A Deep Segmentation Network of Stent Structs Based on IoT for Interventional Cardiovascular Diagnosis

[EN] The Internet of Things (IoT) technology has been widely introduced to the existing medical system. An eHealth system based on IoT devices has gained widespread popularity. In this article, we propose an IoT eHealth framework to provide an autonomous solution for patients with interventional cardiovascular diseases. In this framework, wearable sensors are used to collect a patient's health data, which is daily monitored by a remote doctor. When the monitoring data is abnormal, the remote doctor will ask for image acquisition of the patient's cardiovascular internal conditions. We leverage edge computing to classify these training images by the local base classifier; thereafter, pseudo-labels are generated according to its output. Moreover, a deep segmentation network is leveraged for the segmentation of stent structs in intravascular optical coherence tomography and intravenous ultrasound images of patients. The experimental results demonstrate that remote and local doctors perform real-time visual communication to complete telesurgery. In the experiments, we adopt the U-net backbone with a pretrained SeResNet34 as the encoder to segment the stent structs. Meanwhile, a series of comparative experiments have been conducted to demonstrate the effectiveness of our method based on accuracy, sensitivity, Jaccard, and dice.This work was supported by the National Key Research and Development Program of China (Grant no. 2020YFB1313703), the National Natural Science Foundation of China (Grant no. 62002304), and the Natural Science Foundation of Fujian Province of China (Grant no. 2020J05002).Huang, C.; Zong, Y.; Chen, J.; Liu, W.; Lloret, J.; Mukherjee, M. (2021). A Deep Segmentation Network of Stent Structs Based on IoT for Interventional Cardiovascular Diagnosis. IEEE Wireless Communications. 28(3):36-43. https://doi.org/10.1109/MWC.001.2000407S364328

Critical roles of PTPN family members regulated by non-coding RNAs in tumorigenesis and immunotherapy

Since tyrosine phosphorylation is reversible and dynamic in vivo, the phosphorylation state of proteins is controlled by the opposing roles of protein tyrosine kinases (PTKs) and protein tyrosine phosphatase (PTPs), both of which perform critical roles in signal transduction. Of these, intracellular non-receptor PTPs (PTPNs), which belong to the largest class I cysteine PTP family, are essential for the regulation of a variety of biological processes, including but not limited to hematopoiesis, inflammatory response, immune system, and glucose homeostasis. Additionally, a substantial amount of PTPNs have been identified to hold crucial roles in tumorigenesis, progression, metastasis, and drug resistance, and inhibitors of PTPNs have promising applications due to striking efficacy in antitumor therapy. Hence, the aim of this review is to summarize the role played by PTPNs, including PTPN1/PTP1B, PTPN2/TC-PTP, PTPN3/PTP-H1, PTPN4/PTPMEG, PTPN6/SHP-1, PTPN9/PTPMEG2, PTPN11/SHP-2, PTPN12/PTP-PEST, PTPN13/PTPL1, PTPN14/PEZ, PTPN18/PTP-HSCF, PTPN22/LYP, and PTPN23/HD-PTP, in human cancer and immunotherapy and to comprehensively describe the molecular pathways in which they are implicated. Given the specific roles of PTPNs, identifying potential regulators of PTPNs is significant for understanding the mechanisms of antitumor therapy. Consequently, this work also provides a review on the role of non-coding RNAs (ncRNAs) in regulating PTPNs in tumorigenesis and progression, which may help us to find effective therapeutic agents for tumor therapy

Urban−rural gradients reveal joint control of elevated CO₂ and temperature on extended photosynthetic seasons

Photosynthetic phenology has large effects on the land-atmosphere carbon exchange. Due to limited experimental assessments, a comprehensive understanding of the variations of photosynthetic phenology under future climate and its associated controlling factors is still missing, despite its high sensitivities to climate. Here, we develop an approach that uses cities as natural laboratories, since plants in urban areas are often exposed to higher temperatures and carbon dioxide (CO₂) concentrations, which reflect expected future environmental conditions. Using more than 880 urban-rural gradients across the Northern Hemisphere (≥30° N), combined with concurrent satellite retrievals of Sun-induced chlorophyll fluorescence (SIF) and atmospheric CO₂, we investigated the combined impacts of elevated CO₂ and temperature on photosynthetic phenology at the large scale. The results showed that, under urban conditions of elevated CO2 and temperature, vegetation photosynthetic activity began earlier (−5.6 ± 0.7 d), peaked earlier (−4.9  ± 0.9 d) and ended later (4.6 ± 0.8 d) than in neighbouring rural areas, with a striking two- to fourfold higher climate sensitivity than greenness phenology. The earlier start and peak of season were sensitive to both the enhancements of CO₂ and temperature, whereas the delayed end of season was mainly attributed to CO₂ enrichments. We used these sensitivities to project phenology shifts under four Representative Concentration Pathway climate scenarios, predicting that vegetation will have prolonged photosynthetic seasons in the coming two decades. This observation-driven study indicates that realistic urban environments, together with SIF observations, provide a promising method for studying vegetation physiology under future climate change

Lumen contour segmentation in ivoct based on n-type cnn

Automatic segmentation of lumen contour plays an important role in medical imaging and diagnosis, which is the first step towards the evaluation of morphology of vessels under analysis and the identification of possible atherosclerotic lesions. Meanwhile, quantitative information can only be obtained with segmentation, contributing to the appearance of novel methods which can be successfully applied to intravascular optical coherence tomography (IVOCT) images. This paper proposed a new end-to-end neural network (N-Net) for the automatic lumen segmentation, using multi-scale features based deep neural network, for IVOCT images. The architecture of the N-Net contains a multi-scale input layer, a N-type convolution network layer and a cross-entropy loss function. The multi-scale input layer in the proposed N-Net is designed to avoid the loss of information caused by pooling in traditional U-Net and also enriches the detailed information in each layer. The N-type convolutional network is proposed as the framework in the whole deep architecture. Finally, the loss function guarantees the degree of fidelity between the output of proposed method and the manually labeled output. In order to enlarge the training set, data augmentation is also introduced. We evaluated our method against loss, accuracy, recall, dice similarity coefficient, jaccard similarity coefficient and specificity. The experimental results presented in this paper demonstrate the superior performance of the proposed N-Net architecture, comparing to some existing networks, for enhancing the precision of automatic lumen segmentation and increasing the detailed information of edges of the vascular lumen

An Early Diagnosis of Oral Cancer based on Three-Dimensional Convolutional Neural Networks

Three-dimensional convolutional neural networks (3DCNNs), a rapidly evolving modality of deep learning, has gained popularity in many fields. For oral cancers, CT images are traditionally processed using two-dimensional input, without considering information between lesion slices. In this paper, we established a 3DCNNs-based image processing algorithm for the early diagnosis of oral cancers, which was compared with a 2DCNNs-based algorithm. The 3D and 2D CNNs were constructed using the same hierarchical structure to profile oral tumors as benign or malignant. Our results showed that 3DCNNs with dynamic characteristics of the enhancement rate image performed better than 2DCNNS with single enhancement sequence for the discrimination of oral cancer lesions. Our data indicate that spatial features and spatial dynamics extracted from 3DCNNs may inform future design of CT-assisted diagnosis system

Integrative proteomic and metabonomic profiling elucidates amino acid and lipid metabolism disorder in CA-MRSA-infected breast abscesses

ObjectiveBacterial culture and drug sensitivity testing have been the gold standard for confirming community-acquired methicillin-resistant Staphylococcus aureus (CA-MRSA) infection in breast abscess with a long history. However, these tests may delay treatment and increase the risk of nosocomial infections. To handle and improve this critical situation, this study aimed to explore biomarkers that could facilitate the rapid diagnosis of CA-MRSA infection.MethodsThis study for the first time applied label-free quantitative proteomics and non-targeted metabonomics to identify potential differentially expressed proteins (DEPs) and differentially expressed metabolites (DEMs) in breast abscess infected with CA-MRSA compared to methicillin-susceptible S. aureus (MSSA). The two omics data were integrated and analyzed using bioinformatics, and the results were validated using Parallel Reaction Monitoring (PRM). Receiver operating characteristic (ROC) curves were generated to evaluate the predictive efficiency of the identified biomarkers for diagnosing CA-MRSA infection.ResultsAfter using the above-mentioned strategies, 109 DEPs were identified, out of which 86 were upregulated and 23 were downregulated. Additionally, a total of 61 and 26 DEMs were initially screened in the positive and negative ion modes, respectively. A conjoint analysis indicated that the amino acid metabolism, glycosphingolipid biosynthesis, and glycerophospholipid metabolism pathways were co-enriched by the upstream DEPs and downstream DEMs, which may be involved in structuring the related network of CA-MRSA infection. Furthermore, three significant DEMs, namely, indole-3-acetic acid, L-(−)-methionine, and D-sedoheptulose 7-phosphate, displayed good discriminative abilities in early identification of CA-MRSA infection in ROC analysis.ConclusionAs there is limited high-quality evidence and multiple omics research in this field, the explored candidate biomarkers and pathways may provide new insights into the early diagnosis and drug resistance mechanisms of CA-MRSA infection in Chinese women

Disentangling the effects of vapor pressure deficit on northern terrestrial vegetation productivity

The impact of atmospheric vapor pressure deficit (VPD) on plant photosynthesis has long been acknowledged, but large interactions with air temperature (T) and soil moisture (SM) still hinder a complete understanding of the influence of VPD on vegetation production across various climate zones. Here, we found a diverging response of productivity to VPD in the Northern Hemisphere by excluding interactive effects of VPD with T and SM. The interactions between VPD and T/SM not only offset the potential positive impact of warming on vegetation productivity but also amplifies the negative effect of soil drying. Notably, for high-latitude ecosystems, there occurs a pronounced shift in vegetation productivity\u27s response to VPD during the growing season when VPD surpasses a threshold of 3.5 to 4.0 hectopascals. These results yield previously unknown insights into the role of VPD in terrestrial ecosystems and enhance our comprehension of the terrestrial carbon cycle\u27s response to global warming

Facilitation of AMPA Receptor Synaptic Delivery as a Molecular Mechanism for Cognitive Enhancement

A small peptide from a neuronal cell adhesion molecule enhances synaptic plasticity in the hippocampus and results in improved cognitive performance in rats

STUB1/CHIP: New insights in cancer and immunity

The STUB1 gene (STIP1 homology and U-box-containing protein 1), located at 16q13.3, encodes the CHIP (carboxyl terminus of Hsc70-interacting protein), an essential E3 ligase involved in protein quality control. CHIP comprises three domains: an N-terminal tetratricopeptide repeat (TPR) domain, a middle coiled-coil domain, and a C-terminal U-box domain. It functions as a co-chaperone for heat shock protein (HSP) via the TPR domain and as an E3 ligase, ubiquitinating substrates through its U-box domain. Numerous studies suggest that STUB1 plays a crucial role in various physiological process, such as aging, autophagy, and bone remodeling. Moreover, emerging evidence has shown that STUB1 can degrade oncoproteins to exert tumor-suppressive functions, and it has recently emerged as a novel player in tumor immunity. This review provides a comprehensive overview of STUB1’s role in cancer, including its clinical significance, impact on tumor progression, dual roles, tumor stem cell-like properties, angiogenesis, drug resistance, and DNA repair. In addition, we explore STUB1’s functions in immune cell differentiation and maturation, inflammation, autoimmunity, antiviral immune response, and tumor immunity. Collectively, STUB1 represents a promising and valuable therapeutic target in cancer and immunology