Statistical-mechanical theory of ultrasonic absorption in molecular liquids

We present results of theoretical description of ultrasonic phenomena in molecular liquids. In particular, we are interested in the development of microscopical, i.e., statistical-mechanical framework capable to explain the long living puzzle of the excess ultrasonic absorption in liquids. Typically, ultrasonic wave in a liquid can be generated by applying the periodically alternating external pressure with the angular frequency that corresponds to the ultrasound. If the perturbation introduced by such process is weak - its statistical-mechanical treatment can be done with the use of the linear response theory. We treat the liquid as a system of interacting sites, so that all the response/aftereffect functions as well as the energy dissipation and generalized (wave-vector and frequency dependent) ultrasonic absorption coefficient are obtained in terms of familiar site-site static and time correlation functions such as static structure factors or intermediate scattering functions. To express the site-site intermediate scattering functions we refer to the site-site memory equations in the mode-coupling approximation for the first-order memory kernels, while equilibrium properties such as site-site static structure factors, direct and total correlation functions are deduced from the integral equation theory of molecular liquids known as RISM or one of its generalizations. All the formalism is phrased in a general manner, hence the obtained results are expected to work for arbitrary type of molecular liquid including simple, ionic, polar, and non-polar liquids.Comment: 14 pages, 1 eps-figure, RevTeX4-forma

Disposable Nafion-Coated Single-Walled Carbon Nanotube Test Strip for Electrochemical Quantitative Determination of Acetaminophen in a Finger-Prick Whole Blood Sample

A disposable electrochemical test strip for the quantitative point-of-care (POC) determination of acetaminophen (paracetamol) in plasma and finger-prick whole blood was fabricated. The industrially scalable dry transfer process of single-walled carbon nanotubes (SWCNTs) and screen printing of silver were combined to produce integrated electrochemical test strips. Nafion coating stabilized the potential of the Ag reference electrode and enabled the selective detection in spiked plasma as well as in whole blood samples. The test strips were able to detect acetaminophen in small 40 mu L samples with a detection limit of 0.8 mu M and a wide linear range from 1 mu M to 2 mM, well within the required clinical range. After a simple 1:1 dilution of plasma and whole blood, a quantitative detection with good recoveries of 79% in plasma and 74% in whole blood was achieved. These results strongly indicate that these electrodes can be used directly to determine the unbound acetaminophen fraction without the need for any additional steps. The developed test strip shows promise as a rapid and simple POC quantitative acetaminophen assay.Peer reviewe

Hospitalizations for acetaminophen overdose: a Canadian population-based study from 1995 to 2004

<p>Abstract</p> <p>Background</p> <p>Acetaminophen overdose (AO) is the most common cause of acute liver failure. We examined temporal trends and sociodemographic risk factors for AO in a large Canadian health region.</p> <p>Methods</p> <p>1,543 patients hospitalized for AO in the Calgary Health Region (population ~1.1 million) between 1995 and 2004 were identified using administrative data.</p> <p>Results</p> <p>The age/sex-adjusted hospitalization rate decreased by 41% from 19.6 per 100,000 population in 1995 to 12.1 per 100,000 in 2004 (<it>P </it>< 0.0005). This decline was greater in females than males (46% vs. 29%). Whereas rates fell 46% in individuals under 50 years, a 50% increase was seen in those ≥ 50 years. Hospitalization rates for intentional overdoses fell from 16.6 per 100,000 in 1995 to 8.6 per 100,000 in 2004 (2004 vs. 1995: rate ratio [RR] 0.49; <it>P </it>< 0.0005). Accidental overdoses decreased between 1995 and 2002, but increased to above baseline levels by 2004 (2004 vs. 1995: RR 1.24;<it>P </it>< 0.0005). Risk factors for AO included female sex (RR 2.19; <it>P </it>< 0.0005), Aboriginal status (RR 4.04; <it>P </it>< 0.0005), and receipt of social assistance (RR 5.15; <it>P </it>< 0.0005).</p> <p>Conclusion</p> <p>Hospitalization rates for AO, particularly intentional ingestions, have fallen in our Canadian health region between 1995 and 2004. Young patients, especially females, Aboriginals, and recipients of social assistance, are at highest risk.</p

Paracetamol in therapeutic dosages and acute liver injury: causality assessment in a prospective case series

Background: Acute liver injury (ALI) induced by paracetamol overdose is a well known cause of emergency hospital admission and death. However, there is debate regarding the risk of ALI after therapeutic dosages of the drug. The aim is to describe the characteristics of patients admitted to hospital with jaundice who had previous exposure to therapeutic doses of paracetamol. An assessment of the causality role of paracetamol was performed in each case. Methods: Based on the evaluation of prospectively gathered cases of ALI with detailed clinical information, thirty-two cases of ALI in non-alcoholic patients exposed to therapeutic doses of paracetamol were identified. Two authors assessed all drug exposures by using the CIOMS/RUCAM scale. Each case was classified into one of five categories based on the causality score for paracetamol. Results: In four cases the role of paracetamol was judged to be unrelated, in two unlikely, and these were excluded from evaluation. In seven of the remaining 26 cases, the RUCAM score associated with paracetamol was higher than that associated with other concomitant medications. The estimated incidence of ALI related to the use of paracetamol in therapeutic dosages was 0.4 per million inhabitants older than 15 years of age and per year (99%CI, 0.2-0.8) and of 10 per million paracetamol users-year (95% CI 4.3-19.4). Conclusions:Our results indicate that paracetamol in therapeutic dosages may be considered in the causality assessment in non-alcoholic patients with liver injury, even if the estimated incidence of ALI related to paracetamol appears to be low

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Identification of novel translational urinary biomarkers for acetaminophen-induced acute liver injury using proteomic profiling in mice

Contains fulltext : 108207.pdf (publisher's version ) (Open Access)Drug-induced liver injury (DILI) is the leading cause of acute liver failure. Currently, no adequate predictive biomarkers for DILI are available. This study describes a translational approach using proteomic profiling for the identification of urinary proteins related to acute liver injury induced by acetaminophen (APAP). Mice were given a single intraperitoneal dose of APAP (0-350 mg/kg bw) followed by 24 h urine collection. Doses of >/=275 mg/kg bw APAP resulted in hepatic centrilobular necrosis and significantly elevated plasma alanine aminotransferase (ALT) values (p<0.0001). Proteomic profiling resulted in the identification of 12 differentially excreted proteins in urine of mice with acute liver injury (p<0.001), including superoxide dismutase 1 (SOD1), carbonic anhydrase 3 (CA3) and calmodulin (CaM), as novel biomarkers for APAP-induced liver injury. Urinary levels of SOD1 and CA3 increased with rising plasma ALT levels, but urinary CaM was already present in mice treated with high dose of APAP without elevated plasma ALT levels. Importantly, we showed in human urine after APAP intoxication the presence of SOD1 and CA3, whereas both proteins were absent in control urine samples. Urinary concentrations of CaM were significantly increased and correlated well with plasma APAP concentrations (r = 0.97; p<0.0001) in human APAP intoxicants, who did not present with elevated plasma ALT levels. In conclusion, using this urinary proteomics approach we demonstrate CA3, SOD1 and, most importantly, CaM as potential human biomarkers for APAP-induced liver injury