An inflammatory pulmonary insult post-traumatic brain injury worsens subsequent spatial learning and neurological outcomes.

J Trauma Acute Care Surg

From the Division of Traumatology, Surgical Critical Care and Emergency Surgery (C.L.J., S.A., R.L., J.M., L.J.K., D.N.H., C.W.S., J.L.P.), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Emergency and Critical Care Medicine (Y.S.), Hachioji Medical Center, Tokyo Medical University, Tokyo, Japan; Department of Medicine (A.J.P.), University of Pennsylvania, Philadelphia, PA; Department of Medicine, Pulmonary, Allergy and Critical Care Division (M.C-S.), University of Pennsylvania, Philadelphia, Pennsylvania; and Center for Brain Injury and Repair, Department of Neurosurgery (D.H.S.), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.

Published: September 2019

AI Article Synopsis

Article Abstract

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.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10497189PMC
http://dx.doi.org/10.1097/TA.0000000000002403DOI Listing

Publication Analysis

Top Keywords

spatial learning
16
cci lps
16
weight loss
12
trials platform
12
brain injury
8
neurological recovery
8
pneumonia tbi
8
pneumonia bioeffector
8
morris water
8
water maze
8

Similar Publications

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 PDF

Spatially aligned graph transfer learning for characterizing spatial regulatory heterogeneity.

Brief 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 PDF

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 PDF

CTCNet: a fine-grained classification network for fluorescence images of circulating tumor cells.

Med 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 PDF

Objective: 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.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!