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HighlightsElectrospinning combined with structural engineering to construct 0D@2D@1D hierarchical heterogeneous interfaces.A “mechanical response” model was constructed to explain the stress transfer mechanism of fibrous membranes with 2D@1D structures.The “One for All” fibrous membrane combines excellent flexibility, mechanical properties, waterproof, and electromagnetic protection.The development of wearable multifunctional electromagnetic protective fabrics with multifunctional, low cost, and high efficiency remains a challenge. Here, inspired by the unique flower branch shape of “Thunberg’s meadowsweet” in nature, a nanofibrous composite membrane with hierarchical structure was constructed. Integrating sophisticated 0D@2D@1D hierarchical structures with multiple heterointerfaces can fully unleash the multifunctional application potential of composite membrane. The targeted induction method was used to precisely regulate the formation site and morphology of the metal–organic framework precursor, and intelligently integrate multiple heterostructures to enhance dielectric polarization, which improves the impedance matching and loss mechanisms of the electromagnetic wave absorbing materials. Due to the synergistic enhancement of electrospinning-derived carbon nanofiber “stems”, MOF-derived carbon nanosheet “petals” and transition metal selenide nano-particle “stamens”, the CoxSey/NiSe@CNSs@CNFs (CNCC) composite membrane obtains a minimum reflection loss value (RLmin) of -68.40 dB at 2.6 mm and a maximum effective absorption bandwidth (EAB) of 8.88 GHz at a thin thickness of 2.0 mm with a filling amount of only 5 wt%. In addition, the multi-component and hierarchical heterostructure endow the fibrous membrane with excellent flexibility, water resistance, thermal management, and other multifunctional properties. This work provides unique perspectives for the precise design and rational application of multifunctional fabrics.

In massive multiple-input and multiple-output (mMIMO) systems, superimposed channel state information (CSI) feedback is developed to improve the occupation of uplink bandwidth resources. Nevertheless, the interference from this superimposed mode degrades the recovery performance of both downlink CSI and uplink data sequences. Although machine learning (ML)-based methods effectively mitigate superimposed interference by leveraging the multi-domain features of downlink CSI, the complex interactions among network model parameters cause a significant burden on system resources. To address these issues, inspired by sensing-assisted communication, we propose a novel superimposed CSI feedback method assisted by inactive sensing information that previously existed but was not utilized at the base station (BS). To the best of our knowledge, this is the first time that inactive sensing information is utilized to enhance superimposed CSI feedback. In this method, a new type of modal data, different from communication data, is developed to aid in interference suppression without requiring additional hardware at the BS. Specifically, the proposed method utilizes location, speed, and path information extracted from sensing devices to derive prior information. Then, based on the derived prior information, denoising processing is applied to both the delay and Doppler dimensions of downlink CSI in the delay—Doppler (DD) domain, significantly enhancing the recovery accuracy. Simulation results demonstrate the performance improvement of downlink CSI and uplink data sequences when compared to both classic and novel superimposed CSI feedback methods. Moreover, against parameter variations, simulation results also validate the robustness of the proposed method.

Virtual reality (VR) produces a virtual manifestation of the real world and has been shown to be useful as a digital education modality. As VR encompasses different modalities, tools, and applications, there is a need to explore how VR has been used in medical education. The objective of this scoping review is to map existing research on the use of VR in undergraduate medical education and to identify areas of future research. We performed a search of 4 bibliographic databases in December 2020. Data were extracted using a standardized data extraction form. The study was conducted according to the Joanna Briggs Institute methodology for scoping reviews and reported in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Of the 114 included studies, 69 (60.5%) reported the use of commercially available surgical VR simulators. Other VR modalities included 3D models (15/114, 13.2%) and virtual worlds (20/114, 17.5%), which were mainly used for anatomy education. Most of the VR modalities included were semi-immersive (68/114, 59.6%) and were of high interactivity (79/114, 69.3%). There is limited evidence on the use of more novel VR modalities, such as mobile VR and virtual dissection tables (8/114, 7%), as well as the use of VR for nonsurgical and nonpsychomotor skills training (20/114, 17.5%) or in a group setting (16/114, 14%). Only 2.6% (3/114) of the studies reported the use of conceptual frameworks or theories in the design of VR. Despite the extensive research available on VR in medical education, there continue to be important gaps in the evidence. Future studies should explore the use of VR for the development of nonpsychomotor skills and in areas other than surgery and anatomy. RR2-10.1136/bmjopen-2020-046986.

BackgroundVirtual reality (VR) is a technology that produces a virtual manifestation of the real world. In recent years, VR has been increasingly used as a tool in medical education. The use of VR in medical education has large potential, as it allows for distance learning and training which may be challenging to deliver in real life. VR encompasses different tools and applications. There is a need to explore how VR has been employed in medical education to date.ObjectiveThe objective of this scoping review is to conceptualise the VR tools available and the applications of VR in undergraduate medical education as reported in the literature. This scoping review will identify any gaps in this field and provide suggestions for future research.Methods and analysisThe relevant studies will be examined using the Joanna Briggs Institute methodological framework for scoping studies. A comprehensive search from a total of six electronic databases and grey literature sources will be performed. The reference list of included studies will be screened for additional studies. The screening and data extraction will be done in parallel and independently by two review authors. Any discrepancies will be resolved through consensus or discussion with a third review author. A data extraction form has been developed using key themes from the research questions. The extracted data will be qualitatively analysed and presented in a diagrammatic or tabular form, alongside a narrative summary, in line with Preferred Reporting Items for Systematic Reviews and Meta-Analysis: extension for Scoping Reviews reporting guidelines.Ethics and disseminationAll data will be collected from published and grey literature. Ethics approval is therefore not a requirement. We will present our findings at relevant conferences and submit them for publications in peer-reviewed journals.

Background The effective treatment and prognosis prediction of bladder cancer (BLCA) remains a medical problem. Ferroptosis is an iron-dependent form of programmed cell death. Ferroptosis is closely related to tumour occurrence and progression, but the prognostic value of ferroptosis-related genes (FRGs) in BLCA remains to be further clarified. In this study, we identified an FRG signature with potential prognostic value for patients with BLCA. Methods The corresponding clinical data and mRNA expression profiles of BLCA patients were downloaded from The Cancer Genome Atlas (TCGA). Univariate Cox regression was used to extract FRGs related to survival time, and a Cox regression model was used to construct a multigene signature. Both principal component analysis (PCA) and single-sample gene set enrichment analysis (ssGSEA) were performed for functional annotation. Results Clinical traits were combined with FRGs, and 15 prognosis-related FRGs were identified by Cox regression. High expression of CISD1, GCLM, CRYAB, SLC7A11, TFRC, ACACA, ZEB1, SQLE, FADS2, ABCC1, G6PD and PGD was related to poor survival in BLCA patients. Multivariate Cox regression was used to construct a prognostic model with 7 FRGs that divided patients into two risk groups. Compared with that in the low-risk group, the overall survival (OS) of patients in the high-risk group was significantly lower ( P  < 0.001). In multivariate regression analysis, the risk score was shown to be an independent predictor of OS (HR = 1.772, P  < 0.01). Receiver operating characteristic (ROC) curve analysis verified the predictive ability of the model. In addition, the two risk groups displayed different immune statuses in ssGSEA and different distributed patterns in PCA. Conclusion Our research suggests that a new gene model related to ferroptosis can be applied for the prognosis prediction of BLCA. Targeting FRGs may be a treatment option for BLCA.

BackgroundTumor immunological heterogeneity potentially influences the prognostic disparities among patients with clear cell renal cell carcinoma (ccRCC); however, there is a lack of macroscopic imaging tools that can be used to predict immune-related gene expression in ccRCC.MethodsA novel non-invasive radiogenomics biomarker was constructed for immune-related gene expression in ccRCC. First, 520 ccRCC transcriptomic datasets from The Cancer Genome Atlas (TCGA) were analyzed using a non-negative matrix decomposition (NMF) clustering to identify immune-related molecular subtypes. Immune-related prognostic genes were analyzed through Cox regression and Gene Set Enrichment Analysis (GSEA). We then built a risk model based on an immune-related gene subset to predict prognosis in patients with ccRCC. CT images corresponding to the ccRCC patients in The Cancer Imaging Archive (TCIA) database were used to extract radiomic features. To stratify immune-related gene expression levels, extracted radiogenomics features were identified according to standard consecutive steps. A nomogram was built to combine radiogenomics and clinicopathological information through multivariate logistic regression to further enhance the radiogenomics model. Mann–Whitney U test and ROC curves were used to assess the effectiveness of the radiogenomics marker.ResultsNMF methods successfully clustered patients into diverse subtypes according to gene expression levels in the tumor microenvironment (TME). The relative abundance of 10 immune cell populations in each tissue was also analyzed. The immune-related genomic signature (consisting of eight genes) of the tumor was shown to be significantly associated with survival in patients with ccRCC in TCGA database. The immune-related genomic signature was delineated by grouping the signature expression as either low- or high-risk. Using TCIA database, we constructed a radiogenomics biomarker consisting of 11 radiomic features that were optimal predictors of immune-related gene signature expression levels, which demonstrated AUC (area under the ROC curve) values of 0.76 and 0.72 in the training and validation groups, respectively. The nomogram built by combining radiomics and clinical pathological information could further improve the predictive efficacy of the radiogenomics model (AUC = 0.81, 074).ConclusionsThe novel prognostic radiogenomics biomarker achieved excellent correlation with the immune-related gene expression status of patients with ccRCC and could successfully stratify the survival status of patients in TCGA database. It is anticipated that this work will assist in selecting precise clinical treatment strategies. This study may also lead to precise theranostics for patients with ccRCC in the future.

Our study investigated the mechanisms by which Icaritin (ICT) suppresses prostate cancer (PCa) progression via modulation of the gut microbiome. Using C57BL/6 and TRAMP mouse models, we found that a high-fat diet (HFD) significantly reduced in-testinal Akkermansiaceae abundance and vitamin K2 levels relative to control di-et-fed mice. ICT supplementation in HFD-fed TRAMP mice restored Akkermansiaceae pop-ulations and elevated vitamin K2 levels, concurrently decreasing serum leptin and in-creasing adiponectin concentrations. Direct gavage with Akkermansiaceae similarly elevated vitamin K2 levels, improved adipokine profiles, and extended survival. These findings demonstrate that ICT exerts anti-tumor effects by modulating gut Akker-man-siaceae, thereby enhancing vitamin K2 production and regulating adipokine se-cretion, which supports its clinical potential for PCa treatment.

Background Kinases play critical role in clear-cell renal cell carcinoma (ccRCC). We aim to exploit novel kinase that is both protumorigenic and drugable in ccRCC. Methods Reproduction of public datasets with validation using microarray was performed to identify candidate gene. Functionality was studied using multi-omics with validation in vitro and in vivo. Results 6-Phosphofructo-2-Kinase/Fructose-2,6-Biphosphatase 4 (PFKFB4) was differentially expressed showing significantly higher expression in tumor than in normal kidney. PFKFB4 overexpression was associated with advanced tumor grade, stage and worsened prognosis. PFKFB4-knockdown significantly impaired fitness in cell proliferation, migration and wound healing. Despite being recurrently deleted on 3p, PFKFN4 mRNA remained actively transcribed by HIF1α. Metabolomics showed overexpressed PFKFB4 showed enriched metabolites in pentose phosphate pathway (PPP). Phosphoproteomics and immunoprecipitation showed PFKFB4 also phosphorylated NCOA3 which interacted with FBP1 to counteract overactive PPP flux, forming a regulatory loop. PFKFB4-knockdown overcame resistance to Sunitinib in vitro and in vivo both in xenograft and tail-vein injection murine models. Conclusion We concluded PFKFB4 was associated with PPP activity and the fine-tuning of which was mediated by its phosphorylation of NCOA3. Targeting PFKFB4 held promise to combat resistance to Sunitinib.

Brown adipose tissue (BAT) dissipates metabolic energy and mediates non-shivering thermogenesis, thereby boosting energy expenditure. Increasing BAT mass and activity is expected to be a promising strategy for combating obesity; however, few medications effectively and safely recruit and activate BAT in humans. Berberine (BBR), a natural compound, is commonly used as a nonprescription drug to treat diarrhea. Here, we reported that 1-month BBR intervention increased BAT mass and activity, reduced body weight, and improved insulin sensitivity in mildly overweight patients with non-alcoholic fatty liver disease. Chronic BBR treatment promoted BAT development by stimulating the expression of brown adipogenic genes, enhanced BAT thermogenesis, and global energy expenditure in diet-induced obese mice and chow-fed lean mice, Consistently, BBR facilitated brown adipocyte differentiation in both mouse and human primary brown preadipocytes. We further found that BBR increased the transcription of PRDM16, a master regulator of brown/beige adipogenesis, by inducing the active DNA demethylation of PRDM16 promoter, which might be driven by the activation of AMPK and production of its downstream tricarboxylic acid cycle intermediate α-Ketoglutarate. Moreover, chronic BBR administration had no impact on the BAT thermogenesis in adipose-specific AMPKa1 and AMPKa2 knockout mice. In summary, we found that BBR intervention promoted recruitment and activation of BAT and AMPK–PRDM16 axis was indispensable for the pro-BAT and pro-energy expenditure properties of BBR. Our findings suggest that BBR may be a promising drug for obesity and related metabolic disorders in humans partially through activating BAT.

Cisplatin-based chemotherapy remains a mainstay for the treatment of bladder cancer (BLCA); however, its clinical efficacy is frequently compromised by the emergence of chemoresistance, which leads to poor patient outcomes. Although known mechanisms—such as alterations in drug efflux, DNA repair, and key signaling pathways—have been implicated, they fail to fully explain the clinical complexity of cisplatin resistance, indicating that additional molecular drivers remain undiscovered. Keratin 14 (KRT14), an intermediate filament protein associated with aggressive BLCA subtypes, is consistently upregulated in cisplatin-resistant tumors, yet its precise mechanistic role in resistance remains unclear. In this study, we elucidate the functional contribution of KRT14 to cisplatin resistance in BLCA using patient-derived tissues, established cell lines, xenograft mouse models, and a suite of molecular interaction assays. Our results demonstrate that KRT14 is significantly upregulated in BLCA tissues, correlates with poor clinical prognosis, and functionally drives cisplatin resistance both in vitro and in vivo. Mechanistically, we identify a novel and direct interaction between KRT14 and the translation initiation factor eIF4H, specifically through the N-terminal Head domain of KRT14. This interaction modulates the association of eIF4H with the core eIF4F complex, thereby selectively promoting the translation of Acyl-CoA Oxidase 2 (ACOX2) mRNA through its 5’ untranslated region. We further show that ACOX2 is essential for mediating the effects of KRT14 on lipid metabolism, cell proliferation, survival, and ultimately, cisplatin resistance. Collectively, our findings reveal that KRT14 contributes to chemoresistance in BLCA not only via its structural roles but also by directly regulating translational machinery through eIF4H, leading to upregulation of the metabolic enzyme ACOX2. The newly defined KRT14–eIF4H–ACOX2 axis orchestrates lipid metabolic reprogramming and cell survival, underscoring the KRT14–eIF4H interface as a promising therapeutic target for overcoming cisplatin resistance in BLCA.