The impact of income level on skeletal muscle health in rural Chinese older residents: a study of mediating effects based on dietary knowledge

China’s rural residents have basically solved the problem of subsistence, but due to aging, the prevalence of sarcopenia (abbreviated as sarcopenia) has been increasing year by year, especially the skeletal muscle health of the rural older residents has not been sufficiently paid attention to, so analyses of the impact of income level on the skeletal muscle health of the older people in rural areas of China are of great practical significance. Based on the annual data of the China Health and Nutrition Survey (CHNS) in 2006, 2009, and 2011, we introduced the mediator variable of dietary knowledge and used the Probit model regression, mediation effect model, and instrumental variable regression to assess the skeletal muscle health status of the rural older people in China and explore the mechanism of the influence of the income level on the skeletal muscle health of the rural older residents in China. The primary objectives of this study were to evaluate the impact of income level on the skeletal muscle health status of older adults living in rural areas of China and to investigate the underlying mechanisms. By analyzing the findings of this study, our aim is to establish a correlation between the economic status and skeletal muscle health of older adults in rural communities, as well as elucidate the influence of income level and dietary knowledge on their skeletal muscle health. Through the attainment of these objectives, we hope to provide valuable insights and recommendations for enhancing skeletal muscle health among the rural older population in China. Based on our research findings, it can be inferred that there was a significant association between the financial status of rural older adults and their skeletal muscle health. Additionally, the prevalence of sarcopenia was lower among individuals with higher income levels, and there was a negative correlation between the prevalence of sarcopenia and the level of dietary knowledge among rural older individuals. The knowledge of dietary knowledge level of rural older people plays a mediating role in the income level and the prevalence of sarcopenia. Moreover, with the change in income level and the increase in age, the change in skeletal muscle health status showed obvious heterogeneity, in which the effect on the relatively younger (65–70 years old) samples was greater. Therefore, sustained income growth remains an effective way to improve the skeletal muscle health of older rural residents. At the same time, improving dietary knowledge and dietary quality among the older people is important in preventing a decline in muscle strength and physical function and in preventing the onset of sarcopenia

Open World Classification with Adaptive Negative Samples

Open world classification is a task in natural language processing with key practical relevance and impact. Since the open or {\em unknown} category data only manifests in the inference phase, finding a model with a suitable decision boundary accommodating for the identification of known classes and discrimination of the open category is challenging. The performance of existing models is limited by the lack of effective open category data during the training stage or the lack of a good mechanism to learn appropriate decision boundaries. We propose an approach based on \underline{a}daptive \underline{n}egative \underline{s}amples (ANS) designed to generate effective synthetic open category samples in the training stage and without requiring any prior knowledge or external datasets. Empirically, we find a significant advantage in using auxiliary one-versus-rest binary classifiers, which effectively utilize the generated negative samples and avoid the complex threshold-seeking stage in previous works. Extensive experiments on three benchmark datasets show that ANS achieves significant improvements over state-of-the-art methods.Comment: Accepted by EMNLP 2021 (Main Track, Long Paper

PD-L1 aptamer-functionalized degradable hafnium oxide nanoparticles for near infrared-II diagnostic imaging and radiosensitization

Immune checkpoint blockade is now recognized as a paradigm-shifting cancer therapeutic strategy, whereas there remains difficulty in accurately predicting immunotherapy efficacy by PD-L1 expression. In addition, radiotherapy for cancer patients faces the problem of insufficient dose of radiotherapy at the tumor site while which have been not tolerated by normal tissues. In this study, we created PD-L1 aptamer-anchored spherical nucleic acids (SNAs) with a shell made of PD-L1 aptamer and indocyanine green (ICG) embedded in a mesoporous hafnium oxide nanoparticle core (Hf@ICG-Apt). Upon low pH irradiation in the tumor sites, the nano-system enabled the release of ICG in the high PD-L1 expression tumor to develop a high tumor-to-background ratio of 7.97 ± 0.76 and enhanced the ICG tumor retention to more than 48 h. Moreover, Hf@ICG-Apt improved radiation therapy (RT) when combined with radiation. Notably, Hf@ICG-Apt showed scarcely any systemic toxicity in vivo. Overall, this research offered a novel approach for applying reliable monitoring of PD-L1 expression and localization and robust RT sensitization against cancer with good biosafety

Development and validation of a preoperative MRI-based radiomics nomogram to predict progression-free survival in patients with clival chordomas

ObjectivesThe aim of this study was to establish and validate a MRI-based radiomics nomogram to predict progression-free survival (PFS) of clival chordoma.MethodsA total of 174 patients were enrolled in the study (train cohort: 121 cases, test cohort: 53 cases). Radiomic features were extracted from multiparametric MRIs. Intraclass correlation coefficient analysis and a Lasso and Elastic-Net regularized generalized linear model were used for feature selection. Then, a nomogram was established via univariate and multivariate Cox regression analysis in the train cohort. The performance of this nomogram was assessed by area under curve (AUC) and calibration curve.ResultsA total of 3318 radiomic features were extracted from each patient, of which 2563 radiomic features were stable features. After feature selection, seven radiomic features were selected. Cox regression analysis revealed that 2 clinical factors (degree of resection, and presence or absence of primary chordoma) and 4 radiomic features were independent prognostic factors. The AUC of the established nomogram was 0.747, 0.807, and 0.904 for PFS prediction at 1, 3, and 5 years in the train cohort, respectively, compared with 0.582, 0.852, and 0.914 in the test cohort. Calibration and risk score stratified survival curves were satisfactory in the train and test cohort.ConclusionsThe presented nomogram demonstrated a favorable predictive accuracy of PFS, which provided a novel tool to predict prognosis and risk stratification. Our results suggest that radiomic analysis can effectively help neurosurgeons perform individualized evaluations of patients with clival chordomas

Label-Free Spectral Imaging Unveils Biochemical Mechanisms of Low-Level Laser Therapy on Spinal Cord Injury

Background/Aims: Low-level laser therapy (LLLT) leads to complex photochemical responses during the healing process of spinal cord injury (SCI). Confocal Raman Microspectral Imaging (in combination with multivariate analysis) was adopted to illustrate the underlying biochemical mechanisms of LLLT treatment on a SCI rat model. Methods: Using transversal tissue sections, the Raman spectra can identify areas neighboring the injury site, glial scar, cavity, and unharmed white matter, as well as their correlated cellular alterations, such as demyelination and up-regulation of chondroitin sulfate proteoglycans (CSPGs). Multivariate data analysis methods are used to depict the underlying therapeutic effects by highlighting the detailed content and distribution variations of the biochemical constituents. Results: It is confirmed that photon-tissue interactions might lead to a decay of the inhibitory response to remyelination by suppressing CSPG expression, as also morphologically demonstrated by reduced glial scar and cavity areas. An inter-group comparison semi-quantitatively confirms changes in lipids, phosphatidic acid, CSPGs, and cholesterol during SCI and its LLLT treatment, paving the way for in vitro and in vivo understanding of the biochemical changes accompanying pathobiological SCI events. Conclusion: The achieved results in this work not only have once again proved the well-known cellular mechanisms of SCI, but further illustrate the underlying biochemical variability during LLLT treatment, which provide a sound basis for developing real-time Raman methodologies to monitor the efficacy of the SCI LLLT treatment

Gene Expression Profiling Identifies Two Chordoma Subtypes Associated with Distinct Molecular Mechanisms and Clinical Outcomes.

PURPOSE: Chordoma is a rare bone tumor with a high recurrence rate and limited treatment options. The aim of this study was to identify molecular subtypes of chordoma that may improve clinical management. EXPERIMENTAL DESIGN: We conducted RNA sequencing in 48 tumors from patients with Chinese skull-base chordoma and identified two major molecular subtypes. We then replicated the classification using a NanoString panel in 48 patients with chordoma from North America. RESULTS: Tumors in one subtype were more likely to have somatic mutations and reduced expression in chromatin remodeling genes, such as PBRM1 and SETD2, whereas the other subtype was characterized by the upregulation of genes in epithelial-mesenchymal transition and Sonic Hedgehog pathways. IHC staining of top differentially expressed genes between the two subtypes in 312 patients with Chinese chordoma with long-term follow-up data showed that the expression of some markers such as PTCH1 was significantly associated with survival outcomes. CONCLUSIONS: Our findings may improve the understanding of subtype-specific tumorigenesis of chordoma and inform clinical prognostication and targeted options

Research on the Influence of Teacher Variables on Students’ Mathematical Achievements

Based on the TIMSS2015 data, this study applied a hierarchical linear model to explore the influence of teacher variables on students’ mathematics scores. Teacher variables were composed of teacher characteristic variables, teacher teaching variables, and teacher profes-sional development variables. The teacher’s characteristic variables were teaching age, gender, education, mathematics major, and mathe-matics education. Teachers’ teaching variables were teaching expecta-tions, teaching cooperation, teaching enthusiasm, classroom discussion, multimedia use, attention to homework, and emphasis on exams. Teacher professional development variables had mathematics knowledge training, mathematics education training, and mathematics curriculum training. Multi-layer linear analysis found that in the teacher’s charac-teristic variables, the teacher’s teaching age, gender, education, and mathematics major have a significant effect on the students’ mathemat-ics scores; In the teacher’s teaching variables, teachers’ teaching expectations, teaching enthusiasm, class discussion, and multimedia use have a significant impact on students’ mathematics scores. In the teacher professional development variables, participation in mathematics knowledge training and mathematics education training had a significant positive impact on students’ mathematics scores