[Retracted] Clinical significance of miR­1298 in cervical cancer and its biological function in vitro.

[This retracts the article DOI: 10.3892/ol.2021.12662.].

Optical flow estimation of coronary angiography sequences based on semi-supervised learning.

Optical flow is widely used in medical image processing, such as image registration, segmentation, 3D reconstruction, and temporal super-resolution. However, high-precision optical flow training datasets for medical images are challenging to produce. The current optical flow estimation models trained on these non-medical datasets, such as KITTI, Sintel, and FlyingChairs are unsuitable for medical images. In this work, we propose a semi-supervised learning mechanism to estimate the optical flow of coronary angiography. Our proposed method only needs the original medical images, segmentation results of regions of interest, and pre-trained models based on other optical flow datasets to train a new optical flow estimation model suitable for medical images. First, we use the coronary segmentation results to perform image enhancement processing on the coronary vascular region to improve the image contrast between the vascular region and the surrounding tissues. Then, we extract the high-precision optical flow of coronary arteries based on the coronary-enhanced images and the pre-trained optical flow estimation model. After estimating the optical flow, we take it and its corresponding original coronary angiography images as the training dataset to train the optical flow estimation network. Furthermore, we generate a large-scale synthetic Flying-artery dataset based on coronary artery segmentation results and original coronary angiography images, which is used to improve and evaluate the accuracy of optical flow estimation for coronary angiography. The experimental results on the coronary angiography datasets demonstrate that our proposed method can significantly improve the optical flow estimation accuracy of coronary angiography sequences compared with other methods.

Clinical significance of miR-1298 in cervical cancer and its biological function in vitro.

Cervical cancer is one of the most malignant tumors in women. miR-1298 was reported to be abnormally expressed and serve crucial role in tumorigenesis of several types of cancer; however, the role of miR-1298 in cervical cancer remains unknown. The present study aimed to evaluate the clinical and biological significance of miR-1298 in cervical cancer. To do so, the expression level of miR-1298 in cervical cancer tissues and cells was evaluated by reverse transcription quantitative PCR. Kaplan-Meier survival analysis and Cox regression analysis were used to explore the prognostic significance of miR-1298 in patients with cervical cancer. Cell Counting Kit-8 and Transwell migration and invasion assays were used to evaluate the effect of miR-1298 on the proliferative, migratory and invasive abilities of cervical cancer cells, respectively. The expression of miR-1298 was lower in cancer tissues and cells compared with normal tissues and cells. Furthermore, miR-1298 expression was associated with lymph node metastasis, tumor diameter and staging from the International Federation of Gynecology and Obstetrics. In addition, patients with low miR-1298 expression had poorer overall survival. These findings suggested that miR-1298 may be considered as an independent prognostic factor for patients with cervical cancer. Furthermore, the results demonstrated that miR-1298 knockdown could promote tumor cell proliferation and migratory and invasive abilities. In addition, nucleus accumbens-associated 1 (NACC1) was demonstrated to be a direct target of miR-1298. Taken together, these findings indicated that miR-1298 overexpression may be considered as a prognostic biomarker for cervical cancer and that miR-1298 may play an inhibitor role in cervical cancer by targeting NACC1.

Life cycle water footprint comparison of biomass-to-hydrogen and coal-to-hydrogen processes.

Water is essential for the industrial production of hydrogen. This study investigates the production of hydrogen from biomass and coal. To date, there are few studies focusing on the water footprint of biomass-to-hydrogen and coal-to-hydrogen processes. This research conducted a life cycle water use analysis on wheat straw biomass and coal to hydrogen via pyrolysis gasification processes. The results show that the water consumption of the entire biomass-to-hydrogen process was 76.77 L/MJ, of which biomass cultivation was the dominant contributor (99%). Conversely, the water consumption of the coal-to-hydrogen process was only 1.06 L/MJ, wherein the coal production stage accounted for only 4.15% for the total water consumption, which is far lower than that of the biomass-to-hydrogen process. The hydrogen production stage of biomass hydrogen production accounted for 76% of the total water consumption when excluding the water consumption of straw growth, whereas that of the coal hydrogen production stage was 96%. This research provides the associated water consumption, within a specified boundary, of both hydrogen production processes, and the influence of major factors on total water consumption was demonstrated using sensitivity analysis.

Coronary artery segmentation in angiographic videos utilizing spatial-temporal information.

BACKGROUND: Coronary artery angiography is an indispensable assistive technique for cardiac interventional surgery. Segmentation and extraction of blood vessels from coronary angiographic images or videos are very essential prerequisites for physicians to locate, assess and diagnose the plaques and stenosis in blood vessels. METHODS: This article proposes a novel coronary artery segmentation framework that combines a three-dimensional (3D) convolutional input layer and a two-dimensional (2D) convolutional network. Instead of a single input image in the previous medical image segmentation applications, our framework accepts a sequence of coronary angiographic images as input, and outputs the clearest mask of segmentation result. The 3D input layer leverages the temporal information in the image sequence, and fuses the multiple images into more comprehensive 2D feature maps. The 2D convolutional network implements down-sampling encoders, up-sampling decoders, bottle-neck modules, and skip connections to accomplish the segmentation task. RESULTS: The spatial-temporal model of this article obtains good segmentation results despite the poor quality of coronary angiographic video sequences, and outperforms the state-of-the-art techniques. CONCLUSIONS: The results justify that making full use of the spatial and temporal information in the image sequences will promote the analysis and understanding of the images in videos.

Coronary angiography video segmentation method for assisting cardiovascular disease interventional treatment.

BACKGROUND: Coronary heart disease is one of the diseases with the highest mortality rate. Due to the important position of cardiovascular disease prevention and diagnosis in the medical field, the segmentation of cardiovascular images has gradually become a research hotspot. How to segment accurate blood vessels from coronary angiography videos to assist doctors in making accurate analysis has become the goal of our research. METHOD: Based on the U-net architecture, we use a context-based convolutional network for capturing more information of the vessel in the video. The proposed method includes three modules: the sequence encoder module, the sequence decoder module, and the sequence filter module. The high-level information of the feature is extracted in the encoder module. Multi-kernel pooling layers suitable for the extraction of blood vessels are added before the decoder module. In the filter block, we add a simple temporal filter to reducing inter-frame flickers. RESULTS: The performance comparison with other method shows that our work can achieve 0.8739 in Sen, 0.9895 in Acc. From the performance of the results, the accuracy of our method is significantly improved. The performance benefit from the algorithm architecture and our enlarged dataset. CONCLUSION: Compared with previous methods that only focus on single image analysis, our method can obtain more coronary information through image sequences. In future work, we will extend the network to 3D networks.

Two-stage crossed beam cooling with 6Li and ¹³³Cs atoms in microgravity.

Applying the direct simulation Monte Carlo (DSMC) method developed for ultracold Bose-Fermi mixture gases research, we study the sympathetic cooling process of 6Li and 133Cs atoms in a crossed optical dipole trap. The obstacles to producing 6Li Fermi degenerate gas via direct sympathetic cooling with 133Cs are also analyzed, by which we find that the side-effect of the gravity is one of the main obstacles. Based on the dynamic nature of 6Li and 133Cs atoms, we suggest a two-stage cooling process with two pairs of crossed beams in microgravity environment. According to our simulations, the temperature of 6Li atoms can be cooled to T = 29.5 pK and T/TF = 0.59 with several thousand atoms, which propose a novel way to get ultracold fermion atoms with quantum degeneracy near pico-Kelvin.

A modified Bitter-type electromagnet and control system for cold atom experiments.

We present a modified Bitter-type electromagnet which features high magnetic field, fine electronic properties and efficient heat removal. The electromagnet is constructed from a stack of copper layers separated by mica layers that have the same shape. A distinctive design of cooling channels on the insulating layers and the parallel ducts between the layers ensures low resistance for cooling water to flow. A continuous current control system is also made to regulate the current through the electromagnet. In our experiment, versatile electromagnets are applied to generate magnetic field and gradient field. From our measurements, a peak magnetic field of 1000 G and a peak gradient field of 80 G/cm are generated in the center of the apparatuses which are 7 cm and 5 cm away from the edge of each electromagnet with a current of 230 A and 120 A, respectively. With the effective feedback design in the current control system and cooling water flow of 3.8 l/min, the stability of the current through the electromagnets can reach 10(-5).