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.

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.

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.

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.

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.

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.

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.