Explore the vast range of titles available.

Incorporating passive heating structures into personal thermal management technologies could effectively mitigate the escalating energy crisis. However, current passive heating materials struggle to balance thickness and insulating capability, resulting in compromised comfort, space efficiency, and limited thermoregulatory performance. Here, a dual air-gelation strategy, is developed to directly synthesize ultrathin and self-sustainable heating metafabric with 3D dual-network structure during electrospinning. Controlling the interactions among polymer, solvent, and water enables the microphase separation of charged jets, while adjusting the distribution of carbon black nanoparticles within charged fluids to form fibrous networks composed of interlaced aerogel micro/nanofibers with heat storage capabilities. With a low thickness of 0.18 mm, the integrated metafabric exhibits exceptional thermal insulation performance (15.8 mW m −1 K −1 ), superhydrophobicity, enhanced mechanical properties, and high breathability while maintaining self-sustainable radiative heating ability (long-lasting warming of 8.8 °C). This strategy provides rich possibilities to develop advanced fibrous materials for smart textiles and thermal management. Aerogels have limited application in textiles due to their fragility and poor processability. Here, authors synthesize an ultrathin aerogel-structured micro/nanofiber metafabric for self-sustained heating by developing a dual air-gelation strategy.

Health science popularization, as an effective strategy for transmitting health knowledge and improving audience health literacy, has received increasing attention in the past two years. This paper takes the official account - \"Oria, a specially grounded nutritionist\" as an example, and uses the communication research method of online ethnography and content analysis to explore how blogger Xie Lifeng, as a KOL in the field of health science popularization, can make a professional voice and do a good job in the path and method of health nutrition science popularization in the We Media era. The study ultimately found that in terms of content production and dissemination, it is necessary to do a good job in health and nutrition science popularization. Self media users need to work together from multiple aspects such as segmented vertical production, creating personalized and distinctive IPs, establishing online community forms, and expanding communication channels and platforms.

Background: ChatGPT is a large language model (LLM) based on artificial intelligence (AI) capable of responding in multiple languages and generating nuanced and highly complex responses. While ChatGPT holds promising applications in medical education, its limitations and potential risks cannot be ignored.Methods: A scoping review was conducted for English articles discussing ChatGPT in the context of medical education published after 2022. A literature search was performed using PubMed/MEDLINE, Embase, and Web of Science databases, and information was extracted from the relevant studies that were ultimately included.Results: ChatGPT exhibits various potential applications in medical education, such as providing personalized learning plans and materials, creating clinical practice simulation scenarios, and assisting in writing articles. However, challenges associated with academic integrity, data accuracy, and potential harm to learning were also highlighted in the literature. The paper emphasizes certain recommendations for using ChatGPT, including the establishment of guidelines. Based on the review, 3 key research areas were proposed: cultivating the ability of medical students to use ChatGPT correctly, integrating ChatGPT into teaching activities and processes, and proposing standards for the use of AI by medical students.Conclusion: ChatGPT has the potential to transform medical education, but careful consideration is required for its full integration. To harness the full potential of ChatGPT in medical education, attention should not only be given to the capabilities of AI but also to its impact on students and teachers.

The plastic deformation of crystalline materials can be understood by considering their structural defects such as disclinations and dislocations. Although also glasses are solids, their structure resembles closely the one of a liquid and hence the concept of structural defects becomes ill-defined. As a consequence it is very challenging to rationalize on a microscopic level the mechanical properties of glasses close to the yielding point and to relate plastic events to structural properties. Here we investigate the topological characteristics of the eigenvector field of the vibrational excitations of a two-dimensional glass model, notably the geometric arrangement of the topological defects as a function of vibrational frequency. We find that if the system is subjected to a quasistatic shear, the location of the resulting plastic events correlate strongly with the topological defects that have a negative charge. Our results provide thus a direct link between the structure of glasses prior their deformation and the plastic events during deformation. It remains challenging to understand the relation between mechanical properties of glasses close to the yielding point and plastic behaviors at microscales. Wu et al. examine the plasticity using topological properties of the vibrational modes and identify a correlation between defects and plastic events.

Intra-articular injection of therapeutics is an effective strategy for treating osteoarthritis (OA), but it is hindered by rapid drug diffusion, thereby necessitating high-frequency injections. Hence, the development of a biofunctional hydrogel for improved delivery is required. In this study, we introduce a liposome-anchored teriparatide (PTH (1–34)) incorporated into a gallic acid-grafted gelatin injectable hydrogel (GLP hydrogel). We show that the GLP hydrogel can form in situ and without affecting knee motion after intra-articular injection in mice. We demonstrate controlled, sustained release of PTH (1–34) from the GLP hydrogel. We find that the GLP hydrogel promotes ATDC5 cell proliferation and protects the IL-1β-induced ATDC5 cells from further OA progression by regulating the PI3K/AKT signaling pathway. Further, we show that intra-articular injection of hydrogels into an OA-induced mouse model promotes glycosaminoglycans synthesis and protects the cartilage from degradation, supporting the potential of this biomaterial for OA treatment. Osteoarthritis is a common disease that causes pain and difficulty moving joints. Here the authors present an injectable gelatin-based hydrogel that slowly releases teriparatide drug to avoid frequent injections, offering a potential solution for patients with osteoarthritis.

This study investigated the relationship between smartphone addiction and sleep quality among college students, focusing on the parallel mediating roles of perceived stress and health-promoting lifestyle. A cross-sectional survey was conducted in March 2025 among 2,317 students from Xuzhou Medical University using an online questionnaire. Data were collected via questionnaires and analyzed using SPSS 21.0. The study used the Smartphone Addiction Scale-Short Version (SAS-SV), the Pittsburgh Sleep Quality Index (PSQI), the Health-Promoting Lifestyle Profile (HPLP-II), and Perceived Stress Scale (PSS). Statistical methods included normality tests, descriptive statistics, and mediation analysis. A prevalence rate of 51.9% for sleep disorders was identified among the university student population. A statistically significant positive correlation was observed between smartphone addiction and poor sleep quality (r = 0.259, p < 0.01). Additionally, perceived stress (r = 0.408, p < 0.01) and health-promoting lifestyle (r = -0.182, p < 0.01) were identified as parallel mediators in this relationship. Mediation analysis indicated a significant total effect of smartphone addiction (SAS-SV) on sleep quality (PSQI) (path c = 0.0863, 95% confidence interval (CI) = 0.0730, 0.0995). Furthermore, a significant direct effect of SAS-SV on PSQI was noted (path c' = 0.0325, 95% CI = 0.0188, 0.0461). The health-promoting lifestyle (HPLP) (path a1b1 = 0.0128, 95% CI = 0.0086, 0.0176) and perceived stress (PSS) (path a2b2 = 0.0410, 95% CI = 0.0332, 0.0491) were found to partially mediate the relationship between SAS-SV and PSQI, accounting for 14.83% and 47.51% of the total effect, respectively. These findings highlight the dual mediating roles of perceived stress and health-promoting lifestyle in the association between smartphone addiction and sleep quality. Smartphone addiction negatively affects sleep quality, both directly and indirectly through increased perceived stress and reduced engagement in a health-promoting lifestyle. Interventions targeting stress management and healthy behaviors are recommended to mitigate these effects.

Developing an efficient method for determination of the DNA-binding proteins, due to their vital roles in gene regulation, is becoming highly desired since it would be invaluable to advance our understanding of protein functions. In this study, we proposed a new method for the prediction of the DNA-binding proteins, by performing the feature rank using random forest and the wrapper-based feature selection using forward best-first search strategy. The features comprise information from primary sequence, predicted secondary structure, predicted relative solvent accessibility, and position specific scoring matrix. The proposed method, called DBPPred, used Gaussian naïve Bayes as the underlying classifier since it outperformed five other classifiers, including decision tree, logistic regression, k-nearest neighbor, support vector machine with polynomial kernel, and support vector machine with radial basis function. As a result, the proposed DBPPred yields the highest average accuracy of 0.791 and average MCC of 0.583 according to the five-fold cross validation with ten runs on the training benchmark dataset PDB594. Subsequently, blind tests on the independent dataset PDB186 by the proposed model trained on the entire PDB594 dataset and by other five existing methods (including iDNA-Prot, DNA-Prot, DNAbinder, DNABIND and DBD-Threader) were performed, resulting in that the proposed DBPPred yielded the highest accuracy of 0.769, MCC of 0.538, and AUC of 0.790. The independent tests performed by the proposed DBPPred on completely a large non-DNA binding protein dataset and two RNA binding protein datasets also showed improved or comparable quality when compared with the relevant prediction methods. Moreover, we observed that majority of the selected features by the proposed method are statistically significantly different between the mean feature values of the DNA-binding and the non DNA-binding proteins. All of the experimental results indicate that the proposed DBPPred can be an alternative perspective predictor for large-scale determination of DNA-binding proteins.

Summary Brucellosis is a zoonotic infectious disease caused by the Brucella spp., and the enhancement of diagnostic techniques is imperative for effective disease control. Currently, the diagnosis of brucellosis predominantly relies on serological tests, bacterial culture, and molecular biology methods. Among these approaches, serological diagnosis is the most widely utilized due to its relative simplicity. However, existing diagnostic antigens encounter challenges, such as cross-reactivity. Consequently, the development of novel antigens with high specificity and sensitivity is essential to improve the accuracy and efficiency of serological diagnosis for brucellosis. In this study, five antigenic proteins—Erythritol kinase, Nucleoside diphosphate kinase (NDK), Adenosylhomocysteinase, the 31 kDa immunogenic protein, and Lyso-ornithine lipid O-acyltransferase—were selected, and B-cell linear epitopes were predicted using bioinformatics tools. Four prediction tools, namely ABCpred, SVMTriP, BCPred, and Bepipred Linear Epitope Prediction 2.0, were employed to screen for overlapping candidate epitopes. Fusion proteins were constructed through prokaryotic expression to serve as antigens for serological diagnosis. The sensitivity and specificity of the fusion protein were evaluated using indirect ELISA to detect human IgG antibodies in serum samples. The results indicated that the fusion protein achieved sensitivity and specificity values of 0.8095 and 0.9949, respectively. Although these values were lower in comparison to traditional antigens such as lipopolysaccharide (LPS) and the Rose Bengal antigen, the fusion protein exhibited improved cross-reactivity. This study successfully developed a multiepitope fusion protein for the diagnosis of brucellosis, thereby providing a foundation for the creation of highly specific and sensitive diagnostic antigens.

There is a debate on whether invertebrates possess an antiviral immunity similar to the interferon (IFN) system of vertebrates. The Vago gene from arthropods encodes a viral-activated secreted peptide that restricts virus infection through activating the JAK-STAT pathway and is considered to be a cytokine functionally similar to IFN. In this study, the first crustacean IFN regulatory factor (IRF)-like gene was identified in Pacific white shrimp, Litopenaeus vannamei . The L. vannamei IRF showed similar protein nature to mammalian IRFs and could be activated during virus infection. As a transcriptional regulatory factor, L. vannamei IRF could activate the IFN-stimulated response element (ISRE)-containing promoter to regulate the expression of mammalian type I IFNs and initiate an antiviral state in mammalian cells. More importantly, IRF could bind the 5′-untranslated region of L. vannamei Vago4 gene and activate its transcription, suggesting that shrimp Vago may be induced in a similar manner to that of IFNs and supporting the opinion that Vago might function as an IFN-like molecule in invertebrates. These suggested that shrimp might possess an IRF-Vago-JAK/STAT regulatory axis, which is similar to the IRF-IFN-JAK/STAT axis of vertebrates, indicating that invertebrates might possess an IFN system-like antiviral mechanism.

Recent years have seen a surge in study interest in acoustic temperature measurement because of its exceptional non-invasiveness, high precision, and fast response characteristics. Its main benefit is that it may rely on the temperature field reconstruction technique to obtain the entire temperature distribution information, circumventing the limitations of point-type thermometry. Studies have shown that the acoustic wave transducer topology is a key factor affecting the reconstruction effect. In engineering, a simple uniform placement or trial-and-error methods are often used to determine the transducer topology. However, these approaches lack adaptability in complex temperature fields, resulting in poor accuracy and stability. In this paper, based on the previous research on high-precision temperature field reconstruction algorithms, an adaptive optimization method of acoustic temperature measurement topology based on multiple sub-objectives is proposed. The method further improves the reconstruction of asymmetric complex temperature fields by constructing a new optimization variable and a new optimization objective. Comparison experiments with existing optimization methods demonstrate the effectiveness of the new variables and objectives. Additionally, the reconstruction performance of the proposed method is thoroughly evaluated. The results indicate that the method enables adaptive optimization of transducer topology. Moreover, the optimized results exhibit high accuracy and stability in reconstructing complex, asymmetric temperature fields.