Critical Care of Acute Heart Failure

Acute heart failure is a life-threatening medical condition. Improving acute heart failure care is important. Early diagnosis and evaluating the etiology are important in acute heart failure. Patients with suspected acute heart failure should have a diagnostic workup, and appropriate pharmacological and nonpharmacological management should be started promptly and in parallel. Diagnosis of acute heart failure should be based on history and symptoms. The physical examination typically presents with some combination of increased congestion and decreased peripheral perfusion, further confirmed by electrocardiogram, chest radiograph, biomarkers, and echocardiogram. The first step in the management of a patient is to address life-threatening issues. Patients with respiratory failure or cardiogenic shock should be treated soon. The next step is the identification of precipitants that needs urgent management. Evidence-based medication to reduce morbidity and mortality for patients with heart failure includes an angiotensin converting enzyme inhibitor, angiotensin receptor blocker, or angiotensin receptor-neprilysin inhibitor; a beta blocker; and a mineralocorticoid receptor antagonist. During an acute heart failure episode, management of these agents depends upon whether the patient has contraindications to therapy such as hemodynamic instability or acute kidney injury. Once the patient is stable, therapies are carefully initiated, reinitiated, or titrated with appropriate follow-up

A Two-stage Architecture for Stock Price Forecasting by Integrating Self-Organizing Map and Support Vector Regression

Stock price prediction has attracted much attention from both practitioners and researchers. However, most studies in this area ignored the non-stationary nature of stock price series. That is, stock price series do not exhibit identical statistical properties at each point of time. As a result, the relationships between stock price series and their predictors are quite dynamic. It is challenging for any single artificial technique to effectively address this problematic characteristics in stock price series. One potential solution is to hybridize different artificial techniques. Towards this end, this study employs a two-stage architecture for better stock price prediction. Specifically, the self-organizing map (SOM) is first used to decompose the whole input space into regions where data points with similar statistical distributions are grouped together, so as to contain and capture the non-stationary property of financial series. After decomposing heterogeneous data points into several homogenous regions, support vector regression (SVR) is applied to forecast financial indices. The proposed technique is empirically tested using stock price series from seven major financial markets. The results show that the performance of stock price prediction can be significantly enhanced by using the two-stage architecture in comparison with a single SVR model

Partially hydrolyzed guar gum supplement reduces high-fat diet increased blood lipids and oxidative stress and ameliorates FeCl3-induced acute arterial injury in hamsters

Increased reactive oxygen species (ROS) and hyperlipidemia can promote arterial thrombus. We evaluated the potential of a partially hydrolyzed guar gum (PHGG) as dietary fiber on lipid profiles and FeCl3-induced arterial thrombosis in the high fat-diet fed hamsters. Our in vitro results found that PHGG is efficient to scavenge O2-•, H2O2, and HOCl. High fat-diet increased plasma triglyceride, total cholesterol, LDL, VLDL, methylguanidine and dityrosine level and accelerated FeCl3-induced arterial thrombosis formation (from 463 ± 51 to 303 ± 45 sec). Low dose PHGG supplement significantly decreased the total cholesterol, LDL, methylguanidine and dityrosine level and delayed the time for arterial thrombosis formation (528 ± 75 sec). High dose PHGG supplement decreased the level in triglyceride, total cholesterol, LDL and VLDL and further delayed the time for arterial thrombus (671 ± 36 sec). The increased Bax protein and decreased Bcl-2 and HSP-70 protein expression was found in the carotid and femoral arteries of high fat-diet hamsters. Low and high dose of PHGG supplement decreased Bax expression and increased Bcl-2 and HSP-70 protein expression. We found that FeCl3 significantly enhanced intercellular adhesion molecule-1 and 4-hydroxynonenal expression in the endothelial site of damaged artery after 150-sec FeCl3 stimulation. PHGG supplement decreased the endothelial ICAM-1 and 4-hydroxynonenal expression after 150-sec FeCl3 stimulation. Based on these results, we conclude that PHGG supplement can increase antioxidant protein expression and thus decrease oxidative stress induced arterial injury

Probing highly collimated photon-jets with deep learning

Many extensions of the standard model (SM) predict the existence of axion-like particles and/or dark Higgs in the sub-GeV scale. Two new sub-GeV particles, a scalar and a pseudoscalar, produced through the Higgs boson exotic decays, are investigated. The decay signatures of these two new particles with highly collimated photons in the final states are discriminated from the ones of SM backgrounds using the Convolutional Neural Networks and Boosted Decision Trees techniques. The sensitivities of searching for such new physics signatures at the Large Hadron Collider are obtained

Impact of Heavy Metals in Ambient Air in Insulin Resistance of Shipyard Welders in Northern Taiwan

Exposure to metals poses potential health risks, including insulin resistance (IR), to those exposed to them in excess. Limited studies have examined such risks in occupational workers, including welders, and these have yielded inconsistent results. Thus, we examined the associations between exposure to welding metals and IR in welders. We recruited 78 welders and 75 administrative staff from a shipyard located in northern Taiwan. Personal exposure to heavy metals, including chromium (Cr), manganese (Mn), iron (Fe), nickel (Ni), copper (Cu), zinc (Zn), and cadmium (Cd), was monitored through particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) and urine analysis by inductively coupled plasma mass spectrometry (ICP–MS). After each participant fasted overnight, blood samples were collected and analyzed for IR assessment through updated homeostasis model assessment (HOMA2) modeling. Air sampling in the personal breathing zone was performed during a Monday shift prior to the blood and urine sample collection the following morning. The welders’ median personal Cr, Mn, Fe, Ni, Cu, and Zn airborne PM2.5 levels and urinary Cd levels were significantly higher than those of the administrative staff. After adjustment for covariates, logarithmic PM2.5-Mn, PM2.5-Fe, PM2.5-Cu, and PM2.5-Zn levels were positively correlated with logarithmic fasting plasma glucose (P-FGAC) levels (PM2.5-Mn: β = 0.0105, 95% C.I.: 0.0027–0.0183; PM2.5-Fe: β = 0.0127, 95% C.I.: 0.0027–0.0227; PM2.5-Cu: β = 0.0193, 95% C.I.: 0.0032–0.0355; PM2.5-Zn: β = 0.0132, 95% C.I.: 0.0005–0.0260). Logarithmic urinary Zn was positively correlated with logarithmic serum insulin and HOMA2-IR levels and negatively correlated with logarithmic HOMA2-insulin sensitivity (%S; βinsulin = 0.2171, 95% C.I.: 0.0025–0.4318; βIR = 0.2179, 95% C.I.: 0.0027–0.4330; β%S = −0.2180, 95% C.I.: −0.4334 to −0.0026). We observed that glucose homeostasis was disrupted by Mn, Fe, Cu, and Zn exposure through increasing P-FGAC and IR levels in shipyard welders

Toward Transparent Sequence Models with Model-Based Tree Markov Model

In this study, we address the interpretability issue in complex, black-box Machine Learning models applied to sequence data. We introduce the Model-Based tree Hidden Semi-Markov Model (MOB-HSMM), an inherently interpretable model aimed at detecting high mortality risk events and discovering hidden patterns associated with the mortality risk in Intensive Care Units (ICU). This model leverages knowledge distilled from Deep Neural Networks (DNN) to enhance predictive performance while offering clear explanations. Our experimental results indicate the improved performance of Model-Based trees (MOB trees) via employing LSTM for learning sequential patterns, which are then transferred to MOB trees. Integrating MOB trees with the Hidden Semi-Markov Model (HSMM) in the MOB-HSMM enables uncovering potential and explainable sequences using available information

Debris Flow Risk Assessment and Land-Use Planning – A Case Study of Jhonglun Hot Spring Area

The Jhonglun Scenic Area in Chiayi County, is famous for its hot spring, the region was hit by debris flow with tremendous losses and resulted with dramatic change of the landscape during Typhoon Morakot in 2009. The most effective strategy for reducing natural hazard risks is through land-use planning. Following the concept of Risk=Hazard*Exposure*Vulnerability, this study conducted risk identification through the collection of landslide inventory and history debris flow hazard mapping of Chiayi DF051 potential debris flow torrent. Together with elements at risk information from field investigations, the risk analysis was conducted with several return periods debris flow simulation to recognize the possible economic losses and fatalities by debris flow. The identified high risk areas in Jhonglun Scenic Area were compared to the current special district planning to understand the spatial distribution of high risk areas. The result shows that some of the designated zones were among the areas with high debris flow risks, which further indicates that land-use planning should consider the consequences of natural hazards. The result of this study provides one of the first steps for land use planning restrictions within the potential debris flow region

Tailoring excitonic states of van der Waals bilayers through stacking configuration, band alignment and valley-spin

Excitons in monolayer semiconductors have large optical transition dipole for strong coupling with light field. Interlayer excitons in heterobilayers, with layer separation of electron and hole components, feature large electric dipole that enables strong coupling with electric field and exciton-exciton interaction, at the cost that the optical dipole is substantially quenched (by several orders of magnitude). In this letter, we demonstrate the ability to create a new class of excitons in transition metal dichalcogenide (TMD) hetero- and homo-bilayers that combines the advantages of monolayer- and interlayer-excitons, i.e. featuring both large optical dipole and large electric dipole. These excitons consist of an electron that is well confined in an individual layer, and a hole that is well extended in both layers, realized here through the carrier-species specific layer-hybridization controlled through the interplay of rotational, translational, band offset, and valley-spin degrees of freedom. We observe different species of such layer-hybridized valley excitons in different heterobilayer and homobilayer systems, which can be utilized for realizing strongly interacting excitonic/polaritonic gases, as well as optical quantum coherent controls of bidirectional interlayer carrier transfer either with upper conversion or down conversion in energy