SOCS5, targeted by miR-155-5p, plays a negative regulatory role in pulmonary hypertension through inhibiting JAK2/STAT3 signaling pathway.

Pulmonary hypertension (PH) is a chronic pulmonary vascular disease and causes massive deaths. Here, we intended to investigate the function and mechanism of SOCS5 in PH. We engineered a hypoxia-induced PH model in mice. HE staining were implemented to evaluate pathological alterations in the lung tissues. The potential mechanism of SOCS5 in regulating hypoxia-induced pulmonary artery smooth muscle cell (PASMC) function was explored in vitro. RT-qPCR and western blot revealed that the level of SOCS5 was decreased both in PH mice and hypoxia-induced HPASMCs. Functional assays were performed for confirming the role of SOCS5 in modulating the cell phenotype and JAK2/STAT3 pathway in HPASMCs. Results revealed that overexpression of SOCS5 suppressed proliferation, migration and contraction of HPASMCs and negatively regulated the JAK2/STAT3 signaling pathway in HPASMCs under hypoxia in vitro, while knockdown of SOCS5 accelerated it. As evidenced by mechanism studies, SOCS5 was targeted and regulated by miR-155-5p, hence affecting on HPASMC proliferation, migration and contraction. These outcomes indicated that the decreased level of SOCS5 in hypoxia-induced HPASMCs promoted the cell proliferation, cell migration, and cell contraction through activating JAK2/STAT3 signaling pathway. Moreover, SOCS5 was targeted by miR-155-5p. All in all, our work hinted that miR-155-5p/SOCS5/JAK2/STAT3 axis played a crucial part in PH.

Plumbagin alleviates obesity-related asthma: Targeting inflammation, oxidative stress, and the AMPK pathway.

BACKGROUND: Obesity-related asthma, a specific type of asthma, tends to have more severe symptoms and more frequent exacerbations, and it is insensitive to standard medications. Plumbagin (PLB) has many positive effects on human health. However, it remains unclear whether PLB protects against obesity-related asthma. The study investigated the effect of PLB on obesity-related asthma. METHODS: Four-week-old male C57BL6/J mice were fed either standard-chow diet or high-fat diet (HFD). The mice were sensitized to 100 µg ovalbumin (OVA) once a week and intraperitoneally injected with 1 mg/kg PLB once daily from Week 10 to 11 and then challenged with 10 µg OVA twice a day on Week 12. The lung tissue and bronchoalveolar lavage fluid (BALF) were collected 48 h after the first OVA challenge. RESULTS: HFD enhanced inflammatory cell infiltration within the airways and increased total inflammatory cell and eosinophil counts, levels of eosinophil-related inflammatory cytokines, including interleukin-4 (IL-4), IL-5, and eotaxin in BALF, and oxidative stress in the lung tissues of asthmatic mice. PLB reduced inflammatory cell infiltration in the airway walls, levels of eosinophil-related inflammatory cytokines in BALF, and oxidative stress in lung tissues of obese asthmatic mice. In addition, PLB restored HFD-induced decreases in adenosine monophosphate-activated protein kinase (AMPK) phosphorylation. CONCLUSION: The study suggested that HFD exacerbated inflammation and oxidative stress, while PLB probably alleviated inflammation and oxidative stress and activated AMPK pathway to attenuate obesity-associated asthma. Thus, PLB likely had the potential to treat obesity-related asthma.

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.

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.

Broadband 400-2400†nm Ge heterostructure nanowire photodetector fabricated by three-dimensional Ge condensation technique.

A 2.7% tensile strained Ge/SiGe heterostructure nanowire (NW) is in-situ fabricated by a three-dimensional Ge condensation method. The NW metal-semiconductor-metal (MSM) photodetector demonstrates an ultra-broadband detection wavelength of 400-2400 nm, showing a high responsivity of >3.46×102 A/W with a photocurrent gain of >4.32×102 at 1550 nm under -2 V. A high normalized photocurrent to dark current ratio (NPDR) of 1.88×1011 W-1 at 1550 nm under -1 V is achieved. The fully complementary metal-oxide-semiconductor (CMOS) compatible, simple and scalable process suggest that the Ge heterostructure NW is promising for low cost, high performance near-infrared or short wavelength infrared focal plane array applications.

In-situ growth of NCNT and encapsulation of Co9S8/Co as a sustainable multifunctional electrocatalyst.

At present, the most effective catalytic materials are noble metals, which large-scale applications are strictly restricted by low reserve and high cost. For the development of sustainable energy, exploring cost-effective non-precious metal materials has therefore become an urgent task. As one of the promising candidates, the hybrid of transition metal sulfide and carbon-nitrogen skeleton attracts great attention due to variability, graded pore structure and high conductivity. Herein, a sustainable multifunctional electrocatalytic material has been designed and achieved by in-situ encapsulating Co9S8/Co in N-doped carbon nanotube (NCNT). Efficient multifunctional electrocatalysis towards oxygen reduction, oxygen evolution and hydrogen evolution reactions are realized. This work highlights the importance of rich Co-N and CoNC coupling centers generated by in-situ engineering transition metal sulfide into carbon-nitrogen skeleton for multifunctional catalytic conversion of sustainable energy. The results here may also be instructive for designing other complexes with perspective for catalysis, sensing, energy storage and conversion.

Light-Trapping Engineering for the Enhancements of Broadband and Spectra-Selective Photodetection by Self-Assembled Dielectric Microcavity Arrays.

Light manipulation has drawn great attention in photodetectors towards the specific applications with broadband or spectra-selective enhancement in photo-responsivity or conversion efficiency. In this work, a broadband light regulation was realized in photodetectors with the improved spectra-selective photo-responsivity by the optimally fabricated dielectric microcavity arrays (MCAs) on the top of devices. Both experimental and theoretical results reveal that the light absorption enhancement in the cavities is responsible for the improved sensitivity in the detectors, which originated from the light confinement of the whispering-gallery-mode (WGM) resonances and the subsequent photon coupling into active layer through the leaky modes of resonances. In addition, the absorption enhancements in specific wavelength regions were controllably accomplished by manipulating the resonance properties through varying the effective optical length of the cavities. Consequently, a responsivity enhancement up to 25% within the commonly used optical communication and sensing region (800 to 980 nm) was achieved in the MCA-decorated silicon positive-intrinsic-negative (PIN) devices compared with the control ones. This work well demonstrated that the leaky modes of WGM resonant dielectric cavity arrays can effectively improve the light trapping and thus responsivity in broadband or selective spectra for photodetection and will enable future exploration of their applications in other photoelectric conversion devices.