Indiana University-Purdue University Indianapolis (IUPUI) / Biomedical data exists in the form of journal articles, research studies, electronic health records, care guidelines, etc. While text mining and natural language processing tools have been widely employed across various domains, these are just taking off in the healthcare space. A primary hurdle that makes it difficult to build artificial intelligence models that use biomedical data, is the limited amount of labelled data available. Since most models rely on supervised or semi-supervised methods, generating large amounts of pre-processed labelled data that can be used for training purposes becomes extremely costly. Even for datasets that are labelled, the lack of normalization of biomedical concepts further affects the quality of results produced and limits the application to a restricted dataset. This affects reproducibility of the results and techniques across datasets, making it difficult to deploy research solutions to improve healthcare services. The research presented in this thesis focuses on reducing the need to create labels for biomedical text mining by using unsupervised recurrent neural networks. The proposed method utilizes word embeddings to generate vector representations of biomedical concepts based on semantics and context. Experiments with unsupervised clustering of these biomedical concepts show that concepts that are similar to each other are clustered together. While this clustering captures different synonyms of the same concept, it also captures the similarities between various diseases and the symptoms that those diseases are symptomatic of. To test the performance of the concept vectors on corpora of documents, a document vector generation method that utilizes these concept vectors is also proposed. The document vectors thus generated are used as an input to clustering algorithms, and the results show that across multiple corpora, the proposed methods of concept and document vector generation outperform the baselines and provide more meaningful clustering. The applications of this document clustering are huge, especially in the search and retrieval space, providing clinicians, researchers and patients more holistic and comprehensive results than relying on the exclusive term that they search for. At the end, a framework for extracting clinical information that can be mapped to electronic health records from preventive care guidelines is presented. The extracted information can be integrated with the clinical decision support system of an electronic health record. A visualization tool to better understand and observe patient trajectories is also explored. Both these methods have potential to improve the preventive care services provided to patients.

Thesis (MScMedSc)--Stellenbosch University, 2012. / Bibliography / ENGLISH ABSTRACT: South Africa is ranked amongst the top tuberculosis (TB) burden countries in the world and the Western Cape has a particularly high incidence of the disease. Previous studies have showed that several genes may play crucial roles in susceptibility to TB. In this study, we investigated the role of three genes previously associated with susceptibility to TB and progression to disease. These genes were Surfactant protein D (SFTPD), Macrophage receptor with collagenous structure (MARCO) and CD14. The proteins from these genes bind M. tuberculosis and are involved in the uptake of the bacteria into macrophages. The study investigated the role of ten polymorphisms from SFTPD and MARCO within a South African Coloured (SAC) population, where tuberculosis is highly prevalent. A casecontrol study design was used and polymorphisms were genotyped with Taqman® genotyping assays and amplification refractory mutation system polymerase chain reaction (ARMS-PCR). The results were analysed for association with disease, linkage disequilibrium, haplotypes and gene-gene interactions. Allele and genotype frequencies were also determined which allowed for comparisons to other populations. Five SNPs were associated with TB: two in SFTPD (rs1923537; rs2255326) and three in MARCO (rs1318645; rs3943679; rs2119112). The associated SNPs were located in regions other than exons and the effects of polymorphisms in these regions are not well understood but studies in other genes have shown them to play a functional role. Gene-gene interaction analysis showed that polymorphisms interacted with each other within and between genes, illustrating the importance of epistasis and the complexity of the genetic influences on TB. In addition to the case-control association studies, the role of the rs2569190 promoter SNP in CD14 was assessed. Gene-expression analysis was conducted with qPCR and a reporter gene assay and results from both of these approaches showed that individuals with the TT genotype had a twofold greater expression level than individuals with the CC genotype. Previously, the TT genotype has been associated with stronger promoter activity and expression of soluble CD14 in serum. Since the TT genotype was present at a higher frequency in the control group, we speculate that greater expression of CD14 may contribute to a more TB resistant phenotype. The work presented in this study illustrates the importance of the host genetic component of TB. Genetic studies will eventually revolutionize the current treatment regime as the identification of vulnerable individuals and populations will aid in the development of personalised medicines. / AFRIKAANSE OPSOMMING: Suid-Afrika is een van die top tuberkulose (TB) lande in die wêreld en die Wes-Kaap het veral ‘n hoë insidensie van die siekte. Vorige studies het gewys dat verskeie gene bydra to die vatbaarheid vir tuberkulose. In hierdie studie het ons drie gene, wat voorheen vir vatbaarheid vir tuberkulose en progressie na die siekte ondersoek is, bestudeer. Hierdie gene is Surfactant protein D (SFTPD), Macrophage receptor with collagenous structure (MARCO) en CD14. Die proteïene van hierdie gene bind M. tuberculosis en is betrokke in die opname van die bakterieë in die makrofages. Hierdie studie het tien polimorfismes van SFTPD en MARCO in die Suid-Afrikaanse Kleurlingbevolking (SAK), wat ‘n hoë TB insidensie het, getoets. Pasiënt-kontrole assosiasie studies is gedoen en polimorfismes is gegenotipeer met Taqman® genotiperingsisteem en die amplifikasie refraktoriese mutasie sisteem polimerase ketting reaksie (ARMS-PCR). Die resultate is geanaliseer vir assosiasies met TB, koppelings disekwilibrium, haplotipes en geen-geen interaksies. Alleel en genotype frekwensies is ook bepaal en vergelyk met die van ander bevolkings. Vyf enkel nukleotied polimorfismes (ENPs) is met TB geassosieer: twee in SFTPD (rs1923537; rs2255326) en drie in MARCO (rs1318645; rs3943679; rs2119112). Die geassosieerde ENPs was nie in eksons nie. Die effek van polimorfismes in areas anders as eksons word nie goed verstaan nie, maar studies het bewys dat hulle wel ‘n funksionele rol kan hê. Geen-geen interaksie analise het gewys dat polimorfismes interaksies met mekaar binne sowel as tussen gene gehad het, wat die belangrikheid van epistase en die kompleksiteit van genetiese invloede op TB illustreer. Tesame met die pasiënt-kontrole assosiasie studies is die rol van die rs2569190 promoter ENP in CD14 ook ondersoek. Geenuitdrukkingsanalise is gedoen met qPKR en rapporteerder geen toetse. Die resultate van beide hierdie benaderings het gewys dat individue met die TT genotipe twee keer soveel uitdrukkingsvlakke gehad het as individue met die CC genotipe. Die TT genotipe is voorheen geassosieer met sterk promoter aktiwiteit en die uitdrukking van oplosbare CD14 in serum. Aangesien die TT genotipe meer in die kontrolegroep gevind is, spekuleer ons dat die hoër uitdrukking van CD14 kan bydra tot ‘n meer TB weerstandbiedende fenotipe. Hierdie werk illustreer die belangrikheid van die gasheer genetiese komponent in TB. Genetiese studies sal in die toekoms die huidige behandeling regime revolusioneer, aangesien die identifikasie van individue en bevolkings met ‘n hoë risiko om TB te ontwikkel sal bydra tot die ontwikkeling van persoonlike medisynes. / The National Research Foundation (NRF); the South African Medical Research Council (MRC); Stellenbosch University and the Harry Crossley Foundation.

<div> <div> <div> <p>Microfluidics is a new and emerging field that has applications in a myriad of microfluidic industrial applications such as biochemical engineering, analytical processing, biomedical engineering and separation of cells. Microfluidics operations are carried out in microfluidic chips, and the traditional method of fabrication is carried out in a cleanroom. However, this fabrication method is very costly and also requires professional trained personnel. In this thesis, a low-cost fabrication platform was developed based on soft-lithography technique developed to fabricate the microfluidic devices with resolution at microscale. This fabrication method is advantageous and novel because it is able to achieve the microscale fabrication capability with simple steps and lower-level laboratory configuration. In the developed fabrication platform, an array of ultraviolet light was illuminated onto a photoresist film that has a negative photomask with a microfluidic design on it. The photoresist film is then developed, and a silicon polymer of polydimethylsiloxane (PDMS) is chosen to be the material for the device. In this work, the performance and resolution of the fabrication system was evaluated using scanning electron microscopy (SEM), polymer resolution test and light intensity analysis. </p> <p>Based on the success of the development of microfluidics fabrication platform, various experiment of mixing and separation was conducted and studied because the utilization of the microfluidic device for mixing and separation is very valuable in biomedical and chemical engineering. Although there are a lot of applications reported, the precise separation and mixing at microscale still meet some difficulties. Mixing in micromixers is extremely time-consuming and requires very long microchannels due to laminar flow and low Reynolds number. Particle separation is also hard to be achieved because the size of micron bioparticles is very small and thus the force is not strong enough to manipulate their motion. The integration of magnetic field is an active method to strengthen both mixing and separation that has been widely applied in the biomedical industry overcome these difficulties because of its compatibility with organic particles. However, most magnetic mixing and separation use bulky permanent magnets that leave a large footprint or electromagnets that generate harmful Joule heat to organic and bio-particles. In this work, microscale magnet made of a mixture of neodymium powder and polydimethylsiloxane was developed and integrated into microfluidic system to achieve both rapid mixing of ferrofluids and separation of microparticles. Systematic experiments were conducted to discuss the effect of various parameters on the performance of magnetic mixing and separation of microparticles. It was found that channel geometry, flow filed, and magnetic properties will affect the transport phenomena of ferrofluid and microparticles, and thus mixing and separation efficiency. These findings are of great significance for the high throughput sorting of cancer cells and its mixing between drug for therapy treatment.</p></div></div></div>

No description available.

<div>Biomedical data exists in the form of journal articles, research studies, electronic health records, care guidelines, etc. While text mining and natural language processing tools have been widely employed across various domains, these are just taking off in the healthcare space.</div><div><br></div><div>A primary hurdle that makes it difficult to build artificial intelligence models that use biomedical data, is the limited amount of labelled data available. Since most models rely on supervised or semi-supervised methods, generating large amounts of pre-processed labelled data that can be used for training purposes becomes extremely costly. Even for datasets that are labelled, the lack of normalization of biomedical concepts further affects the quality of results produced and limits the application to a restricted dataset. This affects reproducibility of the results and techniques across datasets, making it difficult to deploy research solutions to improve healthcare services.</div><div><br></div><div>The research presented in this thesis focuses on reducing the need to create labels for biomedical text mining by using unsupervised recurrent neural networks. The proposed method utilizes word embeddings to generate vector representations of biomedical concepts based on semantics and context. Experiments with unsupervised clustering of these biomedical concepts show that concepts that are similar to each other are clustered together. While this clustering captures different synonyms of the same concept, it also captures the similarities between various diseases and the symptoms that those diseases are symptomatic of.</div><div><br></div><div>To test the performance of the concept vectors on corpora of documents, a document vector generation method that utilizes these concept vectors is also proposed. The document vectors thus generated are used as an input to clustering algorithms, and the results show that across multiple corpora, the proposed methods of concept and document vector generation outperform the baselines and provide more meaningful clustering. The applications of this document clustering are huge, especially in the search and retrieval space, providing clinicians, researchers and patients more holistic and comprehensive results than relying on the exclusive term that they search for.</div><div><br></div><div>At the end, a framework for extracting clinical information that can be mapped to electronic health records from preventive care guidelines is presented. The extracted information can be integrated with the clinical decision support system of an electronic health record. A visualization tool to better understand and observe patient trajectories is also explored. Both these methods have potential to improve the preventive care services provided to patients.</div>

The potential for private sector healthcare business in Africa has been forecasted to reach $35 billion by 2016, with South Africa being regarded as the most industrially advanced country on the continent. South Africa’s entry to modern biotechnology is fairly recent, though, with companies in the private sector still in a developmental phase, and most having limited bioproduct ranges. While considerable research has been conducted in the past to attempt to define the biotechnology environment of South Africa, as yet, a concise overview is lacking. In particular, a synopsis of the biomedical or commercial health technology environment has not been forthcoming for entrepreneurs to refer to as a ‘roadmap’. The purpose of this study was to perform a comprehensive study on the attributes that should be met for a successful, sustainable health technology venture (HTV) to be started in South Africa; while identifying the opportunities and threats that have existed in the South African market; thereby, affecting their success and sustainability to date. In this study, two phases of research were conducted. The first was a small-sampled mixed-methods (both qualitative and quantitative) study involving 21 medical devices, biogenerics, diagnostics, and contract services companies. The second was a quantitative study, involving 107 vaccines, biogenerics, therapeutics, nutraceuticals, reagents, diagnostics, medical devices, biotools, contract services and public services companies. Inferential statistical tests were conducted on the data, including Pearson’s Chi-Square, ANOVA, bivariate correlation, linear regression, logistic regression and multinomial logistic regression. From the study, the overall proportion of business sustainability for HTVs was found to be 66.7%, and at least 30% were unsustainable (or not yet at a level of sustainability). Variations were observed in the overall rate of sustainability for companies, based on their core functional classification, location, production type, size and start-up or R&D spending. By converting the observed frequencies of activity level, as an indication of sustainability, into a probability, it was possible to observe the company type that was most, and least likely to succeed in South Africa. Based on the statistical observations in this study, the HTV type most likely to succeed in South Africa, with a 63.7% probability of reaching sustainability, is a ‘vaccines’, ‘biotools’ or ‘public services’ company from Johannesburg with at least 20 employees; that has developed its goods or services internally, but manufactured externally and spent between R20 million–and–R30 million on its R&D or start-up. Conversely, least likely to succeed (3.2% probability) is a nutraceutical company from Cape Town with between six and 20 employees, that has developed and produced internally, and which has spent between R1 million–and–R10million on its start-up. / Life and Consumer Sciences / M.Sc (Life Sciences)

Islam, its texts and lived practice, finds growing importance within the global discourse on bioethics, as there is an increasing Muslim population and burgeoning interest in biomedical research and biotechnologies in the Muslim world. The aim of this thesis is to assess if and how Islam influences the ethical decision making of researchers, REC (researcher ethics committee) members, guideline developers and Islamic scholars in the biomedical research context. I began addressing this question by first reviewing the literature that has been published to explore the role that Islam plays in the literature on biomedical research ethics. There is evidence that some Muslim countries have developed "Islamic" guidelines. That is, guidelines with the explicit aim of setting out Islamic values and stating their relevance to the ethics of research. A review of research guidelines employed within countries with a significant Muslim population, was carried out, to investigate the role of Islam in such guidelines. The literature and guideline review revealed that although international guidelines have been adapted to incorporate Islamic views, studies have shown that the latter are of limited practical application within a "Muslim country" setting. An empirical study was carried out in two case study sites to assess the extent to which Islam influences ethical decision making within the context of biomedical research. 56 semi-structured interviews were carried out in Malaysia (38) and Iran (18) with researchers, REC members, guideline developers and Islamic scholars to understand whether Islam influences what they consider to be an ethico-legal problem, and if the latter emerges, then how such issues are addressed. The empirical study indicates five main conclusions. The first is that Islam and its institutional forms do impact ethical decision making in the day-to-day practice of biomedical research in countries with a Muslim population and/or in the research careers of Muslim researchers. Secondly, it shows that there are many distinctive mechanisms, such as the involvement of Islamic scholars, the process of ijtihad (independent reasoning) and the production of fatawah (legal edicts), by which Islam does identify and develop ethical views about biomedical matters. Thirdly, HIV/AIDS poses major challenges to the world of Islam as it does the rest of world. The epidemic raises issues that touch on cultural sensitivities that are important to Islamic societies and this study has shown that no simple or single response was observed to the ethical issues arising from HIV/AIDS. Fourthly, researchers face practical challenges when deliberating women's autonomy in contexts where Islam is appropriated within 'male dominated' contexts. The role and status of women is disputed in such contexts with views ranging from women needing their husband's permission to leave the home to men and women having equal freedoms. Finally, this study describes and analyses how the personal faith of researchers and their deep commitment to Islamic ethics and law influences their understanding of their legal and moral accountability and ethico-legal decision making. It shows that researchers adopt multiple roles and are required to balance numerous value systems and priorities and face moral anxiety and frustration when these different moral sources are in conflict. Overall, this study indicates that, in the countries studied, Islam does influence biomedical research ethics, and that this can be appreciated through the growing reference to Islam and its scriptural sources in biomedical research ethics literature, research ethics guidelines and the role of Islam in the day-to-day practice of biomedical researchers in the case study sites, that has been captured in the empirical study.

The potential for private sector healthcare business in Africa has been forecasted to reach $35 billion by 2016, with South Africa being regarded as the most industrially advanced country on the continent. South Africa’s entry to modern biotechnology is fairly recent, though, with companies in the private sector still in a developmental phase, and most having limited bioproduct ranges. While considerable research has been conducted in the past to attempt to define the biotechnology environment of South Africa, as yet, a concise overview is lacking. In particular, a synopsis of the biomedical or commercial health technology environment has not been forthcoming for entrepreneurs to refer to as a ‘roadmap’. The purpose of this study was to perform a comprehensive study on the attributes that should be met for a successful, sustainable health technology venture (HTV) to be started in South Africa; while identifying the opportunities and threats that have existed in the South African market; thereby, affecting their success and sustainability to date. In this study, two phases of research were conducted. The first was a small-sampled mixed-methods (both qualitative and quantitative) study involving 21 medical devices, biogenerics, diagnostics, and contract services companies. The second was a quantitative study, involving 107 vaccines, biogenerics, therapeutics, nutraceuticals, reagents, diagnostics, medical devices, biotools, contract services and public services companies. Inferential statistical tests were conducted on the data, including Pearson’s Chi-Square, ANOVA, bivariate correlation, linear regression, logistic regression and multinomial logistic regression. From the study, the overall proportion of business sustainability for HTVs was found to be 66.7%, and at least 30% were unsustainable (or not yet at a level of sustainability). Variations were observed in the overall rate of sustainability for companies, based on their core functional classification, location, production type, size and start-up or R&D spending. By converting the observed frequencies of activity level, as an indication of sustainability, into a probability, it was possible to observe the company type that was most, and least likely to succeed in South Africa. Based on the statistical observations in this study, the HTV type most likely to succeed in South Africa, with a 63.7% probability of reaching sustainability, is a ‘vaccines’, ‘biotools’ or ‘public services’ company from Johannesburg with at least 20 employees; that has developed its goods or services internally, but manufactured externally and spent between R20 million–and–R30 million on its R&D or start-up. Conversely, least likely to succeed (3.2% probability) is a nutraceutical company from Cape Town with between six and 20 employees, that has developed and produced internally, and which has spent between R1 million–and–R10million on its start-up. / Life and Consumer Sciences / M. Sc. (Life Sciences)

Prediction and understanding of the regulatory effects of non-coding DNA is an extensive research area in genomics. Convolutional neural networks have been used with success in the past to predict regulatory features, making chromatin feature predictions based solely on non-coding DNA sequences. Non-coding DNA shares various similarities with the human spoken language. This makes Language models such as the transformer attractive candidates for deciphering the non-coding DNA language. This thesis investigates how well the transformer model, usually used for NLP problems, predicts chromatin features based on genome sequences compared to convolutional neural networks. More specifically, the CNN DeepSEA, which is used for regulatory feature prediction based on noncoding DNA, is compared with the transformer DNABert. Further, this study explores the impact different parameters and training strategies have on performance. Furthermore, other models (DeeperDeepSEA and DanQ) are also compared on the same tasks to give a broader comparison value. Lastly, the same experiments are conducted on modified versions of the dataset where the labels cover different amounts of the DNA sequence. This could prove beneficial to the transformer model, which can understand and capture longrange dependencies in natural language problems. The replication of DeepSEA was successful and gave similar results to the original model. Experiments used for DeepSEA were also conducted on DNABert, DeeperDeepSEA, and DanQ. All the models were trained on different datasets, and their results were compared. Lastly, a Prediction voting mechanism was implemented, which gave better results than the models individually. The results showed that DeepSEA performed slightly better than DNABert, regarding AUC ROC. The Wilcoxon Signed-Rank Test showed that, even if the two models got similar AUC ROC scores, there is statistical significance between the distribution of predictions. This means that the models look at the dataset differently and might be why combining their prediction presents good results. Due to time restrictions of training the computationally heavy DNABert, the best hyper-parameters and training strategies for the model were not found, only improved. The Datasets used in this thesis were gravely unbalanced and is something that needs to be worked on in future projects. This project works as a good continuation for the paper Whole-genome deep-learning analysis identifies contribution of non-coding mutations to autism risk, Which uses the DeepSEA model to learn more about how specific mutations correlate with Autism Spectrum Disorder. / Arbetet kring hur icke-kodande DNA påverkar genreglering är ett betydande forskningsområde inom genomik. Convolutional neural networks (CNN) har tidigare framgångsrikt använts för att förutsäga reglerings-element baserade endast på icke-kodande DNA-sekvenser. Icke-kod DNA har ett flertal likheter med det mänskliga språket. Detta gör språkmodeller, som Transformers, till attraktiva kandidater för att dechiffrera det icke-kodande DNA-språket. Denna avhandling undersöker hur väl transformermodellen kan förutspå kromatin-funktioner baserat på gensekvenser jämfört med CNN. Mer specifikt jämförs CNN-modellen DeepSEA, som används för att förutsäga reglerande funktioner baserat på icke-kodande DNA, med transformern DNABert. Vidare undersöker denna studie vilken inverkan olika parametrar och träningsstrategier har på prestanda. Dessutom jämförs andra modeller (DeeperDeepSEA och DanQ) med samma experiment för att ge ett bredare jämförelsevärde. Slutligen utförs samma experiment på modifierade versioner av datamängden där etiketterna täcker olika mängder av DNA-sekvensen. Detta kan visa sig vara fördelaktigt för transformer modellen, som kan förstå beroenden med lång räckvidd i naturliga språkproblem. Replikeringen av DeepSEA experimenten var lyckad och gav liknande resultat som i den ursprungliga modellen. Experiment som användes för DeepSEA utfördes också på DNABert, DeeperDeepSEA och DanQ. Alla modeller tränades på olika datamängder, och resultat på samma datamängd jämfördes. Slutligen implementerades en algoritm som kombinerade utdatan av DeepDEA och DNABERT, vilket gav bättre resultat än modellerna individuellt. Resultaten visade att DeepSEA presterade något bättre än DNABert, med avseende på AUC ROC. Wilcoxon Signed-Rank Test visade att, även om de två modellerna fick liknande AUC ROC-poäng, så finns det en statistisk signifikans mellan fördelningen av deras förutsägelser. Det innebär att modellerna hanterar samma information på olika sätt och kan vara anledningen till att kombinationen av deras förutsägelser ger bra resultat. På grund av tidsbegränsningar för träning av det beräkningsmässigt tunga DNABert hittades inte de bästa hyper-parametrarna och träningsstrategierna för modellen, utan förbättrades bara. De datamängder som användes i denna avhandling var väldigt obalanserade, vilket måste hanteras i framtida projekt. Detta projekt fungerar som en bra fortsättning för projektet Whole-genome deep-learning analysis identifies contribution of non-coding mutations to autism risk, som använder DeepSEA-modellen för att lära sig mer om hur specifika DNA-mutationer korrelerar med autismspektrumstörning.