Browse Articles

Near-infrared spectral variation in Ryugu particles and implication for rapid space weathering by solar UV radiation

Identification of genomic variants associated with colorectal cancer heredity in indigenous populations of the Amazon

Astaxanthin supplementation in Arabian racing horses mitigates oxidative stress and inflammation in peripheral blood mononuclear cells through enhanced mitophagy

Abstract Astaxanthin, a strong antioxidant carotenoid, has shown promising features in mitigating inflammation and oxidative stress and so that has been considered as a supplement for high-performance animals. In this study, we aimed to evaluate the effects of astaxanthin on oxidative stress, inflammation, and mitochondrial health in peripheral blood mononuclear cells (PBMC) isolated from Arabian racehorses. Horse-derived peripheral blood mononuclear cells exposed to hydrogen peroxide (H₂O₂) presented increased reactive oxygen species (ROS) accumulation and overexpression of pro-inflammatory cytokines such as IL-1β, IL-6, IFN-γ, and TNF-α. The addition of astaxanthin to cell culture reduced H₂O₂-induced inflammatory response by decreasing the expression levels of all the tested pro-inflammatory cytokines. Moreover, astaxanthin displayed a potential antioxidant response by increasing the expression of genes related to antioxidative defense, such as NRF1, SOD2, and GPX. Interestingly, PBMCs isolated from the horses orally supplemented with astaxanthin increased the expression of the mitophagy-related genes PINK1 and PARKIN. Moreover, genes related to mitochondrial dynamics and energy production, such as PPARGC1B, NDUFA9, and MRPL24, as well as genes associated with mitochondrial function, structure and dynamics, such as PIGBOS, MRLP24, PUSL1 and TFAM were upregulated in PBMCs isolated from astaxanthin supplemented horses. Altogether, these findings indicate that astaxanthin may be a beneficial dietary supplement for equine health, supporting resilience against oxidative stress and inflammatory challenges, and improving the recovery and performance of racing horses.

Structure function in photoplethysmographic signal dynamics for physiological assessment

Comprehensive identification of dysregulated extracellular matrix molecules in the corneal endothelium of patients with Fuchs endothelial corneal dystrophy

A fractal gripper with switchable mode for geometry adaptive manipulation

Investigation of sestrin-2 levels and thiol-disulfide homeostasis in polyp tissue of patients with nasal polyps

An experimental study on effect of curvatures of upstream tube on thermal performance of downstream tube in cross-flow of air

The research of predictive models for road traffic fatalities in Shandong Province, China

Profiling the cell-specific small non-coding RNA transcriptome of the human placenta

Investigation of cadmium removal using tin oxide nanoflowers through process optimization, isotherms and kinetics

Community evolution prediction based on feature change patterns in social networks

Comparable neural and behavioural performance in dominant and non-dominant hands during grasping tasks

Post-integration based point-line feature visual SLAM in low-texture environments

Abstract To address the issues of weak robustness and low accuracy of traditional SLAM data processing algorithms in weak texture environments such as low light and low contrast, this paper first studies and improves the data feature extraction method, optimizing the AGAST-based feature extraction algorithm to adaptively adjust the extraction threshold according to the gradient size of different data features. Meanwhile, a fusion-based incremental loop closure detection method is proposed, which integrates the similarity scores of multi-dimensional data features based on the Borda counting strategy, thereby enhancing the accuracy of loop closure detection. The performance of loop closure detection was evaluated on public datasets (such as KITTI sequences 00, 05, and 06), achieving an average AP value of 92.03%. The overall system performance was evaluated on the EuRoC dataset, with the results showing a root mean square error range from 0.0061 to 0.0281 m, demonstrating the excellent accuracy and robustness of the proposed method in large-scale data processing.

A software reliability model for open source big data systems based on Weibull–Weibull distribution

Comprehensive single-cell transcriptome analysis of autologous platelet-rich plasma therapy on human thin endometrium

Influence of particle size and dielectric environment on radiative lifetimes of colloidal cadmium selenide single photon emitters

Abstract High repetition rate single-photon emitters are essential for all-optical quantum information processing, communications and metrology. The spontaneous emission lifetimes of colloidal cadmium selenide quantum dots are typically of the order of 10 ns, severely limiting their brightness and therefore their potential applications in quantum devices. Here we report on single-photon emission at room temperature with nanosecond lifetime from cadmium selenide quantum dots embedded in a polymer matrix. The study shows that the emission lifetime can be tuned by appropriately choosing the particle size and the dielectric constant of the surrounding medium. The quantum dots are synthesized using a green synthesis protocol and surface passivated using oleic acid. A Hanbury Brown and Twiss setup attached to an in-house constructed confocal microscope is used to efficiently couple and characterize the single-photon emission from an array of quantum dots. Detailed analysis of the second-order correlation function ( $$g^{(2)}(\tau )$$ ) of single-photon emission from cadmium selenide quantum dots reveals the particle-size dependence of emission lifetimes. The study also shows that the quality of single-photon emission, as revealed by $$g^{(2)}(0)$$ , reduces with increasing particle-size in the strongly confined regime.

Performance of ChatGPT and Microsoft Copilot in Bing in answering obstetric ultrasound questions and analyzing obstetric ultrasound reports

Abstract To evaluate and compare the performance of publicly available ChatGPT-3.5, ChatGPT-4.0 and Microsoft Copilot in Bing (Copilot) in answering obstetric ultrasound questions and analyzing obstetric ultrasound reports. Twenty questions related to obstetric ultrasound were answered and 110 obstetric ultrasound reports were analyzed by ChatGPT-3.5, ChatGPT-4.0 and Copilot, with each question and report being posed three times to them at different times. The accuracy and consistency of each response to twenty questions and each analysis result in the report were evaluated and compared. In answering twenty questions, both ChatGPT-3.5 and ChatGPT-4.0 outperformed Copilot in accuracy (95.0% vs. 80.0%) and consistency (90.0% and 85.0% vs. 75.0%). However, no statistical difference was found among them. When analyzing obstetric ultrasound reports, ChatGPT-3.5 and ChatGPT-4.0 demonstrated superior accuracy compared to Copilot ( P  < 0.05), and all three showed high consistency and the ability to provide recommendations. The overall accuracy and consistency of ChatGPT-3.5, ChatGPT-4.0, and Copilot were 83.86%, 84.13% vs. 77.51% in accuracy, and 87.30%, 93.65% vs. 90.48% in consistency, respectively. These large language models (ChatGPT-3.5, ChatGPT-4.0 and Copilot) have the potential to assist clinical workflows by enhancing patient education and patient clinical communication around common obstetric ultrasound issues. With inconsistent and sometimes inaccurate responses, along with cybersecurity concerns, physician supervision is crucial in the use of these models.

Therapeutic effects and potential mechanisms of caffeine on obese polycystic ovary syndrome: bioinformatic analysis and experimental validation

Prior therapeutic experiences and treatment expectations are differentially associated with pain-related disability in individuals with chronic pain

Abstract Many individuals suffering from chronic pain do not benefit sufficiently from treatment. Prior treatment experiences and treatment expectations play a significant role in perceived symptom severity and treatment-related outcomes in many chronic diseases. Their role in chronic pain, however, remains underexplored. Therefore, the present study investigated the role of treatment experiences and treatment expectations for pain-related disability in individuals suffering from chronic pain. Participants suffering from chronic pain who were receiving treatment (pharmacotherapy, physiotherapy, and/or psychotherapy) completed questionnaires as part of an online survey. Prior improvement, worsening, and side effect experiences and their relation with treatment expectations were assessed with the generic rating scale for previous treatment experiences, treatment expectations, and treatment effects (GEEE), and pain-related disability via the pain disability questionnaire (PDI). Multiple linear regressions were performed to determine how prior treatment experiences related to treatment expectations and whether prior experiences and current treatment expectations were associated with pain-related disability. In total, 212 participants (86.3% female) were included. Prior worsening experience as well as stronger worsening and side effect expectations were associated with higher pain-related disability. Screening patients for different expectation domains could be an important strategy to detect and target potentially relevant factors influencing pain-related disability and treatment outcome.