Image1_Pyroptosis-related genes prognostic model for predicting targeted therapy and immunotherapy response in soft tissue sarcoma.TIF

Several studies have highlighted the potential of pyroptosis as a target for cancer treatment. This article focuses on the specific roles and clinical implications of pyroptosis-related genes (PRGs) in soft tissue sarcoma (STS). By analyzing differentially expressed PRGs in STS compared to normal tissue, our study evaluates the interactions, biological functions, and prognostic values of PRGs in STS. Through LASSO COX regression analysis, a five-gene survival related-risk score (PLCG1, PYCARD, CASP8, NOD1, and NOD2) was created, which examined both in TCGA cohort and training cohort (GSE21050, GSE30929, and GSE63157). Furthermore, we developed a nomogram incorporating clinic factors and the risk scores of the PRGs, which showed decent accuracy of prediction as evidenced by calibration curves. Additionally, our study analyzed the Tumor Immune Dysfunction and Exclusion Algorithm (TIDE) and IMvigor 210 cohorts to investigate the immunotherapy response, and found that immunotherapy was more beneficial for patients with minimal risk of PRGs than those exhibiting greater risk. Finally, GDSC and CAMP databases were used to screen for effective chemotherapy or targeted drugs that are sensitive to the high-risk populations, including doxorubicin, imatinib, and sorafenib. In conclusion, this study provides a comprehensive analysis of the PRG landscape in STS and constructs a novel risk model to predict prognosis and different therapeutic responses of STS patients, which is helpful for achieving precision medicine.</p

DataSheet1_Pyroptosis-related genes prognostic model for predicting targeted therapy and immunotherapy response in soft tissue sarcoma.ZIP

Several studies have highlighted the potential of pyroptosis as a target for cancer treatment. This article focuses on the specific roles and clinical implications of pyroptosis-related genes (PRGs) in soft tissue sarcoma (STS). By analyzing differentially expressed PRGs in STS compared to normal tissue, our study evaluates the interactions, biological functions, and prognostic values of PRGs in STS. Through LASSO COX regression analysis, a five-gene survival related-risk score (PLCG1, PYCARD, CASP8, NOD1, and NOD2) was created, which examined both in TCGA cohort and training cohort (GSE21050, GSE30929, and GSE63157). Furthermore, we developed a nomogram incorporating clinic factors and the risk scores of the PRGs, which showed decent accuracy of prediction as evidenced by calibration curves. Additionally, our study analyzed the Tumor Immune Dysfunction and Exclusion Algorithm (TIDE) and IMvigor 210 cohorts to investigate the immunotherapy response, and found that immunotherapy was more beneficial for patients with minimal risk of PRGs than those exhibiting greater risk. Finally, GDSC and CAMP databases were used to screen for effective chemotherapy or targeted drugs that are sensitive to the high-risk populations, including doxorubicin, imatinib, and sorafenib. In conclusion, this study provides a comprehensive analysis of the PRG landscape in STS and constructs a novel risk model to predict prognosis and different therapeutic responses of STS patients, which is helpful for achieving precision medicine.</p

Image2_Pyroptosis-related genes prognostic model for predicting targeted therapy and immunotherapy response in soft tissue sarcoma.TIF

Several studies have highlighted the potential of pyroptosis as a target for cancer treatment. This article focuses on the specific roles and clinical implications of pyroptosis-related genes (PRGs) in soft tissue sarcoma (STS). By analyzing differentially expressed PRGs in STS compared to normal tissue, our study evaluates the interactions, biological functions, and prognostic values of PRGs in STS. Through LASSO COX regression analysis, a five-gene survival related-risk score (PLCG1, PYCARD, CASP8, NOD1, and NOD2) was created, which examined both in TCGA cohort and training cohort (GSE21050, GSE30929, and GSE63157). Furthermore, we developed a nomogram incorporating clinic factors and the risk scores of the PRGs, which showed decent accuracy of prediction as evidenced by calibration curves. Additionally, our study analyzed the Tumor Immune Dysfunction and Exclusion Algorithm (TIDE) and IMvigor 210 cohorts to investigate the immunotherapy response, and found that immunotherapy was more beneficial for patients with minimal risk of PRGs than those exhibiting greater risk. Finally, GDSC and CAMP databases were used to screen for effective chemotherapy or targeted drugs that are sensitive to the high-risk populations, including doxorubicin, imatinib, and sorafenib. In conclusion, this study provides a comprehensive analysis of the PRG landscape in STS and constructs a novel risk model to predict prognosis and different therapeutic responses of STS patients, which is helpful for achieving precision medicine.</p

Epitaxial Growth and Composition-Dependent Optical Properties of Vertically Aligned ZnS_1−<i>x</i>Se_<i>x</i> Alloy Nanowire Arrays

Vertically aligned ZnS1−xSex alloy nanowire arrays covering the entire compositional range were grown on GaAs (111)B substrates by metal organic chemical vapor deposition (MOCVD) using Ga nanoparticles as catalysts. The ZnS1−xSex nanowires obtained are predominantly zincblende in structure and preferentially grow along the [111] direction. They show a narrow distribution in composition, which is the result of good growth controls afforded by MOCVD. The relationship between composition of the nanowires and the precursors was studied and determined; it allows the predetermination of the composition of the alloy nanowires. In low temperature (77 K) cathodoluminescence spectra, the band edge emission of ZnS1−xSex nanowires is found to shift from 3.214 to 2.779 eV, and its line width significantly decreases from 213 to 44 meV, when the Se content increases from 0.45 to 1. This phenomenon is understood in terms of the chemical disorder in the alloy

Table1_Pyroptosis-related genes prognostic model for predicting targeted therapy and immunotherapy response in soft tissue sarcoma.DOCX

Several studies have highlighted the potential of pyroptosis as a target for cancer treatment. This article focuses on the specific roles and clinical implications of pyroptosis-related genes (PRGs) in soft tissue sarcoma (STS). By analyzing differentially expressed PRGs in STS compared to normal tissue, our study evaluates the interactions, biological functions, and prognostic values of PRGs in STS. Through LASSO COX regression analysis, a five-gene survival related-risk score (PLCG1, PYCARD, CASP8, NOD1, and NOD2) was created, which examined both in TCGA cohort and training cohort (GSE21050, GSE30929, and GSE63157). Furthermore, we developed a nomogram incorporating clinic factors and the risk scores of the PRGs, which showed decent accuracy of prediction as evidenced by calibration curves. Additionally, our study analyzed the Tumor Immune Dysfunction and Exclusion Algorithm (TIDE) and IMvigor 210 cohorts to investigate the immunotherapy response, and found that immunotherapy was more beneficial for patients with minimal risk of PRGs than those exhibiting greater risk. Finally, GDSC and CAMP databases were used to screen for effective chemotherapy or targeted drugs that are sensitive to the high-risk populations, including doxorubicin, imatinib, and sorafenib. In conclusion, this study provides a comprehensive analysis of the PRG landscape in STS and constructs a novel risk model to predict prognosis and different therapeutic responses of STS patients, which is helpful for achieving precision medicine.</p

Image3_Pyroptosis-related genes prognostic model for predicting targeted therapy and immunotherapy response in soft tissue sarcoma.TIF

Several studies have highlighted the potential of pyroptosis as a target for cancer treatment. This article focuses on the specific roles and clinical implications of pyroptosis-related genes (PRGs) in soft tissue sarcoma (STS). By analyzing differentially expressed PRGs in STS compared to normal tissue, our study evaluates the interactions, biological functions, and prognostic values of PRGs in STS. Through LASSO COX regression analysis, a five-gene survival related-risk score (PLCG1, PYCARD, CASP8, NOD1, and NOD2) was created, which examined both in TCGA cohort and training cohort (GSE21050, GSE30929, and GSE63157). Furthermore, we developed a nomogram incorporating clinic factors and the risk scores of the PRGs, which showed decent accuracy of prediction as evidenced by calibration curves. Additionally, our study analyzed the Tumor Immune Dysfunction and Exclusion Algorithm (TIDE) and IMvigor 210 cohorts to investigate the immunotherapy response, and found that immunotherapy was more beneficial for patients with minimal risk of PRGs than those exhibiting greater risk. Finally, GDSC and CAMP databases were used to screen for effective chemotherapy or targeted drugs that are sensitive to the high-risk populations, including doxorubicin, imatinib, and sorafenib. In conclusion, this study provides a comprehensive analysis of the PRG landscape in STS and constructs a novel risk model to predict prognosis and different therapeutic responses of STS patients, which is helpful for achieving precision medicine.</p

Image4_Pyroptosis-related genes prognostic model for predicting targeted therapy and immunotherapy response in soft tissue sarcoma.TIF

Several studies have highlighted the potential of pyroptosis as a target for cancer treatment. This article focuses on the specific roles and clinical implications of pyroptosis-related genes (PRGs) in soft tissue sarcoma (STS). By analyzing differentially expressed PRGs in STS compared to normal tissue, our study evaluates the interactions, biological functions, and prognostic values of PRGs in STS. Through LASSO COX regression analysis, a five-gene survival related-risk score (PLCG1, PYCARD, CASP8, NOD1, and NOD2) was created, which examined both in TCGA cohort and training cohort (GSE21050, GSE30929, and GSE63157). Furthermore, we developed a nomogram incorporating clinic factors and the risk scores of the PRGs, which showed decent accuracy of prediction as evidenced by calibration curves. Additionally, our study analyzed the Tumor Immune Dysfunction and Exclusion Algorithm (TIDE) and IMvigor 210 cohorts to investigate the immunotherapy response, and found that immunotherapy was more beneficial for patients with minimal risk of PRGs than those exhibiting greater risk. Finally, GDSC and CAMP databases were used to screen for effective chemotherapy or targeted drugs that are sensitive to the high-risk populations, including doxorubicin, imatinib, and sorafenib. In conclusion, this study provides a comprehensive analysis of the PRG landscape in STS and constructs a novel risk model to predict prognosis and different therapeutic responses of STS patients, which is helpful for achieving precision medicine.</p

From local to nonlocal high-Q plasmonic metasurfaces

The physics of bound states in the continuum (BICs) allows to design and demonstrate optical resonant structures with large values of the quality factor ($Q$-factor) by employing dielectric structures with low losses. However, BIC is a general wave phenomenon that should be observed in many systems, including the metal-dielectric structures supporting plasmons where the resonances are hindered by losses. Here we develop a comprehensive strategy to achieve high-$Q$ resonances in plasmonic metasurfaces by effectively tailoring the resonant modes from local and nonlocal regimes

Orientation and Structure Controllable Epitaxial Growth of ZnS Nanowire Arrays on GaAs Substrates

Single-crystalline, well-aligned ZnS nanowire arrays were epitaxially grown on GaAs substrates by metal−organic chemical vapor deposition using Ag and Au nanoparticles as catalysts. Au nanoparticles of different size were used to control the diameter of the nanowires. Under the growth conditions of this study, the arrays were found to align predominantly along the [111]B direction of the substrates, a property which can be utilized to orient the nanowires along chosen directions. Electron microscopy studies of nanowires of different diameters reveal, for the first time, that ZnS nanowires undergo a structural transformation from wurtzite to zinc blende with increasing diameter. The critical diameter is determined to be in the range 30−70 nm, in reasonable agreement with predictions of recent theoretical models. In nanowires whose diameters are near this critical range, a high density of stacking faults is found, consistent with the bistability of the two structures. Good controls of the orientation and structure of nanowire arrays are important for tailoring their properties to satisfy different requirements of potential nanodevices

Porous PVA‑<i>g</i>‑SPA/PVA-SA Catalytic Composite Membrane via Lyophilization for Esterification Enhancement

A catalytic composite membrane was developed for the enhancement of esterification by lyophilization for the first time. The catalytic composite membrane was composed of a poly­(vinyl alcohol) (PVA)-sodium alginate (SA) separation layer and a spongy porous catalytic layer cross-linked by PVA and 4-sulfophthalic acid (SPA). Fourier transform infrared (FTIR) spectroscopy and X-ray photoelectron spectroscopy (XPS) results indicated the successful synthesis of the catalytic composite membrane. The membrane properties were evaluated by ethanol dehydration and esterification. The conversion rate of acetic acid reached 95.9% after 12 h. Compared with batch reactions, the conversion rate increased by 24.4%. After five cycles, the membrane still maintained outstanding catalytic activity. The resistance of mass transfer was analyzed, and the results showed that the porous structure reduced the catalytic layer resistance to total resistance from 70.27 to 32.88%. The composite membrane with a spongy porous catalytic layer exhibited superior dehydration and catalytic performance