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Associations of weight change patterns with hyperuricemia risk in U.S. adults
Equivalence assessment of weight bearing cone beam CT and multidetector CT through 3D Knee bone modelling
Abstract Weight-bearing cone beam computed tomography (WB-CBCT, or simply WBCT), which captures high-resolution 3D images in a natural standing position, has gained increasing interest in recent years. This study examines the potential of WBCT as an alternative to multidetector computed tomography (MDCT) for 3D bone modelling. We generated 3D knee joint models from manually annotated WBCT and MDCT scans, performed rigid registration of these models, and assessed their similarity by evaluating the mean difference, standard deviation, and confidence intervals of the aligned models. The mean differences were computed as the average surface distances between corresponding WBCT and MDCT 3D bone models after rigid registration, providing a quantitative measure of their geometric similarity. Validation was conducted using both patient and cadaver scans to assess WBCT’s clinical applicability under realistic conditions and its technical reliability with controlled samples. Our findings reveal an average absolute difference of less than 0.35 mm for patient scans and 0.30 mm for cadaveric scans between WBCT and MDCT. The patella demonstrated the smallest mean difference (-0.20 mm to 0.10 mm) and standard deviation (0.28 mm to 0.55 mm) across all scans. These results confirm the comparability of WBCT to MDCT for 3D bone modelling, highlighting WBCT’s capacity to deliver appropriate image quality for the clinical assessment of bone joints.
Genomic characterisation of nasal isolates of coagulase-negative Staphylococci from healthy medical students reveals novel Staphylococcal cassette chromosome mec elements
Discovery and genomic characterization of Ulleung virus harbored by Crocidura utsuryoensis on Ulleung Island in Republic of Korea
Lavender Exosome-Like nanoparticles attenuate UVB-Induced Photoaging via miR166-Mediated inflammation and collagen regulation
How much energy is wasted from making a cup of tea?
Peeling tape produces strong electric fields via stick–slip friction that drive chemical reactions
Adhesive interfaces store significant energy due to interlocking molecular chain entanglement and van der Waals forces. When two adhesive surfaces are separated, triboelectric effects induce charge transfer, generating a strong electric field at the peeling interface. This effect offers different opportunities for initiating chemical reactions. Here, we report that the stick–slip friction involved in peeling tape produces electric fields on the order of 10 9 V/m, as measured by the vibrational Stark shift observed by confocal Raman spectroscopy during tape peeling. This field is sufficiently strong to ionize water and produce the H 4 O 2 + cation, a hydroxyl radical adduct with a hydronium ion. We further demonstrate that this electric field can drive a variety of electron transfer reactions. Our findings suggest that tribocharging presents a promising, energy-efficient avenue for electric-field-driven green chemistry.
Identification of biomarkers related to metabolically healthy or unhealthy obesity in children and adolescents with depressive disorders: a cross-sectional study
A hybrid prediction and multi-objective optimization framework for limestone calcined clay cement concrete mixture design
Hemodynamic assessment of rupture risk during growth stages in middle cerebral artery aneurysms treated with coiling
Gut microbiota constituents may affect hypertrophic scarring risk through interaction with specific immune cells in a two-step, two-sample Mendelian randomization study
Gut microbiota differences in children classified by extreme physical fitness and physical activity levels from a Chinese Cross-Sectional study
Targeting ESKAPE pathogens with ZnS and Au@ZnS Core–Shell nanoconjugates for improved biofilm control
Experimental and DEM based characterization of flowability of non-spherical and spherical bauxite particles
Risk factors for multidrug resistant and carbapenem resistant Pseudomonas aeruginosa bloodstream infections among inpatients in Central and East China
Generative AI without guardrails can harm learning: Evidence from high school mathematics
Generative AI is poised to revolutionize how humans work, and has already demonstrated promise in significantly improving human productivity. A key question is how generative AI affects learning—namely, how humans acquire new skills as they perform tasks. Learning is critical to long-term productivity, especially since generative AI is fallible and users must check its outputs. We study this question via a field experiment where we provide nearly a thousand high school math students with access to generative AI tutors. To understand the differential impact of tool design on learning, we deploy two generative AI tutors: one that mimics a standard ChatGPT interface (“GPT Base”) and one with prompts designed to safeguard learning (“GPT Tutor”). Consistent with prior work, our results show that having GPT-4 access while solving problems significantly improves performance (48% improvement in grades for GPT Base and 127% for GPT Tutor). However, we additionally find that when access is subsequently taken away, students actually perform worse than those who never had access (17% reduction in grades for GPT Base)—i.e., unfettered access to GPT-4 can harm educational outcomes. These negative learning effects are largely mitigated by the safeguards in GPT Tutor. Without guardrails, students attempt to use GPT-4 as a “crutch” during practice problem sessions, and subsequently perform worse on their own. Thus, decision-makers must be cautious about design choices underlying generative AI deployments to preserve skill learning and long-term productivity.