Number Of Neurons Research Articles

Enhancing deep stochastic configuration networks: Efficient training via low-rank matrix approximation

Deep Stochastic Configuration Networks (DeepSCN) represent a class of randomized learner models distinguished by their universal approximation capabilities, rapid learning processes, and ease of implementation. These models surpass other randomized learners due to an embedded supervisory mechanism. Although the feature matrix is meticulously constructed, the predictive accuracy of DeepSCN also critically depends on the quality of the model parameters obtained from solving the corresponding system of linear equations. As the number of hidden neurons increases, the resulting expansion of the feature matrix renders matrix inversion computationally expensive, potentially unstable, and memory-intensive. This study addresses these challenges by integrating randomized algorithm-based low-rank matrix approximation to compute model parameters efficiently. A theoretical error bound for the obtained model parameters is derived, ensuring precision and accuracy. The effectiveness of the algorithm is validated on benchmark datasets for both classification and regression tasks. The results demonstrate that this integration significantly enhances the reliability and stability of DeepSCN, improving performance and scalability while greatly reducing computational demands. The method maintains stability despite substantial variations in the number of hidden neurons and mitigates overfitting caused by matrix inversion, ensuring stable and reliable training outcomes. This study advances DeepSCN training methodologies and large-scale data processing.

Nicotine in Vapor Exposure Decreases Hippocampal Neurons in Rats

Background: Recently, many people have considered electronic cigarettes to be a safer alternative to conventional tobacco cigarettes, even though they still contain harmful substances such as nicotine, which is associated with a decrease in hippocampal neurons. Objective: This study aimed to examine the effect of exposure to e-cigarettes on hippocampal neurons in rats. Methods: An experimental study was performed using 26 tissue samples obtained from Wistar rats. The rats were randomly assigned to four groups and treated over an eight-week period as follows: the control group (K) (n=7) had no vapor exposure; the V0 group (n=6) received vapor without nicotine (0 mg/ml); the V6 group (n=7) was exposed to vapor and 6 mg/ml nicotine; and the V12 group (n=6) was exposed to vapor and 12 mg/ml nicotine. Hippocampal neurons were examined using hematoxylin-eosin staining at 400x magnification across 7 fields of view. Data were analyzed using the Kruskal-Wallis test with the Mann-Whitney post-hoc. Results: The number of hippocampal neurons was significantly reduced in both the V12 and V0 groups compared to that in the control group (p<0.05). The lowest number of hippocampal neurons was observed in the V0 group (0 mg/ml nicotine), followed by the V12 group (12 mg/ml), and then the V6 group (6 mg/ml).Conclusion: The present study showed that the variation of nicotine in vapor exposure decreases hippocampal neurons in rats.

Machine Learning as a Tool for Auxiliary Diagnosis of Osteoporosis

By making predictions from the model created with machine learning, specifically supervised learning, this essay focuses on the auxiliary diagnosis of osteoporosis. The chosen dataset includes highly relevant indexes that determine the diagnosis of osteoporosis. After doing the feature selection and one-hot encoding to preprocess the dataset, we then split the dataset into validation and training sets to avoid overfitting. Then, to train the model for prediction, we used the neuron network, chose ReLU and Sigmoid functions as activation functions, and changed the number of neurons and epochs to find the best-fit model. Through further measurement, evaluation, and application, including plotting the binary cross entropy error diagram and analyzing the classification report, the model is examined as highly accurate and desirable. With an appreciable degree of accuracy, this model for auxiliary diagnosis is able to prevent disease development or deterioration by detecting early symptoms and vulnerable populations, reduce the probability for doctors to misdiagnose, and improve the efficiency and quality of the osteoporosis diagnosis, therefore enhancing patient experiences.

Emerging signs of Alzheimer-like tau hyperphosphorylation and neuroinflammation in the brain post recovery from COVID-19.

Coronavirus disease 2019 (COVID-19) has been suggested to increase the risk of memory decline and Alzheimer's disease (AD), the main cause of dementia in the elderly. However, direct evidence about whether COVID-19 induces AD-like neuropathological changes in the brain, especially post recovery from acute infection, is still lacking. Here, using postmortem human brain samples, we found abnormal accumulation of hyperphosphorylated tau protein in the hippocampus and medial entorhinal cortex within 4-13 months post clinically recovery from acute COVID-19, together with prolonged activation of glia cells and increases in inflammatory factors, even though no SARS-COV-2 invasion was detected in these regions. By contrast, COVID-19 did not change beta-amyloid deposition and hippocampal neuron number, and had limited effects on AD-related pathological phenotypes in olfactory circuits including olfactory bulb, anterior olfactory nucleus, olfactory tubercle, piriform cortex and lateral entorhinal cortex. These results provide neuropathological evidences linking COVID-19 with prognostic increase of risk for AD.

Reversal of neurodevelopmental impairment and cognitive enhancement by pharmacological intervention with the polyphenol polydatin in a Down syndrome model

Intellectual disability (ID) is the unavoidable hallmark of Down syndrome (DS), a genetic condition due to triplication of chromosome 21. ID in DS is largely attributable to neurogenesis and dendritogenesis alterations taking place in the prenatal/neonatal period, the most critical time window for brain development. There are currently no treatments for ID in DS. Considering the timeline of brain development, treatment aimed at improving the neurological phenotypes of DS should be initiated as early as possible and use safe agents. The goal of this study was to establish whether it is possible to improve DS-linked neurodevelopmental defects through early treatment with polydatin, a natural polyphenol. We used the Ts65Dn mouse model of DS and focused on the hippocampus, a brain region fundamental for long-term memory. We found that in Ts65Dn mice of both sexes treated with polydatin from postnatal (P) day 3 to P15 there was full restoration of neurogenesis, neuron number, and dendritic development. These effects were accompanied by normalization of Cyclin D1 and DSCAM levels, which may account for the rescue of neurogenesis and dendritogenesis, respectively. Importantly, in Ts65Dn mice treated with polydatin from P3 to adolescence (∼P50) there was full restoration of hippocampus-dependent memory, indicating a pro-cognitive outcome of treatment. No adverse effects were observed on the body and brain weight. The efficacy and safety of polydatin in a model of DS prospect the possibility of its use during early life stages for amelioration of DS-linked neurodevelopmental alterations.

On human nanoscale synaptome: Morphology modeling and storage estimation.

One of the key challenges in neuroscience is to generate the human nanoscale connectome which requires comprehensive knowledge of synaptome forming the neural microcircuits. The synaptic architecture determines limits of individual mental capacity and provides the framework for understanding neurologic disorders. Here, I address morphology modeling and storage estimation for the human synaptome at the nanoscale. A synapse is defined as a pair of pairs [(presynaptic_neuron),(presynaptic_axonal_terminal);(postsynaptic_neuron),(postsynaptic_dendritic_terminal)]. Center coordinates, radius, and identifier characterize a dendritic or axonal terminal. A synapse comprises topology with the paired neuron and terminal identifiers, location with terminal coordinates, and geometry with terminal radii. The storage required for the synaptome depends on the number of synapses and storage necessary for a single synapse determined by a synaptic model. I introduce three synaptic models: topologic with topology, point with topology and location, and geometric with topology, location, and geometry. To accommodate for a wide range of variations in the numbers of neurons and synapses reported in the literature, four cases of neurons (30;86;100;138 billion) and three cases of synapses per neuron (1,000;10,000;30,000) are considered with three full and simplified (to reduce storage) synaptic models resulting in total 72 cases of storage estimation. The full(simplified) synaptic model of the entire human brain requires from 0.21(0.14) petabytes (PB) to 28.98(18.63) PB for the topologic model, from 0.57(0.32) PB to 78.66(43.47) PB for the point model, and from 0.69(0.38) PB to 95.22(51.75) PB for the geometric model. The full(simplified) synaptic model of the cortex needs from 86.80(55.80) TB to 2.60(1.67) PB for the topologic model, from 235.60(130.02) TB to 7.07(3.91) PB for the point model, and from 285.20(155.00) TB to 8.56(4.65) PB for the geometric model. The topologic model is sufficient to compute the connectome's topology, but it is still too big to be stored on today's top supercomputers related to neuroscience. Frontier, the world's most powerful supercomputer for 86 billion neurons can handle the nanoscale synaptome in the range of 1,000-10,000 synapses per neuron. To my best knowledge, this is the first big data work attempting to provide storage estimation for the human nanoscale synaptome.

Neurodegeneration: Effects of calorie restriction on the brain sirtuin protein levels

BackgroundCalorie restriction (CR) is suggested to activate protective mechanisms in neurodegenerative diseases (NDDs). Despite existing literature highlighting the protective role of Sirtuin (SIRT) proteins against age-related neurodegeneration (ND), no study has explored the total levels of SIRT 1, 3, and 6 proteins simultaneously in brain homogenates by ELISA following intermittent calorie restriction. Applying CR protocols in mice to induce stress, we aimed to determine whether ND would be more pronounced with ad libitum (AL) or with CR. MethodsMice were randomly assigned to ad libitum (AL), Chronic CR (CCR), or Intermittent CR (ICR) groups at 10 weeks of baseline age (BL). SIRT 1, 3, and 6 protein levels were measured in the homogenized whole-brain supernatants of 49/50 weeks old mice by the ELISA method. Neuronal morphology was evaluated by the cresyl violet on the hippocampus. Neurodegeneration (ND) was assessed by the fluoro-jade and ImageJ was used for quantifications. ResultsIn the ICR group, SIRT1 levels were elevated compared to both the AL and BL groups. Similarly, the CCR group exhibited higher SIRT1 values compared to the AL and BL groups. While SIRT3 levels were higher in both the ICR and CCR groups compared to the AL and BL groups, this disparity did not reach statistical significance. SIRT6 levels were also higher in the ICR group compared to both the BL and AL groups, with the CCR group showing higher values compared to the BL and AL groups as well. Image quantification demonstrated significant neurodegeneration in the AL group compared to the CCR and ICR group, with no observed alterations in nerve cell morphology and number. ConclusionThis study revealed that the levels of SIRT 1, SIRT 3, and SIRT 6 in brain tissue were notably elevated, and there was less evidence of ND at the 50-week mark in groups undergoing continuous calorie restriction and intermittent calorie restriction compared to baseline and ad libitum groups. Our findings illustrate that CR promotes increased SIRT expression in the mouse brain, thereby potentially mitigating neurodegeneration.

Forebrain Eml1 depletion reveals early centrosomal dysfunction causing subcortical heterotopia.

Subcortical heterotopia is a cortical malformation associated with epilepsy, intellectual disability, and an excessive number of cortical neurons in the white matter. Echinoderm microtubule-associated protein like 1 (EML1) mutations lead to subcortical heterotopia, associated with abnormal radial glia positioning in the cortical wall, prior to malformation onset. This perturbed distribution of proliferative cells is likely to be a critical event for heterotopia formation; however, the underlying mechanisms remain unexplained. This study aimed to decipher the early cellular alterations leading to abnormal radial glia. In a forebrain conditional Eml1 mutant model and human patient cells, primary cilia and centrosomes are altered. Microtubule dynamics and cell cycle kinetics are also abnormal in mouse mutant radial glia. By rescuing microtubule formation in Eml1 mutant embryonic brains, abnormal radial glia delamination and heterotopia volume were significantly reduced. Thus, our new model of subcortical heterotopia reveals the causal link between Eml1's function in microtubule regulation and cell position, both critical for correct cortical development.

High-dose dexamethasone regulates microglial polarization via the GR/JAK1/STAT3 signaling pathway after traumatic brain injury.

Although microglial polarization and neuroinflammation are crucial cellular responses after traumatic brain injury, the fundamental regulatory and functional mechanisms remain insufficiently understood. As potent anti-inflammatory agents, the use of glucocorticoids in traumatic brain injury is still controversial, and their regulatory effects on microglial polarization are not yet known. In the present study, we sought to determine whether exacerbation of traumatic brain injury caused by high-dose dexamethasone is related to its regulatory effects on microglial polarization and its mechanisms of action. In vitro cultured BV2 cells and primary microglia and a controlled cortical impact mouse model were used to investigate the effects of dexamethasone on microglial polarization. Lipopolysaccharide, dexamethasone, RU486 (a glucocorticoid receptor antagonist), and ruxolitinib (a Janus kinase 1 antagonist) were administered. RNA-sequencing data obtained from a C57BL/6 mouse model of traumatic brain injury were used to identify potential targets of dexamethasone. The Morris water maze, quantitative reverse transcription-polymerase chain reaction, western blotting, immunofluorescence and confocal microscopy analysis, and TUNEL, Nissl, and Golgi staining were performed to investigate our hypothesis. High-throughput sequencing results showed that arginase 1, a marker of M2 microglia, was significantly downregulated in the dexamethasone group compared with the traumatic brain injury group at 3 days post-traumatic brain injury. Thus dexamethasone inhibited M1 and M2 microglia, with a more pronounced inhibitory effect on M2 microglia in vitro and in vivo. Glucocorticoid receptor plays an indispensable role in microglial polarization after dexamethasone treatment following traumatic brain injury. Additionally, glucocorticoid receptor activation increased the number of apoptotic cells and neuronal death, and also decreased the density of dendritic spines. A possible downstream receptor signaling mechanism is the GR/JAK1/STAT3 pathway. Overactivation of glucocorticoid receptor by high-dose dexamethasone reduced the expression of M2 microglia, which plays an anti-inflammatory role. In contrast, inhibiting the activation of glucocorticoid receptor reduced the number of apoptotic glia and neurons and decreased the loss of dendritic spines after traumatic brain injury. Dexamethasone may exert its neurotoxic effects by inhibiting M2 microglia through the GR/JAK1/STAT3 signaling pathway.

Neuronal Death: Now You See It, Now You Don't.

Fatally injured neurons may necrose and rupture immediately, or they may initiate a programmed cell death pathway and then wait for microglial phagocytosis. Biochemical and histopathologic assays of neuronal death assess the numbers of neurons awaiting phagocytosis at a particular time point after injury. This number varies with the fraction of neurons that have necrosed vs initiated programmed cell death, the time elapsed since injury, the rate of phagocytosis, and the assay's ability to detect neurons at different stages of programmed cell death. Many of these variables can be altered by putatively neurotoxic and neuroprotective interventions independent of the effects on neuronal death. This complicates analyses of neurotoxicity and neuroprotection and has likely contributed to difficulties with clinical translation of neuroprotective strategies after brain injury. Time-resolved assays of neuronal health, such as ongoing expression of transgenic fluorescent proteins, are a useful means of avoiding these problems.

Scalable Hypothalamic Arcuate Neuron Differentiation from Human Pluripotent Stem Cells Suitable for Modeling Metabolic and Reproductive Disorders.

The hypothalamus, composed of several nuclei, is essential for maintaining our body's homeostasis. The arcuate nucleus (ARC), located in the mediobasal hypothalamus, contains neuronal populations with eminent roles in energy and glucose homeostasis as well as reproduction. These neuronal populations are of great interest for translational research. To fulfill this promise, we used a robotic cell culture platform to provide a scalable and chemically defined approach for differentiating human pluripotent stem cells (hPSCs) into pro-opiomelanocortin (POMC), somatostatin (SST), tyrosine hydroxylase (TH) and gonadotropin-releasing hormone (GnRH) neuronal subpopulations with an ARC-like signature. This robust approach is reproducible across several distinct hPSC lines and exhibits a stepwise induction of key ventral diencephalon and ARC markers in transcriptomic profiling experiments. This is further corroborated by direct comparison to human fetal hypothalamus, and the enriched expression of genes implicated in obesity and type 2 diabetes (T2D). Genome-wide chromatin accessibility profiling by ATAC-seq identified accessible regulatory regions that can be utilized to predict candidate enhancers related to metabolic disorders and hypothalamic development. In depth molecular, cellular, and functional experiments unveiled the responsiveness of the hPSC-derived hypothalamic neurons to hormonal stimuli, such as insulin, neuropeptides including kisspeptin, and incretin mimetic drugs such as Exendin-4, highlighting their potential utility as physiologically relevant cellular models for disease studies. In addition, differential glucose and insulin treatments uncovered adaptability within the generated ARC neurons in the dynamic regulation of POMC and insulin receptors. In summary, the establishment of this model represents a novel, chemically defined, and scalable platform for manufacturing large numbers of hypothalamic arcuate neurons and serves as a valuable resource for modeling metabolic and reproductive disorders.

Machine learning-based approach for assessing the seismic vulnerability of reinforced concrete frame buildings

Recent artificial intelligence (AI) advancements have significantly impacted the field of structural engineering by offering new solutions to enhance safety, efficiency, and cost-effectiveness in the design, construction, and maintenance of infrastructure and urban areas. This study has investigated a machine learning-based approach to estimate the seismic vulnerability of reinforced concrete (RC) building frames. The study utilized a probabilistic technique to assess the simulation of a 4-story RC building frame in the face of epistemic uncertainty. Data was collected using the Monte-Carlo approach, which created a machine learning (ML) model for predicting structural damage. The problem was formulated as both a regression-based and a classification-based model. An artificial neural network (ANN) feed-forward network architecture model was used to solve the classification problems. The optimal number of neurons in the hidden layer was determined to produce the best estimation model. Three different models were combined for the regression model, including LASSO regression, random forest, and gradient boosting, by implementing the stacking generalization. The investigation results indicate that the stacked predicted model exhibits less variance than other ML models. The classification and regression-based algorithms can forecast damage states with a high degree of accuracy, ranging from 87 to 95 percent. In conclusion, the study has demonstrated the effectiveness of machine learning techniques for predicting the seismic vulnerability of RC building frames. With the continued advancement of AI and big data, these methods will likely play a crucial role in the structural engineering field.

ARL11 knockdown alleviates spinal cord injury by inhibiting neuroinflammation and M1 activation of microglia in mice

Spinal cord injury (SCI) is a severe central nervous system injury and microglia are major participants in neuroinflammation after injury. ADP-ribosylation factor-like GTPase 11 (ARL11) is a GTP-binding protein. Whether ARL11 is involved in the SCI progression is unknown. In the impactor-induced moderate SCI mouse model, ARL11 protein and mRNA expression were significantly increased in the injury site. LPS (100 ng/mL) and IFN-γ (20 ng/mL) were incubated with BV2 cells (immortalized mouse microglial cell line) to drive them into an M1-like phenotype. ARL11 up-regulation was also observed in activated microglia in SCI mice and LPS and IFN-γ treated BV2 cells. Basso Mouse Scale scores and inclined plate test revealed that ARL11 deletion promoted motor function recovery in SCI mice. Pathological examination showed ARL11 knockdown reduced spinal cord tissue damage, increased neuron numbers, and inhibited neuronal apoptosis in SCI mice. ARL11 knockdown notably inhibited IL-1β and IL-6 production in vivo and in vitro. Furthermore, ARL11 deletion significantly inhibited iNOS protein and mRNA expression in vivo and in vitro, and COX-2 expression in vivo. Mechanism studies revealed that ARL11 silencing decreased phosphorylated ERK1/2 protein expression. Additionally, ELF1 knockdown significantly inhibited ARL11 protein and mRNA expression in vitro. ELF1 acted as a transcription activator in regulating ARL11 expression by binding to the promoter. In conclusion, ARL11 knockdown protects neurons by inhibiting M1 microglia-induced neuroinflammation, thereby promoting motor functional recovery in SCI mice. This may occur in part under the regulation of ELF1. Our study provides a new molecular target for SCI treatment.

Adult‐onset neuronal ceroid lipofuscinosis in a smooth‐haired dachshund

AbstractA 5‐year‐old, smooth‐haired miniature dachshund exhibited a progressive history of frequent episodes of aggression towards objects, head pressing, circling and intermittent pelvic limb ataxia over a period of 2 weeks. Visual impairment was also noticed several months prior. Neurolocalisation was consistent with forebrain disease. Blood and urine analysis, magnetic resonance imaging of the brain and cerebrospinal fluid analysis were all unremarkable. The patient was euthanased due to disease progression, and postmortem histopathological analysis and electronic microscopy identified the accumulation of intracellular granular material in a number of neurons, mainly located in the thalamus and hypothalamus. This supported a diagnosis of neuronal ceroid lipofuscinosis. Genetic testing showed no mutations in the causative genes reported for this breed. This case reports the occurrence of a potentially novel subtype of neuronal ceroid lipofuscinosis, due to a currently unidentified genetic mutation, leading to an adult onset of clinical signs in the dachshund.

Investigation of Persistent Photoconductivity of Gallium Nitride Semiconductor and Differentiation of Primary Neural Stem Cells.

A gallium nitride (GaN) semiconductor is one of the most promising materials integrated into biomedical devices to play the roles of connecting, monitoring, and manipulating the activity of biological components, due to its excellent photoelectric properties, chemical stability, and biocompatibility. In this work, it was found that the photogenerated free charge carriers of the GaN substrate, as an exogenous stimulus, served to promote neural stem cells (NSCs) to differentiate into neurons. This was observed through the systematic investigation of the effect of the persistent photoconductivity (PPC) of GaN on the differentiation of primary NSCs from the embryonic rat cerebral cortex. NSCs were directly cultured on the GaN surface with and without ultraviolet (UV) irradiation, with a control sample consisting of tissue culture polystyrene (TCPS) in the presence of fetal bovine serum (FBS) medium. Through optical microscopy, the morphology showed a greater number of neurons with the branching structures of axons and dendrites on GaN with UV irradiation. The immunocytochemical results demonstrated that GaN with UV irradiation could promote the NSCs to differentiate into neurons. Western blot analysis showed that GaN with UV irradiation significantly upregulated the expression of two neuron-related markers, βIII-tubulin (Tuj-1) and microtubule-associated protein 2 (MAP-2), suggesting that neurite formation and the proliferation of NSCs during differentiation were enhanced by GaN with UV irradiation. Finally, the results of the Kelvin probe force microscope (KPFM) experiments showed that the NSCs cultured on GaN with UV irradiation displayed about 50 mV higher potential than those cultured on GaN without irradiation. The increase in cell membrane potential may have been due to the larger number of photogenerated free charges on the GaN surface with UV irradiation. These results could benefit topical research and the application of GaN as a biomedical material integrated into neural interface systems or other bioelectronic devices.

Assessment of Solubility Behavior of a Copolymer in Supercritical CO2 and Organic Solvents: Neural Network Prediction and Statistical Analysis.

In the industrial sector, understanding the behavior of block copolymers in supercritical solvents is crucial. While qualitative agreement with polymer solubility curves has been evaluated using complex theoretical models in many cases, quantitative predictions remain challenging. This study aimed to create a rapid and accurate artificial neural network (ANN) model to predict the lower critical solubility and upper critical solubility space of an atypical block copolymer, poly(styrene-co-octafluoropentyl methacrylate) (PSOM), in different supercritical solvent systems over a wide range of temperatures (51.75-182.05 °C) and high pressure (3.28-200.86 MPa). The experimental data set used in this study included one copolymer, five supercritical solvents, one cosolvent, and one initiator. It consisted of seven unique copolymer-solvent combinations (252 cloud point pressures) used to predict the model quantitatively and qualitatively. To predict the PSOM-solvent interactions, the study considered two different input systems: a six-variable system, a five-variable system, and one target output. Initially, we used a three-layer feed-forward neural network to select the best learning algorithm (Levenberg-Marquardt) from 14 different algorithms, considering one sample PSOM-solvent system. Then, the network topology was optimized by varying hidden neuron numbers from 2 to 80 for all seven PSOM-solvent combination systems. The predicted cloud point pressures were in excellent agreement with the experimental cloud point pressures, confirming the model's accuracy. It is clear from the results of a minimum mean square error (≤1.90 × 10-27) and maximum linear regression R 2 (≥0.99) during training, validation, and testing of all the data sets. Further, the ANN model accuracy was tested by statistical analysis, confirming the model's ability to accurately capture the miscibility regions of polymers, enabling efficient processing of various polymer materials. This data-driven approach facilitates the prediction of coexistence curves for other polymers and complex macromolecular systems.

Curcumin attenuated neuroinflammation via the TLR4/MyD88/NF-κB signaling way in the juvenile rat hippocampus following kainic acid-induced epileptic seizures.

The study examined curcumin's impart on relieving neuroinflammation of juvenile rats in kainic acid (KA) induced epileptic seizures by inhibiting the TLR4/MyD88/NF-κB pathway. There were five groups: control, KA, KA + curcumin (KC), KA + oxcarbazepine (OXC) (KO), KA + curcumin + OXC (KCO) groups. KA was stereotactically injected into right hippocampus following intraperitoneal injection of curcumin or (and) OXC for seven days. The rats in the above groups were randomly divided into three subgroups (at 6h, 24h, and 72h of KA administration) following the seizure degree assessed. The number of NeuN (+) neurons and GFAP (+) astrocytes was counted. The gene and protein levels of TLR4, MyD88, and NF-κB were detected. Compared with the KA group, the seizure latency was longer, and the incidence of status epilepticus (SE) was lower in the KC, KO, and KCO groups. The most significant changes were in the KCO group. At 72h following KA injected, the number of neurons was the least, and the number of astrocytes was the most in the KA group. The number of neurons was the most and the number of astrocytes was the least in the KCO group. At 24h, the mRNA and protein levels of TLR4, MyD88, and NF-κB in the KA group were the most. The above valves were the least in the KCO group. Therefore, curcumin could enhance anti-epileptic effect of OXC, protect injured neurons and reduce proliferated glial cells of the hippocampus of epileptic rats by inhibiting inflammation via the TLR4/MyD88/NF-κB pathway.

CNPY2 protects against ER stress and is expressed by corticostriatal neurons together with CTIP2 in a mouse model of Huntington's disease.

Canopy Homolog 2 (CNPY2) is an endoplasmic reticulum (ER) localized protein belonging to the CNPY gene family. We show here that CNPY2 is protective against ER stress induced by tunicamycin in neuronal cells. Overexpression of CNPY2 enhanced, while downregulation of CNPY2 using shRNA expression, reduced the viability of neuroblastoma cells after tunicamycin. Likewise, recombinant CNPY2 increased survival of cortical neurons in culture after ER stress. CNPY2 reduced the activating transcription factor 6 (ATF6) branch of ER stress and decreased the expression of CCAT/Enhancer-Binding Protein Homologous Protein (CHOP) involved in cell death. Immunostaining using mouse brain sections revealed that CNPY2 is expressed by cortical and striatal neurons and is co-expressed with the transcription factor, COUPTF-interacting protein 2 (CTIP2). In transgenic N171-82Q mice, as a model for Huntington's disease (HD), the number of CNPY2-immunopositive neurons was increased in the cortex together with CTIP2. In the striatum, however, the number of CNPY2 decreased at 19 weeks of age, representing a late-stage of pathology. Striatal cells in culture were shown to be more susceptible to ER stress after downregulation of CNPY2. These results demonstrate that CNPY2 is expressed by corticostriatal neurons involved in the regulation of movement. CNPY2 enhances neuronal survival by reducing ER stress and is a promising factor to consider in HD and possibly in other brain diseases.

Molecular pathology, developmental changes and synaptic dysfunction in (pre-) symptomatic human C9ORF72-ALS/FTD cerebral organoids

A hexanucleotide repeat expansion (HRE) in C9ORF72 is the most common genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Human brain imaging and experimental studies indicate early changes in brain structure and connectivity in C9-ALS/FTD, even before symptom onset. Because these early disease phenotypes remain incompletely understood, we generated iPSC-derived cerebral organoid models from C9-ALS/FTD patients, presymptomatic C9ORF72-HRE (C9-HRE) carriers, and controls. Our work revealed the presence of all three C9-HRE-related molecular pathologies and developmental stage-dependent size phenotypes in cerebral organoids from C9-ALS/FTD patients. In addition, single-cell RNA sequencing identified changes in cell type abundance and distribution in C9-ALS/FTD organoids, including a reduction in the number of deep layer cortical neurons and the distribution of neural progenitors. Further, molecular and cellular analyses and patch-clamp electrophysiology detected various changes in synapse structure and function. Intriguingly, organoids from all presymptomatic C9-HRE carriers displayed C9-HRE molecular pathology, whereas the extent to which more downstream cellular defects, as found in C9-ALS/FTD models, were detected varied for the different presymptomatic C9-HRE cases. Together, these results unveil early changes in 3D human brain tissue organization and synaptic connectivity in C9-ALS/FTD that likely constitute initial pathologies crucial for understanding disease onset and the design of therapeutic strategies.

Investigating Egocentric Tuning in Hippocampal CA1 Neurons.

Navigation requires integrating sensory information with a stable schema to create a dynamic map of an animal's position using egocentric and allocentric coordinate systems. In the hippocampus, place cells encode allocentric space, but their firing rates may also exhibit directional tuning within egocentric or allocentric reference frames. We compared experimental and simulated data to assess the prevalence of tuning to egocentric bearing (EB) among hippocampal cells in rats foraging in an open field. Using established procedures, we confirmed egocentric modulation of place cell activity in recorded data; however, simulated data revealed a high false-positive rate (FPR). When we accounted for false positives by comparing with shuffled data that retain correlations between the animal's direction and position, only a very low number of hippocampal neurons appeared modulated by EB. Our study highlights biases affecting FPRs and provides insights into the challenges of identifying egocentric modulation in hippocampal neurons.