Brain Disease Research Articles

Structural Variants at the LMNB1 Locus: Deciphering Pathomechanisms in Autosomal Dominant Adult-Onset Demyelinating Leukodystrophy.

We aimed to elucidate the pathogenic mechanisms underlying autosomal dominant adult-onset demyelinating leukodystrophy (ADLD), and to understand the genotype/phenotype correlation of structural variants (SVs) in the LMNB1 locus. Since the discovery of 3D genome architectures and topologically associating domains (TADs), new pathomechanisms have been postulated for SVs, regardless of gene dosage changes. ADLD is a rare genetic disease associated with duplications (classical ADLD) or noncoding deletions (atypical ADLD) in the LMNB1 locus. High-throughput chromosome conformation capture, RNA sequencing, histopathological analyses of postmortem brain tissues, and clinical and neuroradiological investigations were performed. We collected data from >20 families worldwide carrying SVs in the LMNB1 locus and reported strong clinical variability, even among patients carrying duplications of the entire LMNB1 gene, ranging from classical and atypical ADLD to asymptomatic carriers. We showed that patients with classic ADLD always carried intra-TAD duplications, resulting in a simple gene dose gain. Atypical ADLD was caused by LMNB1 forebrain-specific misexpression due to inter-TAD deletions or duplications. The inter-TAD duplication, which extends centromerically and crosses the 2 TAD boundaries, did not cause ADLD. Our results provide evidence that astrocytes are key players in ADLD pathology. Our study sheds light on the 3D genome and TAD structural changes associated with SVs in the LMNB1 locus, and shows that a duplication encompassing LMNB1 is not sufficient per se to diagnose ADLD, thereby strongly affecting genetic counseling. Our study supports breaking TADs as an emerging pathogenic mechanism that should be considered when studying brain diseases. ANN NEUROL 2024.

Multi-modal flexible and inexpensive plasmonic metasurface for wide range of refractive index sensing

Abstract A plasmonic metasurface containing nanobumps of sub-wavelength feature size arranged in a hexagonal pattern on a flexible substrate and covered with a thin film of gold is investigated as a refractive index sensor. The chosen polymer patterns coated with gold aid in activating the surface plasmon polariton modes. Using numerical calculations, it is shown that this surface can exhibit plasmonic effect with an extremely shallow pattern height of 92.5 nm and minimal thickness of 25 nm of gold over it. The excitation of the plasmonic modes is confirmed using electric field profiles calculated at the relevant wavelengths. As the surface is highly sensitive to changes in the cladding index, and the chosen design aids in exciting three plasmon modes that are suitably well-separated in wavelength, this surface can be used for an extremely wide range of refractive index sensing because each mode contributes uniquely to a different range of refractive index. The results establish that the metasurface is suitable for a variety of applications, including gas detection with a sensitivity of 633 nm/RIU using mode-1, identifying SARS-CoV-2 viral molecules with a sensitivity of 428 nm/RIU using mode-2 and 238 nm/RIU using mode-3, and discriminating between normal and diseased brain tissues in the cerebrospinal fluid in the high-index range using mode-3. The prototype metasurface is made using a cost-effective soft lithography technique using an economical master mould. The inexpensive technique of fabrication, use of very thin metal film, and wavelength of detection lying within the visible to near infrared range imply a low-cost sensor. The structural and optical characterization of the prototype validates the numerical study of the sample.

Alzheimer's Disease and Vascular Dementia, Connecting and Differentiating Features.

Alzheimer's disease (AD) and vascular dementia (VD) are the leading causes of dementia, presenting a significant challenge in differential diagnosis. While their clinical presentations can overlap, their underlying pathologies are distinct. AD is characterized by the accumulation of amyloid plaques and neurofibrillary tangles, leading to progressive neurodegeneration. VD, on the other hand, arises from cerebrovascular insults that disrupt blood flow to the brain, causing neuronal injury and cognitive decline. Despite distinct etiologies, AD and VD share common risk factors such as hypertension, diabetes, and hyperlipidemia. Recent research suggests a potential role for oral microbiota in both diseases, warranting further investigation. The diagnostic dilemma lies in the significant overlap of symptoms including memory loss, executive dysfunction, and personality changes. The absence of definitive biomarkers and limitations of current neuroimaging techniques necessitate a multi-modal approach integrating clinical history, cognitive assessment, and neuroimaging findings. Promising avenues for improved diagnosis include the exploration of novel biomarkers like inflammatory markers, MMPs, and circulating microRNAs. Additionally, advanced neuroimaging techniques hold promise in differentiating AD and VD by revealing characteristic cerebrovascular disease patterns and brain atrophy specific to each condition. By elucidating the complexities underlying AD and VD, we can refine diagnostic accuracy and optimize treatment strategies for this ever-growing patient population. Future research efforts should focus on identifying disease-specific biomarkers and developing more effective neuroimaging methods to achieve a definitive diagnosis and guide the development of targeted therapies.

Harmonic Wavelet Neural Network for Discovering Neuropathological Propagation Patterns in Alzheimer's Disease.

Emerging research indicates that the degenerative biomarkers associated with Alzheimer's disease (AD) exhibit a non-random distribution within the cerebral cortex, instead following the structural brain network. The alterations in brain networks occur much earlier than the onset of clinical symptoms, thereby affecting the progression of brain disease. In this context, the utilization of computational methods to ascertain the propagation patterns of neuropathological events would contribute to the comprehension of the pathophysiological mechanism involved in the evolution of AD. Despite the encouraging findings achieved by existing graph-based deep learning approaches in analyzing irregular graph data, their applications in identifying the spreading pathway of neuropathology are limited due to two disadvantages. They include (1) lack of a common brain network as an unbiased reference basis for group comparison, and (2) lack of an appropriate mechanism for the identification of propagation patterns. To this end, we propose a proof-of-concept harmonic wavelet neural network (HWNN) to predict the early stage of AD and localize disease-related significant wavelets, which can be used to characterize the spreading pathways of neuropathological events across the brain network. The extensive experiments constructed on both synthetic and real datasets demonstrate that our proposed method achieves superior performance in classification accuracy and statistical power of identifying propagation patterns, compared with other representative approaches.

Spatial dynamics of mammalian brain development and neuroinflammation by multimodal tri-omics mapping.

The ability to spatially map multiple layers of the omics information over different time points allows for exploring the mechanisms driving brain development, differentiation, arealization, and alterations in disease. Herein we developed and applied spatial tri-omic sequencing technologies, DBiT ARP-seq (spatial ATAC-RNA-Protein-seq) and DBiT CTRP-seq (spatial CUT&Tag- RNA-Protein-seq) together with multiplexed immunofluorescence imaging (CODEX) to map spatial dynamic remodeling in brain development and neuroinflammation. A spatiotemporal tri-omic atlas of the mouse brain was obtained at different stages from postnatal day P0 to P21, and compared to the regions of interest in the human developing brains. Specifically, in the cortical area, we discovered temporal persistence and spatial spreading of chromatin accessibility for the layer-defining transcription factors. In corpus callosum, we observed dynamic chromatin priming of myelin genes across the subregions. Together, it suggests a role for layer specific projection neurons to coordinate axonogenesis and myelination. We further mapped the brain of a lysolecithin (LPC) neuroinflammation mouse model and observed common molecular programs in development and neuroinflammation. Microglia, exhibiting both conserved and distinct programs for inflammation and resolution, are transiently activated not only at the core of the LPC lesion, but also at distal locations presumably through neuronal circuitry. Thus, this work unveiled common and differential mechanisms in brain development and neuroinflammation, resulting in a valuable data resource to investigate brain development, function and disease.

A sparse transformer generation network for brain imaging genetic association

Brain imaging and genetic data are commonly utilized to investigate brain aging and diseases, particularly Alzheimer’s Disease (AD). Imaging genetics analyze the associations between neuroimaging and genetic data to reveal potential pathological mechanisms for more accurate diagnosis. Many existing methods have limitations in performing fine-grained association analysis between genetic data and phenotypic features extracted from predefined regions of interest in imaging data. To address this issue, this paper proposes a sparse transformer association analysis (STAA) framework that integrates phenotype and genotype feature extraction, identification, and association analysis into a unified model. A key component of the framework is a cross-modal generation network that connects genetic variants with imaging data, enhancing the understanding of the genetic associations underlying imaging patterns in AD and brain aging. Validated using the simulated data and real ADNI dataset, STAA shows superior performance in age regression and AD diagnosis, identifying key genetic and imaging biomarkers and providing association analysis of genetic imaging expression patterns at the voxel level.

Contrastive voxel clustering for multiscale modeling of brain network

Resting-state functional magnetic resonance imaging (fMRI) provides an efficient way to analyze the functional connectivity between brain regions. A comprehensive understanding of brain functionality requires a unified description of multi-scale layers of neural structure. However, existing brain network modeling methods often simplify this property by averaging Blood oxygen level dependent (BOLD) signals at the brain region level for fMRI-based analysis with the assumption that BOLD signals are homogeneous within each brain region, which ignores the heterogeneity of voxels within each Region of Interest (ROI). This study introduces a novel multi-stage self-supervised learning framework for multiscale brain network analysis, which effectively delineates brain functionality from voxel to ROIs and up to sample level. A Contrastive Voxel Clustering (CVC) module is proposed to simultaneously learn the voxel-level features and clustering assignments, which ensures the retention of informative clustering features at the finest voxel-level and concurrently preserves functional connectivity characteristics. Additionally, based on the extracted features and clustering assignments at the voxel level by CVC, a Brain ROI-based Graph Neural Network (BR-GNN) is built to extract functional connectivity features at the brain ROI-level and used for sample-level prediction, which integrates the functional clustering maps with the pre-established structural ROI maps and creates a more comprehensive and effective analytical tool. Experiments are performed on two datasets, which illustrate the effectiveness and generalization ability of the proposed method by analyzing voxel-level clustering results and brain ROIs-level functional characteristics. The proposed method provides a multiscale modeling framework for brain functional connectivity analysis, which will be further used for other brain disease identification. Code is available at https://github.com/yanliugroup/fmri-cvc.

Exploring How Antibody Format Drives Clearance from the Brain.

Monoclonal antibodies (mAbs) have high binding specificity and affinity, making them attractive for treating brain diseases. However, their effectiveness is limited by poor blood-brain barrier (BBB) penetration and rapid central nervous system (CNS) clearance. Our group identified blood-brain barrier modulator (BBBM) peptides that improved mAb penetration across the BBB into the brain. In this study, we investigated the pharmacokinetics of a mAb delivered to the brain using BBBMs after intravenous (IV) administration and explored the impact of antibody format (size, neonatal Fc receptor (FcRn) binding, hyaluronic acid binding) on brain clearance following direct injection into the central nervous system (CNS) via intracerebroventricular (ICV) injection. IRDye800CW-labeled antibodies were administered into C57BL/6 mice via ICV or IV injection, and organ concentrations were measured after various time points. When a mAb was coadministered with a BBBM peptide, the permeation of mAb across the BBB was increased compared to mAb alone at early time points; however, the mAb was cleared within 2 h from the brain. ICV experiments revealed that an antibody Fab fragment had a higher brain exposure than a mAb, and that a Fab fused to a hyaluronic acid binding domain (Fab-VG1) showed remarkable improvement in brain exposure. These findings suggest that BBBMs and antibody format optimization may be promising strategies for enhancing brain retention of therapeutic antibodies.

Adaptive node feature extraction in graph-based neural networks for brain diseases diagnosis using self-supervised learning

Electroencephalography (EEG) has demonstrated significant value in diagnosing brain diseases. In particular, brain networks have gained prominence as they offer additional valuable insights by establishing connections between EEG signal channels. While brain connections are typically delineated by channel signal similarity, there lacks a consistent and reliable strategy for ascertaining node characteristics. Conventional node features such as temporal and frequency domain properties of EEG signals prove inadequate for capturing the extensive EEG information. In our investigation, we introduce a novel adaptive method for extracting node features from EEG signals utilizing a distinctive task-induced self-supervised learning technique. By amalgamating these extracted node features with fundamental edge features constructed using Pearson correlation coefficients, we showed that the proposed approach can function as a plug-in module that can be integrated to many common GNN networks (e.g., GCN, GraphSAGE, GAT) as a replacement of node feature selections module. Comprehensive experiments are then conducted to demonstrate the consistently superior performance and high generality of the proposed method over other feature selection methods in various of brain disorder prediction tasks, such as depression, schizophrenia, and Parkinson’s disease. Furthermore, compared to other node features, our approach unveils profound spatial patterns through graph pooling and structural learning, shedding light on pivotal brain regions influencing various brain disorder prediction based on derived features.

Regulation of GABAergic neurotransmission by purinergic receptors in brain physiology and disease.

Purinergic receptors regulate the processing of neural information in the hippocampus and cerebral cortex, structures related to cognitive functions. These receptors are activated when astrocytic and neuronal populations release adenosine triphosphate (ATP) in an autocrine and paracrine manner, following sustained patterns of neuronal activity. The modulation by these receptors of GABAergic transmission has only recently been studied. Through their ramifications, astrocytes and GABAergic interneurons reach large groups of excitatory pyramidal neurons. Their inhibitory effect establishes different synchronization patterns that determine gamma frequency rhythms, which characterize neural activities related to cognitive processes. During early life, GABAergic-mediated synchronization of excitatory signals directs the experience-driven maturation of cognitive development, and dysfunctions concerning this process have been associated with neurological and neuropsychiatric diseases. Purinergic receptors timely modulate GABAergic control over ongoing neural activity and deeply affect neural processing in the hippocampal and neocortical circuitry. Stimulation of A2 receptors increases GABA release from presynaptic terminals, leading to a considerable reduction in neuronal firing of pyramidal neurons. A1 receptors inhibit GABAergic activity but only act in the early postnatal period when GABA produces excitatory signals. P2X and P2Y receptors expressed in pyramidal neurons reduce the inhibitory tone by blocking GABAA receptors. Finally, P2Y receptor activation elicits depolarization of GABAergic neurons and increases GABA release, thus favoring the emergence of gamma oscillations. The present review provides an overall picture of purinergic influence on GABAergic transmission and its consequences on neural processing, extending the discussion to receptor subtypes and their involvement in the onset of brain disorders, including epilepsy and Alzheimer's disease.

Protective signature of xanthohumol on cognitive function of APP/PS1 mice: a urine metabolomics approach by age.

Alzheimer's disease (AD) has an increasing prevalence, complicated pathogenesis and no effective cure. Emerging evidences show that flavonoid compounds such as xanthohumol (Xn) could play an important role as a dietary supplement or traditional Chinese herbal medicine in the management of diseases such as AD. This study aims to analyze the target molecules of Xn in the prevention and treatment of AD, and its potential mechanism from the perspective of metabolites. APP/PS1 mice 2- and 6-months old were treated with Xn for 3months, respectively, the younger animals to test for AD-like brain disease prevention and the older animals to address therapeutic effects on the disease. Memantine (Mem) was selected as positive control. Behavioral tests were performed to assess the course of cognitive function. Urine samples were collected and analyzed by high-performance liquid chromatography (HPLC) with tandem mass spectrometry (MS/MS) coupled with online Compound Discoverer software. Morris Water Maze (MWM) tests showed that Xn, like Mem, had a therapeutic but not a preventive effect on cognitive impairment. The expression levels of urinary metabolites appeared to show an opposite trend at different stages of Xn treatment, downregulated in the prevention phase while upregulated in the therapy phase. In addition, the metabolic mechanisms of Xn during preventive treatment were also different from that during therapeutic treatment. The signaling pathways metabolites nordiazepam and genistein were specifically regulated by Xn but not by Mem in the disease prevention stage. The signaling pathway metabolite ascorbic acid was specifically regulated by Xn in the therapeutic stage. In conclusion, dietary treatment with Xn altered the urinary metabolite profile at different stages of administration in APP/PS1 mice. The identified potential endogenous metabolic biomarkers and signal pathways open new avenues to investigate the pathogenesis and treatment of AD.

Indications for a brain biopsy in neurological diseases of unknown etiology: The role of magnetic resonance imaging findings and liquid biopsy in yielding definitive pathological diagnoses

Brain biopsies are often considered for patients who cannot be diagnosed with various laboratory test results. However, physicians tend to be hesitant regarding their application in possibly non-neoplastic brain diseases, due to the invasiveness and risks. The aim was to determine the indications for brain biopsies in cases of neurological diseases of unknown etiology. We retrospectively evaluated diagnostic accuracy, laboratory findings (including a liquid biopsy for malignant lymphoma), magnetic resonance imaging (MRI) characteristics and the post-treatment outcomes of patients undergoing brain biopsies for neurological diseases of unknown etiology. The data of patients who had undergone a brain biopsy during their admission to Niigata University Hospital, between 2011 and 2024, were reviewed. Moreover, the laboratory data and MRI findings between patients with definitive and nonspecific biopsy diagnoses were compared. Twenty-six patients underwent a brain biopsy, and a definitive diagnosis was obtained in 14 patients (53.8%). Even in cases where a nonspecific diagnosis was made, biopsy findings helped rule out malignancy and guide clinical diagnosis and treatment decisions. The liquid biopsy for malignant lymphoma was performed in eight patients, with one yielding a positive result, consistent with primary central nervous system lymphoma. The sensitivity and specificity of liquid biopsy were 0.5 and 1, respectively. Diffusely contrasted cortical lesions and the presence of mass effects on MRI, were significantly associated with a definitive diagnosis, compared to a nonspecific diagnosis. In conclusion, brain MRI and liquid biopsies can assist in determining the appropriate indications for brain biopsies in neurological diseases of unknown etiology.

A case report of lymphoproliferative disease in brain following therapies with mycophenolate Mofetil and Fingolimod and literature review

Anti-N-methyl-d-aspartate receptor (NMDAR) encephalitis is an autoimmune disorder. With the method of indirect immunofluorescence assay (IIF), more anti-NMDAR encephalitis patients have been discovered when its first onset. But it was rare that anti-NMDAR encephalitis overlapped with multiple sclerosis (MS) documented in literatures. Here, we present a case who initially developed anti-NMDAR encephalitis and MS. Furthermore, we concluded the characteristics of patients who were diagnosed as anti-NMDAR encephalitis overlapping with MS. Additionally, due to the relapsing process, mycophenolate mofetil and sequentially fingolimod for the treatment were taken, which subsequently led to the development of a lymphoproliferative disease in his brain and other organs. This case illustrates the complex role of immunosuppressive agents.

Monitoring of Electrophysiological Functions in Brain‐on‐a‐Chip and Brain Organoids

Though animal models are still the gold standard for fundamental biological studies and drug evaluation for brain diseases, concerns arise from an apparent lack of reflecting the human genetics and pathophysiology. Recently, human avatars such as brain‐on‐a‐chip and brain organoids which are generated in a 3D manner using multiple types of human‐originated cells have risen as alternative testing models. Particularly in monitoring the functional neuronal cells that express action potentials in brain‐on‐a‐chip or brain organoids, various methods of measuring their electrophysiological function have been suggested for the study of brain‐related disease. Recent methodologies for analyzing the electrophysiology of different types of cells in brain‐on‐a‐chip and brain organoids are summarized in this review. We first emphasize the inherent features of brain‐on‐a‐chip and brain organoids from the perspective of the cell culture environment and accessibility to cells in the deep layer. The applicable monitoring techniques are then overviewed based on these features. Finally, we discuss the unmet needs for electrophysiology monitoring in advanced human brain avatar models.

Small Scale, Big Impact: Nanotechnology-Enhanced Drug Delivery for Brain Diseases.

Central nervous system (CNS) diseases, ranging from brain cancers to neurodegenerative disorders like dementia and acute conditions such as strokes, have been heavily burdening healthcare and have a direct impact on patient quality of life. A significant hurdle in developing effective treatments is the presence of the blood-brain barrier (BBB), a highly selective barrier that prevents most drugs from reaching the brain. The tight junctions and adherens junctions between the endothelial cells and various receptors expressed on the cells make the BBB form a nonfenestrated and highly selective structure that is crucial for brain homeostasis but complicates drug delivery. Nanotechnology offers a novel pathway to circumvent this barrier, with nanoparticles engineered to ferry drugs across the BBB, protect drugs from degradation, and deliver medications to the designated area. After years of development, nanoparticle optimization, including sizes, shapes, surface modifications, and targeting ligands, can enable nanomaterials tailored to specific brain drug delivery settings. Moreover, smart nano drug delivery systems can respond to endogenous and exogenous stimuli that control subsequent drug release. Here, we address the importance of the BBB in brain disease treatment, summarize different delivery routes for brain drug delivery, discuss the cutting-edge nanotechnology-based strategies for brain drug delivery, and further offer valuable insights into how these innovations in nanoparticle technology could revolutionize the treatment of CNS diseases, presenting a promising avenue for noninvasive, targeted therapeutic interventions.

Developing EMDR for psychosis from a Power, Threat, Meaning Framework perspective

Evidence has been accumulating for the crucial role that trauma plays in the development of psychotic experiences. As such, there has been increased interest in the use of trauma-focused therapies, like Eye Movement Desensitisation and Reprocessing therapy (EMDR), for people who experience psychosis. However, the medical model has traditionally understood psychosis as a brain disease, leading to conceptual confusion regarding the role of trauma memory networks in the development of psychotic experiences. This paper proposes a trauma-informed understanding of psychosis in accordance with the Power, Threat, Meaning Framework (PTMF). The commonalities between the PTMF and the key models employed in EMDR, i.e. the Adaptive Information Processing and Structural Dissociation models, are explored.

Neurolipid systems: A new target for the treatment of dementia.

AbstractNeurolipids comprise a diverse class of bioactive lipids that include molecules capable of activating G protein‐coupled receptors, thereby inducing systemic effects that contribute to the maintenance of homeostasis. Dementia, a non‐specific brain disorder characterized by a common set of signs and symptoms, usually arises subsequent to brain injuries or diseases and is often associated with the aging process. Individuals affected by dementia suffer from the disruption of several neurotransmitter and neuromodulatory systems, among which neurolipids play an important role, including the endocannabinoid, lysophosphatidic acid and sphingosine 1‐phosphate systems. In this review, we present an overview of the most recent and pertinent findings regarding the involvement of these neurolipidic systems in dementia, including data from a wide range of both in vitro and in vivo experiments as well as clinical trials.

APOER2 splicing repertoire in Alzheimer's disease: Insights from long-read RNA sequencing.

Disrupted alternative splicing plays a determinative role in neurological diseases, either as a direct cause or as a driver in disease susceptibility. Transcriptomic profiling of aged human postmortem brain samples has uncovered hundreds of aberrant mRNA splicing events in Alzheimer's disease (AD) brains, associating dysregulated RNA splicing with disease. We previously identified a complex array of alternative splicing combinations across apolipoprotein E receptor 2 (APOER2), a transmembrane receptor that interacts with both the neuroprotective ligand Reelin and the AD-associated risk factor, APOE. Many of the human APOER2 isoforms, predominantly featuring cassette splicing events within functionally important domains, are critical for the receptor's function and ligand interaction. However, a comprehensive repertoire and the functional implications of APOER2 isoforms under both physiological and AD conditions are not fully understood. Here, we present an in-depth analysis of the splicing landscape of human APOER2 isoforms in normal and AD states. Using single-molecule, long-read sequencing, we profiled the entire APOER2 transcript from the parietal cortex and hippocampus of Braak stage IV AD brain tissues along with age-matched controls and investigated several functional properties of APOER2 isoforms. Our findings reveal diverse patterns of cassette exon skipping for APOER2 isoforms, with some showing region-specific expression and others unique to AD-affected brains. Notably, exon 15 of APOER2, which encodes the glycosylation domain, showed less inclusion in AD compared to control in the parietal cortex of females with an APOE ɛ3/ɛ3 genotype. Also, some of these APOER2 isoforms demonstrated changes in cell surface expression, APOE-mediated receptor processing, and synaptic number. These variations are likely critical in inducing synaptic alterations and may contribute to the neuronal dysfunction underlying AD pathogenesis.

Multimodal Data Integration for Early Alzheimer’s Detection Using Random Forest and Support Vector Machines

Alzheimer's is a very challenging brain disease to recognize, diagnose, and treat correctly when it appears in its earliest forms. The primary contribution of this research study is about machine learning models, techniques, and approaches. In contrast, Random Forest and Support Vector Machine (SVM) are particularly suitable for identifying and staging Alzheimer's disease stages using multimodal data sources. In this paper, the aim was to develop well-performing predictive models to help diagnose Alzheimer's disease at an early stage by combining neuroimaging data (MRI/PET images), imaging-based biomarkers, both structural and functional measures from MRI(P) /PET image analysis along with subject-specific demographics like age using clinical features in a probabilistic fashion obtained from the Alzheimer's Disease Neuro-Imaging Initiative (ADNI) database. The methodology focuses on data pre-processing, feature selection, and model building using supervised learning algorithms. The accuracy of the Random Forest model is 78%, having a high performance in classifying some classes while showing different marks of performances across other courses. SVM reached an accuracy of 61%, or the model's performance is good in some classes and not reliable to identify samples from the others. The findings of this study underscore the capabilities and limits of these machine learning models in identifying Alzheimer’s disease and highlight the importance of feature engineering, data pre-processing, and model tuning to increase performance and correct class unevenness and misclassification.

The Effect of Maternal High-Fat or High-Carbohydrate Diet during Pregnancy and Lactation on Cytochrome P450 2D (CYP2D) in the Liver and Brain of Rat Offspring.

Cytochrome P450 2D (CYP2D) is important in psychopharmacology as it is engaged in the metabolism of drugs, neurosteroids and neurotransmitters. An unbalanced maternal diet during pregnancy and lactation can cause neurodevelopmental abnormalities and increases the offspring's predisposition to neuropsychiatric diseases. The aim of the present study was to evaluate the effect of maternal modified types of diet: a high-fat diet (HFD) and high-carbohydrate diet (HCD) during pregnancy and lactation on CYP2D in the liver and brain of male offspring at 28 (adolescent) or 63 postnatal days (young adult). The CYP2D activity and protein level were measured in the liver microsomes and the levels of mRNAs of CYP2D1, 2D2 and 2D4 were investigated both in the liver and brain. In the liver, both HFD and HCD increased the mRNA levels of all the three investigated CYP2D genes in adolescents, but an opposite effect was observed in young adults. The CYP2D protein level increased in adolescents but not in young adults. In contrast, young adults showed significantly decreased CYP2D activity. Similar effect of HFD on the CYP2D mRNAs was observed in the prefrontal cortex, while the effect of HCD was largely different than in the liver (the CYP2D2 expression was not affected, the CYP2D4 expression was decreased in young adults). In conclusion, modified maternal diets influence the expression of individual CYP2D1, CYP2D2 and CYP2D4 genes in the liver and brain of male offspring, which may affect the metabolism of CYP2D endogenous substrates and drugs and alter susceptibility to brain diseases and pharmacotherapy outcome.