High KIAA1522 expression predicts a poor prognosis in patients with hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a highly malignant tumor associated with a poor prognosis, and the molecular mechanisms remain poorly understood. KIAA1522 expression is upregulated in various types of tumor tissue; however, its function remains unknown in HCC. Bioinformatics analysis was undertaken using Oncomine, OncoLnc and other databases, in order to determine KIAA1522 expression in HCC and to analyze its association with postoperative prognosis. Reverse transcription-quantitative PCR was performed to detect KIAA1522 mRNA expression in primary HCC and adjacent normal tissues, while KIAA1522 protein expression was assessed via immunohistochemical staining. KIAA1522 expression and clinicopathological characteristics of primary HCC were evaluated, and their association with patient prognosis was analyzed. The Oncomine database results indicated that KIAA1522 expression in HCC and normal liver tissues was significantly different. RT-qPCR analysis demonstrated that KIAA1522 mRNA expression was significantly higher in HCC tissues compared with that in adjacent normal tissues. Immunohistochemical analysis indicated that expression rate of KIAA1522 protein was significantly higher in primary HCC tissues compared with that in normal liver tissues. The OncoLnc database results demonstrated that KIAA1522 expression was significantly associated with short-term survival. Kaplan-Meier survival analysis indicated that high KIAA1522 protein expression was significantly associated with short-term survival for patients with HCC. Multivariate Cox regression analysis demonstrated that tumor size, Tumor-Node-Metastasis stage and high KIAA1522 protein expression were independent predictors of a poor prognosis in patients with primary HCC. Furthermore, high KIAA1522 expression was significantly associated with postoperative survival time in primary HCC, and thus may be a potential molecular marker for prognosis in patients with this cancer type.

Clinical Practicality Study of the Difficulty Scoring Systems DSS-B and DSS-ER in Laparoscopic Liver Resection.

BACKGROUND: Laparoscopic liver resection (LLR) is a high-risk and difficult minimally invasive surgery that requires a comprehensive preoperative evaluation and strict technical training. The Ban Difficulty Scoring System (DSS-B) and the Difficulty Scoring System Based on the Extent of Resection (DSS-ER) are difficulty scoring systems used in LLR. The aim of this study was to explore the clinical practicality of the DSS-B and DSS-ER in LLR. METHODS: Differences in perioperative data were compared among different difficulty groups. The DSS-B and DSS-ER were used to evaluate the difficulty of LLR in 199 patients with tumors. Furthermore, the DSS-ER was used to evaluate the difficulty of LLR in 50 patients with intrahepatic bile duct stones (IBDSs). Finally, the correlation between the DSS-B and DSS-ER were explored. RESULT: In 199 patients who underwent LLR for tumors, the results of an intergroup comparison using the DSS-B groupings showed that operation time, intraoperative blood loss, the intraoperative blood transfusion rate, hepatic portal blockage, conversion to open surgery rate, and the postoperative hospital stay were significantly different among the groups (P < .05). Differences in perioperative data among the difficult groups were similar between the DSS-ER and DSS-B groups. A total of 50 patients who underwent LLR for IBDS were grouped based on the DSS-ER, and intergroup comparisons showed that operation time, intraoperative blood loss, the intraoperative blood transfusion rate, and the hepatic portal block rate were significantly different among the groups (P < .05). Moreover, there was a significant difference in DSS-B scores among the DSS-ER groups (P < .001). CONCLUSION: The DSS-B and DSS-ER accurately classify the degree of difficulty in LLR and therefore provide significant guidance to clinical doctors working and training in LLR.

A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images.

A new hybrid vehicle detection scheme which integrates the Viola-Jones (V-J) and linear SVM classifier with HOG feature (HOG + SVM) methods is proposed for vehicle detection from low-altitude unmanned aerial vehicle (UAV) images. As both V-J and HOG + SVM are sensitive to on-road vehicles' in-plane rotation, the proposed scheme first adopts a roadway orientation adjustment method, which rotates each UAV image to align the roads with the horizontal direction so the original V-J or HOG + SVM method can be directly applied to achieve fast detection and high accuracy. To address the issue of descending detection speed for V-J and HOG + SVM, the proposed scheme further develops an adaptive switching strategy which sophistically integrates V-J and HOG + SVM methods based on their different descending trends of detection speed to improve detection efficiency. A comprehensive evaluation shows that the switching strategy, combined with the road orientation adjustment method, can significantly improve the efficiency and effectiveness of the vehicle detection from UAV images. The results also show that the proposed vehicle detection method is competitive compared with other existing vehicle detection methods. Furthermore, since the proposed vehicle detection method can be performed on videos captured from moving UAV platforms without the need of image registration or additional road database, it has great potentials of field applications. Future research will be focusing on expanding the current method for detecting other transportation modes such as buses, trucks, motors, bicycles, and pedestrians.

Pedestrian Detection and Tracking from Low-Resolution Unmanned Aerial Vehicle Thermal Imagery.

Driven by the prominent thermal signature of humans and following the growing availability of unmanned aerial vehicles (UAVs), more and more research efforts have been focusing on the detection and tracking of pedestrians using thermal infrared images recorded from UAVs. However, pedestrian detection and tracking from the thermal images obtained from UAVs pose many challenges due to the low-resolution of imagery, platform motion, image instability and the relatively small size of the objects. This research tackles these challenges by proposing a pedestrian detection and tracking system. A two-stage blob-based approach is first developed for pedestrian detection. This approach first extracts pedestrian blobs using the regional gradient feature and geometric constraints filtering and then classifies the detected blobs by using a linear Support Vector Machine (SVM) with a hybrid descriptor, which sophisticatedly combines Histogram of Oriented Gradient (HOG) and Discrete Cosine Transform (DCT) features in order to achieve accurate detection. This research further proposes an approach for pedestrian tracking. This approach employs the feature tracker with the update of detected pedestrian location to track pedestrian objects from the registered videos and extracts the motion trajectory data. The proposed detection and tracking approaches have been evaluated by multiple different datasets, and the results illustrate the effectiveness of the proposed methods. This research is expected to significantly benefit many transportation applications, such as the multimodal traffic performance measure, pedestrian behavior study and pedestrian-vehicle crash analysis. Future work will focus on using fused thermal and visual images to further improve the detection efficiency and effectiveness.