Developmental Neurobiology最新文献

Current research on risk factors of dementia predominantly focuses on genetics and lifestyle, yet maternal influences are understudied; we aimed to examine how maternal age at childbirth affects offspring's cognitive function in middle and older age. Data were derived from 3549 offspring in the China Health and Retirement Longitudinal Study (CHARLS; 2011–2018). Maternal age was categorized into four groups (< 22, 22–28, 29–35, and > 35 years). Linear mixed‑effects models were used to estimate baseline differences and longitudinal rates of change, with covariates adjusted in a hierarchical sequence (demographic, socioeconomic, lifestyle, and health‑related). The negative association between advanced maternal age (> 35 years) and offspring baseline cognitive score attenuated with sequential covariate adjustment but remained significant: it was −0.290 SD (95% CI: −0.486, −0.093) in Model 1 and −0.223 SD (95% CI: −0.422, −0.024) in the final Model 4. No significant difference in the rate of cognitive decline was observed across maternal age groups. This finding suggests that advanced maternal age is associated with poorer cognitive performance at baseline in middle and older age. Future studies should replicate these findings in diverse global populations and settings with longer follow-up periods to clarify the complex relationship between maternal age at childbirth and offspring's cognitive aging. Patients or the public were not involved in the design, conduct, reporting, or dissemination plans of our research.

Epilepsy is a neurological disorder of the brain that generates seizures due to abnormal electrical activity. The diagnosis and management of the disease primarily depend on recordings of the EEG. A multistage methodology for seizure detection with enhanced accuracy and reliability is proposed in this work. Isolation of key components of the EEG signal is performed to carry out robust segmentation using CNN and RNN. Features will be extracted through DBFFN and Hjorth parameters, which obtain the optimal features after optimization through salp swarm optimization and firefly optimization algorithm. Data classification has been done using Naive Bayes and Random Forest with an accuracy of 99.47%, sensitivity of 99.78%, specificity of 99.70%, and an F1 score of 99.51%. Performances are higher in comparison with other techniques. DBFFN feature extraction methodology with firefly optimization was identified to be effective in enhancing seizure detection performance. Unlike existing single-path or handcrafted feature-based methods, the proposed DBFFN with dual metaheuristic optimization (SSO–FOA) jointly captures complementary spatial–temporal and spectral EEG features while reducing redundancy, resulting in superior accuracy, robustness, and computational efficiency. This computationally efficient system emerges as a reliable real-world seizure detection tool in epilepsy management.

Medulloblastoma (MB) is the most common malignant brain tumor in children, classified into four molecular subgroups: WNT, sonic hedgehog (SHH), Group 3, and Group 4. The SHH pathway is crucial in the pathogenesis of SHH-subgroup MB (SHH-MB), influenced by mutations in patched homolog 1 (PTCH1), smoothened (SMO), and suppressor-of-fused (SUFU). Targeting this route has shown potential, with vismodegib, an SMO inhibitor, demonstrating effectiveness in both preclinical and clinical investigations. Notwithstanding its therapeutic promise, vismodegib's systemic toxicity, especially bone abnormalities in pediatric patients, constrains its application. Novel techniques, such as nanoparticle formulations and intraventricular administration, have optimized medication distribution, diminished toxicity, and augmented effectiveness. Research indicates vismodegib's efficacy in enhancing progression-free survival (PFS) and addressing tumor-specific genetic abnormalities. Nonetheless, obstacles, such as resistance stemming from SMO mutations and developmental toxicity, remain. Prospects encompass the enhancement of drug delivery methods, the integration of SHH inhibitors with alternative therapeutics, and the advancement of next-generation inhibitors to surmount resistance. Hence, these developments offer potential for improving outcomes in SHH-MB while reducing side effects, especially in pediatric populations.

The prevalence of epileptic disorders is notably elevated in the elderly population. Meanwhile, epilepsy in aged patients is characterized by a distinct set of characteristics. However, the pecularities of molecular mechanisms underlying senile epileptogenesis still remain to be fully elucidated. The objective of the present study was to elucidate the specificity of seizure behavior and glutamatergic transmission in the hippocampus during the development of limbic/mesial temporal lobe epilepsy (TLE) in aging (18-month-old) Krushinsky–Molodkina (KM) audiogenic rats. To reproduce TLE conditions, animals were exposed to repetitive audiogenic seizure (AGS) stimulations, audiogenic kindling, for a period of 14 days. Behavioral analysis revealed that, similar to adult KM rats, audiogenic kindling in aging animals induced a progressive increase in the duration and severity of the limbic AGS phase, post-tonic clonus. However, in contrast to adults, in aging KM rats, the severity of the brainstem AGS component decreased during kindling, while limbic seizure progression was significantly faster, indicating faster epileptization of the limbic structures, including the hippocampus. Further, a comparison of aging KM rats, exposed to 14-day kindling, with non-stimulated (naïve) controls of the same age revealed seizure-induced loss of the hippocampal cells. These alterations were accompanied by a decrease in glutaminase, vesicular glutamate transporters, and AMPA receptor subunits, suggesting a downregulation of glutamate production and glutamatergic transmission. The obtained results collectively indicate that the process of aging in KM rats is associated with accelerated TLE establishment, which is accompanied by significant alterations in the key components of the glutamatergic system in the hippocampus.

The current literature on automatic seizure detection based on EEG has obtained significant accuracy, but most of them still have difficulties in processing the highly non-linear, non-stationary, and patient-specific EEG signals. Models typically need vast quantities of training data; they do not generalize to other datasets, and they are sensitive to noise and changes in channels, making them less robust and applicable in clinical practice. To overcome them, this paper will assume a channel transformer-based generative adversarial and multi-instance attention network with a nutcracker optimizer (CTGA-MinsAN-NutO) to identify seizures reliably. The suggested structure incorporates adaptive guided multi-layer side window box filter decomposition (AGM-LSWBFD) to perform well in denoising and multi-directional shearlet transform domain (MDSTD) to carry out more efficient feature extraction. The model outperforms current benchmarks and shows better robustness in identifying ictal and interictal states, achieving 99.1% accuracy and 93.5% recall when evaluated on the Bonn as well as CHB-MIT datasets.

Long-term physiological monitoring using wearable wireless systems represents a paradigm change in next-generation e-health applications. Specifically, electroencephalography (EEG) represents a noninvasive and trustworthy way of recording brain activity and is a likely candidate for the early diagnosis of autism spectrum disorder (ASD). Yet, conventional methods involving the streaming of raw EEG signals to outside servers for classification consume significant energy and drastically shorten the operational life of wearable sensors. In response to these gaps, this research introduced an energy-aware, sensor-based scheme for ASD detection during early childhood from EEG signals. The system exploits on-node signal denoising via chaotic signal models, feature extraction by dual tree discrete wavelet transform (DT-DWT), and lightweight feature selection by parrot optimization (PO). The core detection is executed via a new Hyperbolic Cross-Head Attention-Based Neural Network (HyperCrossNet) that proposes deep reversible learning in conjunction with spatial and channel-oriented attention mechanisms. The network weights are then optimized by the Pied Kingfisher Optimization Algorithm (PKO) for improved accuracy. Experimental outcomes indicate 99.92% classification, 99.91% recall, and a 99.90% F1-score not mentioning that it has lowered considerably the amount of energy used to transmit the raw data. This effective design enables real-time wearable detection useful and applicable to long-term monitoring.

Neural stem cells (NSCs) play a crucial role in neural regeneration following spinal cord injury (SCI) owing to their self-proliferative and multidirectional differentiation capabilities. This study examined the biological properties of adult spinal cord-derived NSCs (sp-NSCs) and the role of Notch receptors in regulating their activation. NSCs were isolated from the spinal cords of 8-week-old C57/BL6 mice, and their biological properties, including the gene expression profile of Notch receptors, were subsequently analyzed using single-cell RNA sequencing (scRNA-Seq) and bioinformatics. The NSCs were subsequently infected with lentiviral vectors encoding Notch1 shRNA, Notch2 shRNA, and a combination of Notch1 and Notch2 shRNA sequences, and evaluated using Sphere assays and EdU staining to determine their activation effects. The expression levels of downstream genes, NICD and Rbpj, in the Notch signaling pathway, as well as the related target genes, Hes1 and Hey1, were subsequently quantified using Western blot analysis. Intact adult mouse sp-NSCs predominantly existed in a quiescent state, with their population increasing significantly with age. Notch receptors served as critical regulators of adult sp-NSC activation. Notably, Notch1 expression was significantly elevated compared to Notch2 and Notch3, demonstrating its predominant role in sustaining NSC activation. In contrast, Notch2 and Notch3 were primarily responsible for maintaining NSCs in a quiescent state. Overall, both Notch1 and Notch2 signals are involved in different regulatory roles that facilitate the activation and fate determination of NSCs via NICD-Rbpj.

The capacity for enhanced experience, modeled as environmental enrichment (EE) in laboratory animals, to drive positive changes in brain circuitry presents a promising avenue in the development of therapies for neurodevelopmental conditions. Less understood are the underlying mechanisms, or potential interactions of EE with other therapeutic approaches. We have previously shown that early exposure to EE can drive the partial repair of miswired uncrossed retinal projections, and the concomitant rescue of a visual behavior, in the Ten-m3 knockout (KO) mouse. This was associated with a highly spatiotemporally localized upregulation of microglial reactivity in the region where the correction was occurring which peaked around postnatal day (P)25. Aiming to confirm a causal role for microglial-mediated engulfment in this process, we assessed the effect of daily injections of minocycline or vehicle saline from P18 to P24 (inclusive) on measures of microglial reactivity and anatomical corrective pruning in P25 Ten-m3 KO mice. While an effect of EE was confirmed at this timepoint, intriguingly, we found that both the vehicle- and minocycline-treated mice had a similar lack of microglial reactivity and showed a marked absence of corrective pruning of their miswired retinal projections. This suggests that the injection procedure itself disrupted the experience-induced microglial-mediated circuit repair. These results underscore the highly sensitive nature of EE-driven corrective pruning actions of microglia and the critical importance of considering and controlling for all aspects of experience.