Human Brain Research Articles

New sesquiterpenoids with neuroprotective effects in vitro and in vivo from the Picrasma chinensis

Three undescribed sesquiterpenes, designed as pichinenoid A-C (1–3), along with nine known ones (4–12) were isolated from the stems and leaves of Picrasma chinensis. The new isolates including their absolute configurations were elucidated based on extensive spectroscopic methods, single crystal X-ray diffraction, and electronic circular dichroism (ECD) experiments, as well as comparison with literature data. Structurally, compounds 1 and 2 are descending sesquiterpenes, while pichinenoid C (3) is a rare sesquiterpene bearing a 2-methylenebut-3-enoic acid moiety at the C-6 side chain. All the isolated compounds were tested for their neuroprotective effects against the H2O2-induced damage on human neuroblastoma SH-SY5Y cells, and most of them showed moderate neuroprotective activity. Especially, compounds 1, 3–5, and 7 showed a potent neuroprotective effect at 25 or 50 μM. Moreover, the neuroprotective effects of compounds 1 and 4 were tested on a 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced Parkinson's disease (PD) mouse model. Results of western blot and immunofluorescence indicated that compound 4 significantly counteract the toxicity of MPTP, and reversed the expression of tyrosine hydroxylase (TH) in substantia nigra (SN) and striatum (ST) of the mouse brain. Interestingly, western blot data suggested compound 4 also enhanced B-cell lymphoma-2 (Bcl-2) and heme oxygenase 1 (HO-1) expressions in the brain tissues from MPTP damaged mouse.

Microarray-based analysis of differential gene expression profile in rotenone-induced Parkinson's disease zebrafish model.

Despite much clinical and laboratory research that has been performed to explore the mechanisms of Parkinson's disease (PD), its pathogenesis remains elusive to date. Therefore, this study aimed to identify possible regulators of neurodegeneration by performing microarray analysis of the zebrafish PD model's brain following rotenone exposure. A total of 36 adult zebrafish were divided into two groups: control (n = 17) and rotenone-treated (n = 19). Fish were treated with rotenone water (5 µg/L water) for 28 days and subjected to locomotor behavior analysis. Total RNA was extracted from the brain tissue after rotenone treatment. The cDNA synthesized was subjected to microarray analysis and subsequently validated by qPCR. Administration of rotenone has significantly reduced locomotor activity in zebrafish (p < 0.05), dysregulated dopamine-related gene expression (dat, th1, and th2, p < 0.001), and reduced dopamine level in the brain (p < 0.001). In the rotenone-treated group, genes involved in cytotoxic T lymphocytes (gzm3, cd8a, p < 0.001) and T cell receptor signaling (themis, lck, p < 0.001) were upregulated significantly. Additionally, gene expression involved in microgliosis regulation (tyrobp, p < 0.001), cellular response to IL-1 (ccl34b4, il2rb, p < 0.05), and regulation of apoptotic process (dedd1, p < 0.001) were also upregulated significantly. The mechanisms of T cell receptor signaling, microgliosis regulation, cellular response to IL-1, and apoptotic signaling pathways have potentially contributed to PD development in rotenone-treated zebrafish.

Improving the performance for automated brain tumor classification on magnetic resonance imaging deep learningbased

&lt;p&gt;Brain tumor is an uncontrolled growth of abnormal cell in the brain. Early diagnosis of brain tumor has a crucial step in this type of cancer, which is fatal. Magnetic resonance imaging (MRI) is one of the examination tools to examine brain anatomy in clinical practice. The high resolution and clear separation of the tissue enable medical experts to identify brain tumor. The earlier of brain tumor is detected, the wider of treatment options. However, manually analysed of brain anatomy on MRI images are time-consuming. Computer-aided diagnosis with automated way is helpful solution to help management with unreliable degrees of automation to trace various tissue boundaries. This study proposes convolutional neural network (CNN) with its excellences to automated features extraction in convolution layer. The popular architectures of CNN, i.e., visual geometry group16 (VGG16), residual network-50 (resNet-50), inceptionV3, mobileNet, and efficientNetB7 in medical image processing are compared to brain tumor classification task. As the results, VGG16 outperformed other architectures of CNN in this study. VGG16 yields 100% accuracy, precision, sensitivity, specificity, and F1-score for testing set data. The results show the excellent performance in classifying brain tumor and no tumor from MRI images that demonstrate the efficiency of system suggested.&lt;/p&gt;

Simultaneous isolation of intact brain cells and cell-specific extracellular vesicles from cryopreserved Alzheimer’s disease cortex

BackgroundThe neuronal and gliaI populations within the brain are tightly interwoven, making isolation and study of large populations of a single cell type from brain tissue a major technical challenge. Concurrently, cell-type specific extracellular vesicles (EVs) hold enormous diagnostic and therapeutic potential in neurodegenerative disorders including Alzheimer’s disease (AD). New MethodPostmortem AD cortical samples were thawed and gently dissociated. Following filtration, myelin and red blood cell removal, cell pellets were immunolabeled with fluorescent antibodies and analyzed by flow cytometry. The cell pellet supernatant was applied to a triple sucrose cushion for brain EV isolation. ResultsNeuronal, astrocyte and microglial cell populations were identified. Cell integrity was demonstrated using calcein AM, which is retained by cells with esterase activity and an intact membrane. For some experiments cell pellets were fixed, permeabilized, and immunolabeled for cell-specific markers. Characterization of brain small EV fractions showed the expected size, depletion of EV negative markers, and enrichment in positive and cell-type specific markers. Comparison with existing methods and conclusionsWe optimized and integrated established protocols, aiming to maximize information obtained from each human autopsy brain sample. The uniqueness of our method lies in its capability to isolate cells and EVs from a single cryopreserved brain sample. Our results not only demonstrate the feasibility of isolating specific brain cell subpopulations for RNA-seq but also validate these subpopulations at the protein level. The accelerated study of EVs from human samples is crucial for a better understanding of their contribution to neuron/glial crosstalk and disease progression.

Detection of miR-203 expression using a nanofluorescent probe to study the neuroprotective effect of sevoflurane in a neuroinflammatory injury rat model

Ischemic cerebrovascular disease has high disability and mortality rates that can result in related sequelae if not treated promptly and properly. Nanofluorescent probes are useful in the early diagnosis of the disease due to their high sensitivity and specificity, thus facilitating early therapeutic measures and observation of disease progression. Here, a novel nanostructure probe was designed to detect the expression of miR-203 in a cerebral ischemia-reperfusion injury (CIRI) model after sevoflurane treatment, and the effects of sevoflurane and neuroinflammatory injury were discussed. A CIRI rat model was established to study miR-203 expression, nerve injury, oxidative stress, and inflammatory factors in the brain tissue of rats treated with sevoflurane. The nanofluorescent probe showed high specificity and sensitivity for miR-203 detection. miR-203 was over-expressed in CIRI rats, which was decreased by sevoflurane treatment. In addition, sevoflurane treatment significantly improved neurological function, learning, and memory in CIRI rats; decreased malondialdehyde, lactate dehydrogenase, and superoxide dismutase levels; and inhibited the expression of tumor necrosis factor α and interleukins 1 and 6. Moreover, an inverse correlation was determined between miR-203 expression and the degree of neuroinflammation. This study demonstrated that miR-203 may be a useful diagnostic and prognostic index for neuroinflammatory injury.

Dl-3-n-butylphthalide promotes microglial phagocytosis and inhibits microglial inflammation via regulating AGE-RAGE pathway in APP/PS1 mice

Alzheimer's disease (AD) stands as the most prevalent neurodegenerative condition worldwide, and its correlation with microglial function is notably significant. Dl-3-n-butylphthalide (NBP), derived from the seeds of Apium graveolens L. (Chinese celery), has demonstrated the capacity to diminish Aβ levels in the brain tissue of Alzheimer’s transgenic mice. Despite this, its connection to neuroinflammation and microglial phagocytosis, along with the specific molecular mechanism involved, remains undefined. In this study, NBP treatment exhibited a substantial improvement in learning deficits observed in AD transgenic mice (APP/PS1 transgenic mice).Furthermore, NBP treatment significantly mitigated the total cerebral Aβ plaque deposition. This effect was attributed to the heightened presence of activated microglia surrounding Aβ plaques and an increase in microglial phagocytosis of Aβ plaques. Transcriptome sequencing analysis unveiled the potential involvement of the AGE (advanced glycation end products) -RAGE (receptor for AGE) signaling pathway in NBP’s impact on APP/PS1 mice. Subsequent investigation disclosed a reduction in the secretion of AGEs, RAGE, and proinflammatory factors within the hippocampus and cortex of NBP-treated APP/PS1 mice.In summary, NBP alleviates cognitive impairment by augmenting the number of activated microglia around Aβ plaques and ameliorating AGE-RAGE-mediated neuroinflammation. These findings underscore the related mechanism of the crucial neuroprotective roles of microglial phagocytosis and anti-inflammation in NBP treatment for AD, offering a potential therapeutic target for the disease.

Neuroinflammation in Glioblastoma: The Role of the Microenvironment in Tumour Progression.

Glioblastoma (GBM) stands as the most aggressive and lethal among the main types of primary brain tumors. It exhibits malignant growth, infiltrating the brain tissue, and displaying resistance toward treatment. GBM is a complex disease characterized by high degrees of heterogeneity. During tumour growth, microglia and astrocytes, among other cells, infiltrate the tumour microenvironment and contribute extensively to gliomagenesis. Tumour-associated macrophages (TAMs), either of peripheral origin or representing brain-intrinsic microglia, are the most numerous nonneoplastic populations in the tumour microenvironment in GBM. The complex heterogeneous nature of GBM cells is facilitated by the local inflammatory tumour microenvironment, which mostly induces tumour aggressiveness and drug resistance. The immunosuppressive tumour microenvironment of GBM provides multiple pathways for tumour immune evasion, contributing to tumour progression. Additionally, TAMs and astrocytes can contribute to tumour progression through the release of cytokines and activation of signalling pathways. In this review, we summarize the role of the microenvironment in GBM progression, focusing on neuroinflammation. These recent advancements in research of the microenvironment hold the potential to offer a promising approach to the treatment of GBM in the coming times.

Neuroinflammation in the Rat Brain After Exposure to Diagnostic Ultrasound

ObjectiveTo date there have been no studies exploring the potential for neuroinflammation as an intracranial bio-effect associated with diagnostic ultrasound during neonatal cranial scans in a mammalian in vivo model. The study described here was aimed at investigating the effects of B-mode and Doppler mode ultrasound on inflammation in the rat brain. MethodsTwelve Wistar rats (7–9 wk old) were divided into a control group and an ultrasound-exposed group (n = 6/group). A craniotomy was performed, followed by 10 min of B-mode and spectral Doppler interrogation of the middle cerebral artery. The control group was subjected to sham treatment, with the transducer held stationary over the craniotomy site, but the ultrasound machine switched off. Animals were euthanized 48 h after exposure, and the brains formalin fixed for immunohistochemical analysis using allograft inflammatory factor 1 (IBA-1) and glial fibrillary acidic protein (GFAP) as markers of microglia and astrocytes, respectively. The numbers of IBA-1- and GFAP-immunoreactive cells were manually counted and expressed as areal density (cells/mm2). Results were analyzed using Student's unpaired t-test and one-way repeated-measures analysis of variance. ResultsThe ultrasound-exposed brain exhibited significant increases in IBA-1 and GFAP immunoreactive cell density in all regions of B-mode and Doppler mode exposure compared with the control group (p < 0.001). ConclusionTen minutes of B-mode and Doppler mode ultrasound may induce neuroinflammatory changes in the rat brain. This suggests that exposure of brain tissue to current diagnostic ultrasound intensities may not be completely without risk.

Kernel rootkit detection multi class on deep learning techniques

The harmful code application known as a rootkit is designed to be loaded and run directly from the operating system's (OSs') Kernel. Rootkits deployed in the Kernel, called Kernel-mode rootkits, can alter the OS. The intention behind these Kernel changes is to conceal the hack. Detecting a Kernel rootkit in a target machine is found to be quite challenging. Numerous techniques can be employed to modify the Kernel of a system. Kernel rootkits also create hidden access for attacks, enabling unauthorized entry to be gained by attackers on the machine. The ultimate consequence is that essential computer data can be modified, personal information can be gathered, and hackers can observe behavior. Synthetic neural networks support artificial intelligence, a branch of deep learning that models the human brain and operates on large datasets. This study proposed the Kernel rootkit detection multi-class deep learning techniques (KRDMCDLT). Deep learning algorithms are utilized to recognize the Kernel rootkit from a batch of data by selecting essential properties for learning tracking models. Thus, by identifying the OS malware, trojan assaults can be stopped before they can access infected data. This Kernel rootkit detection was tested in a Google Cloud Platform (GCP) computing system.

Quantifying Morphologies of Developing Neuronal Cells using Deep Learning with Imperfect Annotations

The functionality of human intelligence relies on the interaction and health of neurons, hence, quantifying neuronal morphologies can be crucial for investigating the functionality of the human brain. This paper proposes a deep learning (DL) based method for segmenting and quantifying neuronal structures in fluorescence microscopy images of developing neuronal cells cultured in vitro. Compared to the majority of supervised DL-based segmentation methods that heavily rely on creating exact corresponding masks of neuronal structures for the preparation of training samples, the proposed approach allows for imperfect annotation of neurons, as it only requires tracing the centrelines of the neurites. This ability accelerates the preparation of training data by several folds. Our proposed framework is built on a modified version of PSPNet with an EfficientNet backbone pre-trained on the CityScapes dataset. To handle the imperfectness of training samples, we incorporated a weighted combination of two loss functions, namely the Dice loss and Lovász loss functions, into our network. We evaluated the proposed framework and several other state-of-the-art methods on a published dataset of approximately 900 manually quantified cultured mouse neurons. Our results indicate a close correlation between the proposed method and manual quantification in terms of neuron length and the number of branches while demonstrating improved analysis speed. Furthermore, the proposed method achieved high accuracy in neuron segmentation, as evidenced by the evaluation of the neurons’ length and number of branches.

Enhancing EEG-based brain-computer interface systems through efficient machine learning classification techniques

Advances in the fields of neuroscience and computer science have greatly enhanced the human brain’s ability to communicate and interact with the surrounding environment. In addition, recent steps in machine learning (ML) have increased the use of electroencephalography (EEG)-based BCIs for artificial intelligence (AI) applications. The prevailing challenge in recording EEG sensor data is that the captured signals are mixed with noise, which makes their effective use difficult. Therefore, strengthening the classification stage becomes extremely important and plays a major role in addressing this problem. In this study, we chose five most widely used classification models that obtained the best results in this field and tested them on two open-source databases. We also focused on improving the hyperparameters of each algorithm to obtain best results. Our results indicate excellent results on the first dataset and acceptable for most models on the second, while RF showed superior performance on both with an accuracy of 100% on the first dataset and 86.47% on the second. This was achieved with the lowest training costs, and better performance compared to previous works we evaluated that used the same databases. These results provide valuable insights and advance the development of brain-computer interface (BCI) technology and design.

Synergistic Effects of Neratinib in Combination With Palbociclib or Miransertib in Brain Cancer Cells.

Aberrant expression and activation of epidermal growth factor receptor (EGFR) resulted in approval of several forms of EGFR inhibitors in the treatment of patients with a wide range of epithelial cancers. However, no EGFR inhibitor has yet been approved for the treatment of patients with brain cancer, indicating that targeting EGFR alone may not be sufficient in some patients. In this study, we investigated the role of all members of the EGFR family, other growth factor receptors, cell-cycle proteins, and downstream cell signaling pathways (e.g., mitogen-activated protein kinase (MAPK), serine/threonine protein kinase (AKT), signal transducer and activator of transcription (STAT3), Src, Abelson murine leukemia viral oncogene homolog (Abl)) on the growth of a panel of human brain cancer cell lines (HBCCLs). We examined the growth response of HBCCLs to treatment with 17 targeted agents compared to two cytotoxic drugs. Of the targeted agents, the irreversible pan-human epidermal growth factor receptor (HER) inhibitors neratinib and afatinib were more effective than erlotinib and lapatinib at inhibiting the growth of all HBCCLs, and the cyclin-dependent kinase (CDK)1/2/5/9 inhibitor dinaciclib was the most potent targeted agent. We found that treatment with Src/Abl/c-kit inhibitor dasatinib, signal transducer and activator of transcription (STAT3) inhibitor stattic, Abl/platelet-derived growth factor receptor (PDGFR)α/vascular endothelial growth factor (VEGFR)2/fibroblast growth factor receptor (FGFR)1 inhibitor ponatinib, and the tropomyosin receptor kinase (TRK)/ROS proto-oncogene 1 receptor tyrosine kinase (ROS)/anaplastic lymphoma kinase (ALK) inhibitor entrectinib, also inhibited the growth of all HBCCLs. Interestingly, these agents were more effective in inhibiting growth of HBCCLs when proliferating at a slower rate. In addition to inhibiting the proliferation of HBCCLs, treatment with neratinib, dinaciclib, dasatinib, stattic and trametinib inhibited the migration of brain tumor cell line A172. Notably, we found that treatment with neratinib in combination with palbociclib (CDK4/6 inhibitor), or miransertib (AKT1/2/3 inhibitor) resulted in synergistic growth inhibition of all HBCCLs. Our results support that repurposing drugs like neratinib in combination with the palbociclib or miransertib may be of therapeutic potential in brain cancer and warrants further investigations.

Human-specific insights into candidate genes and boosted discoveries of novel loci illuminate roles of neuroglia in reading disorders.

Reading disorders (RD) are human-specific neuropsychological conditions associated with decoding printed words and/or reading comprehension. So far only a handful of candidate genes segregated in families and 42 loci from genome-wide association study (GWAS) have been identified that jointly provided little clues of pathophysiology. Leveraging human-specific genomic information, we critically assessed the RD candidates for the first time and found substantial human-specific features within. The GWAS candidates (i.e., population signals) were distinct from the familial counterparts and were more likely pleiotropic in neuropsychiatric traits and to harbor human-specific regulatory elements (HSREs). Candidate genes associated with human cortical morphology indeed showed human-specific expression in adult brain cortices, particularly in neuroglia likely regulated by HSREs. Expression levels of candidate genes across human brain developmental stages showed a clear pattern of uplifted expression in early brain development crucial to RD development. Following the new insights and loci pleiotropic in cognitive traits, we identified four novel genes from the GWAS sub-significant associations (i.e., FOXO3, MAPT, KMT2E and HTT) and the Semaphorin gene family with functional priors (i.e., SEMA3A, SEMA3E and SEMA5B). These novel genes were related to neuronal plasticity and disorders, mostly conserved the pattern of uplifted expression in early brain development and had evident expression in cortical neuroglial cells. Our findings jointly illuminated the association of RD with neuroglia regulation-an emerging hotspot in studying neurodevelopmental disorders, and highlighted the need of improving RD phenotyping to avoid jeopardizing future genetic studies of RD.

Neurosteroids and their Therapeutic Potential: Remembering the Contribution of Dr. Sabina Luchetti, Netherlands Institute for Neuroscience, Amsterdam, Netherlands

Abstract: This article traces the career of Dr. Sabina Luchetti (1969–2021), a noted physician (medical doctor, specialized in Neurology at Tor Vergata University of Rome, Italy), a dedicated neuroscientist (Ph.D. in Neuroscience at Tor Vergata University and IRCCS Santa Lucia of Rome), and a member of a renowned Netherlands group (senior researcher at Professor Swaab Laboratory of the Netherlands Institute for Neuroscience, Amsterdam, Netherlands), working in the field of brain function and diseases. She is particularly involved in the study of natural compounds, such as neurosteroids and their biosynthetic pathways in neurodegenerative and neuroinflammation- related disorders, working on post-mortem human brains. : This editorial outlines Dr. Luchetti's wide range of interests, discloses her superior fund of knowledge, and recollects her humanitarian spirit, all of which contribute to creating a great sense of belonging to any group of researchers whom she worked with. The impact of Dr. Luchetti's work will continue to be felt for many years. : From the bench to the bedside, her work has indirectly contributed to shedding light on the neurosteroids’ potential therapeutic effects, considering that neurosteroids and their analogues (some of which are over-the-counter) are now used to treat depression, epilepsy, and substance abuse disorders. Moreover, the potential therapeutic effects of allopregnanolone with respect to its capability to promote neuroregeneration and neuroprotection are a promising basis for future treatment of neurodegenerative diseases.

HRCM: A Hierarchical Regularizing Mechanism for Sparse and Imbalanced Communication in Whole Human Brain Simulations

HRCM: A Hierarchical Regularizing Mechanism for Sparse and Imbalanced Communication in Whole Human Brain Simulations

AI-powered Neuroprosthetics for brain-computer interfaces (BCIs)

Imagine a world where individuals with lost or impaired sensory or motor function can regain independence and control through technology. This is the promise of neuroprosthetics, a rapidly evolving field that bridges the gap between the nervous system and external devices. Neuroprosthetics encompass a range of implanted or external devices designed to: Substitute for a malfunctioning part of the nervous system. Assist in the recovery or enhancement of lost or impaired function. Augment existing capabilities, creating new possibilities. These devices interact with the nervous system using various methods, including: Electrical stimulation: Directly stimulating nerves or brain tissue to evoke desired responses. Recording brain activity: Capturing electrical signals generated by the brain for further processing and interpretation. Common applications of neuroprosthetics include: Cochlear implants: Restoring hearing in individuals with severe hearing loss. Deep brain stimulation (DBS): Treating movement disorders like Parkinson's disease and essential tremor. Bionic limbs: Providing control of prosthetic arms and legs for individuals with limb loss. Brain-computer interfaces (BCIs): Enabling communication and control of external devices using brain signals alone. Neuroprosthetics offer a glimpse into the future of medicine and technology. With ongoing advancements, these devices have the potential to revolutionize how we treat neurological conditions, restore lost abilities, and even enhance human potential. However, significant challenges remain, including ensuring long-term safety, improving accuracy and reliability, and addressing ethical considerations. As research continues, neuroprosthetics holds immense potential to improve the lives of millions and redefine what it means to be human. The integration of artificial intelligence (AI) with neuroprosthetics has marked a significant milestone in the development of brain-computer interfaces (BCIs). This emerging synergy aims to enhance the quality of life for individuals with disabilities by restoring lost sensory, motor, and cognitive functions. This review article explores the advancements in AI-powered neuroprosthetics for BCIs, focusing on their design, functionality, and the ethical considerations that accompany their integration into medical practice.

Characterization of tritiated JNJ-GluN2B-5 (3-[3H] 1-(azetidin-1-yl)-2-(6-(4-fluoro-3-methyl-phenyl)pyrrolo[3,2-b]pyridin-1-yl)ethanone), a high affinity GluN2B radioligand with selectivity over sigma receptors.

Here, we describe the characterization of a radioligand selective for GluN2B-containing NMDA receptors, 3-[3H] 1-(azetidin-1-yl)-2-(6-(4-fluoro-3-methyl-phenyl)pyrrolo[3,2-b]pyridin-1-yl)ethanone ([3H]-JNJ- GluN2B-5). In rat cortical membranes, the compound bound to a single site, and the following kinetic parameters were measured; association rate constant Kon = 0.0066 ± 0.0006 min-1 nM-1, dissociation rate constant Koff = 0.0210 ± 0.0001 min-1 indicating calculated KD = Koff/Kon = 3.3 ± 0.4 nM, (mean ± SEM, n = 3). The equilibrium dissociation constant determined from saturation binding experiments in rat cortex was KD of 2.6 ± 0.3 nM (mean ± SEM, n = 3). In contrast to the widely used GluN2B radioligand [3H]-Ro 25-6981, whose affinity Ki for sigma 1 and sigma 2 receptors are 2 and 189 nM, respectively, [3H]-JNJ-GluN2B-5 exhibits no measurable affinity for sigma 1 and sigma 2 receptors (Ki > 10 μM for both) providing distinct selectivity advantages. Anatomical distribution of [3H]-JNJ-GluN2B-5 binding sites in rat, mouse, dog, monkey, and human brain tissue was studied using invitro autoradiography, which showed high specific binding in the hippocampus and cortex and negligible binding in the cerebellum. Enhanced selectivity for GluN2B-containing receptors translated to a good signal-to-noise ratio in both invitro radioligand binding and invitro autoradiography assays. In conclusion, [3H]-JNJ-GluN2B-5 is a high-affinity GluN2B radioligand with excellent signal-to-noise ratio and unprecedented selectivity.

Hypobaria During Aeromedical Evacuation and Systemic Inflammation after Porcine Traumatic Brain Injury.

Traumatic brain injury (TBI)-related morbidity is caused largely by secondary injury resulting from hypoxia, excessive sympathetic drive, and uncontrolled inflammation. Aeromedical evacuation (AE) is utilized by the military for transport of wounded soldiers to higher levels of care. We hypothesized that the hypobaric, hypoxic conditions of AE may exacerbate uncontrolled inflammation following TBI that could contribute to more severe TBI-related secondary injury. Thirty-six female pigs were used to test TBI vs. TBI sham, hypoxia vs. normoxia, and hypobaria vs. ground conditions. TBI was induced by controlled cortical injury, hypobaric conditions of 12,000 feet were established in an altitude chamber, and hypoxic exposure was titrated to 85% SpO2 while at altitude. Serum cytokines, UCH-L1 and TBI biomarkers were analyzed via ELISA. Gross analysis and staining of cortex and hippocampus tissue was completed for glial fibrillary acidic protein (GFAP) and phosphorylated tau (p-tau). Serum IL-1b, IL-6, and TNFα were significantly elevated following TBI in pigs exposed to altitude-induced hypobaria/hypoxia, as well as hypobaria alone, compared to ground level/normoxia. No difference in TBI biomarkers following TBI or hypobaric, hypoxic exposure was noted. No difference in brain tissue GFAP or p-tau when comparing the most different conditions of sham TBI+ground/normoxia to the TBI+hypobaria/hypoxia group was noted. The hypobaric environment of AE induces systemic inflammation following TBI. Severe inflammation may play a role in exacerbating secondary injury associated with TBI and contribute to worse neurocognitive outcomes. Measures should be taken to minimize barometric and oxygenation changes during AE following TBI.

Assessing the radiofrequency shielding effect of titanium mesh on diffusion-weighted imaging: a comparative study of the twice-refocused spin-echo and Stejskal-Tanner sequences.

This study compared twice-refocused spin-echo sequence (TRSE) and Stejskal-Tanner sequence (ST) to evaluate their respective effects on the image quality of magnetic resonance (MR) diffusion-weighted imaging in the presence of radiofrequency (RF) shielding effect of titanium mesh in cranioplasty. A 1.5-T MR scanner with a Head/Neck coil 20 channels and a phantom simulating the T2 and apparent diffusion coefficient (ADC) value of the human brain were used. Imaging was performed with and without titanium mesh placed on the phantom in TRSE and ST, and normalized absolute average deviation (NAAD), Dice similarity coefficient (DSC), and ADC values were calculated. The NAAD values were significantly lower for TRSE than for ST in the area below the titanium mesh, and the drop rates due to titanium mesh were 14.1% for TRSE and 9.8% for ST. The DSC values were significantly lower for TRSE than for ST. The ADC values were significantly higher for TRSE than for ST without titanium mesh. The ADC values showed no significant difference between TRSE and ST with titanium mesh. The ST had a lower RF shielding effect of titanium mesh than the TRSE.

Endothelial PDGF-D contributes to neurovascular protection after ischemic stroke by rescuing pericyte functions

Ischemic stroke induces neovascularization of the injured tissue as an attempt to promote structural repair and neurological recovery. Angiogenesis is regulated by pericytes that potently react to ischemic stroke stressors, ranging from death to dysfunction. Platelet-derived growth factor (PDGF) receptor (PDGFR)β controls pericyte survival, migration, and interaction with brain endothelial cells. PDGF-D a specific ligand of PDGFRβ is expressed in the brain, yet its regulation and role in ischemic stroke pathobiology remains unexplored. Using experimental ischemic stroke mouse model, we found that PDGF-D is transiently induced in brain endothelial cells at the injury site in the subacute phase. To investigate the biological significance of PDGF-D post-ischemic stroke regulation, its subacute expression was either downregulated using siRNA or upregulated using an active recombinant form. Attenuation of PDGF-D subacute induction exacerbates neuronal loss, impairs microvascular density, alters vascular permeability, and increases microvascular stalling. Increasing PDGF-D subacute bioavailability rescues neuronal survival and improves neurological recovery. PDGF-D subacute enhanced bioavailability promotes stable neovascularization of the injured tissue and improves brain perfusion. Notably, PDGF-D enhanced bioavailability improves pericyte association with brain endothelial cells. Cell-based assays using human brain pericyte and brain endothelial cells exposed to ischemia-like conditions were applied to investigate the underlying mechanisms. PDGF-D stimulation attenuates pericyte loss and fibrotic transition, while increasing the secretion of pro-angiogenic and vascular protective factors. Moreover, PDGF-D stimulates pericyte migration required for optimal endothelial coverage and promotes angiogenesis. Our study unravels new insights into PDGF-D contribution to neurovascular protection after ischemic stroke by rescuing the functions of pericytes.