Occupational exposure to strong inorganic acid mists containing sulfuric acid has been recognized as a carcinogen (Group 1) since 1992. An augmented, secondary data analysis of a population-based case-control study of lung cancer was conducted to assess lung cancer-specific risks using 772 lung cancer cases diagnosed between 1981 and 1985. Individually matched controls--on age, gender, and borough of residence--were identified. Lifetime exposure to 10 acidic agents, including strong inorganic acids and some gases, was assessed from complete lifetime occupational histories in terms of concentration, frequency, and reliability of the various exposure assessments. Smoking-adjusted odds ratios and 95% confidence intervals were determined for overall and histology-categorized lung cancers using conditional logistic regression. No excess risk for overall lung cancer was associated with any of the acids, and effect modification by gender could not be identified. The absence of an acid lung cancer effect reinforces more recent toxicological data that suggest specificity to the larynx.
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http://dx.doi.org/10.1179/107735211799031077 | DOI Listing |
Sci Rep
December 2024
Interventional Oncology, Johnson & Johnson Enterprise Innovation, Inc, 10th Floor 255 Main St, 02142, Cambridge, Boston, MA, USA.
The introduction of anti-PD-1/PD-L1 therapies revolutionized treatment for advanced non-small cell lung cancer (NSCLC), yet response rates remain modest, underscoring the need for predictive biomarkers. While a T cell inflamed gene expression profile (GEP) has predicted anti-PD-1 response in various cancers, it failed in a large NSCLC cohort from the Stand Up To Cancer-Mark (SU2C-MARK) Foundation. Re-analysis revealed that while the T cell inflamed GEP alone was not predictive, its performance improved significantly when combined with gene signatures of myeloid cell markers.
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December 2024
Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA.
Background: Benzodiazepines are the third most misused medication, with many patients having their first exposure during a surgical episode. We sought to characterize factors associated with new persistent benzodiazepine use (NPBU) among patients undergoing cancer surgery.
Patients And Methods: Patients who underwent cancer surgery between 2013 and 2021 were identified using the IBM-MarketScan database.
Sci Rep
December 2024
Department of Radiology, Veterans Health Service Medical Center, Seoul, Republic of Korea.
This study aimed to compare computed tomography (CT) findings between basaloid lung squamous cell carcinoma (SCC) and non-basaloid SCC. From July 2003 to April 2021, 39 patients with surgically proven basaloid SCC were identified. For comparison, 161 patients with surgically proven non-basaloid SCC from June 2018 to January 2019 were selected consecutively.
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December 2024
Precision Medicine Center, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510000, China.
Polyomavirus enhancer activator 3 (PEA3), an ETS transcription factor, has been documented to regulate the development and metastasis of human cancers. Nonetheless, a thorough analysis examining the relationship between the PEA3 subfamily members and tumour development, prognosis, and the tumour microenvironment (TME) across various cancer types has not yet been conducted. The expression profiles and prognostic significance of the PEA3 subfamily were evaluated using data from the GEO, TCGA, and PrognoScan databases, in conjunction with COX regression analyses and the Kaplan-Meier Plotter.
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December 2024
The Engineering & Technical College of Chengdu University of Technology, Xiaoba Road, Leshan, 614000, China.
Many conditions, such as pulmonary edema, bleeding, atelectasis or collapse, lung cancer, and shadow formation after radiotherapy or surgical changes, cause Lung Opacity. An unsupervised cross-domain Lung Opacity detection method is proposed to help surgeons quickly locate Lung Opacity without additional manual annotations. This study proposes a novel method based on adversarial learning to detect Lung Opacity on chest X-rays.
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