Time to retire F1-binary score for action unit detection.

Detecting action units is an important task in face analysis, especially in facial expression recognition. This is due, in part, to the idea that expressions can be decomposed into multiple action units. To evaluate systems that detect action units, F1-binary score is often used as the evaluation metric. In this paper, we argue that F1-binary score does not reliably evaluate these models due largely to class imbalance. Because of this, F1-binary score should be retired and a suitable replacement should be used. We justify this argument through a detailed evaluation of the negative influence of class imbalance on action unit detection. This includes an investigation into the influence of class imbalance in train and test sets and in new data (i.e., generalizability). We empirically show that F1-micro should be used as the replacement for F1-binary.

Correlation analysis of occupational stress and metabolic syndrome among employees of a power grid enterprise in China.

BACKGROUND: Being in a state of high occupational stress may disrupt the metabolic balance of the body, thus increasing the risk of metabolic diseases. However, the evidence about the relationship between occupational stress and metabolic syndrome was limited. OBJECTIVES: To explore the association between occupational stress and metabolic syndrome (MetS) in employees of a power grid enterprise. METHODS: A total of 1091 employees were recruited from a power grid enterprise in China. Excluding those who failed to complete the questionnaire and those who had incomplete health check-ups, 945 subjects were included in the study. Assessment of occupational stress was used by job demand-control (JDC) and effort-reward imbalance (ERI) questionnaires, respectively. The information on body mass index (BMI), systolic blood pressure (SBP), and diastolic blood pressure (DBP) were collected. The levels of high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), and fasting blood glucose (FBG) in the fasting venous blood samples were measured. Logistic regression analysis and multiple linear regression methods were used to analyze the correlation between JDC and ERI models of occupational stress, metabolic syndrome, and its components, respectively. RESULTS: The prevalence of MetS was 8.4% and 9.9% in JDC and ERI model high occupational stress employees, respectively. ERI model occupational stress and smoking are significantly associated with the risk of MetS. ERI ratio was significantly associated with lower HDL-C levels. Gender, age, marital status, smoking, high-temperature and high-altitude work were significantly associated with metabolic component levels. CONCLUSION: Our study revealed a high detection rate of occupational stress in both JDC and ERI models among employees of a power grid enterprise. ERI model occupational stress, demanding more attention, was associated with the risk of MetS as well as its components such as HDL-C.

The burdens and benefits of managing and engaging: a diary study approach to person-centred risk assessment of top executives and works council members.

The present diary study investigates the impact of daily effort-reward imbalance (ERI), subjective stress and the cortisol awakening response (CAR) as an objective measure on work engagement of top managers and high-level works council members (N = 45) on three consecutive working days. In the scope of psychosocial risk assessment, we argue that focusing on ERI as a generalized work characteristic might be more suitable for work re-design of higher leadership positions because of their highly dynamic and unpredictable psychosocial work characteristics, while at the same time having more access to job resources. The analyses reveal that both baseline and daily ERI, as well as subjective stress, influence work engagement. Our results suggest that interventions to reduce daily levels of ERI may improve the work environment of top managers and works councils by promoting work engagement and related positive health outcomes in the scope of person-centred risk assessment.

The relationship between effort-reward imbalance and quality of working life among medical caregivers: mediating effects of job burnout.

Background: To determine the relationship between effort-reward imbalance (ERI) and quality of working life (QWL) among medical caregivers and the mediating role of job burnout. Methods: This was a cross-sectional survey. A total of 787 medical caregivers at seven hospitals from Sichuan and Chongqing, China, between May to September 2023 were included in this observational study. The General Information Questionnaire, Effort-Reward Imbalance Questionnaire (ERI), Maslach Burnout Inventory-General Survey (MBI-GS), and Quality of Working Life Scale (QWL7-32) were used for data collection. SPSS 26.0 and PROCESSv3.3 were used for all data analyses, including descriptive statistics. Results: A total of 820 questionnaires were distributed, of which only 787 were valid (return rate; 95.98%). The QWL score of medical caregivers was 126.94 ± 16.69. However, QWL scores were significantly different depending on age, number of children, family support status, department, years of experience, night shift status, number of night shifts per month, number of hours worked per day, monthly income, and occurrence of errors or adverse events (p < 0.05). Furthermore, job burnout and ERI were negatively correlated with QWL (p < 0.01). Job burnout mediated (95% CI = -0.365, -0.260) the relationship between ERI and QWL, accounting for 58.65% of the total effect. Conclusion: Medical caregivers have a medium level of QWL. Job burnout partially mediates the relationship between ERI and QWL. Medical caregiver managers can improve QWL by directly intervening in occupational stress and indirectly intervening in job burnout.

Customised-sampling approach for pipe failure prediction in water distribution networks.

This paper presents a new methodology for addressing imbalanced class data for failure prediction in Water Distribution Networks (WDNs). The proposed methodology relies on existing approaches including under-sampling, over-sampling, and class weighting as primary strategies. These techniques aim to treat the imbalanced datasets by adjusting the representation of minority and majority classes. Under-sampling reduces data in the majority class, over-sampling adds data to the minority class, and class weighting assigns unequal weights based on class counts to balance the influence of each class during machine learning (ML) model training. In this paper, the mentioned approaches were used at levels other than "balance point" to construct pipe failure prediction models for a WDN with highly imbalanced data. F1-score, and AUC-ROC, were selected to evaluate model performance. Results revealed that under-sampling above the balance point yields the highest F1-score, while over-sampling below the balance point achieves optimal results. Employing class weights during training and prediction emphasises the efficacy of lower weights than the balance. Combining under-sampling and over-sampling to the same ratio for both majority and minority classes showed limited improvement. However, a more effective predictive model emerged when over-sampling the minority class and under-sampling the majority class to different ratios, followed by applying class weights to balance data.

Lowered ratio of corticospinal excitation to inhibition predicts greater disability, poorer motor and cognitive function in multiple sclerosis.

Objective: Investigate excitatory-inhibitory (E/I) (im)balance using transcranial magnetic stimulation (TMS) in individuals with Multiple Sclerosis (MS) and determine its validity as a neurophysiological biomarker of disability. Methods: Participants with MS (n = 83) underwent TMS, cognitive, and motor function assessments. TMS-induced motor evoked potential amplitudes (excitability) and cortical silent periods (inhibition) were assessed bilaterally through recruitment curves. The E/I ratio was calculated as the ratio of excitation to inhibition. Results: Participants with greater disability (Expanded Disability Status Scale, EDSS≥3) exhibited lower excitability and increased inhibition compared to those with lower disability (EDSS<3). This resulted in lower E/I ratios in the higher disability group. Individuals with higher disability presented with asymmetrical E/I ratios between brain hemispheres, a pattern not present in the group with lower disability. In regression analyses controlling for demographics, lowered TMS-probed E/I ratio predicted variance in disability (R2 = 0.37, p < 0.001), upper extremity function (R2 = 0.35, p < 0.001), walking speed (R2 = 0.22, p = 0.005), and cognitive performance (R2 = 0.25, p = 0.007). Receiver Operating Characteristic curve analysis confirmed 'excellent' discriminative ability of the E/I ratio in distinguishing high and low disability. Finally, excitation superiorly correlated with the E/I ratio than overall inhibition in both hemispheres (p ≤ 0.01). Conclusion: The E/I ratio is a potential neurophysiological biomarker of disability level in MS, especially when assessed in the hemisphere corresponding to the weaker body side. Interventions aimed at increasing cortical excitation or reducing inhibition may restore E/I balance potentially stalling progression or improving function in MS.

The effect of spatio-temporal sample imbalance in epidemiologic surveillance using opportunistic samples: An ecological study using real and simulated self-reported COVID-19 symptom data.

Open surveys complementing surveillance programs often yield opportunistically sampled data characterised by spatio-temporal imbalance. We set up our study to understand to what extent spatio-temporal statistical models using such data achieve in describing epidemiological trends. We used self-reported symptomatic COVID-19 data from two Belgian regions, Flanders and the Brussels-Capital Region. These data were collected in a large-scale open survey with spatio-temporally imbalanced participation rates. We compared incidence estimates of both self-reported symptoms and test-confirmed COVID-19 cases obtained through generalised linear mixed models correcting for spatio-temporal correlation. We additionally simulated symptom incidences under different sampling strategies to explore the impact of sample imbalance, sample size and disease incidence, on trend detection. Our study shows that spatio-temporal sample imbalance generally does not lead to bad model performances in spatio-temporal trend estimation and high-risk area detection. Except for low-incidence diseases, collecting large samples will often be more essential than ensuring spatio-temporally sample balance.

Gut dysbiosis contributes to the development of Budd-Chiari syndrome through immune imbalance.

Budd-Chiari syndrome (B-CS) is a rare and lethal condition characterized by hepatic venous outflow tract blockage. Gut microbiota has been linked to numerous hepatic disorders, but its significance in B-CS pathogenesis is uncertain. First, we performed a case-control study (Ncase = 140, Ncontrol = 63) to compare the fecal microbiota of B-CS and healthy individuals by metagenomics sequencing. B-CS patients' gut microbial composition and activity changed significantly, with a different metagenomic makeup, increased potentially pathogenic bacteria, including Prevotella, and disease-linked microbial function. Imbalanced cytokines in patients were demonstrated to be associated with gut dysbiosis, which led us to suspect that B-CS is associated with gut microbiota and immune dysregulation. Next, 16S ribosomal DNA sequencing on fecal microbiota transplantation (FMT) mice models examined the link between gut dysbiosis and B-CS. FMT models showed damaged liver tissues, posterior inferior vena cava, and increased Prevotella in the disturbed gut microbiota of FMT mice. Notably, B-CS-FMT impaired the morphological structure of colonic tissues and increased intestinal permeability. Furthermore, a significant increase of the same cytokines (IL-5, IL-6, IL-9, IL-10, IL-17A, IL-17F, and IL-13) and endotoxin levels in B-CS-FMT mice were observed. Our study suggested that gut microbial dysbiosis may cause B-CS through immunological dysregulation. IMPORTANCE: This study revealed that gut microbial dysbiosis may cause Budd-Chiari syndrome (B-CS). Gut dysbiosis enhanced intestinal permeability, and toxic metabolites and imbalanced cytokines activated the immune system. Consequently, the escalation of causative factors led to their concentration in the portal vein, thereby compromising both the liver parenchyma and outflow tract. Therefore, we proposed that gut microbial dysbiosis induced immune imbalance by chronic systemic inflammation, which contributed to the B-CS development. Furthermore, Prevotella may mediate inflammation development and immune imbalance, showing potential in B-CS pathogenesis.

A unified Foot and Mouth Disease dataset for Uganda: evaluating machine learning predictive performance degradation under varying distributions.

In Uganda, the absence of a unified dataset for constructing machine learning models to predict Foot and Mouth Disease outbreaks hinders preparedness. Although machine learning models exhibit excellent predictive performance for Foot and Mouth Disease outbreaks under stationary conditions, they are susceptible to performance degradation in non-stationary environments. Rainfall and temperature are key factors influencing these outbreaks, and their variability due to climate change can significantly impact predictive performance. This study created a unified Foot and Mouth Disease dataset by integrating disparate sources and pre-processing data using mean imputation, duplicate removal, visualization, and merging techniques. To evaluate performance degradation, seven machine learning models were trained and assessed using metrics including accuracy, area under the receiver operating characteristic curve, recall, precision and F1-score. The dataset showed a significant class imbalance with more non-outbreaks than outbreaks, requiring data augmentation methods. Variability in rainfall and temperature impacted predictive performance, causing notable degradation. Random Forest with borderline SMOTE was the top-performing model in a stationary environment, achieving 92% accuracy, 0.97 area under the receiver operating characteristic curve, 0.94 recall, 0.90 precision, and 0.92 F1-score. However, under varying distributions, all models exhibited significant performance degradation, with random forest accuracy dropping to 46%, area under the receiver operating characteristic curve to 0.58, recall to 0.03, precision to 0.24, and F1-score to 0.06. This study underscores the creation of a unified Foot and Mouth Disease dataset for Uganda and reveals significant performance degradation in seven machine learning models under varying distributions. These findings highlight the need for new methods to address the impact of distribution variability on predictive performance.

Mal De Debarquement Syndrome: An Often Unrecognized and Unreported Condition.

Mal de debarquement syndrome (MdDS), also known as "the sickness of disembarkment," is characterized by a persistent bobbing, rocking, or swaying sensation reported by patients long after they have completed travel on a boat or other forms of extended transportation. A detailed patient history, focusing on specific inquiries about recent boat or ship travel, is crucial for a timely diagnosis. The syndrome is unique in that reintroducing similar movements, such as driving, swinging, or returning to the boat, alleviates symptoms temporarily. We describe the case history of a 28-year-old male who experienced a persistent illusion of ground movement for six months following a fishing expedition. The patient reported alleviated symptoms when re-exposed to movements such as driving or swinging. The patient had undergone extensive medical workups and imaging tests under multiple physicians before being diagnosed with MdDS. MdDS is a commonly misdiagnosed, underdiagnosed, unreported, and unrecognized condition. Diagnosing MdDS requires a detailed medical and travel history, accompanying an understanding that the symptoms improve upon re-exposure to the same or similar motion.

[Mechanisms of aging and age-related homeodynamics of bio-elementome.]

This article presents a data science review and our own evaluation on bio-element mediated aging of the human body from the point of view of homeodynamics of bioelementome. The study of bio-element basis of aging is currently one of the actively developing fields in gerontology. During postnatal ontogenesis, the bio-elementome shows no signs of stability. Being extremely dependent on endogenous and exogenous circumstances, the levels of macro- and microelements can either remain within the normal range or undergo significant changes, especially with the body aging. These bio-element developments appear to be very important in terms of a large number of currently known molecular, subcellular, cellular, and tissue mechanisms of aging (oxidative stress, loss of proteostasis, excessive telomere attrition, epigenetic landscape alterations, apoptosis, altered intercellular communication, and many others). Better understanding of metabolic pathways of essential bio-elements (intake in the gastrointestinal tract; absorption, including due to interaction with specific transporting proteins; spread through the circulatory system and the entire body; inclusion in specialized macromolecules and participation in their composition in biochemical processes; excretion from the body), as well as realizing their role in the mechanisms of senile tissue and organ involution, and features of age-related homeodynamics can significantly improve existing knowledge on the biology of aging.

A Single Bout of On-Ice Training Leads to Increased Interlimb Asymmetry in Competitive Youth Hockey Athletes.

Interlimb asymmetry (ILA) refers to an anatomical or physiological imbalance between contralateral limbs, which can influence neuromuscular function. Investigating the influence of neuromuscular fatigue on ILA may be critical for optimizing training programs, injury rehabilitation, and sport-specific performance. The purpose of this study was to determine if a single bout of ice hockey-specific exercise creates or exacerbates lower-limb ILA. Before and after an on-ice training session, 33 youth ice-hockey athletes (14.9 [1.7] y; 11 females) performed 3 repetitions of a maximal vertical countermovement jump (CMJ), an eccentric hamstring contraction, and maximal isometric hip adduction and abduction contractions. Force- and power-related variables were analyzed to determine limb-specific neuromuscular function. The on-ice session reduced maximal isometric hip adduction (left: 7.3% [10.3%]; right: 9.5% [9.6%]) and abduction (left: 4.9% [6.9%]; right: 5.0% [8.1%]) force, but did not impair (P ≥ .10) CMJ performance (jump height, relative peak power, braking duration, and total duration). After the on-ice session, ILA was greater for CMJ propulsive impulse (6.3% [2.9%] vs 5.1% [2.6%]), CMJ braking rate of force development (19.3% [7.6%] vs 15.2% [6.4%]), and peak isometric hip adduction force (6.7% [5.5%] vs 6.1% [4.1%]). In conclusion, hockey-specific exercise leads to increased ILA for multiple force-related metrics, which may be a compensatory mechanism to maintain bilateral task performance when fatigued.

DECNet: Left Atrial Pulmonary Vein Class Imbalance Classification Network.

In clinical practice, the anatomical classification of pulmonary veins plays a crucial role in the preoperative assessment of atrial fibrillation radiofrequency ablation surgery. Accurate classification of pulmonary vein anatomy assists physicians in selecting appropriate mapping electrodes and avoids causing pulmonary arterial hypertension. Due to the diverse and subtly different anatomical classifications of pulmonary veins, as well as the imbalance in data distribution, deep learning models often exhibit poor expression capability in extracting deep features, leading to misjudgments and affecting classification accuracy. Therefore, in order to solve the problem of unbalanced classification of left atrial pulmonary veins, this paper proposes a network integrating multi-scale feature-enhanced attention and dual-feature extraction classifiers, called DECNet. The multi-scale feature-enhanced attention utilizes multi-scale information to guide the reinforcement of deep features, generating channel weights and spatial weights to enhance the expression capability of deep features. The dual-feature extraction classifier assigns a fixed number of channels to each category, equally evaluating all categories, thus alleviating the learning bias and overfitting caused by data imbalance. By combining the two, the expression capability of deep features is strengthened, achieving accurate classification of left atrial pulmonary vein morphology and providing support for subsequent clinical treatment. The proposed method is evaluated on datasets provided by the People's Hospital of Liaoning Province and the publicly available DermaMNIST dataset, achieving average accuracies of 78.81% and 83.44%, respectively, demonstrating the effectiveness of the proposed approach.

The complex interplay between autism spectrum disorder and gut microbiota in children: A comprehensive review.

Autism spectrum disorder (ASD) is characterized by defects in social communication and interaction along with restricted interests and/or repetitive behavior. Children with ASD often also experience gastrointestinal (GI) problems in fact incidence of GI problems in ASD is estimated up to 80 percent. Intestinal microbiota, which is a collection of trillions of microorganisms both beneficial and potentially harmful bacteria living inside the gut, has been considered one of the key elements of gut disorders. The goal of this review is to explore potential link between gut microbiota and ASD in children, based on the recently available data. This review discusses recent advances in this rapidly expanding area of neurodevelopmental disorders, which focuses on what is known about the changes in composition of gut bacteria in children with ASD, exploration of possible mechanisms via which gut microbiota might influence the brain and thus lead to appearance of ASD symptoms, as well as potential treatments that involve modulation of gut flora to improve symptoms in children with ASD, i.e., probiotics, postbiotics or changes in the diet. Of course, it's important to keep in mind inherent difficulties in proving of existence of causal relationships between gut bacteria and ASD. There are significant gaps in understanding of the mechanism of gut-brain axis and the mechanisms that underlie ASD. Standardized approaches for research in this area are needed. This review would provide an overview of this exciting emerging field of research.

Infant versus noninfant formulas and cow's milk: Transition based on age or weight?

Infant formulas are meant to be used until 1 year of age, at which point children are transitioned to non-infant formulas or cow's milk, depending on their remaining dietary intake. Noninfant formulas and cow's milk are appropriate for children who have an average weight at that 1-year mark (9-9.5 kg); however, can contribute significant protein and/or electrolytes to children who are underweight for age, particularly if they still rely heavily on formula feeding for their caloric intake. In this short communication, we present several cases of patients who received excessive amounts of nutrients for age following the formula transition at the 1-year mark. We also provide recommendations for clinicians to consider when faced with underweight infants who are meant to be transitioning off infant formulas.

Impaired olfactory system in metabolic imbalance-related neuropathology.

Olfactory dysfunction, influenced by factors such as aging and environmental stress, is linked to various neurological disorders. The olfactory bulb's connections to brain areas like the hypothalamus, piriform cortex, entorhinal cortex, and limbic system make olfactory dysfunction a contributor to a range of neuropathological conditions. Recent research has underscored that olfactory deficits are prevalent in individuals with both metabolic syndrome and dementia. These systemic metabolic alterations correlate with olfactory impairments, potentially affecting brain regions associated with the olfactory bulb. In cases of metabolic syndrome, phenomena such as insulin resistance and disrupted glucose metabolism may result in compromised olfactory function, leading to multiple neurological issues. This review synthesizes key findings on the interplay between metabolic-induced olfactory dysfunction and neuropathology. It emphasizes the critical role of olfactory assessment in diagnosing and managing neurological diseases related to metabolic syndrome.

Balancing Acts: Tackling Data Imbalance in Machine Learning for Predicting Myocardial Infarction in Type 2 Diabetes.

Type 2 Diabetes (T2D) is a prevalent lifelong health condition. It is predicted that over 500 million adults will be diagnosed with T2D by 2040. T2D can develop at any age, and if it progresses, it may cause serious comorbidities. One of the most critical T2D-related comorbidities is Myocardial Infarction (MI), known as heart attack. MI is a life-threatening medical emergency, and it is important to predict it and intervene in a timely manner. The use of Machine Learning (ML) for clinical prediction is gaining pace, but the class imbalance in predictive models is a key challenge for establishing a trustworthy deployment of the technology. This may lead to bias and overfitting in the ML models, and it may cause misleading interpretations of the ML outputs. In our study, we showed how systematic use of Class Imbalance Handling (CIH) techniques may improve the performance of the ML models. We used the Connected Bradford dataset, consisting of over one million real-world health records. Three commonly used CIH techniques, Oversampling, Undersampling, and Class Weighting (CW) have been used for Naive Bayes (NB), Neural Network (NN), Random Forest (RF), Support Vector Machine (SVM), and Ensemble models. We report that CW overperforms among the other techniques with the highest Accuracy and F1 values of 0.9948 and 0.9556, respectively. Applying the most appropriate CIH techniques for the ML models using real-world healthcare data provides promising results for helping to reduce the risk of MI in patients with T2D.

Insights into the State of the Art of Urogenital Schistosomiasis with a Focus on Infertility.

Schistosomiasis is a neglected tropical disease that affects developing countries worldwide and is caused by several species of parasites from the Schistosoma genus. Chronic infection is characterized by the formation of granulomas around the parasite eggs, the leading cause of pathology. The hepatosplenic clinical form is one of the most common, but urogenital schistosomiasis is another relevant clinical presentation responsible for infertility in men and women. Inflammatory response, anatomical deformations, and endocrine/biochemical changes are involved in the development of infertility. Schistosome parasites can synthesize catechol estrogen-like molecules and affect the sexual hormone balance in their host. Here, we review many aspects of the pathology of urogenital schistosomiasis, specifically infertility, and point to the biochemical and endocrinal elements that must be investigated in the future.

An early sepsis prediction model utilizing machine learning and unbalanced data processing in a clinical context.

Background: Early and accurate diagnoses of sepsis patients are essential to reduce the mortality. However, the sepsis is still diagnosed in a traditional way in China despite the increasing number of related studies, which may to some extent lead to delays in the treatment. Methods: The study included 2,385 patients, including 364 with sepsis, collected from the First Affiliated Hospital of Anhui Medical University and partner hospitals from April to July 2022. External validation was conducted using the MIMIC-III database (over 60,000 patients from 2001 to 2012) and the eICU Collaborative Research Database (139,000 patients from 2014 to 2015). Multiple algorithm models, along with the SHapley Additive exPlanations (SHAP) analysis, are applied to explore the main risk factors for the accurate prediction of the sepsis. Multiple Imputations for filling missing data and the Synthetic Minority Oversampling (SMOTE) balancing method for balancing data are used for the data processing. Result: Eighteen diagnostic features are used in the predictive model for early sepsis. The Random Forest model has the best performance among all the models, with an Area Under the Curve (AUC) of 87% and an F1-score (F1) of 77%. Moreover, the interpretation from the SHAP analysis is generally consistent with the current clinical situation. Conclusion: The study revealed the relationship between these 18 clinical features and diagnostic outcomes. The results indicate that patients with laboratory values of Systolic Blood Pressure, Albumin, and Heart Rate exceeding certain thresholds are at a high likelihood of developing sepsis.

Astrocyte-Neuron Interactions Contributing to Amyotrophic Lateral Sclerosis Progression.

Amyotrophic lateral sclerosis (ALS) is a complex disease impacting motor neurons of the brain, brainstem, and spinal cord. Disease etiology is quite heterogeneous with over 40 genes causing the disease and a vast ~90% of patients having no prior family history. Astrocytes are major contributors to ALS, particularly through involvement in accelerating disease progression. Through study of genetic forms of disease including SOD1, TDP43, FUS, C9orf72, VCP, TBK1, and more recently patient-derived cells from sporadic individuals, many biological mechanisms have been identified to cause intrinsic or glial-mediated neurotoxicity to motor neurons. Overall, many of the normally supportive and beneficial roles that astrocytes contribute to neuronal health and survival instead switch to become deleterious and neurotoxic. While the exact pathways may differ based on disease-origin, altered astrocyte-neuron communication is a common feature of ALS. Within this chapter, distinct genetic forms are examined in detail, along with what is known from sporadic patient-derived cells. Overall, this chapter highlights the interplay between astrocytes and neurons in this complex disease and describes the key features underlying: astrocyte-mediated motor neuron toxicity, excitotoxicity, oxidative/nitrosative stress, protein dyshomeostasis, metabolic imbalance, inflammation, trophic factor withdrawal, blood-brain/blood-spinal cord barrier involvement, disease spreading, and the extracellular matrix/cell adhesion/TGF-ß signaling pathways.