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CCR Translation for This Article from Plectin-1 as a Novel Biomarker for Pancreatic Cancer

Purpose: We are in great need of specific biomarkers to detect pancreatic ductal adenocarcinoma (PDAC) at an early stage, ideally before invasion. Plectin-1 (Plec1) was recently identified as one such biomarker. However, its suitability as a specific biomarker for human pancreatic cancer, and its usability as an imaging target, remain to be assessed.Experimental Design: Specimens of human PDAC, chronic pancreatitis, and normal pancreata were evaluated by immunohistochemistry and Western blot analysis. To validate Plec1 as an imaging target, Plec1-targeting peptides (tPTP) were used as a contrast agent for single photon emission computed tomography in an orthotopic and liver metastasis murine model of PDAC.Results: Plec1 expression was noted to be positive in all PDACs but negative in benign tissues. Plec1 expression increases during pancreatic carcinogenesis. It was found to be misexpressed in only 0% to 3.85% of early PDAC precursor lesions (PanIN I/II) but in 60% of PanIN III lesions. Plec1 expression was further noted to be retained in all metastatic foci assayed and clearly highlighted these metastatic deposits in lymph nodes, liver, and peritoneum. In vivo imaging using tPTP specifically highlighted the primary and metastatic tumors. Biodistribution studies performed after imaging show that the primary pancreatic tumors and liver metastases retained 1.9- to 2.9-fold of tPTP over normal pancreas and 1.7-fold over normal liver.Conclusions: Plec1 is the first biomarker to identify primary and metastatic PDAC by imaging and may also detect preinvasive PanIN III lesions. Strategies designed to image Plec1 could therefore improve detection and staging. Clin Cancer Res; 17(2); 302–9. ©2010 AACR.

This single-centre retrospective study reports the dynamics of the incidence of candida bloodstream infection (CBSI) in 145 patients receiving venovenous extracorporeal membrane oxygenation (ECMO) for respiratory support between January 2014 and December 2018. The incidence rate and odds ratio (OR) of CBSI were calculated, stratified by week of ECMO exposure. Weekly incidence increased throughout the ECMO run, with an increasing trend in OR (P=0.005), and a window of continued risk after decannulation was observed. Of the 13 patients who developed CBSI, five (38%) received empirical micafungin treatment before positive culture due to clinical suspicion. There is a need for prospective studies aiming to improve ECMO diagnostic stewardship practices and discourage unnecessary antifungal prophylaxis or empiric management.

Breastfeeding is universally recognized as the preferred method of infant nutrition, but is sometimes abbreviated because of fear of harm to the infant from maternal medication. The amount of medication that enters breast milk varies based on the maternal serum concentration and the pharmacologic properties of the medication. When prescribing medications for a breastfeeding patient, those with the lowest risk to the infant should be selected, and dosing should be before the infant's longest sleep interval. Prescribers should use current, accurate resources. LactMed is a convenient, government-sponsored, authoritative resource that lists safety information for many medications and is available free online. When mental health conditions occur during lactation, priority should be given to effectively treating the mother, often with medications that were effective during pregnancy. Most antidepressants are compatible with breastfeeding. Stimulant medications may decrease milk supply. Insulin, metformin, and second-generation sulfonylureas are generally preferred to treat diabetes mellitus during breastfeeding, but newer agents require caution because they have not been studied in lactation. Inhaled and nasal treatments for asthma and allergic rhinitis are unlikely to affect breastfed infants. Acetaminophen and ibuprofen are preferred analgesics during lactation. Maternal opioid use can cause infant sedation. Herbal supplements are concerning for risk of impurities and lack of study of effects on breastfed infants. Nonhormonal and progestin-only contraceptives are preferred over combination oral contraceptives. Contrast for computed tomography or magnetic resonance imaging is not concerning during lactation, but use of radiopharmaceuticals, such as iodine 131, can accumulate in the lactating breast and increase risk to the infant.

Background Health utilities from value sets for the EQ-5D-5L are commonly used in economic evaluations. We examined whether modeling spatial correlation among health states could improve the precision of the value sets. Methods Using data from 7 EQ-5D-5L valuation studies, we compared the predictive precision of the published linear model, a recently proposed cross-attribute level effects (CALE) model, and 2 Bayesian models with spatial correlation. Predictive precision was quantified through the root mean squared error (RMSE) for out-of-sample predictions of state-level mean utilities on omitting individual states, as well as omitting blocks of states. Results In all 7 countries, on omitting single health states, Bayesian models with spatial correlation improved upon the published linear model: the RMSEs for the originally published models, 0.050, 0.051, 0.060, 0.061, 0.039, 0.050, and 0.087 for Canada, China, Germany, Indonesia, Japan, Korea, and the Netherlands, respectively, could be reduced to 0.043, 0.042, 0.051, 0.054, 0.037, 0.037, and 0.085, respectively. On omitting blocks of health states, Bayesian models with spatial correlation led to smaller RMSEs in 3 countries, while the CALE model led to smaller RMSEs in the remaining 4 countries. Discussion: Bayesian models incorporating spatial correlation and CALE models are promising for improving the precision of value sets for the EQ-5D-5L. The differential performance of the Bayesian models on omitting single states versus blocks of states suggests that designing valuation studies to capture more health states may further improve precision. We suggest that Bayesian and CALE models be considered as candidates when creating value sets and that alternative designs be explored; this is vital as the prediction errors in value sets need to be smaller than the minimal important difference of the instrument. Highlights The accuracy of value sets of multi-attribute utility instruments is typically of the same order of magnitude as the instrument’s minimal important difference and would benefit from improvement. Bayesian models with spatial correlation have been shown to improve value set accuracy in isolated cases. We showed that Bayesian approaches with spatial correlation improved predictive precision in 7 EQ-5D-5L valuation studies. We recommend that Bayesian models incorporating spatial correlation be considered when creating value sets and have provided code for fitting them.

The Composite Asthma Severity Index (CASI) is a comprehensive tool to assess asthma severity, which has been applied in the research setting.To evaluate, in an outpatient setting, whether a CASI score accurately predicts asthma severity or control as determined by means of subspecialist assessment. Asthma Control Test (ACT) and childhood ACT (C-ACT) scores were generated to provide additional context for CASI scores in relationship to assessments using another clinical tool.Children aged 5 to 18 years with a physician diagnosis of persistent asthma were recruited from a tertiary care center. A pediatric pulmonologist made determinations on each participant's asthma severity and control during a clinic visit. A CASI and ACT/C-ACT score was generated for each patient. Logistic regression and Spearman correlations were used to determine how well CASI scores predicted physician assessments. Agreement between ACT/C-ACT scores and physician assessment of asthma control was determined in supplemental analyses.CASI scores strongly predicted physician assessment of severity (Spearman correlation = 0.61, P.001); unadjusted odds ratio (OR) equal to 36.67 (95% confidence interval [CI]: 8.83-152.34); and adjusted OR equal to 32.76 (95% CI: 85.70-188.44). In supplemental analyses, ACT/C-ACT scores strongly predicted physician assessment of control (Spearman correlation = 0.72, P.001) with an unadjusted OR equal to 42.12 (95% CI: 13.34-133.00) and adjusted OR equal to 55.34 (95% CI: 13.62-224.89).Use of the CASI was feasible and accurately predicted physician assessments of asthma severity and control in this sample, which are not distinct entities. The CASI is a robust tool that may be used successfully in ambulatory pediatric asthma care.

The EQ-5D-5L is a commonly used instrument for assessing the utility of different health states. Health state utility values are a key component of health technology evaluations. Such evaluations are used to support evidence-based decisions surrounding health resource allocations and therefore rely on the accuracy of the valuation set used. This paper takes an alternative approach to developing an EQ-5D-5L value set for Canada. The aim is to introduce a robust method that is likely to generate a value set with improved accuracy and that can be used to generate value sets for other populations without the need for modification. The common approach to developing a valuation set for preference-based instruments is to ask a population sample to value a subset of the health states using an established preference elicitation technique. The relationship between the elicited health states and the preferences is used to inform a model to predict the utility values for the unsampled health states described by the instrument. The true relationship is unknown and the functional forms chosen in the modelling process vary across valuation studies. We use nonparametric local constant regression to estimate an EQ-5D-5L value set for Canada and propose this method as an alternative for value set development because it does not require the specification of a functional form at the outset. Compared to the existing valuation model for Canada, the nonparametric method improves in-sample fit, reducing the average squared prediction error by 94.46% and the mean absolute error by 79.37%. In four of five sets of out-of-sample studies, this new approach performs significantly better than 9 comparison models. Despite lacking any restriction on the functional form of the resulting valuations, the valuation set generated by this new approach is logically consistent. 100% of the pairs of health states in which one state is dominant have health state values which respect this ordering. The value set also appears to differ substantially from the comparators. Overall, the results suggest that nonparametric regression is a promising tool for the estimation of EQ-5D-5L valuation sets and may be a good option in a standardised methodology for value set development.

Since December 2019, the global pandemic caused by the highly infectious novel coronavirus 2019-nCoV (COVID-19) has been rapidly spreading. As of April 2020, the outbreak has spread to over 210 countries, with over 2,400,000 confirmed cases and over 170,000 deaths [1]. COVID-19 causes a severe pneumonia characterized by fever, cough, and shortness of breath. Similar coronavirus outbreaks have occurred in the past causing severe pneumonia like COVID-19, most recently, severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV). However, over time, SARS-CoV and MERS-CoV were shown to cause extra-pulmonary signs and symptoms including hepatitis, acute renal failure, encephalitis, myositis, and gastroenteritis. Similarly, sporadic reports of COVID-19 related extrapulmonary manifestations emerge. Unfortunately, there is no comprehensive summary of the multi-organ manifestations of COVID-19, making it difficult for clinicians to quickly educate themselves about this highly contagious and deadly pathogen. What's more, is that SARS-CoV and MERS-CoV are the closest humanity has come to combating something similar to COVID-19, however, there exists no comparison between the manifestations of any of these novel coronaviruses. In this review, we summarize the current knowledge of the manifestations of the novel coronaviruses SARS-CoV, MERS-CoV, and COVID-19, with a particular focus on the latter, and highlight their differences and similarities.

Objectives Survivors of moderate and severe traumatic brain injury (TBI) require substantial care, much of which is ultimately provided by friends and family. We sought to describe the unmet needs of informal caregivers. Design Qualitative, semistructured interviews with informal caregivers of moderate and severe TBI survivors were conducted 72 hours, 1 month, 3 months, and 6 months after injury. Setting Intensive care unit of a level 1 trauma center. Participants Informal caregivers were friends or family who planned to provide care for the patient. Patients were 18 years or older with a moderate to severe TBI, and not expected to imminently die of their injuries. Measurements and main results Eighteen patient-caregiver dyads were enrolled. Fifty-three interviews with caregivers were completed and analyzed over the course of 6 months. Three themes were identified in the qualitative analysis: caregiver burden, caregiver health-related quality of life, and caregiver needs for information and support. Conclusions This study provides new information about the experience of informal caregivers during the 6 months after their friend or family member survived a moderate to severe TBI. Interventions to promote caregiving may be a substantial opportunity to improve patient-centered outcomes following TBI.

ZUSAMMENFASSUNGIn jüngster Zeit wurden verschiedene stechmückenübertragene Krankheiten erstmalig in Europa autochthon übertragen. Eine Kombination von Veränderungen greift hier ineinander. Zunächst wurden nicht heimische und sich invasiv ausbreitende kompetente Stechmücken (Insekten) nach Europa eingeschleppt. Außerdem nimmt die Reisetätigkeit kontinuierlich zu, und damit die Einschleppung potenziell übertragbarer Pathogene. Der Klimawandel verbessert die Lebensbedingungen der Stechmücken, fördert deren Etablierung in neuen Gebieten und mit wärmeren Temperaturen werden zunehmend Bedingungen für die Krankheitsübertragung erfüllt. Bisher konnten viele solcher Reisekrankheiten nicht übertragen werden, da die kompetenten Vektoren fehlten. Es kann deshalb sein, dass solche Krankheiten nicht richtig oder frühzeitig erkannt werden und somit weitere Übertragungen erfolgen können. Es gilt, solche neuen Risiken durch fächerübergreifende Kommunikation einzugrenzen.

We describe a method to reduce vacuum ultraviolet (VUV) pulse pileup (PPU) in x-ray pulse-height Silicon Drift Detector (SDD) signals. An Amptek FAST SDD, with C1 (Si3N4) window, measures bremsstrahlung emitted from PFRC-2 plasma to extract the electron temperature (T e) and density (n e). The C1 window has low transmissivity for photons with energy below 200 eV though will transmit some VUV and soft x-ray photons, which PFRC-2 plasmas abundantly emit. Multi-VUV-photon PPU contaminates the interpretation of x rays with energy [Formula: see text] 100 eV, particularly in a low-energy exponential tail. The predicted low transmissivity of ∼1 μm thick Mylar [polyethylene terephthalate (PET)] to photons of energy [Formula: see text] eV led to the selection of Mylar as the candidate filter to reduce VUV PPU. Experiments were conducted on an x-ray tube with a graphite target and on a quasi-Maxwellian tenuous plasma (n e ∼ 109 cm−3) with effective temperatures reaching 1500 eV. A Mylar filter thickness of 850 nm is consistent with the results. The Mylar-filter-equipped SDD was then used on the PFRC-2 plasma, showing a substantial reduction in the low-energy x-ray signal, supporting our hypothesis of the importance of VUV PPU. We describe the modeling and experiments performed to characterize the effect of the Mylar filter on SDD measurements.