Background: Severe traumatic brain injury (TBI) patients are at high risk for early aspiration and pneumonia. How pneumonia impacts neurological recovery after TBI is not well characterized. We hypothesized that, independent of the cerebral injury, pneumonia after TBI delays and worsens neurological recovery and cognitive outcomes.
Methods: Fifteen CD1 male mice were randomized to sham craniotomy or severe TBI (controlled cortical impact [CCI] - velocity 6 m/s, depth 1.0 mm) ± intratracheal lipopolysaccharide (LPS-2 mg/kg in 0.1 mL saline) as a pneumonia bioeffector. Neurological functional recovery by Garcia Neurologic Testing (GNT) and body weight loss were recorded daily for 14 days. On Days 6-14, animals underwent Morris Water Maze learning and memory testing with cued trials (platform visible), spatial learning trials (platform invisible, spatial cues present), and probe (memory) trials (platform removed, spatial clues present). Intergroup differences were assessed by the Kruskal-Wallis test with Bonferroni correction (p < 0.05).
Results: Weight loss was greatest in the CCI + LPS group (maximum 24% on Day 3 vs. 8% [Sham], 7% [CCI], both on Day 1). GNT was lowest in CCI + LPS during the first week. Morris Water Maze testing demonstrated greater spatial learning impairment in the CCI + LPS group vs. Sham or CCI counterparts. Cued learning and long-term memory were worse in CCI + LPS and CCI as compared to Sham.
Conclusion: A pneumonia bioeffector insult after TBI worsens weight loss and mortality in a rodent model. Not only is spatial learning impaired, but animals are more debilitated and have worse neurologic performance. Understanding the adverse effects of pneumonia on TBI recovery is the first step d patients.
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http://dx.doi.org/10.1097/TA.0000000000002403 | DOI Listing |
PLoS One
January 2025
IBM Research, Rio de Janeiro, Brazil.
For optimizing production yield while limiting negative environmental impact, sustainable agriculture benefits from real-time, on-the-spot chemical analysis of soil at low cost. Colorimetric paper sensors are ideal candidates, however, their automated readout and analysis in the field is needed. Using mobile technology for paper sensor readout could, in principle, enable the application of machine-learning models for transforming colorimetric data into threshold-based classes that represent chemical concentration.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
Spatially resolved transcriptomics (SRT) technologies facilitate the exploration of cell fates or states within tissue microenvironments. Despite these advances, the field has not adequately addressed the regulatory heterogeneity influenced by microenvironmental factors. Here, we propose a novel Spatially Aligned Graph Transfer Learning (SpaGTL), pretrained on a large-scale multi-modal SRT data of about 100 million cells/spots to enable inference of context-specific spatial gene regulatory networks across multiple scales in data-limited settings.
View Article and Find Full Text PDFJ Comput Neurosci
January 2025
Program in Neuroscience, Indiana University Bloomington, Bloomington, IN, USA.
Hippocampal representations of space and time seem to share a common coding scheme characterized by neurons with bell-shaped tuning curves called place and time cells. The properties of the tuning curves are consistent with Weber's law, such that, in the absence of visual inputs, width scales with the peak time for time cells and with distance for place cells. Building on earlier computational work, we examined how neurons with such properties can emerge through self-supervised learning.
View Article and Find Full Text PDFMed Biol Eng Comput
January 2025
Anhui BioX-Vision Biological Technology Co., Ltd, Hefei, 230031, Anhui, China.
The identification and categorization of circulating tumor cells (CTCs) in peripheral blood are imperative for advancing cancer diagnostics and prognostics. The intricacy of various CTCs subtypes, coupled with the difficulty in developing exhaustive datasets, has impeded progress in this specialized domain. To date, no methods have been dedicated exclusively to overcoming the classification challenges of CTCs.
View Article and Find Full Text PDFObjective: To assist in the rapid clinical identification of brain tumor types while achieving segmentation detection, this study investigates the feasibility of applying the deep learning YOLOv5s algorithm model to the segmentation of brain tumor magnetic resonance images and optimizes and upgrades it on this basis.
Methods: The research institute utilized two public datasets of meningioma and glioma magnetic resonance imaging from Kaggle. Dataset 1 contains a total of 3,223 images, and Dataset 2 contains 216 images.
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