Variability in performance of genetic-enhanced DXA-BMD prediction models across diverse ethnic and geographic populations: A risk prediction study.

BACKGROUND: Osteoporosis is a major global health issue, weakening bones and increasing fracture risk. Dual-energy X-ray absorptiometry (DXA) is the standard for measuring bone mineral density (BMD) and diagnosing osteoporosis, but its costliness and complexity impede widespread screening adoption. Predictive modeling using genetic and clinical data offers a cost-effective alternative for assessing osteoporosis and fracture risk. This study aims to develop BMD prediction models using data from the UK Biobank (UKBB) and test their performance across different ethnic and geographical populations. METHODS AND FINDINGS: We developed BMD prediction models for the femoral neck (FNK) and lumbar spine (SPN) using both genetic variants and clinical factors (such as sex, age, height, and weight), within 17,964 British white individuals from UKBB. Models based on regression with least absolute shrinkage and selection operator (LASSO), selected based on the coefficient of determination (R2) from a model selection subset of 5,973 individuals from British white population. These models were tested on 5 UKBB test sets and 12 independent cohorts of diverse ancestries, totaling over 15,000 individuals. Furthermore, we assessed the correlation of predicted BMDs with fragility fractures risk in 10 years in a case-control set of 287,183 European white participants without DXA-BMDs in the UKBB. With single-nucleotide polymorphism (SNP) inclusion thresholds at 5×10-6 and 5×10-7, the prediction models for FNK-BMD and SPN-BMD achieved the highest R2 of 27.70% with a 95% confidence interval (CI) of [27.56%, 27.84%] and 48.28% (95% CI [48.23%, 48.34%]), respectively. Adding genetic factors improved predictions slightly, explaining an additional 2.3% variation for FNK-BMD and 3% for SPN-BMD over clinical factors alone. Survival analysis revealed that the predicted FNK-BMD and SPN-BMD were significantly associated with fragility fracture risk in the European white population (P < 0.001). The hazard ratios (HRs) of the predicted FNK-BMD and SPN-BMD were 0.83 (95% CI [0.79, 0.88], corresponding to a 1.44% difference in 10-year absolute risk) and 0.72 (95% CI [0.68, 0.76], corresponding to a 1.64% difference in 10-year absolute risk), respectively, indicating that for every increase of one standard deviation in BMD, the fracture risk will decrease by 17% and 28%, respectively. However, the model's performance declined in other ethnic groups and independent cohorts. The limitations of this study include differences in clinical factors distribution and the use of only SNPs as genetic factors. CONCLUSIONS: In this study, we observed that combining genetic and clinical factors improves BMD prediction compared to clinical factors alone. Adjusting inclusion thresholds for genetic variants (e.g., 5×10-6 or 5×10-7) rather than solely considering genome-wide association study (GWAS)-significant variants can enhance the model's explanatory power. The study highlights the need for training models on diverse populations to improve predictive performance across various ethnic and geographical groups.

A multiethnic whole genome sequencing study to identify novel loci for bone mineral density.

At present, there have only been a few DNA sequencing-based studies to explore the genetic determinants of bone mineral density (BMD). We carried out the largest whole genome sequencing analysis to date for femoral neck and spine BMD (n = 4981), with one of the highest average sequencing depths implemented thus far at 22×, in a multiethnic sample (58% Caucasian and 42% African American) from the Louisiana Osteoporosis Study (LOS). The LOS samples were combined with summary statistics from the GEFOS consortium and several independent samples of various ethnicities to perform GWAS meta-analysis (n = 44 506). We identified 31 and 30 genomic risk loci for femoral neck and spine BMD, respectively. The findings substantiate many previously reported susceptibility loci (e.g. WNT16 and ESR1) and reveal several others that are either novel or have not been widely replicated in GWAS for BMD, including two for femoral neck (IGF2 and ZNF423) and one for spine (SIPA1). Although we were not able to uncover ethnicity specific differences in the genetic determinants of BMD, we did identify several loci which demonstrated sex-specific associations, including two for women (PDE4D and PIGN) and three for men (TRAF3IP2, NFIB and LYSMD4). Gene-based rare variant association testing detected MAML2, a regulator of the Notch signaling pathway, which has not previously been suggested, for association with spine BMD. The findings provide novel insights into the pathophysiological mechanisms of osteoporosis.

Ultrasensitive Fluorescent Microsensors Based on Aptamers Modified with SYBR Green I for Visual Quantitative Detection of Organophosphate Pesticides.

Organophosphate pesticides (OPs) are widely utilized in agricultural production, and the residues threaten public health and environmental safety due to their toxicity. Herein, a novel and simple DNA aptamer-based sensor has been fabricated for the rapid, visual, and quantitative detection of profenofos and isocarbophos. The proposed DNA aptamers with a G-quadruplex spatial structure could be recognized by SYBR Green I (SG-I), resulting in strong green fluorescence emitted by SG-I. The DNA aptamers exhibit a higher specific binding ability to target OP molecules through aromatic ring stacking, disrupting the interaction between SG-I and DNA aptamers to induce green fluorescence quenching. Meanwhile, the fluorescence wavelength of G-quadruplex fluorescence emission peaks changes, accompanied by an obvious fluorescence variation from green to blue. SG-I-modified aptasensor without any additive reference fluorescence units for use in multicolor fluorescence assay for selective monitoring of OPs was first developed. The developed aptasensor provides a favorable linear range from 0 to 200 nM, with a low detection limit of 2.48 and 3.01 nM for profenofos and isocarbophos, respectively. Moreover, it offers high selectivity and stability in real sample detection with high recoveries. Then, a self-designed portable smartphone sensing platform was successfully used for quantitative result outputs, demonstrating experience in designing a neotype sensing strategy for point-of-care pesticide monitoring.

Odontogenesis-Empowered Extracellular Vesicles Safeguard Donor-Recipient Stem Cell Interplay to Support Tooth Regeneration.

Harnessing the developmental events of mesenchymal condensation to direct postnatal dental stem cell aggregation represents a cutting-edge and promising approach to tooth regeneration. Tooth avulsion is among the most prevalent and serious dental injuries, and odontogenic aggregates assembled by stem cells from human exfoliated deciduous teeth (SHED) have proven effective in revitalizing avulsed teeth after replantation in the clinical trial. However, whether and how SHED aggregates (SA) communicate with recipient components and promote synergistic tissue regeneration to support replanted teeth remains elusive. Here, it is shown that SA-mediated avulsed tooth regeneration involves periodontal restoration and recovery of recipient Gli1+ stem cells, which are mobilized and necessarily contribute to the reestablishment of the tooth-periodontal ligament-bone interface. Mechanistically, the release of extracellular vesicles (EVs) is revealed indispensable for the implanted SA to mobilize recipient Gli1+ cells and regenerate avulsed teeth. Furthermore, SHED aggregates-released EVs (SA-EVs) are featured with odontogenic properties linked to tissue regeneration, which enhance migration, proliferation, and differentiation of Gli1+ cells. Importantly, local application of SA-EVs per se empowers recipient Gli1+ cells and safeguards regeneration of avulsed teeth. Collectively, the findings establish a paradigm in which odontogenesis-featured EVs govern donor-recipient stem cell interplay to achieve tooth regeneration, inspiring cell-free translational regenerative strategies.

Investigating the angular distortion impact on vehicular optical camera communication (OCC) systems.

Optical camera communication (OCC) shows promise for optical wireless communication (OWC) in vehicular networks. However, vehicle mobility-induced angular distortions hinder system throughput by degrading non-isotropic vehicular OCC channel gain. Few of the prior works have ever made a comprehensive analysis of their impact, especially based on the pixel value which reflects the camera imaging features. To address this knowledge gap, a pixel value-described vehicular OCC system model accounting for transmitter imaging location and intensity from the geometry and radiometry aspects is presented in this paper with common types of the offset and rotation angles included. We integrate a MATLAB-based simulated vehicular OCC system with an experimentally designed testbed for validation and performance analysis. For a single-time snapshot, we investigate the impacts of common angular distortion types in vehicular OCC systems on maximum pixel value, imaging location, and communication-related metrics. Furthermore, we statistically analyze their influences by considering two driving scenarios with respective angular distributions. The angular distortion characterization from this work is expected to lay a stepping stone to addressing mobility in vehicular OCC systems.

A new hip fracture risk index derived from FEA-computed proximal femur fracture loads and energies-to-failure.

Hip fracture risk assessment is an important but challenging task. Quantitative CT-based patient-specific finite element (FE) analysis (FEA) incorporates bone geometry and bone density in the proximal femur. We developed a global FEA-computed fracture risk index to increase the prediction accuracy of hip fracture incidence. PURPOSE: Quantitative CT-based patient-specific finite element (FE) analysis (FEA) incorporates bone geometry and bone density in the proximal femur to compute the force (fracture load) and energy necessary to break the proximal femur in a particular loading condition. The fracture loads and energies-to-failure are individually associated with incident hip fracture, and provide different structural information about the proximal femur. METHODS: We used principal component analysis (PCA) to develop a global FEA-computed fracture risk index that incorporates the FEA-computed yield and ultimate failure loads and energies-to-failure in four loading conditions of 110 hip fracture subjects and 235 age- and sex-matched control subjects from the AGES-Reykjavik study. Using a logistic regression model, we compared the prediction performance for hip fracture based on the stratified resampling. RESULTS: We referred the first principal component (PC1) of the FE parameters as the global FEA-computed fracture risk index, which was the significant predictor of hip fracture (p-value < 0.001). The area under the receiver operating characteristic curve (AUC) using PC1 (0.776) was higher than that using all FE parameters combined (0.737) in the males (p-value < 0.001). CONCLUSIONS: The global FEA-computed fracture risk index increased hip fracture risk prediction accuracy in males.

Antifreeze Polysaccharides from Wheat Bran: The Structural Characterization and Antifreeze Mechanism.

Exploring a novel natural cryoprotectant and understanding its antifreeze mechanism allows the rational design of future sustainable antifreeze analogues. In this study, various antifreeze polysaccharides were isolated from wheat bran, and the antifreeze activity was comparatively studied in relation to the molecular structure. The antifreeze mechanism was further revealed based on the interactions of polysaccharides and water molecules through dynamic simulation analysis. The antifreeze polysaccharides showed distinct ice recrystallization inhibition activity, and structural analysis suggested that the polysaccharides were arabinoxylan, featuring a xylan backbone with a majority of Araf and minor fractions of Manp, Galp, and Glcp involved in the side chain. The antifreeze arabinoxylan, characterized by lower molecular weight, less branching, and more flexible conformation, could weaken the hydrogen bonding of the surrounding water molecules more evidently, thus retarding the transformation of water molecules into the ordered ice structure.

MicroRNA miR-722 Inhibits Cyprinid Herpesvirus 3 Replication via Targeting the Viral Immune Evasion Protein ORF89, Which Negatively Regulates IFN by Degrading IRF3.

Cyprinid herpesvirus 3 (CyHV-3) has caused severe economic losses to carp culture, but its pathogenicity is far from clear. Our previous study has revealed that microRNA (miR)-722 was upregulated during CyHV-3 infection, indicating that miR-722 might play an important role in CyHV-3 replication. In this study, we found that overexpression of miR-722 inhibited CyHV-3 replication and promoted IFN expression. The putative target gene of miR-722 was searched over the CyHV-3 genome, and ORF89 was identified and validated as a target gene of miR-722. Overexpression of ORF89 markedly reduced the expression of IFN and IFN-stimulated genes. Mechanistically, ORF89 interacted with and degraded IFN regulatory factor 3 (IRF3), and inhibited the entry of IRF3 into the nucleus by suppressing the dimerization of IRF3. Moreover, ORF89-mediated suppression of IFN expression could be restored by adding miR-722. To our knowledge, our findings confirm a novel virus-host combat, in which CyHV-3 evades host antiviral immunity by its ORF89 protein, whereas host miR-722, upregulated on CyHV-3 infection, targets ORF89 to impede CyHV-3 replication.

Effect of biochars on the immobilization and form of Cadmium (Cd) in simulated Cd deposition of iron rich soils.

Atmospheric deposition of Cd poses a serious threat to ecosystem security. Biochar is widely used for polluted soil remediation, however, whether biochar already applied to the soil can reduce the hazards of newly deposited Cd remains to be studied. Thus, an indoor cultural experiment and static adsorption method were conducted to study the isothermal and kinetic adsorption processes of three types of biochar (rice husk, rubber wood, and tobacco stem biochars) on Cd in iron rich soils and the effect of biochar on the morphological distribution of Cd in the soil and the soil pH. The results showed that the soil with biochar in our study could quickly fix "the new deposited Cd" in the soil in 3 h with the maximum adsorption capacity in rubber wood biochar-treated sample (3227.34 mg/kg）. The addition of all three biochar treatments significantly increased the soil pH and reduced the soil exchange state Cd content, with a 13.69-17.32% increase in the pH and a 13.22-54.39% reduction in the exchange state Cd content when contrasted with the control, which could promote those Cd converting into unavailable Cd (carbonate-bound form Cd, Fe-Mn oxide-bound form Cd, or residual form Cd) for crops. In summary, the addition of three kinds of biochar treatments could effectively reduce the ecological and environmental risk of soil that was contaminated by Cd and could provide a reliable theoretical basis for the effect of biochar on the improvement of the quality of soil that is contaminated by heavy metals.

MS-YOLOv8-Based Object Detection Method for Pavement Diseases.

Detection of pavement diseases is crucial for road maintenance. Traditional methods are costly, time-consuming, and less accurate. This paper introduces an enhanced pavement disease recognition algorithm, MS-YOLOv8, which modifies the YOLOv8 model by incorporating three novel mechanisms to improve detection accuracy and adaptability to varied pavement conditions. The Deformable Large Kernel Attention (DLKA) mechanism adjusts convolution kernels dynamically, adapting to multi-scale targets. The Large Separable Kernel Attention (LSKA) enhances the SPPF feature extractor, boosting multi-scale feature extraction capabilities. Additionally, Multi-Scale Dilated Attention in the network's neck performs Spatially Weighted Dilated Convolution (SWDA) across different dilatation rates, enhancing background distinction and detection precision. Experimental results show that MS-YOLOv8 increases background classification accuracy by 6%, overall precision by 1.9%, and mAP by 1.4%, with specific disease detection mAP up by 2.9%. Our model maintains comparable detection speeds. This method offers a significant reference for automatic road defect detection.