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Abstract Objective Exploring the correlation between bone turnover marks (BTMs) with lumbar BMD in middle-aged populations. Methods The cross-sectional analysis fetched data came from NHANES. The level of serum bone alkaline phosphatase (sBAP) and urinary N-telopeptide (uNTx) were regarded as representative of bone turnover. Lumbar BMD was the outcome of the study. Multivariable linear regression models were utilized to detect the correlation of sBAP and uNTx with Lumbar BMD. Results The level of sBAP and uNTx was negatively correlated with lumbar BMD in every multivariable linear regression. For sBAP, this inverse correlation was stable in both men and women (P

The tumor control probability (TCP) model has been used for estimating the response of the radiation (photon) therapy for a given treatment dose (distribution). In Taiwan, boron neutron capture therapy (BNCT) is still at the stage of the clinical trials without standard dose prescription. In this study, universal survival curve (USC) model was selected as the TCP model for BNCT. The tumor response and dose distribution from protocol I of the clinical trial of the recurrent head and neck (H&N) cancer conducted by Taipei Veterans General Hospital and National Tsing Hua University were used to verify the TCP model established in this study. The results showed that, using the USC model as a biological model of dose conversion, the TCP calculated by the generalized Equivalent Uniform Dose (gEUD)–based TCP model can be used to well correlate the relationship between the tumor response and dose distribution of the patients of recurrent H&N cancer. The result shows that 25% and 60% of TCP correspond to partial response and complete response of H&N cancer, respectively. This study also indicated that, when BNCT was used to treat recurrent H&N cancer, the minimum dose was an important factor on the efficacy of the treatment. Minimum dose of 18 Gy-w corresponds to at least 60% of TCP.

Abstract Biomarker discovery and development are popular for detecting the subtle diseases. However, biomarkers are needed to be validated and approved, and even fewer are ever used clinically. Imaging biomarkers have a crucial role in the treatment of cancer patients because they provide objective information on tumor biology, the tumor's habitat, and the tumor’s signature in the environment. Tumor changes in response to an intervention complement molecular and genomic translational diagnosis as well as quantitative information. Neuro-oncology has become more prominent in diagnostics and targeted therapies. The classification of tumors has been actively updated, and drug discovery, and delivery in nanoimmunotherapies are advancing in the field of target therapy research. It is important that biomarkers and diagnostic implements be developed and used to assess the prognosis or late effects of long-term survivors. An improved realization of cancer biology has transformed its management with an increasing emphasis on a personalized approach in precision medicine. In the first part, we discuss the biomarker categories in relation to the courses of a disease and specific clinical contexts, including that patients and specimens should both directly reflect the target population and intended use. In the second part, we present the CT perfusion approach that provides quantitative and qualitative data that has been successfully applied to the clinical diagnosis, treatment and application. Furthermore, the novel and promising multiparametric MR imageing approach will provide deeper insights regarding the tumor microenvironment in the immune response. Additionally, we briefly remark new tactics based on MRI and PET for converging on imaging biomarkers combined with applications of bioinformatics in artificial intelligence. In the third part, we briefly address new approaches based on theranostics in precision medicine. These sophisticated techniques merge achievable standardizations into an applicatory apparatus for primarily a diagnostic implementation and tracking radioactive drugs to identify and to deliver therapies in an individualized medicine paradigm. In this article, we describe the critical principles for imaging biomarker characterization and discuss the current status of CT, MRI and PET in finiding imaging biomarkers of early disease.

The purpose of this study was to analyze visual attention during students' learning of Toulmin's argument pattern (TAP) and the effects of epistemic beliefs in science on the understanding of argument components. A total of 43 undergraduates were recruited through the Internet. Online questionnaires targeting beliefs about knowledge in science [Conley, A. M., Pintrich, P. R., Vekiri, I., & Harrison, D. (2004). Changes in epistemological beliefs in elementary science students. "Contemporary Educational Psychology," 29(2), 186-204] and beliefs about justification for knowing in science [Ferguson, L. E., & Bråten, I. (2013). Student profiles of knowledge and epistemic beliefs: Changes and relations to multiple-text comprehension. "Learning and Instruction," 25, 49-61] were used to assess students' epistemic beliefs. Identification tests for the understanding of TAP were performed before and after an argument reading treatment designed as a self-learning activity. The whole reading process was recorded by the eye-tracking system. To explore the effect of the reading treatment, participants were divided into a progress (P) group and a no-progress (NP) group based on their learning gains. The results indicated that students' epistemic beliefs in science and their learning strategies would affect their TAP learning performance. The analyses of visual attention distribution indicated that integration information indicated by inter-scanning counts would enhance students' learning performance. In addition, the LSA results suggested that understanding of data is of primary importance when learning TAP. Suggestions on how to guide students in learning TAP are provided based on the findings.

Although AR technology seems to be promising for helping students learn complicated and abstract concepts, whether the positive effects of this new technology can be applied to the learning of three-dimensional geometric concepts, and what factors might mediate its effectiveness are questions worth further investigation. This study used the eye-tracking method to examine middle school students' learning of three-dimensional geometric concepts with AR-based instruction in comparison with a group of students who learned via paper-based instruction. Motivation and conceptual achievements were assessed using the Instructional Materials Motivation Survey (IMMS) questionnaire and Geometry Achievement Test (GAT), respectively. The results showed that while AR learners performed the same as the paper-based learners in the post concept test, the two groups differed significantly in terms of their motivation orientations and visual attention patterns. AR students were more satisfied with and engaged more in the corresponding instruction. To explore in-depth the effectiveness of the AR system, the AR participants were divided into improved and unimproved learners. The former displayed higher confidence in and perceived greater relevance of the AR instruction. Their attention to and engagement in the simple geometric concepts was higher. Correlation and regression analyses indicated that the performance of paper-based learners was affected by their visual attention, while the performance of the AR learners was predicted by their beliefs in the relevance of the material to the learning goals. Noticeably, for the AR learners, their motivation was significantly associated with their visual attention.