Jingcheng Du

Graphene oxide doped conducting polymer nanocomposite film for electrode-tissue interface

One of the most significant components for implantable bioelectronic devices is the interface between the microelectrodes and the tissue or cells for disease diagnosis or treatment. To make the devices work efficiently and safely in vivo, the electrode-tissue interface should not only be confined in micro scale, but also possesses excellent electrochemical characteristic, stability and biocompatibility. Considering the enhancement of many composite materials by combining graphene oxide (GO) for its multiple advantages, we dope graphene oxide into poly(3,4-ethylenedioxythiophene) (PEDOT) forming a composite film by electrochemical deposition for electrode site modification. As a consequence, not only the enlargement of efficient surface area, but also the development of impedance, charge storage capacity and charge injection limit contribute to the excellent electrochemical performance. Furthermore, the stability and biocompatibility are confirmed by numerously repeated usage test and cell proliferation and attachment examination, respectively. As electrode-tissue interface, this biomaterial opens a new gate for tissue engineering and implantable electrophysiological devices.

Optimization on machine learning based approaches for sentiment analysis on HPV vaccines related tweets

BackgroundAnalysing public opinions on HPV vaccines on social media using machine learning based approaches will help us understand the reasons behind the low vaccine coverage and come up with corresponding strategies to improve vaccine uptake.ObjectiveTo propose a machine learning system that is able to extract comprehensive public sentiment on HPV vaccines on Twitter with satisfying performance.MethodWe collected and manually annotated 6,000 HPV vaccines related tweets as a gold standard. SVM model was chosen and a hierarchical classification method was proposed and evaluated. Additional feature sets evaluation and model parameters optimization was done to maximize the machine learning model performance.ResultsA hierarchical classification scheme that contains 10 categories was built to access public opinions toward HPV vaccines comprehensively. A 6,000 annotated tweets gold corpus with Kappa annotation agreement at 0.851 was created and made public available. The hierarchical classification model with optimized feature sets and model parameters has increased the micro-averaging and macro-averaging F score from 0.6732 and 0.3967 to 0.7442 and 0.5883 respectively, compared with baseline model.ConclusionsOur work provides a systematical way to improve the machine learning model performance on the highly unbalanced HPV vaccines related tweets corpus. Our system can be further applied on a large tweets corpus to extract large-scale public opinion towards HPV vaccines.

Fabrication and degradation characteristic of sputtered iridium oxide neural microelectrodes for FES application

This paper shows the fabrication process of the reactively sputtered iridium oxide film (SIROF) microelectrodes under different oxygen flows and characters the electrochemical performances of the iridium oxide neural microelectrodes which are suffered from stimulus-evoked degradation. The SIROF microelectrodes prepared under 25 sccm oxygen flow shows the least degradation from continuous electrical stimulation (two million phases). That the charge storage capacity is only decreased by 9.6 % and the 1 kHz impedance is only increased by 4.23 %. Hence, the 25 sccm one can be an ideal microelectrode modification material for electrical stimulation with the least degradation.

Biotic and abiotic molecule dopants determining the electrochemical performance, stability and fibroblast behavior of conducting polymer for tissue interface

Because the growth activities of cells considerably depend on the surface characteristics of tissue culture substrates, tissue–substrate interface is a crucial factor in modulating the behavior of cells in tissue engineering. Conducting polymers with excellent biocompatibility act as ideal tissue interface material because they can be facilely fabricated into multiple structures, patterned to undergo electrical stimulation and modified with different dopants. Meanwhile, the performance of conducting polymers is significantly influenced by the characteristics of negatively charged dopants. Herein, six kinds of biotic and abiotic molecules with electronegative groups are used as counterions to dope poly(3,4-ethylenedioxythiophene) (PEDOT). A comprehensive evaluation of the properties of PEDOT, including electrochemistry, electrical stimulation, stability and biocompatibility is provided for further comparison and analysis. This work would reinforce our understanding of the dopant-dependent performance of conducting polymers for tissue engineering and applications in electrophysiological recording and stimulation.

Flexible intramuscular micro tube electrode combining electrical and chemical interface

With the rapidly developed micromachining technology, various kinds of sophisticated microelectrodes integrated with micro fluidic channels are design and fabricated for not only electrophysiological recording and stimulation, but also chemical drug delivery. As many efforts have been devoted to develop rigid microprobes for neural research of brain, few researchers concentrate on fabrication of flexible microelectrodes for intramuscular electrophysiology and chemical interfacing. Since crude wire electrodes still prevail in functional electrical stimulation (FES) and electromyography (EMG) recording of muscle, here we introduce a flexible micro tube electrode combining electrical and chemical pathway. The proposed micro tube electrode is manufactured based on polymer capillary, which provide circumferential electrode site contacting with electro-active tissue and is easy to manufactured with low cost.

Leveraging machine learning-based approaches to assess human papillomavirus vaccination sentiment trends with Twitter data

BackgroundAs one of the serious public health issues, vaccination refusal has been attracting more and more attention, especially for newly approved human papillomavirus (HPV) vaccines. Understanding public opinion towards HPV vaccines, especially concerns on social media, is of significant importance for HPV vaccination promotion.MethodsIn this study, we leveraged a hierarchical machine learning based sentiment analysis system to extract public opinions towards HPV vaccines from Twitter. English tweets containing HPV vaccines-related keywords were collected from November 2, 2015 to March 28, 2016. Manual annotation was done to evaluate the performance of the system on the unannotated tweets corpus. Followed time series analysis was applied to this corpus to track the trends of machine-deduced sentiments and their associations with different days of the week.ResultsThe evaluation of the unannotated tweets corpus showed that the micro-averaging F scores have reached 0.786. The learning system deduced the sentiment labels for 184,214 tweets in the collected unannotated tweets corpus. Time series analysis identified a coincidence between mainstream outcome and Twitter contents. A weak trend was found for “Negative” tweets that decreased firstly and began to increase later; an opposite trend was identified for “Positive” tweets. Tweets that contain the worries on efficacy for HPV vaccines showed a relative significant decreasing trend. Strong associations were found between some sentiments (“Positive”, “Negative”, “Negative-Safety” and “Negative-Others”) with different days of the week.ConclusionsOur efforts on sentiment analysis for newly approved HPV vaccines provide us an automatic and instant way to extract public opinion and understand the concerns on Twitter. Our approaches can provide a feedback to public health professionals to monitor online public response, examine the effectiveness of their HPV vaccination promotion strategies and adjust their promotion plans.

Poly(3,4-ethylenedioxythiophene)/graphene oxide composite coating for electrode-tissue interface

Owing to interacting with the living tissue directly, the electrode-tissue interface largely determines the performance of the whole bioelectronics devices. The miniaturization of biomedical electronic components requires interface materials to possess properties including excellent electrical performance, good biocompatibility and compatibility with microelectronic fabrication process. Considering the unique characteristics and wide applications in biomedical domain of conducting polymer and graphene, composite film consists of poly(3,4-ethylenedioxythiophene) (PEDOT) and graphene oxide (GO) is proposed as electrode-tissue interface in this work. The facilely electrochemically synthesized PEDOT/GO coating on microelectrodes shows low impedance, high charge storage capacity and good biocompatibility to act as electrode-tissue interface. As a result, the composite film is a potential biomaterial as electrode-tissue interface for tissue engineering and further implantable electrophysiological devices.

Extracting psychiatric stressors for suicide from social media using deep learning

BackgroundSuicide has been one of the leading causes of deaths in the United States. One major cause of suicide is psychiatric stressors. The detection of psychiatric stressors in an at risk population will facilitate the early prevention of suicidal behaviors and suicide. In recent years, the widespread popularity and real-time information sharing flow of social media allow potential early intervention in a large-scale population. However, few automated approaches have been proposed to extract psychiatric stressors from Twitter. The goal of this study was to investigate techniques for recognizing suicide related psychiatric stressors from Twitter using deep learning based methods and transfer learning strategy which leverages an existing annotation dataset from clinical text.MethodsFirst, a dataset of suicide-related tweets was collected from Twitter streaming data with a multiple-step pipeline including keyword-based retrieving, filtering and further refining using an automated binary classifier. Specifically, a convolutional neural networks (CNN) based algorithm was used to build the binary classifier. Next, psychiatric stressors were annotated in the suicide-related tweets. The stressor recognition problem is conceptualized as a typical named entity recognition (NER) task and tackled using recurrent neural networks (RNN) based methods. Moreover, to reduce the annotation cost and improve the performance, transfer learning strategy was adopted by leveraging existing annotation from clinical text.Results & conclusionsTo our best knowledge, this is the first effort to extract psychiatric stressors from Twitter data using deep learning based approaches. Comparison to traditional machine learning algorithms shows the superiority of deep learning based approaches. CNN is leading the performance at identifying suicide-related tweets with a precision of 78% and an F-1 measure of 83%, outperforming Support Vector Machine (SVM), Extra Trees (ET), etc. RNN based psychiatric stressors recognition obtains the best F-1 measure of 53.25% by exact match and 67.94% by inexact match, outperforming Conditional Random Fields (CRF). Moreover, transfer learning from clinical notes for the Twitter corpus outperforms the training with Twitter corpus only with an F-1 measure of 54.9% by exact match. The results indicate the advantages of deep learning based methods for the automated stressors recognition from social media.

A pilot study of mining association between psychiatric stressors and symptoms in tweets

Suicide is a significant public health issue, causing huge impacts on individuals as well as their families. Psychiatric stressors are major suicide risk factors and can profoundly impact a person in many aspects. In order to facilitate the understanding of psychiatric stressors and the associated symptoms, we extracted stressors and symptoms terms from major online knowledge repositories and psychiatric clinical notes. The current vocabulary collection contains 1,292 psychiatric symptoms and 715 psychiatric stressors, which was leveraged to study the associations between stressors and symptoms in a corpus of suicide related tweets. Using Chi-Square test with Bonferroni correction, 3,500 symptom–stressor pairs were identified with significant association (p-value<0.01).

A signal detection method for temporal variation of adverse effect with vaccine adverse event reporting system data

BackgroundTo identify safety signals by manual review of individual report in large surveillance databases is time consuming; such an approach is very unlikely to reveal complex relationships between medications and adverse events. Since the late 1990s, efforts have been made to develop data mining tools to systematically and automatically search for safety signals in surveillance databases. Influenza vaccines present special challenges to safety surveillance because the vaccine changes every year in response to the influenza strains predicted to be prevalent that year. Therefore, it may be expected that reporting rates of adverse events following flu vaccines (number of reports for a specific vaccine-event combination/number of reports for all vaccine-event combinations) may vary substantially across reporting years. Current surveillance methods seldom consider these variations in signal detection, and reports from different years are typically collapsed together to conduct safety analyses. However, merging reports from different years ignores the potential heterogeneity of reporting rates across years and may miss important safety signals.MethodReports of adverse events between years 1990 to 2013 were extracted from the Vaccine Adverse Event Reporting System (VAERS) database and formatted into a three-dimensional data array with types of vaccine, groups of adverse events and reporting time as the three dimensions. We propose a random effects model to test the heterogeneity of reporting rates for a given vaccine-event combination across reporting years. The proposed method provides a rigorous statistical procedure to detect differences of reporting rates among years. We also introduce a new visualization tool to summarize the result of the proposed method when applied to multiple vaccine-adverse event combinations.ResultWe applied the proposed method to detect safety signals of FLU3, an influenza vaccine containing three flu strains, in the VAERS database. We showed that it had high statistical power to detect the variation in reporting rates across years. The identified vaccine-event combinations with significant different reporting rates over years suggested potential safety issues due to changes in vaccines which require further investigation.ConclusionWe developed a statistical model to detect safety signals arising from heterogeneity of reporting rates of a given vaccine-event combinations across reporting years. This method detects variation in reporting rates over years with high power. The temporal trend of reporting rate across years may reveal the impact of vaccine update on occurrence of adverse events and provide evidence for further investigations.