1,455 research outputs found

    Predictive Factors for and Complications of Bronchiectasis in Common Variable Immunodeficiency Disorders

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    Bronchiectasis is a frequent complication of common variable immunodeficiency disorders (CVID). In a cohort of patients with CVID, we sought to identify predictors of bronchiectasis. Secondly, we sought to describe the impact of bronchiectasis on lung function, infection risk, and quality of life. We conducted an observational cohort study of 110 patients with CVID and an available pulmonary computed tomography scan. The prevalence of bronchiectasis was 53%, with most of these patients (54%) having mild disease. Patients with bronchiectasis had lower median serum immunoglobulin (Ig) concentrations, especially long-term IgM (0 vs 0.25 g/l; p < 0.01) and pre-treatment IgG (1.3 vs 3.7 g/l; p < 0.01). CVID patients with bronchiectasis had worse forced expiratory volume in one second (2.10 vs 2.99 l; p < 0.01) and an annual decline in forced expiratory volume in one second of 25 ml/year (vs 8 ml/year in patients without bronchiectasis; p = 0.01). Patients with bronchiectasis also reported more annual respiratory tract infections (1.77 vs 1.25 infections/year, p = 0.04) and a poorer quality of life (26 vs 14 points in the St George's Respiratory Questionnaire; p = 0.02). Low serum immunoglobulin M concentration identifies patients at risk for bronchiectasis in CVID and may play a role in pathogenesis. Bronchiectasis is relevant because it is associated with frequent respiratory tract infections, poorer lung function, a greater rate of lung function decline, and a lower quality of life

    The challenges of deploying artificial intelligence models in a rapidly evolving pandemic

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    The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2, emerged into a world being rapidly transformed by artificial intelligence (AI) based on big data, computational power and neural networks. The gaze of these networks has in recent years turned increasingly towards applications in healthcare. It was perhaps inevitable that COVID-19, a global disease propagating health and economic devastation, should capture the attention and resources of the world's computer scientists in academia and industry. The potential for AI to support the response to the pandemic has been proposed across a wide range of clinical and societal challenges, including disease forecasting, surveillance and antiviral drug discovery. This is likely to continue as the impact of the pandemic unfolds on the world's people, industries and economy but a surprising observation on the current pandemic has been the limited impact AI has had to date in the management of COVID-19. This correspondence focuses on exploring potential reasons behind the lack of successful adoption of AI models developed for COVID-19 diagnosis and prognosis, in front-line healthcare services. We highlight the moving clinical needs that models have had to address at different stages of the epidemic, and explain the importance of translating models to reflect local healthcare environments. We argue that both basic and applied research are essential to accelerate the potential of AI models, and this is particularly so during a rapidly evolving pandemic. This perspective on the response to COVID-19, may provide a glimpse into how the global scientific community should react to combat future disease outbreaks more effectively.Comment: Accepted in Nature Machine Intelligenc

    Identification of major hemorrhage in trauma patients in the prehospital setting: diagnostic accuracy and impact on outcome.

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    BACKGROUND: Hemorrhage is the most common cause of potentially preventable death after injury. Early identification of patients with major hemorrhage (MH) is important as treatments are time-critical. However, diagnosis can be difficult, even for expert clinicians. This study aimed to determine how accurate clinicians are at identifying patients with MH in the prehospital setting. A second aim was to analyze factors associated with missed and overdiagnosis of MH, and the impact on mortality. METHODS: Retrospective evaluation of consecutive adult (≥16 years) patients injured in 2019-2020, assessed by expert trauma clinicians in a mature prehospital trauma system, and admitted to a major trauma center (MTC). Clinicians decided to activate the major hemorrhage protocol (MHPA) or not. This decision was compared with whether patients had MH in hospital, defined as the critical admission threshold (CAT+): administration of ≥3 U of red blood cells during any 60-minute period within 24 hours of injury. Multivariate logistical regression analyses were used to analyze factors associated with diagnostic accuracy and mortality. RESULTS: Of the 947 patients included in this study, 138 (14.6%) had MH. MH was correctly diagnosed in 97 of 138 patients (sensitivity 70%) and correctly excluded in 764 of 809 patients (specificity 94%). Factors associated with missed diagnosis were penetrating mechanism (OR 2.4, 95% CI 1.2 to 4.7) and major abdominal injury (OR 4.0; 95% CI 1.7 to 8.7). Factors associated with overdiagnosis were hypotension (OR 0.99; 95% CI 0.98 to 0.99), polytrauma (OR 1.3, 95% CI 1.1 to 1.6), and diagnostic uncertainty (OR 3.7, 95% CI 1.8 to 7.3). When MH was missed in the prehospital setting, the risk of mortality increased threefold, despite being admitted to an MTC. CONCLUSION: Clinical assessment has only a moderate ability to identify MH in the prehospital setting. A missed diagnosis of MH increased the odds of mortality threefold. Understanding the limitations of clinical assessment and developing solutions to aid identification of MH are warranted. LEVEL OF EVIDENCE: Level III-Retrospective study with up to two negative criteria. STUDY TYPE: Original research; diagnostic accuracy study

    Malignant melanoma of the rectum: a case report

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    <p>Abstract</p> <p>Introduction</p> <p>Anorectal melanoma represents an unusual but important presentation of rectal malignancy. There have only been a few cases reported and the optimum management for this condition is still undecided, however, prompt diagnosis is essential. We have outlined current treatment options.</p> <p>Case presentation</p> <p>We report a case of malignant melanoma of the rectum in a 55-year-old Caucasian man presenting as an emergency with rectal bleeding. Biopsies were taken of the fleshy mass found on digital examination, which confirmed malignant melanoma. No distant metastases were found. He underwent an abdominoperineal resection. We report the surgical management of this rare and aggressive malignancy.</p> <p>Conclusion</p> <p>Treatment options for this condition are divergent. Surgical management varies from wide local excision to abdominoperineal resection. Clinical awareness in both medical and surgical clinics is required for prompt diagnosis and treatment.</p

    Multifractality in Human Heartbeat Dynamics

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    Recent evidence suggests that physiological signals under healthy conditions may have a fractal temporal structure. We investigate the possibility that time series generated by certain physiological control systems may be members of a special class of complex processes, termed multifractal, which require a large number of exponents to characterize their scaling properties. We report on evidence for multifractality in a biological dynamical system --- the healthy human heartbeat. Further, we show that the multifractal character and nonlinear properties of the healthy heart rate are encoded in the Fourier phases. We uncover a loss of multifractality for a life-threatening condition, congestive heart failure.Comment: 19 pages, latex2e using rotate and epsf, with 5 ps figures; to appear in Nature, 3 June, 199

    Automatically extracting functionally equivalent proteins from SwissProt

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    In summary, FOSTA provides an automated analysis of annotations in UniProtKB/Swiss-Prot to enable groups of proteins already annotated as functionally equivalent, to be extracted. Our results demonstrate that the vast majority of UniProtKB/Swiss-Prot functional annotations are of high quality, and that FOSTA can interpret annotations successfully. Where FOSTA is not successful, we are able to highlight inconsistencies in UniProtKB/Swiss-Prot annotation. Most of these would have presented equal difficulties for manual interpretation of annotations. We discuss limitations and possible future extensions to FOSTA, and recommend changes to the UniProtKB/Swiss-Prot format, which would facilitate text-mining of UniProtKB/Swiss-Prot

    Selective serotonin reuptake inhibitors in the treatment of generalized anxiety disorder

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    Selective serotonin reuptake inhibitors have proven efficacy in the treatment of panic disorder, obsessive–compulsive disorder, post-traumatic stress disorder and social anxiety disorder. Accumulating data shows that selective serotonin reuptake inhibitor treatment can also be efficacious in patients with generalized anxiety disorder. This review summarizes the findings of randomized controlled trials of selective serotonin reuptake inhibitor treatment for generalized anxiety disorder, examines the strengths and weaknesses of other therapeutic approaches and considers potential new treatments for patients with this chronic and disabling anxiety disorder
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