Identifying the climatic drivers of honey bee disease in England and Wales.

Honey bee colony health has received considerable attention in recent years, with many studies highlighting multifactorial issues contributing to colony losses. Disease and weather are consistently highlighted as primary drivers of colony loss, yet little is understood about how they interact. Here, we combined disease records from government honey bee health inspections with meteorological data from the CEDA to identify how weather impacts EFB, AFB, CBP, varroosis, chalkbrood and sacbrood. Using R-INLA, we determined how different meteorological variables influenced disease prevalence and disease risk. Temperature caused an increase in the risk of both varroosis and sacbrood, but overall, the weather had a varying effect on the six honey bee diseases. The risk of disease was also spatially varied and was impacted by the meteorological variables. These results are an important step in identifying the impacts of climate change on honey bees and honey bee diseases.

Socioeconomic status and cardiovascular health in the COVID-19 pandemic.

The goals of this review are to evaluate the impact of socioeconomic (SE) status on the general health and cardiovascular health of individuals during the COVID-19 pandemic and also discuss the measures to address disparity. SE status is a strong predictor of premature morbidity and mortality within general health. A lower SE status also has implications of increased cardiovascular disease (CVD) mortality and poorer CVD risk factor profiles. CVD comorbidity is associated with a higher case severity and mortality rate from COVID-19, with both CVD and COVID-19 sharing important risk factors. The COVID-19 pandemic has adversely affected people of a lower SE status and of ethnic minority group, who in the most deprived regions are suffering double the mortality rate of the least deprived. The acute stress, economic recession and quarantine restrictions in the wake of COVID-19 are also predicted to cause a decline in mental health. This could pose substantial increase to CVD incidence, particularly with acute pathologies such as stroke, acute coronary syndrome and cardiogenic shock among lower SE status individuals and vulnerable elderly populations. Efforts to tackle SE status and CVD may aid in reducing avoidable deaths. The implementation of 'upstream' interventions and policies demonstrates promise in achieving the greatest population impact, aiming to protect and empower individuals. Specific measures may involve risk factor targeting restrictions on the availability and advertisement of tobacco, alcohol and high-fat and salt content food, and targeting SE disparity with healthy and secure workplaces.

Challenges in the management of older patients with acute coronary syndromes in the COVID-19 pandemic.

Ischaemic heart disease (IHD), in particular acute coronary syndrome (ACS), comprising ST-elevation myocardial infarction, non-ST-elevation myocardial infarction and unstable angina, is the leading cause of death worldwide. Age is a major predictor of adverse outcome following ACS. COVID-19 infection seems to escalate the risk in older patients with heart disease. Increasing odds of in-hospital death is associated with older age following COVID-19 infection. Importantly, it seems older patients with comorbidities such as cardiovascular disease (CVD), in particular IHD, diabetes and hypertension, are at the highest risk of mortality following COVID-19 infection. The evidence is sparse on the optimal care of older patients with ACS with lack of robust randomised controlled trials. In this setting, with the serious threat imposed by the COVID-19 pandemic in the context of rapidly evolving knowledge with much unknown, it is important to weigh the risks and benefits of treatment strategies offered to older patients. In cases where risks outweigh the benefits, it might not be an unreasonable option to treat such patients with a conservative or a palliative approach. Further evidence to elucidate whether invasive management is beneficial in older patients with ACS is required out-with the COVID-19 pandemic. Though it is hoped that the actual acute phase of COVID-19 infection will be short lived, it is vital that important clinical research is continued, given the long-term benefits of ongoing clinical research for patients with long-term conditions, including CVD. This review aimed to evaluate the challenges and the management strategies in the care of older patients presenting with ACS in the context of the COVID-19 pandemic.

Spatio-spectral regularization to improve magnetic resonance spectroscopic imaging quantification.

Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive technique able to provide the spatial distribution of relevant biochemical compounds commonly used as biomarkers of disease. Information provided by MRSI can be used as a valuable insight for the diagnosis, treatment and follow-up of several diseases such as cancer or neurological disorders. Obtaining accurate metabolite concentrations from in vivo MRSI signals is a crucial requirement for the clinical utility of this technique. Despite the numerous publications on the topic, accurate quantification is still a challenging problem due to the low signal-to-noise ratio of the data, overlap of spectral lines and the presence of nuisance components. We propose a novel quantification method, which alleviates these limitations by exploiting a spatio-spectral regularization scheme. In contrast to previous methods, the regularization terms are not expressed directly on the parameters being sought, but on appropriate transformed domains. In order to quantify all signals simultaneously in the MRSI grid, while introducing prior information, a fast proximal optimization algorithm is proposed. Experiments on synthetic MRSI data demonstrate that the error in the estimated metabolite concentrations is reduced by a mean of 41% with the proposed scheme. Results on in vivo brain MRSI data show the benefit of the proposed approach, which is able to fit overlapping peaks correctly and to capture metabolites that are missed by single-voxel methods due to their lower concentrations. Copyright © 2016 John Wiley & Sons, Ltd.

Hybrid sparse regularization for magnetic resonance spectroscopy.

Magnetic resonance spectroscopy imaging (MRSI) is a powerful non-invasive tool for characterising markers of biological processes. This technique extends conventional MRI by providing an additional dimension of spectral information describing the abnormal presence or concentration of metabolites of interest. Unfortunately, in vivo MRSI suffers from poor signal-to-noise ratio limiting its clinical use for treatment purposes. This is due to the combination of a weak MR signal and low metabolite concentrations, in addition to the acquisition noise. We propose a new method that handles this challenge by efficiently denoising MRSI signals without constraining the spectral or spatial profiles. The proposed denoising approach is based on wavelet transforms and exploits the sparsity of the MRSI signals both in the spatial and frequency domains. A fast proximal optimization algorithm is then used to recover the optimal solution. Experiments on synthetic and real MRSI data showed that the proposed scheme achieves superior noise suppression (SNR increase up to 60%). In addition, this method is computationally efficient and preserves data features better than existing methods.