Biomarkers in the prediction and management of acute coronary syndromes: current perspectives

Emanuele Gilardi, Paolo Iacomini, Davide Marsiliani, Guido De Marco, Marcello CovinoDepartment of Emergency Medicine, Catholic University of the Sacred Heart, A Gemelli Hospital, Rome, ItalyAbstract: A large branch of research has focused on the search for biomarkers for early detection of myocardial cell injuries. Most of these studies have evaluated patients presenting to the emergency department, underlining the need for an ideal biomarker for rapid recognition of acute coronary syndrome (ACS). In the recent past, diagnosis of ACS in the emergency department has been based mostly on clinical information and electrocardiographic findings, and markers of generic cell damage have been used to support clinical suspicion. Over the last few years, the role of markers has taken up increasingly more space in non-life-threatening conditions, confining the clinical examination of the patient to the mere waiting for results of blood tests after the electrocardiograph. Currently, the biomarkers most widely used for the diagnosis of ACS are cardiac troponins. Since their introduction into clinical practice, several generations of commercial cardiac troponin assays have been validated in analytical and clinical trials. Development of newer high-sensitivity assays seems to have improved the value of cardiac troponin as both a diagnostic and risk indicator. Several other biomarkers of ACS apart from cardiac troponin have been investigated, but most still require validation in further studies. Among these, pregnancy-associated plasma protein-A, ischemia-modified albumin, and heart-type fatty acid binding protein seem to be the most promising markers under investigation for their possible usefulness in the emergency department setting for early diagnosis of ACS. In conclusion, a multimarker approach could be the future of research. In this review, we highlight the old and new markers, especially the most studied and widely used in clinical practice in recent years, particularly those that can help the clinician to make a rapid and confident diagnosis of ACS.Keywords: biomarkers, acute coronary syndrome, myocardial infarction, emergency departmen

Development and preliminary investigation of a modular chamber for calibration of relative humidity instruments

In the scope of the project HUMEA – Expansion of European research capabilities in humidity measurement within the EURAMET EMPIR program, the modular chamber for calibration of relative humidity instruments was designed, manufactured and characterized. The modular chamber consists of arbitrary numbers of aluminum blocks each of which provides accommodation for the one relative humidity probe and also has the fittings for pressure and temperature probes as well as ports for gas sampling and/or supplying. The gas can be supplied from the dew/frost point generator or the larger climatic chamber. In the latter case, the airflow through the chamber can be enhanced by using an additional fan. The preliminary study was carried out to investigate the improvement in temperature uniformity using a new chamber in combination with two climatic chambers. The investigation results show significant improvement in temperature uniformity thus lowering the uncertainties of the calibration of relative humidity instruments

A robust method to quantify low molecular weight contaminants in heparin: detection of tris(2-n-butoxyethyl) phosphate

Recently, oversulfated chondroitin sulfate (OSCS) was identified in contaminated heparin preparations, which were linked to several adverse clinical events and deaths. Orthogonal analytical techniques, namely nuclear magnetic resonance (NMR) and capillary electrophoresis (CE), have since been applied by several authors for the evaluation of heparin purity and safety. NMR identification and quantification of residual solvents and non-volatile low molecular contaminants with USP acceptance levels of toxicity was achieved 40-fold faster than the traditional GC-headspace technique, which takes similar to 120 min against similar to 3 min to obtain a (1)H NMR spectrum with a signal/noise ratio of at least 1000/1. the procedure allowed detection of Class 1 residual solvents at 2 ppm and quantification was possible above 10 ppm. 2D NMR techniques (edited-HSQC (1)H/(13)C) permitted visualization of otherwise masked EDTA signals at 3.68/59.7 ppm and 3.34/53.5 ppm, which may be overlapping mononuclear heparin signals, or those of ethanol and methanol. Detailed NMR and ESI-MS/MS studies revealed a hitherto unknown contaminant, tris(2-n-butoxyethyl) phosphate (TBEP), which has potential health risks.Brazilian agency Fundacao AraucariaBrazilian agency FINEP (PRONEX-CARBOIDRATOS, PADCT II/SBIO)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Univ Fed Parana, Dept Bioquim & Biol Mol, BR-81531980 Curitiba, PR, BrazilIst Ric Chim & Biochim G Ronzoni, I-20133 Milan, ItalyUniversidade Federal de São Paulo, Dept Bioquim & Biol Mol, BR-04044020 São Paulo, SP, BrazilUniv Liverpool, Sch Biol Sci, Liverpool L69 7ZB, Merseyside, EnglandUniversidade Federal de São Paulo, Dept Bioquim & Biol Mol, BR-04044020 São Paulo, SP, BrazilWeb of Scienc

A machine-learning parsimonious multivariable predictive model of mortality risk in patients with Covid-19

The COVID-19 pandemic is impressively challenging the healthcare system. Several prognostic models have been validated but few of them are implemented in daily practice. The objective of the study was to validate a machine-learning risk prediction model using easy-to-obtain parameters to help to identify patients with COVID-19 who are at higher risk of death. The training cohort included all patients admitted to Fondazione Policlinico Gemelli with COVID-19 from March 5, 2020, to November 5, 2020. Afterward, the model was tested on all patients admitted to the same hospital with COVID-19 from November 6, 2020, to February 5, 2021. The primary outcome was in-hospital case-fatality risk. The out-of-sample performance of the model was estimated from the training set in terms of Area under the Receiving Operator Curve (AUROC) and classification matrix statistics by averaging the results of fivefold cross validation repeated 3-times and comparing the results with those obtained on the test set. An explanation analysis of the model, based on the SHapley Additive exPlanations (SHAP), is also presented. To assess the subsequent time evolution, the change in paO2/FiO2 (P/F) at 48 h after the baseline measurement was plotted against its baseline value. Among the 921 patients included in the training cohort, 120 died (13%). Variables selected for the model were age, platelet count, SpO2, blood urea nitrogen (BUN), hemoglobin, C-reactive protein, neutrophil count, and sodium. The results of the fivefold cross-validation repeated 3-times gave AUROC of 0.87, and statistics of the classification matrix to the Youden index as follows: sensitivity 0.840, specificity 0.774, negative predictive value 0.971. Then, the model was tested on a new population (n = 1463) in which the case-fatality rate was 22.6%. The test model showed AUROC 0.818, sensitivity 0.813, specificity 0.650, negative predictive value 0.922. Considering the first quartile of the predicted risk score (low-risk score group), the case-fatality rate was 1.6%, 17.8% in the second and third quartile (high-risk score group) and 53.5% in the fourth quartile (very high-risk score group). The three risk score groups showed good discrimination for the P/F value at admission, and a positive correlation was found for the low-risk class to P/F at 48 h after admission (adjusted R-squared = 0.48). We developed a predictive model of death for people with SARS-CoV-2 infection by including only easy-to-obtain variables (abnormal blood count, BUN, C-reactive protein, sodium and lower SpO2). It demonstrated good accuracy and high power of discrimination. The simplicity of the model makes the risk prediction applicable for patients in the Emergency Department, or during hospitalization. Although it is reasonable to assume that the model is also applicable in not-hospitalized persons, only appropriate studies can assess the accuracy of the model also for persons at home

Contrarian effect in opinion forming: insights from Greta Thunberg phenomenon

In recent months, the figure of Greta Thunberg and the theme of climate changings quickly became the focus of the debate. This has led to a polarization effect in opinion forming about the climate subject. Starting from the analysis of this phenomenon, we develop an opinion dynamics model in which several types of contrarian agents are considered. Each agent is supposed to have an opinion on several topics related to each other; thus, the opinions being formed on these topics are also mutually dependent. The aim of the paper is to investigate the indirect effects of contrarian agents on the collective opinion about these topics. Several numerical tests are presented in order to highlight the main features of the model