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Search Results (1,280)

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17 pages, 593 KiB  
Review
The Role of GFAP in Post-Mortem Analysis of Traumatic Brain Injury: A Systematic Review
by Matteo Antonio Sacco, Saverio Gualtieri, Alessandro Pasquale Tarallo, Maria Cristina Verrina, Jasmine Calafiore, Aurora Princi, Stefano Lombardo, Francesco Ranno, Alessandro Di Cello, Santo Gratteri and Isabella Aquila
Int. J. Mol. Sci. 2025, 26(1), 185; https://doi.org/10.3390/ijms26010185 (registering DOI) - 28 Dec 2024
Viewed by 330
Abstract
Traumatic brain injuries (TBIs) are a leading cause of mortality and morbidity, particularly in forensic settings where determining the cause of death and timing of injury is critical. Glial fibrillary acidic protein (GFAP), a biomarker specific to astrocytes, has emerged as a valuable [...] Read more.
Traumatic brain injuries (TBIs) are a leading cause of mortality and morbidity, particularly in forensic settings where determining the cause of death and timing of injury is critical. Glial fibrillary acidic protein (GFAP), a biomarker specific to astrocytes, has emerged as a valuable tool in post-mortem analyses of TBI. A PRISMA-based literature search included studies examining GFAP in human post-mortem samples such as brain tissue, cerebrospinal fluid (CSF), serum, and urine. The results highlight that GFAP levels correlate with the severity of brain injury, survival interval, and pathological processes such as astrocyte damage and blood–brain barrier disruption. Immunohistochemistry, ELISA, and molecular techniques were commonly employed for GFAP analysis, with notable variability in protocols and thresholds among studies. GFAP demonstrated high diagnostic accuracy in distinguishing TBI-related deaths from other causes, particularly when analyzed in CSF and serum. Furthermore, emerging evidence supports its role in complementing other biomarkers, such as S100B and NFL, to improve diagnostic precision. However, the review also identifies significant methodological heterogeneity and gaps in standardization, which limit the generalizability of findings. Future research should focus on establishing standardized protocols, exploring biomarker combinations, and utilizing advanced molecular tools to enhance the forensic application of GFAP. Full article
(This article belongs to the Collection New Advances in Molecular Toxicology)
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<p>Searching method using PRISMA flowchart.</p>
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17 pages, 312 KiB  
Review
Antioxidant and Anti-Inflammatory Properties of Melatonin in Secondary Traumatic Brain Injury
by Mariusz Sieminski, Michalina Reimus, Maria Kałas and Ewelina Stępniewska
Antioxidants 2025, 14(1), 25; https://doi.org/10.3390/antiox14010025 (registering DOI) - 28 Dec 2024
Viewed by 361
Abstract
Traumatic brain injury (TBI) is a disease resulting from external physical forces acting against the head, leading to transient or chronic damage to brain tissue. Primary brain injury is an immediate and, therefore, rather irreversible effect of trauma, while secondary brain injury results [...] Read more.
Traumatic brain injury (TBI) is a disease resulting from external physical forces acting against the head, leading to transient or chronic damage to brain tissue. Primary brain injury is an immediate and, therefore, rather irreversible effect of trauma, while secondary brain injury results from a complex cascade of pathological processes, among which oxidative stress and neuroinflammation are the most prominent. As TBI is a significant cause of mortality and chronic disability, with high social costs all over the world, any form of therapy that may mitigate trauma-evoked brain damage is desirable. Melatonin, a sleep–wake-cycle-regulating neurohormone, exerts strong antioxidant and anti-inflammatory effects and is well tolerated when used as a drug. Due to these properties, it is very reasonable to consider melatonin as a potential therapeutic molecule for TBI treatment. This review summarizes data from in vitro studies, animal models, and clinical trials that focus on the usage of melatonin in TBI. Full article
33 pages, 4365 KiB  
Article
Unravelling Secondary Brain Injury: Insights from a Human-Sized Porcine Model of Acute Subdural Haematoma
by Thomas Kapapa, Vanida Wernheimer, Andrea Hoffmann, Tamara Merz, Fabia Zink, Eva-Maria Wolfschmitt, Oscar McCook, Josef Vogt, Martin Wepler, David Alexander Christian Messerer, Claire Hartmann, Angelika Scheuerle, René Mathieu, Simon Mayer, Michael Gröger, Nicole Denoix, Enrico Clazia, Peter Radermacher, Stefan Röhrer and Thomas Datzmann
Cells 2025, 14(1), 17; https://doi.org/10.3390/cells14010017 - 27 Dec 2024
Viewed by 343
Abstract
Traumatic brain injury (TBI) remains one of the leading causes of death. Because of the individual nature of the trauma (brain, circumstances and forces), humans experience individual TBIs. This makes it difficult to generalise therapies. Clinical management issues such as whether intracranial pressure [...] Read more.
Traumatic brain injury (TBI) remains one of the leading causes of death. Because of the individual nature of the trauma (brain, circumstances and forces), humans experience individual TBIs. This makes it difficult to generalise therapies. Clinical management issues such as whether intracranial pressure (ICP), cerebral perfusion pressure (CPP) or decompressive craniectomy improve patient outcome remain partly unanswered. Experimental drug approaches for the treatment of secondary brain injury (SBI) have not found clinical application. The complex, cellular and molecular pathways of SBI remain incompletely understood, and there are insufficient experimental (animal) models that reflect the pathophysiology of human TBI to develop translational therapeutic approaches. Therefore, we investigated different injury patterns after acute subdural hematoma (ASDH) as TBI in a post-hoc approach to assess the impact on SBI in a long-term, human-sized porcine TBI animal model. Post-mortem brain tissue analysis, after ASDH, bilateral ICP, CPP, cerebral oxygenation and temperature monitoring, and biomarker analysis were performed. Extracerebral, intraparenchymal–extraventricular and intraventricular blood, combined with brainstem and basal ganglia injury, influenced the experiment and its outcome. Basal ganglia injury affects the duration of the experiment. Recognition of these different injury patterns is important for translational interpretation of results in this animal model of SBI after TBI. Full article
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<p>Macroscopic findings of a pig brain removed after acute subdural hematoma (ASDH) with the view (<b>A</b>) from above, (<b>B</b>) from the right, (<b>C</b>) from the left side and (<b>D</b>) in coronal sections from frontal to sub-occipital. In A–C fronto-parietal cortex after implantation of the neuro-monitoring probes and the right-sided ASDH. In (<b>D</b>), blood deposits on the right side of the cortex as evidence of intraparenchymal bleeding. (<b>E</b>,<b>F</b>): Exemplary courses of intracranial pressure (ICP) and partial oxygen pressure (PtO<sub>2</sub>, PO<sub>2</sub>) in mmHg over time for the right side (with ASDH) and the left side (control) of the animals analysed. There was an increase in ICP values and a decrease in the PtO<sub>2</sub> values for the right ASDH hemisphere and subsequently for the control side after ASDH is applied (<b>E</b>). In the further course (<b>F</b>), ICP was significantly higher on the right than on the left side and the continuous drop of PtO<sub>2</sub> values was more pronounced on the right than on the left.</p>
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<p>Injury pattern and distribution of haemorrhage after the experiment: (<b>A</b>) only extracerebral, (<b>B</b>) intraparenchymal–extraventricular, (<b>C</b>) intraventricular, (<b>D</b>) brain stem involvement (arrow) and (<b>E</b>) basal ganglia involvement (arrow).</p>
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<p>Median and interquartile representation of the duration of the experiment (survival of the animal) according to the division into (<b>A</b>) haemorrhage distributions, (<b>B</b>) occurrence of brainstem injuries and (<b>C</b>) occurrence of basal ganglia injuries. The significance of the difference between the injury patterns with and without basal ganglia injury in (<b>C</b>) is <span class="html-italic">p</span> = 0.0001 = *** (Mann–Whitney U test).</p>
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<p>The exclusion of the animals from the experiment and the length of the experiment after application of the acute subdural hematoma. (<b>A</b>) There was a difference of animals with basal ganglia lesions and without basal ganglia injuries (<span class="html-italic">p</span> = 0.004, Log rank (Mantel–Cox)). (<b>B</b>) Animals with intraventricular blood distribution dropped out earlier than other animals, followed by animals with intraparenchymal blood distribution (<span class="html-italic">p</span> = 0.228, Log rank (Mantel–Cox). (<b>C</b>) Animals with brainstem lesions dropped out earlier than those without (<span class="html-italic">p</span> = 0.072, Log rank (Mantel–Cox). The dotted lines show the corresponding 95% confidence interval.</p>
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<p>Total modified Glasgow Coma Scale scores (MGCS) over time (3 = minimum, 18 = maximum), showing scores at 4, 30 and 54 h of the current study (<b>A</b>–<b>C</b>). In general, the animals show a deterioration in scores after the application of trauma (acute subdural haematoma), in this case at hour 30, and then a recovery. Both non-extracerebral damage and damage to the brainstem and basal ganglia resulted in lower scores than without such damage. This suggests a clinical equivalent of brain damage.</p>
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<p>Body temperature of animals differentiated by (<b>A</b>) occurrence of basal ganglia injury, (<b>B</b>) occurrence of brainstem injuries and (<b>C</b>) different haemorrhage distribution. Results of the mixed-model approach (restricted maximum likelihood, REML): animals without basal ganglia injury showed higher body temperature than animals with basal ganglia injuries (<span class="html-italic">p</span> = 0.007) (<b>A</b>). The distribution for brainstem injuries and haemorrhage type revealed no significant results. The conditional R<sup>2</sup> values are 0.489 for basal ganglia injury, 0.612 for brainstem injury and 0.609 for haemorrhage distributions.</p>
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<p>Time course of the neuromonitoring values over the experiment (0 to 54 h) separated by intraparenchymal and intraventricular haemorrhage. Extracerebral (<span class="html-italic">N</span> = 3) cases were excluded due to early dropout and the low number of values. (<b>A</b>) Intracranial pressure = ICP, (<b>B</b>) cerebral perfusion pressure = CPP, (<b>C</b>) partial tissue (brain) oxygen saturation = PtO2, (<b>D</b>) brain temperature in grade Celsius. ICP 8 h: haemorrhage side and control side and ICP 24 h: haemorrhage side, <span class="html-italic">p</span> ≤ 0.029 (**); Mann–Whitney-U Test (<b>A</b>). CPP 24 h: haemorrhage side, <span class="html-italic">p</span> = 0.05 (**) and CPP 48 h: control side, <span class="html-italic">p</span> = 0.046 (**); Mann–Whitney-U Test (<b>B</b>).</p>
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<p>Time course of the neuromonitoring values over the experiment (0 to 54 h) separated by the occurrence of brainstem (<b>left</b>) and basal ganglia (<b>right</b>) injury. (<b>A</b>,<b>B</b>) Intracranial pressure = ICP, (<b>C</b>,<b>D</b>) cerebral perfusion pressure = CPP, (<b>E</b>,<b>F</b>) partial tissue (brain) oxygen saturation = PtO<sub>2</sub>, and (<b>G</b>,<b>H</b>) brain temperature in grade Celsius. Significant differences in animals with and without basal ganglia injury: ICP, <span class="html-italic">p</span> = 0.044 (12 h, control side) (<b>B</b>), CPP, <span class="html-italic">p</span> = 0.027 (24 h, control side) (<b>D</b>), PtO<sub>2</sub>, <span class="html-italic">p</span> = 0.044 (12 h, control side) and 0.017 (36 h, haemorrhage side) (<b>F</b>), temperature, <span class="html-italic">p</span> = 0.012 (24 h, haemorrhage side) and <span class="html-italic">p</span> = 0.002 (24 h, control side) (<b>H</b>). Significant differences in animals with and without brainstem injury: temperature, <span class="html-italic">p</span> = 0.036 (36 h, haemorrhage side) (<b>G</b>).</p>
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<p>Time course of the biomarkers (1): S110ß and, (2): MAP2 separated based on the injury patterns. (<b>A</b>,<b>D</b>) Haemorrhage type, (<b>B</b>,<b>E</b>) occurrence of brainstem injuries and (<b>C</b>,<b>F</b>) occurrence of basalganglia injuries. The biomarkers S100ß (<span class="html-italic">p</span> = 0.0153 = *, Kruskal–Wallis-H) and MAP2 (<span class="html-italic">p</span> = 0.0126 = *, Kruskal–Wallis-H) showed significantly higher cumulative concentrations in animals with intraventricular injuries.</p>
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<p>Time course of the biomarkers (1): NSE and (2): GFAP separated based on the injury patterns. (<b>A</b>,<b>D</b>) Haemorrhage type, (<b>B</b>,<b>E</b>) occurrence of brainstem injuries and (<b>C</b>,<b>F</b>) occurrence of basalganglia injuries. The biomarker GFAP (<span class="html-italic">p</span> = 0.0003 = ***, Kruskal–Wallis-H) showed significantly higher cumulative concentrations in animals with intraventricular injuries.</p>
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21 pages, 2732 KiB  
Article
Predictive Modeling of Long-Term Care Needs in Traumatic Brain Injury Patients Using Machine Learning
by Tee-Tau Eric Nyam, Kuan-Chi Tu, Nai-Ching Chen, Che-Chuan Wang, Chung-Feng Liu, Ching-Lung Kuo and Jen-Chieh Liao
Diagnostics 2025, 15(1), 20; https://doi.org/10.3390/diagnostics15010020 - 25 Dec 2024
Viewed by 213
Abstract
Background: Traumatic brain injury (TBI) research often focuses on mortality rates or functional recovery, yet the critical need for long-term care among patients dependent on institutional or Respiratory Care Ward (RCW) support remains underexplored. This study aims to address this gap by employing [...] Read more.
Background: Traumatic brain injury (TBI) research often focuses on mortality rates or functional recovery, yet the critical need for long-term care among patients dependent on institutional or Respiratory Care Ward (RCW) support remains underexplored. This study aims to address this gap by employing machine learning techniques to develop and validate predictive models that analyze the prognosis of this patient population. Method: Retrospective data from electronic medical records at Chi Mei Medical Center, encompassing 2020 TBI patients admitted to the ICU between January 2016 and December 2021, were collected. A total of 44 features were included, utilizing four machine learning models and various feature combinations based on clinical significance and Spearman correlation coefficients. Predictive performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and validated with the DeLong test and SHAP (SHapley Additive exPlanations) analysis. Result: Notably, 236 patients (11.68%) were transferred to long-term care centers. XGBoost with 27 features achieved the highest AUC (0.823), followed by Random Forest with 11 features (0.817), and LightGBM with 44 features (0.813). The DeLong test revealed no significant differences among the best predictive models under various feature combinations. SHAP analysis illustrated a similar distribution of feature importance for the top 11 features in XGBoost, with 27 features, and Random Forest with 11 features. Conclusions: Random Forest, with an 11-feature combination, provided clinically meaningful predictive capability, offering early insights into long-term care trends for TBI patients. This model supports proactive planning for institutional or RCW resources, addressing a critical yet often overlooked aspect of TBI care. Full article
(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
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<p>Flowchart showing the ML training process and its integration into the hospital system for TBI patients in ICU.</p>
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<p>AI prediction device for TBI in ICU, insight into the system architecture and modules.</p>
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<p>Receiver operating characteristic curves (ROC), area under the curve (AUC), for transfer to institute or respiratory care center prediction: (<b>a</b>) 44 features to train the ML model; (<b>b</b>) 27 features which were significant in transfer to institute or respiratory care center; (<b>c</b>) 18 features which were significant and Spearman correlation coefficient &gt; 0.1; (<b>d</b>) 11 features which were significant and Spearman correlation coefficient &gt; 0.2. Logistic Regression (orange), Random Forest (black), LightGBM (green), XGBoost (pink), and stacking (purple).</p>
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<p>SHAP Analysis Results: (<b>a</b>) SHAP global explanation on the 27-feature model (XGBoost model); (<b>b</b>) SHAP absolute value of each feature on the 27-feature model (XGBoost model).</p>
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<p>SHAP Analysis Results: (<b>a</b>) SHAP global explanation on the 11-feature model (Random Forest model); (<b>b</b>) SHAP absolute value of each feature on the 11-feature model (Random Forest model).</p>
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<p>Interface presentation of AI in practical application within the Chi Mei Hospital healthcare system.</p>
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17 pages, 3293 KiB  
Article
Comprehensive Transcriptome-Wide Profiling of 5-Methylcytosine Modifications in Long Non-Coding RNAs in a Rat Model of Traumatic Brain Injury
by Zhijun Xiang, Yixing Luo, Jiangtao Yu, Haoli Ma and Yan Zhao
Curr. Issues Mol. Biol. 2024, 46(12), 14497-14513; https://doi.org/10.3390/cimb46120871 - 23 Dec 2024
Viewed by 269
Abstract
Traumatic brain injury (TBI) poses a major global health challenge, leading to serious repercussions for those affected and imposing considerable financial strains on families and healthcare systems. RNA methylation, especially 5-methylcytosine (m5C), plays a crucial role as an epigenetic modification in [...] Read more.
Traumatic brain injury (TBI) poses a major global health challenge, leading to serious repercussions for those affected and imposing considerable financial strains on families and healthcare systems. RNA methylation, especially 5-methylcytosine (m5C), plays a crucial role as an epigenetic modification in regulating RNA at the level of post-transcriptional regulation. However, the impact of TBI on the m5C methylation profile of long non-coding RNAs (lncRNAs) remains unexplored. In the present study, we conducted a thorough transcriptome-wide examination of m5C methylation in lncRNAs in a rat TBI model utilizing MeRIP-Seq. Our results revealed significant differences in the amount and distribution of m5C methylation in lncRNAs between TBI and control groups, indicating profound changes in m5C methylation following TBI. Bioinformatic analyses linked these specifically methylated transcripts to pathways involved in immune response, neural repair, and lipid metabolism, providing insight into possible mechanisms underlying TBI pathology. These findings offer novel perspectives on the post-transcriptional modifications in lncRNA m5C methylation following TBI, which may contribute to understanding the disease mechanisms and developing targeted therapeutic strategies. Full article
(This article belongs to the Special Issue Chemical Biology of Nucleic Acid Modifications)
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Graphical abstract
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<p><b>Experimental procedure:</b> (<b>A</b>). The general process of the experiment; (<b>B</b>). HE staining of coronal brain slices from the rat TBI model; and (<b>C</b>). HE staining of coronal sections of brain tissue from the sham group.</p>
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<p><b>Overview of lncRNA m<sup>5</sup>C methylation in both sham and TBI groups:</b> (<b>A</b>). Visualization of m<sup>5</sup>C peaks at the chromosomal level in TBI and sham groups. (<b>B</b>). Venn diagram showing the number of m<sup>5</sup>C methylation peaks detected in lncRNAs in TBI and sham groups. (<b>C</b>). Venn diagram showing the number of lncRNAs with m<sup>5</sup>C peaks in TBI and sham groups. (<b>D</b>). Pie charts depicting the distribution of methylated lncRNA sources in TBI and sham groups.</p>
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<p><b>Characteristics of post-TBI lncRNA m<sup>5</sup>C methylation and GO analysis:</b> (<b>A</b>). Number of significantly up-regulated and down-regulated m<sup>5</sup>C peaks in TBI rats (<span class="html-italic">p</span> &lt; 0.05, FC &gt; 2). (<b>B</b>). Number of m<sup>5</sup>C peaks on each lncRNA in TBI and sham rats (<span class="html-italic">p</span> &lt; 0.05, fold change &gt; 2). (<b>C</b>). Significantly enriched GO categories for hyper-methylated lncRNAs. <span class="html-italic">Incomplete GO term displayed: BP: Protein kinase C-activating G protein-coupled receptor signaling pathway; Positive regulation of vascular endothelial growth factor receptor signaling pathway.</span> (<b>D</b>). String diagrams showing the connections between different GO categories of hyper-methylated lncRNAs. (<b>E</b>). Significantly enriched GO categories for hypo-methylated lncRNAs. <span class="html-italic">Incomplete GO term displayed: MF: Beta-N-acetylglucosaminylglycopeptide beta-1,4-galactosyltransferase activity; Lysine N-acetyltransferase activity, acting on acetyl phosphate as donor. BP: positive regulation of cytokine-mediated signaling pathway</span>; positive regulation of phosphatidylinositol 3-kinase signaling pathway. (<b>F</b>). String diagrams showing the connections between different GO categories of hypo-methylated lncRNAs.</p>
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<p><b>Changes in lncRNA expression and GO analysis after TBI:</b> (<b>A</b>). Volcano plot displaying the lncRNAs that were significantly up-regulated and down-regulated after TBI (fold change &gt; 2, <span class="html-italic">p</span>-value &lt; 0.05). (<b>B</b>). Number of up-regulated and down-regulated lncRNAs (fold change &gt; 2, <span class="html-italic">p</span>-value &lt; 0.05). (<b>C</b>). Cluster analysis of differentially expressed lncRNAs. (<b>D</b>). Significantly enriched GO categories for up-regulated lncRNAs. <span class="html-italic">Incomplete GO term displayed: BP: positive regulation of cytokine-mediated signaling pathway.</span> (<b>E</b>). String diagrams showing the connections between different GO categories of the up-regulated lncRNAs. (<b>F</b>). Significantly enriched GO categories for down-regulated lncRNAs. <span class="html-italic">Incomplete GO terms displayed: BP: antigen processing and presentation of peptide antigen via MHC class I; antigen processing and presentation of endogenous peptide antigen via MHC class Ib; positive regulation of high voltage-gated calcium channel activity.</span> (<b>G</b>). String diagrams showing the connections between different GO categories of the down-regulated lncRNAs.</p>
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<p><b>Combined analysis of m<sup>5</sup>C methylation and lncRNA expression after TBI:</b> (<b>A</b>). The nine-quadrant plot illustrates the correlation between lncRNA m<sup>5</sup>C methylation and the corresponding lncRNA expression. Top-right quadrant: both methylation and gene expression are up-regulated (m<sup>5</sup>C-fold change &gt; 2 and Exp-fold change &gt; 2). Top-left quadrant: methylation is down-regulated while gene expression is up-regulated (m<sup>5</sup>C-fold change &lt; −2 and Exp-fold change &gt; 2). Bottom-left quadrant: both methylation and gene expression are down-regulated (m<sup>5</sup>C-fold change &lt; −2 and Exp-fold change &lt; −2). Bottom-right quadrant: methylation is down-regulated while gene expression is up-regulated (m<sup>5</sup>C-fold change &gt; 2 and Exp-fold change &lt; −2). (<b>B</b>). Venn diagrams showing the overlap between the number of differentially methylated genes and the number of differentially expressed lncRNAs.</p>
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10 pages, 666 KiB  
Systematic Review
Long-Term Return to Work After Mild and Moderate Traumatic Brain Injury: A Systematic Literature Review
by Emilia Westarp, Tim Jonas Hallenberger, Karl-Olof Lövblad, Thomas Mokrusch, Claudio Bassetti and Raphael Guzman
Clin. Transl. Neurosci. 2024, 8(4), 31; https://doi.org/10.3390/ctn8040031 - 20 Dec 2024
Viewed by 284
Abstract
Background: Traumatic brain injury (TBI) is referred to as a “silent epidemic” due to its limited awareness in the general public. Nevertheless, it can cause chronic, lifelong physical and cognitive impairments with severe impact on quality of life, resulting in high healthcare costs [...] Read more.
Background: Traumatic brain injury (TBI) is referred to as a “silent epidemic” due to its limited awareness in the general public. Nevertheless, it can cause chronic, lifelong physical and cognitive impairments with severe impact on quality of life, resulting in high healthcare costs and loss of employment. To evaluate the outcome after mild and moderate TBI, “return to work (RTW)” is a relevant parameter, reflecting the socio-economic consequences of TBI. Our study aims to summarize RTW-rates to raise awareness on the impact of non-severe TBI. Methods: We performed a systematic literature review screening the databases Medline, Embase and Web of Science for studies reporting RTW in mild to moderate TBI. Studies that reported on RTW after mild or moderate TBI (defined by GCS > 9) in adults, with a minimum follow-up of six months were included. Risk of bias was assessed using the QUIPS tool. Results: We included 13 studies with a total 22,550 patients. The overall RTW rate after at least six months, varies between 37% and 98%. Full RTW is reported in six of the included 13 studies and varies between 12% and 67%. In six studies (46%) the RTW-rate by the end of follow-up was ≤60%, with four studies being from high-income countries. Conclusion: Mild and moderate TBI have a high impact on employment rates with diverging rates for RTW even between high-income countries. Increasing the societal awareness of this silent epidemic is of utmost importance and is one of the missions of the Swiss Brain Health Plan. Full article
(This article belongs to the Special Issue Brain Health)
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<p>Flow chart for study selection.</p>
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22 pages, 2399 KiB  
Review
The Role of Macronutrients and Gut Microbiota in Neuroinflammation Post-Traumatic Brain Injury: A Narrative Review
by Antonella Cotoia, Ioannis Alexandros Charitos, Alberto Corriero, Stefania Tamburrano and Gilda Cinnella
Nutrients 2024, 16(24), 4359; https://doi.org/10.3390/nu16244359 - 18 Dec 2024
Viewed by 495
Abstract
Traumatic brain injury (TBI) represents a multifaceted pathological condition resulting from external forces that disrupt neuronal integrity and function. This narrative review explores the intricate relationship between dietary macronutrients, gut microbiota (GM), and neuroinflammation in the TBI. We delineate the dual aspects of [...] Read more.
Traumatic brain injury (TBI) represents a multifaceted pathological condition resulting from external forces that disrupt neuronal integrity and function. This narrative review explores the intricate relationship between dietary macronutrients, gut microbiota (GM), and neuroinflammation in the TBI. We delineate the dual aspects of TBI: the immediate mechanical damage (primary injury) and the subsequent biological processes (secondary injury) that exacerbate neuronal damage. Dysregulation of the gut–brain axis emerges as a critical factor in the neuroinflammatory response, emphasizing the role of the GM in mediating immune responses. Recent evidence indicates that specific macronutrients, including lipids, proteins, and probiotics, can influence microbiota composition and in turn modulate neuroinflammation. Moreover, specialized dietary interventions may promote resilience against secondary insults and support neurological recovery post-TBI. This review aims to synthesize the current preclinical and clinical evidence on the potential of dietary strategies in mitigating neuroinflammatory pathways, suggesting that targeted nutrition and gut health optimization could serve as promising therapeutic modalities in TBI management. Full article
(This article belongs to the Special Issue Implications of Diet and the Gut Microbiome in Neuroinflammation)
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<p>The gut microbiota undergoes significant changes following traumatic brain injury (TBI), characterized by the depletion of beneficial species such as <span class="html-italic">Lactobacillus gasseri</span> and <span class="html-italic">Eubacterium ventriosum</span> and an increase in potentially harmful species like <span class="html-italic">Marvinbryantia formatexigens</span> and <span class="html-italic">Eubacterium sulci</span>. This dysbiosis may exacerbate systemic inflammation and negatively impact neurological recovery through the gut–brain axis.</p>
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<p>The main biochemical actions by the GM-friendly bacteria on the CNS.</p>
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<p>The figure demonstrates the influence of the CNS on the gut microbiota and vice versa. It also illustrates the hypothesis of the interconnection between the gut–brain axis and other axes of the microbiota and their influence on homeostasis for the health of the host.</p>
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<p>Some probiotic bacteria that influence the function and structure of neurons.</p>
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23 pages, 842 KiB  
Systematic Review
Neuromechanical Models of Mild Traumatic Brain Injury Conditioned on Reaction Time: A Systematic Review and Meta-Analysis
by Avinash Baskaran, Ross D. Hoehn and Chad G. Rose
J. Clin. Med. 2024, 13(24), 7648; https://doi.org/10.3390/jcm13247648 - 16 Dec 2024
Viewed by 441
Abstract
The accurate, repeatable, and cost-effective quantitative characterization of mild traumatic brain injuries (mTBIs) is crucial for safeguarding the long-term health and performance of high-risk groups, including athletes, emergency responders, and military personnel. However, gaps remain in optimizing mTBI assessment methods, especially regarding the [...] Read more.
The accurate, repeatable, and cost-effective quantitative characterization of mild traumatic brain injuries (mTBIs) is crucial for safeguarding the long-term health and performance of high-risk groups, including athletes, emergency responders, and military personnel. However, gaps remain in optimizing mTBI assessment methods, especially regarding the integration of neuromechanical metrics such as reaction time (RT) in predictive models. Background/Objectives: This review synthesizes existing research on the use of neuromechanical probabilistic models as tools for assessing mTBI, with an emphasis on RT’s role in predictive diagnostics. Methods: We examined 57 published studies on recent sensing technologies such as advanced electromyographic (EMG) systems that contribute data for probabilistic neural imaging, and we also consider measurement models for real-time RT tracking as a diagnostic measure. Results: The analysis identifies three primary contributions: (1) a comprehensive survey of probabilistic approaches for mTBI characterization based on RT, (2) a technical examination of these probabilistic algorithms in terms of reliability and clinical utility, and (3) a detailed outline of experimental requirements for using RT-based metrics in psychomotor tasks to advance mTBI diagnostics. Conclusions: This review provides insights into implementing RT-based neuromechanical metrics within experimental frameworks for mTBI diagnosis, suggesting that such metrics may enhance the sensitivity and utility of assessment and rehabilitation protocols. Further validation studies are recommended to refine RT-based probabilistic models for mTBI applications. Full article
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<p>Existing mTBI assessment tools quantify patients’ reaction dynamics through physical observations by clinicians and are often overly coarse, generalizing, and challenging to perform accurately at the point of injury. Novel methods, including myography and portable neuro-imaging, have enabled robust, fine, and accurate measurement of reaction dynamics associated with mTBI at the point of injury, but the scientific literature reports a wide variety of disparate modeling approaches that frustrate methods to integrate RT into conventional mTBI assessment. This results in a broken chain between fast, reliable RT measurement and mTBI assessment. This work addresses this challenge by presenting and comparing models in the literature for mTBI characterization based on RT, examining the reliability and clinical utility of these models and detailing experimental requirements for using these models in mTBI diagnostics. This provides engineers, clinicians, and researchers with a guide to selecting and implementing mTBI models.</p>
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<p>Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart illustrating the data retrieval protocol used in this work.</p>
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<p>We extracted features pertaining to seven data categories (listed above) from each of the 57 studies selected for review. Quantitative items, including sample size, and statistical significance are used later on to characterize the neuromechanical models used in the studies.</p>
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<p>Above, a histogram of model types used in the selected studies (<b>top</b>) and a map linking inputs to outputs through models used in the selected studies (<b>bottom</b>) are shown. The lines shown in the map represent hypotheses. The thicker dark lines represent the most common hypotheses evaluated, the thinner dark lines represent the lesS common, and the dotted lines represent the least common hypotheses evaluated. The most commonly evaluated hypotheses were that experimental conditions including cognitive constraints (GCC, e.g., distraction tasks) and reaction time constraints (RTC, e.g., timed tasks) can be linked to neural activity (NAM) and cognitive performance (CPM) through time–frequency analysis and stochastic modeling.</p>
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<p>Visual representations of the log sum of Stouffer Z-scores and BMA weights across the various models tested are shown. The study indicates high utility and reliability of analytical time–frequency models and less reliability of machine learning models.</p>
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18 pages, 6778 KiB  
Article
An Interpretable CatBoost Model Guided by Spectral Morphological Features for the Inversion of Coastal Water Quality Parameters
by Baofeng Chen, Yunzhi Chen and Hongmei Chen
Water 2024, 16(24), 3615; https://doi.org/10.3390/w16243615 - 15 Dec 2024
Viewed by 535
Abstract
Chlorophyll-a (Chla) and total suspended solid (TSS) concentrations are important parameters for water quality assessment, and in recent years, machine learning has been shown to have great potential in this field. However, current water quality parameter inversion models lack interpretability and rarely consider [...] Read more.
Chlorophyll-a (Chla) and total suspended solid (TSS) concentrations are important parameters for water quality assessment, and in recent years, machine learning has been shown to have great potential in this field. However, current water quality parameter inversion models lack interpretability and rarely consider the morphological characteristics of the spectrum. To address this limitation, we used Sentinel-3 OLCI data to construct an interpretable CatBoost model guided by spectral morphological characteristics for remote sensing monitoring of Chla and TSS along the coast of Fujian. The results show that the coastal waters of Fujian Province can be divided into five clusters, and the areas of different clusters will change with the alternation of seasons. Clusters 2 and 4 are the main types of coastal waters. The CatBoost model combined with spectral feature engineering has a high accuracy in predicting Chla and TSS, among which Chla is slightly better than TSS (R2 = 0.88, MSE = 8.21, MAPE = 1.10 for Chla predictions; R2 = 0.77, MSE = 380.49, MAPE = 2.48 for TSS predictions). We further conducted an interpretability analysis on the model output and found that the combination of BRI and TBI indexes composed of bands such as b8, b9, and b10 and the fluctuation of spectral curves will have a significant impact on the prediction of model output. The interpretable CatBoost model based on spectral morphological features proposed in this study can provide an effective technical means of estimating the chlorophyll-a and total suspended particulate matter concentrations in the coastal areas of Fujian. Full article
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<p>Location of Fujian Province and study area.</p>
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<p>GLORIA data points used in the study.</p>
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<p>Research flow chart.</p>
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<p>(<b>a</b>) Average spectral curve of each category. (<b>b</b>) Chla concentration distribution of different clusters. (<b>c</b>) TSS concentration distribution of different clusters.</p>
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<p>Clustering results of coastal water bodies in Fujian Province in different seasons. Figures (<b>a</b>–<b>d</b>) show the average maps of water classification for spring, summer, autumn, and winter.</p>
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<p>The structure of the CatBoost algorithm.</p>
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<p>Prediction results of the CatBoost model on the test set (the red line is the trend line).</p>
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<p>Interpretability results of spectral features for CatBoost inversion model of Chla and TSS by SHAP analysis. Figure (<b>a</b>) shows the local explainable results of Chla, Figure (<b>b</b>) shows the global explainable results of Chla, Figure (<b>c</b>) shows the local explainable results of TSS, and Figure (<b>d</b>) shows the global explainable results of TSS. (In the left column chart, one dot represents a sample, where warmer colors indicate larger values of the feature, and vice versa. The wider the distribution of SHAP values for a feature, the larger its global SHAP value, indicating that the feature has a greater impact on the model. In the right column chart, the white numbers on the blue bar represent the average absolute SHAP value [<a href="#B44-water-16-03615" class="html-bibr">44</a>].)</p>
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<p>Annual average concentration distribution map of Chla and TSS along the coast of Fujian Province from 2021 to 2023. (<b>a</b>–<b>c</b>) is the average concentration of Chla, and (<b>d</b>–<b>f</b>) is the average concentration of TSS.</p>
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<p>Average Chla and TSS concentration values in different seasons along the coast of Fujian Province from 2021 to 2023. The four graphs on the left (<b>a</b>–<b>d</b>) show the average concentration of Chla, while the four graphs on the right (<b>e</b>–<b>h</b>) show the average concentration of TSS.</p>
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17 pages, 5155 KiB  
Article
Neuroprotective Effects of Functionalized Hydrophilic Carbon Clusters: Targeted Therapy of Traumatic Brain Injury in an Open Blast Rat Model
by Parasuraman Padmanabhan, Jia Lu, Kian Chye Ng, Dinesh Kumar Srinivasan, Kumar Sundramurthy, Lizanne Greer Nilewski, William K. A. Sikkema, James M. Tour, Thomas A. Kent, Balázs Gulyás and Jan Carlstedt-Duke
Biomedicines 2024, 12(12), 2832; https://doi.org/10.3390/biomedicines12122832 - 13 Dec 2024
Viewed by 379
Abstract
Traumatic brain injury (TBI) causes multiple cerebrovascular disruptions and oxidative stress. These pathological mechanisms are often accompanied by serious impairment of cerebral blood flow autoregulation and neuronal and glial degeneration. Background/Objectives: Multiple biochemical cascades are triggered by brain damage, resulting in reactive oxygen [...] Read more.
Traumatic brain injury (TBI) causes multiple cerebrovascular disruptions and oxidative stress. These pathological mechanisms are often accompanied by serious impairment of cerebral blood flow autoregulation and neuronal and glial degeneration. Background/Objectives: Multiple biochemical cascades are triggered by brain damage, resulting in reactive oxygen species production alongside blood loss and hypoxia. However, most currently available early antioxidant therapies lack capacity and hence sufficient efficacy against TBI. The aim of this study was to test a novel catalytic antioxidant nanoparticle to alleviate the damage occurring in blast TBI. Methods: TBI was elicited in an open blast rat model, in which the rats were exposed to the effects of an explosive blast. Key events of the post-traumatic chain in the brain parenchyma were studied using immunohistochemistry. The application of a newly developed biologically compatible catalytic superoxide dismutase mimetic carbon-based nanocluster, a poly-ethylene-glycol-functionalized hydrophilic carbon cluster (PEG-HCC), was tested post-blast to modulate the components of the TBI process. Results: The PEG-HCC was shown to significantly ameliorate neuronal loss in the brain cortex, the dentate gyrus, and hippocampus when administered shortly after the blast. There was also a significant increase in endothelial activity to repair blood–brain barrier damage as well as the modulation of microglial and astrocyte activity and an increase in inducible NO synthase in the cortex. Conclusions: We have demonstrated qualitatively and quantitatively that the previously demonstrated antioxidant properties of PEG-HCCs have a neuroprotective effect after traumatic brain injury following an explosive blast, acting at multiple levels of the pathological chain of events elicited by TBI. Full article
(This article belongs to the Special Issue Emerging Trends in Traumatic Brain Injury)
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<p>The timeline of the acute experiment from 3 h pre−blast to 3 h post−blast, showing transportation, blast exposure, and injection of the animals.</p>
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<p>The post-traumatic events in brain parenchyma, their interrelationships, and the neuronal markers used for their visualization and quantification in the present study. BBB—blood–brain barrier; CNPase—2′,3′-cyclic-nucleotide 3′-phosphodiesterase; GFAP—glial fibrillary acidic protein; Iba1—ionized calcium-binding adaptor molecule 1; iNOS—inducible nitric oxide synthase; NeuN—neuronal nuclei; RECA-1—rat endothelial cell antigen 1.</p>
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<p>(<b>A</b>) Cortex nuclei immunofluorescence analysis of neurons stained with antibodies to neuronal nuclei (NeuN) (green) and counterstained with 4′,6-diamidino-2-phenylindole (DAPI) for nuclei (blue): examples from the CONTROL (C), PEG (P), and SALINE (S) groups analyzed 3 (P3, S3) and 14 (P14, S14) days post-blast. (<b>B</b>) Average levels of NeuN (normalized to the control) in the PEG and SALINE groups. Coloured bar = mean; open bar = 1 S.D. (<b>C</b>) Statistically significant differences (+ refers to <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>(<b>A</b>) Dentate gyrus nuclear immunofluorescence analysis, labelling neurons with antibodies to neuronal nuclei (NeuN) (green) and counterstained with 4′,6-diamidino-2-phenylindole (DAPI) for nuclei (blue): examples from the CONTROL (C), PEG (P), and SALINE (S) groups analyzed 3 (P3, S3) and 14 (P14, S14) days post-blast. (<b>B</b>) Average levels of NeuN (normalized to the control) in the PEG and SALINE groups. Coloured bar = mean; open bar = 1 S.D. (<b>C</b>) Statistically significant differences (+ refers to <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>(<b>A</b>) Hippocampus nuclear immunofluorescence analysis labelling neurons with antibodies to neuronal nuclei (NeuN) (green) antibodies and counterstained with 4′,6-diamidino-2-phenylindole (DAPI) for nuclei (blue): examples from the CONTROL (C), PEG (P), and SALINE (S) groups analyzed 3 (P3, S3) and 14 (P14, S14) days post-blast. (<b>B</b>) Average levels of NeuN (normalized to the control) in the PEG and SALINE groups. Coloured bar = mean; open bar = 1 S.D. (<b>C</b>) Statistically significant differences (+ refers to <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>(<b>A</b>) Rat endothelial cell antigen (RECA-1) (red) immunofluorescence labelling of cortical neurons that are counterstained with 4′,6-diamidino-2-phenylindole (DAPI) for nuclei (blue): examples from the CONTROL (C), PEG (P), and SALINE (S) groups analyzed 3 (P3, S3) and 14 (P14, S14) days post-blast. (<b>B</b>) Average levels of RECA-1 (normalized to the control) in the PEG and SALINE groups. Coloured bar = mean; open bar = 1 S.D. (<b>C</b>) Statistically significant differences (+ refers to <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>(<b>A</b>) Immunofluorescence labelling of neurons that are stained with antibodies to ionized calcium-binding adaptor molecule 1 (Iba1) (green) and counterstained with 4′,6-diamidino-2-phenylindole (DAPI) (blue) for nuclei in the cortex: examples from the PEG (P) and SALINE (S) groups analyzed 3 (P3, S3) and 14 (P14, S14) days post-blast. The smaller frames are higher-magnification views showing representative microglia cells. (<b>B</b>) The average number of labelled cells/field of view in the PEG and SALINE groups. Coloured bar = mean; open bar = 1 S.D. (<b>C</b>) Statistically significant differences (+ refers to <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>(<b>A</b>) Immunofluorescence labelling of neurons that are stained with antibodies to glial fibrillary acidic protein (GFAP) (green) and counterstained with 4′,6-diamidino-2-phenylindole (DAPI) (blue) for nuclei in the cortex: examples from the CONTROL (C), PEG (P), and SALINE (S) groups analyzed 3 (P3, S3) and 14 (P14, S14) days post-blast. (<b>B</b>) The average levels of GFAP (normalized to the control) in the PEG and SALINE groups. Coloured bar = mean; open bar = 1 S.D. (<b>C</b>) Statistically significant differences (+ refers to <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>(<b>A</b>) Immunofluorescence labelling of neurons that are stained with antibodies to inducible nitic oxide synthase (iNOS) (green) and counterstained with 4′,6-diamidino-2-phenylindole (DAPI) (blue) for nuclei in the cortex: examples from the CONTROL (C), PEG (P), and SALINE (S) groups analyzed 3 (P3, S3) and 14 (P14, S14) days post-blast. (<b>B</b>) The average levels of iNOS (normalized to the control) in the PEG and SALINE groups. Coloured bar = mean; open bar = 1 S.D. (<b>C</b>) Statistically significant differences (+ refers to <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>(<b>A</b>) Immunofluorescence labelling of neurons that are stained with antibodies to 2′,3′-cyclic-nucleotide 3′-phosphodiesterase (CNPase) (red) and counterstained with 4′,6-diamidino-2-phenylindole (DAPI) (blue) for nuclei in the cortex: examples from the CONTROL (C), PEG (P), and SALINE (S) groups analyzed 3 (P3, S3) and 14 (P14, S14) days post-blast. (<b>B</b>) Average levels of CNPase (normalized to the control) in the PEG and SALINE groups. Coloured bar = mean; open bar = 1 S.D. (<b>C</b>) Statistically significant differences (+ refers to <span class="html-italic">p</span> &lt; 0.05).</p>
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14 pages, 613 KiB  
Article
Patterns of Brain Injury and Clinical Outcomes Related to Trauma from Collisions Involving Motor Vehicles
by Bharti Sharma, Aubrey May B. Agcon, George Agriantonis, Samantha R. Kiernan, Navin D. Bhatia, Kate Twelker, Zahra Shafaee and Jennifer Whittington
J. Clin. Med. 2024, 13(24), 7500; https://doi.org/10.3390/jcm13247500 - 10 Dec 2024
Viewed by 365
Abstract
Background: Despite improvements in technology and safety measures, injuries from collisions involving motor vehicles (CIMVs) continue to be prevalent. Therefore, our goal is to investigate the different patterns of head injuries associated with CIMVs. Method: This is a single-center, retrospective study [...] Read more.
Background: Despite improvements in technology and safety measures, injuries from collisions involving motor vehicles (CIMVs) continue to be prevalent. Therefore, our goal is to investigate the different patterns of head injuries associated with CIMVs. Method: This is a single-center, retrospective study of patients with motor vehicle-related trauma between 1 January 2016–31 December 2023. Patients were identified based on the International Classification of Diseases (ICD) injury codes and the Abbreviated Injury Severity (AIS) for body region involvement. Result: 536 patients met the inclusion criteria. The majority of the injured population includes pedestrians (46.8%), followed by motorcycle drivers (25.2%), bicyclists (18.7%), and motor vehicle drivers (9.3%). The male-to-female ratios for bicyclists and motorcyclists were 13.7:1 and 11.9:1, respectively, which is higher compared with motor vehicle occupants and pedestrians, with ratios of 2.3:1 and 1.5:1. Patients with blunt trauma (99.63%) were higher than penetrating trauma (0.37%). In most cases, the head had the highest AIS score, with a mean of 3.7. Additionally, the median Injury Severity Score (ISS) was 20. Skull fractures were the most prevalent, followed by hemorrhages, lacerations, contusions, and abrasions. Conclusions: The most prevalent injuries were head injuries and fractures. Fractures were the most common, followed by hemorrhage, laceration, contusion, and abrasion. These findings underscore the high incidence of TBI and fractures in such CIMVs, highlighting the need for targeted trauma interventions and the need for injury prevention strategies to mitigate these severe outcomes. Full article
(This article belongs to the Special Issue Clinical Advances in Traumatic Brain Injury)
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<p>Receiver Operating Characteristic (ROC) curve comparison for four predictive models evaluating discharge outcomes.</p>
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23 pages, 4842 KiB  
Article
Evaluation of Snowboarding Helmets in Mitigation of the Biomechanical Responses of Head Surrogate
by Atul Harmukh and Shailesh G. Ganpule
Appl. Sci. 2024, 14(23), 11460; https://doi.org/10.3390/app142311460 - 9 Dec 2024
Viewed by 671
Abstract
Traumatic brain injury (TBI) during snowboarding sports is a major concern. A robust evaluation of existing snowboarding helmets is desired. Head kinematics (i.e., linear acceleration, angular velocity, angular acceleration) and associated brain responses (brain pressure, equivalent (von Mises) stress, and maximum principal strain) [...] Read more.
Traumatic brain injury (TBI) during snowboarding sports is a major concern. A robust evaluation of existing snowboarding helmets is desired. Head kinematics (i.e., linear acceleration, angular velocity, angular acceleration) and associated brain responses (brain pressure, equivalent (von Mises) stress, and maximum principal strain) of the head are a predominant cause of TBI or concussion. The conventional snowboarding helmet, which mitigates linear acceleration, is typically used in snow sports. However, the role of conventional snowboarding helmets in mitigating angular head kinematics is marginal or insignificant. In recent years, new anti-rotational technologies (e.g., MIPS, WaveCel) have been developed that seek to reduce angular kinematics (i.e., angular velocity, angular acceleration). However, investigations regarding the performance of snowboarding helmets in terms of the mitigation of head kinematics and brain responses are either extremely limited or not available. Toward this end, we have evaluated the performance of snowboarding helmets (conventional and anti-rotational technologies) against blunt impact. We also evaluated the performance of newly developed low-cost, silica-based anti-rotational pads by integrating them with conventional helmets. Helmets were mounted on a head surrogate–Hybrid III neck assembly. The head surrogate consisted of skin, skull, dura mater, and brain. The geometry of the head surrogate was based on the GHBMC head model. Substructures of the head surrogate was manufactured using additive manufacturing and/or molding. A linear impactor system was used to simulate/recreate snowfield hazards (e.g., tree stump, rock, pole) loading. Following the ASTM F2040 standard, an impact velocity of 4.6 ± 0.2 m/s was used. The head kinematics (i.e., linear acceleration, angular velocity, angular acceleration) and brain simulant pressures were measured in the head surrogate. Further, using the concurrent simulation, the brain simulant responses (i.e., pressure, von Mises stress, and maximum principal strain) were computed. The front and side orientations were considered. Our results showed that the helmets with anti-rotation technologies (i.e., MIPS, WaveCel) significantly reduced the angular kinematics and brain responses compared to the conventional helmet. Further, the performance of the silica pad-based anti-rotational helmet was comparable to the existing anti-rotational helmets. Lastly, the effect of a comfort liner on head kinematics was also investigated. The comfort liner further improved the performance of anti-rotational helmets. Overall, these results provide important data and novel insights regarding the performance of various snowboarding helmets. These data have utility in the design and development of futuristic snowboarding helmets and safety protocols. Full article
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<p>(<b>a</b>) Photographs of the 3D-printed head surrogate integrated with Hybrid III neck (the skin is not shown for the clarity of the photographs), (<b>b</b>) a midsagittal view of the head surrogate for the visualization of the skin, skull, dura mater, and brain simulant.</p>
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<p>Photographs of helmets used in this study (<b>a</b>) conventional helmet, (<b>b</b>) MIPS helmet, (<b>c</b>) WaveCel helmet, (<b>d</b>) silica pad helmet (for the clarity of the photograph of the helmet, only 2 silicas are shown; however, 10 silica pads are present in between the comfort liner and impact mitigation liner).</p>
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<p>(<b>a</b>) A schematic of the linear impactor setup, (<b>b</b>) a photograph depicting the instrumentation in the head surrogate used for front orientation (skin is not shown for the clarity of the photograph), (<b>c</b>) a photograph depicting the instrumentation in the head surrogate used for the side orientation (skin is not shown for the clarity of the photograph), (<b>d</b>) a schematic depicting the pressure sensor locations within the brain simulant for front orientation impact, (<b>e</b>) a schematic depicting the pressure sensor locations within the brain simulant for side orientation impact (the pressure sensor is shown in the top corner).</p>
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<p>Kinematic response (peak angular velocity, peak angular acceleration, peak linear acceleration) of the head surrogate, (<b>a</b>–<b>c</b>) and (<b>d</b>–<b>f</b>) depict the kinematic response for front and side orientations, respectively. * Indicates statistically significant changes (<span class="html-italic">p</span> &lt; 0.05) compared to no helmet. ** indicates statistically significant changes (<span class="html-italic">p</span> &lt; 0.05) compared to the conventional helmet.</p>
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<p>Kinetic response (brain simulant pressures) of the head surrogate: positive and negative values represent the coup and contrecoup pressures, respectively, in the brain simulant. (<b>a</b>,<b>b</b>) depicts the brain simulant pressures for the front and side orientations, respectively. * Indicates statistically significant changes (<span class="html-italic">p</span> &lt; 0.05) compared to no helmet. ** Indicates statistically significant changes (<span class="html-italic">p</span> &lt; 0.05) compared to the conventional helmet.</p>
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<p>Kinematic response of head surrogate with conventional and anti-rotational helmets (‘with comfort liner’ and ‘without comfort liner’). (<b>a</b>–<b>c</b>) and (<b>d</b>–<b>f</b>) depict the kinematic response for front and side orientations, respectively. * Shows the significant changes (<span class="html-italic">p</span> &lt; 0.05) in the ‘without comfort liner’ helmet configurations compared to the ‘with comfort liner’ helmet configurations.</p>
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<p>Spatiotemporal evolution of brain simulant pressure in the front (<b>a</b>) and side (<b>b</b>) orientations. Here, rows A, B, C, D, and E represent the no-helmet, conventional helmet, MIPS helmet, WaveCel helmet, and silica pad helmet configurations, respectively.</p>
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<p>Spatiotemporal evolution of equivalent (von Mises) stress (<b>a</b>,<b>b</b>), and MPS (<b>c</b>,<b>d</b>) in the brain simulant for front and side orientations. Here, rows A, B, C, D, and E represent the no-helmet, conventional helmet, MIPS helmet, WaveCel helmet, and silica pad helmet configurations, respectively.</p>
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15 pages, 1335 KiB  
Article
Lactate Is a Strong Predictor of Poor Outcomes in Patients with Severe Traumatic Brain Injury
by Bharti Sharma, Winston Jiang, Yashoda Dhole, George Agriantonis, Navin D. Bhatia, Zahra Shafaee, Kate Twelker and Jennifer Whittington
Biomedicines 2024, 12(12), 2778; https://doi.org/10.3390/biomedicines12122778 - 6 Dec 2024
Viewed by 552
Abstract
Background: Lactate is a byproduct of glycolysis, often linked to oxygen deprivation. This study aimed to examine how lactate levels (LLs) affect clinical outcomes in patients with severe TBI, hypothesizing that higher LLs would correlate with worse outcomes. Methods: This is a [...] Read more.
Background: Lactate is a byproduct of glycolysis, often linked to oxygen deprivation. This study aimed to examine how lactate levels (LLs) affect clinical outcomes in patients with severe TBI, hypothesizing that higher LLs would correlate with worse outcomes. Methods: This is a level 1 single-center, retrospective study of patients with severe TBI between 1 January 2020 and 31 December 2023, inclusive. Results: Single-factor ANOVA indicated a significant decrease in LLs with increasing age. Linear regression models showed the same for hospital admission, Intensive Care Unit (ICU) admission LLs, and death LLs. Prognostic scores such as Injury Severity Scores (ISS) and Glasgow Coma Score (GCS) showed a strong correlation with both Hospital admission and ICU admission LLs. ANOVA indicated higher LLs with increasing ISS and increasing LLs with decreasing GCS. Linear regressions revealed a strong positive correlation between ISS and LLs. On linear regression, the LL measured at hospital admission and ICU admission was positively associated with the length of stay (LOS) in the hospital, LOS in the ICU, ventilator days, and mortality. Linear regression models showed that a decreased delta LL during ICU admission led to an increased LOS at the hospital and the ICU, as well as a higher number of days on a ventilator. Discussion: We discovered that high LLs were linked to higher AIS and GCS scores, longer stays in the hospital and ICU, more days requiring a ventilator, and higher mortality rates in patients with severe TBI. Conclusions: LLs can be considered a strong predictor of poor clinical outcomes in patients with severe TBI. Full article
(This article belongs to the Section Neurobiology and Clinical Neuroscience)
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<p>This figure outlines the distributions of injury mechanisms for each of the six traumatic brain injury (TBI) diagnoses. Each diagnosis displays the number of patients within each of the seven mechanisms of injury categories. It shows the different classifications of intracranial injuries with stratification by the mechanism of injury and the number of incidences of each that was seen in our study sample, with falls being by far the most common mechanism of injury, particularly in subdural and subarachnoid hemorrhage.</p>
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<p>Linear regression analysis demonstrates a statistically significant correlation between admission LLs and hospital length of stay (LOS), measured in days. The line of best fit with confidence intervals shows a gradual upward trend and a positive correlation between the lactate level at admission and the hospital LOS. The correlation coefficient between lactate level at admission and hospital LOS was 0.7903.</p>
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<p>This illustrates a box plot indicating average LLs among patients who survived their injury and those who experienced death during hospitalization. Average admission LL was significantly higher for cases with mortality. Average LLs upon admission to the trauma bay among patients who survived their injury were lower than those who died during the hospitalization, with 0 indicating patients who survived and 1 indicating patients who did not.</p>
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16 pages, 1286 KiB  
Review
Resuscitation and Initial Management After Moderate-to-Severe Traumatic Brain Injury: Questions for the On-Call Shift
by Jesús Abelardo Barea-Mendoza, Mario Chico-Fernández, Maria Angeles Ballesteros, Alejandro Caballo Manuel, Ana M. Castaño-Leon, J. J. Egea-Guerrero, Alfonso Lagares, Guillermo Morales-Varas, Jon Pérez-Bárcena, Luis Serviá Goixart and Juan Antonio Llompart-Pou
J. Clin. Med. 2024, 13(23), 7325; https://doi.org/10.3390/jcm13237325 - 2 Dec 2024
Viewed by 880
Abstract
Traumatic brain injury (TBI) is a leading cause of disability and mortality globally, stemming from both primary mechanical injuries and subsequent secondary responses. Effective early management of moderate-to-severe TBI is essential to prevent secondary damage and improve patient outcomes. This review provides a [...] Read more.
Traumatic brain injury (TBI) is a leading cause of disability and mortality globally, stemming from both primary mechanical injuries and subsequent secondary responses. Effective early management of moderate-to-severe TBI is essential to prevent secondary damage and improve patient outcomes. This review provides a comprehensive guide for the resuscitation and stabilization of TBI patients, combining clinical experience with current evidence-based guidelines. Key areas addressed in this study include the identification and classification of severe TBI, intubation strategies, and optimized resuscitation targets to maintain cerebral perfusion. The management of coagulopathy and special considerations for patients with concomitant hemorrhagic shock are discussed in depth, along with recommendations for neurosurgical interventions. This article further explores the role of multimodal neuromonitoring and targeted temperature management to mitigate secondary brain injury. Finally, it discusses end-of-life care in cases of devastating brain injury (DBI). This practical review integrates foundational and recent advances in TBI management to aid in reducing secondary injuries and enhancing long-term recovery, presenting a multidisciplinary approach to support acute care decisions in TBI patients. Full article
(This article belongs to the Special Issue Neurocritical Care: New Insights and Challenges)
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<p>The HEAD bundle for intubation.</p>
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<p>Approach to reversal of anticoagulation in traumatic brain hemorrhage. PCC: four-factor prothrombin complex concentrate; INR: international normalized ratio; CT: computed tomography; max: maximum; IU: international units; g: grams; mg: milligrams. * In patients with TBI and normal CT, it is advisable to discontinue anticoagulation, perform an observation period, and repeat CT before restarting anticoagulation. † Activated charcoal is used in spontaneous cerebral hemorrhage if the last intake is less than 8 h. In traumatic brain hemorrhage, it may interfere with the initial care of major trauma.</p>
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<p>Neuroimaging workflow in moderate-to-severe TBI. * If DAI is suspected (unexplained neurological findings), schedule an MRI. † In patients with early (&lt;2–3 h from injury) initial pathological CT scan, consider earlier follow-up.</p>
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26 pages, 14168 KiB  
Article
Enhancing Leaf Area Index Estimation in Southern Xinjiang Fruit Trees: A Competitive Adaptive Reweighted Sampling-Successive Projections Algorithm and Three-Band Index Approach with Fractional-Order Differentiation
by Mamat Sawut, Xin Hu, Asiya Manlike, Ainiwan Aimaier, Jintao Cui and Jiaxi Liang
Forests 2024, 15(12), 2126; https://doi.org/10.3390/f15122126 - 1 Dec 2024
Viewed by 585
Abstract
The Leaf Area Index (LAI) is a key indicator for assessing fruit tree growth and productivity, and accurate estimation using hyperspectral technology is essential for monitoring plant health. This study aimed to improve LAI estimation accuracy in apricot, jujube, and walnut trees in [...] Read more.
The Leaf Area Index (LAI) is a key indicator for assessing fruit tree growth and productivity, and accurate estimation using hyperspectral technology is essential for monitoring plant health. This study aimed to improve LAI estimation accuracy in apricot, jujube, and walnut trees in Xinjiang, China. Canopy hyperspectral data were processed using fractional-order differentiation (FOD) from 0 to 2.0 orders to extract spectral features. Three feature selection methods—Competitive Adaptive Reweighted Sampling (CARS), Successive Projections Algorithm (SPA), and their combination (CARS-SPA)—were applied to identify sensitive spectral bands. Various band combinations were used to construct three-band indices (TBIs) for optimal LAI estimation. Random forest (RF) models were developed and validated for LAI prediction. The results showed that (1) the reflectance spectra of jujube and walnut trees were similar, while apricot spectra differed. (2) The correlation between fractional-order differential spectra and LAI was highest at orders 1.4 and 1.7, outperforming integer-order spectra. (3) The CARS-SPA selected a smaller set of feature bands in the 1100~2500 nm, reducing collinearity and improving spectral index construction. (4) The RF model using TBI4 demonstrated high R², low RMSE, and an RPD value > 2, indicating optimal prediction accuracy. This approach holds promise for hyperspectral LAI monitoring in fruit trees. Full article
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
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Figure 1
<p>Overview of the study area and sampling point distribution. (<b>a</b>) is sampling point distribution, (<b>b</b>) is location of Xinjiang, (<b>c</b>) is location of study, (<b>d</b>) is apricot canopy, (<b>e</b>) is jujube canopy, (<b>f</b>) is walnut canopy, (<b>g</b>) is apricot leaf, (<b>h</b>) is jujube leaf, (<b>i</b>) is walnut leaf.</p>
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<p>Flowchart of this study.</p>
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<p>Distribution of LAI for three fruit tree species.</p>
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<p>Reflectance spectral curves of different fruit trees.</p>
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<p>The 0~2.0 order differential spectral mean curves for different fruit trees.</p>
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<p>Feature band distribution of apricot, jujube, and walnut. Among them, (<b>a</b>–<b>c</b>) show the distribution of the CARS, SPA, CARS-SPA screening feature bands of apricot trees respectively. (<b>d</b>–<b>f</b>) show the distribution of the CARS, SPA, CARS-SPA screening feature bands of jujube trees respectively. (<b>g</b>–<b>i</b>) show the distribution of the CARS, SPA, CARS-SPA screening feature bands of apricot trees respectively.</p>
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<p>Results of multicollinearity analysis for CARS-SPA-selected feature bands based on FOD-1.7 transformation.</p>
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<p>Correlation (r) between LAI and TBIs for apricot.</p>
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<p>Correlation (r) between LAI and TBIs for jujube.</p>
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<p>Correlation (r) between LAI and TBIs for walnut.</p>
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<p>Scatter diagrams of predicted and measured LAI values of the optimal estimation model.</p>
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<p>Residual normal distribution of optimal inversion models for different fruit trees.</p>
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<p>Correlation (r) between mixed data set based on TBI4 and LAI.</p>
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<p>Scatter diagrams of predicted and measured LAI values of the optimal estimation model based on Mixed Sample Set.</p>
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<p>Residual normal distribution of the optimal estimation model based on Mixed Sample Set.</p>
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