Quantifying contribution of Lipoprotein (a) to atherogenic lipoprotein burden: a novel particle-based approach

Background: Elevated lipoprotein (a) [Lp(a)] is an independent risk factor for atherosclerotic cardiovascular disease (ASCVD). As clinical LDL cholesterol [LDL-C] incorporates cholesterol from Lp(a) [Lp(a)-C], there is interest in quantifying the contribution of Lp(a)-C to LDL-C given implications for risk assessment, diagnosis, and treatment. Estimating Lp(a)-C is subject to inaccuracies; measuring Lp(a) particle number [Lp(a)-P] is more accurate. Objective: To capture how Lp(a) contributes to the atherogenic lipoprotein burden, we demonstrate a particle-based approach using readily available measures of Lp(a)-P and apolipoprotein B (apoB). Methods: Using the Very Large Database of Lipids (VLDbL), we compared Lp(a)-P (nmol/L) with all atherogenic particles (non-HDL-P). Non-HDL-P was calculated by converting apoB mass to molar concentration using the preserved molecular weight of apoB100 (512 kg/mol). We calculated the percentage of Lp(a)-P relative to non-HDL-P by Lp(a)-P deciles and stratified across sex, age, triglycerides, LDL-C, and non-HDL-C. Results: 158,260 patients from the VLDbL were included. The fraction Lp(a)-P/non-HDL-P increased with rising Lp(a)-P. Lp(a)-P comprised on average 3% of atherogenic particles among the study population and 15% at the highest Lp(a)-P decile. Findings were similar when stratified by sex. When stratified by age, Lp(a)-P/non-HDL-P was highest among the youngest and oldest patients. Lp(a)-P/non-HDL-P decreased at higher levels of triglycerides and LDL-C owing to larger contributions from VLDL and LDL. Conclusions: We demonstrate a particle-based approach to quantify the contribution of Lp(a) to total atherogenic particle burden using validated and widely available laboratory assays. Future research applying this method could define clinically meaningful thresholds and inform use in risk assessment and management.

Structural and physical properties of 99 complex bulk chalcogenides crystals using first-principles calculations

Chalcogenide semiconductors and glasses have many applications in the civil and military fields, especially in relation to their electronic, optical and mechanical properties for energy conversion and in enviormental materials. However, they are much less systemically studied and their fundamental physical properties for a large class chalcogenide semiconductors are rather scattered and incomplete. Here, we present a detailed study using well defined first-principles calculations on the electronic structure, interatomic bonding, optical, and mechanical properties for 99 bulk chalcogenides including thirteen of these crytals which have never been calculated. Due to their unique composition and structures, these 99 bulk chalcogenides are divided into two main groups. The first group contains 54 quaternary crystals with the structure composition (A2BCQ4) (A = Ag, Cu; B = Zn, Cd, Hg, Mg, Sr, Ba; C = Si, Ge, Sn; Q = S, Se, Te), while the second group contains scattered ternary and quaternary chalcogenide crystals with a more diverse composition (AxByCzQn) (A = Ag, Cu, Ba, Cs, Li, Tl, K, Lu, Sr; B = Zn, Cd, Hg, Al, Ga, In, P, As, La, Lu, Pb, Cu, Ag; C = Si, Ge, Sn, As, Sb, Bi, Zr, Hf, Ga, In; Q = S, Se, Te; $$\hbox {x} = 1$$ x = 1 , 2, 3; $$\hbox {y} = 0$$ y = 0 , 1, 2, 5; $$\hbox {z} = 0$$ z = 0 , 1, 2 and $$\hbox {n} = 3$$ n = 3 , 4, 5, 6, 9). Moreover, the total bond order density (TBOD) is used as a single quantum mechanical metric to characterize the internal cohesion of these crystals enabling us to correlate them with the calculated properties, especially their mechanical properties. This work provides a very large database for bulk chalcogenides crucial for the future theoretical and experimental studies, opening opportunities for study the properties and potential application of a wide variety of chalcogenides.

Pandemic Accelerates Datacracy And The Goals Of Sustainable Development Goals

In December 2019, an entropy emerged which caused many changes to occur in the world today. A virus that causes the whole world to quickly organize and condition itself. SARS-CoV2 or SARS Coronavirus 2 or better known as Covid-19 is a new corona virus that is responsible for these changes. Covid-19, which was first known to infect a resident of China in December 2019, has now spread to 214 countries and all regions of the world, and affecting every aspect of everyday human life. All nations strive to position themselves in response to this entropy. Especially in the 21st century today. Where development of AI is a prototype that is continuously being cared for by all countries today. Many countries are taking advantage from big tickets of AI in the face of a pandemic. One of them is datacracy. A concept that accumulates all data, all information, then extrapolated from all data and information that has been obtained using certain algorithms which are made into a very large database. Datacracy will be one of the pillars in order to realize the goals that contained in the global action of Sustainable Development Goals (SDGs). Datacracy will also be very helpful in realizing some of the goals of the 17 goals in the Sustainable Development Goals (SDGs) which are expected to be achieved by 2030. This essay will discuss how the contribution of AI in dealing with the pandemic as well as speeding up datacracy and so that it can contributes to the realization of the goals from global action Sustainable Development Goals (SDGs).

Abstract 17033: Inflammation and Lipids Components: Insights From the VLDL Study

Background: Dyslipidemia and inflammation independently contribute to atherosclerosis. The associations between different lipid parameters and inflammatory markers is not fully understood. Hypothesis: LDL-C, triglycerides, and HDL-C do not predict inflammation equally. Methods: We analyzed data from 784,233 patients from the second harvest of the Very Large Database of Lipid study with lipids and hsCRP measured. The prevalence of having hsCRP≥2 mg/L was compared in 20 quantiles of non-HDL-C, LDL-C, HDL-C and triglycerides. Using linear regression, we estimated the correlations between hsCRP and lipids and to what degree individual lipid components explain the variation in hsCRP values. We then examined these association by sex and age (≥65 vs <65 years) categories. Results: The median hsCRP of the population was 2.3 mg/L (IQR, 1.0-5.7). The proportion of patients with hsCRP≥2 mg/L progressively increased with higher non-HDL-C, LDL-C, triglycerides quintiles, but decreased with higher HDL-C quintiles. All lipid measures directly correlated with hsCRP value except HDL-C which was inversely correlated (P<0.001 for all). LDL-C and non-HDL-C values explained very little of the variance seen in hsCRP (univariate R 2 = 0.5% and 0.1%, respectively). Triglyceride levels were the strongest predictor of hsCRP (standardized β, 0.21; P<0.001) and explained 4.5% of variability in hsCRP values. HDL-C was the second best (albeit an inverse) predictor of hsCRP (standardized β, -0.19; P<0.001) and explained 3.8% of its variance. Regardless LDL-C quantile, the prevalence of having hsCRP ≥2 mg/L was lower in those also have low triglyceride or high HDL-C levels (Figure). These associations were consistent by sex and age categories. Conclusion: Of major lipid and lipoprotein cholesterol fractions, triglycerides and HDL-C correlated most strongly and non-HDL-C and LDL-C contributed the least to hsCRP.

Global Scatterometer Observations of the Structure of Tropical Cyclone Wind Fields

A very large database containing 24 years of scatterometer passes is analyzed to investigate the surface wind fields within tropical cyclones. The analysis confirms the left–right asymmetry of the wind field with the strongest winds directly to the right of the tropical cyclone center (Northern Hemisphere). At values greater than 2 times the radius to maximum winds, the asymmetry is approximately equal to the storm velocity of forward movement. Observed wind inflow angle (i.e., storm motion not subtracted) is shown to vary both radially and azimuthally within the tropical cyclone. The smallest observed wind inflow angles are found in the left-front quadrant with the largest values in the right-rear quadrant. As the velocity of forward movement increases and the central pressure decreases, observed inflow angles ahead of the storm decrease and those behind the storm increase. In the right-rear quadrant, the observed inflow angle increases with radius from the storm center. In all other quadrants, the observed inflow angle is approximately constant as a function of radial distance.

Forensic Analysis of Commercial Inks by Laser-Induced Breakdown Spectroscopy (LIBS)

Laser-induced breakdown spectroscopy (LIBS) was tested for all of the relevant issues in forensic examinations of commercial inks, including classification of pen inks on one paper type and on different paper types, determination of the deposition order of layered inks, and analysis of signatures and toners on one questioned document. The scope of this work was to determine the potential of a single LIBS setup that is compatible with portable instruments for different types of ink analysis, rather than building a very large database for inks and papers. We identified up to seven metals characteristic for the examined inks, which allowed to fully discriminate all eight black inks on one type of printing paper. When the inks were tested on ten different papers, the correct classification rates for some of them were reduced for reasons thoroughly studied and explained. The replicated tests on three crossing points, each one involving a pair of blue or black inks, were successful in five cases out of six. In the test simulating documents of forensic interest (questioned documents), LIBS was able to correctly identify the differences in three inks used for signatures on one of the three pages and the use of different printing inks on each page of the document.

The Use of Data Science to Analyse Physiology of Oxygen Delivery in the Extracorporeal Circulation

Background Recent scientific reports have brought into light a new concept of goal-directed perfusion (GDP) that aims to recreate physiological conditions in which the risk of end-organ malperfusion is minimalized. The aim of our study was to analyse patients’ interim physiology while on cardiopulmonary bypass based on the haemodynamic and tissue oxygen delivery measurements. We also aimed to create a universal formula that may help in further implementation of the GDP concept. Methods We retrospectively analysed patients operated on at the Wroclaw University Hospital between June 2017 and December 2018. Since our observations provided an extensive amount of data, including the patients' demographics, surgery details and the perfusion-related data, the Data Science methodology was applied. Results A total of 272 (mean age 62.5±12.4, 74% male) cardiac surgery patients were included in the study. To study the relationship between haemodynamic and tissue oxygen parameters, the data for three different values of DO2i (280 ml/min/m2, 330 ml/min/m2 and 380ml/min/m2), were evaluated. Each set of those lines showed a descending function of CI in Hb concentration for the set DO2i. Conclusions Modern calculation tools make it possible to create a common data platform from a very large database. Using that methodology we created models of haemodynamic compounds describing tissue oxygen delivery. The obtained unique patterns may both allow the adaptation of the flow in relation to the patient’s unique morphology that changes in time and contribute to wider and safer implementation of perfusion strategy which has been tailored to every patient’s individual needs.

The use of data science to analyse physiology of oxygen delivery in the extracorporeal circulation

Background Recent scientific reports have brought into light a new concept of goal-directed perfusion (GDP) that aims to recreate physiological conditions in which the risk of end-organ malperfusion is minimalized. The aim of our study was to analyse patients’ interim physiology while on cardiopulmonary bypass based on the haemodynamic and tissue oxygen delivery measurements. We also aimed to create a universal formula that may help in further implementation of the GDP concept. Methods We retrospectively analysed patients operated on at the Wroclaw University Hospital between June 2017 and December 2018. Since our observations provided an extensive amount of data, including the patients’ demographics, surgery details and the perfusion-related data, the Data Science methodology was applied. Results A total of 272 (mean age 62.5 ± 12.4, 74% male) cardiac surgery patients were included in the study. To study the relationship between haemodynamic and tissue oxygen parameters, the data for three different values of DO2i (280 ml/min/m2, 330 ml/min/m2 and 380 ml/min/m2), were evaluated. Each set of those lines showed a descending function of CI in Hb concentration for the set DO2i. Conclusions Modern calculation tools make it possible to create a common data platform from a very large database. Using that methodology we created models of haemodynamic compounds describing tissue oxygen delivery. The obtained unique patterns may both allow the adaptation of the flow in relation to the patient’s unique morphology that changes in time and contribute to wider and safer implementation of perfusion strategy which has been tailored to every patient’s individual needs.

The Use of Data Science to Analyse Physiology of Oxygen Delivery in the Extracorporeal Circulation

Background Recent scientific reports brought into light a new concept of goal-directed perfusion (GDP) that aims to recreate physiological conditions in which the risk of end-organ malperfusion is minimalized. The aim of our study was to analyse patient’s interim physiology while on cardiopulmonary bypass based on the haemodynamic and tissue oxygen delivery measurements. We also aimed to create a universal formula that may help in the further implementation of the GDP concept. Methods We retrospectively analysed patients operated at Wroclaw University Hospital between June 2017 and December 2018. Since our observations provided a huge amount of data, including the patient's demographics, surgery details, and the perfusion-related data, the Data Science methodology was applied. Results A total of 272 (mean age 62.5±12.4, 74% male) cardiac surgery patients were included in the study. To study the relationship between haemodynamic and tissue oxygen parameters, the data for three different values of DO2i (280 ml/min/m2, 330 ml/min/m2 and 380ml/min/m2), were evaluated. Each set of those lines showed a descending function of CI in Hb concentration for the set DO2i. Conclusions Modern calculation tools make it possible to create a common data platform from a very large database. Using that methodology, we created models of haemodynamic compounds describing tissue oxygen delivery. The obtained unique patterns may both allow the adaptation of the flow in relation to the patient’s unique morphology that changes in time and contributes to the wider and safer implementation of tailored to individual patient’s needs perfusion strategy.