Brain Disease Research Articles

Senescent cell transplantation into the skin induces age-related peripheral dysfunction and cognitive decline.

Cellular senescence is an established cause of cell and tissue aging. Senescent cells have been shown to increase in multiple organs during aging, including the skin. Here we hypothesized that senescent cells residing in the skin can spread senescence to distant organs, thereby accelerating systemic aging processes. To explore this hypothesis, we initially observed an increase in several markers of senescence in the skin of aging mice. Subsequently, we conducted experiments wherein senescent fibroblasts were transplanted into the dermis of young mice and assessed various age-associated parameters. Our findings reveal that the presence of senescent cells in the dermal layer of young mice leads to increased senescence in both proximal and distal host tissues, alongside increased frailty, and impaired musculoskeletal function. Additionally, there was a significant decline in cognitive function, concomitant with increased expression of senescence-associated markers within the hippocampus brain area. These results support the concept that the accumulation of senescent cells in the skin can exert remote effects on other organs including the brain, potentially explaining links between skin and brain disorders and diseases and, contributing to physical and cognitive decline associated with aging.

A Rare Case of Early Infantile Neuronal Ceroid Lipofuscinosis: Clinical and Diagnostic Insights

The Aim of the Study: The aim of this work is to show the different specific clinical and paraclinical aspects of ceroid lipofuscinosis in its early infantile form named Santavuori-Haltia disease through a new case.Given its rarety especially in consanguineous populations for better understanding the clinical presentation,progression and management of the disease. Introduction: The neuronal ceroid lipofuscinoses are a group of genetic brain diseases with an autosomal recessive manner characterized by the accumulation of an autofluorescent material in multiple tissues of patients. Clinically, they are progressive decline of mental and motor capacities, epilepsy and visual loss through retinal degeneration . In this work we report the early infantile neuronal ceroid lipofuscinosis or Santavuori-Haltia disease. Report Case: This is a 12-month-old infant from consanguineous parents who has presented with flexion spasms with eyelid clonia since the age of 1 month. The examination found microcephaly, and the neurological examination showed horizontal nystagmus with the absence of ocular pursuit and the presence of a pyramidal and extrapyramidal syndrome. Brain MRI shows delayed myelination. Skin biopsy confirms diagnosis. The Arg122Trp genetic mutation found is the most common one in this disease. Conclusion: Santaviori-Haltia disease is a rare and serious disease, the prognosis is severe, it must be mentioned in the face of psychomotor regression and a picture of encephalopathy. Confirmation of the diagnosis is based on histological and molecular biology data.

Deciphering mouse brain spatial diversity via glyco-lipidomic mapping

Gangliosides in the brain play a crucial role in modulating the integrity of vertebrate central nervous system in a region-specific manner. However, to date, a comprehensive structural elucidation of complex intact ganglioside isomers has not been achieved, resulting in the elusiveness into related molecular mechanism. Here, we present a glycolipidomic approach for isomer-specific and brain region-specific profiling of the mouse brain. Considerable region-specificity and commonality in specific group of regions are highlighted. Notably, we observe a similarity in the abundance of major isomers, GD1a and GD1b, within certain regions, which provides significant biological implications with interpretation through the lens of a theoretical retrosynthetic state-transition network. Furthermore, A glycocentric-omics approaches using gangliosides and N-glycans reveal a remarkable convergence in spatial dynamics, providing valuable insight into molecular interaction network. Collectively, this study uncovers the spatial dynamics of intact glyco-conjugates in the brain, which are relevant to regional function and accelerates the discovery of potential therapeutic targets for brain diseases.

A brain-network operator for modeling disease: a first data-based application for Parkinson’s disease

AbstractThe complexity of our brains can be described as a multi-layer network: neurons, neural agglomerates, and lobes. Neurological diseases are often related to malfunctions in this network. We propose a conceptual model of the brain, describing the disease as the result of an operator affecting and disrupting the network organization. We adopt the formalism of operators, matrices, and tensor products adapted from theoretical physics. This novel approach can be tested and instantiated for different diseases, balancing mathematical formalism and data-driven findings, including pathologies where aging is included as a risk factor. We quantitatively model the K-operator from real data of Parkinson’s Disease, from the Parkinson’s Progression Markers Initiative (PPMI) upon concession by the University of Southern California. The networks are reconstructed from fMRI analysis, resulting in a matrix acting on the healthy brain and giving as output the diseased brain. We finally decompose the K-operator into the tensor product of its submatrices and we are able to assess its action on each region of interest (ROI) characterizing the brain for the specific considered samples. We also approximate the time-dependent K-operator from the fMRI of the same patient at the baseline and at the first follow-up. Our results confirm the findings of the literature on the topic. Also, these applications confirm the feasibility of the proposed analytic technique. Further research developments can compare operators for different patients and for different diseases, looking for commonalities and aiming to develop a comprehensive theoretical approach.

Overcoming the Blood–Brain Barrier: Multifunctional Nanomaterial‐Based Strategies for Targeted Drug Delivery in Neurological Disorders

The development of effective therapies for neurological disorders is a growing area of research due to the increasing prevalence of these conditions. Some neurological disorders that are prevalent and remain difficult to treat are glioma, neurodegenerative disease, ischemic stroke, and traumatic brain injury. Subsequently, the therapeutic efficacy of small molecules, proteins, and oligonucleotides remains a challenge due to the presence of the blood–brain barrier (BBB), a highly selective semipermeable membrane. To this end, multifunctional nanomaterials have emerged as promising vehicles for targeted drug delivery to the brain, due to their ability to transport therapeutics across the BBB selectively. The design of advanced nanomaterial‐based drug delivery systems capable of overcoming the BBB is influenced by many factors, such as fabrication technique and surface modification. This review explores the diverse range of nanomaterials, including polymer, lipid, gold, magnetic, and carbon‐based nanostructures, capable of effectively passing the BBB. These materials cross the BBB via a variety of established transport mechanisms for targeted delivery of therapeutics to the brain. Moreover, the structure and function of the BBB are highlighted and the potential for nanotechnology to aid the treatment of neurological disorders based on their ability to undergo transcytosis into the brain is highlighted.

The origins of ADNI.

A brief history of events surrounding the conceptualization and original implementation of the Alzheimer's Disease Neuroimaging Initiative (ADNI) as a public-private partnership (PPP) is provided from the perspective of three individuals directly involved from the outset. Potential barriers and how they were addressed are summarized, especially the decision to make all data freely accessible in real-time. Decisions made at the beginning of ADNI are revisited in light of what has been learned over the past 20 years, especially the importance of the investment in cerebrospinal fluid (CSF) and blood measures and the commitment to data sharing. The key elements of ADNI's success from the authors' perspective are also summarized. HIGHLIGHTS: Informal interactions among colleagues were the beginning of something big. An NIH Director's personal decision on open data sharing has had perhaps the greatest impact of any single decision in the past several decades in terms of advancing clinical biomarker research. After 20 years, blood-based biomarkers of brain disease may soon take the place of brain imaging for purposes of diagnosis and drug development.

8596 Brain Type 3 Deiodinase Is Induced in Two Disease Models That Lead to Cognitive Impairment: Liver Dysfunction and Alzheimer Disease

Abstract Disclosure: S. Wajner: None. T. Oliveira: None. M. Dreher: None. R. Ribeiro: None. C. Gonçalves: None. M.R. Alveres-da-Silva: None. Thyroid hormone is the leading regulator of cell energy production in most tissues, mainly the brain. While the activation process of T4 into T3 depends on D1 and D2 deiodinases, type 3 is the main enzyme that inactivates T3. Several mechanisms, among them oxidative stress, led by disease, imbalances and induces D3, diminishing T3 levels. The response of D3 in the brain in the context of different disease models has yet to be studied. Here we evaluated D3 induction in the brain in two animal disease models, one systemic and the other local. Methodology: To the metabolic dysfunction-associated steatosis liver disease model Male/adult Sprague Dawley rats (n=20) were assigned to control group (standard diet-2.93kcal/g) or high-fat-diet group (CDHF-4.3kcal/g). In the streptozotocin-induced (STZ) Alzheimer's model Adult Wistar rats (n=16) were allocated to the control group (5uL of citrate) or 5uL of streptozotocin. Sham animals were used as controls. D3 expression, oxidative stress parameters, endoplasmic stress and mitochondrial amount measured in the brain. Levels of D3 increased in the brain (∼30% in each group, P<0.0001) in both MASLD and STZ groups. Cerebral tissue from both groups had augmented carbonyl levels (P<0.001) and reduced sulfhydryl (P<0.001). Glutathione was diminished. Antioxidant defenses endoplasmic reticulum function and mitochondrial concentration were altered (P=0.001). The augmented T3 inactivation by D3 dysfunction in brain due to oxidative stress disrupts ER-mitochondrial contact interaction, changing the function of organelles in both brain disease models, shedding light to new mechanisms of action of thyroid hormone in severe brain disease. Presentation: 6/3/2024

Salience network segregation mediates the effect of tau pathology on mild behavioral impairment.

A recently developed mild behavioral impairment (MBI) diagnostic framework standardizes the early characterization of neuropsychiatric symptoms in older adults. However, the joint contributions of Alzheimer's disease (AD) pathology and brain function to MBI remain unclear. We test a novel model assessing direct relationships between AD biomarker status and MBI symptoms, as well as mediated effects through segregation of the salience and default-mode networks, using data from 128 participants with diagnosis of amnestic mild cognitive impairment or mild dementia-AD type. We identified a mediated effect of tau positivity on MBI through functional segregation of the salience network from the other high-level, association networks. There were no direct effects of AD biomarkers status on MBI. Our findings suggest that tau pathology contributes to MBI primarily by disrupting salience network function and emphasize the role of the salience network in mediating relationships between neuropathological changes and behavioral manifestations. Network segregation mediates Alzheimer's disease (AD) pathology impact on mild behavioral impairment (MBI). The salience network is pivotal in linking tau pathology and MBI. This study used path analysis with AD biomarkers and network integrity. The study evaluated the roles of salience, default mode, and frontoparietal networks. This is the first study to integrate MBI with AD biomarkers and network functionality.

N-Pep-Zn Improves Cognitive Functions and Acute Stress Response Affected by Chronic Social Isolation in Aged Spontaneously Hypertensive Rats (SHRs).

Aging and chronic stress are regarded as the most important risk factors of cognitive decline. Aged spontaneously hypertensive rats (SHRs) represent a suitable model of age-related vascular brain diseases. The aim of this study was to explore the effects of chronic isolation stress in aging SHRs on their cognitive functions and response to acute stress, as well as the influence of the chronic oral intake of N-Pep-Zn, the Zn derivative of N-PEP-12. Nine-month-old SHRs were subjected to social isolation for 3 months (SHRiso group), and one group received N-pep-Zn orally (SHRisoP, 1.5 mg/100 g BW). SHRs housed in groups served as the control (SHRsoc). The behavioral study included the following tests: sucrose preference, open field, elevated plus maze, three-chamber sociability and social novelty and spatial learning and memory in a Barnes maze. Levels of corticosterone, glucose and proinflammatory cytokines in blood plasma as well as salivary amylase activity were measured. Restraint (60 min) was used to test acute stress response. Isolation negatively affected the SHRs learning and memory in the Barnes maze, while the treatment of isolated rats with N-Pep-Zn improved their long-term memory and working memory impairments, making the SHRisoP comparable to the SHRsoc group. Acute stress induced a decrease in the relative thymus weight in the SHRiso group (but not SHRsoc), whereas treatment with N-Pep-Zn prevented thymus involution. N-pep-Zn mitigated the increment in blood cortisol and glucose levels induced by acute stress. N-pep-Zn enhanced the adaptive capabilities towards chronic (isolation) and acute (immobilization) stress in aged SHRs and prevented cognitive disturbances induced by chronic isolation, probably affecting the hypothalamo-pituitary-adrenal, sympathetic, and immune systems.

Analysis of epilepsy-associated variants in HCN3 - Functional implications and clinical observations.

This case study investigates the role of hyperpolarization-activated, cyclic nucleotide-gated (HCN) ion channels, which are integral membrane proteins crucial for regulating neuronal excitability. HCN channels are composed of four subunits (HCN1-4), with HCN1, HCN2, and HCN4 previously linked to epilepsy. However, the role of the HCN3 in epileptogenesis remains underexplored. We recruited a cohort of 298 epilepsy patients to screen for genetic variants in the HCN3 (NM_020897.3) using Sanger sequencing. We identified rare variants and conducted functional assays to evaluate their pathogenicity. We identified three rare heterozygous variants in HCN3: c.1370G > A (R457H), c.1982G > A (R661Q), and c.1982G > A(P630L). Invitro functional analyses demonstrated that these variants affected the expression level of HCN3 protein without altering its membrane localization. Whole-cell voltage-clamp experiments showed that two variants (R457H and R661Q) significantly reduced current density in cells, while P630L has no effect on ion channel current. Our findings suggest that the identified HCN3 genetic variants disrupt HCN ion channel function, highlighting HCN3 as a novel candidate gene involved in epileptic disorders. This expands the genetic landscape of epilepsy and provides new insights into its molecular underpinnings. Epilepsy is a brain disease that can be caused by mutations in specific genes. We found three rare variants in HCN3 gene in 298 patients with epilepsy, and two of the three mutations could be pathogenic and cause epilepsy and another one is single-nucleotide polymorphism, which could have no effect and no contribution to the development of epilepsy.

REVEALS: an open-source multi-camera GUI for rodent behavior acquisition.

Deciphering the rich repertoire of mouse behavior is crucial for understanding the functions of both the healthy and diseased brain. However, the current landscape lacks effective, affordable, and accessible methods for acquiring such data, especially when employing multiple cameras simultaneously. We have developed REVEALS (Rodent Behavior Multi-Camera Laboratory Acquisition), a graphical user interface for acquiring rodent behavioral data via commonly used USB3 cameras. REVEALS allows for user-friendly control of recording from one or multiple cameras simultaneously while streamlining the data acquisition process, enabling researchers to collect and analyze large datasets efficiently. We release this software package as a stand-alone, open-source framework for researchers to use and modify according to their needs. We describe the details of the graphical user interface implementation, including the camera control software and the video recording functionality. We validate results demonstrating the graphical user interface's stability, reliability, and accuracy for capturing rodent behavior using DeepLabCut in various behavioral tasks. REVEALS can be incorporated into existing DeepLabCut, MoSeq, or other custom pipelines to analyze complex behavior. In summary, REVEALS offers an interface for collecting behavioral data from single or multiple perspectives, which, when combined with deep learning algorithms, enables the scientific community to identify and characterize complex behavioral phenotypes.

Segmentation of acute ischemic stroke lesions based on deep feature fusion

Acute ischemic stroke (AIS) is a common brain disease worldwide, and diagnosing AIS requires effectively utilizing information from multiple Computed Tomography Perfusion (CTP) maps. As far as we know, most methods independently process each CTP map or fail to fully utilize medical prior information when integrating the information from CTP maps. Considering the characteristics of AIS lesions, we propose a method for efficient information fusion of CTP maps to achieve accurate segmentation results. We propose Window Multi-Head Cross-Attention Net (WMHCA-Net), which employs a multi-path U-shaped architecture for encoding and decoding. After encoding, multiple independent windowed cross-attentions are used to deeply integrate information from different maps. During the decoding phase, a Channel Cross-Attention (CCA) module is utilized to enhance information recovery during upsampling. We also added a segmentation optimization module to optimize low-resolution segmentation results, improving the overall performance. Finally, experimental results demonstrate that our proposed method exhibits strong balance and excels across multiple metrics. It can provide more accurate AIS lesion segmentation results to assist doctors in evaluating patient conditions. Our code are available at https://github.com/MTVLab/WMHCA-Net.

Large-Scale Mechanistic Models of Brain Circuits with Biophysically and Morphologically Detailed Neurons.

Understanding the brain requires studying its multiscale interactions from molecules to networks. The increasing availability of large-scale datasets detailing brain circuit composition, connectivity, and activity is transforming neuroscience. However, integrating and interpreting this data remains challenging. Concurrently, advances in supercomputing and sophisticated modeling tools now enable the development of highly detailed, large-scale biophysical circuit models. These mechanistic multiscale models offer a method to systematically integrate experimental data, facilitating investigations into brain structure, function, and disease. This review, based on a Society for Neuroscience 2024 MiniSymposium, aims to disseminate recent advances in large-scale mechanistic modeling to the broader community. It highlights (1) examples of current models for various brain regions developed through experimental data integration; (2) their predictive capabilities regarding cellular and circuit mechanisms underlying experimental recordings (e.g., membrane voltage, spikes, local-field potential, electroencephalography/magnetoencephalography) and brain function; and (3) their use in simulating biomarkers for brain diseases like epilepsy, depression, schizophrenia, and Parkinson's, aiding in understanding their biophysical underpinnings and developing novel treatments. The review showcases state-of-the-art models covering hippocampus, somatosensory, visual, motor, auditory cortical, and thalamic circuits across species. These models predict neural activity at multiple scales and provide insights into the biophysical mechanisms underlying sensation, motor behavior, brain signals, neural coding, disease, pharmacological interventions, and neural stimulation. Collaboration with experimental neuroscientists and clinicians is essential for the development and validation of these models, particularly as datasets grow. Hence, this review aims to foster interest in detailed brain circuit models, leading to cross-disciplinary collaborations that accelerate brain research.

Advances in Chiral Metabolomic Profiling and Biomarker Discovery.

Chiral metabolomics entails the enantioselective measurement of the metabolome present in a biological system. Over recent years, it has garnered significant interest for its potential in discovering disease biomarkers and aiding clinical diagnostics. D-Amino acids and D-hydroxy acids, traditionally overlooked as unnatural, are now emerging as novel signaling molecules and potential biomarkers for a range of metabolic disorders, brain diseases, kidney disease, diabetes, and cancer. Despite their significance, simultaneous measurements of multiple classes of chiral metabolites in a biological system remain challenging. Hence, limited information is available regarding the metabolic pathways responsible for synthesizing D-amino/hydroxy acid and their associated pathophysiological mechanisms in various diseases. Capitalizing on recent advancements in sensitive analytical techniques, researchers have developed various targeted chiral metabolomic methods for the analysis of chiral biomarkers. Here, we highlight the pivotal role of chiral metabolic profiling studies in disease diagnosis, prognosis, and therapeutic interventions. Furthermore, we describe cutting-edge chromatographic and mass spectrometry methods that enable enantioselective analysis of chiral metabolites. These advanced techniques are instrumental in unraveling the complexities of disease biomarkers, contributing to the ongoing efforts in disease biomarker discovery.

Widespread transposable element dysregulation in human aging brains with Alzheimer's disease.

Transposable element (TE) dysregulation is associated with neuroinflammation in Alzheimer's disease (AD) brains. Yet, TE quantitative trait loci (teQTL) have not been well characterized in human aged brains with AD. We leveraged large-scale bulk and single-cell RNA sequencing, whole-genome sequencing (WGS), and xQTL from three human AD brain biobanks to characterize TE expression dysregulation and experimentally validate AD-associated TEs using CRISPR interference (CRISPRi) assays in human induced pluripotent stem cell (iPSC)-derived neurons. We identified 26,188 genome-wide significant TE expression QTLs (teQTLs) in human aged brains. Subsequent colocalization analysis of teQTLs with AD genetic loci identified AD-associated teQTLs and linked locus TEs. Using CRISPRi assays, we pinpointed a neuron-specific suppressive role of the activated short interspersed nuclear element (SINE; chr11:47608036-47608220) on expression of C1QTNF4 via reducing neuroinflammation in human iPSC-derived neurons. We identified widespread TE dysregulation in human AD brains and teQTLs offer a complementary analytic approach to identify likely AD risk genes. Widespread transposable element (TE) dysregulations are observed in human aging brains with degrees of neuropathology, apolipoprotein E (APOE) genotypes, and neuroinflammation in Alzheimer's disease (AD). A catalog of TE quantitative trait loci (teQTLs) in human aging brains was created using matched RNA sequencing and whole-genome sequencing data. CRISPR interference assays reveal that an upregulated intergenic TE from the MIR family (chr11: 47608036-47608220) suppresses expression of its nearest anti-inflammatory gene C1QTNF4 in human induced pluripotent stem cell-derived neurons.

Tibolone treatment after traumatic brain injury exerts a sex-specific and Y chromosome-dependent regulation of methylation and demethylation enzymes and estrogen receptors in the cerebral cortex

Tibolone, a synthetic steroid used for the treatment of climacteric symptoms, displays sex-specific protective actions in experimental models of brain diseases. Previous in vitro findings suggest that tibolone reduces oxidative stress and neuroinflammation through the regulation of DNA methylation and the activation of estrogen receptors (ERs) α and β. In this study, we assessed whether tibolone regulates the expression of genes coding for DNA methylation and demethylation enzymes and ERs in the injured cerebral cortex of animals suffering a traumatic brain injury. The four-core genotype mouse model was used to determine whether the effect of tibolone on gene regulation was influenced by gonads or by cell-autonomous actions of sex chromosomes. Tibolone treatment resulted in sex-specific modification in the expression of genes coding for DNA methyl transferases (Dnmt) 3a, and 3b, for growth arrest and DNA-damage-inducible proteins (Gadd) 45β and 45γ, and for ERα and ERβ. In contrast, tibolone did not affect the expression of genes coding for Dnmt1, Gadd45α, and ten-eleven translocation methylcytosine dioxygenases 1–3. The sex-specific effect of tibolone on Dnmt3a expression depended on gonadal sex. In contrast, the presence or absence of the Y chromosome determined the effect of tibolone on Dnmt3b, Gadd45β, Gadd45γ, ERα and ERβ expression. These findings suggest that tibolone exerts a sex-specific regulation of DNA methylation and ER expression in the injured cerebral cortex that is determined by a combination of gonadal effects and cell-autonomous actions of sex chromosome genes.

Comparison of single shot and multishot diffusion-weighted imaging in 5-T magnetic resonance imaging for brain disease diagnosis.

Diffusion-weighted imaging (DWI) with single-shot echo-planar imaging (ssEPI) is a valuable tool for detecting acute brain lesions but does suffer from image distortions. Multishot echo-planar imaging (msEPI) is a technique for reducing such distortions. This study aimed to compare the image quality and diagnostic efficacy of ssEPI- and msEPI-DWI at 5.0 T for brain disease detection. This study retrospectively reviewed images of 107 consecutive patients with suspected brain diseases who underwent ssEPI- and msEPI-DWI at 5.0 T at the First Affiliated Hospital of University of Science and Technology of China from August 2023 to September 2023. Two radiologists independently graded image quality and measured the image distortion. Signal-to-noise ratio, contrast-to-noise ratio, and apparent diffusion coefficient (ADC) were calculated and compared between ssEPI- and msEPI-DWI. Image quality scores were compared using the Wilcoxon test and other continuous variables by the paired t-test. The diagnostic accuracy of ADC values in distinguishing lesions from normal-appearing tissues was measured with the area under the curve (AUC). Image quality evaluation and distortion analysis revealed that msEPI-DWI significantly outperformed ssEPI-DWI (two-sided P<0.001). No significant difference was observed in signal-to-noise ratio, contrast-to-noise ratio, or ADC values between msEPI- and ssEPI-DWI (two-sided P≥0.601). The ADC values of msEPI- and ssEPI-DWI showed strong correlations for both lesions (r=0.97) and contralateral normal tissues (r=0.91) (two-sided P<0.001). Compared to those of the contralateral white matter, ADC values of low-grade gliomas (LGGs) were significantly higher [ssEPI-DWI: 1,119.9±273.1 vs. 805.1±73.9; msEPI-DWI: 1,196.2±355.6 vs. 757.3±98.0 (unit: ×10-6 mm2/s)], while the ADC values of acute cerebral infarction (ACI) lesions were significantly lower [ssEPI-DWI: 603.9±273.2 vs. 888.9±212.0; msEPI-DWI: 538.0±281.2 vs. 905.0±188.9 (unit: ×10-6 mm2/s)] (two-sided P≤0.003). The AUCs for detecting LGGs were excellent for both ssEPI-DWI [AUC =0.934; 95% confidence interval (CI): 0.84-1.00] and msEPI-DWI (AUC =0.944; 95% CI: 0.86-1.00) (two-sided P<0.001; two-sided DeLong test: P=0.833). As compared to ssEPI-DWI, msEPI-DWI, when performed at 5.0 T, demonstrated superior image quality and less anatomical distortion in a wide spectrum of brain diseases and showed promising diagnostic performance for LGGs and ACI. In the future, msEPI-DWI at 5.0 T could become clinically routine in the diagnosis and grading of brain disorders.

Genetic and vascular risk factors for ischemic stroke and cortical morphometry in individuals without a history of stroke: A UK Biobank observational cohort study

BackgroundStroke risk factors may contribute to cognitive decline and dementia by altering brain tissue integrity. If their effects on brain are nonnegligible, the target regions for stroke rehabilitation with brain stimulation identified by cross-sectional case-control studies may be biased due to the pre-existing brain differences caused by these risk factors. Here, we investigated the effects of stroke risk factors on cortical thickness (CT) and surface area (SA) in individuals without a history of stroke. MethodsIn this observational study, we used data from the UK Biobank cohort to explore the effects of polygenic risk score for ischemic stroke (PRSIS), systolic blood pressure (SBP), diastolic blood pressure (DBP), glycated hemoglobin (HbA1c), triglycerides (TG), and low-density lipoprotein (LDL) on CT and SA of 62 cerebral regions. We excluded non-Caucasian participants and participants with missing data, unqualified brain images, or a history of stroke or any other brain diseases. We constructed a multivariate linear regression model for each phenotype to simultaneously test the effect of each factor and interaction between factors. The results were verified by sensitivity analyses of SDP or DBP input and adjusting for body-mass index, high-density lipoprotein cholesterol, or smoking and alcohol intake. By excluding participants with abnormal blood pressure, glucose, or lipid, we tested whether vascular risk factor within normal range also affected cortical phenotypes. To determine clinical relevance of our findings, we also investigated the effects of stroke risk factors and cortical phenotypes on cognitive decline assessed by fluid intelligence score (FIQ) and the mediation of cortical phenotype for the association between stroke risk factor and FIQ. ResultsThe study consisted of 27 120 eligible participants. Stroke risk factors were associated with 16 CT and two SA phenotypes in both main and sensitivity analyses (all p < 0.0004, Bonferroni corrected), which could explain portions of variances (partial R2, median 0.62 % [IQR 0.44–0.75 %] in main analyses) in these phenotypes. Among the 18 cortical phenotypes associated with stroke risk factors, we identified 26 specific predictor-phenotype associations (all p < 0.0026), including the positive associations between PRSIS and SA and between HbA1c and CT, negative associations of SBP and TG with CT, and mixed associations of PRSIS and DBP with CT. Neither LDL nor interactions between risk factors affected cortical phenotypes. Of the 16 associations between vascular risk factors and cortical phenotypes, ten were still significant after excluding participants with abnormal vascular risk assessments and diagnoses. Stroke risk factors were associated with FIQ in all analyses (p < 0.0004; partial R2, range 0.22–0.3 %), of which the associations of PRSIS and SBP with cognitive decline were mediated by CT phenotypes. ConclusionsStroke risk factors have substantial effects on cortical morphometry and cognitive decline in middle-aged and older people, which should be considered in the prevention of dementia and in the identification of target regions for stroke rehabilitation with brain stimulation.

Tuning a bioengineered hydrogel for studying astrocyte reactivity in glioblastoma

Astrocytes play many essential roles in the central nervous system (CNS) and are altered significantly in disease. These reactive astrocytes contribute to neuroinflammation and disease progression in many pathologies, including glioblastoma (GB), an aggressive form of brain cancer. Current in vitro platforms do not allow for accurate modeling of reactive astrocytes. In this study, we sought to engineer a simple bioengineered hydrogel platform that would support the growth of primary human astrocytes and allow for accurate analysis of various reactive states. After validating this platform using morphological analysis and qPCR, we then used the platform to begin investigating how astrocytes respond to GB derived extracellular vesicles (EVs) and soluble factors (SF). These studies reveal that EVs and SFs induce distinct astrocytic states. In future studies, this platform can be used to study how astrocytes transform the tumor microenvironment in GB and other diseases of the CNS. Statement of significanceRecent work has shown that astrocytes help maintain brain homeostasis and may contribute to disease progression in diseases such as glioblastoma (GB), a deadly primary brain cancer. In vitro models allow researchers to study basic mechanisms of astrocyte biology in healthy and diseased conditions, however current in vitro systems do not accurately mimic the native brain microenvironment. In this study, we show that our hydrogel system supports primary human astrocyte culture with an accurate phenotype and allows us to study how astrocytes change in response to a variety of inflammatory signals in GB. This platform could be used further investigate astrocyte behavior and possible therapeutics that target reactive astrocytes in GB and other brain diseases.

The Bidirectional Association Between Metabolic Syndrome and Long-COVID-19.

The rapid global spread of a new coronavirus disease known as COVID-19has led to a significant increase in mortality rates, resulting in an unprecedented worldwide pandemic. The impact of COVID-19, particularly its long-term effects, has also had a profound effect on the health and well-being of individuals.Metabolic syndrome increases the risk of heart and brain diseases, presenting a significant danger to human well-being. The prognosis of long COVID and the progression of metabolic syndrome interact with each other, but there is currently a lack of systematic reports.In this paper, the pathogenesis, related treatment and prognosis of long COVID and metabolic syndrome are systematically reviewed.