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Globally, fish have been severely affected by the widespread, chronic degradation of fresh waters, with a substantial proportion of species declining in abundance or range in recent decades. This has especially been the case in densely populated countries with an industrial heritage and intensive agriculture, where the majority of river catchments have been affected by deteriorations in water quality and changes in land use. This study used a spatially and temporally extensive dataset, encompassing 16,124 surveys at 1180 sites representing a wide range of river typologies and pressures, to examine changes in the fish populations of England's rivers over four decades (1980s-2010s).

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Background: Sepsis, a critical global health challenge, accounted for approximately 20% of worldwide deaths in 2017. Although the Sequential Organ Failure Assessment (SOFA) score standardizes the diagnosis of organ dysfunction, early sepsis detection remains challenging due to its insidious symptoms. Current diagnostic methods, including clinical assessments and laboratory tests, frequently lack the speed and specificity needed for timely intervention, particularly in vulnerable populations such as older adults, intensive care unit (ICU) patients, and those with compromised immune systems.

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Mitogen-activated protein kinase 1 (MAPK1) is a serine/threonine kinase that plays a crucial role in the MAP kinase signaling transduction pathway. This pathway plays a crucial role in various cellular processes, including cell proliferation, differentiation, adhesion, migration, and survival. Besides, many chemotherapeutic drugs targeting the MAPK pathway are used in clinical practice, and novel inhibitors of MAPK1 with improved specificity and efficacy are required.

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Purpose: The integration of artificial intelligence (AI), particularly deep learning (DL), with optical coherence tomography (OCT) offers significant opportunities in the diagnosis and management of glaucoma. This article explores the application of various DL models in enhancing OCT capabilities and addresses the challenges associated with their clinical implementation.

Methods: A review of articles utilizing DL models was conducted, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, and large language models (LLMs).

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Targeting Protein-Protein Interactions in Hematologic Malignancies.

Annu Rev Pathol

January 2025

Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA; email:

Over the last two decades, there have been extensive efforts to develop small-molecule inhibitors of protein-protein interactions (PPIs) as novel therapeutics for cancer, including hematologic malignancies. Despite the numerous challenges associated with developing PPI inhibitors, a significant number of them have advanced to clinical studies in hematologic patients in recent years. The US Food and Drug Administration approval of the very first PPI inhibitor, venetoclax, demonstrated the real clinical value of blocking protein-protein interfaces.

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