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.

Automated Detection of MT Indications with CRACKVIEW AI

Magnetic particle testing (MT) has been used in non-destructive testing for over 80 years. Although there were attempts to automate it in the last century, it is still largely done manually today. Many forges and foundries already have a very high degree of automation in production, so that most of the personnel costs are spent on the final inspection. Especially in high-wage countries, these companies are under great cost pressure and the exhibit the problem of finding enough qualified personnel and keeping costs low at the same time. As a result of this growing pressure, there has been an increasing number of enquiries from test system manufacturers for fully automated MT systems in recent years. The advances in artificial intelligence (AI) in the last decade, show a promising way to realize the automated evaluation of indications in MT systems. The AI method of deep learning uses neural networks, that simulate the working principle of a human brain, is trained by a large amount of data. In case annotated images of crack indications. After the training the network is capable of detecting crack indications independent of the part geometry or crack position on the part itself. This presentation shows the CRACKVIEW AI system developed by KARL DEUTSCH for the automated evaluation in magnetic particle testing and presents results from its application.

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.

Synergistic effect of chrysin and kaempferol in ameliorating Cerebral Ischemic Reperfusion injury in rat by controlling expression of proinflammatory mediators NF-κB and STAT3.

The two flavonoids kaempferol and chrysin are known to possess anti-inflammatory and antioxidant activities. In addition, these two flavonoids were reported to display synergistic effects against inflammation. The present study aims to provide an analysis of the combined effects of kaempferol and chrysin on ischemic rat brain induced by endothelin-1. The neurological deficit score and infarct area of the brain were determined post drug treatment. Histopathological sections displayed the morphological changes in the brain tissue. The brain tissues were processed for assessing the antioxidant and anti-inflammatory activity by measuring superoxide dismutase activity, catalase activity, level of reduced glutathione, brain malondialdehyde, and amount of calcium. The expression level of inflammatory molecules was analyzed by western blotting and immunohistochemistry. The infarct area, neurological score and NF-κB and STAT3 expression levels were significantly reduced. The analysis of neuroprotective synergistic activity of kaempferol and chrysin indicated the therapeutic potential of the combination in alleviating cerebral ischemia by controlling expression of proinflammatory mediators.

Biochemical changes and bioaccumulation of manganese in Astyanax lacustris (Teleostei: Characidae).

Major tailings dam failures have occurred recently around the world and resulted in severe environmental impacts, such as metal contamination. Manganese is a metal highly associated with mining activities, largely detected in mining dam collapses. This metal is considered necessary for different organisms, but it can be toxic and cause oxidative stress and genetic damage in fishes. In this study, we investigated the toxic effects of manganese on Astyanax lacustris, by exposing the fish individually to different concentrations of this metal (2.11, 5.00, and 10.43 mg/L) for 96 h. To assess the effects of manganese, we used biochemical biomarkers (glutathione S-transferase, catalase, and acetylcholinesterase enzyme activity) and the manganese bioaccumulation in different tissues (liver and gills). The obtained data showed that only at concentrations of 5.00 mg/L and 10.43 mg/L the activity of glutathione S-transferase differed significantly. Additionally, the acetylcholinesterase activity in the brain tissue was inhibited. The highest level of manganese bioaccumulation was observed in the liver and branchial tissue. Overall, we concluded that high concentrations of manganese may cause physiological changes in Astyanax lacustris.

Practical measurements distinguishing physiological and pathological stereoelectroencephalography channels based on high-frequency oscillations in the human brain.

The present study aimed to identify various distinguishing features for use in the accurate classification of stereoelectroencephalography (SEEG) channels based on high-frequency oscillations (HFOs) inside and outside the epileptogenic zone (EZ). HFOs were detected in patients with focal epilepsy who underwent SEEG. Subsequently, HFOs within the seizure-onset and early spread zones were defined as pathological HFOs, whereas others were defined as physiological. Three features of HFOs were identified at the channel level, namely, morphological repetition, rhythmicity, and phase-amplitude coupling (PAC). A machine-learning (ML) classifier was then built to distinguish two HFO types at the channel level by application of the above-mentioned features, and the contributions were quantified. Further verification of the characteristics and classifier performance was performed in relation to various conscious states, imaging results, EZ location, and surgical outcomes. Thirty-five patients were included in this study, from whom 166 104 pathological HFOs in 255 channels and 53 374 physiological HFOs in 282 channels were entered into the analysis pipeline. The results revealed that the morphological repetitions of pathological HFOs were markedly higher than those of the physiological HFOs; this was also observed for rhythmicity and PAC. The classifier exhibited high accuracy in differentiating between the two forms of HFOs, as indicated by an area under the curve (AUC) of 0.89. Both PAC and rhythmicity contributed significantly to this distinction. The subgroup analyses supported these findings. The suggested HFO features can accurately distinguish between pathological and physiological channels substantially improving its usefulness in clinical localization. In this study, we computed three quantitative features associated with HFOs in each SEEG channel and then constructed a machine learning-based classifier for the classification of pathological and physiological channels. The classifier performed well in distinguishing the two channel types under different levels of consciousness as well as in terms of imaging results, EZ location, and patient surgical outcomes.