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Metastatic malignancies of the oral cavity are rare lesions, accounting for only 1-4% of all oral malignancies, and can occur in the jaw bones, the oral soft tissues, or even both. Although hepatocellular carcinoma is the most common primary hepatic tumor, no more than 1% of the cases show oral involvement. When metastatic tumor involves the oral cavity, the most frequent site is the posterior angle of the mandible. Histologically, hence, immunohistochemical markers are used for diagnosis. Glypican-3 and HepPar1 are the markers that can be used to confirm the microscopic diagnosis of HCC. Very rarely, hepatocellular carcinoma (HCC) metastasizes to the oral cavity, and such cases have a poor prognosis due to delay in diagnosis. We present a 74-year-old male with a metastasis of HCC in the left mandibular body as the first manifestation. Histologic examination confirmed metastatic hepatocellular carcinoma in the oral mucosa with immunohistochemical (IHC) markers. A review of pertinent literature was performed. Given the rarity of the disease, treatment principles are based mainly on retrospective series and case reports. We report an exceptionally unusual presentation with few cases (<70) reported in the literature, thus representing a diagnostic challenge.
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BACKGROUND: Obesity is one of the most important public health problems for university students. The objective of the study was to evaluate the association between body mass index (BMI) and body fat percentage (%BF) with waist circumference (WC) as a cardiometabolic risk factor (CMR) among university students. METHODS: A cross-sectional study was carried out in 2,048 students from a private university located in Lima, Peru. Anthropometric data (weight, height, %BF, and WC) were collected. Chi-square test was used. Association analysis was performed using multiple logistic regression. RESULTS: The findings indicated that 36.9% and 61.1% of men were overweight and had higher %BF, respectively, compared to women. Women (OR, 0.22; 95% CI, 0.17, 0.29), Peruvian students (OR, 0.59; 95% CI, 0.39, 0.90), and students enrolled in the faculty of health sciences (OR = 0.76; 95% CI, 0.62, 0.94) are less likely to manifest CMR. Also, excess body weight (OR, 17.28; 95% CI, 13.21, 22.59) and a high %BF (OR, 4.55; 95% CI, 3.55, 5.84) were strongly associated with CMR. CONCLUSION: CMRs are a public health problem among university students. Therefore, it is important to carry out healthy lifestyle programs to promote better control and prevention, particularly among male students and those who have excess weight and body fat.
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In this paper, we propose a compact and low-complexity binary feature descriptor for video analytics. Our binary descriptor encodes the motion information of a spatio-temporal support region into a low-dimensional binary string. The descriptor is based on a binning strategy and a construction that binarizes separately the horizontal and vertical motion components of the spatio-temporal support region. We pair our descriptor with a novel Fisher Vector (FV) scheme for binary data to project a set of binary features into a fixed length vector in order to evaluate the similarity between feature sets. We test the effectiveness of our binary feature descriptor with FVs for action recognition, which is one of the most challenging tasks in computer vision, as well as gait recognition and animal behavior clustering. Several experiments on the KTH, UCF50, UCF101, CASIA-B, and TIGdog datasets show that the proposed binary feature descriptor outperforms the state-of-the-art feature descriptors in terms of computational time and memory and storage requirements. When paired with FVs, the proposed feature descriptor attains a very competitive performance, outperforming several state-of-the-art feature descriptors and some methods based on convolutional neural networks.
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Over the past decade, video anomaly detection has been explored with remarkable results. However, research on methodologies suitable for online performance is still very limited. In this paper, we present an online framework for video anomaly detection. The key aspect of our framework is a compact set of highly descriptive features, which is extracted from a novel cell structure that helps to define support regions in a coarse-to-fine fashion. Based on the scene's activity, only a limited number of support regions are processed, thus limiting the size of the feature set. Specifically, we use foreground occupancy and optical flow features. The framework uses an inference mechanism that evaluates the compact feature set via Gaussian Mixture Models, Markov Chains, and Bag-of-Words in order to detect abnormal events. Our framework also considers the joint response of the models in the local spatio-temporal neighborhood to increase detection accuracy. We test our framework on popular existing data sets and on a new data set comprising a wide variety of realistic videos captured by surveillance cameras. This particular data set includes surveillance videos depicting criminal activities, car accidents, and other dangerous situations. Evaluation results show that our framework outperforms other online methods and attains a very competitive detection performance compared with state-of-the-art non-online methods.
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This paper proposes a view-invariant gait recognition framework that employs a unique view invariant model that profits from the dimensionality reduction provided by Direct Linear Discriminant Analysis (DLDA). The framework, which employs gait energy images (GEIs), creates a single joint model that accurately classifies GEIs captured at different angles. Moreover, the proposed framework also helps to reduce the under-sampling problem (USP) that usually appears when the number of training samples is much smaller than the dimension of the feature space. Evaluation experiments compare the proposed framework's computational complexity and recognition accuracy against those of other view-invariant methods. Results show improvements in both computational complexity and recognition accuracy.
