Browse Articles

Major Adverse Cardiovascular Events 1 Year After Discharge in Out-of-Hospital Cardiac Arrest Survivors

Synergistic combination of ceftazidime and avibactam with Aztreonam against MDR Klebsiella pneumoniae in ICU patients

Abstract The proliferation of multidrug-resistant, metallo-beta-lactamase-producing Klebsiella pneumoniae (MBL-producing K. pneumoniae) poses a major threat to public health resulting in increasing treatment costs, prolonged hospitalization, and mortality rate. Treating such bacteria presents substantial hurdles for clinicians. The combination of Aztreonam (ATM) and ceftazidime/avibactam (CAZ/AVI) is likely the most successful approach. The study evaluated the in vitro activity of CAZ/AVI in combination with ATM against MBL-producing K. pneumoniae clinical isolates collected from Suez Canal University Hospital patients. Carbapenem-resistant K. pneumoniae were isolated and identified from different specimens. The presence of metallo-β-lactamases was detected phenotypically by modified carbapenem inactivation method (mCIM) and EDTA-CIM (eCIM) testing, and genotypically for the three metallo-β-lactamase genes: blaNDM, blaIMP, and blaVIM by conventional PCR method. The synergistic effect of CAZ/AVI with ATM against MBL-producing K. pneumoniae was detected by ceftazidime-avibactam combination disks and E-test for antimicrobial susceptibility testing. Out of the 65 K. pneumoniae isolates recovered, 60% (39/65) were carbapenem-resistant (CRKP). According to the mCIM and eCIM tests, 89.7% (35/39) of CRKP isolates were carbapenemase-positive, and 68.6% (24/35) were metallo-β-lactamase (MBL)-positive. By using the conventional PCR, at least one of the MBL genes was present in each metallo-bata-lactamase-producing isolate: 8.3% carried the blaVIM gene, 66.7% the blaNDM, and 91.7% the blaIMP gene. After doing the disk combination method for ceftazidime-avibactam plus Aztreonam, 62.5% of the isolates shifted from resistance to sensitivity. Also, ceftazidime/avibactam plus Aztreonam resistance was reduced markedly among CRKP using the E-test. The addition of Aztreonam to ceftazidime/avibactam is an effective therapeutic option against MBL-producing K. pneumoniae. Clinical Trials Registry: Pan African Clinical Trials Registry. Trial No.: PACTR202410744344899. Trial URL: https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=32000

Identifying new classes of financial price jumps with wavelets

We introduce an unsupervised classification framework that leverages a multiscale wavelet representation of time-series and apply it to stock price jumps. In line with previous work, we recover the fact that time-asymmetry of volatility is the major feature that separates exogenous, news-induced jumps from endogenously generated jumps. Local mean-reversion and trend are found to be two additional key features, allowing us to identify new classes of jumps. Using our wavelet-based representation, we investigate the endogenous or exogenous nature of cojumps, which occur when multiple stocks experience price jumps within the same minute. Perhaps surprisingly, our analysis suggests that a significant fraction of cojumps result from an endogenous contagion mechanism.

Lipoprotein(a) as a Pharmacological Target: Premises, Promises, and Prospects

Atherosclerotic cardiovascular disease is a major health concern worldwide and requires effective preventive measures. Lp(a) (lipoprotein [a]) has recently garnered attention as an independent risk factor for astherosclerotic cardiovascular disease, with proinflammatory and prothrombotic mechanisms contributing to its atherogenicity. On an equimolar basis, Lp(a) is ~5 to 6 times more atherogenic than particles that have been widely associated with adverse cardiovascular outcomes, such as LDL (low-density lipoprotein). Lp(a) can enter the vessel wall, leading to the accumulation of oxidized phospholipids in the arterial intima, which are crucial for initiating plaque inflammation and triggering vascular disease progression. In addition, Lp(a) may cause atherothrombosis through interactions between apoA (apolipoprotein A) and the platelet PAR-1 (protease-activated receptor 1) receptor, as well as competitive inhibition of plasminogen. Because Lp(a) is mostly determined on genetic bases, a 1-time assessment in a lifetime can suffice to identify patients with elevated levels. Mendelian randomization studies and post hoc analyses of randomized trials of LDL cholesterol–lowering drugs showed a causal link between Lp(a) concentrations and cardiovascular outcomes, with therapeutic reduction of Lp(a) expected to contribute to estimated cardiovascular risk mitigation. Many Lp(a)-lowering drugs, including monoclonal antibodies, small interfering ribonucleic acids, antisense oligonucleotides, small molecules, and gene editing compounds, are at different stages of clinical investigation and show promise for clinical use. In particular, increased Lp(a) testing and treatment are expected to have a substantial impact at the population level, enabling the identification of high-risk individuals and the subsequent prevention of a large number of cardiovascular events. Ongoing phase 3 trials will further elucidate the cardiovascular benefits of Lp(a) reduction over the long term, offering potential avenues for targeted interventions and improved cardiovascular outcomes.

Non-isolated common-grounded high step-up DC-DC converter with continuous source current suitable for low power applications

Temperature-dependent polar lignification of a seed coat suberin layer promoting dormancy in <i>Arabidopsis thaliana</i>

The seed is a landmark plant adaptation where the embryo is sheltered by a protective seed coat to facilitate dispersion. In Arabidopsis , the seed coat, derived from ovular integuments, plays a critical role in maintaining dormancy, ensuring germination occurs during a favorable season. Dormancy is enhanced by cold temperatures during seed development by affecting seed coat permeability through changes in apoplastic barriers. However, their localization and composition are poorly understood. This study identifies and investigates a polar barrier in the seed coat’s outer integument (oi1) cells. We present histological, biochemical, and genetic evidence showing that cold promotes polar seed coat lignification of the outer integument 1 (oi1) cells and suberization throughout the entire oi1 cell boundary. The polar oi1 barrier is regulated by the transcription factors MYB107 and MYB9. MYB107, in particular, is crucial for the lignified polar oi1 barrier formation under cold temperatures. The absence of the oi1 barrier in mutant seeds correlates with increased permeability and reduced dormancy. Our findings elucidate how temperature-induced modifications in seed coat composition regulate dormancy, highlighting the roles of suberin and lignin in this process.

Unipolar Voltage Mapping to Predict Recovery of Left Ventricular Ejection Fraction in Patients With Recent-Onset Nonischemic Cardiomyopathy

BACKGROUND: The ability to predict recovery of left ventricular ejection fraction (LVEF) in response to guideline-directed therapy among patients with nonischemic cardiomyopathy is desired. We sought to determine whether left ventricular endocardial unipolar voltage measured during invasive electroanatomic mapping could be used to predict LVEF recovery among those with recent-onset nonischemic cardiomyopathy. METHODS: We analyzed the left ventricular voltage maps of patients included in the eMAP trial (Electrogram-Guided Myocardial Advanced Phenotyping; NCT03293381), a prospective, nonrandomized, interventional trial conducted at 2 institutions between 2017 and 2020. Patients had recent-onset nonischemic cardiomyopathy defined by LVEF ≤45% and development of symptoms or signs of heart failure within the past 6 months. Detailed voltage maps of the left ventricular endocardium were generated using the Carto electroanatomic mapping system. Abnormal unipolar amplitude was defined as &lt;8.27 mV. The primary end point was recovery of LVEF (Recovery) defined by a 1-year LVEF ≥50% or ≥45% with ≥10% increase from baseline. RESULTS: Of the 29 enrolled patients (median age, 49 years [25th percentile, 39; 75th percentile, 59], 8 females [27.6%]), LVEF recovered in 13 (44.8%) by 1-year follow-up. The percentage of total endocardial surface area with unipolar voltage abnormality (AUA) was significantly lower among Recovery patients than No Recovery patients (18.2% [25th percentile, 6.4; 75th percentile, 22.4] versus 80.0% [25th percentile, 29.5; 75th percentile, 90.9]; P =0.004). Percent AUA was associated with lower likelihood of Recovery (odds ratio, 0.64 per 10% increase in AUA; 95% CI, 0.47–0.88; P =0.006). A 28% cutoff value for percent AUA was 92% sensitive and 75% specific with an area under the receiver operating characteristic curve of 0.81 (95% CI, 0.63–0.99; P =0.001) for predicting recovery versus no recovery. The majority of patients (12 of 13; 92.3%) with a percent AUA &gt;28% did not recover. CONCLUSIONS: Left ventricular unipolar voltage abnormality is a potent predictor of LVEF recovery among patients recently diagnosed with nonischemic cardiomyopathy. Detailed left ventricular unipolar voltage mapping could therefore be used as a valuable prognostic tool in guiding treatment decisions.

Serum biomarkers associated with health impacts of high residential radon exposure: a metabolomic pilot study

Robust inattentive discrete choice

Rational inattention models characterize optimal decision-making in data-rich environments. In such environments, it can be costly to look carefully at all of the information. Some information is much more salient for the decision at hand and merits closer scrutiny. The inattention decision model formalizes this choice and deduces how best to navigate through the potentially vast array of data when making decisions. In the rational formulation, the decision-maker commits fully to a subjective prior distribution over the possible states of the world that could be realized. We relax this assumption and look for a robustly optimal solution to the inattention problem by allowing the decision-maker to be ambiguity averse with respect to this prior. We feature a setup that is deliberately simple by a) assuming a discrete set of choices, b) using Shannon’s mutual information to quantify attention costs, and c) imposing relative entropy with respect to a baseline probability distribution to quantify prior divergence. We provide necessary and sufficient conditions for the robust solution and develop numerical methods to solve it. In comparison to the rational solution with no prior uncertainty, our decision-maker slants priors in more cautious or pessimistic directions when deducing how to allocate attention over the range of available information. This approach implements a form of robustness to prior misspecification, or equivalently, a form of ambiguity aversion. We explore some examples that show how the robust solution differs from the rational solution with a commitment to a subjective prior distribution and how it differs from imposing risk aversion.

Transcatheter Repair Versus Surgery for Atrial Versus Ventricular Functional Mitral Regurgitation: A Post Hoc Analysis of the MATTERHORN Trial

LFD-YOLO: a lightweight fall detection network with enhanced feature extraction and fusion

Abstract Falls are one of the significant safety hazards for the elderly. Current object detection models for fall detection often suffer from high computational complexity, limiting their deployment on resource-constrained edge devices. Although lightweight models can reduce computational requirements, they typically compromise detection accuracy. To address these challenges, and considering the more lightweight architecture of YOLOv5 compared to other YOLO series models such as YOLOv8, we propose a lightweight fall detection model based on YOLOv5, named Lightweight Fall Detection YOLO (LFD-YOLO). Our method introduces a novel lightweight feature extraction module, Cross Split RepGhost (CSRG), which reduces information loss during feature map transmission. We also integrate an Efficient Multi-scale Attention (EMA) to enhance focus on the human pose. Moreover, we propose a Weighted Fusion Pyramid Network (WFPN) and utilize Group Shuffle Convolutions (GSConv) to reduce the model’s computational complexity and improve the efficiency of multi-scale feature fusion. Additionally, we design an Inner Weighted Intersection over Union (Inner-WIoU) loss to accelerate model convergence and enhance generalization. We construct a Person Fall Detection Dataset (PFDD) dataset covering diverse scenarios. Experimental results on the PFDD and the publicly available Falling Posture Image Dataset (FPID) datasets show that, compared to YOLOv5s, LFD-YOLO improves mAP0.5 by 1.5% and 1.7%, respectively, while reducing the number of parameters and calculations by 19.2% and 21.3%. Furthermore, compared to YOLOv8s, LFD-YOLO reduces the number of parameters and calculations by 48.6% and 56.1%, respectively, while improving mAP0.5 by 0.3% and 0.5%. These results demonstrate that LFD-YOLO achieves higher detection accuracy and lower computational complexity, making it well-suited for fall detection tasks.

Publisher Correction: Genetic diversity and origin of Kazakh Tobet Dogs

Turbulence enhances wave attenuation of seagrass in combined wave–current flows

The wave attenuation properties of seagrasses are key to accurately predict how effective these plants are at protecting coasts from erosion and floods. While recent studies have significantly advanced the understanding of seagrass wave attenuation in pure-wave conditions, the presence of a current introduces several complications that have yet to be fully explored. In the present study, we quantify the wave attenuation of seagrass canopies in the presence of a current parallel to the direction of wave propagation via experiments conducted with dynamically scaled mimics of seagrass installed in a laboratory flume facility. The dataset we present is the largest of its kind and spans a broad range of wave properties, current velocities, water depths, and plant densities for a total of over 300 experiments. Using our experimental results, we show that the commonly employed approach of modeling wave attenuation as a result of vegetation drag works well for a range of conditions but underpredicts systematically when turbulence generated by the interaction between the seagrass canopy and the current is sufficiently strong. We then employ phenomenological arguments and experimental data to identify a nondimensional parameter that effectively quantifies the relative importance of turbulence and drag in dictating the overall observed wave attenuation. Moreover, we propose a simple but physically based modeling approach that is consistent with the proposed phenomenology and can be used for applications in coastal waters.

Study of the degradation and microstructural characteristics of granite porphyry with freeze–thaw cycles

Thermal-solutal-induced bistability of evaporating multicomponent liquid thin films

Volatile multicomponent liquid films show rich dynamics, due to the complex interplay of gradients in temperature and in solute concentrations. Here, we study the evaporation dynamics of a tricomponent liquid film, consisting of water, ethanol, and trans-anethole oil (known as “ouzo”). With the preferential evaporation of ethanol, cellular convective structures are observed both in the thermal patterns and in the nucleated oil droplet patterns. However, the feature sizes of these two patterns can differ, indicating dual instability mechanisms dominated by either temperature or solute concentration. Using numerical simulations, we quantitatively compare the contributions of temperature and solute concentration on the surface tension. Our results reveal that the thermal Marangoni effect predominates at the initial evaporation stage, resulting in cellular patterns in thermal images, while the solutal Marangoni effect gradually becomes dominant. By regulating the transition time of this thermal-solutal-induced bistability and the nucleation time of oil microdroplets in the ternary mixture, the oil droplet patterns can be well controlled. This capability not only enhances our understanding of the evaporation dynamics but also paves the way for precise manipulation of nucleation and deposition processes at larger scales.

Deep attention model for arrhythmia signal classification based on multi-objective crayfish optimization algorithmic variational mode decomposition

Anomalous suppression of large-scale density fluctuations in classical and quantum spin liquids

Classical spin liquids (CSLs) are intriguing states of matter that do not exhibit long-range magnetic order and are characterized by an extensive ground-state degeneracy. Adding quantum fluctuations, which induce dynamics between these different classical ground states, can give rise to quantum spin liquids (QSLs). QSLs are highly entangled quantum phases of matter characterized by fascinating emergent properties, such as fractionalized excitations and topological order. One such exotic quantum liquid is the Z 2 QSL, which can be regarded as a resonating valence bond (RVB) state formed from superpositions of dimer coverings of an underlying lattice. In this work, we unveil a hidden large-scale structural property of archetypal CSLs and QSLs known as hyperuniformity, i.e., normalized infinite-wavelength density fluctuations are completely suppressed in these systems. In particular, we first demonstrate that classical ensembles of close-packed dimers and their corresponding quantum RVB states are perfectly hyperuniform in general. Subsequently, we focus on a ruby-lattice spin liquid that was recently realized in a Rydberg-atom quantum simulator, and show that the QSL remains effectively hyperuniform even in the presence of a finite density of spinon and vison excitations, as long as the dimer constraint is still largely preserved. Moreover, we demonstrate that metrics based on the framework of hyperuniformity can be used to distinguish the QSL from other proximate quantum phases. These metrics can help identify potential QSL candidates, which can then be further analyzed using more advanced, computationally intensive quantum numerics to confirm their status as true QSLs.

Multifactor prediction model for stock market analysis based on deep learning techniques

Aqueous power source integrated on a microfluidic chip

The growing demand for portable sensors for point-of-care (POC) and onsite health monitoring has led to significant interest in developing suitable power sources. In this study, we developed a microfluidic chip-integrated reverse electrodialysis (μRED) system for ecofriendly power generation with monolithic operation. Leveraging its fully ionic characteristic, μRED was successfully applied to an ionic diode, thereby demonstrating its capability for seamless integration. The feasibility of operating a bipolar electrode sensor without an external power supply was demonstrated, highlighting its broad applicability in electrochemical portable sensors. μRED has great potential for future applications, including electrochemical sensors for POC diagnostics and wearable devices.

An optimized detection model for micro-terrain around transmission lines

Abstract Detecting micro-terrain is essential for the effective layout and maintenance of transmission lines. To address the issues of detection incompleteness, classification ambiguity, and inefficiency in traditional methods, particularly the challenge of distinguishing between saddle and canyon micro-terrain, this paper optimizes the calculation of micro-terrain features and the strategy of micro-terrain detection, and explores a detection method of micro-terrain around transmission lines based on the GPU parallel random forest. This paper employs the GPU parallel random forest model as the extraction framework, leveraging the computational speed advantage of GPU parallel technology for handling large datasets and the robustness inherent in the ensemble approach of random forests. The DEM data of 49 transmission lines in the study area was used for micro-terrain detection experiments. Most of these 49 routes are situated in mountainous regions with complex terrain and contain diverse micro-terrain categories along their paths, rendering them highly representative. The experimental results demonstrate that the proposed method effectively identifies atypical micro-terrain types and four typical micro-terrain types—saddle, canyon, alpine watershed, and uplift—with a classification accuracy of 97.96% and a Kappa coefficient of 0.974. Compared to the traditional method, which achieves a classification accuracy of 75.19% and a Kappa coefficient of 0.642, the proposed method demonstrates a clear improvement in performance. Moreover, by employing the parallel model, the acceleration ratios for training and classification reach 50.57 and 109.06, respectively, significantly improving the efficiency of micro-terrain detection for large-scale regions. These findings could significantly enhance transmission line maintenance and layout planning by providing more accurate micro-terrain data, enabling better decision-making and resource allocation for infrastructure development and disaster risk mitigation.