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To construct and validate a deep learning radiomics (DLR) model based on X-ray images for predicting and distinguishing acute and chronic osteoporotic vertebral fractures (OVFs). A total of 942 cases (1076 vertebral bodies) with both vertebral X-ray examination and MRI scans were included in this study from three hospitals. They were divided into a training cohort (n = 712), an internal validation cohort (n = 178), an external validation cohort (n = 111), and a prospective validation cohort (n = 75). The ResNet-50 model architecture was used for deep transfer learning (DTL), with pre-training performed on RadImageNet and ImageNet datasets. DTL features and radiomics features were extracted from lateral X-ray images of OVFs patients and fused together. A logistic regression model with the least absolute shrinkage and selection operator was established, with MRI showing bone marrow edema as the gold standard for acute OVFs. The performance of the model was evaluated using receiver operating characteristic curves. Eight machine learning classification models were evaluated for their ability to distinguish between acute and chronic OVFs. The Nomogram was constructed by combining clinical baseline data to achieve visualized classification assessment. The predictive performance of the best RadImageNet model and ImageNet model was compared using the Delong test. The clinical value of the Nomogram was evaluated using decision curve analysis (DCA). Pre-training resulted in 34 and 39 fused features after feature selection and fusion. The most effective machine learning algorithm in both DLR models was Light Gradient Boosting Machine. Using the Delong test, the area under the curve (AUC) for distinguishing between acute and chronic OVFs in the training cohort was 0.979 and 0.972 for the RadImageNet and ImageNet models, respectively, with no statistically significant difference between them (P = 0.235). In the internal validation cohort, external validation cohort, and prospective validation cohort, the AUCs for the two models were 0.967 vs 0.629, 0.886 vs 0.817, and 0.933 vs 0.661, respectively, with statistically significant differences in all comparisons (P < 0.05). The deep learning radiomics nomogram (DLRN) was constructed by combining the predictive model of RadImageNet with clinical baseline features, resulting in AUCs of 0.981, 0.974, 0.895, and 0.902 in the training cohort, internal validation cohort, external validation cohort, and prospective validation cohort, respectively. Using the Delong test, the AUCs for the fused feature model and the DLRN in the training cohort were 0.979 and 0.981, respectively, with no statistically significant difference between them (P = 0.169). In the internal validation cohort, external validation cohort, and prospective validation cohort, the AUCs for the two models were 0.967 vs 0.974, 0.886 vs 0.895, and 0.933 vs 0.902, respectively, with statistically significant differences in all comparisons (P < 0.05). The Nomogram showed a slight improvement in predictive performance in the internal and external validation cohort, but a slight decrease in the prospective validation cohort (0.933 vs 0.902). DCA showed that the Nomogram provided more benefits to patients compared to the DLR models. Compared to the ImageNet model, the RadImageNet model has higher diagnostic value in distinguishing between acute and chronic OVFs. Furthermore, the diagnostic performance of the model is further improved when combined with clinical baseline features to construct the Nomogram. [ABSTRACT FROM AUTHOR]

In rheumatoid arthritis (RA), macrophages infiltrate joints, while fibroblast-like synovial cells proliferate abnormally, forming a barrier against drug delivery, which hinders effective drug delivery to joint focus. Here we firstly designed a pH-responsive size-adjustable nanoparticle, composed by methotrexate (MTX)-human serum albumin (HSA) complex coating with pH-responsive liposome (Lipo/MTX-HSA) for delivering drugs specifically to inflamed joints in acidic environments. We showed in vitro that the nanoparticles can induce mitochondrial dysfunction, promote apoptosis of fibroblast-like synoviocytes and macrophages, further reduce the secretion of inflammatory factors (TNF-α, IL-1β, MMP-9), and regulate the inflammatory microenvironment. We also demonstrated similar effects in a rat model of arthritis, in which Lipo/MTX-HSA accumulated in arthritic joints, and at low pH, liposome phospholipid bilayer cleavage released small-sized MTX-HSA, which effectively reduced the number of fibroblast-synoviocytes and macrophages in joints, alleviated joint inflammation, and repaired bone erosion. These findings suggest that microenvironment-responsive size-adjustable nanoparticles show promise as a treatment against rheumatoid arthritis. Abnormal proliferation of fibroblast synoviocytes poses a physical barrier to effective nanoparticle delivery. We designed size-adjustable nano-delivery systems by preparing liposomes with cholesterol hemisuccinate (CHEM), which were subsequently loaded with small-sized albumin nanoparticles encapsulating the cytotoxic drug MTX (MTX-HSA), termed Lipo/MTX-HSA. Upon tail vein injection, Lipo/MTX-HSA could be aggregated at the site of inflammation via the ELVIS effect in the inflamed joint microenvironment. Specifically, intracellular acidic pH-triggered dissociation of liposomes promoted the release of MTX-HSA, which was further targeted to fibroblasts or across fibroblasts to macrophages to exert anti-inflammatory effects. The results showed that liposomes with adjustable particle size achieved efficient drug delivery, penetration and retention in joint sites; the strategy exerted significant anti-inflammatory effects in the treatment of rheumatoid arthritis by inducing mitochondrial dysfunction to promote apoptosis in fibrosynoviocytes and macrophages. [Display omitted] [ABSTRACT FROM AUTHOR]

Metallic materials have attracted the considerable attentions owing to their remarkable tribological potentials, rendering them indispensable in the fields of manufacturing, aerospace, medical, and daily life. In the future, their utilization is poised to extend into electronic devices, energy storage, sensors, catalysts and other realms in order to enhance performance metrics, bolster stability and reduce costs. Notwithstanding various research have been undertaken on metal-based composites, the important influencing factors and wear mechanisms of metal-base friction interfaces have not been comprehensively reviewed. Therefore, this review summarizes these main influencing factors (e.g., material properties, service conditions, additives and energy effects) on tribological behaviors of metal interfaces, subsequently generalizes their wear mechanisms such as abrasion, adhesion, corrosion and fatigue. Finally, their development prospects and difficulties of the interface tribology are discussed in detail, thereby providing the in-depth insight of these fields for enriching actual applications. [ABSTRACT FROM AUTHOR]

Smart energy plays an important role in alleviating energy poverty in China in the context of the new technological revolution and industrial change. This paper uses China's smart pilot policy as a quasi-natural experiment, employing China's provincial panel data from 2011 to 2022, measuring energy poverty indices based on a projection pursuit evaluation model, and conducting a quasi-natural experiment using a synthetic control method to assess the effect of smart energy on energy poverty and specific mechanisms of action. The study found that smart energy has a specific and significant impact on energy poverty, but the effect differs in different regions depending on the construction cycle and energy endowment. In the mechanism test, smart energy significantly alleviates energy poverty through the mechanisms of improving energy supply capacity, improving energy payment capacity, and improving energy cleanliness. The results of the study provide empirical references for regional solutions to energy poverty and the planning of smart energy construction. [ABSTRACT FROM AUTHOR]

• Taxifolin extracted from Larix olgensis roots inhibits the proliferation/migration of melanoma. • Taxifolin mediates the MAPK pathway to exert its antimelanoma effect. • Taxifolin suppresses nucleic translocation of β-catenin via inhibiting Rac1/JNK signaling. Metastatic melanoma is a fatal cancer. Despite the advances in targeted therapy and immunotherapy for patients with melanoma, drug resistance and low response rates pose a considerable challenge. Taxifolin is a multifunctional natural compound with emerging antitumor potentials. However, its utility in melanoma treatment remains unclear. The study aimed to investigate the effect of purified Taxifolin from Larix olgensis roots (Changbai Mountain, China) on melanoma and explore the underlying mechanism. Purified Taxifolin from Larix olgensis roots was evaluated for its antimelanoma effects in vitro and in vivo settings. RNA-seq analysis was performed to explore the underlying mechanism. Purified Taxifolin (> 99 %) from Larix olgensis roots inhibited the proliferation and migration of B16F10 melanoma cells at 200 and 400 μM, and of A375 cells at 100 and 200 μM. Taxifolin administered at 60 mg/kg suppressed tumor growth and metastasis in mouse models without causing significant toxicity. Taxifolin modulated USP18/Rac1/JNK/β-catenin axis to exert its antitumor effect. These findings indicate that Taxifolin derived from Larix olgensis roots may be a promising antimelanoma therapy. [Display omitted] [ABSTRACT FROM AUTHOR]

Bismuth telluride-based materials are well known for their excellent thermoelectric (TE) properties within the near-room temperature range. However, the poor thermoelectric performance of their n-type counterparts has posed a major hindrance to the application of bismuth telluride-based thermoelectric generator (TEG). In this study, a dual optimization approach was implemented. Specifically, 2D ceramic nano-sized hexagonal boron nitride (h-BN) was introduced into n-type Bi 2 Te 2.7 Se 0.3 (BTS) to enhance its performance, aligning it with p-type materials. It is indicated that ceramic h-BN decoration leads to the emergence of a high density of dislocations, which boosts the phonon scattering and reduces lattice thermal conductivity significantly. Meanwhile, a high carrier mobility is maintained to ensure an improved power factor. With the addition of moderate ceramic h-BN, the composite material achieved a ZT value of 1.1 at 400 K. The geometric configuration of TEG was optimized using finite element methods to strike the optimal balance between output power (P o u t ) and efficiency (η). Based on the structure optimization results, the predicted P o u t and η increased by 48 % and 36 %, respectively. The fabricated TEG demonstrated to achieve an efficiency of 6.4 %. This work allows the evolution from bismuth telluride-based prototype to a functional TEG advanced in room-temperature heat recovery. [ABSTRACT FROM AUTHOR]

Dissolve organic matters (DOM) usually showed negative effect on the removal of inorganic arsenic (As) in groundwater by electrochemical approaches, yet which parts of sub-component within DOM played the role was lack of evidence. Herein, we investigated the effects of land-source humic-like acid (HA) on groundwater As(III) removal using air cathode iron electrocoagulation, based on the parallel factor analysis of three-dimensional excitation–emission matrix and statistical methods. Our results showed that the land-source HA contained five kinds of components and all components presented significantly negative correlations with the removal of both As(III) and As(V). However, the high aromatic fulvic-like acid and low aromatic humic-like acid components of land-source HA presented the opposite correlations with the concentration of As(III) during the reaction. The high aromaticity fulvic-like components of land-source HA (Sigma-Aldrich HA, SAHA) produced during the reaction facilitated the oxidation of As(III) due to its high electron transfer capacities and good solubility in wide pH range, but the low aromaticity humic-like ones worked against the oxidation of As(III). Our findings offered the novel insights for the flexible activities of DOM in electron Fenton system. [Display omitted] • Humic acid presented negative correlations with the removal of both As(III) and As(V). • High aromatic fulvic-like acid facilitated the oxidation of As(III). • Electron transfer capacities of humic substance had flexible effects on As(III) removal. [ABSTRACT FROM AUTHOR]

Damping technology has been widely used because of its good vibration control effect. However, due to the strong nonlinearity of the added dampers, accurately predicting the seismic response of damped structures remains a challenge. This study investigates the application of interpretable machine learning (ML)-based and deep learning-based approaches to the prediction of the maximum inter-storey displacement of a damped structure. A comprehensive database consisting of 13,855 structural responses to ground motions was collected. Seven traditional interpretable ML algorithms including random forest and extreme gradient boosting (XGBoost), a convolutional neural network based on large receptive field, and seismic wave transformer (SWT) model based on a transformer network were developed. The predictions show that the error of the SWT based on unsupervised feature extraction is reduced by 50.90% compared with that of the optimal XGBoost in ensemble learning. Although the SWT has the highest global accuracy, XGBoost is found to have a smaller error when the structure is in a linear state with peak ground acceleration as partition index, so an aggregation model (AM)-based structural response prediction method was also proposed. The accuracy of the AM improved by 27.95% compared with that of the SWT. In contrast with other ML models, the proposed AM is more advantageous in terms of computational efficiency and accuracy. • Numerical model of a damped steel frame was developed using OpenSees based on shaking table test results. • A prediction dataset was built based on the numerical results of a damped structure. • Seven interpretable machine learning-based techniques were utilised to predict the maximum structural inter-storey drift. • The deep learning algorithm based on unsupervised feature extraction can obtain more satisfactory results when the structure enters strong nonlinearity. • The developed aggregation model exhibits higher prediction accuracy. [ABSTRACT FROM AUTHOR]