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Documents submitted to the Faculty of the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Master of Fine Arts in the Department of Art.Master of Fine Art

Artificial Intelligence Techniques That May Be Applied to Primary Care Data to Facilitate Earlier Diagnosis of Cancer : Systematic Review

Acknowledgments This research was funded by the National Institute for Health Research (NIHR) Policy Research Programme, conducted through the Policy Research Unit in Cancer Awareness, Screening, and Early Diagnosis, PR-PRU-1217-21601. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. This work was also supported by the CanTest Collaborative (funded by Cancer Research UK C8640/A23385), of which FW and WH are directors and JE, HS, and NdW are associate directors. HS is additionally supported by the Houston Veterans Administration Health Services Research and Development Center for Innovations in Quality, Effectiveness, and Safety (CIN13-413) and the Agency for Healthcare Research and Quality (R01HS27363). The funding sources had no role in the study design, data collection, data analysis, data interpretation, writing of the report, or the decision to submit for publication. The authors would like to thank Isla Kuhn, Reader Services Librarian, University of Cambridge Medical Library, for her help in developing the search strategy.Peer reviewedPublisher PD

Artificial Intelligence Techniques That May Be Applied to Primary Care Data to Facilitate Earlier Diagnosis of Cancer: Systematic Review.

BACKGROUND: More than 17 million people worldwide, including 360,000 people in the United Kingdom, were diagnosed with cancer in 2018. Cancer prognosis and disease burden are highly dependent on the disease stage at diagnosis. Most people diagnosed with cancer first present in primary care settings, where improved assessment of the (often vague) presenting symptoms of cancer could lead to earlier detection and improved outcomes for patients. There is accumulating evidence that artificial intelligence (AI) can assist clinicians in making better clinical decisions in some areas of health care. OBJECTIVE: This study aimed to systematically review AI techniques that may facilitate earlier diagnosis of cancer and could be applied to primary care electronic health record (EHR) data. The quality of the evidence, the phase of development the AI techniques have reached, the gaps that exist in the evidence, and the potential for use in primary care were evaluated. METHODS: We searched MEDLINE, Embase, SCOPUS, and Web of Science databases from January 01, 2000, to June 11, 2019, and included all studies providing evidence for the accuracy or effectiveness of applying AI techniques for the early detection of cancer, which may be applicable to primary care EHRs. We included all study designs in all settings and languages. These searches were extended through a scoping review of AI-based commercial technologies. The main outcomes assessed were measures of diagnostic accuracy for cancer. RESULTS: We identified 10,456 studies; 16 studies met the inclusion criteria, representing the data of 3,862,910 patients. A total of 13 studies described the initial development and testing of AI algorithms, and 3 studies described the validation of an AI algorithm in independent data sets. One study was based on prospectively collected data; only 3 studies were based on primary care data. We found no data on implementation barriers or cost-effectiveness. Risk of bias assessment highlighted a wide range of study quality. The additional scoping review of commercial AI technologies identified 21 technologies, only 1 meeting our inclusion criteria. Meta-analysis was not undertaken because of the heterogeneity of AI modalities, data set characteristics, and outcome measures. CONCLUSIONS: AI techniques have been applied to EHR-type data to facilitate early diagnosis of cancer, but their use in primary care settings is still at an early stage of maturity. Further evidence is needed on their performance using primary care data, implementation barriers, and cost-effectiveness before widespread adoption into routine primary care clinical practice can be recommended.CRU

Risk factors for genital infections in people initiating SGLT2 inhibitors and their impact on discontinuation

Introduction: To identify risk factors, absolute risk, and impact on treatment discontinuation of genital infections with sodium-glucose co-transporter-2 inhibitors (SGLT2i). Research design and methods: We assessed the relationship between baseline characteristics and genital infection in 21 004 people with type 2 diabetes initiating SGLT2i and 55 471 controls initiating dipeptidyl peptidase-4 inhibitors (DPP4i) in a UK primary care database. We assessed absolute risk of infection in those with key risk factors and the association between early genital infection and treatment discontinuation. Results: Genital infection was substantially more common in those treated with SGLT2i (8.1% within 1 year) than DPP4i (1.8%). Key predictors of infection with SGLT2i were female sex (HR 3.64; 95% CI 3.23 to 4.11) and history of genital infection; <1 year before initiation (HR 4.38; 3.73 to 5.13), 1–5 years (HR 3.04; 2.64 to 3.51), and >5 years (HR 1.79; 1.55 to 2.07). Baseline HbA1c was not associated with infection risk for SGLT2i, in contrast to DPP4i where risk increased with higher HbA1c. One-year absolute risk of genital infection with SGLT2i was highest for those with a history of prior infection (females 23.7%, males 12.1%), compared with those without (females 10.8%, males 2.7%). Early genital infection was associated with a similar discontinuation risk for SGLT2i (HR 1.48; 1.21–1.80) and DPP4i (HR 1.58; 1.21–2.07). Conclusions: Female sex and history of prior infection are simple features that can identify subgroups at greatly increased risk of genital infections with SGLT2i therapy. These data can be used to risk-stratify patients. High HbA1c is not a risk factor for genital infections with SGLT2i

The waking brain: an update

Wakefulness and consciousness depend on perturbation of the cortical soliloquy. Ascending activation of the cerebral cortex is characteristic for both waking and paradoxical (REM) sleep. These evolutionary conserved activating systems build a network in the brainstem, midbrain, and diencephalon that contains the neurotransmitters and neuromodulators glutamate, histamine, acetylcholine, the catecholamines, serotonin, and some neuropeptides orchestrating the different behavioral states. Inhibition of these waking systems by GABAergic neurons allows sleep. Over the past decades, a prominent role became evident for the histaminergic and the orexinergic neurons as a hypothalamic waking center

The state of the Martian climate

60°N was +2.0°C, relative to the 1981–2010 average value (Fig. 5.1). This marks a new high for the record. The average annual surface air temperature (SAT) anomaly for 2016 for land stations north of starting in 1900, and is a significant increase over the previous highest value of +1.2°C, which was observed in 2007, 2011, and 2015. Average global annual temperatures also showed record values in 2015 and 2016. Currently, the Arctic is warming at more than twice the rate of lower latitudes

Immediate chest X-ray for patients at risk of lung cancer presenting in primary care: randomised controlled feasibility trial

Background: Achieving earlier stage diagnosis is one option for improving lung cancer outcomes in the United Kingdom. Patients with lung cancer typically present with symptoms to general practitioners several times before referral or investigation. Methods: We undertook a mixed methods feasibility individually randomised controlled trial (the ELCID trial) to assess the feasibility and inform the design of a definitive, fully powered, UK-wide, Phase III trial of lowering the threshold for urgent investigation of suspected lung cancer. Patients over 60, with a smoking history, presenting with new chest symptoms to primary care, were eligible to be randomised to intervention (urgent chest X-ray) or usual care. Results: The trial design and materials were acceptable to GPs and patients. We randomised 255 patients from 22 practices, although the proportion of eligible patients who participated was lower than expected. Survey responses (89%), and the fidelity of the intervention (82% patients X-rayed within 3 weeks) were good. There was slightly higher anxiety and depression in the control arm in participants aged >75. Three patients (1.2%) were diagnosed with lung cancer. Conclusions: We have demonstrated the feasibility of individually randomising patients at higher risk of lung cancer, to a trial offering urgent investigation or usual care

Predicting range shifts of African apes under global change scenarios

Aim: Modelling African great ape distribution has until now focused on current or past conditions, while future scenarios remain scarcely explored. Using an ensemble forecasting approach, we predicted changes in taxon-specific distribution under future scenarios of climate, land use and human populations for (1) areas outside protected areas (PAs) only (assuming complete management effectiveness of PAs), (2) the entire study region and (3) interspecies range overlap. Location: Tropical Africa. Methods: We compiled occurrence data (n = 5,203) on African apes from the IUCN A.P.E.S. database and extracted relevant climate-, habitat- and human-related predictors representing current and future (2050) conditions to predict taxon-specific range change under a best- and a worst-case scenario, using ensemble forecasting. Results The predictive performance of the models varied across taxa. Synergistic interactions between predictors are shaping African ape distribution, particularly human-related variables. On average across taxa, a range decline of 50% is expected outside PAs under the best scenario if no dispersal occurs (61% in worst scenario). Otherwise, an 85% range reduction is predicted to occur across study regions (94% worst). However, range gains are predicted outside PAs if dispersal occurs (52% best, 21% worst), with a slight increase in gains expected across study regions (66% best, 24% worst). Moreover, more than half of range losses and gains are predicted to occur outside PAs where interspecific ranges overlap. Main Conclusions: Massive range decline is expected by 2050, but range gain is uncertain as African apes will not be able to occupy these new areas immediately due to their limited dispersal capacity, migration lag and ecological constraints. Given that most future range changes are predicted outside PAs, Africa's current PA network is likely to be insufficient for preserving suitable habitats and maintaining connected ape populations. Thus, conservation planners urgently need to integrate land use planning and climate change mitigation measures at all decision-making levels both in range countries and abroad

Population dynamics and genetic connectivity in recent chimpanzee history

Knowledge on the population history of endangered species is critical for conservation, but whole-genome data on chimpanzees (Pan troglodytes) is geographically sparse. Here, we produced the first non-invasive geolocalized catalog of genomic diversity by capturing chromosome 21 from 828 non-invasive samples collected at 48 sampling sites across Africa. The four recognized subspecies show clear genetic differentiation correlating with known barriers, while previously undescribed genetic exchange suggests that these have been permeable on a local scale. We obtained a detailed reconstruction of population stratification and fine-scale patterns of isolation, migration, and connectivity, including a comprehensive picture of admixture with bonobos (Pan paniscus). Unlike humans, chimpanzees did not experience extended episodes of long-distance migrations, which might have limited cultural transmission. Finally, based on local rare variation, we implement a fine-grained geolocalization approach demonstrating improved precision in determining the origin of confiscated chimpanzees

Psychosocial determinants of sustained maternal functional impairment: longitudinal findings from a pregnancy-birth cohort study in rural Pakistan

Function is an important marker of health throughout the life course, however, in low-and-middle-income-countries, little is known about the burden of functional impairment as women transition from pregnancy to the first year post-partum. Leveraging longitudinal data from 960 women participating in the Share Child Cohort in Pakistan, this study sought to (1) characterize functional trajectories over time among women in their perinatal period and (2) assess predictors of chronic poor functioning following childbirth. We used a group-based trajectory modeling approach to examine maternal patterns of function from the third trimester of pregnancy through 12 months post-partum. Three trajectory groups were found: persistently well-functioning (51% of women), poor functioning with recovery (39% of women), and chronically poor functioning (10% of women). When compared to mothers in the highest functioning group, psychosocial characteristics (e.g., depression, stress, and serious life events) were significantly associated with sustained poor functioning one-year following child-birth. Mothers living in nuclear households were more likely to experience chronic poor functioning. Higher education independently predicted maternal function recovery, even when controlling for psychosocial characteristics. Education, above and beyond socio-economic assets, appears to play an important protective role in maternal functional trajectories following childbirth. Public health implications related to maternal function and perinatal mental health are discussed