Functional polymers for biomedical application : synthesis and applications

Aromatic and aliphatic diacid chlorides were used to condense naturally occurring diamino acids and their esterified derivatives. It was anticipated the resulting functional polyamides would biodegrade to physiologically acceptable compounds and show pH dependant solubility could be used for biomedical applications ranging from enteric coatings to hydrosoluble drug delivery vehicles capable of targeting areas of low physiological pH. With these applications in mind the polymers were characterised by infra red spectroscopy, gel permeation chromatography and in the case of aqueous soluble polymers by potentiometric titration. Thin films of poly (lysine ethyl ester isophthalamide) plasticised with poly (caprolactone) were cast from DMSO/chloroform solutions and their mechanical properties measured on a Hounsfield Hti tensiometer. Interfacial synthesis was investigated as a synthetic route for the production of linear functional polyamides. High molecular weight polymer was obtained only when esterified diamino acids were condensed with aromatic diacid chlorides. The method was unsuitable for the production of copolymers of free and esterified amino acids with a diacid chloride. A novel miscible mixed solvent single phase reaction was investigated for production of copolymers of esterified and non-esterified amino acids with diacid chlorides. Aliphatic diacid chlorides were unsuitable for condensing diamino acids using this technique because of high rates of hydrolysis. The technique gave high molecular weight homopolymers from esterified diamino acids and aromatic diacid chlorides.

Watermarking biomedical time series data

This thesis addresses the problem of information hiding in low dimensional digital data focussing on issues of privacy and security in Electronic Patient Health Records (EPHRs). The thesis proposes a new security protocol based on data hiding techniques for EPHRs. This thesis contends that embedding of sensitive patient information inside the EPHR is the most appropriate solution currently available to resolve the issues of security in EPHRs. Watermarking techniques are applied to one-dimensional time series data such as the electroencephalogram (EEG) to show that they add a level of confidence (in terms of privacy and security) in an individual’s diverse bio-profile (the digital fingerprint of an individual’s medical history), ensure belief that the data being analysed does indeed belong to the correct person, and also that it is not being accessed by unauthorised personnel. Embedding information inside single channel biomedical time series data is more difficult than the standard application for images due to the reduced redundancy. A data hiding approach which has an in built capability to protect against illegal data snooping is developed. The capability of this secure method is enhanced by embedding not just a single message but multiple messages into an example one-dimensional EEG signal. Embedding multiple messages of similar characteristics, for example identities of clinicians accessing the medical record helps in creating a log of access while embedding multiple messages of dissimilar characteristics into an EPHR enhances confidence in the use of the EPHR. The novel method of embedding multiple messages of both similar and dissimilar characteristics into a single channel EEG demonstrated in this thesis shows how this embedding of data boosts the implementation and use of the EPHR securely.

Charge-balanced copolymer hydrogels:potential as biomedical materials

Polyzwitterionic-containing hydrogel materials been proposed for use in biomaterial applications. Polyzwitterions contain anions and cations in the same monomeric unit, unlike polyampholytes which contain them in different monomeric units. The use of cationic and anionic monomers in stoichiometrically equivalent proportions produces charge-balanced polyampholytes (PA) copolymers. Membranes prepared using either betaine-containing (BT) polyzwitterionic copolymers or PA copolymers can share similar properties, but the range of EWCs offered by membranes incorporating BT and PA monomers is greater than that for conventional neutral hydrogels and methacrylic acid-based systems. Here we compare properties of BT-containing and PA-containing copolymer membranes, relevant to their potential as biomedical materials. Membranes of the copolymers were prepared as previously described. Surface energy was determined using a GBX Digidrop (GBX Scientific Instruments), with diidomethane and water as probes. The absorption of proteins was determined by soaking the membranes in 1mg/ml protein solutions for a predetermined time, and measuring UV absorption of the membranes at certain wavelengths. The BT and PA copolymer membranes displayed similar values for the polar components and dispersive components of total surface free energy. This was perhaps not surprising when the structures of the monomers were considered. The BT and PA copolymer membranes displayed differences in their protein absorption over time, with the PA demonstrating higher uptake of protein than the BT. In addition to the aforementioned greater EWC range, the use of BT and PA copolymer membranes also avoids some of the problems associated with net anionicity. Comparison of the BT copolymer with the “pseudo” zwitterionic PA copolymers shows that controlled molecular architecture is required to gain the benefits of balancing the charges present in the copolymers in a way that will make them beneficial to hydrogel design.

Biomedical events extraction using the hidden vector state model

Objective: Biomedical events extraction concerns about events describing changes on the state of bio-molecules from literature. Comparing to the protein-protein interactions (PPIs) extraction task which often only involves the extraction of binary relations between two proteins, biomedical events extraction is much harder since it needs to deal with complex events consisting of embedded or hierarchical relations among proteins, events, and their textual triggers. In this paper, we propose an information extraction system based on the hidden vector state (HVS) model, called HVS-BioEvent, for biomedical events extraction, and investigate its capability in extracting complex events. Methods and material: HVS has been previously employed for extracting PPIs. In HVS-BioEvent, we propose an automated way to generate abstract annotations for HVS training and further propose novel machine learning approaches for event trigger words identification, and for biomedical events extraction from the HVS parse results. Results: Our proposed system achieves an F-score of 49.57% on the corpus used in the BioNLP'09 shared task, which is only 2.38% lower than the best performing system by UTurku in the BioNLP'09 shared task. Nevertheless, HVS-BioEvent outperforms UTurku's system on complex events extraction with 36.57% vs. 30.52% being achieved for extracting regulation events, and 40.61% vs. 38.99% for negative regulation events. Conclusions: The results suggest that the HVS model with the hierarchical hidden state structure is indeed more suitable for complex event extraction since it could naturally model embedded structural context in sentences.

From biomedical literature to knowledge:mining protein-protein interactions

To date, more than 16 million citations of published articles in biomedical domain are available in the MEDLINE database. These articles describe the new discoveries which accompany a tremendous development in biomedicine during the last decade. It is crucial for biomedical researchers to retrieve and mine some specific knowledge from the huge quantity of published articles with high efficiency. Researchers have been engaged in the development of text mining tools to find knowledge such as protein-protein interactions, which are most relevant and useful for specific analysis tasks. This chapter provides a road map to the various information extraction methods in biomedical domain, such as protein name recognition and discovery of protein-protein interactions. Disciplines involved in analyzing and processing unstructured-text are summarized. Current work in biomedical information extracting is categorized. Challenges in the field are also presented and possible solutions are discussed.

Nanopatterned UV curable hydrogels for biomedical applications

With an increasing use of emerging patterning technologies such as UV-NIL in biotechnological applications there is at the same time a raising demand for new material for such applications. Here we present a PEG based precursor mixed with a photoinitiator to make it UV sensitive as a new material aimed at biotechnological applications. Using HSQ patterned quartz stamps we observed excellent pattern replication indicating good flow properties of the resist. We were able to obtain imprints with <20 nm residual layer. The PEG based resist has hydrogel properties and it swelling in water was observed by AFM.

The education and training of technicians in the construction industry

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Interferometric microstructured polymer optical fiber ultrasound sensor for optoacoustic endoscopic imaging in biomedical applications

We report a characterization of the acoustic sensitivity of microstructured polymer optical fiber interferometric sensors at ultrasonic frequencies from 100kHz to 10MHz. The use of wide-band ultrasonic fiber optic sensors in biomedical ultrasonic and optoacoustic applications is an open alternative to conventional piezoelectric transducers. These kind of sensors, made of biocompatible polymers, are good candidates for the sensing element in an optoacoustic endoscope because of its high sensitivity, its shape and its non-brittle and non-electric nature. The acoustic sensitivity of the intrinsic fiber optic interferometric sensors depends strongly of the material which is composed of. In this work we compare experimentally the intrinsic ultrasonic sensitivities of a PMMA mPOF with other three optical fibers: a singlemode silica optical fiber, a single-mode polymer optical fiber and a multimode graded-index perfluorinated polymer optical fiber. © 2014 SPIE.

Controlled synthesis and processing of a poly(L-lactide-co-ε-caprolactone) copolymer for biomedical use as an absorbable monofilament surgical suture

Poly(L-lactide-co-ε-caprolactone) 75:25% mol, P(LL-co-CL), was synthesized via bulk ring-opening polymerisation (ROP) using a novel tin(II)alkoxide initiator, [Sn(Oct)]2DEG, at 130oC for 48 hrs. The effectiveness of this initiator was compared withthe well-known conventional tin(II) octoateinitiator, Sn(Oct)2. The P(LL-co-CL) copolymersobtained were characterized using a combination of analytical technique including: nuclear magnetic resonance spectroscopy (NMR), differential scanning calorimetry (DSC), thermogravimetry (TG) and gel permeation chromatography (GPC). The P(LL-co-CL) was melt-spun into monofilament fibres of uniform diameter and smooth surface appearance. Modification of the matrix morphology was then built into the as-spun fibresvia a series of controlled off-line annealing and hot-drawing steps. © (2014) Trans Tech Publications, Switzerland.

Distributive tactile sensing using fibre Bragg grating sensors for biomedical applications

Artificial tactile sensing systems using the distributive tactile sensing technique and fibre Bragg grating sensors are presented. A one-dimensional arrangement, with possible applications in an endoscope, is compared with a similar arrangement using conventional electronic sensors. A two-dimensional sensing surface is described, with potential applications in human balance and gait analysis, capable of detecting simultaneously the position and shape of an object placed upon it. It is believed that this work represents the first use of fibre Bragg grating sensors in a distributive sensing regime.

Event trigger identification for biomedical events extraction using domain knowledge

Motivation: In molecular biology, molecular events describe observable alterations of biomolecules, such as binding of proteins or RNA production. These events might be responsible for drug reactions or development of certain diseases. As such, biomedical event extraction, the process of automatically detecting description of molecular interactions in research articles, attracted substantial research interest recently. Event trigger identification, detecting the words describing the event types, is a crucial and prerequisite step in the pipeline process of biomedical event extraction. Taking the event types as classes, event trigger identification can be viewed as a classification task. For each word in a sentence, a trained classifier predicts whether the word corresponds to an event type and which event type based on the context features. Therefore, a well-designed feature set with a good level of discrimination and generalization is crucial for the performance of event trigger identification. Results: In this article, we propose a novel framework for event trigger identification. In particular, we learn biomedical domain knowledge from a large text corpus built from Medline and embed it into word features using neural language modeling. The embedded features are then combined with the syntactic and semantic context features using the multiple kernel learning method. The combined feature set is used for training the event trigger classifier. Experimental results on the golden standard corpus show that >2.5% improvement on F-score is achieved by the proposed framework when compared with the state-of-the-art approach, demonstrating the effectiveness of the proposed framework. © 2014 The Author 2014. The source code for the proposed framework is freely available and can be downloaded at http://cse.seu.edu.cn/people/zhoudeyu/ETI_Sourcecode.zip.