The rapid and accurate identification of biological tissue types in resected specimens is critical to ensure complete tumor excision during surgery.By leveraging inherent electromagnetic property variations among tiss...The rapid and accurate identification of biological tissue types in resected specimens is critical to ensure complete tumor excision during surgery.By leveraging inherent electromagnetic property variations among tissues,this study presents a novel dual-port electromagnetic method that employs two-port S-parameters for quantitative tissue discrimination.The proposed technology leverages differences in the broadband electromagnetic properties among biological tissues,which are manifested as distinct attenu-ation characteristics during signal transmission.This approach allows for the successful differentiation of various tissue types,such as skin,muscle,fat,and tumor tissues,in ex vivo tumor-bearing mouse models.Specifically designed for biological tissue detection,this dual-port framework is the first to achieve a calibration-free operation and facilitate the detection of tumors with a size as small as 0.1 mm.Experimental validation in tumor-bearing mouse models demonstrated robust differentiation among skin,fat,muscle,and tumor tissues.Consistent measurements across multiple orientations were achieved,with a specific absorption rate below 0.0091 W/kg confirming operational safety.The transmission characteristics reveal significant bioelectromagnetic interactions,providing physical insights into tissue dielectric properties.This method provides a promising platform for clinical diagnostics and precision surgical guidance.展开更多
Objective To identify pyroptosis,apoptosis,and necroptosis(PANoptosis)-related genes(PRGs)in clear cell renal cell carcinoma(ccRCC)for patient stratification and prognosis prediction.Methods We used differential expre...Objective To identify pyroptosis,apoptosis,and necroptosis(PANoptosis)-related genes(PRGs)in clear cell renal cell carcinoma(ccRCC)for patient stratification and prognosis prediction.Methods We used differential expression analysis and weighted gene co-expression network analysis(WGCNA)to identify ccRCC-specific PRGs.A prognostic model,the PANoptosis-index(PANI),was constructed using least absolute shrinkage and selection operator(LASSO)and Cox regression.The PANI model,comprising PRGs,was validated through single-cell RNA-sequencing(scRNA-seq),immunohistochemistry,and reverse transcription-quantitative polymerase chain reaction(RT-qPCR).Patient cohorts were categorized into high-and low-PANI groups,and the model’s performance was appraised using various metrics.External validation was performed with the E-MTAB-1980 dataset.Functional and gene set enrichment analyses distinguished biological differences between groups.Mutational landscapes and tumor immune microenvironments were compared.Sensitivity to immunotherapy and antineoplastic drugs was also predicted using PANI.The effects of Z-DNA-binding protein 1(ZBP1)on cell proliferation and migration were assessed by cell counting kit-8(CCK-8)and Transwell assays.Results We identified five PRGs(ZBP1,tumor necrosis factor superfamily protein 14(TNFSF14),cyclin-dependent kinase inhibitor 3(CDKN3),parathyroid hormone-like hormone(PTHLH),and heme-oxygenase 1(HMOX1))constituting PANI,independently associated with ccRCC patient prognosis.The PANI-based nomogram,integrated with clinical factors,demonstrated high predictive accuracy for prognosis.High-PANI patients exhibited distinct co-mutation patterns in ccRCC driver genes and lower survival probabilities,with an enriched immune-related functional profile,indicating an activated immune environment.These patients also showed increased sensitivity to immunotherapy and antineoplastic drugs.The knockdown of ZBP1,a key PRG in the PANI,significantly reduced ccRCC cell proliferation and migration.Conclusions PANI provides precise prognosis and immunotherapy response predictions for ccRCC patients,facilitating individualized treatment strategies.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.62476285)the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation(Grant No.GZC20252693).
文摘The rapid and accurate identification of biological tissue types in resected specimens is critical to ensure complete tumor excision during surgery.By leveraging inherent electromagnetic property variations among tissues,this study presents a novel dual-port electromagnetic method that employs two-port S-parameters for quantitative tissue discrimination.The proposed technology leverages differences in the broadband electromagnetic properties among biological tissues,which are manifested as distinct attenu-ation characteristics during signal transmission.This approach allows for the successful differentiation of various tissue types,such as skin,muscle,fat,and tumor tissues,in ex vivo tumor-bearing mouse models.Specifically designed for biological tissue detection,this dual-port framework is the first to achieve a calibration-free operation and facilitate the detection of tumors with a size as small as 0.1 mm.Experimental validation in tumor-bearing mouse models demonstrated robust differentiation among skin,fat,muscle,and tumor tissues.Consistent measurements across multiple orientations were achieved,with a specific absorption rate below 0.0091 W/kg confirming operational safety.The transmission characteristics reveal significant bioelectromagnetic interactions,providing physical insights into tissue dielectric properties.This method provides a promising platform for clinical diagnostics and precision surgical guidance.
基金supported by the Zhejiang Provincial Natural Science Foundation of China(No.LZ22H160008)the Zhejiang Medical Science and Technology Project(Nos.2022RC059 and 2023KY931),China.
文摘Objective To identify pyroptosis,apoptosis,and necroptosis(PANoptosis)-related genes(PRGs)in clear cell renal cell carcinoma(ccRCC)for patient stratification and prognosis prediction.Methods We used differential expression analysis and weighted gene co-expression network analysis(WGCNA)to identify ccRCC-specific PRGs.A prognostic model,the PANoptosis-index(PANI),was constructed using least absolute shrinkage and selection operator(LASSO)and Cox regression.The PANI model,comprising PRGs,was validated through single-cell RNA-sequencing(scRNA-seq),immunohistochemistry,and reverse transcription-quantitative polymerase chain reaction(RT-qPCR).Patient cohorts were categorized into high-and low-PANI groups,and the model’s performance was appraised using various metrics.External validation was performed with the E-MTAB-1980 dataset.Functional and gene set enrichment analyses distinguished biological differences between groups.Mutational landscapes and tumor immune microenvironments were compared.Sensitivity to immunotherapy and antineoplastic drugs was also predicted using PANI.The effects of Z-DNA-binding protein 1(ZBP1)on cell proliferation and migration were assessed by cell counting kit-8(CCK-8)and Transwell assays.Results We identified five PRGs(ZBP1,tumor necrosis factor superfamily protein 14(TNFSF14),cyclin-dependent kinase inhibitor 3(CDKN3),parathyroid hormone-like hormone(PTHLH),and heme-oxygenase 1(HMOX1))constituting PANI,independently associated with ccRCC patient prognosis.The PANI-based nomogram,integrated with clinical factors,demonstrated high predictive accuracy for prognosis.High-PANI patients exhibited distinct co-mutation patterns in ccRCC driver genes and lower survival probabilities,with an enriched immune-related functional profile,indicating an activated immune environment.These patients also showed increased sensitivity to immunotherapy and antineoplastic drugs.The knockdown of ZBP1,a key PRG in the PANI,significantly reduced ccRCC cell proliferation and migration.Conclusions PANI provides precise prognosis and immunotherapy response predictions for ccRCC patients,facilitating individualized treatment strategies.
