Scientific Reports最新文献

Studies have used extensive clinical information to predict time-to-heart failure (HF) in patients with and without diabetes mellitus (DM). We aimed to determine a screening method using only computed tomography calcium scoring (CTCS) to assess HF risk. We analyzed CTCS scans from 1,998 patients (336 with type 2 diabetes) from a no-charge coronary artery calcium score registry (CLARIFY Study, Clinicaltrials.gov NCT04075162). We used deep learning to segment epicardial adipose tissue (EAT) and engineered radiomic features of calcifications ("calcium-omics") and EAT ("fat-omics"). We developed models incorporating radiomics to predict risk of incident HF in patients with and without type 2 diabetes. At a median follow-up of 1.7 years, 5% had incident HF. In the overall cohort, fat-omics (C-index: 77.3) outperformed models using clinical factors, EAT volume, Agatston score, calcium-omics, and calcium-and-fat-omics to predict HF. For DM patients, the calcium-omics model (C-index: 81.8) outperformed other models. In conclusion, CTCS-based models combining calcium and fat-omics can predict incident HF, outperforming prediction scores based on clinical factors.Please check article title if captured correctly.YesPlease check and confirm that the authors and their respective affiliations have been correctly identified and amend if necessary.Yes.

A pan-cancer analysis summarizing the overall changes in mRNA and protein stability of ADM9, as well as its oncogenic function on immune cell line modulation and checkpoints within the tumor microenvironment (TME), is lacking, despite the fact that ADM9 up-regulation is correlated with the progression of many cancers. Therefore, in this study, we comprehensively analyzed the role of ADAM9 expression and its prognostic value in different cancers to fill this gap. Multiple bioinformatics databases such as Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Clinical Proteomic Tumor Analysis Consortium (CPTAC) were used to evaluate the ADAM9 genetic alternation, phosphorylation, and methylation, and indicated highly positive correlated genes that might play a critical interaction with ADAM9 and their molecular function with GO analysis. We also evaluate the effect of higher ADAM9 with prominent immune modulatory genes and immune infiltration especially in liver cancer pathogenesis stimulates lower NK cell effector functions based on its role in MICA shedding and increasing the Tregs infiltration. Immunohistochemistry (IHC) staining from 90 pathologically verified samples proved the positive correlation between ADAM9 and tumor stages and proved the higher expression of ADAM9 correlated genes (SNX9, APP, TNF, CDH1, ITGAV, MAD2L2) in HCC pathogenesis. In conclusion, this pan-cancer study provides a comprehensive understanding of the prognostic value of ADAM9 in various tumors emphasizing its importance to be considered as an innovative treatment approach, especially in tumor immunity shortly.

It aimed to analyze the value of deep learning algorithm combined with magnetic resonance imaging (MRI) in the risk diagnosis and prognosis of endometrial cancer (EC). Based on the deep learning convolutional neural network (CNN) architecture residual network with 101 layers (ResNet-101), spatial attention and channel attention modules were introduced to optimize the model. A retrospective collection of MRI image data from 210 EC patients was used for model segmentation and reconstruction, with 140 cases as the test set and 70 cases as the validation set. The performance was compared with traditional ResNet-101 model, ResNet-101 model based on spatial attention mechanism (SA-ResNet-101), and ResNet-101 model based on channel attention mechanism (CA-ResNet-101), using accuracy (AC), precision (PR), recall (RE), and F1 score as evaluation metrics. Among the 70 cases in the validation set, there were 45 cases of low-risk EC and 25 cases of high-risk EC. Using ROC curve analysis, it was found that the area under the curve (AUC) for the diagnosis of high-risk EC of the proposed model in this article (0.918) was visibly larger as against traditional ResNet-101 (0.613), SA-ResNet-101 (0.760), and CA-ResNet-101 models (0.758). The AC, PR, RE, and F1 values of the proposed model for the diagnosis of EC risk were visibly higher (P  < 0.05). In the validation set, postoperative recurrence occurred in 13 cases and did not occur in 57 cases. Using ROC curve analysis, it was found that the AUC for postoperative recurrence prediction of the patients by the proposed model (0.926) was visibly larger as against traditional ResNet-101 (0.620), SA-ResNet-101 (0.729), and CA-ResNet-101 models (0.767). The AC, PR, RE, and F1 values of the proposed model for postoperative recurrence prediction were visibly higher (P  < 0.05). The proposed model in this article, assisted by MRI, presented superior performance in diagnosing high-risk EC patients, with higher sensitivity (Sen) and specificity (Spe), and also demonstrated excellent predictive AC in postoperative recurrence prediction.

The focus of the current work is the study of the effect of the photo-catalytic activity of ZnO nanoparticles. The photocatalytic destruction of methylene blue dye, a common water contaminant, was used to assess the photocatalytic efficiency of the ZnO nanoparticles from its aqueous solution by using ZnO nanoparticles thin film under UV light and laser irradiation. Sol-gel methods prepared ZnO nanoparticle thin films. X-ray diffraction and a field-emitted scanning electron microscope were utilized to examine the structure of the produced ZnO nanoparticles. An extended characterization by laser-based fluorescence and UV-visible spectroscopic techniques. The effects of operational parameters such as photo-catalyst load and contact time on photocatalytic degradation of methylene blue were investigated. The recent study's findings showed that irradiation with a UV laser increases with power density 25 µW/cm², the photo-catalytic rate. The UV spectra show decay for the band at 664nm decreased and the concentration of M.B. in monomer form decayed to 26% of the original concentration in 24 h, while the band at 612 which is related to the dimer M.B. molecules was not affected. The laser irradiation did the same for monomer M.B. molecules in only 3 h, while the dimer decreased to 28% of its original concentration. The reaction mechanism has been discussed by molecular modelling. Quantum mechanical calculations at B3LYP/6-311g(d,p) level indicated that methylene blue changed from dimers to monomers in the existence of ZnO. The current results present a method for degrading M.B. not only in wastewater but also in the industrial waste scale.

The ability to learn novel motor skills is essential for patients with Parkinson's disease (PD) to regain activities of daily living. However, the underlying mechanisms of motor learning in PD remain unclear. To identify motor features that are distinctively manifested in PD during motor learning, we quantified a rich set of variables reflecting various aspects of the learning process in a virtual throwing task. While the performance outcome improved similarly over 3 days of practice for both PD patients and age-matched controls, further analysis revealed distinct learning processes between the two groups. PD patients initially performed with a slow release velocity and gradually increased it as practice progressed, whereas the control group began with an unnecessarily rapid release velocity, which they later stabilized at a lower value. Performance characteristics related to the timing of ball release and the inter-release interval did not show significant group differences, although they were modulated across practice in both groups. After one week, both groups retained the performance outcomes and underlying kinematics developed over practice. This study underscores the importance of analyzing the multi-faceted learning process to characterize motor skill learning in PD. The findings may provide insights into PD pathophysiology and inform rehabilitation strategies.

The coronavirus disease 2019 (COVID-19) has a significant impact on the global population, particularly on individuals with chronic kidney disease (CKD). COVID-19 patients with CKD will face a considerably higher risk of mortality than the general population. This study developed a predictive model for assessing mortality in COVID-19-affected CKD patients, providing personalized risk prediction to optimize clinical management and reduce mortality rates. We developed machine learning algorithms to analyze 219 patients' clinical laboratory test data retrospectively. The performance of each model was assessed using a calibration curve, decision curve analysis, and receiver operating characteristic (ROC) curve. It was found that the LightGBM model showed the most satisfied performance, with an area under the ROC curve of 0.833, sensitivity of 0.952, and specificity of 0.714. Prealbumin, neutrophil percent, respiratory index in arterial blood, half-saturated pressure of oxygen, carbon dioxide in serum, glucose, neutrophil count, and uric acid were the top 8 significant variables in the prediction model. Validation by 46 patients demonstrated acceptable accuracy. This model can serve as a powerful tool for screening CKD patients at high risk of COVID-19-related mortality and providing decision support for clinical staff, enabling efficient allocation of resources, and facilitating timely and targeted management for those who need the relevant interference urgently.

The characterisation of novel materials presents a challenge that requires new and original developments. To face some of these demands for making measurements at the nanoscale, a new microsphere-assisted white light interference nanoscope performing local reflectance mapping is presented. This technique presents the advantages of being non-destructive, full-field and label-free. A 145 μm diameter microsphere, glued to the end of an optical fiber, is inserted inside the white light interference microscope to improve the lateral resolution from 940 nm to 520 nm. The acquisition and the Fourier transform processing of a stack of interference images superimposed on the virtual image produced by the microsphere allows the extraction of the local reflectance over a wavelength range of 460 nm to 900 nm and a field of view of 8 μm in diameter. The enhancement in the lateral resolution of the reflectance is demonstrated through the spectral distinction of neighboring ripples on a laser-textured colored stainless-steel sample that cannot be resolved without the microsphere, on regions with a surface of 279 × 279 nm² horizontally spaced 279 nm apart. Future improvements could potentially lead to a lateral resolution of reflectance measurement over a 100 nm diameter area in air, paving the way to sub-diffraction reflectance mapping.

The rapid advancement of artificial intelligence has significantly expanded the role of service robots in everyday life. This expansion necessitates the accurate recognition and prediction of human intentions to provide timely and appropriate services. However, existing methods often struggle to perform effectively in complex and unstructured environments. To address this challenge, we propose the Large language model and Knowledge graph based Intention Recognition Framework (LKIRF), which combines large language model (LLM) with knowledge graphs (KG) to enhance the intention recognition capabilities of service robots. Our approach constructs an offline KG from human motion and environmental data and builds an online reasoning graph through real-time interaction, utilizing LLM for interpretation. Experimental results indicate that compared to traditional methods, LKIRF not only improves prediction accuracy across various scenarios but also enhances the transparency and interpretability of the intention reasoning process.

As a van der Waals (vdW) layered semiconductor material, lead iodide (PbI₂) possessing a direct bandgap with strong photoluminescence emission in visible range has gained wide attention in applications of photonic and optoelectronic devices. Here, upconversion photoluminescence (UPL) in exfoliated PbI₂ flakes is demonstrated at room temperature and elevated temperatures. The linear power dependence of UPL emission with 532 nm excitation suggests the one-photon involved multiphonon-assisted UPL emission process, which is revealed by the temperature-dependent UPL emission measurement. Meanwhile, the nonlinear power dependence of UPL emission with 561 nm excitation indicates the transition of UPL emission mechanism from linear to nonlinear regime, and the temperature-dependent UPL emission study further shows that the upconversion is contributed by both the multiphonon-assisted UPL process and the two-photon absorption induced PL process. This study will provide an insight to the understanding of photon upconversion in vdW layered semiconductors and advancing applications in temperature-controlled photon upconversion, tunable photonics, photodetection and imaging.