Impact-resistant supercapacitor by hydrogel-infused lattice.

The safety of energy storage devices is increasingly crucial due to the growing requirements for application under harsh conditions. Effective methods for enhancing robustness without compromising functionality are necessary. Here we present an impact-resistant, ready-to-use supercapacitor constructed from self-healable hydrogel electrolyte-infused lattice electrodes. Three-dimensional-printed carbon-coated silicon oxycarbide current collectors provide mechanical protection, with compressive stress, Young's modulus, and energy absorption up to 70.61 MPa, 2.75 GPa, and 92.15 kJ/m3, respectively. Commercially viable polyaniline and self-healable polyvinyl alcohol hydrogel are used as active coatings and electrolytes. I-wrapped package structured supercapacitor electrode exhibits a static specific capacitance of 585.51 mF/cm3 at 3 mA/cm3, with an energy density of 97.63 µWh/cm3 at a power density of 0.5 mW/cm3. It maintains operational integrity under extreme conditions, including post-impact with energy of 0.3 J/cm3, dynamic loading ranging from 0 to 18.83 MPa, and self-healing after electrolyte damage, demonstrating its promise for applications in extreme environments.

A reversible implantable memristor for health monitoring applications.

Conventional implantable electronics based on von Neumann architectures encounter significant limitations in computing and processing vast biological information due to computational bottlenecks. The memristor with integrated memory-computing and low power consumption offer a promising solution to overcome the computational bottleneck and Moore's law limitations of traditional silicon-based implantable devices, making them the most promising candidates for next-generation implantable devices. In this work, a highly stable memristor with an Ag/BaTiO3/MnO2/FTO structure was fabricated, demonstrating retention characteristics exceeding 1200 cycles and endurance above 1000 s. The device successfully exhibited three-stage responses to biological signals after implantation in SD (Sprague-Dawley) rats. Importantly, the memristor perform remarkable reversibility, maintaining over 100 cycles of stable repetition even after extraction from the rat. This study provides a new perspective on the biomedical application of memristors, expanding the potential of implantable memristive devices in intelligent medical fields such as health monitoring and auxiliary diagnostics.

Programmable and Modularized Gas Sensor Integrated by 3D Printing.

The rapid advancement of intelligent manufacturing technology has enabled electronic equipment to achieve synergistic design and programmable optimization through computer-aided engineering. Three-dimensional (3D) printing, with the unique characteristics of near-net-shape forming and mold-free fabrication, serves as an effective medium for the materialization of digital designs into usable devices. This methodology is particularly applicable to gas sensors, where performance can be collaboratively optimized by the tailored design of each internal module including composition, microstructure, and architecture. Meanwhile, diverse 3D printing technologies can realize modularized fabrication according to the application requirements. The integration of artificial intelligence software systems further facilitates the output of precise and dependable signals. Simultaneously, the self-learning capabilities of the system also promote programmable optimization for the hardware, fostering continuous improvement of gas sensors for dynamic environments. This review investigates the latest studies on 3D-printed gas sensor devices and relevant components, elucidating the technical features and advantages of different 3D printing processes. A general testing framework for the performance evaluation of customized gas sensors is proposed. Additionally, it highlights the superiority and challenges of programmable and modularized gas sensors, providing a comprehensive reference for material adjustments, structure design, and process modifications for advanced gas sensor devices.

Adipokine vaspin maintains angiogenesis and neurological function during cerebral ischemia-reperfusion via suppressing endoplasmic reticulum stress.

INTRODUCTION: Visceral adipose tissue-derived serine protease inhibitor (vaspin) is an adipokine. It has been reported that decreased serum vaspin levels are significantly associated with stroke severity and prognosis. OBJECTIVE: This article aims to explore the theoretical feasibility of vaspin supplementation for cerebral ischemia-reperfusion (I/R) injury. METHODS: The I/R mouse models were constructed by the middle cerebral artery occlusion (MCAO) method, and the effects of vaspin on cerebral infarction, neurological function, angiogenesis and endoplasmic reticulum (ER) stress were explored. To verify the mediation of ER stress in the regulation of vaspin, human brain microvascular endothelial cells (HBMECs) were subjected to ER stress agonist tunicamycin in vitro. The impacts of vaspin and tunicamycin on oxygen glucose deprivation/ recovery (OGD/R)-induced cell viability, apoptosis, and angiogenesis were examined. RESULTS: Vaspin inhibited blood-brain barrier breakdown and infarction occurred in the brain tissue of the I/R mice. Vaspin also enhanced cerebral neovascularization and reduced the apoptosis. Additional tunicamycin increased the apoptosis of HBMECs and inhibited angiogenesis, reversing the protective effect of vaspin on cells. CONCLUSION: Together, this study reveals that vaspin supplementation reduces cerebral infarction and works against neurological dysfunction. It maintains the survival and angiogenesis capacity of HBMECs by inhibiting ER stress.

Approved immune checkpoint inhibitors in hepatocellular carcinoma: a large-scale meta-analysis and systematic review.

A meta-analysis was performed to assess the benefits and safety profile of approved immune checkpoint inhibitors in hepatocellular carcinoma patients. Eligible studies were searched from Cochrane, Embase, and PubMed databases based on a well-established strategy. Following the exclusion of ineligible studies, 12 studies were included in this meta-analysis. Compared with control group, immune checkpoint inhibitors were associated with improved ORR (OR 3.03, 95% CI 2.26-4.05, P < 0.00001), SD (OR 0.77, 95% CI 0.62-0.95, P = 0.02), OS (HR 0.75, 95% CI 0.68-0.83, P < 0.00001), and PFS (HR 0.74, 95% CI 0.63-0.87, P < 0.0003). However, no significant differences were observed in DCR (OR 1.33, 95% CI 0.97-1.81, P = 0.07), PD (OR 0.90, 95% CI 0.67-1.21, P = 0.48), and all caused any-grade adverse events (OR 1.22, 95% CI 0.62-2.39, P = 0. 57), all caused ≥ grade 3 adverse events (OR 1.10, 95% CI 0.97-1.25, P = 0.14), treatment-related any-grade adverse events (OR 1.13, 95% CI 0.55-2.32, P = 0.73), and treatment-related ≥ grade 3 events (OR 0.82, 95% CI 0.34-1.97, P = 0.65) between the two groups. After subgroup analysis conducted, patients in the immune checkpoint inhibitor group compared with targeted drug group showed significant improvements in OS (HR 0.74, 95% CI 0.66-0.84, P < 0.00001) and PFS (HR 0.75, 95% CI 0.61-0.91, P = 0.004). Immune checkpoint inhibitors have demonstrated peculiar benefits in the treatment of HCC with an acceptable safety profile. Compared to targeted drugs, immune checkpoint inhibitors still offer advantages in the treatment of hepatocellular carcinoma. However, there is still considerable room for further improvement.

DCT-Net: An effective method to diagnose retinal tears from B-scan ultrasound images.

Retinal tears (RTs) are usually detected by B-scan ultrasound images, particularly for individuals with complex eye conditions. However, traditional manual techniques for reading ultrasound images have the potential to overlook or inaccurately diagnose conditions. Thus, the development of rapid and accurate approaches for the diagnosis of an RT is highly important and urgent. The present study introduces a novel hybrid deep-learning model called DCT-Net to enable the automatic and precise diagnosis of RTs. The implemented model utilizes a vision transformer as the backbone and feature extractor. Additionally, in order to accommodate the edge characteristics of the lesion areas, a novel module called the residual deformable convolution has been incorporated. Furthermore, normalization is employed to mitigate the issue of overfitting and, a Softmax layer has been included to achieve the final classification following the acquisition of the global and local representations. The study was conducted by using both our proprietary dataset and a publicly available dataset. In addition, interpretability of the trained model was assessed by generating attention maps using the attention rollout approach. On the private dataset, the model demonstrated a high level of performance, with an accuracy of 97.78%, precision of 97.34%, recall rate of 97.13%, and an F1 score of 0.9682. On the other hand, the model developed by using the public funds image dataset demonstrated an accuracy of 83.82%, a sensitivity of 82.69% and a specificity of 82.40%. The findings, therefore present a novel framework for the diagnosis of RTs that is characterized by a high degree of efficiency, accuracy and interpretability. Accordingly, the technology exhibits considerable promise and has the potential to serve as a reliable tool for ophthalmologists.

Memristor-Based Neuromorphic Chips.

In the era of information, characterized by an exponential growth in data volume and an escalating level of data abstraction, there has been a substantial focus on brain-like chips, which are known for their robust processing power and energy-efficient operation. Memristors are widely acknowledged as the optimal electronic devices for the realization of neuromorphic computing, due to their innate ability to emulate the interconnection and information transfer processes witnessed among neurons. This review paper focuses on memristor-based neuromorphic chips, which provide an extensive description of the working principle and characteristic features of memristors, along with their applications in the realm of neuromorphic chips. Subsequently, a thorough discussion of the memristor array, which serves as the pivotal component of the neuromorphic chip, as well as an examination of the present mainstream neural networks, is delved. Furthermore, the design of the neuromorphic chip is categorized into three crucial sections, including synapse-neuron cores, networks on chip (NoC), and neural network design. Finally, the key performance metrics of the chip is highlighted, as well as the key metrics related to the memristor devices are employed to realize both the synaptic and neuronal components.

Computational study of the balloon dilation steps on transcatheter aortic valve replacement.

Balloon dilation is a commonly used assistant method in transcatheter aortic valve replacement (TAVR) and plays an important role during valve implantation procedure. The balloon dilation steps need to be fully considered in TAVR numerical simulations. This study aims to establish a TAVR simulation procedure with two different balloon dilation steps to analyze the impact of balloon dilation on the results of TAVR implantation. Two cases of aortic stenosis were constructed based on medical images. An implantation simulation procedure with self-expandable valve was established, and multiple models including different simulation steps such as balloon pre-dilation and balloon post-dilation were constructed to compare the different effects on vascular stress, stent morphology and paravalvular leakage. Results show that balloon pre-dilation of TAVR makes less impact on post-operative outcomes, while post-dilation can effectively improve the implantation morphology of the stent, which is beneficial to the function and durability of the valve. It can effectively improve the adhesion of the stent and reduce the paravalvular leakage volume more than 30% after implantation. However, balloon post-dilation may also lead to about 20% or more increased stress on the aorta and increase the risk of damage. The balloon dilation makes an important impact on the TAVR outcomes. Balloon dilation needs to be fully considered during pre-operative analysis to obtain a better clinical result.