Obioma Ezekobe is a GP in an urgent care centre in Central Middlesex Hospital. She believes that the public need to be educated about the use of NHS resources, and be taught when it is appropriate to seek care. If you would like to contribute to this collection, please email a brief audio recording to voices@bmj.com or phone +44 (20) 3058 7427...

The BMJ has been campaigning for an end to “too much medicine” - the pernicious effect of marketing on the range of tests and treatments that doctors offer patients - tests and treatments which are motivated by the financial reward to the system, than the health of the individual. A new analysis on BMJ.com takes a look at what’s happening in the...

In glioma patients, differentiation between tumor progression (TP) and treatment-related changes (TRCs) remains challenging. Difficulties in classifying imaging alterations may result in a delay or an unnecessary discontinuation of treatment. PET using O-(2-¹⁸F-fluoroethyl)-l-tyrosine (¹⁸F-FET) has been shown to be a useful tool for detecting TP and TRCs. Methods: We retrospectively evaluated 127 consecutive patients with World Health Organization grade II–IV glioma who underwent ¹⁸F-FET PET imaging to distinguish between TP and TRCs. ¹⁸F-FET PET findings were verified by neuropathology (40 patients) or clinicoradiologic follow-up (87 patients). Maximum tumor-to-brain ratios (TBR_max) of ¹⁸F-FET uptake and the slope of the time–activity curves (20–50 min after injection) were determined. The diagnostic accuracy of ¹⁸F-FET PET parameters was evaluated by receiver-operating-characteristic analysis and ² testing. The prognostic value of ¹⁸F-FET PET was estimated using the Kaplan–Meier method. Results: TP was diagnosed in 94 patients (74%) and TRCs in 33 (26%). For differentiating TP from TRCs, receiver-operating-characteristic analysis yielded an optimal ¹⁸F-FET TBR_max cutoff of 1.95 (sensitivity, 70%; specificity, 71%; accuracy, 70%; area under the curve, 0.75 ± 0.05). The highest accuracy was achieved by a combination of TBR_max and slope (sensitivity, 86%; specificity, 67%; accuracy, 81%). However, accuracy was poorer when tumors harbored isocitrate dehydrogenase (IDH) mutations (91% in IDH-wild-type tumors, 67% in IDH-mutant tumors, P < 0.001). ¹⁸F-FET PET results correlated with overall survival (P < 0.001). Conclusion: In our neurooncology department, the diagnostic performance of ¹⁸F-FET PET was convincing but slightly inferior to that of previous reports.

Posttreatment high-grade gliomas are usually monitored with contrast-enhanced MRI, but its diagnostic accuracy is limited as it cannot adequately distinguish between true tumor progression and treatment-related changes. According to recent Response Assessment in Neuro-Oncology recommendations, PET overcomes this limitation. However, it is currently unknown which tracer yields the best results. Therefore, a systematic review and metaanalysis were performed to compare the diagnostic accuracy of the different PET tracers in differentiating tumor progression from treatment-related changes in high-grade glioma patients. Methods: PubMed, Web of Science, and Embase were searched systematically. Study selection, data extraction, and quality assessment were performed independently by 2 authors. Metaanalysis was performed using a bivariate random-effects model when at least 5 studies were included. Results: The systematic review included 39 studies (11 tracers). ¹⁸F-FDG (12 studies, 171 lesions) showed a pooled sensitivity and specificity of 84% (95% confidence interval, 72%–92%) and 84% (95% confidence interval, 69%–93%), respectively. O-(2-¹⁸F-fluoroethyl)-l-tyrosine (¹⁸F-FET) (7 studies, 172 lesions) demonstrated a sensitivity of 90% (95% confidence interval, 81%–95%) and specificity of 85% (95% confidence interval, 71%–93%). For S-¹¹C-methyl)-l-methionine (¹¹C-MET) (8 studies, 151 lesions), sensitivity was 93% (95% confidence interval, 80%–98%) and specificity was 82% (95% confidence interval, 68%–91%). The numbers of included studies for the other tracers were too low to combine, but sensitivity and specificity ranged between 93%–100% and 0%–100%, respectively, for ¹⁸F-FLT; 85%–100% and 72%–100%, respectively, for 3,4-dihydroxy-6-¹⁸F-fluoro-l-phenylalanine (¹⁸F-FDOPA); and 100% and 70%–88%, respectively, for ¹¹C-choline. Conclusion: ¹⁸F-FET and ¹¹C-MET, both amino-acid tracers, showed a comparably higher sensitivity than ¹⁸F-FDG in the differentiation between tumor progression and treatment-related changes in high-grade glioma patients. The evidence for other tracers is limited; thus, ¹⁸F-FET and ¹¹C-MET are preferred when available. Our results support the incorporation of amino-acid PET tracers for the treatment evaluation of high-grade gliomas.

Radiomics is a rapidly evolving field of research concerned with the extraction of quantitative metrics—the so-called radiomic features—within medical images. Radiomic features capture tissue and lesion characteristics such as heterogeneity and shape and may, alone or in combination with demographic, histologic, genomic, or proteomic data, be used for clinical problem solving. The goal of this continuing education article is to provide an introduction to the field, covering the basic radiomics workflow: feature calculation and selection, dimensionality reduction, and data processing. Potential clinical applications in nuclear medicine that include PET radiomics-based prediction of treatment response and survival will be discussed. Current limitations of radiomics, such as sensitivity to acquisition parameter variations, and common pitfalls will also be covered.

Frederick S Fein
Nov 1, 1990; 13:1169-1179
Supplement 4: Diabetes 80 Black Populations: Current State of Knowledge

OBJECTIVE

To determine the effect of tirzepatide, a dual agonist of glucose-dependent insulinotropic polypeptide and glucagon-like peptide 1 receptors, on biomarkers of nonalcoholic steatohepatitis (NASH) and fibrosis in patients with type 2 diabetes mellitus (T2DM).

OBJECTIVE

Diabetes is a major risk factor for renal function decline and failure. The availability of multiplex panels of biochemical markers provides the opportunity to identify novel biomarkers that can better predict changes in renal function than routinely available clinical markers.

OBJECTIVE

Plasma protein N-glycan profiling integrates information on enzymatic protein glycosylation, which is a highly controlled ubiquitous posttranslational modification. Here we investigate the ability of the plasma N-glycome to predict incidence of type 2 diabetes and cardiovascular diseases (CVDs; i.e., myocardial infarction and stroke).

Time -- Philosophy.

Paris : Société d’éditions scientifiques, 1895.

Paris : G. Carre, 1895.

Boyu Ren, Sergio Bacallado, Stefano Favaro, Tommi Vatanen, Curtis Huttenhower, Lorenzo Trippa.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 494--517.

Abstract:
Detecting associations between microbial compositions and sample characteristics is one of the most important tasks in microbiome studies. Most of the existing methods apply univariate models to single microbial species separately, with adjustments for multiple hypothesis testing. We propose a Bayesian analysis for a generalized mixed effects linear model tailored to this application. The marginal prior on each microbial composition is a Dirichlet process, and dependence across compositions is induced through a linear combination of individual covariates, such as disease biomarkers or the subject’s age, and latent factors. The latent factors capture residual variability and their dimensionality is learned from the data in a fully Bayesian procedure. The proposed model is tested in data analyses and simulation studies with zero-inflated compositions. In these settings and within each sample, a large proportion of counts per microbial species are equal to zero. In our Bayesian model a priori the probability of compositions with absent microbial species is strictly positive. We propose an efficient algorithm to sample from the posterior and visualizations of model parameters which reveal associations between covariates and microbial compositions. We evaluate the proposed method in simulation studies, and then analyze a microbiome dataset for infants with type 1 diabetes which contains a large proportion of zeros in the sample-specific microbial compositions.

Susheela P. Singh, Ana-Maria Staicu, Robert R. Dunn, Noah Fierer, Brian J. Reich.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2341--2362.

Abstract:
The advent of high-throughput sequencing technologies has made data from DNA material readily available, leading to a surge of microbiome-related research establishing links between markers of microbiome health and specific outcomes. However, to harness the power of microbial communities we must understand not only how they affect us, but also how they can be influenced to improve outcomes. This area has been dominated by methods that reduce community composition to summary metrics, which can fail to fully exploit the complexity of community data. Recently, methods have been developed to model the abundance of taxa in a community, but they can be computationally intensive and do not account for spatial effects underlying microbial settlement. These spatial effects are particularly relevant in the microbiome setting because we expect communities that are close together to be more similar than those that are far apart. In this paper, we propose a flexible Bayesian spike-and-slab variable selection model for presence-absence indicators that accounts for spatial dependence and cross-dependence between taxa while reducing dimensionality in both directions. We show by simulation that in the presence of spatial dependence, popular distance-based hypothesis testing methods fail to preserve their advertised size, and the proposed method improves variable selection. Finally, we present an application of our method to an indoor fungal community found within homes across the contiguous United States.

Gabriela V. Cohen Freue, David Kepplinger, Matías Salibián-Barrera, Ezequiel Smucler.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2065--2090.

Abstract:
In large-scale quantitative proteomic studies, scientists measure the abundance of thousands of proteins from the human proteome in search of novel biomarkers for a given disease. Penalized regression estimators can be used to identify potential biomarkers among a large set of molecular features measured. Yet, the performance and statistical properties of these estimators depend on the loss and penalty functions used to define them. Motivated by a real plasma proteomic biomarkers study, we propose a new class of penalized robust estimators based on the elastic net penalty, which can be tuned to keep groups of correlated variables together in the selected model and maintain robustness against possible outliers. We also propose an efficient algorithm to compute our robust penalized estimators and derive a data-driven method to select the penalty term. Our robust penalized estimators have very good robustness properties and are also consistent under certain regularity conditions. Numerical results show that our robust estimators compare favorably to other robust penalized estimators. Using our proposed methodology for the analysis of the proteomics data, we identify new potentially relevant biomarkers of cardiac allograft vasculopathy that are not found with nonrobust alternatives. The selected model is validated in a new set of 52 test samples and achieves an area under the receiver operating characteristic (AUC) of 0.85.

Youyi Zhang, Jeffrey S. Morris, Shivali Narang Aerry, Arvind U. K. Rao, Veerabhadran Baladandayuthapani.

Source: The Annals of Applied Statistics, Volume 13, Number 3, 1957--1988.

Abstract:
Technological innovations have produced large multi-modal datasets that include imaging and multi-platform genomics data. Integrative analyses of such data have the potential to reveal important biological and clinical insights into complex diseases like cancer. In this paper, we present Bayesian approaches for integrative analysis of radiological imaging and multi-platform genomic data, where-in our goals are to simultaneously identify genomic and radiomic, that is, radiology-based imaging markers, along with the latent associations between these two modalities, and to detect the overall prognostic relevance of the combined markers. For this task, we propose Radio-iBAG: Radiomics-based Integrative Bayesian Analysis of Multiplatform Genomic Data , a multi-scale Bayesian hierarchical model that involves several innovative strategies: it incorporates integrative analysis of multi-platform genomic data sets to capture fundamental biological relationships; explores the associations between radiomic markers accompanying genomic information with clinical outcomes; and detects genomic and radiomic markers associated with clinical prognosis. We also introduce the use of sparse Principal Component Analysis (sPCA) to extract a sparse set of approximately orthogonal meta-features each containing information from a set of related individual radiomic features, reducing dimensionality and combining like features. Our methods are motivated by and applied to The Cancer Genome Atlas glioblastoma multiforme data set, where-in we integrate magnetic resonance imaging-based biomarkers along with genomic, epigenomic and transcriptomic data. Our model identifies important magnetic resonance imaging features and the associated genomic platforms that are related with patient survival times.

Kurtis Shuler, Marilou Sison-Mangus, Juhee Lee.

Source: Bayesian Analysis, Volume 15, Number 2, 559--578.

Abstract:
We propose a Bayesian sparse multivariate regression method to model the relationship between microbe abundance and environmental factors for microbiome data. We model abundance counts of operational taxonomic units (OTUs) with a negative binomial distribution and relate covariates to the counts through regression. Extending conventional nonlocal priors, we construct asymmetric nonlocal priors for regression coefficients to efficiently identify relevant covariates and their effect directions. We build a hierarchical model to facilitate pooling of information across OTUs that produces parsimonious results with improved accuracy. We present simulation studies that compare variable selection performance under the proposed model to those under Bayesian sparse regression models with asymmetric and symmetric local priors and two frequentist models. The simulations show the proposed model identifies important covariates and yields coefficient estimates with favorable accuracy compared with the alternatives. The proposed model is applied to analyze an ocean microbiome dataset collected over time to study the association of harmful algal bloom conditions with microbial communities.

The bright blue pigment that adorns the Bust of Nefertiti’s crown can now be used to study molecular biology

One major goal of integrative and comparative biology is to understand and explain the interaction between the performance and behavior of animals in their natural environment. The Caviomorph, Octodon degu, is a native rodent species from Chile, and represents a unique model to study physiological and behavioral traits, including cognitive and sensory abilities. Degus live in colonies and have a well-structured social organization, with a mostly diurnal–crepuscular circadian activity pattern. More notable is the fact that in captivity, they reproduce and live between 5 and 7 yr and show hallmarks of neurodegenerative diseases (including Alzheimer's disease), diabetes, and cancer.

Changes in BMI category appear to be common in young children and are associated with cardiometabolic risk in cross-sectional studies. However, there are few longitudinal studies and little information from multiethnic samples of US middle school children.

Findings demonstrate that shifts in BMI category are common in middle-school-aged children and associated with clinically meaningful changes in cardiometabolic risk factors. Programs to promote decreases in BMI, prevent increases, and moderate risk are indicated. (Read the full article)

Infantile hemangiomas have a period of rapid growth in early infancy. Most hemangioma growth is completed by 5 months of age, but the majority of patients are not seen by a specialist until after the growth phase is complete.

The most rapid hemangioma growth is between 1 and 2 months of life, much earlier than previously believed. Patients with high-risk hemangiomas should be followed closely, and treatment initiation should be considered before or during the most rapid growth phase. (Read the full article)

The presence of elevated cardiometabolic risk factors, such as obesity, high glucose or lipid levels, and smoking, in adolescents has been shown to be associated with earlier onset of chronic conditions, such as diabetes and heart disease.

Obesity, smoking, and elevated glucose increases the risk of dying before the age of 55 years. This is the first study to focus on risk factors and mortality among adolescents and young adults in a nationally representative US sample. (Read the full article)

The systemic nonselective β-blocker propranolol hydrochloride is increasingly used as first-line management for infantile hemangiomas. Superficial nonulcerating lesions do not require systemic medications. Case series have suggested the efficacy of timolol; however, its safety has been questioned.

This randomized controlled trial indicates that timolol maleate 0.5% gel is a well-tolerated, safe, and effective treatment of superficial infantile hemangiomas. (Read the full article)

Resistance exercise is known to have a robust effect on glycemic control and cardiometabolic health among children and adolescents, even in the absence of weight loss.

Normalized strength capacity is associated with lower cardiometabolic risk clustering in boys and girls, even after adjustment for cardiorespiratory fitness, level of physical activity, and BMI. (Read the full article)

Acute kidney injury (AKI) occurs in up to 50% of children after cardiopulmonary bypass and is associated with adverse outcomes. Renal biomarkers have been shown to predict postoperative AKI, but few studies have examined cardiac biomarkers for risk classification.

Preoperative levels of creatine kinase-MB and heart-type fatty acid binding protein are strongly associated with the development of postoperative AKI after pediatric cardiac surgery and can be used to improve preoperative clinical risk prediction. (Read the full article)

Insulin resistance plays a role in obesity. Recently it has been associated with increased risk of AD. Aβ42 and PSEN1 are molecules associated with increased risk of later AD. Patients affected by AD show elevated levels of plasma Aβ42.

Levels of Aβ42 and PSEN1 are significantly elevated in obese adolescents and correlated with the degree of both adiposity and systemic insulin resistance. (Read the full article)

Evidence on the cardiometabolic consequences of sedentary behavior in youth is inconsistent and mostly relies on cross-sectional studies. Studies with objective measures of sedentary time have found limited evidence of cross-sectional associations with adiposity markers but no other outcomes.

Objectively assessed daily sedentary time was not prospectively associated with cardiometabolic outcomes. Moderate to vigorous physical activity was beneficially associated with body fat mass, insulin, high-density lipoprotein cholesterol, and clustered cardiometabolic score. (Read the full article)

OBJECTIVES:

Infantile hemangiomas (IHs) are common; some cases require timely referral and treatment to prevent complications. We developed and validated a reliable instrument for timely and adequate referral of patients with IH to experts by nonexpert primary physicians.

The partnership between Reliance Retail Whatsapp - inked on Wednesda -and could mean more competition for the likes of BigBasket and Grofers and others like Amazon and Flipkart looking to get a share of the food and groceries pie.

Canada has opened Service centers across the country for taking biometrics related to foreign nationals who apply to stay in it.A New Approach for EfficiencyBefore December 3rd, such immigration candidates, who were applying away from Canada, needed…

રિલાયન્સે ગત 31મી ડિસેમ્બરના રોજ Amazon અને Flipkartને ટક્કર આપવા માટે પોતાનું નવું ઈ-કૉમર્સ સાહસ 'જિયો માર્ટ' (JioMart) લૉંચ કર્યું હતું.