Cardiac surgery-associated acute kidney injury (CSA-AKI) is a major complication that results in increased morbidity and mortality after cardiac surgery. Most established prediction models are ...limited to the analysis of nonlinear relationships and fail to fully consider intraoperative variables, which represent the acute response to surgery. Therefore, this study utilized an artificial intelligence-based machine learning approach thorough perioperative data-driven learning to predict CSA-AKI.
A total of 671 patients undergoing cardiac surgery from August 2016 to August 2018 were enrolled. AKI following cardiac surgery was defined according to criteria from Kidney Disease: Improving Global Outcomes (KDIGO). The variables used for analysis included demographic characteristics, clinical condition, preoperative biochemistry data, preoperative medication, and intraoperative variables such as time-series hemodynamic changes. The machine learning methods used included logistic regression, support vector machine (SVM), random forest (RF), extreme gradient boosting (XGboost), and ensemble (RF + XGboost). The performance of these models was evaluated using the area under the receiver operating characteristic curve (AUC). We also utilized SHapley Additive exPlanation (SHAP) values to explain the prediction model.
Development of CSA-AKI was noted in 163 patients (24.3%) during the first postoperative week. Regarding the efficacy of the single model that most accurately predicted the outcome, RF exhibited the greatest AUC (0.839, 95% confidence interval CI 0.772-0.898), whereas the AUC (0.843, 95% CI 0.778-0.899) of ensemble model (RF + XGboost) was even greater than that of the RF model alone. The top 3 most influential features in the RF importance matrix plot were intraoperative urine output, units of packed red blood cells (pRBCs) transfused during surgery, and preoperative hemoglobin level. The SHAP summary plot was used to illustrate the positive or negative effects of the top 20 features attributed to the RF. We also used the SHAP dependence plot to explain how a single feature affects the output of the RF prediction model.
In this study, machine learning methods were successfully established to predict CSA-AKI, which determines risks following cardiac surgery, enabling the optimization of postoperative treatment strategies to minimize the postoperative complications following cardiac surgeries.
Magnetic particles are widely used as signal labels in a variety of biological sensing applications, such as molecular detection and related strategies that rely on ligand-receptor binding. In this ...review, we explore the fundamental concepts involved in designing magnetic particles for biosensing applications and the techniques used to detect them. First, we briefly describe the magnetic properties that are important for bio-sensing applications and highlight the associated key parameters (such as the starting materials, size, functionalization methods, and bio-conjugation strategies). Subsequently, we focus on magnetic sensing applications that utilize several types of magnetic detection techniques: spintronic sensors, nuclear magnetic resonance (NMR) sensors, superconducting quantum interference devices (SQUIDs), sensors based on the atomic magnetometer (AM), and others. From the studies reported, we note that the size of the MPs is one of the most important factors in choosing a sensing technique.
Metal nanoparticles are extensively studied due to their unique chemical and physical properties, which differ from the properties of their respective bulk materials. Likewise, the properties of ...heterogeneous bimetallic structures are far more attractive than those of single-component nanoparticles. For example, the incorporation of a second metal into a nanoparticle structure influences and can potentially enhance the optical/plasmonic and magnetic properties of the material. This review focuses on the enhanced optical/plasmonic and magnetic properties offered by bimetallic nanoparticles and their corresponding impact on biological applications. In this review, we summarize the predominant structures of bimetallic nanoparticles, outline their synthesis methods, and highlight their use in biological applications, both diagnostic and therapeutic, which are dictated by their various optical/plasmonic and magnetic properties.
This study improved the shoreline detection performance based on the U-Net model by combining Sentinel-1 Synthetic Aperture Radar (SAR) and Digital Elevation Model (DEM) data. The U-Net network was ...first modified to enhance feature extraction by using the MobileNetV3 backbone architecture and convolutional block attention module (CBAM). To alleviate the performance degradation of shoreline detection caused by radar shadow, especially in coastal areas with large terrain undulations, SAR and DEM data were combined as input to U-Net. Furthermore, this study evaluated the shoreline detection performance using the statistical analysis based on the proposed probabilistic model of distance difference between the detected shoreline and reference data which was provided by Construction and Planning Agency Ministry of the Interior (CPAMI), Taiwan government. The experiment was conducted based on two self-built datasets, one containing 4061 SAR images and the other containing 3822 SAR images and corresponding DEM data, both collected in the coastal areas of Taiwan from 2016 to 2019. The experimental results showed that compared with the U-Net network using SAR data, the modified U-Net has achieved superior performance in shoreline detection for various coastal landforms. Moreover, the addition of DEM data reduced the influence of radar shadow, making shoreline detection results more consistent with reference data. Finally, the generalization ability of the modified U-Net in shoreline detection was also verified by testing images from regions not included in the built dataset.
The electron positive boron atom usually does not contribute to the frontier orbitals for several lower‐lying electronic transitions, and thus is ideal to serve as a hub for the spiro linker of ...light‐emitting molecules, such that the electron donor (HOMO) and acceptor (LUMO) moieties can be spatially separated with orthogonal orientation. On this basis, we prepared a series of novel boron complexes bearing electron deficient pyridyl pyrrolide and electron donating phenylcarbazolyl fragments or triphenylamine. The new boron complexes show strong solvent‐polarity dependent charge‐transfer emission accompanied by a small, non‐negligible normal emission. The slim orbital overlap between HOMO and LUMO and hence the lack of electron correlation lead to a significant reduction of the energy gap between the lowest lying singlet and triplet excited states (ΔET‐S) and thereby the generation of thermally activated delay fluorescence (TADF).
Reducing the gap: Using a boron atom as the spiro linker between an electron‐deficient pyridyl pyrrolide and an electron‐donating phenylcarbazolyl or triphenylamine fragment, boron complexes with a narrow HOMO–LUMO orbital overlap, small singlet–triplet energy gap (down to 38 meV), and strong thermally activated delayed fluorescence (TADF) were prepared. For the first time boron‐complex‐based OLEDs show a significant TADF contribution.
The lack of structural information impeded the access of efficient luminescence for the exciplex type thermally activated delayed fluorescence (TADF). We report here the pump-probe Step-Scan Fourier ...transform infrared spectra of exciplex composed of a carbazole-based electron donor (CN-Cz2) and 1,3,5-triazine-based electron acceptor (PO-T2T) codeposited as the solid film that gives intermolecular charge transfer (CT), TADF, and record-high exciplex type cyan organic light emitting diodes (external quantum efficiency: 16%). The transient infrared spectral assignment to the CT state is unambiguous due to its distinction from the local excited state of either the donor or the acceptor chromophore. Importantly, a broad absorption band centered at ~2060 cm
was observed and assigned to a polaron-pair absorption. Time-resolved kinetics lead us to conclude that CT excited states relax to a ground-state intermediate with a time constant of ~3 µs, followed by a structural relaxation to the original CN-Cz2:PO-T2T configuration within ~14 µs.
Summary
We extend the synthetic control method to evaluate the distributional effects of policy intervention in the possible presence of poor matching. The counterfactuals (or intervention effects) ...are identified by matching a vector of pre‐intervention quantile residuals of the treated unit and a convex combination of its potential‐control counterparts. The residuals are orthogonal to a set of observable common factors that control for the potentially poor matching. We also apply our method to a set of case studies that explore the distributional effects of state‐level minimum‐wage hikes in the USA.
Proton transfer involving site-specific hydrogen-bonding interactions is one of the most fundamental and important reactions in chemistry and biology. Deliberately triggering this reaction by ...photoexcitation enables unique and insightful mechanistic analyses. This Review describes a particularly effective method that involves exciting a photoacid containing both an amine and a basic residue and monitoring the ensuing excited-state intramolecular proton-transfer (ESIPT) reactions. Replacing a H atom on the amine with another substituent R modulates the acidity of the amine and allows for the excited-state hydrogen-bond strength to be tuned over a very broad range. In this way, one can draw empirical correlations between N−H bond distances, acidity, hydrogen-bond strength and the ESIPT kinetics and thermodynamics. For example, stronger intramolecular N−H···N hydrogen bonding leads to faster and more exergonic ESIPT. Tuning the amine and basic residues allows one to switch the ESIPT mechanism between the kinetic and thermodynamic regimes, such that molecules can generate ratiometric emission, which is suitable for white-light generation and two-colour imaging. The identity of the amine substituent R not only affects the acidity but can be differentially sensitive towards the local chemical environment. Thus, the R group transduces environmental changes into modified ESIPT rates and/or mechanisms. Such studies open new frontiers in the fundamental aspects of proton transfer in amines, as well as their largely unexplored potential applications.This Review describes excited-state intramolecular proton-transfer (ESIPT) reactions with amines as proton donors. Systematic variation of N−H bond strength, acidity and reaction rate enables ESIPT kinetics and thermodynamics to be correlated and new molecules to be designed for sensing and optoelectronics applications.
Human body fluids (biofluids) contain various proteins, some of which reflect individuals' physiological conditions or predict diseases. Therefore, the analysis of biofluids can provide substantial ...information on novel biomarkers for clinical diagnosis and prognosis. In the past decades, mass spectrometry (MS)-based technologies have been developed as proteomic strategies not only for the identification of protein biomarkers but also for biomarker verification/validation in body fluids for clinical applications. The main advantage of targeted MS-based methodologies is the accurate and specific simultaneous quantitation of multiple biomarkers with high sensitivity. Here, we review MS-based methodologies that are currently used for the targeted quantitation of protein components in human body fluids, especially in plasma, urine, cerebrospinal fluid, and saliva. In addition, the currently used MS-based methodologies are summarized with a specific focus on applicable clinical sample types, MS configurations, and acquisition modes.
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