Association between dietary copper intake and bone mineral density in children and adolescents aged 8-19 years: A cross-sectional study.

PURPOSE: Some studies showed the possible role of copper intake on bone mineral density (BMD) in adults or the elderly, but the association remained uncertain in children and adolescents. Our research explored the association between copper intake and BMD in individuals aged 8-19 years from the National Health and Nutrition Examination Survey (NHANES) 2011-2016. METHODS: In the present study, 6,965 individuals aged 8-19 (mean age 13.18 ± 3.38 years) were enrolled from the NHANES 2011-2016. Copper intake was evaluated by averaging two 24-hour copper dietary intake recalls. Multivariate linear regression analyses were used to explore the association between copper intake and total BMD, subtotal BMD, and total spine BMD in children and adolescents. Stratified analyses and interaction tests were performed by age, gender, and race. RESULTS: Participants of the higher quartile of copper intake were more likely to be older, men, Non-Hispanic White, and Other Hispanic. They have higher values of poverty income ratio (PIR), serum phosphorus, blood urea nitrogen, serum vitamin D, and BMD and lower values of body mass index (BMI), cholesterol, total protein, and serum cotinine. In the fully adjusted model, we found positive associations between copper intake and total BMD (ß = 0.013, 95CI: 0.006, 0.019)), subtotal BMD (ß = 0.020, 95CI: 0.015, 0.024), and total spine BMD (ß = 0.014, 95CI: 0.009, 0.019). Stratified analyses showed that the association was stronger in men, individuals aged 14-19, Non-Hispanic White, and Other Hispanic. CONCLUSIONS: Our study suggests that copper intake is positively associated with BMD in U.S. children and adolescents. The study emphasizes the role of copper intake on bone health in the early stages of life. However, more investigations are needed to verify our findings and their underlying mechanisms.

The efficacy of expanded non-invasive prenatal testing (NIPT) in a high-risk twin pregnancies cohort.

INTRODUCTION: Our objective was to evaluate the efficacy of expanded non-invasive prenatal testing (NIPT) that includes both trisomies and copy number variants (CNVs) in high-risk twin pregnancies. MATERIAL AND METHODS: A prospective, double-blinded cohort study was conducted, enrolling 73 high-risk twin pregnancies characterized by increased risk of genetic disorders due to factors such as increased nuchal translucency, structural anomalies, fetal growth restriction, and other factors associated with chromosomal abnormality. Participants underwent invasive karyotyping and chromosomal microarray analysis, alongside separate expanded NIPT for research purposes. The sensitivity, specificity, positive predictive value, and negative predictive value of expanded NIPT were calculated. RESULTS: The cohort included 24 monochorionic and 49 dichorionic twin pregnancies. The median cell-free fetal DNA concentration in expanded NIPT was 16.7% (range 3.86%-49.1%), with a test failure rate of 1.4% (1/73). High-risk findings for trisomy 21/13/18 were identified in five cases (6.8%), Turner syndrome in one case (1.4%), and CNVs indicative of high risk for clinically significant microdeletion/microduplication syndromes (MMS) in ten cases (13.7%). Of these, 56 cases (76.7%) tested NIPT negative, revealing one false-negative for 45, X and five false-negatives for CNVs. Expanded NIPT achieved a detection rate of 100% (5/5) for trisomy 21/13/18 with a false-positive rate of 0% (0/5), a detection rate of 33.3% (1/3) for sex chromosome abnormalities with a false-positive rate of 0% (0/3), and a detection rate of 66.7% (4/6) for MMS with a false-positive rate of 3.0% (2/67). The positive predictive values for trisomy T21/13/18, sex chromosome abnormalities, and known MMS were 100% (5/5), 100% (1/1), and 66.7% (4/6) in the expanded NIPT, respectively. CONCLUSIONS: The expanded NIPT demonstrated high detection rates for common trisomies and moderate detection rates for prenatal MMS in high-risk twin pregnancies. Further studies with large sample sizes in low-risk populations are needed.

Incremental RPN: Hierarchical Region Proposal Network for Apple Leaf Disease Detection in Natural Environments.

Apple leaf diseases can seriously affect apple production and quality, and accurately detecting them can improve the efficiency of disease monitoring. Owing to the complex natural growth environment, apple leaf lesions may be easily confused with background noise, leading to poor performance. In this study, a cascaded Incremental Region Proposal Network (Inc-RPN) is proposed to accurately detect apple leaf diseases in natural environments. The proposed Inc-RPN has a two-layer RPN architecture, where the precursor RPN is leveraged to generate diseased leaf proposals, and the successor RPN focuses on extracting target disease spots based on diseased leaf proposals. In the successor RPN, a low-level feature aggregation module is designed to fully utilize the bridged features and preserve the semantic information of the target disease spots. An incremental module is also leveraged to extract aggregated diseased leaf features and target disease spot features. Finally, a novel position anchor generator is designed to generate anchors based on diseased leaf proposals. The experimental results show that the proposed Inc-RPN performs very well on the FALD_CED and Apple Leaf Disease datasets, showing that it can accurately perform apple leaf disease detection tasks.

Osthole, a Coumarin from Cnidium monnieri: A Review on Its Pharmacology, Pharmacokinetics, Safety, and Innovative Drug Delivery Platforms.

Osthole, a coumarin compound mainly derived from Cnidium monnieri (L.), has attracted much interest from the scientific community owing to its multiple therapeutic properties. However, its pharmacological mechanism, pharmacokinetics, and toxicological effects are far from clear. Furthermore, the potential drug delivery platforms of osthole remain to be comprehensively delineated. The present review aimed to systematically summarize the most up-to-date information related to pharmacology, pharmacokinetics, and safety issues related to osthole, and discuss the investigations of novel drug delivery platforms. The information herein discussed was retrieved from authoritative databases, including PubMed, Web of Science, Google Scholar, Chinese National Knowledge Infrastructure (CNKI) and so on, reviewing information published up until February of 2024. New evidence shows that osthole induces a sequence of therapeutic actions and has a moderate absorption rate and rapid metabolic characteristics. In addition, this phytoconstituent possesses potential hepatotoxicity, and caution should be exercised against the risk of the drug combination. Furthermore, given its needy solubility in aqueous medium and non-organizational targeting, novel drug delivery methods have been designed to overcome these shortcomings. Given the properties of osthole, its therapeutic benefits ought to be elucidated in a greater array of comprehensive research studies, and the molecular mechanisms underlying these benefits should be explored.

Speciation of selenium-containing small molecules in urine and cell lysate by CE-ICPMS with in-capillary enrichment.

The quantitative speciation of selenium in biological systems is highly important for evaluating health status and elucidating transformations of Se species in physiological and pathological processes. Hyphenation of capillary electrophoresis with inductively coupled plasma mass spectrometry (CE-ICPMS) is promising for this purpose. However, the unfavorable or insufficient sensitivity for selenium analysis with CE-ICPMS seriously limits its practical applications in biological analysis, e.g., cell analysis. Therefore, it is crucial to improve the detection sensitivity for Se species. In this study, CE-ICPMS sensitivities for five selenium species (selenocystamine (SeA), methyl-2-acetamido-2-deoxy-1-seleno-ß-d-galactopyranoside (SeSug 1), selenomethionine (SeMet), Se-Methylselenocysteine (MeSeCys) and selenocystine (SeCys)) were improved by in-capillary stacking via pH gradient between the zones of sample-leading buffer and the incorporation of isopropanol. The improvement on sensitivity of up to 9.9 folds was achieved in different biological samples, with LODs of 0.29-0.52 µg L-1. This approach was further applied for Se speciation in cell lysate, urine and culture medium. It showed that SeMet was more readily reduced in the medium and favorably accumulated by HepG2, HuH-7 and HCCLM3 cells with respect to SeSug 1 and MeSeCys. In cells, all the three Se species were largely transformed into other Se species. Furthermore, more than 70 % of SeMet reduced in medium was transformed into unknown Se species after 48-h interaction with cells.

Integrated transcriptomic analysis and machine learning for characterizing diagnostic biomarkers and immune cell infiltration in fetal growth restriction.

Background: Fetal growth restriction (FGR) occurs in 10% of pregnancies worldwide. Placenta dysfunction, as one of the most common causes of FGR, is associated with various poor perinatal outcomes. The main objectives of this study were to screen potential diagnostic biomarkers for FGR and to evaluate the function of immune cell infiltration in the process of FGR. Methods: Firstly, differential expression genes (DEGs) were identified in two Gene Expression Omnibus (GEO) datasets, and gene set enrichment analysis was performed. Diagnosis-related key genes were identified by using three machine learning algorithms (least absolute shrinkage and selection operator, random forest, and support vector machine model), and the nomogram was then developed. The receiver operating characteristic curve, calibration curve, and decision curve analysis curve were used to verify the validity of the diagnostic model. Using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), the characteristics of immune cell infiltration in placental tissue of FGR were evaluated and the candidate key immune cells of FGR were screened. In addition, this study also validated the diagnostic efficacy of TREM1 in the real world and explored associations between TREM1 and various clinical features. Results: By overlapping the genes selected by three machine learning algorithms, four key genes were identified from 290 DEGs, and the diagnostic model based on the key genes showed good predictive performance (AUC = 0.971). The analysis of immune cell infiltration indicated that a variety of immune cells may be involved in the development of FGR, and nine candidate key immune cells of FGR were screened. Results from real-world data further validated TREM1 as an effective diagnostic biomarker (AUC = 0.894) and TREM1 expression was associated with increased uterine artery PI (UtA-PI) (p-value = 0.029). Conclusion: Four candidate hub genes (SCD, SPINK1, TREM1, and HIST1H2BB) were identified, and the nomogram was constructed for FGR diagnosis. TREM1 was not only associated with a variety of key immune cells but also correlated with increased UtA-PI. The results of this study could provide some new clues for future research on the prediction and treatment of FGR.

Indefinite Robust Linear Quadratic Optimal Regulator for Discrete-Time Uncertain Singular Markov Jump Systems.

The robust LQ optimal regulator problem for discrete-time uncertain singular Markov jump systems (SMJSs) is solved by introducing a new quadratic cost function established by the penalty function method, which combines the penalty function and the weighting matrices. First, the indefinite robust optimal regulator problem for uncertain SMJSs is transformed into the robust optimal regulator problem with positive definite weighting matrices for uncertain Markov jump systems (MJSs). The transformed robust LQ problem is settled by the robust least-squares method, and the condition of the existence and analytic form of the robust optimal regulator are proposed. On the infinite horizon, the optimal state feedback is obtained, which can guarantee the regularity, causality, and stochastic stability of the corresponding optimal closed-loop system and eliminate the uncertain parameters of the closed-loop system. A numerical example and a practical example of DC motor are used to verify the validity of the conclusions.

ECCT: Efficient Contrastive Clustering via Pseudo-Siamese Vision Transformer and Multi-view Augmentation.

Image clustering aims to divide a set of unlabeled images into multiple clusters. Recently, clustering methods based on contrastive learning have attracted much attention due to their ability to learn discriminative feature representations. Nevertheless, existing clustering algorithms face challenges in capturing global information and preserving semantic continuity. Additionally, these methods often exhibit relatively singular feature distributions, limiting the full potential of contrastive learning in clustering. These problems can have a negative impact on the performance of image clustering. To address the above problems, we propose a deep clustering framework termed Efficient Contrastive Clustering via Pseudo-Siamese Vision Transformer and Multi-view Augmentation (ECCT). The core idea is to introduce Vision Transformer (ViT) to provide the global view, and improve it with Hilbert Patch Embedding (HPE) module to construct a new ViT branch. Finally, we fuse the features extracted from the two ViT branches to obtain both global view and semantic coherence. In addition, we employ multi-view random aggressive augmentation to broaden the feature distribution, enabling the model to learn more comprehensive and richer contrastive features. Our results on five datasets demonstrate that ECCT outperforms previous clustering methods. In particular, the ARI metric of ECCT on the STL-10 (ImageNet-Dogs) dataset is 0.852 (0.424), which is 10.3% (4.8%) higher than the best baseline.

Factor structure and measurement invariance of the University Demand-Resource Questionnaire: further evidence from Hungarian university students.

Purpose: The present study aimed to further examine the factor structure and measurement invariance of the UDRQ among a sample of Hungarian university students. Methods: Firstly, the factor structure of the UDRQ was examined among 837 Hungarian university students. Specifically, two measurement models (first-order model and second-order model) were constructed and compared. Secondly, the internal consistency reliability of the UDRQ was examined. Thirdly, measurement invariance of the UDRQ was evaluated across genders. Finally, measurement invariance of the UDRQ was evaluated across two different samples. Results: It was found that the first-order model outperformed the second-order model and better represented the factor structure of the UDRQ subscales. Results of Cronbach's alpha and Composite Reliability suggested that the internal consistency reliabilities of the two UDRQ subscales were satisfactory. Measurement invariance analysis revealed that the UDRQ measurement model was strict invariant across genders and samples. Conclusion: The findings of the present study indicated that the UDRQ displayed satisfactory reliability and validity and could be used to assess demands and resources of Hungarian university students.

Identification of susceptibility modules and characteristic genes to osteoarthritis by WGCNA.

The susceptibility modules and characteristic genes of patients with osteoarthritis (OA) were determined by weighted gene co-expression network analysis (WGCNA), and the role of immune cells in OA related microenvironment was analyzed. GSE98918 and GSE117999 data sets are from GEO database. R language was used to conduct difference analysis for the new data set after merging. The formation of gene co-expression network, screening of susceptibility modules and screening of core genes are all through WGCNA. GO and KEGG enrichment analyses were used for Hub genes. The characteristic genes of the disease were obtained by Lasso regression screening. SSGSEA was used to estimate immune cell abundance in sample and a series of correlation analyses were performed. WGCNA was used to form 6 gene co-expression modules. The yellow-green module is identified as the susceptible module of OA. 202 genes were identified as core genes. Finally, RHOT2, FNBP4 and NARF were identified as the characteristic genes of OA. The results showed that the characteristic genes of OA were positively correlated with plasmacytoid dendritic cells, NKT cells and immature dendritic cells, but negatively correlated with active B cells. MDSC were the most abundant immune cells in cartilage. This study identified the Hippo signaling pathway, mTOR signaling pathway, and three characteristic genes (RHOT2, FNBP4, NARF) as being associated with osteoarthritis (OA). These three genes are downregulated in the cartilage of OA patients and may serve as biomarkers for early diagnosis and targeted therapy. Proper regulation of immune cells may aid in the treatment of OA. Future research should focus on developing tools to detect these genes and exploring their therapeutic applications.