Hemodynamic precision to guide surgical timing for neonates with congenital diaphragmatic hernia: a narrative review
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Severity of neonatal influenza infection is driven by type I interferon and oxidative stress
Neonates exhibit increased susceptibility to respiratory viral infections, attributed to inflammation at the developing pulmonary air-blood interface. IFN I are antiviral cytokines critical to control viral replication, but also promote inflammation. Previously, we established a neonatal murine influenza virus (IV) model, which demonstrates increased mortality. Here, we sought to determine the role of IFN I in this increased mortality. We found that three-day-old IFNAR-deficient mice are highly protected from IV-induced mortality. In addition, exposure to IFNβ 24 h post IV infection accelerated death in WT neonatal animals but did not impact adult mortality. In contrast, IFN IIIs are protective to neonatal mice. IFNβ induced an oxidative stress imbalance specifically in primary neonatal IV-infected pulmonary type II epithelial cells (TIIEC), not in adult TIIECs. Moreover, neonates did not have an infection-induced increase in antioxidants, including a key antioxidant, superoxide dismutase 3, as compared to adults. Importantly, antioxidant treatment rescued IV-infected neonatal mice, but had no impact on adult morbidity. We propose that IFN I exacerbate an oxidative stress imbalance in the neonate because of IFN I-induced pulmonary TIIEC ROS production coupled with developmentally regulated, defective antioxidant production in response to IV infection. This age-specific imbalance contributes to mortality after respiratory infections in this vulnerable population.
Failed mobility transition in an ideal setting and implications for building a green city
The mobility sector significantly contributes to the climate crisis, impacting several Sustainable Development Goals (SDGs) such as good health (SDG 3), sustainable cities (SDG 11), climate action (SDG 13), and life on land (SDG 15). Despite broad consensus on the need for mobility transformation, practical implementation is contentious due to diverse stakeholder interests. Tübingen, a green showcase city in Germany, exemplifies this challenge. Although ideal for green mobility, a tramway project was rejected in a referendum. This case-study highlights that mobility transition is not just a technical issue but a discourse-communicative challenge, emphasising the role of socially embedded narratives. The study aims to explain the referendum’s rejection by analysing discourses, identifying argumentation patterns, and providing insights for future projects. Using Hajer’s Discourse Coalitions approach and Discourse Network Analysis, the study found that the discourse was dynamic and polarised. The pro-tramway coalition’s communication deficiencies and the opposing coalition’s strong narrative connectivity influenced the outcome. Recommendations for effective communication strategies in future projects are provided.
Error-driven upregulation of memory representations
Learning an association does not always succeed on the first attempt. Previous studies associated increased error signals in posterior medial frontal cortex with improved memory formation. However, the neurophysiological mechanisms that facilitate post-error learning remain poorly understood. To address this gap, participants performed a feedback-based association learning task and a 1-back localizer task. Increased hemodynamic responses in posterior medial frontal cortex were found for internal and external origins of memory error evidence, and during post-error encoding success as quantified by subsequent recall of face-associated memories. A localizer-based machine learning model displayed a network of cognitive control regions, including posterior medial frontal and dorsolateral prefrontal cortices, whose activity was related to face-processing evidence in the fusiform face area. Representation strength was higher during failed recall and increased during encoding when subsequent recall succeeded. These data enhance our understanding of the neurophysiological mechanisms of adaptive learning by linking the need for learning with increased processing of the relevant stimulus category.
Outcomes of spontaneous pneumothorax in neonates: treatments vs. expectant management
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Surgical video workflow analysis via visual-language learning
Surgical video workflow analysis has made intensive development in computer-assisted surgery by combining deep learning models, aiming to enhance surgical scene analysis and decision-making. However, previous research has primarily focused on coarse-grained analysis of surgical videos, e.g., phase recognition, instrument recognition, and triplet recognition that only considers relationships within surgical triplets. In order to provide a more comprehensive fine-grained analysis of surgical videos, this work focuses on accurately identifying triplets <instrument, verb, target> from surgical videos. Specifically, we propose a vision-language deep learning framework that incorporates intra- and inter- triplet modeling, termed I2TM, to explore the relationships among triplets and leverage the model understanding of the entire surgical process, thereby enhancing the accuracy and robustness of recognition. Besides, we also develop a new surgical triplet semantic enhancer (TSE) to establish semantic relationships, both intra- and inter-triplets, across visual and textual modalities. Extensive experimental results on surgical video benchmark datasets demonstrate that our approach can capture finer semantics, achieve effective surgical video understanding and analysis, with potential for widespread medical applications.
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