Automatic Prediction of Rheumatoid Arthritis Disease Activity from the Electronic Medical Records

Objective: We aimed to mine the data in the Electronic Medical Record to automatically discover patients' Rheumatoid Arthritis disease activity at discrete rheumatology clinic visits. We cast the problem as a document classification task where the feature space includes concepts from the clinical narrative and lab values as stored in the Electronic Medical Record. Materials and Methods The Training Set consisted of 2792 clinical notes and associated lab values. Test Set 1 included 1749 clinical notes and associated lab values. Test Set 2 included 344 clinical notes for which there were no associated lab values. The Apache clinical Text Analysis and Knowledge Extraction System was used to analyze the text and transform it into informative features to be combined with relevant lab values. Results: Experiments over a range of machine learning algorithms and features were conducted. The best performing combination was linear kernel Support Vector Machines with Unified Medical Language System Concept Unique Identifier features with feature selection and lab values. The Area Under the Receiver Operating Characteristic Curve (AUC) is 0.831 (σ = 0.0317), statistically significant as compared to two baselines (AUC = 0.758, σ = 0.0291). Algorithms demonstrated superior performance on cases clinically defined as extreme categories of disease activity (Remission and High) compared to those defined as intermediate categories (Moderate and Low) and included laboratory data on inflammatory markers. Conclusion: Automatic Rheumatoid Arthritis disease activity discovery from Electronic Medical Record data is a learnable task approximating human performance. As a result, this approach might have several research applications, such as the identification of patients for genome-wide pharmacogenetic studies that require large sample sizes with precise definitions of disease activity and response to therapies

Ultra-Small Nanoparticles of Pd-Pt-Ni Alloy Octahedra with High Lattice Strain for Efficient Oxygen Reduction Reaction

The design and synthesis of ultra-small-sized Pt-based catalyst with specific effects for enhancing the oxygen reduction reaction (ORR) is an effective way to improve the utilization of Pt. Herein, Pt-Pd-Ni octahedra nanoparticles characterized by the ultra-small size of 4.71 nm were synthesized by a Pd seed-inducing-growth route. Initially, Pd nanocubes were synthesized under solvothermal conditions; subsequently, Pt-Ni was deposited in the Pd seed solution. The Pd seeds were oxidized into Pd2+ and combined with Pt2+ and Ni2+ in the solution and finally formed the ternary alloy small-sized octahedra. In the synthesis process of the ultra-small Pt-Pd-Ni octahedra, Pd nanocube seed played an important role. In addition, the size of the Pt-Pd-Ni octahedra could be regulated by adjusting the concentration rate of Pt-Ni. The ultra-small Pt-Pd-Ni octahedra formation by depositing Pt-Ni with a feeding ratio of 2:1 showed good ORR activity, and the high half-wave potential was 0.933 V. In addition, the Pt-Pd-Ni octahedra showed an enhanced mass activity of 0.93 A mg−1 Pt+Pd in ORR, which was 5.81 times higher than commercial Pt/C. The theoretical calculation shows that compared to Pt/C, the small-sized ternary alloy octahedra had an obvious contraction strain effect (contraction rate: 3.49%). The alloying effect affected the d-band center of the Pt negative shift. In the four-electron reaction, Pt-Pd-Ni ultra-small octahedra exhibited the lowest overpotential, resulting in the adsorption performance to become optimized. Therefore, the Pd seed-inducing-growth route provides a new idea for exploring the synthesis of small-sized nanoparticle catalysts

Functional Interaction of E1AF and Sp1 in Glioma Invasion▿

Transcription factor E1AF is widely known to play critical roles in tumor metastasis via directly binding to the promoters of genes involved in tumor migration and invasion. Here, we report for the first time E1AF as a novel binding partner for ubiquitously expressed Sp1 transcription factor. E1AF forms a complex with Sp1, contributes to Sp1 phosphorylation and transcriptional activity, and functions as a mediator between epidermal growth factor and Sp1 phosphorylation and activity. Sp1 functions as a carrier bringing E1AF to the promoter region, thus activating transcription of glioma-related gene for β1,4-galactosyltransferase V (GalT V; EC 2.4.1.38). Biologically, E1AF functions as a positive invasion regulator in glioma in cooperation with Sp1 partly via up-regulation of GalT V. This report describes a new mechanism of glioma invasion involving a cooperative effort between E1AF and Sp1 transcription factors

Error analysis of the best performing classifier.

<p>Out of 429 misclassified cases (using DAS28 derived dichotomous labels as gold standard), the majority are from the Moderate and Low disease activity categories.</p

Dataset characteristics.

<p>Dataset characteristics.</p

Ranges of lab values.

<p>(Left) Range of lab values for Moderate/High (MH) disease activity cases vs. Range of lab values for Low/Remission (LR) disease activity cases among 1320 correctly classified notes. (Right) Range of lab values for Moderate/High (MH) disease activity cases vs. Range of lab values for Low/Remission (LR) disease activity cases among 429 misclassified notes.</p

Portability testing.

<p>Portability testing.</p

Histogram of DAS28 scores for 25 discordant cases.

<p>These discordant cases are between DAS labels and domain expert labels among 93 random samples from the Training Set (the remaining 68 cases were concordant).</p