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In this Letter, we present a robust, wide-range, and precise monitoring scheme for transmitter (Tx) impairments in coherent digital subcarrier multiplexing (DSCM) systems. The proposed scheme employs frequency-domain pilot tones (FPTs) to compensate for frequency offset (FO), polarization aliasing, and carrier phase noise, thus isolating Tx impairments from channel distortions. It then implements 4 × 4 real-valued MIMO to compensate for Tx impairments by equalizing symmetric subcarriers. Tx impairment monitoring is derived from the equalizer coefficients. By considering the phase shift caused by Tx impairments, a wide-range and precise monitoring of Tx impairments including IQ skew, IQ phase, and gain imbalances is achieved. We experimentally validated our approach using a 48-GBaud, four-subcarrier, dual-polarization coherent DSCM system. The results confirm the method's capability for a wide-range, robust, and precise Tx impairment monitoring in coherent DSCM systems, maintaining performance even in the presence of ultra-fast polarization variation.
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BACKGROUND: Tumor heterogeneity of immune infiltration of cells plays a decisive role in hepatocellular carcinoma (HCC) therapy response and prognosis. This study investigated the effect of different subtypes of CD8+T cells on the HCC tumor microenvironment about its prognosis. METHODS: Single-cell RNA sequencing, transcriptome, and single-nucleotide variant data from LUAD patients were obtained based on the GEO, TCGA, and HCCD18 databases. CD8+ T cells-associated subtypes were identified by consensus clustering analysis, and genes with the highest correlation with prognostic CD8+ T cell subtypes were identified using WGCNA. The ssGSEA and ESTIMATE algorithms were used to calculate pathway enrichment scores and immune cell infiltration levels between different subtypes. Finally, the TIDE algorithm, CYT score, and tumor responsiveness score were utilized to predict patient response to immunotherapy. RESULTS: We defined 3 CD8+T cell clusters (CD8_0, CD8_1, CD8_2) based on the scRNA- seq dataset (GSE149614). Among, CD8_2 was prognosis-related risk factor with HCC. We screened 30 prognosis genes from CD8_2, and identified 3 molecular subtypes (clust1, clust2, clust3). Clust1 had better survival outcomes, higher gene mutation, and enhanced immune infiltration. Furthermore, we identified a 12 genes signature (including CYP7A1, SPP1, MSC, CXCL8, CXCL1, GCNT3, TMEM45A, SPP2, ME1, TSPAN13, S100A9, and NQO1) with excellent prediction performance for HCC prognosis. In addition, High-score patients with higher immune infiltration benefited less from immunotherapy. The sensitivity of low-score patients to multiple drugs including Parthenolide and Shikonin was significantly higher than that of high-score patients. Moreover, high-score patients had increased oxidative stress pathways scores, and the RiskScore was closely associated with oxidative stress pathways scores. And the nomogram had good clinical utility. CONCLUSION: To predict the survival outcome and immunotherapy response for HCC, we developed a 12-gene signature based on the heterogeneity of the CD8+ T cells.
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Tumor-specific T cells (TSTs) are essential components for the success of personalized tumor-infiltrating lymphocyte (TIL)-based adoptive cellular therapy (ACT). Therefore, the selection of a common biomarker for screening TSTs in different tumor types, followed by ex vivo expansion to clinical number levels can generate the greatest therapeutic effect. However, studies on shared biomarkers for TSTs have not been realized yet. The present review summarizes the similarities and differences of a number of biomarkers for TSTs in several tumor types studied in the last 5 years, and the advantages of combining biomarkers. In addition, the review discusses the possible shortcomings of current biomarkers and highlights strategies to identify TSTs accurately using intercellular interactions. Finally, the development of TSTs in personalized TIL-based ACT for broader clinical applications is explored.