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Several studies have indicated that dysregulation of long non-coding RNAs (lncRNAs) participates in the initiation and progression of cancer. The lncRNA MIR4435-2HG was previously reported to act as an oncogene in human cancer, including liver cancer. However, its role in the pathogenesis in liver cancer is largely unclear. The present study aimed to reveal the molecular mechanism by which MIR4435-2HG regulates liver cancer. The expression levels of MIR4435-2HG in liver cancer and adjacent normal tissues were analyzed using The Cancer Genome Atlas database. MIR4435-2HG expression was validated by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) in cancer cells in vitro. The target genes of MIR4435-2HG were predicted using bioinformatics analysis. Interactions between miR-136-5p, MIR4435-2HG and B3GNT5 were detected using luciferase reporter assays, and their effects on cell viability, migration and invasion were assessed using Cell Counting Kit-8, wound healing and Transwell assays. The effects of miR-136-5p and MIR4435-2HG on B3GNT5 expression were confirmed by western blot analysis. The results revealed that MIR4435-2HG expression was upregulated in primary liver cancer and liver cancer cell lines, and was positively associated with advanced tumor stage, metastasis and poor prognosis in patients with liver cancer. Knockdown of MIR4435-2HG significantly inhibited the proliferation, migration and invasion of liver cancer cells. Furthermore, miR-136-5p was determined to be a direct target of MIR4435-2HG and suppressed MIR4435-2HG expression by binding with the seed region of the 3′-UTR of MIR4435-2HG in liver cancer cells. Functional studies showed that the inhibitory effects of MIR4435-2HG knockdown on cell proliferation, migration and invasion were significantly rescued by inhibiting miR-136-5p. Furthermore, the target gene, B3GNT5, of miR-136-5p was confirmed by bioinformatics analysis and RT-qPCR. In addition, B3GNT5 expression was regulated by the MIR4435-2HG/miR-136-5p axis. In conclusion, the present study indicated that MIR4435-2HG facilitated the progression of liver cancer via the MIR4435-2HG/miR-136-5p/B3GNT5 axis, which demonstrated that MIR4435-2HG may be a potential biomarker for the prognosis and treatment of liver cancer. [ABSTRACT FROM AUTHOR]

Objective . Deep learning (DL) methods have been widely utilized in ultrasound (US) image segmentation tasks. However, current DL segmentation methods for US images are typically developed only for lesion segmentation of specific organs; e.g. breast or thyroid US. So far, there is currently no general-purpose lesion segmentation framework for US images that can be implemented across various organs in computer aided diagnosis scenarios. Considering that most lesion locations in US images have abnormal ultrasonic echo intensities or patterns that may be visually distinct from surrounding normal tissues or organs, it is thus possible to develop a universal lesion segmentation framework for US images (named as ULS4US), focusing on effectively identifying and segmenting lesions of various sizes in different organs. Approach . The proposed ULS4US framework comprises three components: (1) a multiple-in multi-out (MIMO) UNet that incorporates multiscale features extracted from the US image and lesion, (2) a novel two-stage lesion-aware learning algorithm that recursively locates and segments the lesions in a reinforced manner, and (3) a lesion-adaptive loss function for the MIMO-UNet that integrates two weighted components and one self-supervised component designed for intra- and inter-branches of network outputs, respectively. Main Results . Compared to six state-of-the-art segmentation models, ULS4US has achieved superior performance (accuracy of 0.956, DSC of 0.836, HD of 7.849, and mIoU of 0.731) in a unified dataset consisting of two public and three private US image datasets, which include over 2200 images of three specific types of organs. Comparative experiments on both individual and unified datasets suggest that ULS4US is likely scalable with additional data. Significance . The study demonstrates the potential of DL-based universal lesion segmentation approaches in clinical US, which would substantially reduce clinician workload and enhance diagnostic accuracy., (© 2023 Institute of Physics and Engineering in Medicine.)

Hepatocellular carcinoma (HCC) is one of the most common and aggressive cancers. HCC shows high prevalence and lethality caused by a variety of etiologic factors. However, the underlying mechanisms and the diagnostic markers identifying patients at risk in advance has not been entirely elucidated. Long non-coding RNAs (lncRNAs) are a subgroup of non-coding RNAs greater than 200 nucleotides in length with no protein-coding capability. With the progress in sequencing technologies and bioinformatic tools, the landscape of lncRNAs is being revealed. Numerous discoveries point out that lncRNAs participate in HCC carcinogenesis and metastasis through altering cell proliferation and invasion ability, apoptosis, and chemo- or radio-sensitivity. Moreover, lncRNA is easy to detect compared to the traditional diagnostic methods. This review summarizes the mechanisms of major lncRNAs in HCC discovered in recent years and lncRNAs as early diagnostic markers for HCC., Competing Interests: None., (IJCEP Copyright © 2021.)

No consensus has been achieved regarding the optimal extent of lymph node (LN) dissection for node-negative ESCC patients. This study aimed to determine the optimal extent of LN dissection for node-negative ESCC patients. We retrospectively reviewed 481 ESCC patients with node-negative resection and no preoperative therapy. Overall survival (OS) was evaluated by the log-rank test and multivariate Cox regression. The 5-year OS was 51.7% and 64.7% for patients with 1-5 and ≥6 negative LNs resected, respectively ( P <0.001). However, there was no significant survival difference between patients with 6-12 negative LNs resected and patients with over 12 negative LNs resected ( P =0.205). Multivariate analysis indicated that the negative LN count was independently associated with better survival. In the subgroup analysis, no optimum lymphadenectomy was defined for T1; the minimum number of LNs that needed to be resected was 6 nodes for T2 and 7 nodes for T3. No survival benefit was observed when extensive lymphadenectomy was performed. The nomogram, including the number of LNs examined, T stage, and histologic differentiation, had more predictive power than TNM staging. The results of our study suggest that ESCC patients with LN-negative tumors should have at least 6 LNs examined for T2 and 7 LNs for T3, but extensive lymphadenectomy is not recommended. The nomogram, including the number of LNs examined, T stage, and histologic differentiation, is a useful clinical tool., Competing Interests: None., (IJCEP Copyright © 2020.)

The recently developed body-wide Automatic Anatomy Recognition (AAR) methodology depends on fuzzy modeling of individual objects, hierarchically arranging objects, constructing an anatomy ensemble of these models, and a dichotomous object recognition-delineation process. The parent-to-offspring spatial relationship in the object hierarchy is crucial in the AAR method. We have found this relationship to be quite complex, and as such any improvement in capturing this relationship information in the anatomy model will improve the process of recognition itself. Currently, the method encodes this relationship based on the layout of the geometric centers of the objects. Motivated by the concept of virtual landmarks (VLs), this paper presents a new one-shot AAR recognition method that utilizes the VLs to learn object relationships by training a neural network to predict the pose and the VLs of an offspring object given the VLs of the parent object in the hierarchy. We set up two neural networks for each parent-offspring object pair in a body region, one for predicting the VLs and another for predicting the pose parameters. The VL-based learning/prediction method is evaluated on two object hierarchies involving 14 objects. We utilize 54 computed tomography (CT) image data sets of head and neck cancer patients and the associated object contours drawn by dosimetrists for routine radiation therapy treatment planning. The VL neural network method is found to yield more accurate object localization than the currently used simple AAR method.

Currently, there are many papers that have been published on the detection and segmentation of lymph nodes from medical images. However, it is still a challenging problem owing to low contrast with surrounding soft tissues and the variations of lymph node size and shape on computed tomography (CT) images. This is particularly very difficult on low-dose CT of PET/CT acquisitions. In this study, we utilize our previous automatic anatomy recognition (AAR) framework to recognize the thoracic-lymph node stations defined by the International Association for the Study of Lung Cancer (IASLC) lymph node map. The lymph node stations themselves are viewed as anatomic objects and are localized by using a one-shot method in the AAR framework. Two strategies have been taken in this paper for integration into AAR framework. The first is to combine some lymph node stations into composite lymph node stations according to their geometrical nearness. The other is to find the optimal parent (organ or union of organs) as an anchor for each lymph node station based on the recognition error and thereby find an overall optimal hierarchy to arrange anchor organs and lymph node stations. Based on 28 contrast-enhanced thoracic CT image data sets for model building, 12 independent data sets for testing, our results show that thoracic lymph node stations can be localized within 2-3 voxels compared to the ground truth.

Cu2ZnSn(S(1-x)Se(x))4 nanocrystals are an emerging family of functional materials with huge potential of industrial applications, however, it is an extremely challenging task to synthesize Cu2ZnSn(S(1-x)Se(x))4 nanocrystals with both tunable energy band and phase purity. Here we show that a green and economic route could be designed for the synthesis of Cu2ZnSn(S(1-x)Se(x))4 nanocrystals with bandgap tunable in the range of 1.5-1.12 eV. Consequently, conduction band edge shifted from -3.9 eV to -4.61 eV (relative to vacuum energy) is realized. The phase purity of Cu2ZnSn(S(1-x)Se(x))4 nanocrystals is substantiated with in-depth combined optical and structural characterizations. Electrocatalytic and thermoelectric performances of Cu2ZnSn(S(1-x)Se(x))4 nanocrystals verify their superior activity to replace noble metal Pt and materials containing heavy metals. This green and economic route will promote large-scale application of Cu2ZnSn(S(1-x)Se(x))4 nanocrystals as solar cell materials, electrocatalysts, and thermoelectric materials.