Role of consolidative thoracic and prophylactic cranial radiation in extensive stage small cell lung cancer in chemo-immunotherapy era.

Introduction: The role of consolidative thoracic and prophylactic brain radiation for extensive stage small cell lung cancer patients is controversial. We investigated the factors associated with the use of any radiation therapy (RT) and whether RT has a benefit to overall survival (OS) in patients receiving any systemic therapy and whether this benefit is the same if Chemotherapy (CT) or chemo-immunotherapy (CT-IO) is used.

Material/methods: The NCDB database was queried from years 2017-2019.

Lymphatic vascular invasion: Diagnostic variability and overall survival impact on patients undergoing surgical resection.

Objective: The diagnostic criteria of lymphatic vascular invasion have not been standardized. Our investigation assesses the factors associated with lymphatic vascular invasion positive tumors and the impact of lymphatic vascular invasion on overall survival for patients with non-small cell lung cancer undergoing (bi)lobectomy with an adequate node dissection.

Methods: The National Cancer Database was queried from the years 2010 to 2015 to find surgical patients who underwent lobectomy with at least 10 lymph nodes examined (adequate node dissection) and with known lymphatic vascular invasion status.

The structure of Mn(II)-bound Rubisco from Spinacia oleracea.

The rate of photosynthesis and, thus, CO fixation, is limited by the rate of ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco). Not only does Rubisco have a relatively low catalytic rate, but it also is promiscuous regarding the metal identity in the active site of the large subunit. In Nature, Rubisco binds either Mg(II) or Mn(II), depending on the chloroplastic ratio of these metal ions; most studies performed with Rubisco have focused on Mg-bound Rubisco.

Quantifying Geographic Atrophy in Age-Related Macular Degeneration: A Comparative Analysis Across 12 Deep Learning Models.

Purpose: AI algorithms have shown impressive performance in segmenting geographic atrophy (GA) from fundus autofluorescence (FAF) images. However, selection of artificial intelligence (AI) architecture is an important variable in model development. Here, we explore 12 distinct AI architecture combinations to determine the most effective approach for GA segmentation.

Strong versus Weak Data Labeling for Artificial Intelligence Algorithms in the Measurement of Geographic Atrophy.

Purpose: To gain an understanding of data labeling requirements to train deep learning models for measurement of geographic atrophy (GA) with fundus autofluorescence (FAF) images.

Design: Evaluation of artificial intelligence (AI) algorithms.

Subjects: The Age-Related Eye Disease Study 2 (AREDS2) images were used for training and cross-validation, and GA clinical trial images were used for testing.