Nerve Cells Research Articles

Unveiling the hidden dangers: a review of non-apoptotic programmed cell death in anesthetic-induced developmental neurotoxicity.

Anesthetic-induced developmental neurotoxicity (AIDN) can arise due to various factors, among which aberrant nerve cell death is a prominent risk factor. Animal studies have reported that repeated or prolonged anesthetic exposure can cause significant neuroapoptosis in the developing brain. Lately, non-apoptotic programmed cell deaths (PCDs), characterized by inflammation and oxidative stress, have gained increasing attention. Substantial evidence suggests that non-apoptotic PCDs are essential for neuronal cell death in AIDN compared to apoptosis. This article examines relevant publications in the PubMed database until April 2024. Only original articles in English that investigated the potential manifestations of non-apoptotic PCD in AIDN were analysed. Specifically, it investigates necroptosis, pyroptosis, ferroptosis, and parthanatos, elucidating the signaling mechanisms associated with each form. Furthermore, this study explores the potential relevance of these non-apoptotic PCDs pathways to the pathological mechanisms underlying AIDN, drawing upon their distinctive characteristics. Despite the considerable challenges involved in translating fundamental scientific knowledge into clinical therapeutic interventions, this comprehensive review offers a theoretical foundation for developing innovative preventive and treatment strategies targeting non-apoptotic PCDs in the context of AIDN.

Silymarin ameliorates motor function and averts neuroinflammation-induced cell death in the rat model of Huntington’s disease

Huntington's disease (HD) is a scarce neurodegenerative disorder defined by chorea (unusual involuntary movements), behavioral presentations, psychiatric features, and cognitive deterioration. Although the precise pathogenic mechanism behind HD has not yet been identified, the most widely acknowledged pathways include excitotoxicity, mitochondrial malfunction, neuroinflammation, neurochemical imbalance, oxidative stress, and apoptosis HD has no efficient therapy. Current medications have drawbacks. Silymarin, a compound made up of standardized extracts obtained from the seeds of the Silybum marianum and polyphenolic flavonolignan, is utilized in therapeutic settings to treat a variety of experimental disorders in animals. Silymarin's key pharmacological activities include anti-cancer, hepatoprotection, antioxidant, cardioprotection, and anti-inflammatory. It also has no adverse side effects on people or animals. The current study aims to provide Silymarin's neuro-pharmacological activities or therapeutic qualities in HD. In this study, Thirty-six male Sprague-Dawley rats (200–220 g, 8 weeks) at the initial of the study were used. Silymarin solution (100 mg/Kg) was administered by oral gavage for 21 days to ameliorate neural damage in rats injected with 3-nitropropionicacid (3-NP) in a preliminary rat model of HD. The results showed that administration of silymarin to HD rats reduced gliosis, improved motor coordination and muscle activity, and increased striatal volume and the number of neurons and glial cells. Our results suggest that silymarin provides a protective environment for nerve cells and can have beneficial effects against the harmful effects of HD.

Research progress in the mechanism of acupuncture regulating microglia in the treatment of Alzheimer’s disease

Alzheimer’s disease (AD) is the most common neurodegenerative disease in the central nervous system, characterized by memory and cognitive dysfunction. Acupuncture is an effective means to alleviate the symptoms of AD. Recent studies have shown that microglia play an important role in the occurrence and development of AD. Acupuncture can regulate the activity of microglia, inhibit neuroinflammation, regulate phagocytosis, and clear Aβ Pathological products such as plaque can protect nerve cells and improve cognitive function in AD patients. This article summarizes the relationship between microglia and AD, as well as the research progress in the mechanism of acupuncture regulating microglia in the treatment of AD. The mechanism of acupuncture regulating microglia in the treatment of AD is mainly reviewed from two aspects: inhibiting neuroinflammatory activity and regulating phagocytic function.

Exploring the therapeutic potential: Apelin-13's neuroprotective effects foster sustained functional motor recovery in a rat model of Huntington's disease.

Huntington's disease (HD) is a hereditary condition considered by the progressive degeneration of nerve cells in the brain, resultant in motor dysfunction and cognitive impairment. Despite current treatment modalities including pharmaceuticals and various therapies, a definitive cure remains elusive. Therefore, this study investigates the therapeutic potential effect of Apelin-13 in HD management. Thirty male Wistar rats were allocated into three groups: a control group, a group with HD, and a group with both HD and administered Apelin-13. Apelin-13 was administered continuously over a 28-day period at a dosage of around 30 mg/kg to mitigate inflammation in rats subjected to 3-NP injection within an experimental HD model. Behavioral tests, such as rotarod, electromyography (EMG), elevated plus maze, and open field assessments, demonstrated that Apelin-13 improved motor function and coordination in rats injected with 3-NP. Apelin-13 treatment significantly increased neuronal density and decreased glial cell counts compared to the control group. Immunohistochemistry analysis revealed reduced gliosis and expression of inflammatory factors in the treatment group. Moreover, Apelin-13 administration led to elevated levels of glutathione and reduced reactive oxygen species (ROS) level in the treated group. Apelin-13 demonstrates neuroprotective effects, leading to improved movement and reduced inflammatory and fibrotic factors in the HD model.

Creation of an in vitro model of GM1 gangliosidosis by CRISPR/Cas9 knocking-out the GLB1 gene in SH-SY5Y human neuronal cell line.

GM1 gangliosidosis is one type of hereditary error of metabolism that occurs due to the absence or reduction of β-galactosidase enzyme content in the lysosome of cells, including neurons. In vitro, the use of neural cell lines could facilitate the study of this disease. By creating a cell model of GM1 gangliosidosis on the SH-SY5Y human nerve cell line, it is possible to understand the main role of this enzyme in breaking down lipid substrate and other pathophysiologic phenomena this disease. To knock-out the human GLB1 gene, guides targeting exons 14 and 16 of the GLB1 gene were designed using the CRISPOR and CHOP-CHOP websites, and high-efficiency guides were selected for cloning in the PX458 vector. After confirming the cloning, the vectors were transformed into DH5α bacteria and then the target vector was extracted and transfected into human nerve cells (SH-SY5Y cell line) by electroporation. After 48 h, GFP+ cells were sorted using the FACS technique and homozygous (compound heterozygous) single cells were isolated using the serial dilution method and sequencing was done to confirm them. Finally, gap PCR tests, X-gal and Periodic acid-Schiff (PAS) staining, and qPCR were used to confirm the knock-out of the human GLB1 gene. Additionally, RNA sequencing data analysis from existing data of the Gene Expression Omnibus (GEO) was used to find the correlation of GLB1 with other genes, and then the top correlated genes were tested for further evaluation of knock-out effects. The nonviral introduction of two guides targeting exons 14 and 16 of the GLB1 gene into SH-SY5Y cells led to the deletion of a large fragment with a size of 4.62 kb. In contrast to the non-transfected cell, X-gal staining resulted in no blue color in GLB1 gene knock-out cells indicating the absence of β-galactosidase enzyme activity in these cells. Real-time PCR (qPCR) results confirmed the RNA-Seq analysis outcomes on the GEO data set and following the GLB1 gene knock-out, the expression of its downstream genes, NEU1 and CTSA, has been decreased. It has been also shown that the downregulation of GLB1-NEU1-CTSA complex gene was involved in suppressed proliferation and invasion ability of knock-out cells. This study proved that using dual guide RNA can be used as a simple and efficient tool for targeting the GLB1 gene in nerve cells and the knockout SH-SY5Y cells can be used as a model investigation of basic and therapeutic surveys for GM1 gangliosidosis disease.

Mechanism Analysis and Response of Digital Financial Inclusion to Labor Economy based on ANN and Contribution Analysis

Abstract Given the inclusiveness of digital inclusive finance (DFI) and its complex impact mechanism on the labor economy, this study uses the characteristics of adaptive and self-learning ability of artificial neural network (ANN) to simulate the process of delivering stimuli to nerve cells in the human brain through linear weighted summarization and functional mapping, and implement the optimization learning algorithm to adjust the weights in the network structure, thus completing the hierarchical analysis of index weight. At the same time, the neural network structure is used to approach the greatest extent and Garson algorithm is used for sensitivity analysis. We use data on the labor economy and digital financial inclusion in Heilongjiang, Jilin, and Liaoning provinces in China from 2011 to 2021 as a training dataset. The study found that (1) the indexes of DFI have different importance to the indexes of labor economy, among which the most important are the number and amount of insurance per capita and the proportion of the number and amount paid by digital technology, which have a normalized importance of 100 and 99.3%, further, R-square coverage is above 0.95, respectively, for labor economy indicators; (2) For different subdivided indicators, the indexes of DFI determine different significance. This study employs tools and policies related to DFI to address labor economy challenges, so as to promote the overall economic construction. This study studies the response and transmission mechanism of the concept of DFI to the labor economy, and explores the labor economy problems such as improving labor productivity and labor mismatch in the economy under its “inclusive” principle. Compared with the traditional weight analysis, it is closer to the real situation and has a stronger ability to fit the reality. In the future, the model could be rebased and measured against absolute indicators and a wider dataset could be adopted for extension to more areas.

Genetic and epidemiological factors in the pathogenesis of neurodegenerative disorders

The aim of this study was to present the current knowledge on the aetiopathogenesis of neurodegenerative disorders, devoting attention not only to neurophysiological and clinical aspects, but above all to the genetic and extra-genomic conditions underlying these diseases. The dynamic development of knowledge and technological progress in medical and related sciences is resulting in changes in the demographic structure of the human population (ageing populations). One of the consequences of these changes is an increase in the incidence of neurodegenerative disorders. Neurodegenerative diseases include Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease. They belong to a group of disorders whose common feature is a pathological process leading to the loss of nerve cells through apoptosis or necrosis. The aetiology of neurodegenerative diseases is multifactorial and, in addition to the involvement of genomic factors, extra-genomic mechanisms have been postulated to be involved in their aetiopathogenesis. The aetiology of numerous neurodegenerative diseases is still not fully understood. Therefore, intensive research has been ongoing for years, focusing, among other aspects, on the causes and course of such conditions. An important direction of research into the pathogenesis of neurodegenerative diseases is assessment of their potential genetic aspect. Many studies also focus on non-genomic factors, highlighting that many of them can have a significant impact on the course of these diseases.

Severe Hypothermia Induces Ferroptosis in Cerebral Cortical Nerve Cells

Severe Hypothermia Induces Ferroptosis in Cerebral Cortical Nerve Cells

LncRNA SOX21-AS1 promotes activation of BV2 cells via epigenetically silencing of SOCS3 and aggravates Parkinson's disease.

LncRNAs perform a crucial impact on microglia's activation in Parkinson's disease (PD). Here, our purpose is to probe the function and involved mechanism of lncRNA SOX21-AS1 on microglial activation in PD. Mice were treated with MPTP, and BV2 cells were treated with LPS/ATP to build PD animal and cell models. Genes' expression was measured using RT-qPCR, immunoblotting, and IHC. ELISA was applied for testing inflammatory factors' levels. Cell viability and apoptosis were tested using kits. RIP and RNA pull-down assay were utilized for monitoring the bond of SOX21-AS1 to EZH2, and ChIP was applied for affirming the bond between EZH2 and SOCS3's promoter. The expression of SOX21-AS1 and SOCS3 was abnormal in PD cell and animal models. Inhibition of SOX21-AS1 repressed LPS/ATP-induced activation in BV2 cells and nerve damage caused by activated BV2 cells, alleviating the pathological features of PD mice. Further studies found that SOX21-AS1 epigenetically inhibited SOCS3 by recruiting EZH2 to SOCS3 promoter. SOX21-AS1 overexpression partially offset the repressive impact of SOCS3 enhancement on BV2 cell activation and the protective effect on nerve cells. SOX21-AS1 enhances LPS/ATP-induced activation of BV2 cells and nerve damage caused by activated BV2 cells though recruiting EZH2 to SOCS3's promoter, thereby alleviating PD progression. Our research supplies new potential target for curing PD.

Acrodermatitis Enteropathica Following a Pull-Through Procedure

Abstract Introduction: Acrodermatitis Enteropathica is a rare condition traditionally expressed by perioral or acral dermatitis, diarrhea, and alopecia. Acrodermatitis Enteropathica can be inherited or acquired, with the acquired form linked to zinc deficiency. The pull-through procedure is a method used to treat Hirschsprung’s disease, a condition in which the large intestine is missing nerve cells that make it difficult to pass stool, which can lead to obstruction and absorption issues. Complications of pull-through procedures have been associated with bowel obstruction and malabsorption of essential nutrients, including zinc. Case Report: We present a 16-month-old male who developed perioral dermatitis, well-demarcated erythematous plaques, and alopecia after a pull-through procedure related to Hirschsprung’s disease. Discussion: Acrodermatitis Enteropathica can present with various cutaneous lesions, and dermatologists should be familiar with the differing morphologies. In this paper, we encourage dermatologists to consider Acrodermatitis Enteropathica as a differential diagnosis for patients who develop cutaneous lesions following pull-through procedures. Keywords: Acrodermatitis Enteropathica, Hirschsprung’s Disease, zinc deficiency, case report

Estimation and optimization of nerve cells’ proliferation on electrospun nanofibrous scaffolds

Estimation and optimization of nerve cells’ proliferation on electrospun nanofibrous scaffolds

Novel Technologies to Address the Lower Motor Neuron Injury and Augment Reconstruction in Spinal Cord Injury.

Lower motor neuron (LMN) damage results in denervation of the associated muscle targets and is a significant yet under-appreciated component of spinal cord injury (SCI). Denervated muscle undergoes a progressive degeneration and fibro-fatty infiltration that eventually renders the muscle non-viable unless reinnervated within a limited time window. The distal nerve deprived of axons also undergoes degeneration and fibrosis making it less receptive to axons. In this review, we describe the LMN injury associated with SCI and its clinical consequences. The process of degeneration of the muscle and nerve is broken down into the primary components of the neuromuscular circuit and reviewed, including the nerve and Schwann cells, the neuromuscular junction, and the muscle. Finally, we discuss three promising strategies to reverse denervation atrophy. These include providing surrogate axons from local sources; introducing stem cell-derived spinal motor neurons into the nerve to provide the missing axons; and finally, instituting a training program of high-energy electrical stimulation to directly rehabilitate these muscles. Successful interventions for denervation atrophy would significantly expand reconstructive options for cervical SCI and could be transformative for the predominantly LMN injuries of the conus medullaris and cauda equina.

Roles of SMAD and SMAD-Associated Signaling Pathways in Nerve Regeneration Following Peripheral Nerve Injury: A Narrative Literature Review.

Although several methods are being applied to treat peripheral nerve injury, a perfect treatment that leads to full functional recovery has not yet been developed. SMAD (Suppressor of Mothers Against Decapentaplegic Homolog) plays a crucial role in nerve regeneration by facilitating the survival and growth of nerve cells following peripheral nerve injury. We conducted a systematic literature review on the role of SMAD in this context. Following peripheral nerve injury, there was an increase in the expression of SMAD1, -2, -4, -5, and -8, while SMAD5, -6, and -7 showed no significant changes; SMAD8 expression was decreased. Specifically, SMAD1 and SMAD4 were found to promote nerve regeneration, whereas SMAD2 and SMAD6 inhibited it. SMAD exerts its effects by promoting neuronal survival and growth through BMP/SMAD1, BMP/SMAD4, and BMP/SMAD7 signaling pathways. Furthermore, it activates nerve regeneration programs via the PI3K/GSK3/SMAD1 pathway, facilitating active regeneration of nerve cells and subsequent functional recovery after peripheral nerve damage. By leveraging these mechanisms of SMAD, novel strategies for treating peripheral nerve damage could potentially be developed. We aim to further elucidate the precise mechanisms of nerve regeneration mediated by SMAD and explore the potential for developing targeted nerve treatments based on these findings.

Internet of Healthcare Things Based Detection of EEG Epileptic Seizures: A Smart System

Background: A seizure is a sudden and uncontrolled electrical activity in the brain that can cause a variety of symptoms, depending on the location and severity of the abnormal activity. It can be a symptom of an underlying neurological disorder or can occur without an apparent cause. Epilepsy is one of the most common causes of seizures. Overactive electrical discharges disrupt normal brain electrical activity and interfere with nerve cell communication. Aim and Objective: A comprehensive analysis of the literature revealed that several CAD system designs have shown to be useful to radiologists in routine medical practice as second-opinion aids for epileptic seizure detection in circumstances where a clear differentiation cannot be formed subjectively. CAD systems are made to help radiologists by automating the examination of medical data and images, improving the efficiency and accuracy of diagnosis. These systems examine patterns in medical imaging using machine learning approaches, which can be quite helpful in spotting small abnormalities that the human eye can miss. Moreover, the objective of this study was to design a smart healthcare system using a combination of DWT, Hjorth, and statistical parameters for seizure detection. Method: In this research article, the authors proposed the framework of the Internet of Healthcare Things (IoHT) for performing seizure detection. The authors used different pre-processing techniques and extracted different features like Hjorth, wavelets, and statistics, which were classified using different machine-learning techniques. This novel methodology combines a number of technologies and techniques to improve seizure detection's precision and dependability. Results: DWT + Hjorth + Statistical parameters with bior 1.5 as the pre-processing technique yielding the best outcomes. 86% accuracy was obtained with kNN for k = 5, 93% accuracy was obtained with a linear kernel for an SVM classifier, and 95.5% accuracy was obtained using a decision tree and logistic regression. The authors also considered another dataset for validation and received 96.83% accuracy with decision tree and logistic regression classifiers considering the bior1.5 wavelet filter as a preprocessing technique. Conclusion: The IoHT framework offers a multi-modal, adaptive method of seizure detection that enables the dynamic modification of detection parameters and the incorporation of extra sensor signals. This improves seizure detection's precision and dependability, which has important implications for patient care and monitoring. This work shows how IoHT and machine learning can be combined to build a reliable, real-time seizure detection system. These developments, which make it possible for prompt interventions and individualized treatment plans, can significantly improve the quality of care for individuals with epilepsy.

Mapping the landscape: A bibliometric perspective on autophagy in spinal cord injury.

Spinal cord injury (SCI) is a severe condition that often leads to persistent damage of nerve cells and motor dysfunction. Autophagy is an intracellular system that regulates the recycling and degradation of proteins and lipids, primarily through lysosomal-dependent organelle degradation. Numerous publications have highlighted the involvement of autophagy in the secondary injury of SCI. Therefore, gaining a comprehensive understanding of autophagy research is crucial for designing effective therapies for SCI. Dates were obtained from Web of Science, including articles and article reviews published from its inception to October 2023. VOSviewer, Citespace, and SCImago were used to visualized analysis. Bibliometric analysis was conducted using the Web of Science data, focusing on various categories such as publications, authors, journals, countries, organizations, and keywords. This analysis was aimed to summarize the knowledge map of autophagy and SCI. From 2009 to 2023, the number of annual publications in this field exhibited wave-like growth, with the highest number of publications recorded in 2020 (44 publications). Our analysis identified Mei Xifan as the most prolific author, while Kanno H emerged as the most influential author based on co-citations. Neuroscience Letters was found to have published the largest number of papers in this field. China was the most productive country, contributing 232 publications, and Wenzhou Medical University was the most active organization, publishing 39 papers. We demonstrated a comprehensive overview of the relationship between autophagy and SCI utilizing bibliometric tools. This article could help to enhance the understanding of the field about autophagy and SCI, foster collaboration among researchers and organizations, and identify potential therapeutic targets for treatment.

Classification of Alzheimer’s Disease Based on Deep Learning Using Medical Images

Neurodegenerative disorders, notably Alzheimer’s, pose an escalating global health challenge. Marked by the degeneration of brain neurons, these conditions lead to a gradual decline in nerve cells. Worldwide, over 55 million people grapple with dementia, with Alzheimer’s prominently impacting the aging demographic. The primary hurdle to early Alzheimer’s detection is the widespread lack of awareness. The main goal is to design and implement an artificial intelligence system using deep learning (DL) to detect Alzheimer’s disease (AD) through medical images and classify them into various stages, such as non-demented, moderate dementia, mild dementia, and very mild dementia. The dataset contains 6400 magnetic resonance images in .jpg format, with standardized dimensions of 176 × 208 pixels. To demonstrate the advantages of data augmentation and transformation techniques, four scenarios were created: two without these techniques, utilizing the Adam and SGD optimizers, and two with these techniques, also employing the Adam and SGD optimizers, respectively. The main results revealed that scenarios utilizing these techniques exhibited more stable performance when validated with a new dataset. Scenario 3, using the Adam optimizer, achieved a weighted average accuracy of 91.83%, whereas scenario 4, employing the SGD optimizer, reached 87.58% accuracy. In contrast, scenarios 1 and 2, which omitted these techniques, obtained low accuracies below 55%. It is concluded that classifying AD with a DL model exceeding 90% accuracy is feasible. This is the importance of utilizing data augmentation and transformation techniques to improve generalizability to input image variations, which is a consistent factor in the healthcare sector.

Cognitive Brain of Homo sapiens: Stress, Emotions, Health, Hormones, Longevity

Replication of new discoveries in the field of genetics and epigenetics, the scientific union of neurobiology and neurophysiology of the quantum brain HOMO SAPIENS of the 21st century, the achievements of biophysics, hybrid neuroimaging and nuclear medicine, the introduction of nutritionology and bioelementology technologies, the 5P of MEDICINE and 5G TECHNOLOGY, compliance of art medicine and modern psychoanalysis, - will allow control the cognitive brain of a 21st century HUMAN. Scientific progress and 33 years of experimental experience of biophysics in the field of neurogeriatrics and neurocardiology, neuroendocrinology and psychoneuroimmunology - made it possible to argue that "nerve cells are restored," the quantum human brain is CONTROLLED, high-quality and healthy LONGEVITY HOMO SAPIENS of the 21st century is achievable. Children who experience chronic stressors are vulnerable to lifelong emotional and physical health problems. Cognitive and mental health affects a person's ability to cope with stress, maintain a relationship paradigm, make healthy choices and lead a fulfilling life. An increase in the expected and average life expectancy of a person is realizable/achievable/feasible when creating conditions/tools/algorithms/technologies for active and high-quality longevity.

Oligodendroglioma: a neurological perspective in Sub-saharan Africa.

Gliomas are a kind of brain cancer that develops from glial cells. Glial cells provide nourishment and energy to nerve cells, and they also preserve the blood-brain barrier. A primary cancer of the central nervous system (CNS) is oligodendroglioma. This suggests that it originates in the brain or spinal cord. While oligodendrogliomas can strike anyone at any age, the age range of 35 to 44 is when they most commonly occur. Oligodendrogliomas are rare in young people and more common in men than women. Based on anecdotal data, patients with oligodendroglioma may present management challenges in Africa. There are delays in diagnosis and referrals due to the scarcity of neuroimaging facilities. A wide range of strategies have been put forth to improve pathology services in low- and middle-income nations. Adequate mentorship, short-term visitor programs, overcoming supply chain constraints, establishing training standards, and establishing the role of pathologists in cancer screening and early diagnosis have all been proposed as solutions to this problem. To sum up, oligodendroglioma is one of the low-grade gliomas this study looked at. Brain cancer is a serious public health concern in Africa. Improved options for screening and therapy are required to better address this problem.

A NSC-34 cell line-derived spheroid model: Potential and challenges for in vitro evaluation of neurodegeneration.

Three-dimensional (3D) spheroid models aim to bridge the gap between traditional two-dimensional (2D) cultures and the complex in vivo tissue environment. These models, created by self-clustering cells to mimic a 3D environment with surrounding extracellular framework, provide a valuable research tool. The NSC-34 cell line, generated by fusing mouse spinal cord motor neurons and neuroblastoma cells, is essential for studying neurodegenerative diseases like amyotrophic lateral sclerosis (ALS), where abnormal protein accumulation, such as TAR-DNA-binding protein 43 (TDP-43), occurs in affected nerve cells. However, NSC-34 behavior in a 3D context remains underexplored, and this study represents the first attempt to create a 3D model to determine its suitability for studying pathology. We generated NSC-34 spheroids using a nonadhesive hydrogel-based template and characterized them for 6 days. Light microscopy revealed that NSC-34 cells in 3D maintained high viability, a distinct round shape, and forming stable membrane connections. Scanning electron microscopy identified multiple tunnel-like structures, while ultrastructural analysis highlighted nuclear bending and mitochondria alterations. Using inducible GFP-TDP-43-expressing NSC-34 spheroids, we explored whether 3D structure affected TDP-43 expression, localization, and aggregation. Spheroids displayed nuclear GFP-TDP-43 expression, albeit at a reduced level compared with 2D cultures and generated both TDP-35 fragments and TDP-43 aggregates. This study sheds light on the distinctive behavior of NSC-34 in 3D culture, suggesting caution in the use of the 3D model for ALS or TDP-43 pathologies. Yet, it underscores the spheroids' potential for investigating fundamental cellular mechanisms, cell adaptation in a 3D context, future bioreactor applications, and drug penetration studies. RESEARCH HIGHLIGHTS: 3D spheroid generation: NSC-34 spheroids, developed using a hydrogel-based template, showed high viability and distinct shapes for 6 days. Structural features: advanced microscopy identified tunnel-like structures and nuclear and mitochondrial changes in the spheroids. Protein dynamics: the study observed how 3D structures impact TDP-43 behavior, with altered expression but similar aggregation patterns to 2D cultures. Research implications: this study reveals the unique behavior of NSC-34 in 3D culture, suggests a careful approach to use this model for ALS or TDP-43 pathologies, and highlights its potential in cellular mechanism research and drug testing applications.

A Comparative Study on Data Balancing Methods for Alzheimer's Disease Classification

Alzheimer's disease is a prevalent neurological disorder affecting millions of people worldwide, often associated with the aging process, leading to the death of nerve cells in the brain and loss of connections. Recently, promising results have been demonstrated in diagnosing Alzheimer's disease using deep learning models, and various approaches for early diagnosis have been proposed. However, the imbalance in health datasets, particularly those containing rare cases, can lead to performance losses and misleading results during model training. This study focuses on these imbalance issues, evaluating the effectiveness of different balancing methods using the Alzheimer's MRI dataset. In this context, the performance of SMOTE, ADASYN, and Weight Balancing methods is compared using a custom model. Experimental results indicate that, compared to the original imbalanced dataset, Weight balancing outperforms in terms of accuracy, precision, recall, and F1 score. While SMOTE and ADASYN show improvement in various metrics, they are considered inferior to the Weight Balancing method. This study contributes to selecting data-balancing methods to enhance the accuracy of deep learning models in Alzheimer's disease classification and emphasizes the importance of addressing class imbalances in health datasets.