Avalanches and power law behavior in aortic dissection progression

Aortic dissection is a devastating cardiovascular disease known for its rapid propagation and high morbidity and mortality. The mechanisms underlying the propagation of aortic dissection are not well understood. Our study reports the discovery of avalanche-like failure of the aorta during dissection propagation that results from the local buildup of strain energy followed by a cascade failure of inhomogeneously distributed interlamellar collagen fibers. An innovative computational model was developed that successfully describes the failure mechanics of dissection propagation. Our study provides the first quantitative agreement between experiment and model prediction of the dissection propagation within the complex extracellular matrix (ECM). Our results may lead to the possibility of predicting such catastrophic events based on microscopic features of the ECM.Published versio

Vascular smooth muscle Sirtuin-1 protects against aortic dissection during Angiotensin II-induced hypertension

BACKGROUND: Sirtuin-1 (SirT1), a nicotinamide adenine dinucleotide(+)-dependent deacetylase, is a key enzyme in the cellular response to metabolic, inflammatory, and oxidative stresses; however, the role of endogenous SirT1 in the vasculature has not been fully elucidated. Our goal was to evaluate the role of vascular smooth muscle SirT1 in the physiological response of the aortic wall to angiotensin II, a potent hypertrophic, oxidant, and inflammatory stimulus. METHODS AND RESULTS: Mice lacking SirT1 in vascular smooth muscle (ie, smooth muscle SirT1 knockout) had drastically high mortality (70%) caused by aortic dissection after angiotensin II infusion (1 mg/kg per day) but not after an equipotent dose of norepinephrine, despite comparable blood pressure increases. Smooth muscle SirT1 knockout mice did not show any abnormal aortic morphology or blood pressure compared with wild-type littermates. Nonetheless, in response to angiotensin II, aortas from smooth muscle SirT1 knockout mice had severely disorganized elastic lamellae with frequent elastin breaks, increased oxidant production, and aortic stiffness compared with angiotensin II-treated wild-type mice. Matrix metalloproteinase expression and activity were increased in the aortas of angiotensin II-treated smooth muscle SirT1 knockout mice and were prevented in mice overexpressing SirT1 in vascular smooth muscle or with use of the oxidant scavenger tempol. CONCLUSIONS: Endogenous SirT1 in aortic smooth muscle is required to maintain the structural integrity of the aortic wall in response to oxidant and inflammatory stimuli, at least in part, by suppressing oxidant-induced matrix metalloproteinase activity. SirT1 activators could potentially be a novel therapeutic approach to prevent aortic dissection and rupture in patients at risk, such as those with hypertension or genetic disorders, such as Marfan's syndrome.R01 HL098028 - NHLBI NIH HHS; HL098028 - NHLBI NIH HHS; HL105287 - NHLBI NIH HHS; T32 HL07224 - NHLBI NIH HH

Automobile components procurement using a DEA-TOPSIS-FMIP approach with all-unit quantity discount and fuzzy factors

Components procurement is a crucial process in supply chain management of the automobile industry. The problem is further complicated by imprecise information and discount policies provided by suppliers. This paper aims to develop a computational approach for assisting automobile components procurement with all-unit quantity discount policy and fuzzy factors, from potential suppliers offering different product portfolios. We propose a two-stage approach consisting of a DEA-TOPSIS (data envelopment analysis procedures followed with a technique for order preference by similarity to an ideal solution) approach for screening suppliers, and subsequentially a fuzzy mixed integer programming (FMIP) model with multiple objectives for optimizing order allocations. The DEA-TOPSIS approach integrates suppliers’ comparative performance and diversity performance into an overall index that improves the ranking of potential suppliers, while the FMIP model features a soft time-window in delivery punctuality and an all-unit quantity discount function in cost. By applying it in a case of automobile components procurement, we show that this two-stage approach effectively supports decision makers in yielding procurement plans for various components offered by many potential suppliers. This paper contributes to integrating multi-attribute decision analysis approach in the form of DEA crossevaluation with TOPSIS and FMIP model for supporting components procurement decisions. First published online 19 November 202

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Prediction of Drug-Drug Interactions with Bupropion and Its Metabolites as CYP2D6 Inhibitors Using a Physiologically-Based Pharmacokinetic Model

The potential of inhibitory metabolites of perpetrator drugs to contribute to drug-drug interactions (DDIs) is uncommon and underestimated. However, the occurrence of unexpected DDI suggests the potential contribution of metabolites to the observed DDI. The aim of this study was to develop a physiologically-based pharmacokinetic (PBPK) model for bupropion and its three primary metabolites—hydroxybupropion, threohydrobupropion and erythrohydrobupropion—based on a mixed “bottom-up” and “top-down” approach and to contribute to the understanding of the involvement and impact of inhibitory metabolites for DDIs observed in the clinic. PK profiles from clinical researches of different dosages were used to verify the bupropion model. Reasonable PK profiles of bupropion and its metabolites were captured in the PBPK model. Confidence in the DDI prediction involving bupropion and co-administered CYP2D6 substrates could be maximized. The predicted maximum concentration (Cmax) area under the concentration-time curve (AUC) values and Cmax and AUC ratios were consistent with clinically observed data. The addition of the inhibitory metabolites into the PBPK model resulted in a more accurate prediction of DDIs (AUC and Cmax ratio) than that which only considered parent drug (bupropion) P450 inhibition. The simulation suggests that bupropion and its metabolites contribute to the DDI between bupropion and CYP2D6 substrates. The inhibitory potency from strong to weak is hydroxybupropion, threohydrobupropion, erythrohydrobupropion, and bupropion, respectively. The present bupropion PBPK model can be useful for predicting inhibition from bupropion in other clinical studies. This study highlights the need for caution and dosage adjustment when combining bupropion with medications metabolized by CYP2D6. It also demonstrates the feasibility of applying the PBPK approach to predict the DDI potential of drugs undergoing complex metabolism, especially in the DDI involving inhibitory metabolites

First vaccination after birth: serious adverse events of Bacillus Calmette-Guérin (BCG) in real-world

The Bacillus Calmette-Guérin (BCG) vaccine is a free vaccine in China, and more than 300 million newborns have been vaccinated. Inevitably, the BCG vaccine will have some rare adverse events on the first day of life (24 hours after birth), but related reports are extremely rare. In this commentary, the authors searched the Chinese legal documents database for documents related to serious adverse events caused by BCG from January 2010 to January 2022. Fourteen pediatric cases were identified, including 7 preterm infants and 7 full-term infants. The events included 4 cases of interstitial pneumonia, 3 cases of lymphadenitis, 3 cases of septicemia, 1 case of myocarditis, 1 case of muscle atrophy, 1 case of epilepsy, and 1 case of disseminated BCG vaccine. The mortality rate of preterm infants was 100% and that of full-term infants was 28.6% (2/7). All deaths occurred within one day. The BCG vaccine has good safety for the vast majority of newborns

A bibliometric analysis of carbon neutrality: Research hotspots and future directions

Global attention has shifted in recent years to climate change and global warming. The international community has set the objective of carbon neutrality to address the climate crisis. Carbon neutrality has drawn significant attention as a crucial step in the fight against climate change, with individual nations having established their carbon neutrality targets. This paper aims to use bibliometric analysis to investigate research hotspots and trends in carbon neutrality research, and accesses the literature through the Web of Science (WoS) core database and undertakes an in-depth examination of 909 publications linked to carbon neutrality around the world using Vosviewer and Bibliometrix software. According to the findings, the number of carbon neutrality publications has increased dramatically in recent years. There are also notable differences in carbon neutrality research across countries and regions. China and the US are the primary drivers and leaders of carbon neutrality research, and developing countries have relatively little carbon neutrality research. Research has concentrated on carbon neutrality’s practical, technical, policy, and economic aspects, as well as renewable energy sources, carbon conversion technologies, and carbon capture and storage technologies are also research hotspots. The paper also outlines opportunities for the advancement of carbon neutrality research in the future, including how it might be further integrated with Artificial intelligence (AI) and the metaverse, and how to attack the difficulties and uncertainties faced by the post-epidemic rebound. This study aids in understanding the current state of the field of carbon neutrality research and can be used to guide future studies

MORPHOLOGICAL EVOLUTION OF Al2O3 AND CNT NANOFLUID DROPLETS DURING SOLIDIFICATION

Nanofluid is an emerging heat transfer fluid with good heat transfer and thermal conductivity properties. It is important to investigate the phase change properties and morphological evolution during the freezing of nanofluid droplets to understand their practical applications. The effect of dynamic wettability on the deformation of a single droplet of aluminum trioxide (Al2O3–H2O) and graphene (CNT–H2O) nanofluids at different mass concentrations and substrate temperatures was investigated by visualizing the droplet freezing. The formation of solid-like and freezing front motions inside the droplet during the freezing process of these droplets was investigated. The solidification process was strongly influenced by the temperature gradient perpendicular to the cold surface and the change in the solid–liquid interface wettability during the phase change, resulting in volume redistribution at the top of the droplet. The freezing shape of Al2O3–H2O nanodroplets resembled a “moon crater,” and the influence of wettability decreased with increasing concentration, leading to a relative increase in the aperture of the top platform. The fully frozen state of the nanofluid droplet had an increasingly pointed tip, with a strong relationship between the substrate temperature and solidification time when the CNT–H2O concentration was 5 times higher and showed no change in the freezing droplet deformation rate under the experimental conditions. The contact angle of the two nanofluid droplets did not fluctuate significantly with increasing concentration, while that of the 1% nanofluid droplets remained at an average value of 85° during freezing. Under different freezing conditions, the freezing shape of Al2O3–H2O droplets tended to increase in diameter as the subcooling temperature decreased, with the final deformation rate of 1% Al2O3–H2O being twice that at 5% concentration, while the contact angle of the same mass concentration of Al2O3–H2O decreased by 1° as the subcooling temperature dropped. The CNT–H2O droplet became sharper at the tip as the subcooling temperature increased, and its contact angle did not change with temperature