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Objective and rationale: Children's clinical pain phenotypes are complex, and there is a lack of objective biological diagnostic markers and cognitive patterns. Detecting physiological signals through wearable devices simplifies disease diagnosis and holds the potential for remote medical applications. Method and results: This research established a pain recognition model based on AI skin potential (SP) signal analysis. A total of 237 subjects participated in this study, comprising 152 boys and 85 girls, ranging in age from 2 to 16 years old. Initially, we preprocessed SP signals and built datasets for pain and non-pain conditions, including 195 pain and 97 non-pain samples. Then, we applied wavelet transform (WT) to capture the time-frequency characteristics of the signals and extract energy features and created a feature set comprising 30 features and selected 10 most relevant ones using the “SelectKBest” function.We compared six algorithms, optimized their parameters, and evaluated the stability and fitting performance of each algorithm. The random forest (RF) algorithm emerged as the best, demonstrating significant performance in pain recognition with an accuracy of 80.3 % and a sensitivity of 92 %. The SP signals generated by children of different genders, ages, and needling positions during indwelling needle puncture were accurately recognized. Conclusion: We developed a comprehensive SP recognition model, innovatively employing WT for SP signal analysis. This time-frequency analysis method, by preserving low-frequency features, is particularly suitable for SP signals. By combining pain monitoring with SP signals and ML, subjective pain experiences are transformed into quantifiable data, achieving high accuracy and real-time measurement capabilities. These advantages provide valuable technical support for clinical pediatric pain management.

BackgroundAcute cadmium (Cd) exposure can cause damage to multiple tissues, with the kidney being the primary target organ. The development of Cd-induced acute kidney injury involves complex mechanisms, in which autophagy and oxidative stress play crucial roles.ObjectiveTo investigate the effect of 10-hydroxy-2-decenoic acid (10-HDA) on kidney injury in mice exposed to cadmium, and provide experimental basis for studying the pathogenesis and prevention of Cd poisoning.MethodsThirty-five male C57BL/6 mice were divided into 7 groups (each of 5 mice): control group (normal saline, intraperitoneal injection), CdCl2 group (4 mg·kg−1, intraperitoneal injection), intervention groups ( 4 mg·kg−1 CdCl2, intraperitoneal Injection + 50, 100, 150, or 200 mg·kg−1 10-HDA, oral gavage), and 10-HDA group (150 mg·kg−1, oral gavage). All treatments were given for 14 d. Twenty-four hours after the last infection, physiological indicators [blood urea nitrogen (BUN), creatinine (CRE), malondialdehyde (MDA), and superoxide dismutase (SOD)], histopathological indicators, autophagy-related proteins (Atg7, Atg5, Beclin-1, and LC3), and mitochondrial autophagy-related proteins (PINK1 and Parkin) were detected to examine the effect of 10-HDA on kidney injury caused by CdCl2.ResultsCompared with the control group, the body weight of mice in the CdCl2 group was significantly reduced (P

With the increasing demand for high temporal and spatial resolution multispectral image sequences, many studies have been carried out on fusion on Landsat-8 and Sentinel-2 images to obtain image sequences with a revisit cycle of 2 and 3 days and a spatial resolution of 10 m. However, current fusion methods suffer from complex computation and loss of spectral and spatial information. To address these issues, a Landsat-8 and Sentinel-2 image fusion based on multiscale smoothing-sharpening filter (MSSF) method is proposed. MSSF combines well the initial spatial prediction obtained from the Landsat-8 image at the target date and the detailed image extracted from the Sentinel-2 image at the reference date. Thin plate spline interpolation with morphological opening-closing algorithm is implemented on the Landsat-8 image at the target date, and the Laplacian of Gaussian enhancement algorithm is applied to the Sentinel-2 image at the reference date in the preprocessing stage. Smoothing-sharpening filter (SSIF) is employed to separate the high and low frequency components of the two preprocessed images. The multiscale SSIF is then utilized to migrate the details from the preprocessed Sentinel-2 image to the preprocessed Landsat-8 image. The performance of MSSF and five compared methods was evaluated qualitatively and quantitatively. Experiments on three remote sensing data sets gathered from different experimental sites confirm that the proposed MSSF method could efficiently generate Sentinel-2-like images with high spatial and spectral resolution.

The floating platform is a critical component of the floating offshore wind turbine (FOWT), and its internal structure design plays a key role in ensuring the safe operation of the FOWT. In this study, the local model of the floating platform was firstly parameterized, and a response surface model was obtained by conducting an orthogonal test. The response surface model was then optimized using a gradient descent algorithm. Finally, the internal structure arrangement was validated through a safety calibration. The optimization results indicate that the maximum stress of the optimized model is reduced by 22.12% compared to the original model, while maintaining the same mass, centroid, and other mass-related parameters. The optimization significantly improves the safety of the structure and provides valuable references for the design and construction of a FOWT platform.

Intervertebral disc degeneration (IVDD) is a worldwide disease that causes low back pain and reduces quality of life. Biotherapeutic strategies based on tissue engineering alternatives, such as intervertebral disc scaffolds, supplemented by drug-targeted therapy have brought new hope for IVDD. In this study, to explore the role and mechanism of MnO2/GelMA composite hydrogels in alleviating IVDD, we prepared composite hydrogels with MnO2 and methacrylate gelatin (GelMA) and characterized them using compression testing and transmission electron microscopy (TEM). Annulus fibrosus cells (AFCs) were cultured in the composite hydrogels to verify biocompatibility by live/dead and cytoskeleton staining. Cell viability assays and a reactive oxygen species (ROS) probe were used to analyze the protective effect of the composite hydrogels under oxidative damage. To explore the mechanism of improving the microenvironment, we detected the expression levels of antioxidant and autophagy-related genes and proteins by qPCR and Western blotting. We found that the MnO2/GelMA composite hydrogels exhibited excellent biocompatibility and a porous structure, which promoted cell proliferation. The addition of MnO2 nanoparticles to GelMA cleared ROS in AFCs and induced the expression of antioxidant and cellular autophagy through the common SIRT1/NRF2 pathway. Therefore, the MnO2/GelMA composite hydrogels, which can improve the disc microenvironment through scavenging intracellular ROS and resisting oxidative damage, have great application prospects in the treatment of IVDD.

Subpixel mapping (SPM) could handle the mixed pixels in coarse original spectral image (COSI) to obtain the fine land-cover class mapping result. In recent years, with the auxiliary spatiotemporal information provided by the same region fine prior spectral image (FPSI), spatiotemporal subpixel mapping (SSPM) has shown greater potential than the traditional SPM methods. However, the inaccurate spatiotemporal information of the FPSI is rarely effective identified due to variation differences in the current SSPM methods, affecting the mapping accuracy. To address the abovementioned issues, SSPM based on priori remote sensing image with variation differences (CVDBI) is proposed. First, the coarse abundance images of COSI and the fine thematic images of FPSI are obtained by unmixing COSI and classifying FPSI. Second, the degradation observation model (DOM) is established to use downsampling matrix to correlate the coarse abundance images of COSI with the ideal thematic images of COSI, and the variation difference observation model (VDOM) is established to use variation difference factor to correlate the fine thematic images of FPSI with the ideal thematic images of COSI. Third, a separable convex optimization model is established for DOM and VDOM. This model optimizes the variation difference factor and the ideal thematic images of COSI. Finally, we use the alternating direction method of multipliers to solve the separable convex optimization problem to produce the final mapping result. Experimental results on the three spectral images show that the proposed CVDBI yields the more accurate mapping result than the traditional SPM methods.