Investigations into the patient voice: a multi-perspective analysis of inflammation

The patient is the expert of their medical journey and their experiences go largely unheard in clinical practice. Understanding the patient is important as bridging gaps in the medical domain enhances clinical knowledge, benefiting patient care in addition to improving quality of life. Valuable solutions to these problems lie at the intersection of Machine learning and sentiment analysis; through ontologies, semantic similarity, and clustering. In this thesis, I present challenges and solutions that explore patient quality of life pertaining to two inflammatory diseases: Uveitis and Inflammatory Bowel Disease, which are immune-mediated inflammatory diseases and often undifferentiated. This thesis explores how a patient’s condition and inflammation influences their voice and quality of life via sentiment analysis, clustering, and semantic characterisations. Methods With guidance from domain experts and a foundation derived from clinical consensus documents, I created an application ontology, Ocular Immune-Mediated Inflammatory Diseases Ontology (OcIMIDo), which was enhanced with patient-preferred terms curated from online forum conversations, using a semi-automated statistical approach - with application of annotating term-frequency and sentiment analysis. Semantic similarity was explored using a preexisting embedding model derived from clinical letters to train other models consisting of patient-generated texts for systematic comparison of the clinician and patient voice. In a final experimental chapter, blood markers were clustered and analysed with their corresponding quantitative quality of life outcomes using patients in the UK Biobank with Inflammatory Bowel Disease. Results OcIMIDo is the first of its kind in ophthalmology and sentiment analysis revealed that first posts were more negative compared to replies. Systematic comparisons of embedding models revealed frequent misspellings from clinicians; use of abbreviations from patients; and patient priorities - models performed better when the clinical domain was extended with equivalent-sized, patient-generated data. Clusters unveiled insight into the presence of inflammatory stress and the relationship with happiness and the presence of a maternal smoking history with a Crohn’s disease diagnosis. Summary Patient-preferred terms prove the patient voice provides meaningful text mining and fruitful sentiment analysis, revealing the role a forum plays on patients; semantic similarity highlighted potential novel disease associations and the patient lexicon; and clustering blood markers featured clusters presenting a relationship with sentiment. In summary, this deeper knowledge of quality of life biomarkers through the patient voice can benefit the clinical domain and patient outcomes as understanding the patient can improve the clinical-patient relationship and communication standards: all benefiting the diagnosis process, developing treatment plans, and shortening these intensive time hauls in clinical practice

Estimating Global ‘‘Blue Carbon’’ Emissions from Conversion and Degradation of Vegetated Coastal Ecosystems

Recent attention has focused on the high rates of annual carbon sequestration in vegetated coastal ecosystems—marshes, mangroves, and seagrasses—that may be lost with habitat destruction (‘conversion’). Relatively unappreciated, however, is that conversion of these coastal ecosystems also impacts very large pools of previously-sequestered carbon. Residing mostly in sediments, this ‘blue carbon’ can be released to the atmosphere when these ecosystems are converted or degraded. Here we provide the first global estimates of this impact and evaluate its economic implications. Combining the best available data on global area, land-use conversion rates, and near-surface carbon stocks in each of the three ecosystems, using an uncertainty-propagation approach, we estimate that 0.15–1.02 Pg (billion tons) of carbon dioxide are being released annually, several times higher than previous estimates that account only for lost sequestration. These emissions are equivalent to 3–19% of those from deforestation globally, and result in economic damages of $US 6–42 billion annually. The largest sources of uncertainty in these estimates stems from limited certitude in global area and rates of land-use conversion, but research is also needed on the fates of ecosystem carbon upon conversion. Currently, carbon emissions from the conversion of vegetated coastal ecosystems are not included in emissions accounting or carbon market protocols, but this analysis suggests they may be disproportionally important to both. Although the relevant science supporting these initial estimates will need to be refined in coming years, it is clear that policies encouraging the sustainable management of coastal ecosystems could significantly reduce carbon emissions from the land-use sector, in addition to sustaining the well-recognized ecosystem services of coastal habitats

Case Reports1. A Late Presentation of Loeys-Dietz Syndrome: Beware of TGFβ Receptor Mutations in Benign Joint Hypermobility

Background: Thoracic aortic aneurysms (TAA) and dissections are not uncommon causes of sudden death in young adults. Loeys-Dietz syndrome (LDS) is a rare, recently described, autosomal dominant, connective tissue disease characterized by aggressive arterial aneurysms, resulting from mutations in the transforming growth factor beta (TGFβ) receptor genes TGFBR1 and TGFBR2. Mean age at death is 26.1 years, most often due to aortic dissection. We report an unusually late presentation of LDS, diagnosed following elective surgery in a female with a long history of joint hypermobility. Methods: A 51-year-old Caucasian lady complained of chest pain and headache following a dural leak from spinal anaesthesia for an elective ankle arthroscopy. CT scan and echocardiography demonstrated a dilated aortic root and significant aortic regurgitation. MRA demonstrated aortic tortuosity, an infrarenal aortic aneurysm and aneurysms in the left renal and right internal mammary arteries. She underwent aortic root repair and aortic valve replacement. She had a background of long-standing joint pains secondary to hypermobility, easy bruising, unusual fracture susceptibility and mild bronchiectasis. She had one healthy child age 32, after which she suffered a uterine prolapse. Examination revealed mild Marfanoid features. Uvula, skin and ophthalmological examination was normal. Results: Fibrillin-1 testing for Marfan syndrome (MFS) was negative. Detection of a c.1270G > C (p.Gly424Arg) TGFBR2 mutation confirmed the diagnosis of LDS. Losartan was started for vascular protection. Conclusions: LDS is a severe inherited vasculopathy that usually presents in childhood. It is characterized by aortic root dilatation and ascending aneurysms. There is a higher risk of aortic dissection compared with MFS. Clinical features overlap with MFS and Ehlers Danlos syndrome Type IV, but differentiating dysmorphogenic features include ocular hypertelorism, bifid uvula and cleft palate. Echocardiography and MRA or CT scanning from head to pelvis is recommended to establish the extent of vascular involvement. Management involves early surgical intervention, including early valve-sparing aortic root replacement, genetic counselling and close monitoring in pregnancy. Despite being caused by loss of function mutations in either TGFβ receptor, paradoxical activation of TGFβ signalling is seen, suggesting that TGFβ antagonism may confer disease modifying effects similar to those observed in MFS. TGFβ antagonism can be achieved with angiotensin antagonists, such as Losartan, which is able to delay aortic aneurysm development in preclinical models and in patients with MFS. Our case emphasizes the importance of timely recognition of vasculopathy syndromes in patients with hypermobility and the need for early surgical intervention. It also highlights their heterogeneity and the potential for late presentation. Disclosures: The authors have declared no conflicts of interes

Evaluating the detection ability of a range of epistasis detection methods on simulated data for pure and impure epistatic models

BACKGROUND: Numerous approaches have been proposed for the detection of epistatic interactions within GWAS datasets in order to better understand the drivers of disease and genetics. METHODS: A selection of state-of-the-art approaches were assessed. These included the statistical tests, fast-epistasis, BOOST, logistic regression and wtest; swarm intelligence methods, namely AntEpiSeeker, epiACO and CINOEDV; and data mining approaches, including MDR, GSS, SNPRuler and MPI3SNP. Data were simulated to provide randomly generated models with no individual main effects at different heritabilities (pure epistasis) as well as models based on penetrance tables with some main effects (impure epistasis). Detection of both two and three locus interactions were assessed across a total of 1,560 simulated datasets. The different methods were also applied to a section of the UK biobank cohort for Atrial Fibrillation. RESULTS: For pure, two locus interactions, PLINK’s implementation of BOOST recovered the highest number of correct interactions, with 53.9% and significantly better performing than the other methods (p = 4.52e − 36). For impure two locus interactions, MDR exhibited the best performance, recovering 62.2% of the most significant impure epistatic interactions (p = 6.31e − 90 for all but one test). The assessment of three locus interaction prediction revealed that wtest recovered the highest number (17.2%) of pure epistatic interactions(p = 8.49e − 14). wtest also recovered the highest number of three locus impure epistatic interactions (p = 6.76e − 48) while AntEpiSeeker ranked as the most significant the highest number of such interactions (40.5%). Finally, when applied to a real dataset for Atrial Fibrillation, most notably finding an interaction between SYNE2 and DTNB

Estimating global "blue carbon" emissions from conversion and degradation of vegetated coastal ecosystems.

Recent attention has focused on the high rates of annual carbon sequestration in vegetated coastal ecosystems--marshes, mangroves, and seagrasses--that may be lost with habitat destruction ('conversion'). Relatively unappreciated, however, is that conversion of these coastal ecosystems also impacts very large pools of previously-sequestered carbon. Residing mostly in sediments, this 'blue carbon' can be released to the atmosphere when these ecosystems are converted or degraded. Here we provide the first global estimates of this impact and evaluate its economic implications. Combining the best available data on global area, land-use conversion rates, and near-surface carbon stocks in each of the three ecosystems, using an uncertainty-propagation approach, we estimate that 0.15-1.02 Pg (billion tons) of carbon dioxide are being released annually, several times higher than previous estimates that account only for lost sequestration. These emissions are equivalent to 3-19% of those from deforestation globally, and result in economic damages of $US 6-42 billion annually. The largest sources of uncertainty in these estimates stems from limited certitude in global area and rates of land-use conversion, but research is also needed on the fates of ecosystem carbon upon conversion. Currently, carbon emissions from the conversion of vegetated coastal ecosystems are not included in emissions accounting or carbon market protocols, but this analysis suggests they may be disproportionally important to both. Although the relevant science supporting these initial estimates will need to be refined in coming years, it is clear that policies encouraging the sustainable management of coastal ecosystems could significantly reduce carbon emissions from the land-use sector, in addition to sustaining the well-recognized ecosystem services of coastal habitats

Global distribution of seagrasses, tidal marshes, and mangroves.

<p>Data sources: Seagrass and saltmarsh coverage data are from the United Nations Environment Programme World Conservation Monitoring Centre (UNEP-WCMC); mangrove coverage data are from UNEP-WCMC in collaboration with the International Society for Mangrove Ecosystems (ISME).</p

Estimates of carbon released by land-use change in coastal ecosystems globally and associated economic impact.

<p>Notes: 1 Pg = 1 billion metric tons. To obtain values per km², multiply by 100. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043542#s2" target="_blank">Methods</a> section for detailed description of inputs and their sources. In brief, data for global extent and conversion rate are recently published ranges (minimum - maximum, and central estimate in parentheses). For near-surface carbon susceptible to land-use conversion (expressed in potential CO₂ emissions <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043542#pone.0043542-Intergovernmental2" target="_blank">[48]</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043542#pone.0043542-Pearson2" target="_blank">[50]</a>), uncertainty range is based on assumption of 25–100% loss C upon land-use impact; thus, the high-end estimate is the literature-derived global mean carbon storage in vegetation and the top meter of sediment only (central estimate is thus 63% loss). Results for carbon loss are non-parametric 90% confidence intervals (median in parentheses) from Monte Carlo uncertainty propagation of the three input variables (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043542#s2" target="_blank">Methods</a>). Economic estimates apply a multiplier of US$ 41 per ton of CO₂ to lower, upper, and central emission estimates (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043542#s2" target="_blank">Methods</a>).</p