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Abstract The growth in carbon emissions poses a severe challenge to global sustainable development, making it imperative to explore the impacts of economic restructuring and technological progress on Carbon Emission Performance (CEP). However, existing studies often lack an integrated analysis of economic restructuring and technological progress, while giving limited attention to the indirect role of Environmental Regulation (ER). This study constructs a multidimensional theoretical framework, breaking down economic restructuring into four dimensions—industrial structure, factor input, ownership, and new-type urbanization (NTU), and refining technological progress into technological innovation and energy efficiency. It uncovers the complex interplay among CEP, economic restructuring, technological progress, and ER. The findings reveal critical insights: (1) industrial structure suppresses CEP, whereas adjustments in factor input and ownership structure significantly enhance it; (2) the relationship between NTU and CEP exhibits a non-linear pattern; (3) compared to the technological innovation, energy efficiency provide a more substantial boost to CEP; and (4) ER positively moderates the impacts of factor input, ownership structure, and NTU. Finally, the study proposes recommendations for holistic economic restructuring and diversified ERs.

Abstract To analyze the clinical features, epidemiological characteristics, risk factors, and complication characteristics of pulmonary tuberculosis (PTB) combined with extrapulmonary tuberculosis (EPTB) in elderly patients (aged > 60 years). Clinical data were collected from pathogen positive elderly patients with PTB hospitalized in a teaching hospital in Zhejiang from 2017 to 2023. We retrospectively analyzed the risk factors for complications of EPTB in elderly patients with PTB by univariate and multivariate logistic regression models. We also described the characteristics of complications in PTB with EPTB. A total of 781 PTB elderly patients were enrolled, of whom 86 (11.01%) had complicated EPTB. The most commonly invasive sites for EPTB were thoracic spine (17.53%), cervical lymph nodes (13.40%), and meninges (12.37%). 60

Abstract In addition to presenting significant diagnostic and treatment challenges, lung adenocarcinoma (LUAD) is the most common form of lung cancer. Using scRNA-Seq and bulk RNA-Seq data, we identify three genes referred to as HMR, FAM83A, and KRT6A these genes are related to necroptotic anoikis-related gene expression. Initial validation, conducted on the GSE50081 dataset, demonstrated the model's ability to categorize LUAD patients into high-risk and low-risk groups with significant survival differences. This model was further applied to predict responses to PD-1/PD-L1 blockade therapies, utilizing the IMvigor210 and GSE78220 cohorts, and showed strong correlation with patient outcomes, highlighting its potential in personalized immunotherapy. Further, LUAD cell lines were analyzed using quantitative PCR (qPCR) and Western blot analysis to confirm their expression levels, further corroborating the model's relevance in LUAD pathophysiology. The mutation landscape of these genes was also explored, revealing their broad implication in various cancer types through a pan-cancer analysis. The study also delved into molecular subclustering, revealing distinct expression profiles and associations with different survival outcomes, emphasizing the model’s utility in precision oncology. Moreover, the diversity of immune cell infiltration, analyzed in relation to the necroptotic anoikis signature, suggested significant implications for immune evasion mechanisms in LUAD. While the findings present a promising stride towards personalized LUAD treatment, especially in immunotherapy, limitations such as the retrospective nature of the datasets and the need for larger sample sizes are acknowledged. Prospective clinical trials and further experimental research are essential to validate these findings and enhance the clinical applicability of our prognostic model.

Abstract Anoikis resistance, a notable factor in osteosarcoma, plays a significant role in tumor invasion and metastasis. This study seeks to identify a distinct gene signature that is specifically associated with the anoikis subcluster in osteosarcoma. Clinical, single-cell, and transcriptional data from TARGET and GEO datasets were used to develop a gene signature for osteosarcoma based on the anoikis subcluster. Univariate Cox and LASSO regression analyses were employed. The signature's predictive value was evaluated using time-dependent ROC and Kaplan–Meier analyses. Functional enrichment analyses and drug sensitivity analyses were conducted. Validation of three modular genes was performed using RT-qPCR and Western blotting. Signature (ZNF583, CGNL1, CXCL13) was developed to predict overall survival in osteosarcoma patients, targeting the anoikis subcluster. The signature demonstrated good performance in external validation. Stratification based on the signature revealed significantly different prognoses. The signature was an independent prognostic factor. The low-risk group showed enhanced immune cell infiltration and improved immune function. Drug sensitivity analysis indicated efficacy of chemotherapy agents. Prognostic nomograms incorporating the signature provided greater predictive accuracy and clinical utility. Signatures related to the anoikis subcluster play a significant role in osteosarcoma progression. Incorporating these findings into clinical decision-making can improve osteosarcoma treatment and patient outcomes.