The Effects of Bivalirudin and Ordinary Heparin on the Incidence of Bleeding Events and the Level of Inflammation after Interventional Therapy for Acute Myocardial Infarction.

Objective: To evaluate and compare the efficacy, bleeding events, and inflammation levels of optimized bivalirudin versus ordinary heparin in the context of percutaneous coronary intervention (PCI) for patients with acute myocardial infarction. This approach will underscore the comprehensive scope of the study, addressing multiple dimensions of clinical outcomes. Methods: This study involved 120 acute myocardial infarction patients treated from January 2022 to January 2023, randomly allocated into two groups: the control group received ordinary heparin, and the observation group received bivalirudin. Both groups underwent percutaneous coronary intervention (PCI). The study specifically measured coagulation indexes such as prothrombin time (PT) and activated partial thromboplastin time (aPTT), and inflammatory markers including C-reactive protein (CRP) and interleukin-6 (IL-6). Additionally, the incidence of bleeding events and major adverse cardiovascular events (MACE) within 30 days post-PCI were recorded, with bleeding events categorized according to the Bleeding Academic Research Consortium (BARC) criteria and MACE defined by the occurrence of death, non-fatal myocardial infarction, or stroke. Results: No significant differences were observed in coagulation indexes and pre-operation inflammation levels between the two groups (P > .05). However, at 7 days post-operation, despite both groups showing reduced inflammation-NLR decreased by 25%, hs-CRP by 30%, and IL-10 increased by 20%-the bivalirudin group exhibited notably lower incidence rates of various bleeding events (mucosal 2% vs 6%, gingival 1% vs 4%, puncture site 3% vs 8%, and hematuria 1% vs 5%) within 30 days post-PCI compared to the heparin group. TIMI blood flow grades 3 (indicating normal flow) were achieved in 85% of the bivalirudin group compared to 70% in the heparin group. The incidence of MACE was comparable between groups with both reporting a 5% occurrence rate (P > .05). Conclusion: The study reveals that while both bivalirudin and ordinary heparin effectively prevent MACE post-acute myocardial infarction intervention, bivalirudin significantly reduces postoperative bleeding events and maintains comparable anti-inflammatory effects. This suggests its preferable use in clinical settings, particularly in patient populations at high risk for bleeding. Future research could further explore the specific patient characteristics that optimize bivalirudin's benefits over heparin, enhancing tailored therapeutic approaches. This could potentially include randomized trials focusing on patients with different baseline bleeding risks.

A miniaturized biosensor for rapid detection of tetracycline based on a graphene field-effect transistor with an aptamer modified gate.

Tetracycline is a broad-spectrum antibiotic for human, poultry and livestock that may cause health damage when enriched in humans. Therefore, it is essential to create a rapid tetracycline assay with high sensitivity, specificity and portability. In this study, a miniaturized tetracycline biosensor based on aptamer-modified graphene field-effect transistor (Apt-SGGT) was fabricated and two detection strategies using transfer characteristic curves and real-time channel current were established for different circumstances. The detection limits of the two strategies were 2.073 pM and 100 pM, respectively. The biosensor also demonstrated outstanding stability, anti-interference and specificity ability. Finally, the biosensor was employed to detect the content of tetracycline in Skim Milk with outstanding recovery rate. We believe that the miniaturized Apt-SGGT biosensor with appropriate detection strategies will provide an ideal portable sensing platform for many important analytes in food with superior selectivity and sensitivity.

Molecular dynamics study on the interfacial properties of mixtures of monomers of polyvinylpyrrolidone (PVP)-based battery binders on graphene and graphite surfaces.

This study investigates the behavior of two different mixtures of monomers of polyvinylpyrrolidone (PVP)-based battery binders, polyvinylpyrrolidone:polyvinylidene difluoride (PVP:PVDF) and polyvinylpyrrolidone:polyacrylic acid (PVP:PAA), at graphene and graphite interfaces using classical molecular dynamics simulations. The aim is to identify the best performing monomer binder blend and carbon-based material for the design of battery-optimized energy devices. The PVP:PAA monomer binder blend and graphite are found to have the best interaction energies, densification upon adsorption, and more ordered structure. The adsorption of both monomer binder blends is strongly guided by the higher affinity of PVP and PAA monomeric molecules for the surfaces compared to PVDF. The structure of adsorbed layers of PVP:PVDF monomer binder blend on graphene and graphite develops more quickly than PVP:PAA, indicating faster kinetics. This study complements a previous density functional theory study recently reported by our group and contributes to a better understanding of the nanoscopic features of relevant interfacial regions involving mixtures of monomers of PVP-based battery binders and different carbon-based materials. The effect of a blend of commonly used monomer binders on carbon-based materials is essential for obtaining tightly bound anode and cathode active materials in lithium-ion batteries, which is crucial for designing battery-optimized energy devices.

Deep Learning-Based Segmentation of 3D Volumetric Image and Microstructural Analysis.

As a fundamental but difficult topic in computer vision, 3D object segmentation has various applications in medical image analysis, autonomous vehicles, robotics, virtual reality, lithium battery image analysis, etc. In the past, 3D segmentation was performed using hand-made features and design techniques, but these techniques could not generalize to vast amounts of data or reach acceptable accuracy. Deep learning techniques have lately emerged as the preferred method for 3D segmentation jobs as a result of their extraordinary performance in 2D computer vision. Our proposed method used a CNN-based architecture called 3D UNET, which is inspired by the famous 2D UNET that has been used to segment volumetric image data. To see the internal changes of composite materials, for instance, in a lithium battery image, it is necessary to see the flow of different materials and follow the directions analyzing the inside properties. In this paper, a combination of 3D UNET and VGG19 has been used to conduct a multiclass segmentation of publicly available sandstone datasets to analyze their microstructures using image data based on four different objects in the samples of volumetric data. In our image sample, there are a total of 448 2D images, which are then aggregated as one 3D volume to examine the 3D volumetric data. The solution involves the segmentation of each object in the volume data and further analysis of each object to find its average size, area percentage, total area, etc. The open-source image processing package IMAGEJ is used for further analysis of individual particles. In this study, it was demonstrated that convolutional neural networks can be trained to recognize sandstone microstructure traits with an accuracy of 96.78% and an IOU of 91.12%. According to our knowledge, many prior works have applied 3D UNET for segmentation, but very few papers extend it further to show the details of particles in the sample. The proposed solution offers a computational insight for real-time implementation and is discovered to be superior to the current state-of-the-art methods. The result has importance for the creation of an approximately similar model for the microstructural analysis of volumetric data.

Designing Low Tortuosity Electrodes through Pattern Optimization for Fast-Charging.

The development of fast-charging technologies is crucial for expediting the progress and promotion of electric vehicles. In addition to innovative material exploration, reduction in the tortuosity of electrodes is a favored strategy to enhance the fast-charging capability of lithium-ion batteries by optimizing the ion-transfer kinetics. To realize the industrialization of low-tortuosity electrodes, a facile, cost-effective, highly controlled, and high-output continuous additive manufacturing roll-to-roll screen printing technology is proposed to render customized vertical channels within electrodes. Extremely precise vertical channels are fabricated by applying the as-developed inks, using LiNi0.6 Mn0.2 Co0.2 O2 as the cathode material. Additionally, the relationship between the electrochemical properties and architecture of the channels, including the pattern, channel diameter, and edge distance between channels, is revealed. The optimized screen-printed electrode exhibited a seven-fold higher charge capacity (72 mAh g-1 ) at a current rate of 6 C and superior stability compared with that of the conventional bar-coated electrode (10 mAh g-1 , 6 C) at a mass loading of 10 mg cm-2 . This roll-to-roll additive manufacturing can potentially be applied to various active materials printing to reduce electrode tortuosity and enable fast charging in battery manufacturing.

Investigations of Si Thin Films as Anode of Lithium-Ion Batteries.

Amorphous silicon thin films having various thicknesses were investigated as a negative electrode material for lithium-ion batteries. Electrochemical characterization of the 20 nm thick thin silicon film revealed a very low first cycle Coulombic efficiency, which can be attributed to the silicon oxide layer formed on both the surface of the as-deposited Si thin film and the interface between the Si and the substrate. Among the investigated films, the 100 nm Si thin film demonstrated the best performance in terms of first cycle efficiency and cycle life. Observations from scanning electron microscopy demonstrated that the generation of cracks was inevitable in the cycled Si thin films, even as the thickness of the film was as little as 20 nm, which was not predicted by previous modeling work. However, the cycling performance of the 20 and 100 nm silicon thin films was not detrimentally affected by these cracks. The poor capacity retention of the 1 µm silicon thin film was attributed to the delamination.

An internal magnetic field strategy to reuse pulverized active materials for high performance: a magnetic three-dimensional ordered macroporous TiO2/CoPt/α-Fe2O3 nanocomposite anode.

A ferromagnetic three-dimensional ordered macroporous TiO2/CoPt/α-Fe2O3 (3DOMTCF) nanocomposite was synthesized via a sol-gel approach templated by poly(methyl methacrylate) (PMMA) microspheres. After magnetization, it exhibited an extremely high reversible capacity and a long cycle life, which were ascribed to the internal magnetic field for reusing pulverized active materials and its unique structure.

Pore orientation effects on the kinetics of mesostructure loss in surfactant templated titania thin films.

The mesostructure loss kinetics are measured as a function of the orientation of micelles in 2D hexagonal close packed (HCP) columnar mesostructured titania thin films using in situ grazing incidence small angle X-ray scattering (GISAXS). Complementary supporting information is provided by ex situ scanning electron microscopy. Pluronic surfactant P123 acts as the template to synthesize HCP structured titania thin films. When the glass substrates are modified with crosslinked P123, the micelles of the HCP mesophase align orthogonal to the films, whereas a mix of parallel and orthogonal alignment is found on unmodified glass. The rate of mesostructure loss of orthogonally oriented (o-HCP) thin films (∼60 nm thickness) prepared on modified substrate is consistently found to be less by a factor of 2.5 ± 0.35 than that measured for mixed orientation HCP films on unmodified substrates. The activation energy for mesostructure loss is only slightly greater for films on modified glass (155 ± 25 kJ mol(-1)) than on unmodified (128 kJ mol(-1)), which implies that the rate difference stems from a greater activation entropy for mesostructure loss in o-HCP titania films. Nearly perfect orthogonal orientation of micelles on modified surfaces contributes to the lower rate of mesostructure loss by supporting the anisotropic stresses that develop within the films during annealing due to continuous curing, sintering and crystallization into the anatase phase during high temperature calcination (>450 °C). Because the film thickness dictates the propagation of orientation throughout the films and the degree of confinement, thicker (∼250 nm) films cast onto P123-modified substrates have a much lower activation energy for mesostructure loss (89 ± 27 kJ mol(-1)) due to the mix of orientations found in the films. Thus, this kinetic study shows that thin P123-templated o-HCP titania films are not only better able to achieve good orthogonal alignment of the mesophase relative to thicker films or films on unmodified substrates, but that alignment of the mesophase in the films stabilizes the mesophase against thermally-induced mesostructure loss.

Bundle-like α'-NaV2O5 mesocrystals: from synthesis, growth mechanism to analysis of Na-ion intercalation/deintercalation abilities.

Bundle-like α'-NaV2O5 mesocrystals were synthesized successfully by a two-step hydrothermal method. Observations using electron microscopy revealed that the obtained NaV2O5 mesocrystals were composed of nanobelts with the preferential growth direction of [010]. The precise crystal structure was further confirmed by Rietveld refinement and Raman spectroscopy. Based on analysis of crystal structure and microscopy, a reaction and growth mechanism, hydrolysis-condensation (oxolation and olation)-ion exchange-self-assembly, was proposed and described in detail. Furthermore, electrochemical measurements were used to analyze the Na-ions intercalation/deintercalation abilities in NaV2O5, and indicated that Na-ions were difficult to extract. Importantly, the DFT theoretical calculation results, which showed that the migration energy of Na-ions was so huge that migration of Na-ions was quite difficult, can explain and support well the results of the electrochemical measurements.

Giant magnetoresistance in non-magnetic phosphoric acid doped polyaniline silicon nanocomposites with higher magnetic field sensing sensitivity.

Phosphoric acid doped conductive polyaniline (PANI) polymer nanocomposites (PNCs) reinforced with silicon nanopowders have been successfully synthesized using a facile surface initiated polymerization (SIP) method. The chemical structures of the nanocomposites are characterized using Fourier transform infrared (FT-IR) spectroscopy. The enhanced thermal stability of the silicon-PANI PNCs compared with pure PANI is obtained using thermogravimetric analysis (TGA). The obtained optical band gap of the PNCs using Ultraviolet-visible diffuse reflectance spectroscopy (UV-vis DRS) decreases with increasing silicon loading. The dielectric properties of the PNCs are strongly related to the silicon loading level. Temperature dependent resistivity analysis reveals a quasi 3-D variable range hopping (VRH) electrical conduction mechanism for the synthesized PNCs. Room temperature giant magnetoresistance (GMR) is observed in the synthesized non-magnetic nanocomposites and analyzed using the wave-function shrinkage model.