Background: It is controversial whether achieving stable disease leads to a survival benefit and whether the importance of achieving stable disease differs between cytotoxic agents and molecular targeted agents. To examine these questions, the authors retrospectively reviewed phase II and III studies in the second-line setting for advanced non-small cell lung cancer using epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) and cytotoxic agents separately.
Methods: The authors chose 45 trials for the chemotherapy group and nine for the EGFR TKI group by searching the PubMed database. All nine trials in the EGFR TKI group concern gefitinib and erlotinib.
Results: The median survival time increased 0.0375 month with each 1% increase in stable disease rate (p = 0.039), and each 1% increase in response rate resulted in 0.0744 (p < 0.001) month of median survival time in the analysis combined with both cytotoxic agents and EGFR TKIs. Main and interaction terms for EGFR TKI treatment were not statistically significant. With respect to time to progression, only response rate showed a statistically significant relationship with survival.
Conclusions: To obtain response seems to be more important than to achieve stable disease for both cytotoxic agents and EGFR TKIs, although achieving stable disease is still valuable. The relationship between survival and response or stable disease appears similar for cytotoxic agents and EGFR TKIs.
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Biomed Phys Eng Express
January 2025
Department of Ophthalmology, Hospital Universitario de Canarias, Carretera Ofra S/N, La Laguna, Santa Cruz de Tenerife, 38320, SPAIN.
This paper systematically evaluates saliency methods as explainability tools for convolutional neural networks trained to diagnose glaucoma using simplified eye fundus images that contain only disc and cup outlines. These simplified images, a methodological novelty, were used to relate features highlighted in the saliency maps to the geometrical clues that experts consider in glaucoma diagnosis. Despite their simplicity, these images retained sufficient information for accurate classification, with balanced accuracies ranging from 0.
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January 2025
Chinese Academy of Sciences Key Laboratory of Tropical Marine Bio Resources and Ecology, Guangdong Key Laboratory of Marine Materia Medica, Innovation Academy of South China Sea Ecology and Environmental Engineering, Guangdong Provincial Observation and Research Station for Coastal Upwelling Ecosystem, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 511458, China.
Rotation of the bacterial flagellum, the first identified biological rotary machine, is driven by its stator units. Knowledge gained about the function of stator units has increasingly led to studies of rotary complexes in different cellular pathways. Here, we report that a tetrameric PilZ family protein, FlgX, is a structural component underneath the stator units in the flagellar motor of .
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Z Qiu, Department of Nuclear Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Shanghai, 200233, China.
Objective: Pleural metastasis (PM) is rare in patients with differentiated thyroid cancer (DTC). Radioiodine (131I) therapy has been the main treatment for postoperative metastasis and recurrence of DTC. However, clinical data on PM from DTC are limited.
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Foot and Mouth Disease Department, National Veterinary Research Institute, Vom, Plateau State, Nigeria.
The global public health risk posed by Salmonella Kentucky (S. Kentucky) is rising, particularly due to the dissemination of antimicrobial resistance genes in human and animal populations. This serovar, widespread in Africa, has emerged as a notable cause of non-typhoidal gastroenteritis in humans.
View Article and Find Full Text PDFAdv Sci (Weinh)
January 2025
Yunnan Key Laboratory of Stem Cell and Regenerative Medicine, Kunming Medical University, Kunming, 650500, China.
Small molecules as nanomedicine carriers offer advantages in drug loading and preparation. Selecting effective small molecules for stable nanomedicines is challenging. This study used artificial intelligence (AI) to screen drug combinations for self-assembling nanomedicines, employing physiochemical parameters to predict formation via machine learning.
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