Hasil untuk "Neurology. Diseases of the nervous system"

Anju Singh, R. Kukreti, L. Saso et al.

Oxidative stress is proposed as a regulatory element in ageing and various neurological disorders. The excess of oxidants causes a reduction of antioxidants, which in turn produce an oxidation–reduction imbalance in organisms. Paucity of the antioxidant system generates oxidative-stress, characterized by elevated levels of reactive species (oxygen, hydroxyl free radical, and so on). Mitochondria play a key role in ATP supply to cells via oxidative phosphorylation, as well as synthesis of essential biological molecules. Various redox reactions catalyzed by enzymes take place in the oxidative phosphorylation process. An inefficient oxidative phosphorylation may generate reactive oxygen species (ROS), leading to mitochondrial dysfunction. Mitochondrial redox metabolism, phospholipid metabolism, and proteolytic pathways are found to be the major and potential source of free radicals. A lower concentration of ROS is essential for normal cellular signaling, whereas the higher concentration and long-time exposure of ROS cause damage to cellular macromolecules such as DNA, lipids and proteins, ultimately resulting in necrosis and apoptotic cell death. Normal and proper functioning of the central nervous system (CNS) is entirely dependent on the chemical integrity of brain. It is well established that the brain consumes a large amount of oxygen and is highly rich in lipid content, becoming prone to oxidative stress. A high consumption of oxygen leads to excessive production of ROS. Apart from this, the neuronal membranes are found to be rich in polyunsaturated fatty acids, which are highly susceptible to ROS. Various neurodegenerative diseases such as Parkinson’s disease (PD), Alzheimer’s disease (AD), Huntington’s disease (HD), and amyotrophic lateral sclerosis (ALS), among others, can be the result of biochemical alteration (due to oxidative stress) in bimolecular components. There is a need to understand the processes and role of oxidative stress in neurodegenerative diseases. This review is an effort towards improving our understanding of the pivotal role played by OS in neurodegenerative disorders.

Anne S. B. Olsen, N. Færgeman

Sphingolipids are highly enriched in the nervous system where they are pivotal constituents of the plasma membranes and are important for proper brain development and functions. Sphingolipids are not merely structural elements, but are also recognized as regulators of cellular events by their ability to form microdomains in the plasma membrane. The significance of such compartmentalization spans broadly from being involved in differentiation of neurons and synaptic transmission to neuronal–glial interactions and myelin stability. Thus, perturbations of the sphingolipid metabolism can lead to rearrangements in the plasma membrane, which has been linked to the development of various neurological diseases. Studying microdomains and their functions has for a long time been synonymous with studying the role of cholesterol. However, it is becoming increasingly clear that microdomains are very heterogeneous, which among others can be ascribed to the vast number of sphingolipids. In this review, we discuss the importance of microdomains with emphasis on sphingolipids in brain development and function as well as how disruption of the sphingolipid metabolism (and hence microdomains) contributes to the pathogenesis of several neurological diseases.

Yanyan Li, Xiaorui Pei, Rui Ding et al.

Md Talha Mohsin, Ismail Abdulrashid

Healthcare data now span EHRs, medical imaging, genomics, and wearable sensors, but most diagnostic models still process these modalities in isolation. This limits their ability to capture early, cross-modal disease signatures. This paper introduces a multimodal foundation model built on a transformer architecture that integrates heterogeneous clinical data through modality-specific encoders and cross-modal attention. Each modality is mapped into a shared latent space and fused using multi-head attention with residual normalization. We implement the framework using a multimodal dataset that simulates early-stage disease patterns across EHR sequences, imaging patches, genomic profiles, and wearable signals, including missing-modality scenarios and label noise. The model is trained using supervised classification together with self-supervised reconstruction and contrastive alignment to improve robustness. Experimental evaluation demonstrates strong performance in early-detection settings, with stable classification metrics, reliable uncertainty estimates, and interpretable attention patterns. The approach moves toward a flexible, pretrain-and-fine-tune foundation model that supports precision diagnostics, handles incomplete inputs, and improves early disease detection across oncology, cardiology, and neurology applications.

J. Mertens, Dylan A. Reid, Shong Lau et al.

Age-associated neurological diseases represent a profound challenge in biomedical research as we are still struggling to understand the interface between the aging process and the manifestation of disease. Various pathologies in the elderly do not directly result from genetic mutations, toxins, or infectious agents but are primarily driven by the many manifestations of biological aging. Therefore, the generation of appropriate model systems to study human aging in the nervous system demands new concepts that lie beyond transgenic and drug-induced models. Although access to viable human brain specimens is limited and induced pluripotent stem cell models face limitations due to reprogramming-associated cellular rejuvenation, the direct conversion of somatic cells into induced neurons allows for the generation of human neurons that capture many aspects of aging. Here, we review advances in exploring age-associated neurodegenerative diseases using human cell reprogramming models, and we discuss general concepts, promises, and limitations of the field.

E. Díaz-Mesa, C. Cárdenes Moreno, A. Morera-Fumero et al.

Introduction Malondialdehyde (MDA) is a product of polyunsaturated fatty acid peroxidation (Del Rio D, et al. A review of recent studies on MDA as toxic molecule and biological marker of oxidative stress. Nutr Metab Cardiovasc Dis. 2005;15:316-28). It is a biomarker of oxidative stress and is involved in the pathophysiology of schizophrenia (Goh et al. Asian J Psychiatr. 2022;67:102932). Schizofrenia is linked to disrupted oxidative balance and inflammation (Więdłocha et al. Brain Sci. 2023;13:490). Prior research has shown connections between biomarkers and circadian rhythms in schizophrenia (Morera & Abreu. Acta Physiol Scand. 2007;43:313-14) and diabetes type 2 (Kanabrocki EL, et al. Circadian variation in oxidative stress biomarkers in healthy and type II diabetic men. Chronobiol Int. 2002;19:423-39). To determinate if MDA levels have a role in schizophrenia and follow a circadian rhythm may be useful. Objectives The aim of our study is to compare diurnal and nocturnal MDA serum levels in patients in acute and stabilized phases of schizophrenia according to CIE-10 to find out if there are variations related with circadian rhythms Methods 47 patients were included in our study in two clinical phases: acute episode and stabilization. Blood samples were collected at 12:00h and at 00:00h. MDA serum levels were measured twice: when patients were decompensated (admission) and at clinical stabilization (discharge). The relationship between quantitative variables at both times was analysed by T-Student test Results There is no significative difference between night and day MDA levels in the acute phase of the schizophrenia (2.22±1.352 vs. 1.93±1.530, p<0.09). There is statistical significance between 12:00 and 00:00 (1.90±1.136 vs. 1.34±0.868, p<0.001) at discharge: it was observed that levels decreased. This result can be interpreted as there is circadian rhythm in stabilized phases. Conclusions MDA levels in patients with schizophrenia do not follow a circadian rhythm in the acute episode. When they are clinically stabilized present a circadian change. These patients lose the circadian rhythm in acute episodes. MDA circadian rhythm may help diagnose the clinical phase and its severity. It is necessary to perform more studies to know its utility as an oxidative biomarker Disclosure of Interest None Declared

Aaron D. Talsma, Jon P. Niemi, Richard E. Zigmond

Abstract Background Since the 1990s, evidence has accumulated that macrophages promote peripheral nerve regeneration and are required for enhancing regeneration in the conditioning lesion (CL) response. After a sciatic nerve injury, macrophages accumulate in the injury site, the nerve distal to that site, and the axotomized dorsal root ganglia (DRGs). In the peripheral nervous system, as in other tissues, the macrophage response is derived from both resident macrophages and recruited monocyte-derived macrophages (MDMs). Unresolved questions are: at which sites do macrophages enhance nerve regeneration, and is a particular population needed. Methods Ccr2 knock-out (KO) and Ccr2 gfp/gfp knock-in/KO mice were used to prevent MDM recruitment. Using these strains in a sciatic CL paradigm, we examined the necessity of MDMs and residents for CL-enhanced regeneration in vivo and characterized injury-induced nerve inflammation. CL paradigm variants, including the addition of pharmacological macrophage depletion methods, tested the role of various macrophage populations in initiating or sustaining the CL response. In vivo regeneration, measured from bilateral proximal test lesions (TLs) after 2 d, and macrophages were quantified by immunofluorescent staining. Results Peripheral CL-enhanced regeneration was equivalent between crush and transection CLs and was sustained for 28 days in both Ccr2 KO and WT mice despite MDM depletion. Similarly, the central CL response measured in dorsal roots was unchanged in Ccr2 KO mice. Macrophages at both the TL and CL, but not between them, stained for the pro-regenerative marker, arginase 1. TL macrophages were primarily CCR2-dependent MDMs and nearly absent in Ccr2 KO and Ccr2 gfp/gfp KO mice. However, there were only slightly fewer Arg1+ macrophages in CCR2 null CLs than controls due to resident macrophage compensation. Zymosan injection into an intact WT sciatic nerve recruited Arg1+ macrophages but did not enhance regeneration. Finally, clodronate injection into Ccr2 gfp KO CLs dramatically reduced CL macrophages. Combined with the Ccr2 gfp KO background, depleting MDMs and TL macrophages, and a transection CL, physically removing the distal nerve environment, nearly all macrophages in the nerve were removed, yet CL-enhanced regeneration was not impaired. Conclusions Macrophages in the sciatic nerve are neither necessary nor sufficient to produce a CL response.

Chenquan Lin, Shuangyang Zhang, Ping Yang et al.

Abstract The prolonged usage of atypical antipsychotic drugs (AAPD) among individuals with schizophrenia often leads to metabolic side effects such as dyslipidemia. These effects not only limit one’s selection of AAPD but also significantly reduce compliance and quality of life of patients. Recent studies suggest that bilirubin plays a crucial role in maintaining lipid homeostasis and may be a potential pre-treatment biomarker for individuals with dyslipidemia. The present study included 644 schizophrenia patients from two centers. Demographic and clinical characteristics were collected at baseline and 4 weeks after admission to investigate the correlation between metabolites, episodes, usage of AAPDs, and occurrence of dyslipidemia. Besides, we explored the combined predictive value of genotypes and baseline bilirubin for dyslipidemia by employing multiple PCR targeted capture techniques to sequence two pathways: bilirubin metabolism-related genes and lipid metabolism-related genes. Our results indicated that there existed a negative correlation between the changes in bilirubin levels and triglyceride (TG) levels in patients with schizophrenia. Among three types of bilirubin, direct bilirubin in the baseline (DBIL-bl) proved to be the most effective in predicting dyslipidemia in the ROC analysis (AUC = 0.627, p < 0.001). Furthermore, the odds ratio from multinomial logistic regression analysis showed that UGT1A1*6 was a protective factor for dyslipidemia (ß = −12.868, p < 0.001). The combination of baseline DBIL and UGT1A1*6 significantly improved the performance in predicting dyslipidemia (AUC = 0.939, p < 0.001). Schizophrenia patients with UGT1A1*6 mutation and a certain level of baseline bilirubin may be more resistant to dyslipidemia and have more selections for AAPD than other patients.

Sung Woo Joo, Young Tak Jo, Woohyeok Choi et al.

Abstract A morphometric similarity (MS) network can be constructed using multiple magnetic resonance imaging parameters of each cortical region. An MS network can be used to assess the similarity between cortical regions. Although MS networks can detect microstructural alterations and capture connections between histologically similar cortical areas, the influence of schizophrenia on the topological characteristics of MS networks remains unclear. We obtained T1- and diffusion-weighted images of 239 healthy controls and 190 individuals with schizophrenia to construct the MS network. Group comparisons of the mean MS of the cortical regions and subnetworks were performed. The strengths of the connections between the cortical regions and the global and nodal network indices were compared between the groups. Clinical associations with the network indices were tested using Spearman’s rho. Compared with healthy controls, individuals with schizophrenia had significant group differences in the mean MS of several cortical regions and subnetworks. Individuals with schizophrenia had both superior and inferior strengths of connections between cortical regions compared with those of healthy controls. We observed regional abnormalities of the MS network in individuals with schizophrenia regarding lower centrality values of the pars opercularis, superior frontal, and superior temporal areas. Specific nodal network measures of the right pars opercularis and left superior temporal areas were associated with illness duration in individuals with schizophrenia. We identified regional abnormalities of the MS network in schizophrenia with the left superior temporal area possibly being a key region in topological organization and cortical connections.

Hannelore Findeis, Hannelore Findeis, Maria Strauß et al.

IntroductionThere is evidence that there is a small group of people with schizophrenia spectrum disorders who are more likely to commit homicide than those in the general population. However, there is limited knowledge about the psychopathology that leads to homicide in this group. The aim of this study was to examine two commonly used definitions of the Threat/Control-Override (TCO) concept, which aims to identify a certain risk of serious violence in patients with schizophrenia spectrum disorders.MethodsThis is a sub analysis of a file-based, retrospective and exploratory cross-sectional study. All forensic homicide offenders with schizophrenia spectrum disorders who were detained at the Forensic Hospital Berlin as of 31 December 2014 were examined for the occurrence of TCO according to two commonly used definitions.ResultsOf a total of 419 forensic patients with schizophrenia spectrum disorders, 78 committed homicide (18.6%). The forensic homicide offenders with schizophrenia spectrum disorders were characterised by being male, unemployed, single and having committed (attempted) manslaughter. Irrespective of the definition used, the entire TCO complex was present in less than a third of the sample. In both definitions, Threat symptoms were slightly less frequent than Control-Override symptoms. While Threat symptoms occurred less frequently in Stompe et al.’s definition, Control-Override symptoms were the most common. With regard to Kröber’s definition of Threat and Control-Override, the situation is exactly the opposite.DiscussionRegarding the entire TCO complex, Kröber’s definition seems a little more open and Stompe et al.’s more strict (38.5% vs. 35.9%). Since TCO only occurs in about one third of the subjects in both definitions, neither definition appears to be conclusive. A combination with proportions from both definitions could be a contribution to a future definition of TCO. The present study provides scarcely published primary data on psychopathology in homicide offenders with schizophrenia spectrum disorders, especially on the much discussed TCO concept in two definitions. In order to determine the most useful definition of TCO, to avoid false positives and to identify clear psychopathological risk symptoms, larger samples and comparative studies with offenders and non-offenders should be conducted in the future.

Jiajin Zhang, Ge Wang, Mannudeep K. Kalra et al.

In medical image analysis, the expertise scarcity and the high cost of data annotation limits the development of large artificial intelligence models. This paper investigates the potential of transfer learning with pre-trained vision-language models (VLMs) in this domain. Currently, VLMs still struggle to transfer to the underrepresented diseases with minimal presence and new diseases entirely absent from the pretraining dataset. We argue that effective adaptation of VLMs hinges on the nuanced representation learning of disease concepts. By capitalizing on the joint visual-linguistic capabilities of VLMs, we introduce disease-informed contextual prompting in a novel disease prototype learning framework. This approach enables VLMs to grasp the concepts of new disease effectively and efficiently, even with limited data. Extensive experiments across multiple image modalities showcase notable enhancements in performance compared to existing techniques.

Rifqi Alfinnur Charisma, Faisal Dharma Adhinata

Potato plants are plants that are beneficial to humans. Like other plants in general, potato plants also have diseases; if this disease is not treated immediately, there will be a significant decrease in food production. Therefore, it is necessary to detect diseases quickly and precisely so that disease control can be carried out effectively and efficiently. Classification of potato leaf disease can be done directly. Still, the symptoms cannot always explain the type of disease that attacks potato leaves because there are many types of diseases with symptoms that look the same. Humans also have deficiencies in determining the results of identification of potato leaf disease, so sometimes the results of identification between individuals can be different. Therefore, the use of Deep Learning for the classification process of potato leaf disease is expected to shorten the time and have a high classification accuracy. This study uses a deep learning method with the DenseNet201 architecture. The choice to use the DenseNet201 algorithm in this study is because the model can identify important features of potato leaves and recognize early signs of emerging diseases. This study aimed to evaluate the effectiveness of the transfer learning method with the DenseNet201 architecture in increasing the classification accuracy of potato leaf disease compared to traditional classification methods. This study uses two types of scenarios, namely, comparing the number of dropouts and comparing the three optimizers. This test produces the best model using dropout 0.1 and Adam optimizer with an accuracy of 99.5% for training, 95.2% for validation, and 96% for the confusion matrix. In this study, using data testing, as many as 40 images were tested into the model that has been built. The test results on this model resulted in a new accuracy for classifying potato leaf disease, namely 92.5%.

Jing Sun, G. Nan

Signaling pathways are critical modulators of a variety of physiological and pathological processes, and the abnormal activation of some signaling pathways can contribute to disease progression in various conditions. As a result, signaling pathways have emerged as an important tool through which the occurrence and development of diseases can be studied, which may then lead to the development of novel drugs. Accumulating evidence supports a key role for extracellular signal-regulated kinase 1/2 (ERK1/2) signaling in the embryonic development of the central nervous system (CNS) and in the regulation of adult brain function. ERK1/2, one of the most well characterized members of the mitogen-activated protein kinase family, regulates a range of processes, from metabolism, motility and inflammation, to cell death and survival. In the nervous system, ERK1/2 regulates synaptic plasticity, brain development and repair as well as memory formation. ERK1/2 is also a potent effector of neuronal death and neuroinflammation in many CNS diseases. This review summarizes recent findings in neurobiological ERK1/2 research, with a special emphasis on findings that clarify our understanding of the processes that regulate the plethora of isoform-specific ERK functions under physiological and pathological conditions. Finally, we suggest some potential therapeutic strategies associated with agents acting on the ERK1/2 signaling to prevent or treat neurological diseases.

I. Qureshi, J. Mattick, M. Mehler

Sumaya Mustofa, Md Mehedi Hasan Munna, Yousuf Rayhan Emon et al.

Leaf disease is a common fatal disease for plants. Early diagnosis and detection is necessary in order to improve the prognosis of leaf diseases affecting plant. For predicting leaf disease, several automated systems have already been developed using different plant pathology imaging modalities. This paper provides a systematic review of the literature on leaf disease-based models for the diagnosis of various plant leaf diseases via deep learning. The advantages and limitations of different deep learning models including Vision Transformer (ViT), Deep convolutional neural network (DCNN), Convolutional neural network (CNN), Residual Skip Network-based Super-Resolution for Leaf Disease Detection (RSNSR-LDD), Disease Detection Network (DDN), and YOLO (You only look once) are described in this review. The review also shows that the studies related to leaf disease detection applied different deep learning models to a number of publicly available datasets. For comparing the performance of the models, different metrics such as accuracy, precision, recall, etc. were used in the existing studies.

Zolzaya Dashdorj, Stanislav Grigorev, Munguntsatsral Dovdondash

In the field of health-care and bio-medical research, understanding the relationship between the symptoms of diseases is crucial for early diagnosis and determining hidden relationships between diseases. The study aimed to understand the extent of symptom types in disease prediction tasks. In this research, we analyze a pre-generated symptom-based human disease dataset and demonstrate the degree of predictability for each disease based on the Convolutional Neural Network and the Support Vector Machine. Ambiguity of disease is studied using the K-Means and the Principal Component Analysis. Our results indicate that machine learning can potentially diagnose diseases with the 98-100% accuracy in the early stage, taking the characteristics of symptoms into account. Our result highlights that types of unusual symptoms are a good proxy for disease early identification accurately. We also highlight that unusual symptoms increase the accuracy of the disease prediction task.

Sophia Krix, Ella Wilczynski, Neus Falgàs et al.

Alzheimer's disease has an increasing prevalence in the population world-wide, yet current diagnostic methods based on recommended biomarkers are only available in specialized clinics. Due to these circumstances, Alzheimer's disease is usually diagnosed late, which contrasts with the currently available treatment options that are only effective for patients at an early stage. Blood-based biomarkers could fill in the gap of easily accessible and low-cost methods for early diagnosis of the disease. In particular, immune-based blood-biomarkers might be a promising option, given the recently discovered cross-talk of immune cells of the central nervous system with those in the peripheral immune system. With the help of machine learning algorithms and mechanistic modeling approaches, such as agent-based modeling, an in-depth analysis of the simulation of cell dynamics is possible as well as of high-dimensional omics resources indicative of pathway signaling changes. Here, we give a background on advances in research on brain-immune system cross-talk in Alzheimer's disease and review recent machine learning and mechanistic modeling approaches which leverage modern omics technologies for blood-based immune system-related biomarker discovery.

Muhammad Shoaib Farooq, Abdullah Mehboob

Citrus diseases have been major issues for citrus growing worldwide for many years they can lead significantly reduce fruit quality. the most harmful citrus diseases are citrus canker, citrus greening, citrus black spot, citrus leaf miner which can have significant economic losses of citrus industry in worldwide prevention and management strategies like chemical treatments. Citrus diseases existing in all over the world where citrus is growing its effects the citrus tree root, citrus tree leaf, citrus tree orange etc. Existing of citrus diseases is highly impact on economic factor that can also produce low quality fruits and increased the rate for diseases management. Sanitation and routine monitoring can be effective in managing certain citrus diseases, but others may require more intensive treatments like chemical or biological control methods.

Fangzhi Wang, Hua Liao, Richard S. J. Tol

We investigate optimal carbon abatement in a dynamic general equilibrium climate-economy model with endogenous structural change. By differentiating the production of investment from consumption, we show that social cost of carbon can be conceived as a reduction in physical capital. In addition, we distinguish two final sectors in terms of productivity growth and climate vulnerability. We theoretically show that heterogeneous climate vulnerability results in a climate-induced version of Baumol's cost disease. Further, if climate-vulnerable sectors have high (low) productivity growth, climate impact can either ameliorate (aggravate) the Baumol's cost disease, call for less (more) stringent climate policy. We conclude that carbon abatement should not only factor in unpriced climate capital, but also be tailored to Baumol's cost and climate diseases.

S. Sorce, K. Krause