Deficiency in the retinoschisin protein (RS1) causes X-linked juvenile retinoschisis (XLRS), a retinal degenerative disease that disrupts retinal layers and forms cystic cavities. In addition to its structural function, RS1 is believed to play a role in retinal development. A zebrafish model may provide insights into the role of Rs1 in the earliest stages of retinal development. To explore this, we created a zebrafish model with RS1 deficiency by knocking down the two homologs, Rs1a and Rs1b. Gene expression and protein presence were assessed in Wildtype Tüpfel Longfin zebrafish at 1, 24, 48, 72, 96, and 120 h post-fertilization (hpf). We then performed morpholino (MO)-mediated knockdown targeting rs1a and rs1b mRNA, using scrambled oligos (SC) as controls. MOs or SCs were injected at the 1-4 cell stage, and samples were collected at 48, 72, 96, and 120 h post-fertilization (hpf). The effects were analyzed using immunohistochemistry (IHC) and RNA sequencing. Expression of rs1a and rs1b was first observed at 48 hpf. The successful knockdown of Rs1 was confirmed via IHC. At 72 hpf, Rs1 protein presence was eliminated without affecting overall embryo development. Transcriptional analysis showed enrichment of genes related to axon guidance at 72 hpf and visual perception at 96 hpf. On IHC, photoreceptor protein levels were lower in MO-injected retinae at 96 and 120 hpf. Our findings align with those observed in rodent and organoid models for XLRS, demonstrate the potential of the zebrafish model for XLRS, and advocate for continued research on Rs1 in zebrafish.

Autism spectrum disorder (ASD) is a multifactorial neurodevelopmental condition defined by social deficits, stereotypical or repetitive behaviors, and anxiety. This study evaluates the therapeutic potential of transauricular vagal nerve stimulation (tVNS) in a valproic acid (VPA)-induced mouse model of ASD. The study comprised three groups: the control + sham (saline-treated offsprings receiving sham stimulation), the autistic + sham (VPA-treated offspring receiving sham stimulation), and the autistic + tVNS (VPA-treated offsprings receiving tVNS). Male C57BL/6 mice exposed to VPA on embryonic day 12.5 were evaluated for behavioral and neurobiological alterations. tVNS was applied twice weekly for 3 weeks to investigate its effects on sociability, anxiety-like behaviors, neurogenesis markers, and apoptosis pathways. Behavioral testing, including the three-chamber test, mirrored chamber test, open field test, and elevated plus maze, revealed that tVNS significantly improved sociability and social preference indices, reduced social anxiety, and decreased general anxiety-like behaviors in VPA-induced mice. Histological and immunohistochemical analyses have shown a decrease in neuron density, brain-derived neurotrophic factor (BDNF), and doublecortin (DCX) expression in the hippocampus, amygdala, and prefrontal cortex of VPA-induced mice. Additionally, the increase in caspase-3 immunoreactivity indicates increased apoptosis. tVNS treatment restored BDNF and DCX levels, promoting neurogenesis and synaptic plasticity while significantly reducing caspase-3-mediated apoptosis in affected brain regions. These findings suggest that tVNS may counteract the neural and behavioral deficits associated with ASD by modulating neurogenesis, neuronal plasticity, and apoptosis. The study highlights tVNS as a potential therapeutic intervention for ASD, emphasizing its role in targeting both behavioral alterations and underlying neurobiological mechanisms.

Alzheimer's disease (AD), the most prevalent form of dementia, is neuropathologically defined by the accumulation of extracellular amyloid-beta (Aβ) plaques and intracellular neurofibrillary tangles of hyperphosphorylated tau. Although traditionally viewed as a neuron-centric disorder, increasing evidence underscores the pivotal role of glial cells-particularly microglia and astrocytes-in AD pathogenesis. Once regarded as passive support cells, glia are now recognized as active participants in neuroinflammation, synaptic dysfunction, and disease progression. Microglia, the resident immune cells of the central nervous system, and astrocytes, key regulators of homeostasis and neurotransmission, undergo significant phenotypic changes in response to AD pathology. These include polarization into pro-inflammatory states, impaired clearance of pathological proteins, and detrimental cross talk that amplifies neuroinflammation and neuronal injury. This review synthesizes current literature on the dualistic roles of glial cells in AD, highlighting their contributions to Aβ and tau pathology, synapse loss, demyelination, neurotransmission deficits, and the neuroinflammatory cycle. Emphasis is placed on the dynamic polarization of glia, the reciprocal interactions between microglia and astrocytes, and their combined impact on neurodegeneration. We further explore both pharmacological and non-pharmacological therapeutic approaches targeting glial function, including anti-inflammatory agents, senolytics, deep brain stimulation, exercise, and dietary interventions. By elucidating the multifaceted involvement of glial cells in AD, this review aims to spotlight emerging therapeutic strategies that go beyond neuronal targets, offering new hope for modifying disease progression and improving patient outcomes.

Establishing a proper balance between neuronal excitation (E) and inhibition (I) is essential for healthy brain development, with alterations in this dynamic linked to neurodevelopmental disorders. Animal models suggest that hippocampal activity rapidly increases in the early postnatal period, believed to support the development and stabilization of E/I neural circuitry. This process has not yet been examined in humans, however. Utilizing longitudinal data from the Developing Human Connectome Project, the present study evaluated the impact of early hippocampal activity and gestational age at birth on later outcomes in a cohort of preterm infants (N = 58). Hippocampal activity was assessed using the amplitude of low-frequency fluctuations (ALFF) derived from resting-state functional magnetic resonance imaging collected at two timepoints in the early postnatal period (prior to 20 weeks following birth). Increases in hippocampal activity during this early postnatal period predicted better cognitive and motor function at 18 months of age. Greater gestational age was associated with greater hippocampal activity increase between timepoints. Interestingly, no significant relationships were found between baseline hippocampal activity and 18-month outcomes, suggesting that dynamic changes rather than static measures may be especially sensitive to preterm birth and subsequent alterations in neurodevelopmental processes. These findings underscore the importance of changes in early hippocampal function and gestational age as key risk factors for future neurodevelopmental concerns.

Adult patterns of behavior can often be explained by developmental experiences. In some cases, developmental experience can have permanent influence on brain and behavior only during specific ages; these phases are called critical or sensitive periods. Epigenetic mechanisms can regulate both maturational and experiential processes in the brain by coordinating transcription of genes involved in organization and plasticity. Epigenetics thus may have particular relevance to critical periods. As such, we employed ChIP-seq to assess accessible regulatory regions, segments of the genome where transcription factors (TFs) bind, using the epigenetic marker H3K27ac. We focused on the auditory forebrain, required for developmental sensory song learning, in juvenile male and female zebra finches (Taeniopygia guttata). Both sexes rely on developmental sensory learning to bias adult behaviors, though males have a defined critical period for this process, whereas it is not clear that females do. Thus, we sought to address two major questions: (1) Are H327ac peaks changing in males as they transition into their critical period, and if so, how?, and (2) How similar are the female H3K27ac peaks at the same ages of development? Our analyses revealed that age and sex affect H3K27ac-based peak profiles and enriched TF binding sites within them, as well as genes annotated to those H3K27ac-defined peaks. These findings provide new insights into how epigenetic regulation may influence auditory forebrain organization and function in the context of changing learning potential across a sensitive developmental period and create a foundation for additional studies.

Transition metal complexes (TMCs) have emerged as promising agents in neurotherapeutics due to their redox activity, coordination flexibility, and ability to interact with biomolecular targets. However, their cytotoxic effects on neural tissues remain insufficiently understood, posing challenges for safe and targeted applications. Computational approaches provide powerful tools for unraveling the mechanisms underlying TMC-induced cytotoxicity, enabling the prediction of biological behavior at the molecular level. This study explores how advanced in silico methods, such as molecular docking, density functional theory (DFT), and molecular dynamics (MD) simulations, are applied to assess the structure, reactivity, and interaction profiles of TMCs in neurological contexts. Particular focus is placed on modeling neurotoxicity mechanisms, evaluating blood-brain barrier penetration, and identifying structure-activity relationships (SARs) relevant to neurodegenerative diseases and pediatric brain cancers. Comparative analyses across different metal centers and ligand frameworks are presented, revealing how variations in electronic structure influence biological outcomes. Moreover, limitations of current computational methodologies are addressed, along with challenges in accurately modeling the neural microenvironment. Opportunities for future research include the integration of machine learning to enhance predictive accuracy, automate compound screening, and guide rational design of neuroactive metal-based drugs. The review also emphasizes the need for standardized protocols to improve reproducibility and biological relevance in computational neurotoxicology. By aligning the capabilities of computational chemistry with the demands of neurobiology, this study highlights a strategic framework for advancing safe, targeted, and effective transition metal-based therapies in the nervous system.

One hallmark of brain maturation in adolescence is increased myelination (fractional anisotropy [FA]) of the axons, although the epigenetic drivers of this stage of neurodevelopment are as yet poorly understood. Our previous study of a longitudinal cohort of normally developing adolescents, aged nine to fourteen, established the connections between changes in DNA methylation (DNAm) at seven cytosine-phosphate-guanine (CpG) sites in genes highly expressed in the brain to grey matter maturation as well as cognitive improvement. Continuing that work, we investigate the relationships between the changes in DNAm of these genes (GRIN2D, GABRB3, KCNC1, SLC12A9, CHD5, STXBP5, and NFASC), four networks of FA change, and scores from seven cognitive tests. The demethylation of the CpGs over time was significantly related to a brain network highlighting FA increases in regions associated with maturation of interhemispheric connectivity. Mediation analysis found that this same network mediated the relationship between decreases in DNAm of four of these genes and increases in overall cognitive performance. These relationships suggest that changes in DNAm of genes involved in myelination and the excitatory/inhibitory balance in the brain might be driving maturation of white matter, which in turn is implicated in the improved cognitive performance seen in adolescents.

Prenatal stress, mediated by elevated glucocorticoid (GC) levels, is a relevant modulator of fetal brain development and a known risk factor for neurodevelopmental disorders. Using the avian embryo as a vertebrate model, we injected corticosterone into the yolk on embryonic day 6 (E6) and assessed neurodevelopmental outcomes at day 14 (E14). Through deep proteomic profiling - quantifying over 6500 proteins - we uncovered a robust molecular signature of stress-induced disruption. Key myelin-associated proteins (myelin basic protein [MBP], PLP1, 2',3'-cyclic-nucleotide 3'-phosphodiesterase [CNP]) were markedly downregulated, indicating impaired oligodendrocyte maturation. These proteomic shifts were corroborated by immunohistochemistry and qPCR. Pathway-level analysis pointed to altered MAPK and AKT signaling as putative mediators of the observed phenotype. Our findings mirror previous mammalian data while highlighting the avian model's unique suitability for mechanistic dissection of prenatal stress effects. This study offers new insight into how early GC exposure impairs glial development, with broader implications for understanding the molecular origins of stress-linked brain vulnerability.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that appears in the earliest ages of the lifespan. The causes of ASD remain anonymous, although immunological, genetic, biological, and psychosocial theories have been proposed. Stresses during early life, such as maternal separation (MS), are one of the psychosocial causes of ASD. The neuroimmune response is complicated in the pathophysiology of ASD. Cuminaldehyde (CA) has different pharmacological effects, such as anti-inflammatory properties. This study aimed to explore effects of CA on autistic-like behaviors in maternally separated mice with respect to its probable anti-neuroinflammatory effects.

Autism spectrum disorder (ASD) presents unique challenges in diagnosis and treatment, necessitating innovative approaches to understanding its underlying neurophysiological mechanisms. Real-time classification of electroencephalography (EEG) data in children with ASD faces significant challenges due to variability in EEG signals caused by individual differences in brain activity, age, and behavioral states, complicating robust algorithm development. This study develops and validates a deep learning-based framework for real-time EEG classification in children with ASD, aiming to enhance diagnostic accuracy and enable timely interventions. The dataset includes EEG recordings from 60 children (30 with ASD and 30 typically developing), representing diverse age groups and behavioral profiles to improve generalizability. Pre-processing removes noise and artifacts through segmentation, short-time Fourier transform (STFT), and independent component analysis (ICA). Grid search optimization (GSO) enhances model performance by systematically searching hyperparameter combinations to find the optimal configuration. A hybrid convolutional neural network (CNN)-long short-term memory (LSTM) framework is proposed, combining convolutional layers for spatial feature extraction with LSTM layers for temporal sequence modeling. This hybrid model is the primary proposed solution for real-time EEG classification due to its ability to capture both spatial and temporal features critical for interpreting sequential EEG data in children with ASD. The model achieves an accuracy of 87.5%, a precision of 85.0%, a recall of 90.0%, and an F1 score of 87.5% implemented using MATLAB software. In comparison, ResNet, a baseline deep CNN model, achieves slightly higher accuracy (89.1%) but lacks temporal modeling capabilities essential for sequential EEG interpretation. Despite ResNet's marginally higher accuracy, the hybrid CNN-LSTM is favored as the final model for its superior temporal modeling, critical in EEG analysis. Future work may include real-time feedback mechanisms, mobile application development, and longitudinal data expansion.

Mammals are born into social groups: even species that become solitary begin life seeking social contact with family members. For solitary mammals, dispersal thus marks a major geographic and social transition from their natal group. This transition may be promoted by reduced social tolerance for and reduced interest in family members, and/or by unrelated factors such as increased exploration and activity. Dispersal may also coincide with other developmental events such as weaning or puberty. We investigated developmental changes in oxytocin receptor density in two solitary hamster species (Syrian hamsters: Mesocricetus auratus and Siberian hamsters: Phodopus sungorus) that disperse to individual burrows in the wild. We quantified oxytocin receptor density prior to and after separation from the natal group to determine whether and how neurobiological changes coincide with changes in social behavior. We also quantified transitions in social behavior across development in Syrian hamsters at 2.5, 4, and 8 weeks. Oxytocin receptor densities and distributions reorganized substantially from pre- to post-dispersal ages in both species. Binding decreased across brain regions, with declines in binding in the endopiriform nucleus of both species, and the greatest reduction in hippocampal CA2 of Syrian hamsters. All metrics of social interest and interaction declined across the 2.5-8 week interval-consistent with transition to a solitary lifestyle-except play behavior which peaked in the characteristic juvenile range. Developmental decline in oxytocin receptor density and oxytocin signaling may support transitions in social behavior in solitary mammals.

This comprehensive literature review was conducted to identify relevant studies on mycotoxins and brain development in children. Existing studies suggest that mycotoxin exposure during critical periods of brain development may lead to neurocognitive impairments in children. Some studies have reported associations between mycotoxin exposure and reduced cognitive abilities, impaired motor skills, and behavioral problems. Additionally, mycotoxins have been shown to disrupt neural signaling pathways and interfere with neurotransmitter function, potentially contributing to neurodevelopmental disorders. This comprehensive review has provided a comprehensive overview of the possible evidence on the association between mycotoxin exposure and brain development in children and identified areas for future research.

Parkinson's disease (PD) is a liberal neurological disorder categorized by tremors, stiffness, and decreased motor function, resulting from the degeneration of dopamine-producing nerve cells in the brain. The limitations of early diagnosis of PD using ML and deep learning (DL) include potential challenges in accessing diverse and representative datasets, as well as the risk of overfitting models to specific populations, hindering the generalizability of diagnostic tools transversely diverse patient groups and demographics. To alleviate these issues, we introduced an end-to-end transformer module, E2E-TM, for precise PD diagnosis. Initially, we acquired both magnetic resonance imaging (MRI) and electroencephalography (EEG) data, underwent noise reduction using the bilateral filter and wavelet decomposition, and performed segmentation and reconstruction on MRI images using Super U-Net to reduce data complexity. Subsequently, false peaks in EEG signals were eliminated on the basis of multiple features, and both datasets were input into the proposed E2E-TM model. The transformer encoder module (TEM) included a multi-scale trunk convolution (Multi-TC) module with a penalty and reward strategy, designed in a parallel manner for feature extraction via trunk convolution. Feature maps were then mapped to their feature points using the dual-way trunk convolutional (DW-TC) module, and dual-parallel attention network (DPANet) was employed to minimize feature dimensionality. Finally, the transformer decoder module (TDM) was developed to entangle and decode the feature maps of both datasets for the classification of the diagnosed outcome. Our proposed E2E-TM model's efficiency is evaluated for proving its efficacy. As a result, our E2E-TM model attained superior diagnosis performance compared to other baseline approaches.

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by impairments in social communication and the presence of restricted, repetitive behaviors. Although there is no cure for ASD, early diagnosis and evidence-based interventions can significantly improve developmental outcomes. However, many children are diagnosed later than recommended, limiting timely access to appropriate support services. This proof-of-concept study examines whether facial morphometric characteristics, analyzed through canonical discriminant analysis (CDA), can differentiate children with ASD from their typically developing (TD) peers. The study included 40 children diagnosed with ASD and 40 age- and gender-matched TD controls. Standardized facial photographs were taken in the Frankfurt Horizontal plane in accordance with biometric photography guidelines. Anthropometric landmarks were identified, and inter-landmark distances were measured using the ImageJ software. CDA was then performed in SPSS 28.0 to develop a statistical classification model. CDA was conducted to differentiate ASD and TD groups based on facial morphometric features. While overall facial morphology alone did not significantly distinguish the groups, specific regions-particularly the eyes and lips-showed significant discriminatory power. The nasal profile demonstrated moderate differentiation, and the strongest separation was achieved when combining overall facial and organ-specific features, with a canonical correlation of 0.74 and a significant Wilks' Lambda (Λ = 0.453, χ²(8) = 58.651, p 〈 0.001). The present findings suggest that specific facial regions, particularly the eyes and lips, may carry morphometric features that significantly differentiate children with ASD from their TD peers. While overall facial morphology alone did not provide sufficient discrimination, combining overall facial and organ-specific measurements improved group separation (canonical correlation = 0.74). These results should be regarded as preliminary, highlighting the potential of facial morphometrics as a supplementary, non-invasive research tool. External validation with larger, ethnically diverse samples remains essential before any clinical or screening applicability can be considered.

Honeybee (Apis mellifera) workers exhibit remarkable behavioral plasticity throughout adult life. In spring and summer, they transition through diverse tasks over a short lifespan of 4-6 weeks. This involves dramatic changes in sensory environment and cognitive demands associated with pronounced structural neuronal plasticity in the mushroom bodies (MBs), high-order brain centers for sensory integration, learning, and memory. This plasticity manifests as age- and experience-related volume increase in sensory input regions of the MB calyces, accompanied by pruning of projection neuron (PN) boutons in synaptic microcircuits within visual and olfactory compartments. As winter approaches, honeybees suspend brood rearing and foraging activities to survive the cold months by forming a tight, thermoregulated cluster. Unique physiological adaptations enable winter bees to live up to 8 months until a new generation emerges in spring. This extended lifespan occurs during a period of reduced sensory input and high metabolic costs raising the question of how such conditions affect structural neuronal plasticity. Using synapsin immunolabeling and 3D confocal-microscopy image analyses of MB synaptic neuropils in whole-mount brains of age-controlled worker bees, we found that winter bees retain a high degree of neuronal plasticity throughout their lifespan. MB calyces exhibit an initial volume increase followed by a period of stagnation to then undergo another expansion at the onset of spring foraging. While olfactory PN boutons exhibit continuous pruning, visual bouton numbers remain stable during winter. We conclude that winter bees retain comparable neuronal capacities to summer bees, despite strong differences in lifespan, physiological, and environmental conditions.

Medical imaging plays a pivotal role in diagnosing and treating various health conditions, especially in early-stage cancer detection. Despite advancements in imaging techniques, the complexity and variability of multimodal medical images, such as MRI and CT scans, pose challenges for accurate diagnosis. Traditional methods often struggle with combining these heterogeneous data sources effectively, limiting the ability to provide timely and precise predictions for early cancer detection. This study proposes a hybrid deep learning framework that integrates multimodal image fusion techniques to improve early cancer prediction. The primary objective of this work is to develop an efficient model that can process diverse medical images, extract meaningful features, and provide accurate classifications for identifying cancerous regions. The techniques employed include Gaussian smoothing for image pre-processing, feature extraction using ORB (Oriented FAST and Rotated BRIEF) for handcrafted features, and the InceptionV4 network for deep learning-based feature extraction. The final stage involves classification using Sparse Logistic Regression and the MS-GWNN classifier, designed to predict the malignancy stage of tumors. The experimental results demonstrate that the proposed approach significantly outperforms traditional methods, achieving a classification accuracy of 93.4%, sensitivity of 91.8%, and specificity of 92.5%. These metrics show superior performance in early detection and risk assessment, especially for high-risk cancer cases. The model is validated using TCIA dataset and displays robust fusion capabilities, leading to high-quality and reliable predictions. Future work will explore the integration of additional imaging modalities, real-time applications for clinical settings, and optimization of fusion strategies. Furthermore, incorporating explainable AI (XAI) can improve the interpretability of the model, enhancing its usability in clinical practice.

Electromagnetic field (EMF) exposure, unavoidable in modern life, is linked to oxidative stress and ferroptosis, processes linked to neurodevelopmental disorders. This study investigated the effects of EMF exposure during different pregnancy trimesters on rat offspring trigeminal ganglia (TGs), focusing on transient receptor potential melastatin 2 (TRPM2) ion channels, and assessed the neuroprotective potential of ferrostatin-1 (Fer), a ferroptosis inhibitor, against EMF-induced damage. Pregnant rats were exposed to 900 MHz EMF for 2 h/day during early (1-7 days, EMF 1), mid (8-14 days, EMF 2), or late (15-21 days, EMF 3) gestation. Fer (2.5 µmol/kg, i.p.) was administered immediately after daily EMF exposure in Fer treatment groups. Offspring TG tissues were analyzed on postnatal Day 28 using histopathological, immunohistochemical, and biochemical approaches. EMF exposure significantly reduced antioxidant capacity and elevated lipid peroxidation, reactive oxygen species (ROS), pro-inflammatory cytokines, apoptotic markers, and TRPM2 activation, with the most pronounced alterations in mid-gestation exposure. Fer administration largely normalized these parameters and reduced structural damage in TG. In conclusion, these findings suggest that prenatal EMF triggers ferroptotic/apoptotic neurodegeneration via TRPM2, and that Fer holds promise as a neuroprotective agent.

Generalized anxiety disorder (GAD) is a chronic psychiatric disease characterized by excessive and uncontrollable worry about common life events. Neurological, neurochemical, genomic, environmental, psychogenic, and immunological factors are thought to be involved in GAD. However, studies conducted to establish any suitable biomarkers for the assessment of anxiety disorder is limited. Hence, we aim to investigate the serum levels of interleukin-6 (IL-6) and transforming growth factor-beta 1 (TGF-β1) in GAD patients.

Silent synapses in multiple sclerosis (MS) represent a key yet underexplored concept in the pathology of this disease, playing a crucial role in cognitive impairments and reduced neuroplasticity. These synapses, due to the inactivity of AMPA receptors under pathological conditions, are unable to efficiently transmit neural signals, leading to disrupted neural communication. This dysfunction is particularly influenced by chronic inflammation, alterations in neurotransmitter dynamics, and a reduction in neurotrophic factors in MS patients. One of the key aspects of understanding silent synapses is that they not only have the potential for reactivation, but they can also contribute to the restoration of neural networks by re-establishing neuroplasticity. Recent research has shown that targeted treatments, including activating NMDA receptors, increasing brain-derived neurotrophic factor (BDNF), and using drugs like ketamine, help restore patients' cognitive function. Apart from pharmacological therapies, non-pharmacological strategies also include cognitive rehabilitation, physical activity, and noninvasive brain stimulation, which might promote synaptic plasticity and consequently quality of life. Therefore, reactivating latent synapses as a novel and interesting therapy strategy could not only improve cognitive performance in MS patients but also open the road for fresh methods to mend the nervous system and increase their quality of life. Though its specific form has not yet been thoroughly investigated, this approach offers great promise to become a viable MS treatment.

Hepatic failure is a severe condition marked by the progressive or sudden loss of liver function, broadly categorized into acute liver failure (ALF), which develops within days to weeks, and chronic liver failure (CLF), which evolves over months or years. Both forms can lead to serious complications such as jaundice, impaired detoxification, portal hypertension, ascites, multi-organ dysfunction, and coagulation disorders. A significant neuropsychiatric consequence of liver failure is hepatic encephalopathy (HE), a spectrum of cognitive, motor, and behavioral abnormalities. Although elevated ammonia levels have long been implicated as a central factor in the pathogenesis of HE, emerging evidence suggests that other metabolic toxins also play critical roles. These include manganese (Mn), altered glucose metabolism, short-chain fatty acids (SCFAs), mercaptans, and gamma-aminobutyric acid (GABA). This review aims to explore the multifactorial metabolic landscape contributing to HE, highlighting the potential synergistic effects and mechanistic roles of these blood-borne precipitates. Understanding these diverse metabolic contributors may pave the way for more comprehensive diagnostic and therapeutic approaches beyond the traditional focus on ammonia.

Hypoxia-induced neonatal seizures (HINSs) are a major cause of long-term cognitive deficits and heightened epilepsy risk in adulthood. Early inflammatory responses following HINS contribute to these pathological outcomes. This study examined the sustained neuroprotective benefits of exosomes derived from mesenchymal stem cells (MSC-exosomes) in a rat model of HINS, leveraging their anti-inflammatory and neuroregenerative properties. Forty-nine male and female Wistar rats were divided into four groups: (1) control + saline, (2) control + exosome, (3) hypoxia + saline, and (4) hypoxia + exosome. Neonatal rats (postnatal day 10) were subjected to hypoxia (5% O for 15 min). Sixty minutes after the onset of hypoxia induction, pups received either MSC-exosomes (30 µg/100 µL) or saline for 12 consecutive days (lactation period). Behavioral tests, hippocampal tissue analysis (for RT-PCR and oxidative stress markers), and pentylenetetrazole (PTZ) kindling were performed at P60-P61. The study revealed that treatment with exosomes improved memory performance and reduced anxiety-like behaviors in the hypoxia-exposed group, as evidenced by the novel object recognition and elevated plus maze tests. These benefits were linked to decreased oxidative stress (lower malondialdehyde/MDA levels), reduced pro-inflammatory markers (interleukin-6 [IL-6] and tumor necrosis factor-α [TNF-α]), and increased anti-inflammatory signaling (higher IL-10) in the hippocampus. Although exosome therapy delayed the onset of epileptogenesis, it did not lessen the intensity of seizures. The results indicate that administering MSC-derived exosomes after HINS can reduce susceptibility to PTZ-induced kindling, alleviate neuroinflammation, regulate oxidative stress, and protect against long-term cognitive impairments. Together, these findings highlight the potential of exosome-based interventions in mitigating the delayed neurological effects of HINS during adolescence.