تجربیات زیسته یک مادر دارای کودک مبتلا به سندرم وردینگ هافمن: مطالعه موردی کیفی The Lived Experiences of the Mother of a Child with Werdnig-Hoffman Syndrome: A Qualitative Case Study

مقدمه: سندوم وردینگ هافمن جزء بیماری‌های تحلیل برنده و پیش‌رونده عصبی- نخاعی محسوب می‌شود که به صورت اتوزومی نهفته به ارث می‌رس

A patient with neurofibroma (schwannoma) in peri-sacral: A case report

Schwannomas are benign tumors of the nerve sheath and are usually single encapsulated and slow growing in peripheral or sympathetic nervous system. In this report a 49 year-old man is presented with one year complain of abdominal pain and intermittent hematospermia. The CT scan of the abdomen showed a 60 × 65 × 60 mm mass in anterior pelvic cavity with deviation to the sacral bone, originated from nerve. Several examinations revealed neurofibroma. Due to the large size of the tumor and it's position to the pelvic nerves, to remove the mass the patient only underwent laparotomy with partial resection. Pathology tests confirmed Ancient Schwannoma with degenerative changes. Radiotherapy was done with the aim of reducing the size of the rest of tumor. In our case, schwannoma was diagnosed incidentally. The size of the tumor indicated a relatively long period from the time that tumor was generated until the time of diagnosis. Despite using paraclinical findings, a definite diagnosis of the disease was made by histopathological tests. © 2015, Mazandaran University of Medical Sciences. All rights reserved

Use of Text Data in Identifying and Prioritizing Potential Drug Repositioning Candidates

New drug development costs between 500 million and 2 billion dollars and takes 10-15 years, with a success rate of less than 10%. Drug repurposing (defined as discovering new indications for existing drugs) could play a significant role in drug development, especially considering the declining success rates of developing novel drugs. In the period 2007-2009, drug repurposing led to the launching of 30-40% of new drugs. Typically, new indications for existing medications are identified by accident. However, new technologies and a large number of available resources enable the development of systematic approaches to identify and validate drug-repurposing candidates with significantly lower cost. A variety of resources have been utilized to identify novel drug repurposing candidates such as biomedical literature, clinical notes, and genetic data. In this dissertation, we focused on using text data in identifying and prioritizing drug repositioning candidates and conducted five studies. In the first study, we aimed to assess the feasibility of using patient reviews from social media to identify potential candidates for drug repurposing. We retrieved patient reviews of 180 medications from an online forum, WebMD. Using dictionary-based and machine learning approaches, we identified disease names in the reviews. Several publicly available resources were used to exclude comments containing known indications and adverse drug effects. After manually reviewing some of the remaining comments, we implemented a rule-based system to identify beneficial effects. The dictionary-based system and machine learning system identified 2178 and 6171 disease names respectively in 64,616 patient comments. We provided a list of 10 common patterns that patients used to report any beneficial effects or uses of medication. After manually reviewing the comments tagged by our rule-based system, we identified five potential drug repurposing candidates. To our knowledge, this was the first study to consider using social media data to identify drug-repurposing candidates. We found that even a rule-based system, with a limited number of rules, could identify beneficial effect mentions in the comments of patients. Our preliminary study shows that social media has the potential to be used in drug repurposing. In the second study, we investigated the significance of extracting information from multiple sentences specifically in the context of drug-disease relation discovery. We used multiple resources such as Semantic Medline, a literature-based resource, and Medline search (for filtering spurious results) and inferred 8,772 potential drug-disease pairs. Our analysis revealed that 6,450 (73.5%) of the 8,772 potential drug-disease relations did not occur in a single sentence. Moreover, only 537 of the drug-disease pairs matched the curated gold standard in the Comparative Toxicogenomics Database (CTD), a trusted resource for drug-disease relations. Among the 537, nearly 75% (407) of the drug-disease pairs occur in multiple sentences. Our analysis revealed that the drug-disease pairs inferred from Semantic Medline or retrieved from CTD could be extracted from multiple sentences in the literature. This highlights the significance of the need for discourse-level analysis in extracting the relations from biomedical literature. In the third and fourth study, we focused on prioritizing drug repositioning candidates extracted from biomedical literature which we refer to as Literature-Based Discovery (LBD). In the third study, we used drug-gene and gene-disease semantic predications extracted from Medline abstracts to generate a list of potential drug-disease pairs. We further ranked the generated pairs, by assigning scores based on the predicates that qualify drug-gene and gene-disease relationships. On comparing the top-ranked drug-disease pairs against the Comparative Toxicogenomics Database, we found that a significant percentage of top-ranked pairs appeared in CTD. Co-occurrence of these high-ranked pairs in Medline abstracts is then used to improve the rankings of the inferred drug-disease relations. Finally, manual evaluation of the top-ten pairs ranked by our approach revealed that nine of them have good potential for biological significance based on expert judgment. In the fourth study, we proposed a method, utilizing information surrounding causal findings, to prioritize discoveries generated by LBD systems. We focused on discovering drug-disease relations, which have the potential to identify drug repositioning candidates or adverse drug reactions. Our LBD system used drug-gene and gene-disease semantic predication in SemMedDB as causal findings and Swanson’s ABC model to generate potential drug-disease relations. Using sentences, as a source of causal findings, our ranking method trained a binary classifier to classify generated drug-disease relations into desired classes. We trained and tested our classifier for three different purposes: a) drug repositioning b) adverse drug-event detection and c) drug-disease relation detection. The classifier obtained 0.78, 0.86, and 0.83 F-measures respectively for these tasks. The number of causal findings of each hypothesis, which were classified as positive by the classifier, is the main metric for ranking hypotheses in the proposed method. To evaluate the ranking method, we counted and compared the number of true relations in the top 100 pairs, ranked by our method and one of the previous methods. Out of 181 true relations in the test dataset, the proposed method ranked 20 of them in the top 100 relations while this number was 13 for the other method. In the last study, we used biomedical literature and clinical trials in ranking potential drug repositioning candidates identified by Phenome-Wide Association Studies (PheWAS). Unlike previous approaches, in this study, we did not limit our method to LBD. First, we generated a list of potential drug repositioning candidates using PheWAS. We retrieved 212,851 gene-disease associations from PheWAS catalog and 14,169 gene-drug relationships from DrugBank. Following Swanson’s model, we generated 52,966 potential drug repositioning candidates. Then, we developed an information retrieval system to retrieve any evidence of those candidates co-occurring in the biomedical literature and clinical trials. We identified nearly 14,800 drug-disease pairs with some evidence of support. In addition, we identified more than 38,000 novel candidates for re-purposing, encompassing hundreds of different disease states and over 1,000 individual medications. We anticipate that these results will be highly useful for hypothesis generation in the field of drug repurposing

Extraction and Classification of Drug-Drug Interaction from Biomedical Text Using a Two-Stage Classifier

One of the critical causes of medical errors is Drug-Drug interaction (DDI), which occurs when one drug increases or decreases the effect of another drug. We propose a machine learning system to extract and classify drug-drug interactions from the biomedical literature, using the annotated corpus from the DDIExtraction-2013 shared task challenge. Our approach applies a two-stage classifier to handle the highly unbalanced class distribution in the corpus. The first stage is designed for binary classification of drug pairs as interacting or non-interacting, and the second stage for further classification of interacting pairs into one of four interacting types: advise, effect, mechanism, and int. To find the set of best features for classification, we explored many features, including stemmed words, bigrams, part of speech tags, verb lists, parse tree information, mutual information, and similarity measures, among others. As the system faced two different classification tasks, binary and multi-class, we also explored various classifiers in each stage. Our results show that the best performing classifier in both stages was Support Vector Machines, and the best performing features were 1000 top informative words and part of speech tags between two main drugs. We obtained an F-Measure of 0.64, showing a 12% improvement over our submitted system to the DDIExtraction 2013 competition

Leakage current and resistive switching mechanisms in SrTiO3

PhD ThesisResistive switching random access memory devices have attracted considerable attention due to exhibiting fast programming, non-destructive readout, low power-consumption, high-density integration, and low fabrication-cost. Resistive switching has been observed in a wide range of materials but the underpinning mechanisms still have not been understood completely. This thesis presents a study of the leakage current and resistive switching mechanisms of SrTiO3 metal-insulator-metal devices fabricated using atomic layer deposition and pulse laser deposition techniques. First, the conduction mechanisms in SrTiO3 are investigated. The leakage current characteristics are highly sensitive to the polarity and magnitude of applied voltage bias, punctuated by sharp increases at high field. The characteristics are also asymmetric with bias and the negative to positive current crossover point always occurs at a negative voltage bias. A model comprising thermionic field emission and tunnelling phenomena is proposed to explain ii the dependence of leakage current upon the device parameters quantitatively. SrTiO3 also demonstrates bipolar switching behaviour where the current-density versus voltage (J-V) characteristics show asymmetry at all temperatures examined, with resistive switching behaviour observed at elevated temperatures. The asymmetry is explained by the relative lack of electron traps at one electrode, which is determined from the symmetric J-V curve obtained at room temperature due to the redistribution of the dominant electrical defects in the film. Evidence is presented for a model of resistive switching that originates from defect diffusion (possibly oxygen vacancies) at high temperatures. Finally, a peculiar resistive switching behaviour was observed in pulse laser deposited SrTiO3. This switching depends on both the amplitude and polarity of the applied voltage, and cannot be described as either bipolar or unipolar resistive switching. This behaviour is termed antipolar due to the opposite polarity of the set voltage relative to the previous reset voltage. The proposed model based on electron injection by tunnelling at interfaces and a Poole-Frenkel mechanism through the bulk is extended to explain the antipolar resistive switching behaviour. This model is quantified by use of a simple mathematical equation to simulate the experimental results

Tailoring the excitation of localized surface plasmon-polariton resonances by focusing radially-polarized beams

We study the interaction of focused radially-polarized light with metal nanospheres. By expanding the electromagnetic field in terms of multipoles, we gain insight on the excitation of localized surface plasmon-polariton resonances in the nanoparticle. We show that focused radially-polarized beams offer more opportunities than a focused plane wave or a Gaussian beam for tuning the near- and far-field system response. These results find applications in nano-optics, optical tweezers, and optical data storage.Comment: 4 pages, 3 figure

Education is the Key to Every Door: Narratives of Immigrant Adult Basic Education Learners

Adult Basic Education (ABE) is a broad concept that aims at educating adults with low education. Immigrant ABE learners comprise a significant portion of the adult learners’ population who seek education through enrolling in ABE programs. These learners have experienced different learning environments due to their social life process and are motivated to make changes in their social status for a better life. Therefore, it is critical to understand their learning needs through their learning and educational experiences in order to develop an inclusive ABE learning environment. The purpose of this narrative study was to understand immigrant ABE learners’ experience in an ABE setting from a post-critical lens. The three research questions that guided this study were “How do immigrant ABE learners describe their educational experience prior to their enrollment in ABE?”, “How do immigrant ABE learners describe their learning experiences?”, and “How do immigrant ABE learners describe the role of education in changing their life situations?”The study was conducted in an ABE organization that offered HiSet preparation classes. The research data were collected through two sets of interviews with six immigrant ABE learners. Another source of the study data was the researcher’s field notes. While each participant’s process inspired certain key observation about their learning experiences, five themes were generated regarding their shared perspectives toward learning and education. They all experienced interrupted educational processes, held low socioeconomic status both in their home countries and in the US, perceived literacy in terms of learning English, had different learning experiences in different learning environments, and advocated the transformative power of education. The findings of this study suggest that the immigrant ABE learners seek to get educated in order to change their social status in terms of having a high income job and support their communities. Learning English would significantly facilitate this process for them, as it empowers them to communicate effectively in the American context