Scoping a public health impact assessment of aquaculture with particular reference to tilapia in the UK

Background. The paper explores shaping public health impact assessment tools for tilapia, a novel emergent aquaculture sector in the UK. This Research Council’s UK Rural Economy and Land Use project embraces technical, public health, and marketing perspectives scoping tools to assess possible impacts of the activity. Globally, aquaculture produced over 65 million tonnes of food in 2008 and will grow significantly requiring apposite global public health impact assessment tools.<p></p> Methods. Quantitative and qualitative methods incorporated data from a tridisciplinary literature. Holistic tools scoped tilapia farming impact assessments. Laboratory-based tilapia production generated data on impacts in UK and Thailand along with 11 UK focus groups involving 90 consumers, 30 interviews and site visits, 9 visits to UK tilapia growers and 2 in The Netherlands.<p></p> Results. The feasibility, challenges, strengths, and weaknesses of creating a tilapia Public Health Impact Assessment are analysed. Occupational and environmental health benefits and risks attached to tilapia production were identified.<p></p> Conclusions. Scoping public health impacts of tilapia production is possible at different levels and forms for producers, retailers, consumers, civil society and governmental bodies that may contribute to complex and interrelated public health assessments of aquaculture projects. Our assessment framework constitutes an innovatory perspective in the field

A Bayesian Image Analysis of the Change in Tumor/Brain Contrast Uptake Induced by Radiation via Reversible Jump Markov Chain Monte Carlo

This work is motivated by a pilot study on the change in tumor/brain contrast uptake induced by radiation via quantitative Magnetic Resonance Imaging. The results inform the optimal timing of administering chemotherapy in the context of radiotherapy. A noticeable feature of the data is spatial heterogeneity. The tumor is physiologically and pathologically distinct from surrounding healthy tissue. Also, the tumor itself is usually highly heterogeneous. We employ a Gaussian Hidden Markov Random Field model that respects the above features. The model introduces a latent layer of discrete labels from an Markov Random Field (MRF) governed by a spatial regularization parameter. We further assume that conditional on the hidden labels, the observed data are independent and normally distributed, We treat the regularization parameter of the MRF, as well as the number of states of the MRF as parameters, and estimate them via the Reversible Jump Markov chain Monte Carlo algorithm. We propose a novel and nontrivial implementation of the Reversible Jump moves. The performance of the method is examined in both simulation studies and real data analysis. We report the pixel-wise posterior mean and standard deviation of the change in contrast uptake marginalized over the number of states and hidden labels. We also compare the performance with a Markov chain with fixed number of states and a parallel Expectation-Maximization approach from a frequentist perspective

Quantitative Magnetic Resonance Image Analysis via the EM Algorithm with Stochastic Variation

Quantitative Magnetic Resonance Imaging (qMRI) provides researchers insight into pathological and physiological alterations of living tissue, with the help of which, researchers hope to predict (local) therapeutic efficacy early and determine optimal treatment schedule. However, the analysis of qMRI has been limited to ad-hoc heuristic methods. Our research provides a powerful statistical framework for image analysis and sheds light on future localized adaptive treatment regimes tailored to the individual’s response. We assume in an imperfect world we only observe a blurred and noisy version of the underlying “true” scene via qMRI, due to measurement errors or unpredictable influences. We use a hidden Markov Random Field to model the unobserved “true” scene and develop a maximum likelihood approach via the Expectation-Maximization algorithm with stochastic variation. An important improvement over previous work is the assessment of variability in parameter estimation, which is the valid basis for statistical inference. Moreover, we focus on recovering the “true” scene rather than segmenting the image. Our research has shown that the approach is powerful in both simulation studies and on a real dataset, while quite robust in the presence of some model assumption violations

Infections after chimeric antigen receptor (CAR)-T-cell therapy for hematologic malignancies.

Chimeric antigen receptor (CAR)-T-cell therapies have revolutionized the management of acute lymphoblastic leukemia, non-Hodgkin lymphoma, and multiple myeloma but come at the price of unique toxicities, including cytokine release syndrome, immune effector cell-associated neurotoxicity syndrome, and long-term "on-target off-tumor" effects. All of these factors increase infection risk in an already highly immunocompromised patient population. Indeed, infectious complications represent the key determinant of non-relapse mortality after CAR-T cells. The temporal distribution of these risk factors shapes different infection patterns early versus late post-CAR-T-cell infusion. Furthermore, due to the expression of their targets on B lineage cells at different stages of differentiation, CD19, and B-cell maturation antigen (BCMA) CAR-T cells induce distinct immune deficits that could require different prevention strategies. Infection incidence is the highest during the first month post-infusion and subsequently decreases thereafter. However, infections remain relatively common even a year after infusion. Bacterial infections predominate early after CD19, while a more equal distribution between bacterial and viral causes is seen after BCMA CAR-T-cell therapy, and fungal infections are universally rare. Cytomegalovirus (CMV) and other herpesviruses are increasingly breported, but whether routine monitoring is warranted for all, or a subgroup of patients, remains to be determined. Clinical practices vary substantially between centers, and many areas of uncertainty remain, including CMV monitoring, antibacterial and antifungal prophylaxis and duration, use of immunoglobulin replacement therapy, and timing of vaccination. Risk stratification tools are available and may help distinguish between infectious and non-infectious causes of fever post-infusion and predict severe infections. These tools need prospective validation, and their integration in clinical practice needs to be systematically studied

Cognitive, behavioural and psychological barriers to the prevention of severe hypoglycaemia: A qualitative study of adults with type 1 diabetes

Objectives: Severe hypoglycaemia affects approximately one in three people with type 1 diabetes and is the most serious side effect of insulin therapy. Our aim was to explore individualistic drivers of severe hypoglycaemia events. Methods: In-depth semi-structured interviews were conducted with a purposive sample of 17 adults with type 1 diabetes and a history of recurrent severe hypoglycaemia, to elicit experiences of hypoglycaemia (symptoms/awareness, progression from mild to severe and strategies for prevention/treatment). Interviews were analysed using an adapted grounded theory approach. Results: Three main themes emerged: hypoglycaemia-induced cognitive impairment, behavioural factors and psychological factors. Despite experiencing early hypoglycaemic symptoms, individuals often delayed intervention due to impaired/distracted attention, inaccurate risk assessment, embarrassment, worry about rebound hyperglycaemia or unavailability of preferred glucose source. Delay coupled with use of a slow-acting glucose source compromised prevention of severe hypoglycaemia. Conclusion: Our qualitative data highlight the multifaceted, idiosyncratic nature of severe hypoglycaemia and confirm that individuals with a history of recurrent severe hypoglycaemia may have specific thought and behaviour risk profiles. Individualised prevention plans are required, emphasising both the need to attend actively to mild hypoglycaemic symptoms and to intervene promptly with an appropriate, patient-preferred glucose source to prevent progression to severe hypoglycaemia

Automating the Calibration of a Neonatal Condition Monitoring System

Abstract. Condition monitoring of premature babies in intensive care can be carried out using a Factorial Switching Linear Dynamical System (FSLDS) [15]. A crucial part of training the FSLDS is the manual calibration stage, where an interval of normality must be identified for each baby that is monitored. In this paper we replace this manual step by using a classifier to predict whether an interval is normal or not. We show that the monitoring results obtained using automated calibration are almost as good as those using manual calibration

Articulated Model Registration of MRI/X-Ray Spine Data

Collection : Lecture Notes in Computer Science ; vol. 6112This paper presents a method based on articulated models for the registration of spine data extracted from multimodal medical images of patients with scoliosis. With the ultimate aim being the development of a complete geometrical model of the torso of a scoliotic patient, this work presents a method for the registration of vertebral column data using 3D magnetic resonance images (MRI) acquired in prone position and X-ray data acquired in standing position for five patients with scoliosis. The 3D shape of the vertebrae is estimated from both image modalities for each patient, and an articulated model is used in order to calculate intervertebral transformations required in order to align the vertebrae between both postures. Euclidean distances between anatomical landmarks are calculated in order to assess multimodal registration error. Results show a decrease in the Euclidean distance using the proposed method compared to rigid registration and more physically realistic vertebrae deformations compared to thin-plate-spline (TPS) registration thus improving alignment.IRS

Clinical trial of lamivudine in children with chronic hepatitis B.

N Engl J Med. 2002 May 30;346(22):1706-13. Clinical trial of lamivudine in children with chronic hepatitis B. Jonas MM, Mizerski J, Badia IB, Areias JA, Schwarz KB, Little NR, Greensmith MJ, Gardner SD, Bell MS, Sokal EM; International Pediatric Lamivudine Investigator Group. of Gastroenterology, Children's Hospital, Boston, MA 02115, USA. Erratum in: N Engl J Med 2002 Sep 19;347(12):955. Kelley, Deirdre [corrected to Kelly, Deirdre]. Comment in: J Hepatol. 2003 May;38(5):698-9. N Engl J Med. 2002 May 30;346(22):1682-3. Abstract BACKGROUND: Lamivudine therapy is effective for chronic hepatitis B infection in adults. We evaluated the efficacy and tolerability of lamivudine as a treatment for chronic infection with hepatitis B virus (HBV) in children. METHODS: Children with chronic hepatitis B were randomly assigned in a 2:1 ratio to receive either oral lamivudine (3 mg per kilogram of body weight; maximum, 100 mg) or placebo once daily for 52 weeks. The primary end point was virologic response (defined by the absence of serum hepatitis B e antigen and serum HBV DNA) at week 52 of treatment. RESULTS: Of the 403 children screened, 191 were randomly assigned to receive lamivudine and 97 to receive placebo. The rate of virologic response at week 52 was higher among children who received lamivudine than among those who received placebo (23 percent vs. 13 percent, P=0.04). Lamivudine therapy was well tolerated and was also associated with higher rates of seroconversion from hepatitis B e antigen to hepatitis B e antibody, normalization of alanine aminotransferase levels, and suppression of HBV DNA. CONCLUSIONS: In children with chronic hepatitis B, 52 weeks of treatment with lamivudine was associated with a significantly higher rate of virologic response than was placebo. PMID: 12037150 [PubMed - indexed for MEDLINE

Commensal Neisseria species share immune suppressive mechanisms with Neisseria gonorrhoeae

Neisseria gonorrhoeae is a highly adapted human sexually transmitted pathogen that can cause symptomatic infections associated with localized inflammation as well as asymptomatic and subclinical infections, particularly in females. Gonococcal infection in humans does not generate an effective immune response in most cases, which contributes to both transmission of the pathogen and reinfection after treatment. Neisseria gonorrhoeae is known to evade and suppress human immune responses through a variety of mechanisms. Commensal Neisseria species that are closely related to N. gonorrhoeae, such as N. cinerea, N. lactamica, N. elongata, and N. mucosa, rarely cause disease and instead asymptomatically colonize mucosal sites for prolonged periods of time without evoking clearing immunologic responses. We have shown previously that N. gonorrhoeae inhibits the capacity of antigen-pulsed dendritic cells to induce CD4+ T cell proliferation in vitro. Much of the suppressive effects of N. gonorrhoeae on dendritic cells can be recapitulated either by outer-membrane vesicles released from the bacteria or by purified PorB, the most abundant outer-membrane protein in Neisseria gonorrhoeae. We show here that three commensal Neisseria species, N. cinerea, N. lactamica and N. mucosa, show a comparable capacity to suppress dendritic cell-induced T cell proliferation in vitro through mechanisms similar to those demonstrated previously for N. gonorrhoeae, including inhibition by purified PorB. Our findings suggest that some immune-evasive properties of pathogenic N. gonorrhoeae are shared with commensal Neisseria species and may contribute to the ability of both pathogens and commensals to cause prolonged mucosal colonization in humans