To assess the value of exosomal miRNAs as biomarkers for Alzheimer disease (AD), the expression of microRNAs was measured in a plasma fraction enriched in exosomes by differential centrifugation, ...using Illumina deep sequencing. Samples from 35 persons with a clinical diagnosis of AD dementia were compared to 35 age and sex matched controls. Although these samples contained less than 0.1 microgram of total RNA, deep sequencing gave reliable and informative results. Twenty miRNAs showed significant differences in the AD group in initial screening (miR-23b-3p, miR-24-3p, miR-29b-3p, miR-125b-5p, miR-138-5p, miR-139-5p, miR-141-3p, miR-150-5p, miR-152-3p, miR-185-5p, miR-338-3p, miR-342-3p, miR-342-5p, miR-548at-5p, miR-659-5p, miR-3065-5p, miR-3613-3p, miR-3916, miR-4772-3p, miR-5001-3p), many of which satisfied additional biological and statistical criteria, and among which a panel of seven miRNAs were highly informative in a machine learning model for predicting AD status of individual samples with 83-89% accuracy. This performance is not due to over-fitting, because a) we used separate samples for training and testing, and b) similar performance was achieved when tested on technical replicate data. Perhaps the most interesting single miRNA was miR-342-3p, which was a) expressed in the AD group at about 60% of control levels, b) highly correlated with several of the other miRNAs that were significantly down-regulated in AD, and c) was also reported to be down-regulated in AD in two previous studies. The findings warrant replication and follow-up with a larger cohort of patients and controls who have been carefully characterized in terms of cognitive and imaging data, other biomarkers (e.g., CSF amyloid and tau levels) and risk factors (e.g., apoE4 status), and who are sampled repeatedly over time. Integrating miRNA expression data with other data is likely to provide informative and robust biomarkers in Alzheimer disease.
Micronutrient deficiencies account for an estimated one million premature deaths annually, and for some nations can reduce gross domestic product
by up to 11%, highlighting the need for food policies ...that focus on improving nutrition rather than simply increasing the volume of food produced
. People gain nutrients from a varied diet, although fish-which are a rich source of bioavailable micronutrients that are essential to human health
-are often overlooked. A lack of understanding of the nutrient composition of most fish
and how nutrient yields vary among fisheries has hindered the policy shifts that are needed to effectively harness the potential of fisheries for food and nutrition security
. Here, using the concentration of 7 nutrients in more than 350 species of marine fish, we estimate how environmental and ecological traits predict nutrient content of marine finfish species. We use this predictive model to quantify the global spatial patterns of the concentrations of nutrients in marine fisheries and compare nutrient yields to the prevalence of micronutrient deficiencies in human populations. We find that species from tropical thermal regimes contain higher concentrations of calcium, iron and zinc; smaller species contain higher concentrations of calcium, iron and omega-3 fatty acids; and species from cold thermal regimes or those with a pelagic feeding pathway contain higher concentrations of omega-3 fatty acids. There is no relationship between nutrient concentrations and total fishery yield, highlighting that the nutrient quality of a fishery is determined by the species composition. For a number of countries in which nutrient intakes are inadequate, nutrients available in marine finfish catches exceed the dietary requirements for populations that live within 100 km of the coast, and a fraction of current landings could be particularly impactful for children under 5 years of age. Our analyses suggest that fish-based food strategies have the potential to substantially contribute to global food and nutrition security.
Structure and function of therapeutic antibodies can be modulated by a variety of post-translational modifications (PTM). Tyrosine (Tyr) sulfation is a type of negatively charged PTM that occurs ...during protein trafficking through the Golgi. In this study, we discovered that an anti-interleukin (IL)-4 human IgG1, produced by transiently transfected HEK293 cells, contained a fraction of unusual negatively charged species. Interestingly, the isolated acidic species exhibited a two-fold higher affinity to IL-4 and a nearly four-fold higher potency compared to the main species. Mass spectrometry (MS) showed the isolated acidic species possessed an +80-Dalton from the expected mass, suggesting an occurrence of Tyr sulfation. Consistent with this hypothesis, we show the ability to control the acidic species during transient expression with the addition of Tyr sulfation inhibitor sodium chlorate or, conversely, enriched the acidic species from 30% to 92% of the total antibody protein when the IL-4 IgG was co-transfected with tyrosylprotein sulfotransferase genes. Further MS and mutagenesis analysis identified a Tyr residue at the light chain complementarity-determining region-1 (CDRL-1), which was sulfated specifically. These results together have demonstrated for the first time that Tyr sulfation at CDRL-1 could modulate antibody binding affinity and potency to a human immune cytokine.
Erdheim-Chester disease (ECD) is a rare histiocytosis that was recently recognized as a neoplastic disorder owing to the discovery of recurrent activating MAPK (RAS-RAF-MEK-ERK) pathway mutations. ...Typical findings of ECD include central diabetes insipidus, restrictive pericarditis, perinephric fibrosis, and sclerotic bone lesions. The histopathologic diagnosis of ECD is often challenging due to nonspecific inflammatory and fibrotic findings on histopathologic review of tissue specimens. Additionally, the association of ECD with unusual tissue tropism and an insidious onset often results in diagnostic errors and delays. Most patients with ECD require treatment, except for a minority of patients with minimally symptomatic single-organ disease. The first ECD consensus guidelines were published in 2014 on behalf of the physicians and researchers within the Erdheim-Chester Disease Global Alliance. With the recent molecular discoveries and the approval of the first targeted therapy (vemurafenib) for BRAF-V600-mutant ECD, there is a need for updated clinical practice guidelines to optimize the diagnosis and treatment of this disease. This document presents consensus recommendations that resulted from the International Medical Symposia on ECD in 2017 and 2019. Herein, we include the guidelines for the clinical, laboratory, histologic, and radiographic evaluation of ECD patients along with treatment recommendations based on our clinical experience and review of literature in the molecular era.
Pulmonary embolism (PE)-related mortality is decreasing in Europe. However, time trends in the USA and Canada remain uncertain because the most recent analyses of PE-related mortality were published ...in the early 2000s.
For this retrospective epidemiological study, we accessed medically certified vital registration data from the WHO Mortality Database (USA and Canada, 2000-17) and the Multiple Cause of Death database produced by the Division of Vital Statistics of the US Centers for Disease Control and Prevention (CDC; US, 2000-18). We investigated contemporary time trends in PE-related mortality in the USA and Canada and the prevalence of conditions contributing to PE-related mortality reported on the death certificates. We also estimated PE-related mortality by age group and sex. A subgroup analysis by race was performed for the USA.
In the USA, the age-standardised annual mortality rate (PE as the underlying cause) decreased from 6·0 deaths per 100 000 population (95% CI 5·9-6·1) in 2000 to 4·4 deaths per 100 000 population (4·3-4·5) in 2006. Thereafter, it continued to decrease to 4·1 deaths per 100 000 population (4·0-4·2) in women in 2017 and plateaued at 4·5 deaths per 100 000 population (4·4-4·7) in men in 2017. Among adults aged 25-64 years, it increased after 2006. The median age at death from PE decreased from 73 years to 68 years (2000-18). The prevalence of cancer, respiratory diseases, and infections as a contributing cause of PE-related death increased in all age categories from 2000 to 2018. The annual age-standardised PE-related mortality was consistently higher by up to 50% in Black individuals than in White individuals; these rates were approximately 50% higher in White individuals than in those of other races. In Canada, the annual age-standardised mortality rate from PE as the underlying cause of death decreased from 4·7 deaths per 100 000 population (4·4-5·0) in 2000 to 2·6 deaths per 100 000 population (2·4-2·8) in 2017; this decline slowed after 2006 across age groups and sexes.
After 2006, the initially decreasing PE-related mortality rates in North America progressively reached a plateau in Canada, while a rebound increase was observed among young and middle-aged adults in the USA. These findings parallel recent upward trends in mortality from other cardiovascular diseases and might reflect increasing inequalities in the exposure to risk factors and access to health care.
None.
With the growing adoption of the electronic health record (EHR) worldwide over the last decade, new opportunities exist for leveraging EHR data for detection of rare diseases. Rare diseases are often ...not diagnosed or delayed in diagnosis by clinicians who encounter them infrequently. One such rare disease that may be amenable to EHR-based detection is acute hepatic porphyria (AHP). AHP consists of a family of rare, metabolic diseases characterized by potentially life-threatening acute attacks and chronic debilitating symptoms. The goal of this study was to apply machine learning and knowledge engineering to a large extract of EHR data to determine whether they could be effective in identifying patients not previously tested for AHP who should receive a proper diagnostic workup for AHP.
We used an extract of the complete EHR data of 200,000 patients from an academic medical center and enriched it with records from an additional 5,571 patients containing any mention of porphyria in the record. After manually reviewing the records of all 47 unique patients with the ICD-10-CM code E80.21 (Acute intermittent hepatic porphyria), we identified 30 patients who were positive cases for our machine learning models, with the rest of the patients used as negative cases. We parsed the record into features, which were scored by frequency of appearance and filtered using univariate feature analysis. We manually choose features not directly tied to provider attributes or suspicion of the patient having AHP. We trained on the full dataset, with the best cross-validation performance coming from support vector machine (SVM) algorithm using a radial basis function (RBF) kernel. The trained model was applied back to the full data set and patients were ranked by margin distance. The top 100 ranked negative cases were manually reviewed for symptom complexes similar to AHP, finding four patients where AHP diagnostic testing was likely indicated and 18 patients where AHP diagnostic testing was possibly indicated. From the top 100 ranked cases of patients with mention of porphyria in their record, we identified four patients for whom AHP diagnostic testing was possibly indicated and had not been previously performed. Based solely on the reported prevalence of AHP, we would have expected only 0.002 cases out of the 200 patients manually reviewed.
The application of machine learning and knowledge engineering to EHR data may facilitate the diagnosis of rare diseases such as AHP. Further work will recommend clinical investigation to identified patients' clinicians, evaluate more patients, assess additional feature selection and machine learning algorithms, and apply this methodology to other rare diseases. This work provides strong evidence that population-level informatics can be applied to rare diseases, greatly improving our ability to identify undiagnosed patients, and in the future improve the care of these patients and our ability study these diseases. The next step is to learn how best to apply these EHR-based machine learning approaches to benefit individual patients with a clinical study that provides diagnostic testing and clinical follow up for those identified as possibly having undiagnosed AHP.
Abstract
Objectives
Identifying all published reports of randomized controlled trials (RCTs) is an important aim, but it requires extensive manual effort to separate RCTs from non-RCTs, even using ...current machine learning (ML) approaches. We aimed to make this process more efficient via a hybrid approach using both crowdsourcing and ML.
Methods
We trained a classifier to discriminate between citations that describe RCTs and those that do not. We then adopted a simple strategy of automatically excluding citations deemed very unlikely to be RCTs by the classifier and deferring to crowdworkers otherwise.
Results
Combining ML and crowdsourcing provides a highly sensitive RCT identification strategy (our estimates suggest 95%–99% recall) with substantially less effort (we observed a reduction of around 60%–80%) than relying on manual screening alone.
Conclusions
Hybrid crowd-ML strategies warrant further exploration for biomedical curation/annotation tasks.
Brown fat can reduce obesity through the dissipation of calories as heat. Control of thermogenic gene expression occurs via the induction of various coactivators, most notably PGC-1α. In contrast, ...the transcription factor partner(s) of these cofactors are poorly described. Here, we identify interferon regulatory factor 4 (IRF4) as a dominant transcriptional effector of thermogenesis. IRF4 is induced by cold and cAMP in adipocytes and is sufficient to promote increased thermogenic gene expression, energy expenditure, and cold tolerance. Conversely, knockout of IRF4 in UCP1+ cells causes reduced thermogenic gene expression and energy expenditure, obesity, and cold intolerance. IRF4 also induces the expression of PGC-1α and PRDM16 and interacts with PGC-1α, driving Ucp1 expression. Finally, cold, β-agonists, or forced expression of PGC-1α are unable to cause thermogenic gene expression in the absence of IRF4. These studies establish IRF4 as a transcriptional driver of a program of thermogenic gene expression and energy expenditure.
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•IRF4 is induced by cold and cAMP in mouse and human brown adipocytes•Targeted overexpression of IRF4 promotes thermogenesis and leanness•Loss of IRF4 reduces thermogenesis and causes obesity and cold intolerance•IRF4 interacts physically and functionally with PGC-1α to promote thermogenesis
Brown fat has thermogenic capacity that can be explored to reduce obesity. These data identify IRF4 as the key central factor that controls the thermogenic gene expression program in brown fat cells, regulating energy expenditure and cold tolerance.
Studies found associations between atopic dermatitis (AD) and various comorbidities.
To appraise evidence of the association between AD and comorbidities among adults.
Our multidisciplinary work ...group conducted a systematic review of the association between AD and selected comorbidities. We applied the Grading of Recommendations, Assessment, Development, and Evaluation for prognosis approach for assessing the certainty of the evidence, providing statements of association based on the available evidence.
Analysis of the evidence resulted in 32 statements. Clear evidence of the association of AD in adults and select allergic, atopic, immune-mediated mental health and bone health conditions and skin infections was identified. There is some evidence supporting an association between AD and substance use, attention deficit hyperactivity disorder, and elements of metabolic syndrome. Evidence suggests a small association with various cardiovascular conditions. The association between AD in adults and autism spectrum disorders, myocardial infarction, stroke, and metabolic syndrome is inconclusive.
This analysis is based on the best available evidence at the time it was conducted. This guideline does not make recommendations for screening or management of comorbidities in adults with AD.
Clinicians should be aware of comorbidities associated with AD. Further research is needed to determine whether screening or management of comorbidities is beneficial for adults with AD.
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