Cancer Stem Cell Microenvironment Models with Biomaterial Scaffolds In Vitro

Defined by its potential for self-renewal, differentiation and tumorigenicity, cancer stem cells (CSCs) are considered responsible for drug resistance and relapse. To understand the behavior of CSC, the effects of the microenvironment in each tissue are a matter of great concerns for scientists in cancer biology. However, there are many complicated obstacles in the mimicking the microenvironment of CSCs even with current advanced technology. In this context, novel biomaterials have widely been assessed as in vitro platforms for their ability to mimic cancer microenvironment. These efforts should be successful to identify and characterize various CSCs specific in each type of cancer. Therefore, extracellular matrix scaffolds made of biomaterial will modulate the interactions and facilitate the investigation of CSC associated with biological phenomena simplifying the complexity of the microenvironment. In this review, we summarize latest advances in biomaterial scaffolds, which are exploited to mimic CSC microenvironment, and their chemical and biological requirements with discussion. The discussion includes the possible effects on both cells in tumors and microenvironment to propose what the critical factors are in controlling the CSC microenvironment focusing the future investigation. Our insights on their availability in drug screening will also follow the discussion

TensorMD: Scalable Tensor-Diagram based Machine Learning Interatomic Potential on Heterogeneous Many-Core Processors

Molecular dynamics simulations have emerged as a potent tool for investigating the physical properties and kinetic behaviors of materials at the atomic scale, particularly in extreme conditions. Ab initio accuracy is now achievable with machine learning based interatomic potentials. With recent advancements in high-performance computing, highly accurate and large-scale simulations become feasible. This study introduces TensorMD, a new machine learning interatomic potential (MLIP) model that integrates physical principles and tensor diagrams. The tensor formalism provides a more efficient computation and greater flexibility for use with other scientific codes. Additionally, we proposed several portable optimization strategies and developed a highly optimized version for the new Sunway supercomputer. Our optimized TensorMD can achieve unprecedented performance on the new Sunway, enabling simulations of up to 52 billion atoms with a time-to-solution of 31 ps/step/atom, setting new records for HPC + AI + MD

Research on performance evaluation and optimization theory for thermal microscope imaging systems

Infrared imaging theory is an important theoretical basis for the design of infrared imaging systems, but there is no research on infrared imaging theory for designing thermal microscope imaging systems. Therefore, we studied the performance evaluation and optimization theory of thermal microscope imaging systems. In this paper, we analyzed the difference in spectral radiant flux between thermal microscope imaging and telephoto thermal imaging. The expression of signal-to-noise ratio of the output image of the thermal microscope imaging systems was derived, based on the analysis of the characteristics of thermal microscope imaging. We studied the performance evaluation model of thermal microscope imaging systems based on the minimum resolvable temperature difference and the minimum detectable temperature difference. Simulation and analysis of different detectors (ideal photon detector and ideal thermal detector) were also carried out. Finally, based on the conclusion of theoretical research, we carried out a system design and image acquisition experiment. The results show that the theoretical study of thermal microscope imaging systems in this paper can provide reference for the performance evaluation and optimization of thermal microscope imaging systems

OpenCL-accelerated first-principles calculations of all-electron quantum perturbations on HPC resources

We have proposed, for the first time, an OpenCL implementation for the all-electron density-functional perturbation theory (DFPT) calculations in FHI-aims, which can effectively compute all its time-consuming simulation stages, i.e., the real-space integration of the response density, the Poisson solver for the calculation of the electrostatic potential, and the response Hamiltonian matrix, by utilizing various heterogeneous accelerators. Furthermore, to fully exploit the massively parallel computing capabilities, we have performed a series of general-purpose graphics processing unit (GPGPU)-targeted optimizations that significantly improved the execution efficiency by reducing register requirements, branch divergence, and memory transactions. Evaluations on the Sugon supercomputer have shown that notable speedups can be achieved across various materials

Adaptive position calibration technique for an optical micro-scanning thermal microscope imaging system

In order to improve the spatial resolution of an optical micro-scanning thermal microscope system, the micro-scanning position must be accurately calibrated. An adaptive calibration method based on image registration and plane coordinate system is proposed. The meaning of calibration is given, and the principle and method of point calibration are introduced in detail and experiments using the real system were done. Different reconstruction methods were applied to reconstruct the visible light image and the real thermal microscope image, and the evaluation scores are given. Results of simulation and real thermal imaging processing show that the method can successfully calibrate the micro-scanning position. The method can significantly improve the oversampled reconstructed image quality, thus enhancing the spatial resolution of the system. This method can also be used in other electro-optical imaging systems

The first clinical data of the SAPIEN 3 aortic valve in the treatment of aortic stenosis in China

BackgroundData on outcomes following transcatheter aortic valve replacement with SAPIEN 3 in China is limited as it was approved by the National Medical Products since 2020. The present study was designed to collect clinical data on the SAPIEN 3 aortic valve in Chinese patients with bicuspid aortic valve and tricuspid aortic valve stenosis.MethodsWe analyzed the patient characteristics, procedural features and procedural outcomes of the first 438 patients (223 for bicuspid aortic valve and 215 tricuspid aortic valve) from 21 provinces in 74 sites treated with the SAPIEN 3 valve system for transcatheter aortic valve replacement between September 2020 and May 2022.ResultsProcedural mortality was 0.7%. 5 cases during the operation were converted to surgery. Among 438 cases, permanent pacemaker implantation was performed in a total of 12 cases (2.7%). The patient had severe leaflet calcification of the aortic valve, with moderate and severe calcification reaching 39.7% and 35.2% respectively. The size of the implanted valves was predominantly 26 mm and 23 mm, reaching 42.5% and 39.5% respectively. The incidence of moderate or severe perivalvular leak in the postoperative period was 0.5%, with a predominance of 90/10 and 80/20 valve deployment height. There was a significant difference in the deployment height of the valve between bicuspid aortic valve and tricuspid aortic valve, with the bicuspid aortic valve having a more deployment height of 90/10. Annulus size in bicuspid aortic valve group was significantly larger than tricuspid aortic valve group. Valve sizing for oversized, within size, and undersized were different between bicuspid aortic valve and tricuspid aortic valve.ConclusionsProcedural success rates were high, with similar and good results for bicuspid aortic valve and tricuspid aortic valve, low perivalvular leak for both valve types, and low permanent pacemaker implantation rates for both valve types. Annulus size, valve sizing and coronary artery height were significantly different in the BAV and TAV group

Technology for the formation of engineered microvascular network models and their biomedical applications

Abstract Tissue engineering and regenerative medicine have made great progress in recent decades, as the fields of bioengineering, materials science, and stem cell biology have converged, allowing tissue engineers to replicate the structure and function of various levels of the vascular tree. Nonetheless, the lack of a fully functional vascular system to efficiently supply oxygen and nutrients has hindered the clinical application of bioengineered tissues for transplantation. To investigate vascular biology, drug transport, disease progression, and vascularization of engineered tissues for regenerative medicine, we have analyzed different approaches for designing microvascular networks to create models. This review discusses recent advances in the field of microvascular tissue engineering, explores potential future challenges, and offers methodological recommendations

Driver Emotion and Fatigue State Detection Based on Time Series Fusion

Studies have shown that driver fatigue or unpleasant emotions significantly increase driving risks. Detecting driver emotions and fatigue states and providing timely warnings can effectively minimize the incidence of traffic accidents. However, existing models rarely combine driver emotion and fatigue detection, and there is space to improve the accuracy of recognition. In this paper, we propose a non-invasive and efficient detection method for driver fatigue and emotional state, which is the first time to combine them in the detection of driver state. Firstly, the captured video image sequences are preprocessed, and Dlib (image open source processing library) is used to locate face regions and mark key points; secondly, facial features are extracted, and fatigue indicators, such as driver eye closure time (PERCLOS) and yawn frequency are calculated using the dual-threshold method and fused by mathematical methods; thirdly, an improved lightweight RM-Xception convolutional neural network is introduced to identify the driver’s emotional state; finally, the two indicators are fused based on time series to obtain a comprehensive score for evaluating the driver’s state. The results show that the fatigue detection algorithm proposed in this paper has high accuracy, and the accuracy of the emotion recognition network reaches an accuracy rate of 73.32% on the Fer2013 dataset. The composite score calculated based on time series fusion can comprehensively and accurately reflect the driver state in different environments and make a contribution to future research in the field of assisted safe driving

Driver Emotion and Fatigue State Detection Based on Time Series Fusion

Studies have shown that driver fatigue or unpleasant emotions significantly increase driving risks. Detecting driver emotions and fatigue states and providing timely warnings can effectively minimize the incidence of traffic accidents. However, existing models rarely combine driver emotion and fatigue detection, and there is space to improve the accuracy of recognition. In this paper, we propose a non-invasive and efficient detection method for driver fatigue and emotional state, which is the first time to combine them in the detection of driver state. Firstly, the captured video image sequences are preprocessed, and Dlib (image open source processing library) is used to locate face regions and mark key points; secondly, facial features are extracted, and fatigue indicators, such as driver eye closure time (PERCLOS) and yawn frequency are calculated using the dual-threshold method and fused by mathematical methods; thirdly, an improved lightweight RM-Xception convolutional neural network is introduced to identify the driver’s emotional state; finally, the two indicators are fused based on time series to obtain a comprehensive score for evaluating the driver’s state. The results show that the fatigue detection algorithm proposed in this paper has high accuracy, and the accuracy of the emotion recognition network reaches an accuracy rate of 73.32% on the Fer2013 dataset. The composite score calculated based on time series fusion can comprehensively and accurately reflect the driver state in different environments and make a contribution to future research in the field of assisted safe driving