Discordant Findings of Skeletal Metastasis Between Tc99m MDP Bone Scans and F18 FDG PET/CT Imaging for Advanced Breast and Lung Cancers—Two Case Reports and Literature Review

Traditionally, Tc99m methyl diphosphate (MDP) bone scintigraphy provides high-sensitivity detection of skeletal metastasis from breast and lung cancers in regular follow-up. Fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT), based on the glucose metabolism of malignant cells, plays a role in describing rumor growth, proliferation of neoplasm and the extent of metastasis. In general, concordant findings of skeletal metastasis are seen on both types of image, especially in cases of breast and lung cancer. However, there were extremely discordant findings of skeletal metastasis between bone scans and F18 FDG PET/CT imaging in two cases among 300 consecutive F18 FDG PET/CT follow-up exams of patients with malignancies, during the past year, in our center. Both cases, one of breast cancer and one of lung cancer, had negative bone scintigraphic findings, but a diffusely high grade of F18 FDG avid marrow infiltration in the axial spine, leading to the diagnosis of stage IV disease in both cases. Owing to variant genetic aberrance of malignance, F18 FDG PET/CT reveals direct evidence of diffuse, rapid neoplasm metabolism in the bone marrow of the spine, but not of secondary osteoblastic reactions in vivo. F18 FDG PET/CT should always be employed in the follow-up of patients with malignancies

GenEpi: gene-based epistasis discovery using machine learning.

BackgroundGenome-wide association studies (GWAS) provide a powerful means to identify associations between genetic variants and phenotypes. However, GWAS techniques for detecting epistasis, the interactions between genetic variants associated with phenotypes, are still limited. We believe that developing an efficient and effective GWAS method to detect epistasis will be a key for discovering sophisticated pathogenesis, which is especially important for complex diseases such as Alzheimer's disease (AD).ResultsIn this regard, this study presents GenEpi, a computational package to uncover epistasis associated with phenotypes by the proposed machine learning approach. GenEpi identifies both within-gene and cross-gene epistasis through a two-stage modeling workflow. In both stages, GenEpi adopts two-element combinatorial encoding when producing features and constructs the prediction models by L1-regularized regression with stability selection. The simulated data showed that GenEpi outperforms other widely-used methods on detecting the ground-truth epistasis. As real data is concerned, this study uses AD as an example to reveal the capability of GenEpi in finding disease-related variants and variant interactions that show both biological meanings and predictive power.ConclusionsThe results on simulation data and AD demonstrated that GenEpi has the ability to detect the epistasis associated with phenotypes effectively and efficiently. The released package can be generalized to largely facilitate the studies of many complex diseases in the near future

High expression FUT1 and B3GALT5 is an independent predictor of postoperative recurrence and survival in hepatocellular carcinoma.

Cancer may arise from dedifferentiation of mature cells or maturation-arrested stem cells. Previously we reported that definitive endoderm from which liver was derived, expressed Globo H, SSEA-3 and SSEA-4. In this study, we examined the expression of their biosynthetic enzymes, FUT1, FUT2, B3GALT5 and ST3GAL2, in 135 hepatocellular carcinoma (HCC) tissues by qRT-PCR. High expression of either FUT1 or B3GALT5 was significantly associated with advanced stages and poor outcome. Kaplan Meier survival analysis showed significantly shorter relapse-free survival (RFS) for those with high expression of either FUT1 or B3GALT5 (P = 0.024 and 0.001, respectively) and shorter overall survival (OS) for those with high expression of B3GALT5 (P = 0.017). Combination of FUT1 and B3GALT5 revealed that high expression of both genes had poorer RFS and OS than the others (P < 0.001). Moreover, multivariable Cox regression analysis identified the combination of B3GALT5 and FUT1 as an independent predictor for RFS (HR: 2.370, 95% CI: 1.505-3.731, P < 0.001) and OS (HR: 2.153, 95% CI: 1.188-3.902, P = 0.012) in HCC. In addition, the presence of Globo H, SSEA-3 and SSEA-4 in some HCC tissues and their absence in normal liver was established by immunohistochemistry staining and mass spectrometric analysis

Development of a Kinesthetic Learning System for Schoolchildren’s Baseball Learning Based on Competence Motivation Theory: Its Effect on Students’ Skill and Motivation

The traditional baseball instruction strategies were mainly conducted by the instructors with oral explanation and exemplification while students had to improve their performance in athletic activities through continuous practice. During the learning process of athletic skills, students oftentimes posed less confidence due to unskilled body movement resulting in lower achievement sense. Finally, they started to reject the engagement in relevant athletic activities and even never practice anymore. Therefore, this research aimed to explore the influence on the learning motivation and the performance of athletic skills made by students in the conventionally instructive mode by introducing the Computer-Aided Design (CAD) instruction strategies of the kinect baseball learning system. Research results indicated: (1) after the kinect baseball learning system was introduced into instruction, it positively affected the learning motivation of students; (2) after the kinect baseball learning system was introduced into instruction, it positively affected the performance of athletic skills of students

SMILEtrack: SiMIlarity LEarning for Occlusion-Aware Multiple Object Tracking

Despite recent progress in Multiple Object Tracking (MOT), several obstacles such as occlusions, similar objects, and complex scenes remain an open challenge. Meanwhile, a systematic study of the cost-performance tradeoff for the popular tracking-by-detection paradigm is still lacking. This paper introduces SMILEtrack, an innovative object tracker that effectively addresses these challenges by integrating an efficient object detector with a Siamese network-based Similarity Learning Module (SLM). The technical contributions of SMILETrack are twofold. First, we propose an SLM that calculates the appearance similarity between two objects, overcoming the limitations of feature descriptors in Separate Detection and Embedding (SDE) models. The SLM incorporates a Patch Self-Attention (PSA) block inspired by the vision Transformer, which generates reliable features for accurate similarity matching. Second, we develop a Similarity Matching Cascade (SMC) module with a novel GATE function for robust object matching across consecutive video frames, further enhancing MOT performance. Together, these innovations help SMILETrack achieve an improved trade-off between the cost ({\em e.g.}, running speed) and performance (e.g., tracking accuracy) over several existing state-of-the-art benchmarks, including the popular BYTETrack method. SMILETrack outperforms BYTETrack by 0.4-0.8 MOTA and 2.1-2.2 HOTA points on MOT17 and MOT20 datasets. Code is available at https://github.com/pingyang1117/SMILEtrack_Officia