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BACKGROUND: Neurological disorders pose a significant health challenge, and their early detection is critical for effective treatment planning and prognosis. Traditional classification of neural disorders based on causes, symptoms, developmental stage, severity, and nervous system effects has limitations. Leveraging artificial intelligence (AI) and machine learning (ML) for pattern recognition provides a potent solution to address these challenges. Therefore, this study focuses on proposing an innovative approach-the Aggregated Pattern Classification Method (APCM)-for precise identification of neural disorder stages. METHOD: The APCM was introduced to address prevalent issues in neural disorder detection, such as overfitting, robustness, and interoperability. This method utilizes aggregative patterns and classification learning functions to mitigate these challenges and enhance overall recognition accuracy, even in imbalanced data. The analysis involves neural images using observations from healthy individuals as a reference. Action response patterns from diverse inputs are mapped to identify similar features, establishing the disorder ratio. The stages are correlated based on available responses and associated neural data, with a preference for classification learning. This classification necessitates image and labeled data to prevent additional flaws in pattern recognition. Recognition and classification occur through multiple iterations, incorporating similar and diverse neural features. The learning process is finely tuned for minute classifications using labeled and unlabeled input data. RESULTS: The proposed APCM demonstrates notable achievements, with high pattern recognition (15.03%) and controlled classification errors (CEs) (10.61% less). The method effectively addresses overfitting, robustness, and interoperability issues, showcasing its potential as a powerful tool for detecting neural disorders at different stages. The ability to handle imbalanced data contributes to the overall success of the algorithm. CONCLUSION: The APCM emerges as a promising and effective approach for identifying precise neural disorder stages. By leveraging AI and ML, the method successfully resolves key challenges in pattern recognition. The high pattern recognition and reduced CEs underscore the method's potential for clinical applications. However, it is essential to acknowledge the reliance on high-quality neural image data, which may limit the generalizability of the approach. The proposed method allows future research to refine further and enhance its interpretability, providing valuable insights into neural disorder progression and underlying biological mechanisms.

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Since the worldwide outbreak of coronavirus disease 2019 (COVID-19) in 2020, various research reports and case reports have been published. It has been found that COVID-19 causes not only respiratory disorders but also thrombosis and gastrointestinal disorders, central nervous system (CNS) disorders, and peripheral neuropathy. Compared to other disorders, there are low number of research reports and low number of summaries on COVID-19-related neural disorders. Therefore, focusing on neural disorders, we outline both basic research and clinical manifestations of COVID-19-related neural disorders.

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It has been hypothesized that the brain optimizes its capacity for computation by self-organizing to a critical point. The dynamical state of criticality is achieved by striking a balance such that activity can effectively spread through the network without overwhelming it and is commonly identified in neuronal networks by observing the behavior of cascades of network activity termed "neuronal avalanches." The dynamic activity that occurs in neuronal networks is closely intertwined with how the elements of the network are connected and how they influence each other's functional activity. In this review, we highlight how studying criticality with a broad perspective that integrates concepts from physics, experimental and theoretical neuroscience, and computer science can provide a greater understanding of the mechanisms that drive networks to criticality and how their disruption may manifest in different disorders. First, integrating graph theory into experimental studies on criticality, as is becoming more common in theoretical and modeling studies, would provide insight into the kinds of network structures that support criticality in networks of biological neurons. Furthermore, plasticity mechanisms play a crucial role in shaping these neural structures, both in terms of homeostatic maintenance and learning. Both network structures and plasticity have been studied fairly extensively in theoretical models, but much work remains to bridge the gap between theoretical and experimental findings. Finally, information theoretical approaches can tie in more concrete evidence of a network's computational capabilities. Approaching neural dynamics with all these facets in mind has the potential to provide a greater understanding of what goes wrong in neural disorders. Criticality analysis therefore holds potential to identify disruptions to healthy dynamics, granted that robust methods and approaches are considered.

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Human brain organoids cultured from human pluripotent stem cells provide a promising platform to recapitulate histological features of the human brain and model neural disorders. However, unlike animal models, brain organoids lack a reproducible topographic organization, which limits their application in modeling intricate biology, such as the interaction between different brain regions. To overcome these drawbacks, brain organoids have been pre-patterned into specific brain regions and fused to form an assembloid that represents reproducible models recapitulating more complex biological processes of human brain development and neurological diseases. This approach has been applied to model interneuron migration, neuronal projections, tumor invasion, oligodendrogenesis, forebrain axis establishment, and brain vascularization. In this review article, we will summarize the usage of this technology to understand the fundamental biology underpinning human brain development and disorders.

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Rosa species, rose hips, are widespread wild plants that have been traditionally used as medicinal compounds for the treatment of a wide variety of diseases. The therapeutic potential of these plants is based on its antioxidant effects caused by or associated with its phytochemical composition, which includes ascorbic acid, phenolic compounds and healthy fatty acids among others. Over the last few years, medicinal interest in rose hips has increased as a consequence of recent research that has studied its potential application as a treatment for several diseases including skin disorders, hepatotoxicity, renal disturbances, diarrhoea, inflammatory disorders, arthritis, diabetes, hyperlipidaemia, obesity and cancer. In this review, the role of different species of Rosa in the prevention of treatment of various disorders related to oxidative stress, is examined, focusing on new therapeutic approaches from a molecular point of view.

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BACKGROUND: Although up to 90% of the eukaryotic genome can be transcribed, only 1-2% of the resultant transcripts encode for proteins, while the remaining can be classified as non-coding RNAs (ncRNAs) which mostly consist of long ncRNAs (lncRNAs) and small ncRNAs. In overall, they have been suggested to target specific regions in the genome and play multi-faceted roles in many important biological processes. SUMMARY: Recent evidence has shown that ncRNAs are abundantly expressed in the brain and many of them are aberrantly regulated in neural disorders. Yet their functional relevance in related physiological and pathological processes has not been adequately understood. Thus, the elucidation of the role of ncRNAs in the brain would greatly enhance the current understanding of neural development and ultimately lead to novel strategies to treat neural diseases. In this report, we reviewed the structure and mechanism of lncRNAs and various classes of small ncRNAs in brain development and neural disorders. PERSPECTIVE: We hope that extensive studies of these ncRNAs would unravel and characterize novel molecular circuits in the brain, and facilitate the development of RNA-based therapeutics for people suffering from neural disorders.

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