Non-thermal ablation of rabbit liver VX2 tumor by pulsed high intensity focused ultrasound with ultrasound contrast agent:Pathological characteristics

AIM： To investigate the pathological characteristics of non-thermal damage induced by pulsed high intensity focused ultrasound （PHIFU） combined with ultrasound contrast agent （UCA）, SonoVue （Bracco SpA, Milan, Italy） in rabbit liver VX2 tumor. METHODS： Liver VX2 tumor models were established in 20 rabbits, which were divided randomly into PHIFU combined with ultrasound contrast agent group （PHIFU ＋ UCA group） and sham group. In the PHIFU ＋ UCA group, 0.2 mL of SonoVue was injected intravenously into the tumor, followed by ultrasound exposure of Isp 5900 W/cm^2. The rabbits were sacrificed one day after ultrasound exposure. Specimens of the exposed tumor tissues were obtained and observed pathologically under light microscope and transmission electron microscope. The remaining tumor tissues were sent for 2,3,5-Triphenyltetrazolium chloride （TTC） staining. RESULTS： Before Trc staining, tumor tissues in both the sham and the PHIFU ＋ UCA groups resembled gray fish meat, After TIC staining, the tumor tissues were uniformly stained red, with a clear boundary between tumor tissue and normal tissue, Histological examination showed signs of tumor cell injury in PHIFU ＋ UCA group, with cytoplasmic vacuoles of various sizes, chromatin margination and karyopyknosis. Electron microscopic examination revealed tumor cell volume reduction, karyopyknosis, chromatin margination, intercellular space widening, the presence of high electro＇n-density apoptotic bodies and vacuoles in cytoplasm. CONCLUSION： The non-thermal effects of PHIFU combined with UCA can be used to ablate rabbit liver VX2 tumors.

Decoding brain responses to pixelized images in the primary visual cortex: implications for visual cortical prostheses

Visual cortical prostheses have the potential to restore partial vision. Still limited by the low-resolution visual percepts provided by visual cortical prostheses, implant wearers can currently only ＂see＂ pixelized images, and how to obtain the specific brain responses to different pixelized images in the primary visual cortex（the implant area） is still unknown. We conducted a functional magnetic resonance imaging experiment on normal human participants to investigate the brain activation patterns in response to 18 different pixelized images. There were 100 voxels in the brain activation pattern that were selected from the primary visual cortex, and voxel size was 4 mm × 4 mm × 4 mm. Multi-voxel pattern analysis was used to test if these 18 different brain activation patterns were specific. We chose a Linear Support Vector Machine（LSVM） as the classifier in this study. The results showed that the classification accuracies of different brain activation patterns were significantly above chance level, which suggests that the classifier can successfully distinguish the brain activation patterns. Our results suggest that the specific brain activation patterns to different pixelized images can be obtained in the primary visual cortex using a 4 mm × 4 mm × 4 mm voxel size and a 100-voxel pattern.

An intelligent electronic capsule system for automated detection of gastrointestinal bleeding

In clinical practice,examination of the hemorrhagic spot (HS) remains difficult.In this paper,we describe a remote controlled capsule (RCC) micro-system with an automated,color-based sensor to identify and localize the HS of the gastrointestinal (GI) tract.In vitro testing of the detecting sensor demonstrated that it was capable of discriminating mimetic intestinal fluid (MIF) with and without the hemoglobin (Hb) when the concentration of Hb in MIF was above 0.05 g/ml.Therefore,this RCC system is able to detect the relatively accurate location of the HS in the GI tract.

FinBrain: when finance meets AI 2.0

Artificial intelligence(AI)is the core technology of technological revolution and industrial transformation.As one of the new intelligent needs in the AI 2.0 era,financial intelligence has elicited much attention from the academia and industry.In our current dynamic capital market,financial intelligence demonstrates a fast and accurate machine learning capability to handle complex data and has gradually acquired the potential to become a'financial brain.'In this paper,we survey existing studies on financial intelligence.First,we describe the concept of financial intelligence and elaborate on its position in the financial technology field.Second,we introduce the development of financial intelligence and review state-of-the-art techniques in wealth management,risk management,financial security,financial consulting,and blockchain.Finally,we propose a research framework called FinBrain and summarize four open issues,namely,explainable financial agents and causality,perception and prediction under uncertainty,risk-sensitive and robust decision-making,and multi-agent game and mechanism design.We believe that these research directions can lay the foundation for the development of AI 2.0 in the finance field.