High temperature carbon–carbon supercapacitor using ionic liquid as electrolyte

This paper presents results about the electrochemical and cycling characterizations of a supercapacitor cell using a microporous activated carbon as the active material and N-butyl-N-methylpyrrolidinium bis(trifluoromethanesulfonyl)imide (PYR14TFSI) ionic liquid as the electrolyte. The microporous activated carbon exhibited a specific capacitance of 60 F g−1 measured from the three-electrode cyclic voltammetry experiments at 20mVs−1 scan rate, with a maximum operating potential range of 4.5V at 60 ◦C. A coin cell assembled with this microporous activated carbon and PYR14TFSI as the electrolyte was cycled for 40,000 cycles without any change of cell resistance (9cm2), at a voltage up to 3.5V at 60 ◦C, demonstrating a high cycling stability as well as a high stable specific capacitance in this ionic liquid electrolyte. These high performances make now this type of supercapacitor suitable for high temperature applications (≥60 ◦C)

Emergency Department Diagnosis and Managment of Influenza

Introduction: Diagnosing influenza in the emergency department (ED) remains a challenge as physicians have no reliable tools to accurately and rapidly diagnose influenza; however, rapid diagnosis is crucial to begin antiviral therapy in patients with complications or at risk of complications from influenza. Centers for Disease Control and Prevention (CDC) Guidelines recommend prompt antiviral treatment for patients who are hospitalized, at extremes of age (65 years old), or have a chronic disease or conditions putting them at increased risk of complications. Methods: First, we determined compliance with CDC antiviral guidelines via a retrospective evaluation of ED patients with confirmed influenza. Then, we created a prospective cohort of ED patients who met CDC criteria for recommended antiviral treatment who were evaluated for influenza by 3 means: clinical diagnosis, a new molecular-based rapid test, and a Polymerase Chain Reaction (PCR) test. Comparing the clinical diagnosis and rapid influenza test to the standard PCR assay allowed for a performance evaluation of both clinician diagnosis, and the new molecular-based rapid test. Finally, a cost-effectiveness analysis was performed to compare influenza testing and treatment strategies. Results: ED providers have poor compliance with CDC guidelines regarding antiviral treatment with only 41% of patients recommended to receive antiviral treatment being treated in the ED. Provider diagnosis for influenza has a poor sensitivity of 36%, especially compared to the molecular-based rapid influenza test which has 95% sensitivity in the same population. Finally, the most cost-effective testing and treatment strategy depends on influenza prevalence with rapid testing as the most cost-effective treatment at low influenza prevalence, and treating all patients without testing as the most cost-effective strategy at high prevalence. Conclusions: The challenges of making a clinical diagnosis of influenza in the ED, and current lack of a rapid sensitive influenza test, likely contribute to poor compliance with current CDC guidelines regarding antiviral administration. Integrating a new highly sensitive molecular-based rapid influenza test into ED clinical care, could improve compliance with CDC guidelines and is cost effective at low influenza prevalence

The Frequency of Influenza and Bacterial Co-infection: A Systematic Review and Meta-Analysis.

AIM: Co-infecting bacterial pathogens are a major cause of morbidity and mortality in influenza. However, there remains a paucity of literature on the magnitude of co-infection in influenza patients. METHOD: A systematic search of MeSH, Cochrane Library, Web of Science, SCOPUS, EMBASE, and PubMed was performed. Studies of humans in which all individuals had laboratory confirmed influenza, and all individuals were tested for an array of common bacterial species, met inclusion criteria. RESULTS: Twenty-seven studies including 3,215 participants met all inclusion criteria. Common etiologies were defined from a subset of eight articles. There was high heterogeneity in the results (I(2) = 95%), with reported co-infection rates ranging from 2% to 65%. Though only a subset of papers were responsible for observed heterogeneity, subanalyses and meta-regression analysis found no study characteristic that was significantly associated with co-infection. The most common co-infecting species were Streptococcus pneumoniae and Staphylococcus aureus, which accounted for 35% (95% CI, 14%-56%) and 28% (95% CI, 16%-40%) of infections, respectively; a wide range of other pathogens caused the remaining infections. An assessment of bias suggested that lack of small-study publications may have biased the results. CONCLUSIONS: The frequency of co-infection in the published studies included in this review suggests that though providers should consider possible bacterial co-infection in all patients hospitalized with influenza, they should not assume all patients are co-infected and be sure to properly treat underlying viral processes. Further, high heterogeneity suggests additional large-scale studies are needed to better understand the etiology of influenza bacterial co-infection. This article is protected by copyright. All rights reserved

Google Flu Trends Spatial Variability Validated Against Emergency Department Influenza-Related Visits

Background: Influenza is a deadly and costly public health problem. Variations in its seasonal patterns cause dangerous surges in emergency department (ED) patient volume. Google Flu Trends (GFT) can provide faster influenza surveillance information than traditional CDC methods, potentially leading to improved public health preparedness. GFT has been found to correlate well with reported influenza and to improve influenza prediction models. However, previous validation studies have focused on isolated clinical locations. Objective: The purpose of the study was to measure GFT surveillance effectiveness by correlating GFT with influenza-related ED visits in 19 US cities across seven influenza seasons, and to explore which city characteristics lead to better or worse GFT effectiveness. Methods: Using Healthcare Cost and Utilization Project data, we collected weekly counts of ED visits for all patients with diagnosis (International Statistical Classification of Diseases 9) codes for influenza-related visits from 2005-2011 in 19 different US cities. We measured the correlation between weekly volume of GFT searches and influenza-related ED visits (ie, GFT ED surveillance effectiveness) per city. We evaluated the relationship between 15 publically available city indicators (11 sociodemographic, two health care utilization, and two climate) and GFT surveillance effectiveness using univariate linear regression. Results: Correlation between city-level GFT and influenza-related ED visits had a median of .84, ranging from .67 to .93 across 19 cities. Temporal variability was observed, with median correlation ranging from .78 in 2009 to .94 in 2005. City indicators significantly associated (P Conclusions: GFT is strongly correlated with ED influenza-related visits at the city level, but unexplained variation over geographic location and time limits its utility as standalone surveillance. GFT is likely most useful as an early signal used in conjunction with other more comprehensive surveillance techniques. City indicators associated with improved GFT surveillance provide some insight into the variability of GFT effectiveness. For example, populations with lower socioeconomic status may have a greater tendency to initially turn to the Internet for health questions, thus leading to increased GFT effectiveness. GFT has the potential to provide valuable information to ED providers for patient care and to administrators for ED surge preparedness

Google Flu Trends Spatial Variability Validated Against Emergency Department Influenza-Related Visits.

BACKGROUND: Influenza is a deadly and costly public health problem. Variations in its seasonal patterns cause dangerous surges in emergency department (ED) patient volume. Google Flu Trends (GFT) can provide faster influenza surveillance information than traditional CDC methods, potentially leading to improved public health preparedness. GFT has been found to correlate well with reported influenza and to improve influenza prediction models. However, previous validation studies have focused on isolated clinical locations. OBJECTIVE: The purpose of the study was to measure GFT surveillance effectiveness by correlating GFT with influenza-related ED visits in 19 US cities across seven influenza seasons, and to explore which city characteristics lead to better or worse GFT effectiveness. METHODS: Using Healthcare Cost and Utilization Project data, we collected weekly counts of ED visits for all patients with diagnosis (International Statistical Classification of Diseases 9) codes for influenza-related visits from 2005-2011 in 19 different US cities. We measured the correlation between weekly volume of GFT searches and influenza-related ED visits (ie, GFT ED surveillance effectiveness) per city. We evaluated the relationship between 15 publically available city indicators (11 sociodemographic, two health care utilization, and two climate) and GFT surveillance effectiveness using univariate linear regression. RESULTS: Correlation between city-level GFT and influenza-related ED visits had a median of .84, ranging from .67 to .93 across 19 cities. Temporal variability was observed, with median correlation ranging from .78 in 2009 to .94 in 2005. City indicators significantly associated (P CONCLUSIONS: GFT is strongly correlated with ED influenza-related visits at the city level, but unexplained variation over geographic location and time limits its utility as standalone surveillance. GFT is likely most useful as an early signal used in conjunction with other more comprehensive surveillance techniques. City indicators associated with improved GFT surveillance provide some insight into the variability of GFT effectiveness. For example, populations with lower socioeconomic status may have a greater tendency to initially turn to the Internet for health questions, thus leading to increased GFT effectiveness. GFT has the potential to provide valuable information to ED providers for patient care and to administrators for ED surge preparedness

Machine learning based prediction models in male reproductive health: Development of a proof-of-concept model for Klinefelter Syndrome in azoospermic patients

Background Due to the highly variable clinical phenotype, Klinefelter Syndrome is underdiagnosed. Objective Assessment of supervised machine learning based prediction models for identification of Klinefelter Syndrome among azoospermic patients, and comparison to expert clinical evaluation. Materials and methods Retrospective patient data (karyotype, age, height, weight, testis volume, follicle-stimulating hormone, luteinizing hormone, testosterone, estradiol, prolactin, semen pH and semen volume) collected between January 2005 and June 2019 were retrieved from a patient data bank of a University Centre. Models were trained, validated and benchmarked based on different supervised machine learning algorithms. Models were then tested on an independent, prospectively acquired set of patient data (between July 2019 and July 2020). Benchmarking against physicians was performed in addition. Results Based on average performance, support vector machines and CatBoost were particularly well-suited models, with 100% sensitivity and >93% specificity on the test dataset. Compared to a group of 18 expert clinicians, the machine learning models had significantly better median sensitivity (100% vs. 87.5%, p = 0.0455) and fared comparably with regards to specificity (90% vs. 89.9%, p = 0.4795), thereby possibly improving diagnosis rate. A Klinefelter Syndrome Score Calculator based on the prediction models is available on . Discussion Differentiating Klinefelter Syndrome patients from azoospermic patients with normal karyotype (46,XY) is a problem that can be solved with supervised machine learning techniques, improving patient care. Conclusions Machine learning could improve the diagnostic rate of Klinefelter Syndrome among azoospermic patients, even more for less-experienced physicians

Dynamic stratification in drying films of colloidal mixtures

In simulations and experiments, we study the drying of films containing mixtures of large and small colloidal particles in water. During drying, the mixture stratifies into a layer of the larger particles at the bottom with a layer of the smaller particles on top. We developed a model to show that a gradient in osmotic pressure, which develops dynamically during drying, is responsible for the segregation mechanism behind stratification

Colouration in amphibians as a reflection of nutritional status : the case of tree frogs in Costa Rica

Colouration has been considered a cue for mating success in many species; ornaments in males often are related to carotenoid mobilization towards feathers and/or skin and can signal general health and nutrition status. However, there are several factors that can also link with status, such as physiological blood parameters and body condition, but there is not substantial evidence which supports the existence of these relationships and interactions in anurans. This study evaluated how body score and blood values interact with colouration in free-range Agalychnis callidryas and Agalychnis annae males. We found significant associations between body condition and plasmatic proteins and haematocrit, as well as between body condition and colour values from the chromaticity diagram. We also demonstrated that there is a significant relation between the glucose and plasmatic protein values that were reflected in the ventral colours of the animals, and haematocrit inversely affected most of those colour values. Significant differences were found between species as well as between populations of A. callidryas, suggesting that despite colour variation, there are also biochemical differences within animals from the same species located in different regions. These data provide information on underlying factors for colouration of male tree frogs in nature, provide insights about the dynamics of several nutrients in the amphibian model and how this could affect the reproductive output of the animals