Bronchopulmonary dysplasia (BPD) is the most common cause of morbidity and mortality in preterm infants. A key histopathological feature of BPD is stunted late lung development, where the process of alveolarization-the generation of alveolar gas exchange units-is impeded, through mechanisms that remain largely unclear. As such, there is interest in the clarification both of the pathomechanisms at play in affected lungs, and the mechanisms of de novo alveoli generation in healthy, developing lungs. A better understanding of normal and pathological alveolarization might reveal opportunities for improved medical management of affected infants. Furthermore, disturbances to the alveolar architecture are a key histopathological feature of several adult chronic lung diseases, including emphysema and fibrosis, and it is envisaged that knowledge about the mechanisms of alveologenesis might facilitate regeneration of healthy lung parenchyma in affected patients. To this end, recent efforts have interrogated clinical data, developed new-and refined existing-in vivo and in vitro models of BPD, have applied new microscopic and radiographic approaches, and have developed advanced cell-culture approaches, including organoid generation. Advances have also been made in the development of other methodologies, including single-cell analysis, metabolomics, lipidomics, and proteomics, as well as the generation and use of complex mouse genetics tools. The objective of this review is to present advances made in our understanding of the mechanisms of lung alveolarization and BPD over the period 1 January 2017-30 June 2019, a period that spans the 50th anniversary of the original clinical description of BPD in preterm infants.
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http://dx.doi.org/10.1152/ajplung.00369.2019 | DOI Listing |
Biochemistry
January 2025
School of Chemistry and Biochemistry, Georgia Institute of Technology, 950 Atlantic Drive, Atlanta, Georgia 30332, United States.
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January 2025
Department of Chemistry, Brandeis University, 415 South Street, Waltham, Massachusetts 02453, United States.
The photocatalytic efficiency of materials such as graphene and noble metal nanoclusters depends on their plasmon lifetimes. Plasmon dephasing and decay in these materials is thought to occur on ultrafast time scales, ranging from a few femtoseconds to hundreds of femtoseconds and longer. Here we focus on understanding the dephasing and decay pathways of excited states in small lithium and silver clusters and in plasmonic states of the π-conjugated molecule anthracene, providing insights that are crucial for interpreting optical properties and photophysics.
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January 2025
Faculty of Medicine and Medical Sciences, University of Balamand, Kalhat, Lebanon.
Background: Transvenous pacemakers (TVP) and leadless pacemakers (LP) are two reliable permanent modalities for the treatment of heart rhythm disorders. Several observational studies explored the safety and efficacy of the two devices. The aim of this meta-analysis study is to present a comparative analysis of the safety of leadless versus transvenous pacemakers.
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January 2025
Cancer Early Detection Advanced Research Center (CEDAR), Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA.
Neurosignaling is increasingly recognized as a critical factor in cancer progression, where neuronal innervation of primary tumors contributes to the disease's advancement. This study focuses on segmenting individual axons within the prostate tumor microenvironment, which have been challenging to detect and analyze due to their irregular morphologies. We present a novel deep learning-based approach for the automated segmentation of axons, AxonFinder, leveraging a U-Net model with a ResNet-101 encoder, based on a multiplexed imaging approach.
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December 2024
Department of Information Technology and Electrical Engineering, ETH Zurich, Sternwartstrasse 7, Zürich, Switzerland.
The LLaMA family, a collection of foundation language models ranging from 7B to 65B parameters, has become one of the most powerful open-source large language models (LLMs) and the popular LLM backbone of multi-modal large language models (MLLMs), widely used in computer vision and natural language understanding tasks. In particular, LLaMA3 models have recently been released and have achieved impressive performance in various domains with super-large scale pre-training on over 15T tokens of data. Given the wide application of low-bit quantization for LLMs in resource-constrained scenarios, we explore LLaMA3's capabilities when quantized to low bit-width.
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