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Since the development of the polymerase chain reaction (PCR) technique, genomic information has been retrievable from lesser amounts of DNA than previously possible. PCR-based amplifications require high-precision instruments to perform temperature cycling reactions; further, they are cumbersome for routine clinical use. However, the use of isothermal approaches can eliminate many complications associated with thermocycling. The application of diagnostic devices for isothermal DNA amplification has recently been studied extensively. In this paper, we describe the basic concepts of several isothermal amplification approaches and review recent progress in diagnostic device development.

Emergency department free-text chief complaints (CCs) are a major data source for syndromic surveillance. CCs need to be classified into syndromic categories for subsequent automatic analysis. However, the lack of a standard vocabulary and high-quality encodings of CCs hinder effective classification. This paper presents a new ontology-enhanced automatic CC classification approach. Exploiting semantic relations in a medical ontology, this approach is motivated to address the CC vocabulary variation problem in general and to meet the specific need for a classification approach capable of handling multiple sets of syndromic categories. We report an experimental study comparing our approach with two popular CC classification methods using a real-world dataset. This study indicates that our ontology-enhanced approach performs significantly better than the benchmark methods in terms of sensitivity, F measure, and F2 measure.

Information and communications technologies have enabled healthcare institutions to accumulate large amounts of healthcare data that include diagnoses, medications, and additional contextual information such as patient demographics. To gain a better understanding of big healthcare data and to develop better data-driven clinical decision support systems, we propose a novel multiple-channel latent Dirichlet allocation (MCLDA) approach for modeling diagnoses, medications, and contextual information in healthcare data. The proposed MCLDA model assumes that a latent health status group structure is responsible for the observed co-occurrences among diagnoses, medications, and contextual information. Using a real-world research testbed that includes one million healthcare insurance claim records, we investigate the utility of MCLDA. Our empirical evaluation results suggest that MCLDA is capable of capturing the comorbidity structures and linking them with the distribution of medications. Moreover, MCLDA is able to identify the pairing between diagnoses and medications in a record based on the assigned latent groups. MCLDA can also be employed to predict missing medications or diagnoses given partial records. Our evaluation results also show that, in most cases, MCLDA outperforms alternative methods such as logistic regressions and the k-nearest-neighbor (KNN) model for two prediction tasks, i.e., medication and diagnosis prediction. Thus, MCLDA represents a promising approach to modeling healthcare data for clinical decision support.

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Metal-oxide-semiconductor (MOS) photodetector with the high-k material enhanced deep depletion at edge was demonstrated. The mechanism of saturated substrate injection current in MOS capacitor was adopted. By building HfO2 based devices that with the direct observation of the enhanced edge charge collection efficiency due to fringing field effect in inversion, we are able to show a photodetector with 3000 times (ratio of photocurrent to dark current) improvement in sensitivity than the conventional SiO2 based tunneling photodiodes (approximate 100 times) in the visible.

This study examines the lithographic capacity of tips in dip-pen nanolithography (DPN). The dependence of the transport rate (R) decay on the area of lithography (A(lith)), the dependence of A(lith) on the lithographic time (t), and the effect of piranha cleaning on the lithographic capacity are considered herein. The dependencies in the line-drawing lithography process are studied using 16-mercaptohexadecanoic acid (MHA) ink. On the basis of the linear decay dependence discovered in the R-A(lith) dependence, piranha treatment can increase the lithographic capacity by up to 35.5-fold, an improvement that may originate from a change in the tip's surface chemistry. Moreover, a theoretical model is derived to describe the A(lith)-t dependence accurately and to predict the tips' lifetime. Furthermore, an experiment involving DPN-based nanostructure fabrication demonstrates the importance of monitoring the tips' transport rate and lifetime. In addition to shedding light on the physical and chemical principles behind DPN, this study provides a comprehensive model for a quantitative analysis of the tips' behavior.