric cancer

Exploring Language as a Source of DIF in a Math Test for English Language Learners

English language learners (ELs) have shown lower performance in mathematics than non-ELs although mathematics is an area that uses the least amount of language among the subjects that are mainly tested. If this differential performance is due to the bias in test items, then validity of using ELs’ test scores in comparison to non-ELs’ is compromised. For this reason, studies have investigated whether the differential performance can be attributed to language load in the tests. The results of these studies were not consistent. Some studies did find its effect, whereas others did not. Some of the difficulties encountered by researchers in past studies investigating DIF include a large difference in sample size between the two groups and unclear distinctions between ELs and non-ELs. This study aims to investigate the source of DIF between ELs and non-ELs using a comparatively large and a better defined/restricted population of ELs. This study will contribute to existing knowledge about English proficiency as a possible cause of differential performance between the two groups. The findings of this study will have implications for test construction and policies for providing testing accommodations (e.g., test language simplification)

Spatiotemporal analysis of EEG signal during consciousness using convolutional neural network

peer reviewedElectroencephalogram (EEG) measurement could help to distinguish the level of consciousness in an individual without requiring a behavioral response, which could be useful as a diagnostic aid in patients with disorders of consciousness. In this study, we explored the EEG-evoked perturbation and analyzed consciousness using event-related spectral perturbation and convolutional neural network. We observed a novel EEG neurophysiological signature that can be used to monitor brain activity during unconsciousness. Also, the performance accuracy in the parietal region was higher than in the frontal region. The sensitivity for conscious experience was 90.9%, whereas sensitivity for unconscious experience was at the chance level in the parietal region. These results could be evidence for the importance of the posterior hot zone and could help shed light on the internal neural dynamics related to conscious experience. © 2018 IEEE

Light Trapping Color Filters for Semitransparent Solar Cells

Semitransparent colorful solar cells equipped with photonically tailored Fabry–Perot (FP) cavities as the back electrode have garnered attention for their prospective application in building integrated photovoltaics (BIPVs). These cells transmit colored light at the FP resonance while reflecting nonresonant light back into the cell, a significant portion of which is also lost into air. Herein, we present a method to enhance light trapping in colorful semitransparent solar cells using closely packed Ag-coated silica particles on a thin Ag layer. This structure simultaneously acts as an effective FP cavity and color filter, scattering off-resonant light to high angles while transmitting the targeted colors. We show that the high-angle scattering originates from antiparallel out-of-plane electric dipoles unique to our design, which promote light trapping. When applied onto a dye-sensitized solar cell (DSSC), our effective Fabry–Perot (EFP) color filters provided a maximum of ∼7% more short-circuit current density (Jsc) than those from DSSCs equipped with planar filters. Furthermore, compared to bare DSSCs and DSSCs including conventional scattering layers, DSSCs equipped with EFP filters showed a maximum of 14.6 and 5.9% higher cell efficiencies (η), respectively. The ability to filter color and improve light trapping suggests alternative pathways for engineering colorful semitransparent solar cells

Standard setting for a novel esophageal conduit questionnaire: CONDUIT Report Card

Abstract Background The purpose of this study was to establish the clinical thresholds for five domains (dysphagia, reflux, dumping-hypoglycemia, dumping-GI symptoms, pain) to support the use of the CONDUIT questionnaire as a screening tool to identify patients who might benefit from an educational or clinical intervention. Methods A panel of 16 experts met to develop descriptions of “poor,” “moderate,” and “good” conduit performance. They were trained to use the modified and extended Angoff standard-setting method. Each judge provided item ratings that reflected borderline good and borderline moderate patients. The average item ratings were summed and transformed to a 0–100 scale to derive final cut scores. Panelist evaluation of the process and confidence with the rating tasks were collected. Results Panelists expressed that the training on the method gave them information they needed to complete their assignment. Among other factors, their experience with patients was most influential on their ratings. On the 0–100 score scale, good/moderate cuts ranged from 7.2 to 20.8, and moderate/poor cuts ranged from 37.9 to 64.3, depending on domains and weights. Standard errors of one or both cut scores increased for dysphagia and dumping-GI with weighting. Conclusions We described the selection and training of panelists and panelists’ evaluations of the processes they were asked to follow in detail to defend the cut scores. Further prospective validation studies are underway to compare cut scores from this study and clinicians’ judgments and further refine the categorization