Clinical value of conventional magnetic resonance imaging combined with diffusion-weighted imaging in predicting pelvic lymph node metastasis of cervical cancer.

Background: In cervical cancer (CC), the involvement of pelvis lymph nodes is a crucial factor for patients' outcome. We aimed to investigate the value of conventional magnetic resonance imaging (MRI) combined with diffusion-weighted imaging (DWI) and the apparent diffusion coefficient (ADC) in predicting CC pelvic lymph node metastasis (PLNM). Methods: This retrospective study included CC patients who received surgical treatments. Surgical pathology results served as the gold standard for investigating the diagnostic performance of conventional MRI combined with DWI. We analyzed the association between tumor ADC and PLNM, as well as other pathological factors. The areas under the receiver operating characteristic curves (AUCs) for ADC in assessing PLNM and pathological factors were evaluated, and optimal cut-off points were obtained. Results: A total of 261 CC patients were analyzed. PLNM patients had significantly lower tumor ADC (0.829 ± 0.144×10-3mm2/s vs. 1.064 ± 0.345×10-3mm2/s, p<0.0001), than non-PLNM CC. The agreement between conventional MRI combined with DWI and pathological results on PLNM diagnosis was substantial (Kappa=0.7031, p<0.0001), with 76% sensitivity, 94.31% specificity, and 90.8% accuracy. The AUC of tumor ADC was 0.703, and the optimal cut-off was 0.95×10-3 mm2/s. In multivariate analysis model 1, tumor ADC<0.95×10-3mm2/s was significantly associated with PLNM (OR, 2.83; 95%CI, 1.08-7.43; p= 0.0346) after adjusting for age and pathological risk factors. In multivariate analysis model 2, tumor ADC<0.95×10-3mm2/s (OR, 4.00; 95%CI, 1.61-9.89; p=0.0027), age<35 years old (OR, 2.93; 95%CI, 1.04-8.30; p=0.0428), increased tumor diameter on MRI (OR, 2.17; 95%CI, 1.18-3.99; p=0.0128), vaginal vault involvement on MRI (OR, 2; 95%CI, 1.002-3.99; p=0.0494) were independent predictors for PLNM. Tumor ADC<0.95×10-3mm2/s was significantly associated with higher risk of tumor diameter ≥4cm (OR, 2.60; 95%CI, 1.43-4.73; p=0.0017), muscular layer infiltration >1/2 (OR, 5.46; 95%CI, 3.19-9.34; p<0.0001), vaginal vault involvement (OR, 2.25; 95%CI, 1.28-3.96; p=0.0051), and lymphovascular space involvement (OR, 3.81; 95%CI, 2.19-6.63; p<0.0001). Conclusion: Conventional MRI combined with DWI had a good diagnostic performance in detecting PLNM. The tumor ADC value in PLNM patients was significantly lower than that in non-PLNM patients. Tumor ADC <0.95×10-3mm2/s, age <35 years old, increased tumor diameter on MRI, vaginal vault involvement on MRI were independent predictors for PLNM.

The KMT1A/TIMP3/PI3K/AKT circuit regulates tumor growth in cervical cancer.

The epigenetic mechanism of tissue inhibitor of metalloproteinase 3 (TIMP3), a well-known tumor suppressor, in cervical cancer (CC) is still unclear. Integrated GEO database, protein interaction network, and a pan-cancer analysis revealed a KMT1A/TIMP3 axis in CC. KMT1A was highly expressed, and TIMP3 was poorly expressed in CC tissues and cells. KMT1A inhibited the activity of TIMP3. Silencing of KMT1A hampered the proliferation, migration, invasion, tumorigenesis and metastases of CC cells in vivo, and increased the apoptosis of cells. TIMP3 downregulation promoted the malignant phenotype and in vivo tumorigenesis and metastasis of CC cells. KMT1A downregulation impaired PI3K/AKT pathway in cells, while TIMP3 silencing promoted PI3K/AKT pathway activity. We propose a novel perspective that KMT1A involves in the growth and metastases via the TIMP3/PI3K/AKT axis in CC. In summary, our study identified a vital role played by KMT1A in the development of CC and the epigenetic mechanism, indicating that targeting KMT1A-related pathways could be conducive to the therapies for CC.

Unsupervised learning for hyperspectral recovery based on a single RGB image.

Hyperspectral imagery often suffers from the degradation of spatial, spectral, or temporal resolution due to the limitations of hyperspectral imaging devices. To address this problem, hyperspectral recovery from a single red-green-blue (RGB) image has recently achieved significant progress via deep learning. However, current deep learning-based methods are all learned in a supervised way under the availability of RGB and correspondingly hyperspectral images, which is unrealistic for practical applications. Hence, we propose to recover hyperspectral images from a single RGB image in an unsupervised way. Moreover, based on the statistical property of hyperspectral images, a customized loss function is proposed to boost the performance. Extensive experiments on the BGU iCVL Hyperspectral Image Dataset demonstrate the effectiveness of the proposed method.

The Overview of Human Localization and Vital Sign Signal Measurement Using Handheld IR-UWB Through-Wall Radar.

Obtaining information (e.g., position, respiration, and heartbeat rates) on humans located behind opaque and non-metallic obstacles (e.g., walls and wood) has prompted the development of non-invasive remote sensing technologies. Due to its excellent features like high penetration ability, short blind area, fine-range resolution, high environment adoption capabilities, low cost and power consumption, and simple hardware design, impulse radio ultra-wideband (IR-UWB) through-wall radar has become the mainstream primary application radar used for the non-invasive remote sensing. IR-UWB through-wall radar has been developed for nearly 40 years, and various hardware compositions, deployment methods, and signal processing algorithms have been introduced by many scholars. The purpose of these proposed approaches is to obtain human information more accurately and quickly. In this paper, we focus on IR-UWB through-wall radar and introduce the key advances in system design and deployment, human detection theory, and signal processing algorithms, such as human vital sign signal measurement methods and moving human localization. Meanwhile, we discuss the engineering pre-processing methods of IR-UWB through-wall radar. The lasts research progress in the field is also presented. Based on this progress, the conclusions and the development directions of the IR-UWB through-wall radar in the future are also preliminarily forecasted.

Deep-learning-based hyperspectral recovery from a single RGB image.

Commercial hyperspectral imaging devices are expensive and tend to suffer from the degradation of spatial, spectral, or temporal resolution. To address these problems, we propose a deep-learning-based method to recover hyperspectral images from a single RGB image. The proposed method learns an end-to-end mapping between an RGB image and corresponding hyperspectral images. Moreover, a customized loss function is proposed to boost the performance. Experimental results on a variety of hyperspectral datasets demonstrate that our proposed method outperforms several state-of-the-art methods in terms of both quantitative measurements and perceptual quality.

PFNet: an unsupervised deep network for polarization image fusion.

Image fusion is the key step to improve the performance of object detection in polarization images. We propose an unsupervised deep network to address the polarization image fusion issue. The network learns end-to-end mapping for fused images from intensity and degree of linear polarization images, without the ground truth of fused images. Customized architecture and loss function are designed to boost performance. Experimental results show that our proposed network outperforms other state-of-the-art methods in terms of visual quality and quantitative measurement.

Overexpressed miR-9 promotes tumor metastasis via targeting E-cadherin in serous ovarian cancer.

MicroRNAs (miRNAs) play critical roles in the development and progression in various cancers. Dysfunctional miR-9 expression remains ambiguous, and no consensus on the metastatic progression of ovarian cancer has been reached. In this study, results from the bioinformatics analysis show that the 3'-UTR of the E-cadherin mRNA was directly regulated by miR-9. Luciferase reporter assay results confirmed that miR-9 could directly target this 3'-UTR. miR-9 and E-cadherin expression in ovarian cancer tissue was quantified by qRT-PCR. Migration and invasion were detected by wound healing and Transwell system assay in SKOV3 and A2780. qRT-PCR and Western blot were performed to detect the epithelial‒mesenchymal transition-associated mRNA and proteins. Immunofluorescence technique was used to analyze the expression and subcellular localization of E-cadherin, N-cadherin, and vimentin. The results showed that miR-9 was frequently upregulated in metastatic serous ovarian cancer tissue compared with paired primary ones. Upregulation of miR-9 could downregulate the expression of E-cadherin but upregulate the expression of mesenchymal markers (N-cadherin and vimentin). Overexpression of miR-9 could promote the cell migration and invasion in ovarian cancer, and these processes could be effectively inhibited via miR-9 inhibitor. Thus, our study demonstrates that miR-9 may promote ovarian cancer metastasis via targeting E-cadherin and a novel potential therapeutic approach to control metastasis of ovarian cancer.