Developmental Neurobiology最新文献

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.

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.

Recent reports have linked biallelic loss-of-function variants in the TBC1D2B gene to neurodevelopmental disorder with seizures and gingival overgrowth (NEDSGO) (OMIM 619323), a rare condition characterized by seizures and gingival hyperplasia. However, due to the limited number of reported cases, the phenotypic diversity of this syndrome remains poorly characterized. This study reports four affected children from a consanguineous family in Türkiye, in whom a novel variant in this gene was identified. All individuals underwent clinical examination, electroencephalography (EEG), brain magnetic resonance imaging (MRI), histopathological evaluation, and genetic analyses. A novel homozygous truncating variant in the TBC1D2B gene was identified. In silico protein structure modeling was performed to investigate the potential impact of the variant. The identified c.323_324delinsAA; p.(Phe108Ter) variant causes premature protein truncation, resulting in the loss of key functional domains, such as Rab-GAP-TBC, coiled-coil, and PH (pleckstrin homology). All patients exhibited developmental delay (DD), epileptic seizures, gingival fibromatosis, and craniofacial anomalies. The growth delay seen in both of our patients, also described in an earlier case with the same gene variant, suggests that this may be a clinical feature of the syndrome. Binding pocket analysis revealed marked reductions in putative protein interaction regions, suggesting a loss-of-function effect due to the mutation. These findings reveal previously unrecognized aspects of both the genetic and clinical spectrum of NEDSGO syndrome caused by variants in the TBC1D2B gene. The resulting data underscore that disruption of structural protein regions directly contributes to the phenotype of this rare disorder.

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.

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.

Diabetic retinopathy (DR), a prevailing manifestation among diabetic patients, occurs as a major sight-threatening disorder. Dectin-1, as an innate immune receptor, has been notified as a critical modulator of diabetes mellitus. In this context, the implication of Dectin-1 in the process of DR that is still a conundrum will be addressed here. The diabetic mouse model was established by intraperitoneal injection of streptozotocin (STZ), and human microglia cells (HMC3) were subjected to high glucose (HG) to create cellular models of diabetes. Glucose level and body weight were recorded in mice. Reverse transcription-quantitative PCR (RT-qPCR) and western blotting checked Dectin-1 expression. RT-qPCR, enzyme-linked immunosorbent assay (ELISA), and western blotting appraised the inflammatory levels. Immunofluorescence staining and western blotting ascertained the expression of IBA-1 and tight junction proteins. Besides, western blotting also examined albumin expression. Terminal-deoxynucleoitidyl transferase mediated nick end labeling (TUNEL) assay and western blotting assayed the apoptotic level. Dectin-1 was highly expressed in both retinal tissues of diabetic mice and HG-exposed HMC3 cells. Dectin-1 antagonist laminarin (LAM) observably repressed microglia activation, inflammatory reaction, and blood–retinal barrier (BRB) leakage both in vitro and in vivo. Moreover, LAM produced anti-apoptotic effect in vivo. To sum up, Dectin-1 inhibitor might block the inflammatory cascade and protect against BRB disruption in DR.