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9 pages, 215 KiB  
Review
Hyperphosphorylated Tau and Cognition in Epilepsy
by Juri-Alexander Witt, Johanna Andernach, Albert Becker and Christoph Helmstaedter
J. Clin. Med. 2025, 14(2), 514; https://doi.org/10.3390/jcm14020514 - 15 Jan 2025
Viewed by 238
Abstract
In light of the growing interest in the bidirectional relationship between epilepsy and dementia, this review aims to provide an overview of the role of hyperphosphorylated tau (pTau) in cognition in human epilepsy. A literature search identified five relevant studies. All of them [...] Read more.
In light of the growing interest in the bidirectional relationship between epilepsy and dementia, this review aims to provide an overview of the role of hyperphosphorylated tau (pTau) in cognition in human epilepsy. A literature search identified five relevant studies. All of them examined pTau burden in surgical biopsy specimens from patients with temporal lobe epilepsy. The prevalence of pTau reported across the five studies, encompassing a total of 142 patients, ranged from 3.5% to 95%. Findings also varied regarding the location of pTau in the hippocampus and/or temporal cortex. Two of five studies (40%) demonstrated an inverse relationship between pTau burden and cognitive performance, one study with regard to executive functions and the other with regard to naming and verbal short-term memory. The only longitudinal study found a significant link between pTau and cognitive decline in verbal learning and memory, and in part also in naming, from the pre- to the postoperative assessment and from three to 12 months postoperatively. Given the heterogeneity of the study cohorts and the neuropsychological and neuropathological methodologies and findings, no clear picture emerges regarding the association between pTau and cognition in temporal lobe epilepsy. Added to this is the multifactorial etiology of cognitive impairment in epilepsy, including the active epilepsy, the underlying and sometimes dynamic pathology, and anti-seizure medication. Some of these factors may affect pTau expression. Further research should aim to investigate pTau longitudinally and noninvasively on a whole-brain level, using targeted neuropsychological outcome measures and controlling for age and other factors potentially influencing cognitive trajectories in epilepsy. Full article
(This article belongs to the Special Issue New Trends in Diagnosis and Treatment of Epilepsy)
27 pages, 856 KiB  
Review
Alzheimer’s Disease and Porphyromonas gingivalis: Exploring the Links
by Ivana Shawkatova, Vladimira Durmanova and Juraj Javor
Life 2025, 15(1), 96; https://doi.org/10.3390/life15010096 - 14 Jan 2025
Viewed by 321
Abstract
Recent research highlights compelling links between oral health, particularly periodontitis, and systemic diseases, including Alzheimer’s disease (AD). Although the biological mechanisms underlying these associations remain unclear, the role of periodontal pathogens, particularly Porphyromonas gingivalis, has garnered significant attention. P. gingivalis, a [...] Read more.
Recent research highlights compelling links between oral health, particularly periodontitis, and systemic diseases, including Alzheimer’s disease (AD). Although the biological mechanisms underlying these associations remain unclear, the role of periodontal pathogens, particularly Porphyromonas gingivalis, has garnered significant attention. P. gingivalis, a major driver of periodontitis, is recognized for its potential systemic effects and its putative role in AD pathogenesis. This review examines evidence connecting P. gingivalis to hallmark AD features, such as amyloid β accumulation, tau hyperphosphorylation, neuroinflammation, and other neuropathological features consistent with AD. Virulence factors, such as gingipains and lipopolysaccharides, were shown to be implicated in blood–brain barrier disruption, neuroinflammation, and neuronal damage. P. gingivalis-derived outer membrane vesicles may serve to disseminate virulence factors to brain tissues. Indirect mechanisms, including systemic inflammation triggered by chronic periodontal infections, are also supposed to exacerbate neurodegenerative processes. While the exact pathways remain uncertain, studies detecting P. gingivalis virulence factors and its other components in AD-affected brains support their possible role in disease pathogenesis. This review underscores the need for further investigation into P. gingivalis-mediated mechanisms and their interplay with host responses. Understanding these interactions could provide critical insights into novel strategies for reducing AD risk through periodontal disease management. Full article
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<p>A schematic illustration showing <span class="html-italic">P. gingivalis</span> colonizing subgingival pockets in periodontitis and spreading its virulence factors via OMVs to the brain, potentially contributing to the development of Alzheimer’s pathology. Image created by the authors using Illustrator.</p>
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14 pages, 6048 KiB  
Article
Leptomeningeal Dissemination in Choroid Plexus Tumors: Magnetic Resonance Imaging Appearance and Risk Factors
by Daniel Nunes do Espirito Santo, Monika Warmuth-Metz, Camelia-Maria Monoranu, Martin Hasselblatt, Christian Thomas, Torsten Pietsch, Jürgen Krauß, Tilmann Schweitzer, Brigitte Bison, Matthias Eyrich, Uwe Kordes, Denise Obrecht-Sturm, Mirko Pham and Annika Quenzer
Children 2025, 12(1), 82; https://doi.org/10.3390/children12010082 - 11 Jan 2025
Viewed by 362
Abstract
Background: Intracranial choroid plexus tumors (CPT) are rare and primarily affect young children. Leptomeningeal dissemination (LMD) has been reported not only in high-grade choroid plexus carcinoma (CPC) but also in lower histological grades; however, a systematic evaluation of CPT-specific imaging characteristics remains lacking. [...] Read more.
Background: Intracranial choroid plexus tumors (CPT) are rare and primarily affect young children. Leptomeningeal dissemination (LMD) has been reported not only in high-grade choroid plexus carcinoma (CPC) but also in lower histological grades; however, a systematic evaluation of CPT-specific imaging characteristics remains lacking. Methods: We analyzed the imaging characteristics of LMD in a single-center pediatric cohort of 22 CPT patients (thirteen choroid plexus papilloma (CPP), six atypical choroid plexus papilloma (aCPP), three CPC), comparing LMD features with those of the primary tumor. Additionally, we examined the correlation between resection status and LMD development during follow-up. Results: At diagnosis, we observed true LMD in three (two CPCs, one CPP) and pseudo-LMD in one case (CPP). During follow-up, two CPP patients developed cystic LMD, and one aCPP patient developed a solid metastasis. LMD had characteristics of the primary tumor in 3/4 cases. Incomplete resection was associated with a higher risk of LMD (p = 0.025). Conclusions: LMD can occur in both high- and lower-grade CPT, presenting at diagnosis as well as in relapsed lower-grade cases. Notable MR-imaging features include pseudo-LMD at diagnosis and cystic LMD in relapsed CPP cases. Incomplete tumor resection may increase the risk of LMD, although further validation is needed. Full article
(This article belongs to the Section Pediatric Radiology)
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<p>Figure presents all cases with true leptomeningeal dissemination (LMD) at diagnosis. (<b>A</b>) Case of a supratentorial choroid plexus carcinoma with simultaneous LMD in the right cerebellopontine angle (<b>a</b>), which, according to imaging characteristics, is very similar to the tumor and exhibits small cystic components. (<b>B</b>) Case of a purely solid plexus papilloma with also solid LMD in the left internal auditory meatus (arrow in <b>b1</b>) as well at the level S1/2 (arrow in <b>b2</b>). (<b>C</b>) Case of a large supratentorial choroid plexus carcinoma with laminar LMD along the anterior brainstem (arrow in <b>c</b>).</p>
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<p>A 1-year-old male CPP patient with pseudo leptomeningeal dissemination at diagnosis. Contrast-enhanced T1-wheigted images show the course from presurgery to pre-chemotherapy in sagittal (<b>A</b>–<b>C</b>) and transverse planes (<b>a</b>–<b>c</b>). (<b>A</b>,<b>a</b>) Contrast enhanced T1-wheighted image shows the primary tumor and the diffuse leptomeningeal contrast enhancement at the skull base, especially in the interpeduncular cistern as demonstrated in (arrow in <b>a</b>). (<b>B</b>,<b>b</b>) The diffuse contrast enhancement was still detectable immediately after surgery. (<b>C</b>,<b>c</b>) However, the contrast enhancement had regressed in an MRI performed 4 weeks later before the start of chemotherapy.</p>
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<p>This Figure shows the two CPP patients, who presented with new cystic leptomeningeal lesions during follow-up after incomplete primary tumor resection. (<b>A</b>) A 3 years old male patient with CPP. The primary tumor had no cystic components. Three months after surgery, first cystic leptomeningeal lesions were noticed (arrow in <b>a</b>). (<b>B</b>) A 6 years old male CPP patient initially presented intratumoral cysts. Cystic leptomeningeal lesions without contrast enhancement appeared four years after diagnosis (arrows in <b>b</b>).</p>
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5 pages, 208 KiB  
Editorial
Post-COVID-19 Neuropathology and Perspectives of Protective Roles of Estrogens
by Rodrigo Portes Ureshino, Roberta Sessa Stilhano, Carla Máximo Prado and Louise Newson
COVID 2025, 5(1), 9; https://doi.org/10.3390/covid5010009 - 9 Jan 2025
Viewed by 660
Abstract
Epidemiological data revealed that COVID-19 (Coronavirus disease 2019) is more prevalent and lethal among the elderly population [...] Full article
(This article belongs to the Special Issue Exploring Neuropathology in the Post-COVID-19 Era)
14 pages, 3952 KiB  
Article
Multivariate Analyses with Two-Step Dimension Reduction for an Association Study Between 11C-Pittsburgh Compound B and Magnetic Resonance Imaging in Alzheimer’s Disease
by Atsushi Kawaguchi and Fumio Yamashita
Bioengineering 2025, 12(1), 48; https://doi.org/10.3390/bioengineering12010048 - 9 Jan 2025
Viewed by 380
Abstract
The neuropathological diagnosis of Alzheimer’s disease (AD) relies on amyloid beta (Aβ) deposition in brain tissues. To study the relationship between Aβ deposition and brain structure, as determined using 11C-Pittsburgh compound B (PiB) and magnetic resonance imaging (MRI), respectively, we developed a [...] Read more.
The neuropathological diagnosis of Alzheimer’s disease (AD) relies on amyloid beta (Aβ) deposition in brain tissues. To study the relationship between Aβ deposition and brain structure, as determined using 11C-Pittsburgh compound B (PiB) and magnetic resonance imaging (MRI), respectively, we developed a regression model with PiB and MRI data as the predictor and response variables, respectively, and proposed a regression method for studying the association between them based on a supervised sparse multivariate analysis with dimension reduction based on a composite paired basis function. By applying this method to imaging data of 61 patients with AD (age: 55–85), the first component showed the strongest correlation with the composite score, owing to the supervised feature. The spatial pattern included the hippocampal and parahippocampal regions for MRI. The peak value was observed in the posterior cingulate and precuneus for PiB. The differences in PiB scores among the diagnosis groups 12 months after PiB imaging were significant between the normal and AD groups (p = 0.0284), but not between the normal and mild cognitive impairment (MCI) groups or the MCI and AD groups (p = 0.3508). Our method may facilitate the development of a dementia biomarker from brain imaging data. Scoring imaging data allows for visualization and the application of traditional analysis, facilitating clinical analysis for better interpretation of results. Full article
(This article belongs to the Special Issue Advances in Brain Magnetic Resonance Imaging)
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Graphical abstract

Graphical abstract
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<p>Composite paired basis functions. The heat color represents the shape of the function.</p>
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<p>True images for the simulation study.</p>
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<p>Spatial pattern results in the SSMA model.</p>
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<p>PiB scores in the SSMA model.</p>
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<p>Probability 2D maps for the result of the simulation study.</p>
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13 pages, 1964 KiB  
Article
Effects of Aging on Orbicularis Oculi Muscle Strength and Ultrastructure in Dermatochalasis: A Pilot Study
by Larysa Krajewska-Węglewicz, Paulina Felczak and Małgorzata Dorobek
J. Clin. Med. 2025, 14(1), 162; https://doi.org/10.3390/jcm14010162 - 30 Dec 2024
Viewed by 376
Abstract
Background: Age-related changes to the orbicularis oculi muscle include impaired eyelid function, such as lagophthalmos, alterations in tear film dynamics, and aesthetic changes like wrinkles, festoons, and the descent of soft tissue. To date, the structural and functional changes that would comprehensively increase [...] Read more.
Background: Age-related changes to the orbicularis oculi muscle include impaired eyelid function, such as lagophthalmos, alterations in tear film dynamics, and aesthetic changes like wrinkles, festoons, and the descent of soft tissue. To date, the structural and functional changes that would comprehensively increase our understanding of orbicularis aging have not been analyzed. This study aims to investigate functional outcomes using surface electromyography and correlate them with ultrastructural changes in orbicularis during aging. Methods: This study enrolled 26 patients aged 37 to 78 years with a clinical diagnosis of dermatochalasis. Patients were divided into two age groups (<60 years; ≥60 years). Ultrastructural and electromyographical examinations were performed, and the electromyographical signals were correlated with the ultrastructural damage in the orbicularis. Results: This study revealed significantly lower values of average voluntary contraction and RMS of the surface electromyography signals in the older age group compared to the younger age group (p = 0.029 and p = 0.045, respectively). There was no statistically significant association between age and muscle damage (χ2(2) = 2.86, p > 0.05). There was no correlation between average voluntary contraction and the degree of ultrastructural damage in both groups (Spearman’s coefficient equaled 0.06923 and 0.64366, respectively). Conclusions: sEMG measurements are valuable for monitoring age-related functional changes in the orbicularis. Aging diminishes the functional capacity of the orbicularis, as evidenced by reduced contraction strength. This study, the first to compare ultrastructural and electromyographical changes in the orbicularis among dermatochalasis patients of different ages, finds that ultrastructural damage to muscle fibers is not directly responsible for the contraction strength decline. Full article
(This article belongs to the Special Issue Advances in Ophthalmic Plastic and Reconstructive Surgery)
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<p>Severely deformed, atrophic, spindle-shaped orbicular muscle (Orb) in a patient aged 37 years. A large, central nucleus (N) and scattered remnants of preserved myofilaments (thin arrow) are located in this fiber. Fragments of other damaged muscles with loss of myofilaments are visible nearby (thick arrow). M—mitochondria. The overall architecture of the muscle is disrupted, with compromised structural integrity and clear signs of degeneration, such as altered fiber shapes and the absence of normal myofibrillar organization. Orign. magn. ×20,000.</p>
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<p>Mildly damaged orbicularis oculi muscle (Orb) on the longitudinal section in patients aged 75 years. Significant Z line (arrow) irregularities and vacuoles (V) in some mitochondria (M) are visible. Vacuoles manifest as clear, membrane-bound spaces within the mitochondria. N—nucleus. Orign. magn. ×15,000.</p>
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16 pages, 7989 KiB  
Article
Glioma Image-Level and Slide-Level Gene Predictor (GLISP) for Molecular Diagnosis and Predicting Genetic Events of Adult Diffuse Glioma
by Minh-Khang Le, Masataka Kawai, Kenta Masui, Takashi Komori, Takakazu Kawamata, Yoshihiro Muragaki, Tomohiro Inoue, Ippei Tahara, Kazunari Kasai and Tetsuo Kondo
Bioengineering 2025, 12(1), 12; https://doi.org/10.3390/bioengineering12010012 - 27 Dec 2024
Viewed by 469
Abstract
The latest World Health Organization (WHO) classification of central nervous system tumors (WHO2021/5th) has incorporated molecular information into the diagnosis of each brain tumor type including diffuse glioma. Therefore, an artificial intelligence (AI) framework for learning histological patterns and predicting important genetic events [...] Read more.
The latest World Health Organization (WHO) classification of central nervous system tumors (WHO2021/5th) has incorporated molecular information into the diagnosis of each brain tumor type including diffuse glioma. Therefore, an artificial intelligence (AI) framework for learning histological patterns and predicting important genetic events would be useful for future studies and applications. Using the concept of multiple-instance learning, we developed an AI framework named GLioma Image-level and Slide-level gene Predictor (GLISP) to predict nine genetic abnormalities in hematoxylin and eosin sections: IDH1/2, ATRX, TP53 mutations, TERT promoter mutations, CDKN2A/B homozygous deletion (CHD), EGFR amplification (EGFRamp), 7 gain/10 loss (7+/10−), 1p/19q co-deletion, and MGMT promoter methylation. GLISP consists of a pair of patch-level GLISP-P and patient-level GLISP-W models, each pair of which is for a genetic prediction task, providing flexibility in clinical utility. In this study, the Cancer Genome Atlas whole-slide images (WSIs) were used to train the model. A total of 108 WSIs from the Tokyo Women’s Medical University were used as the external dataset. In cross-validation, GLISP yielded patch-level/case-level predictions with top performances in IDH1/2 and 1p/19q co-deletion with average areas under the curve (AUCs) of receiver operating characteristics of 0.75/0.79 and 0.73/0.80, respectively. In external validation, the patch-level/case-level AUCs of IDH1/2 and 1p/19q co-deletion detection were 0.76/0.83 and 0.78/0.88, respectively. The accuracy in diagnosing IDH-mutant astrocytoma, oligodendroglioma, and IDH-wild-type glioblastoma was 0.66, surpassing the human pathologist average of 0.62 (0.54–0.67). In conclusion, GLISP is a two-stage AI framework for histology-based prediction of genetic events in adult gliomas, which is helpful in providing essential information for WHO 2021 molecular diagnoses. Full article
(This article belongs to the Special Issue Computational Pathology and Artificial Intelligence)
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Figure 1
<p>(<b>A</b>) Data mining process. We accessed cBioportal to retrieve the information of filtered and high-quality information about genetic events, including <span class="html-italic">IDH1/2</span>, <span class="html-italic">ATRX</span>, <span class="html-italic">TP53</span>, and <span class="html-italic">TERT</span> promoter mutations, <span class="html-italic">CDKN2A/B</span> homozygous deletion, <span class="html-italic">EGFR</span> amplification, 7 gain/10 loss chromosomal abnormalities, 1p/19q co-deletion, and <span class="html-italic">MGMT</span> hypermethylation. (<b>B</b>) Materials of the study. Among 1123 cases (n = 617 of the TCGA-GBM and n = 516 of the TCGA-LGG), simple nucleotide variation (SNV) and copy number variation (CNV) are available in 877 and 1019 cases, respectively. The number of corresponding SNV-available and CNV-available slides are 1257 and 1484, respectively. (<b>C</b>) The bar graph shows the availability of data in the 9 genetic events of interest. (<b>D</b>) The representatives of 256 × 256 pixels are used in the deep learning training process. (<b>E</b>) The study design. Since the data is heterogeneous, the cases are randomly assigned to train and test data, independently of other tasks. TCGA: The Cancer Genome Atlas. WSI: whole slide image. TWMU: Tokyo Women’s Medical University.</p>
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<p>(<b>A</b>) In the training process, random flipping, random rotation, color jittering, and Gaussian blurring of the training patches are randomly applied for data augmentation. (<b>B</b>) The table shows the technical details of input data and hyperparameters of the training process. (<b>C</b>) The architecture of the patch-level model (GLISP-P) includes four layers of convolutional operations and a fully connected layer. The structure of the GLISP-W consists of two multi-layer perceptrons (MLP) with an attention-pooling operation in between. MIL: multiple instance learning. FC: fully connected layer.</p>
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<p>The GLISP-incorporating diagnostic workflow performs AI-based molecular diagnosis. GLISP models are beneficial in an integrated workflow. First, the pathologist examines and recognizes the glioma morphology of a brain tumor. GLISP<sub>IDH</sub> then categorizes the tumor based on IDH status. If an IDH-mutant phenotype is predicted, subsequent examinations of molecular statuses by GLISP<sub>1p/19q-codel</sub>, GLISP<sub>CHD</sub>, and the presence of microvascular proliferation (MVP) and/or necrosis can provide a more molecular-integrated diagnosis compared to the classic diagnosis. If an IDH-wildtype phenotype is detected, MVP/necrosis, as well as molecular statuses predicted by GLISP<sub>TERT</sub>, GLISP<sub>EGFR</sub>, and GLISP<sub>7+10−</sub>, can be useful for further molecular diagnosis.</p>
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<p>Patch-level (<b>A</b>) and WSI-level (<b>B</b>) evaluation of GLISP in predicting <span class="html-italic">IDH1/2</span> mutation and 1p/19q, including receiver operating characteristics (ROC) analysis and confusion matrix. (<b>C</b>) Most informative patches for positive (<b>left</b>) and negative (<b>right</b>) prediction of GLISP are measured by the patch-level DeepLIFT scores.</p>
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<p>Subpopulation analysis. (<b>A</b>) The negative predictive values of predicting 7+/10–, <span class="html-italic">EGFR</span> amplification, <span class="html-italic">TERT</span> promoter mutation in IDH-wildtype phenotype (context 1). (<b>B</b>) The receiver operating characteristics (ROC) analysis with 95%CI band of predicting <span class="html-italic">CDKN2A/B</span> homozygous deletion in IDH-mutant phenotype (context 2). The ROC analyses of predicting 1p/19q co-deletion in IDH-mutant phenotype in TCGA (<b>C</b>) and TWMU datasets (<b>D</b>) (context 3).</p>
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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 1219
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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13 pages, 1823 KiB  
Article
Postoperative Vision-Related Quality of Life After Sphenoid Wing Meningioma Surgery: Impact of Radiomic Shape Features and Age
by Alim Emre Basaran, Martin Vychopen, Clemens Seidel, Alonso Barrantes-Freer, Felix Arlt, Erdem Güresir and Johannes Wach
J. Clin. Med. 2025, 14(1), 40; https://doi.org/10.3390/jcm14010040 - 25 Dec 2024
Viewed by 356
Abstract
Background: Sphenoid wing meningiomas (SWM) frequently compress structures of the optic pathway, resulting in significant visual dysfunction characterized by vision loss and visual field deficits, which profoundly impact patients’ quality of life (QoL), daily activities, and independence. The objective of this study was [...] Read more.
Background: Sphenoid wing meningiomas (SWM) frequently compress structures of the optic pathway, resulting in significant visual dysfunction characterized by vision loss and visual field deficits, which profoundly impact patients’ quality of life (QoL), daily activities, and independence. The objective of this study was to assess the impact of SWM surgery on patient-reported outcome measures (PROMs) regarding postoperative visual function. Methods: The Visual Function Score Questionnaire (VFQ-25) is a validated tool designed to assess the impact of visual impairment on quality of life. The questionnaire was distributed to a previously published study population in which shape radiomics were correlated with new cranial nerve deficits after SWM surgery. Results: A total of 42 patients (42/74; 56.8%) responded to the questionnaire. Of the 42 patients, 30 were female (71%) and 12 were male (29%). The multivariable analysis demonstrated that lower sphericity reflecting irregular SWM shape was associated with poorer VFQ-25 (OR: 6.8, 95% CI: 1.141.8, p = 0.039), while age was associated with lower VFQ-25 (OR: 27, 95% CI: 2.7−272.93, p = 0.005), too. Analysis of the subcategories of the VFQ-25 revealed significantly reduced general vision (p = 0.045), social functioning (p = 0.045), and peripheral vision (p = 0.017) in those with SWM with low sphericity. Conclusions: The study highlights that SWM surgery impacts postoperative visual function, with age and irregular SWM shape being associated with poorer postoperative VFQ-25 scores. VFQ-25 is a feasible tool to assess vision outcome in SWM surgery and has clinical potential for longitudinal follow-up evaluations. Irregular SWM shape should be considered during preoperative treatment planning and patient consultation regarding functional outcome. Full article
(This article belongs to the Special Issue Neuro-Oncology: Diagnosis and Treatment)
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<p>Flowchart summarizing the selection process of participants for the study on MSWM. Out of 101 neurosurgically treated patients from January 2010 to December 2021, 27 were excluded due to additional malignancies, recurrent meningioma, or prior intracranial surgery. The final cohort included 74 patients with MSWM, of which 42 provided informed consent to participate in the study.</p>
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<p>(<b>A</b>) The ROC curve shows the discriminative ability of patient age at the time of surgery to predict postoperative visual outcomes. The area under the curve (AUC) is 0.65, with a 95% Confidence Interval (CI) ranging from 0.474 to 0.823, indicating moderate predictive accuracy. A cut-off value of age ≥ 62 years was determined, yielding a sensitivity of 57.1% and a specificity of 85.7%. The Youden’s index, which balances sensitivity and specificity, is 0.43. (<b>B</b>) The ROC curve evaluates the ability of tumor sphericity to predict postoperative visual outcomes. The AUC for tumor sphericity is 0.52, with a 95% CI ranging from 0.329 to 0.705, indicating low predictive accuracy. A cut-off value of sphericity ≤ 0.86 was identified, resulting in a sensitivity of 47.6% and a specificity of 76.2%. The Youden’s index for this model is 0.24. Both panels display ROC curves (light blue) plotted against a reference line (red) representing random chance, with the x-axis showing 1-specificity and the y-axis showing sensitivity.</p>
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<p>(<b>A</b>) Compares the postoperative visual function scores between patients with low and high tumor sphericity across the same health domains. Asterisks (**) above the columns indicate statistically significant differences in visual outcome scores between different daily health domains. In both figures, the height of bars represents the visual function scores for each group, and error bars show the standard deviation, illustrating variability within each group. (<b>B</b>) Compares the postoperative visual function scores between patients in low and high age groups across the same health domains.</p>
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<p>(<b>A</b>) A forest plot showing factors associated with postoperative VR-QoL in MWSM, with odds ratios (OR) and 95% confidence intervals (CI) from a multivariable logistic regression. The red dashed line at OR = 1.00 represents no effect. Significant predictors include age (cut-off ≥ 62; OR = 26.965, 95% CI: 2.664–272.927, <span class="html-italic">p</span> = 0.005) and sphericity (cut-off ≤ 0.86; OR = 6.769, 95% CI: 1.096–41.784, <span class="html-italic">p</span> = 0.039). Non-significant factors such as sex and cavernous sinus invasion had CIs crossing the null line. (<b>B</b>) A bubble plot correlating total composite VFQ-25 scores (x-axis) with age (y-axis), with colors representing sex (red: males, blue: females) and bubble size reflecting tumor shape (larger: regular, smaller: irregular). The plot highlights variations in QoL scores by tumor shape, sex, and age, suggesting interdependencies affecting VR-QoL.</p>
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14 pages, 710 KiB  
Review
A Role of Inflammation in Charcot–Marie–Tooth Disorders—In a Perspective of Treatment?
by Joanna Kamińska and Andrzej Kochański
Int. J. Mol. Sci. 2025, 26(1), 15; https://doi.org/10.3390/ijms26010015 - 24 Dec 2024
Viewed by 424
Abstract
Despite the fact that there are published case reports and model work providing evidence of inflammation in Charcot–Marie–Tooth disorders (CMTs), in clinical practice, CMT and inflammatory neuropathies are always classified as two separate groups of disorders. This sharp separation of chronic neuropathies into [...] Read more.
Despite the fact that there are published case reports and model work providing evidence of inflammation in Charcot–Marie–Tooth disorders (CMTs), in clinical practice, CMT and inflammatory neuropathies are always classified as two separate groups of disorders. This sharp separation of chronic neuropathies into two groups has serious clinical implications. As a consequence, the patients harboring CMT mutations are practically excluded from pharmacological anti-inflammatory treatments. In this review, we present that neuropathological studies of peripheral nerves taken from some patients representing familial aggregation of CMTs revealed the presence of inflammation within the nerves. This shows that neurodegeneration resulting from germline mutations and the inflammatory process are not mutually exclusive. We also point to reports demonstrating that, at the clinical level, a positive response to anti-inflammatory therapy was observed in some patients diagnosed with CMTs, confirming the role of the inflammatory component in CMT. We narrowed a group of more than 100 genes whose mutations were found in CMT-affected patients to the seven most common (MPZ, PMP22, GJB1, SEPT9, LITAF, FIG4, and GDAP1) as being linked to the coexistence of hereditary and inflammatory neuropathy. We listed studies of mouse models supporting the idea of the presence of an inflammatory process in some CMTs and studies demonstrating at the cellular level the presence of an inflammatory response. In the following, we discuss the possible molecular basis of some neuropathies involving neurodegenerative and inflammatory processes at both the clinical and morphological levels. Finally, we discuss the prospect of a therapeutic approach using immunomodulation in some patients affected by CMTs. Full article
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<p>Three scenarios for the development of inflammation in CMT: mutation leads to cell death, and residual cells activate the immune system (<b>1</b>); mutation results in activation of the innate immune response (<b>2</b>); pathogen-activated immune response is not effectively silenced (<b>3</b>). The association of these scenarios to different subtypes of CMT is given. Created in BioRender.</p>
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18 pages, 11130 KiB  
Article
First Description of the Role of the Relationship Between Serum Amyloid P Components and Nuclear Factors/Pro-Cytokines During Critical Periods of Toxoplasmic Encephalitis
by Gungor Cagdas Dincel, Hasan Tarik Atmaca and Saeed El-Ashram
Brain Sci. 2024, 14(12), 1298; https://doi.org/10.3390/brainsci14121298 - 23 Dec 2024
Viewed by 986
Abstract
Background/Objectives: Toxoplasma gondii (T. gondii), an obligate food-borne intracellular parasite, causes severe neuropathology by establishing a persistent infection in the host brain. We have previously shown that T. gondii infection induces severe neuropathology in the brain manifested by increased nitric [...] Read more.
Background/Objectives: Toxoplasma gondii (T. gondii), an obligate food-borne intracellular parasite, causes severe neuropathology by establishing a persistent infection in the host brain. We have previously shown that T. gondii infection induces severe neuropathology in the brain manifested by increased nitric oxide production, oxidative stress, glial activation/BBB damage, increased pro-inflammatory cytokine glia maturation factor-beta and induced apoptosis. Methods: The aim of this experimental study was to investigate the serum amyloid P (SAP) components, nuclear factor kappa B (NF-κB), interleukin-1 beta (IL-1β), caspase 1 (Casp 1), tumor necrosis factor-alpha (TNF-α) and complement 3 (C3) gene expressions on the 10th, 20th and 30th days after infection with T. gondii in the neuroimmunopathogenesis of toxoplasmic encephalitis (TE) in mouse brains by real-time quantitative polymerase chain reaction. The study also aimed to determine whether there was a correlation between the markers included in the study on these critical days, which had not previously been investigated. The mRNA expression levels of SAP components, NF-κB, IL-1β, Casp 1, TNF-α and C3 were examined. Results: The most notable outcome of this investigation was the observation that SAP components exhibited a 13.9-fold increase on day 10 post-infection, followed by a rapid decline in the subsequent periods. In addition, IL-1β expression increased 20-fold, while SAP components decreased 13-fold on day 20 after infection. Additionally, the TNF-α, Casp 1 and NF-κB expression levels were consistently elevated to above normal levels at each time point. Conclusions: This study identified SAP components, NF-κB, IL-1β, Casp 1 and TNF-α expressions as playing critical roles in TE neuroimmunopathogenesis. Furthermore, to the best of our knowledge, this is the first study to investigate SAP components during the transition from acute systemic infection to early/medium chronic and chronic infection and to explore the relationship between SAP components and other nuclear factors/pro-cytokines. Full article
(This article belongs to the Special Issue New Advances in Neuroimmunology and Neuroinflammation)
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<p>Histopathology of toxoplasmic encephalitis stained by hematoxylin and eosin. (<b>A</b>) <span class="html-italic">Toxoplasma gondii</span> tissue cysts (arrow). Necrotic/degenerative neuronal cells (arrowheads). The 10th day of infection, 20× magnification. (<b>B</b>) Severe perivascular mononuclear cell infiltration (arrowheads) and gliosis focus (arrow). The 20th day of infection, 10× magnification. (<b>C</b>) Glia proliferation (arrow) in the brain. The 30th day of infection, 20× magnification. (<b>D</b>) Glia proliferation and perivascular mononuclear cell infiltration (arrow). The 30th day of infection, 20× magnification.</p>
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<p>Immunohistochemical stain for <span class="html-italic">Toxoplasma gondii</span> in the brain. (<b>A</b>) Immunopositivity for <span class="html-italic">Toxoplasma gondii</span> antigens in neurons (arrowheads) and glial cells (arrows). The 10th day of infection, 20× magnification. (<b>B</b>) Immunopositivity for <span class="html-italic">Toxoplasma gondii</span> antigens in necrotic neurons (arrow) and glial cells (arrowheads). The 20th day of infection, 20× magnification. (<b>C</b>) Immunopositivity for <span class="html-italic">Toxoplasma gondii</span> antigens in the glial proliferation area (arrowhead) and vessel endothelial cells (arrow). The 20th day of infection, 20× magnification. (<b>D</b>) Immunopositivity for <span class="html-italic">Toxoplasma gondii</span> antigens in the glial proliferation area (arrowhead) and vessel endothelial cells (arrow). The black rectangle highlights two distinct tissue cysts, each enclosed within the rectangle. The 30th day of infection, 20× magnification.</p>
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<p>mRNA expression levels of SAP in toxoplasmic encephalitis. (*** <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Statistical analysis of the SAP, NF-kB and C3 mRNA expression levels.</p>
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<p>mRNA expression levels of NF-κB in toxoplasmic encephalitis (** <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.005).</p>
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<p>mRNA expression levels of C3 in toxoplasmic encephalitis. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>mRNA expression levels of IL-1β in toxoplasmic encephalitis. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.005.</p>
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<p>Statistical analysis of IL-1β, Casp1 and TNF-α expression.</p>
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<p>mRNA expression levels of Casp1 in toxoplasmic encephalitis. ** <span class="html-italic">p</span> &lt; 0.005.</p>
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<p>mRNA expression levels of TNF-α in toxoplasmic encephalitis. * <span class="html-italic">p</span> &lt; 0.05 ** <span class="html-italic">p</span> &lt; 0.005.</p>
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27 pages, 2428 KiB  
Review
The Emerging Role of PCSK9 in the Pathogenesis of Alzheimer’s Disease: A Possible Target for the Disease Treatment
by Gabriella Testa, Serena Giannelli, Erica Staurenghi, Rebecca Cecci, Lucrezia Floro, Paola Gamba, Barbara Sottero and Gabriella Leonarduzzi
Int. J. Mol. Sci. 2024, 25(24), 13637; https://doi.org/10.3390/ijms252413637 - 20 Dec 2024
Viewed by 837
Abstract
Alzheimer’s disease (AD) is a multifactorial neurodegenerative disease mainly caused by β-amyloid (Aβ) accumulation in the brain. Among the several factors that may concur to AD development, elevated cholesterol levels and brain cholesterol dyshomeostasis have been recognized to play a relevant role. Proprotein [...] Read more.
Alzheimer’s disease (AD) is a multifactorial neurodegenerative disease mainly caused by β-amyloid (Aβ) accumulation in the brain. Among the several factors that may concur to AD development, elevated cholesterol levels and brain cholesterol dyshomeostasis have been recognized to play a relevant role. Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a protein primarily known to regulate plasma low-density lipoproteins (LDLs) rich in cholesterol and to be one of the main causes of familial hypercholesterolemia. In addition to that, PCSK9 is also recognized to carry out diverse important activities in the brain, including control of neuronal differentiation, apoptosis, and, importantly, LDL receptors functionality. Moreover, PCSK9 appeared to be directly involved in some of the principal processes responsible for AD development, such as inflammation, oxidative stress, and Aβ deposition. On these bases, PCSK9 management might represent a promising approach for AD treatment. The purpose of this review is to elucidate the role of PCSK9, whether or not cholesterol-related, in AD pathogenesis and to give an updated overview of the most innovative therapeutic strategies developed so far to counteract the pleiotropic activities of both humoral and brain PCSK9, focusing in particular on their potentiality for AD management. Full article
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<p>PCSK9 involvement in brain cholesterol dyshomeostasis. Circulating PCSK9 prevents recycling of LDL receptors, inducing hypercholesterolemia, inflammation, and oxidative stress, thus leading to BBB damage that allows PCSK9 to enter into the brain. Inside the brain, PCSK9 affects receptors and transporters involved in astrocyte-synthetized cholesterol and cholesterol uptake by neurons. Abbreviations: ABC, ATP-binding cassette transporter; ApoE, Apolipoprotein E; ApoER2, Apolipoprotein E receptor 2; BBB, blood-brain barrier; CSF, cerebrospinal fluid; HDL, high-density lipoprotein; LDLR, low-density lipoprotein receptor; LRP1, lipoprotein receptor-related protein 1; PCSK9, Proprotein convertase subtilisin/kexin type 9; and VLDLR; very low-density lipoprotein receptor.</p>
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<p>Application of anti-PCSK9 pharmacological tools for AD treatment. Drugs developed to target circulating PCSK9 appear suitable for AD cure by counteracting amyloidogenesis and by reducing hypercholesterolemia, inflammation, and oxidative stress, thus preventing BBB damage. PCSK9-targeting drugs able to cross the BBB could be suggested to delay the neurodegenerative AD progression, exerting their activities directly inside the brain. Abbreviations: AAV, adeno-associated virus; EV, extracellular vesicle; mAb, monoclonal antibody; miRNA, microRNA; and siRNA, small interfering RNA.</p>
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<p>PCSK9 activities associated with Alzheimer’s disease onset and development and the relative factors involved. Abbreviations: ABCA1, ATP-binding cassette transporter A1; ApoE, Apolipoprotein E; ApoER2, Apolipoprotein E receptor 2; BACE1, beta-site amyloid precursor protein-cleaving enzyme-1; BBB, blood-brain barrier; LDLR, low-density lipoprotein receptor; LRP1, lipoprotein receptor-related protein 1; NF-κB, nuclear factor kappa B; NLRP3, NOD-like receptor protein 3; TLR4, Toll-like receptor 4; VLDLR; very low-density lipoprotein receptor.</p>
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15 pages, 4094 KiB  
Article
Mossy Fiber Sprouting in Temporal Lobe Epilepsy: The Impact of Netrin-1, DCC, and Gene Expression Changes
by Melis Onay, Patrick N. Harter, Katherina Weber, Albrecht Piiper, Marcus Czabanka, Karl H. Plate, Thomas M. Freiman, Florian Gessler and Barbara Puhahn-Schmeiser
Biomedicines 2024, 12(12), 2869; https://doi.org/10.3390/biomedicines12122869 - 17 Dec 2024
Viewed by 687
Abstract
Background: Temporal lobe epilepsy (TLE) is the most common form of drug-resistant epilepsy, often associated with hippocampal sclerosis (HS), which involves selective neuronal loss in the Cornu Ammonis subregion 1 CA1 and CA4 regions of the hippocampus. Granule cells show migration and mossy [...] Read more.
Background: Temporal lobe epilepsy (TLE) is the most common form of drug-resistant epilepsy, often associated with hippocampal sclerosis (HS), which involves selective neuronal loss in the Cornu Ammonis subregion 1 CA1 and CA4 regions of the hippocampus. Granule cells show migration and mossy fiber sprouting, though the mechanisms remain unclear. Microglia play a role in neurogenesis and synaptic modulation, suggesting they may contribute to epilepsy. This study examines the role of microglia and axonal guidance molecules in neuronal reorganization in TLE. Methods: Nineteen hippocampal samples from patients with TLE undergoing epilepsy surgery were analyzed. Microglial activity (M1/M2-like microglia) and neuronal guidance molecules were assessed using microscopy and semi-automated techniques. Gene expression was evaluated using the nCounter Expression Profiling method. Results: Neuronal cell loss was correlated with decreased activity of the M1 microglial phenotype. In the CA2 region, neuronal preservation was linked to increased mossy fiber sprouting and microglial presence. Neuronal markers such as Deleted in Colorectal Cancer (DCC) and Synaptopodin were reduced in areas of cell death, while Netrin-1 was elevated in the granule cell layer, potentially influencing mossy fiber sprouting. The nCounter analysis revealed downregulation of genes involved in neuronal activity (e.g., NPAS4, BCL-2, GRIA1) and upregulation of IκB, indicating reduced neuroinflammation. Conclusions: This study suggests reduced neuroinflammation in areas of neuronal loss, while regions with preserved neurons showed mossy fiber sprouting associated with microglia, Netrin-1, and DCC. Full article
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<p>Subregions of healthy hippocampi (noHS) and sclerotic hippocampi (HS) stained with CD68. Activated microglia is recognizable by thickened and retracted branches. In the bar diagrams, bars and error bars indicate medians and IQR. (<b>A</b>) CA1 subregion of noHS. (<b>B</b>) CA1 subregion of HS. (<b>C</b>) CA2 subregion of noHS. (<b>D</b>) CA2 subregion of HS—here you can detect a significant overexpression of Iba-1. (<b>E</b>) CA3 subregion of noHS. (<b>F</b>) CA3 subregion of HS. (<b>G</b>) CA4 subregion of noHS. (<b>H</b>) CA4 subregion of HS. (<b>I</b>) Granule cell layer of noHS. (<b>J</b>) Granule cell layer of HS.</p>
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<p>The levels of DCC in noHS and HS hippocampal slices. (<b>A</b>) Expression of DCC in noHS. (<b>B</b>) Expression of DCC in HS revealing a loss of DCC in all subregions. (<b>C</b>) One-factorial analysis of DCC-positive stained area/tissue area mm<sup>2</sup> in HS and noHS. The semi-automatized analysis shows a significant downregulation of DCC in subregion CA2, CA3, CA4, and GCL in the sclerotic tissue. Bars and error bars indicate medians and IQR. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>IHC staining of Netrin-1 in noHS and HS in hippocampal slices. (<b>A</b>) Expression of Netrin-1 in noHS (<b>B</b>) and in HS, displaying a loss of Netrin-1 in almost all subregions. (<b>C</b>) One-factorial analysis of Netrin-1-positive area/tissue area mm<sup>2</sup> in HS. A significant upregulation is detectable in the GCL region of HS. Bars and error bars indicate medians and IQR. ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Cell type analyses according to the expression of different cell type scores in noHS and HS. For the investigation, six samples were used per group. Each point represents a sample. Boxes, bars and error bars indicate medians and IQR. The score is calculated from the cell type abundances by taking the logarithm of the expression of the genes specific to the cell types. (<b>A</b>) Astrocytes score (<b>B</b>) Oligodendrocytes score—An increased oligodendrocytes score (genes that occur in oligodendrocytes) is detectable in the sclerosis but there are no significant differences. (<b>C</b>) Microglia score—Surprisingly, an increased number of genes encoding for macrophages in general is apparent, even though the results are not significant. (<b>D</b>) Activated microglia score—The genes specifically associated with activated microglia exhibit higher expression in the noHS group, although without a significant result.</p>
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<p>Differential levels of mRNAs and proteins between non-sclerotic (noHS) and sclerotic tissue (HS) are presented in volcano plots. (<b>A</b>) Differential levels of mRNAs and proteins involved in activated microglia. The largest differences were seen in the proteins NPAS4, FLT1, and BCL2, but none reached statistical significance. (<b>B</b>) Differential levels of cytokine mRNAs and proteins in HS and noHS. The highest differences were seen in VEGFA and FLT1, which, however, did not reach statistical significance. (<b>C</b>) Differential levels of mRNAs and proteins involved in vesicle trafficking. The highest differences are observed in NPAS4, ARC, KCNA1, and GRIA1; however, this did not reach statistical significance. (<b>D</b>) Differential levels of mRNAs involved in axon and dendrite structure in HS and noHS. The highest differences were seen in ARC, KCNA1, and GRIA1, again without reaching statistical significance.</p>
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<p>Exemplary scheme of the expression of signaling influenced by cAMP in HS. The pathway was created by the summarization of pathway scores, which were calculated on the principal component analysis of the pathway genes’ normalized expression (refer to the <a href="#sec2-biomedicines-12-02869" class="html-sec">Section 2</a> for details). The mRNAs, which were found to be downregulated are blue colored, whereas upregulated mRNAs are gold colored. Interestingly, the expression of IκB was upregulated, which, in turn, leads to reduced activity of NFκB, as the NFκB pathway is inhibited by IκB. This could be interpreted as a sign of reduced neuroinflammation.</p>
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20 pages, 6109 KiB  
Article
Maresin-like 1 Ameliorates Neuropathology of Alzheimer’s Disease in Brains of a Transgenic Mouse Model
by Pallavi Shrivastava, Yan Lu, Shanchun Su, Yuichi Kobayashi, Yuhai Zhao, Nathan Lien, Abdul-Razak Masoud, Walter J. Lukiw and Song Hong
Biomedicines 2024, 12(12), 2865; https://doi.org/10.3390/biomedicines12122865 - 17 Dec 2024
Viewed by 740
Abstract
(1) Background: Impeded resolution of inflammation contributes substantially to the pathogenesis of Alzheimer’s disease (AD); consequently, resolving inflammation is pivotal to the amelioration of AD pathology. This can potentially be achieved by the treatment with specialized pro-resolving lipid mediators (SPMs), which should resolve [...] Read more.
(1) Background: Impeded resolution of inflammation contributes substantially to the pathogenesis of Alzheimer’s disease (AD); consequently, resolving inflammation is pivotal to the amelioration of AD pathology. This can potentially be achieved by the treatment with specialized pro-resolving lipid mediators (SPMs), which should resolve neuroinflammation in brains. (2) Methods: Here, we report the histological effects of long-term treatment with an SPM, maresin-like 1 (MarL1), on AD pathogenesis in a transgenic 5xFAD mouse model. (3) Results: MarL1 treatment reduced Aβ overload, curbed the loss of neurons in brains especially cholinergic neurons associated with cleaved-caspase-3-associated apoptotic degeneration, reduced microgliosis and the pro-inflammatory M1 polarization of microglia, curbed the AD-associated decline in anti-inflammatory Iba1+Arg-1+-M2 microglia, inhibited phenotypic switching to pro-inflammatory N1 neutrophils, promoted the blood–brain barrier-associated tight-junction protein claudin-5 and decreased neutrophil leakage in 5xFAD brains, and induced the switch of neutrophils toward the inflammation-resolving N2 phenotype. (4) Conclusions: Long-term administration of MarL1 mitigates AD-related neuropathogenesis in brains by curbing neuroinflammation and neurodegeneration, based on the histological results. These findings provide preclinical leads and mechanistic insights for the development of MarL1 into an effective modality to ameliorate AD pathogenesis. Full article
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<p>MarL1 treatment ameliorated AD neuropathology in brains of 5xFAD mice. (<b>A</b>) Immunostaining of NeuN (green) and Amyloid-β<sub>1–42</sub> (red) in CA3 and dentate gyrus (DG) of hippocampus. White arrows mark some Aβ<sub>1–42</sub> deposition in hippocampal regions. Panels a–f: 10× magnification; scale bar: 180 μm. (<b>B</b>) Quantification of NeuN<sup>+</sup> and Amyloid-β<sub>1–42</sub><sup>+</sup> staining intensities of hippocampus (mean fluorescence intensity—MFI). Data are means ± SEM. Wildtype <span class="html-italic">n</span> = 6, 5xFAD <span class="html-italic">n</span> = 6, and 5xFAD+MarL1 <span class="html-italic">n</span> = 5. *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>MarL1 protected cholinergic neurons (ChAT<sup>+</sup>) and inhibited apoptotic cleaved caspase-3 activity in brains of 5xFAD mice. (<b>A</b>) Immunostaining of ChAT (green) and cleaved caspase-3 (red) in striatum (Panels a–c): 10× magnification; scale bar: 180 µm. White arrows mark cleaved caspase-3<sup>+</sup> cholinergic neurons in zoomed-in images (Panels d–f). Scale bar: 35 µm. (<b>B</b>) Quantification of ChAT and caspase-3 in striatum. Left: mean fluorescence intensity MFI for ChAT<sup>+</sup>; middle: MFI for cleaved caspase-3<sup>+</sup>; right: count of cells stained positive for both ChAT and cleaved-caspase-3. Data are means ± SEM. Wildtype <span class="html-italic">n</span> = 6, 5xFAD <span class="html-italic">n</span> = 6, and 5xFAD+MarL1 <span class="html-italic">n</span> = 5. *** <span class="html-italic">p</span> &lt; 0.001 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>MarL1 suppressed pro-inflammatory M1 phenotype polarization of microglia in brains of 5xFAD mice. (<b>A</b>) Immunostaining of microglia with Iba-1 (green) and CD68 (red) in CA1 region of hippocampus from 5xFAD transgenic mice (Panels a–c: 10× magnification; scale bar: 180 µm. Panels d–f: zoomed-in images; scale bar: 30 µm). White arrows mark Iba-1<sup>+</sup>CD68<sup>+</sup> microglia. (<b>B</b>) Quantification of Iba-1<sup>+</sup> and CD68<sup>+</sup> in hippocampus. Left: mean fluorescence intensity MFI of Iba-1<sup>+</sup>; middle: MFI of CD68<sup>+</sup>; right: count of microglia stained positive for both Iba-1<sup>+</sup> and CD68<sup>+</sup>. (<b>C</b>) Quantification of microglia based on phenotype characterization (ramified, partially ramified, partially amoeboid, amoeboid) in hippocampus. Data are means ± SEM. Wildtype <span class="html-italic">n</span> = 6, 5xFAD <span class="html-italic">n</span> = 6, and 5xFAD+MarL1 <span class="html-italic">n</span> = 5 for (<b>B</b>). Wildtype <span class="html-italic">n</span> = 6, 5xFAD <span class="html-italic">n</span> = 6, and 5xFAD+MarL1 <span class="html-italic">n</span> = 6 for (<b>C</b>). **** <span class="html-italic">p</span> &lt; 0.0001, *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01, and * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>MarL1 promoted anti-inflammatory M2 phenotype polarization of microglia in brains of 5xFAD mice. (<b>A</b>) Immunostaining of microglia with Iba-1 (green) and Arg1 (red) in cortex (Panels a–c: 20× magnification; scale bar: 90 µm. Panels d–f: zoomed-in images; scale bar: 30 µm). White arrows mark Iba1<sup>+</sup>Arg1<sup>+</sup> microglia. Red arrows mark microglial aggregation in cortex of 5xFAD mice. (<b>B</b>) Quantification of Iba-1 and Arg1 in cortex. Left: mean fluorescence intensity MFI of Iba1<sup>+</sup>; middle: MFI of Arg1<sup>+</sup>; right: count of microglia stained positive for both Iba1 and Arg1 in cortex. Data are means ± SEM. Wildtype <span class="html-italic">n</span> = 6, 5xFAD <span class="html-italic">n</span> = 6, and 5xFAD+MarL1 <span class="html-italic">n</span> = 5. *** <span class="html-italic">p</span> &lt; 0.001 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>MarL1 attenuated the AD-associated compromise of blood–brain barrier tight-junctions as well as neutrophil infiltration into brains of 5xFAD mice. (<b>A</b>) Immunostaining of Gr-1 (green) for neutrophils and claudin-5 (red) for tight-junctions of the vasculatures in cortex. Panels a–c show images from cortex (4× magnification; scale bar: 460 µm). Panels d–f show zoomed-in images; scale bar: 65 µm. White arrows mark some Gr-1<sup>+</sup> cells outside the vasculature in parenchyma in zoomed-in images. Yellow arrows mark some claudin-5<sup>+</sup> vasculatures. Neutrophil swarming is evident in Panels b and e. (<b>B</b>) Quantification of Gr-1<sup>+</sup> and claudin-5<sup>+</sup> in MFI in cortex. Data are means ± SEM. Wildtype <span class="html-italic">n</span> = 6, 5xFAD <span class="html-italic">n</span> = 6, and 5xFAD+MarL1 <span class="html-italic">n</span> = 5. *** <span class="html-italic">p</span> &lt; 0.001 and * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>MarL1 treatment suppressed pro-inflammatory N1 polarization of neutrophils infiltrated into AD-pathogenic brains in 5xFAD mice. (<b>A</b>) Immunostaining of Gr-1 (green) for neutrophils and iNOs (red), an inflammatory marker. Panels a–c show hippocampus (4× magnification; scale bar: 460 µm). Panels d–f show zoomed-in images; scale bar: 40 µm. White arrows mark some Gr-1<sup>+</sup>iNOs<sup>+</sup> cells and yellow arrows mark only Gr-1-positive cells in zoomed-in panels. (<b>B</b>) Quantification of Gr-1<sup>+</sup> and iNOs<sup>+</sup> in hippocampus. Left: MFI of Gr-1<sup>+</sup>; middle: MFI of iNOs<sup>+</sup>; right: Pearson’s coefficient for quantification of co-localization of Gr-1 and iNOs. Data are means ± SEM. Wildtype <span class="html-italic">n</span> = 6, 5xFAD <span class="html-italic">n</span> = 6, and 5xFAD+MarL1 <span class="html-italic">n</span> = 5. *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt;0.01, and * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>MarL1 treatment induced anti-inflammatory N2 phenotypic polarization of neutrophils infiltrated into AD-pathogenic brains in 5xFAD mice. (<b>A</b>) Immunostaining of Gr-1 (green) for neutrophils and Arg1 (red), an anti-inflammatory marker. Panels a–c show hippocampus (4× magnification, scale bar: 460 µm). Panels d–f show zoomed-in images; scale bar: 40 µm. White arrows mark Gr-1<sup>+</sup> cells and yellow arrows mark Gr-1<sup>+</sup>Arg1<sup>+</sup> cells in zoomed-in panels. (<b>B</b>) Quantification of Gr-1<sup>+</sup> and Arg1<sup>+</sup> in hippocampus. Left: MFI of Gr-1<sup>+</sup>; middle: MFI of Arg1<sup>+</sup>; right: Pearson’s coefficient for quantification of co-localization of Gr-1 and Arg1. Data are means ± SEM. Wildtype <span class="html-italic">n</span> = 6, 5xFAD <span class="html-italic">n</span> = 6, and 5xFAD+MarL1 <span class="html-italic">n</span> = 5. *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01, and * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>A graphic summary.</p>
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22 pages, 4439 KiB  
Article
Artificial Intelligence-Assisted Comparative Analysis of the Overlapping Molecular Pathophysiology of Alzheimer’s Disease, Amyotrophic Lateral Sclerosis, and Frontotemporal Dementia
by Zihan Wei, Meghna R. Iyer, Benjamin Zhao, Jennifer Deng and Cassie S. Mitchell
Int. J. Mol. Sci. 2024, 25(24), 13450; https://doi.org/10.3390/ijms252413450 - 15 Dec 2024
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Abstract
The overlapping molecular pathophysiology of Alzheimer’s Disease (AD), Amyotrophic Lateral Sclerosis (ALS), and Frontotemporal Dementia (FTD) was analyzed using relationships from a knowledge graph of 33+ million biomedical journal articles. The unsupervised learning rank aggregation algorithm from SemNet 2.0 compared the most important [...] Read more.
The overlapping molecular pathophysiology of Alzheimer’s Disease (AD), Amyotrophic Lateral Sclerosis (ALS), and Frontotemporal Dementia (FTD) was analyzed using relationships from a knowledge graph of 33+ million biomedical journal articles. The unsupervised learning rank aggregation algorithm from SemNet 2.0 compared the most important amino acid, peptide, and protein (AAPP) nodes connected to AD, ALS, or FTD. FTD shared 99.9% of its nodes with ALS and AD; AD shared 64.2% of its nodes with FTD and ALS; and ALS shared 68.3% of its nodes with AD and FTD. The results were validated and mapped to functional biological processes using supervised human supervision and an external large language model. The overall percentages of mapped intersecting biological processes were as follows: inflammation and immune response, 19%; synapse and neurotransmission, 19%; cell cycle, 15%; protein aggregation, 12%; membrane regulation, 11%; stress response and regulation, 9%; and gene regulation, 4%. Once normalized for node count, biological mappings for cell cycle regulation and stress response were more prominent in the intersection of AD and FTD. Protein aggregation, gene regulation, and energetics were more prominent in the intersection of ALS and FTD. Synapse and neurotransmission, membrane regulation, and inflammation and immune response were greater at the intersection of AD and ALS. Given the extensive molecular pathophysiology overlap, small differences in regulation, genetic, or environmental factors likely shape the underlying expressed disease phenotype. The results help prioritize testable hypotheses for future clinical or experimental research. Full article
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Figure 1

Figure 1
<p>A Venn diagram of three diseases (AD, ALS, and FTD) illustrating intersections and unions for AAPP (amino acid, peptide, proteins) node type. FTD is represented by the light yellow circles; AD is represented by the aqua blue circles; ALS is represented by the light red circles. Intersections are shown in percentages for each disease. (<b>A</b>) FTD; (<b>B</b>) AD; (<b>C</b>) ALS.</p>
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<p>CompositeView of top 1% AAPP (amino acid peptide protein) nodes from AD, ALS, and FTD. CompositeView is a visualization tool for SemNet 2.0 that allows for the aggregation and visualization of multiple simulation outputs into a compressed form [<a href="#B24-ijms-25-13450" class="html-bibr">24</a>]. The source nodes are colored in green, while the target nodes are colored in blue with names next to them. There are large amounts of source nodes shared only between AD and ALS; meanwhile, the AD and FTD and ALS and FTD disease pairs (circled in orange) fewer nodes. The majority of nodes are shared by all three diseases. Nodes exclusive to only one disease are labeled outside the apex: AD has many exclusive nodes; ALS has a few exclusive nodes; and FTD had no exclusive nodes.</p>
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<p>Sunburst diagram of high-ranking AAPP nodes for each disease intersection mapped to their biological processes. The width of each segment represents the number of source nodes. The inner ring shows the relative counts of high-ranking intersecting nodes belonging to each biological process. Clockwise from the top of the inner circle: synapse and neurotransmission (dark green); inflammation and immune response (navy); cell cycle regulation (dark purple); protein aggregation (green); membrane regulation (turquoise); energetics and metabolism (dark orange); stress response regulation (brown); gene regulation and expression (obsidian). The outer ring shows the relative counts of the mapped biological processes for each disease intersection: AD ∩ ALS, AD ∩ FTD, ALS ∩ FTD.</p>
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<p>Bar chart quantitatively illustrating the biological mapping of the top 1% of high-ranking AAPP nodes based on all disease intersections: AD ∩ ALS, AD ∩ FTD, and ALS ∩ FTD.</p>
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<p>Bar chart illustrating the normalized biological mapping of the top 1% of high-ranking AAPP nodes adjusted for differences in node count between intersecting diseases: AD ∩ ALS, AD ∩ FTD, and ALS ∩ FTD.</p>
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<p>Z-scores to examine standardized differences in the biological process mappings of disease intersections compared to the overall average for each biological process: AD ∩ ALS, ALS ∩ FTD, and AD ∩ FTD.</p>
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<p>Overall framework for artificial intelligence-based comparative analysis of the molecular pathophysiology overlap of AD, ALS, and FTD. Over 33 million journal articles from PubMed were used to construct the knowlege graph used by SemNet 2.0. The open-source SemNet 2.0 software [<a href="#B21-ijms-25-13450" class="html-bibr">21</a>] and its post-visualization software, CompositeView [<a href="#B24-ijms-25-13450" class="html-bibr">24</a>], were used to determine which Unified Medical Lanaguge System (UMLS) amino acid, peptide, and protein (AAPP) nodes are most important to AD, ALS, FTD, and, namely, their intersections. Next, the top 1% of nodes were mapped to eight biological processes using cross-domain text mining natural language processing and a large language model. Finally, three human evaluators provided a check on the top 1% of nodes via full text article review and on the corresponding biological mappings.</p>
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