Background and objectives: Brain-derived neurotrophic factor (BDNF) is one of the most studied neurotrophins. Low BDNF concentrations have been noted in patients with traditional cardiovascular disease risk factors and have been associated with the increased risk of stroke/transient ischemic attack (TIA). We aimed to study the correlation of BDNF serum levels with acute stroke severity and its potential role as a biomarker in predicting functional outcome. Materials and methods: We systematically searched PubMed, Web of Science, and the Cochrane database using specific keywords. The endpoints examined were the correlation of BDNF with functional outcome, the National Institute of Health stroke scale (NIHSS) measured at the acute phase, and stroke infarct volume. We also compared serum BDNF levels between stroke patients and healthy controls. Results: Twenty-six records were included from the initial 3088 identified. Twenty-five studies reported NIHSS and BDNF levels on the first day after acute stroke. Nine studies were further meta-analyzed. A statistically significant negative correlation between NIHSS and BDNF levels during the acute phase of stroke was noted (COR: −0.3013, 95%CI: (−0.4725; −0.1082), z = −3.01, p = 0.0026). We also noted that BDNF levels were significantly lower in patients with stroke compared to healthy individuals. Due to the heterogeneity of studies, we only conducted a qualitative analysis regarding serum BDNF and functional outcome, while no correlation between BDNF levels and stroke infarct volume was noted. Conclusions: We conclude that in the acute stroke phase, stroke severity is negatively correlated with BDNF levels. Concurrently, patients with acute stroke have significantly lower BDNF levels in serum compared to healthy controls. No correlations between BDNF and stroke infarct volume or functional outcome at follow-up were noted.

Functional neurological disorder (FND), formerly called conversion disorder, is a condition characterized by neurological symptoms that lack an identifiable organic purpose. These signs, which can consist of motor, sensory, or cognitive disturbances, are not deliberately produced and often vary in severity. Its diagnosis is predicated on clinical evaluation and the exclusion of other medical or psychiatric situations. Its treatment typically involves a multidisciplinary technique addressing each of the neurological symptoms and underlying psychological factors via a mixture of medical management, psychotherapy, and supportive interventions. Recent advances in neuroimaging and a deeper exploration of its epidemiology, pathophysiology, and clinical presentation have shed new light on this disorder. This paper synthesizes the current knowledge on FND, focusing on its epidemiology and underlying mechanisms, neuroimaging insights, and the differentiation of FND from feigning or malingering. This review highlights the phenotypic heterogeneity of FND and the diagnostic challenges it presents. It also discusses the significant role of neuroimaging in unraveling the complex neural underpinnings of FND and its potential in predicting treatment response. This paper underscores the importance of a nuanced understanding of FND in informing clinical practice and guiding future research. With advancements in neuroimaging techniques and growing recognition of the disorder’s multifaceted nature, the paper suggests a promising trajectory toward more effective, personalized treatment strategies and a better overall understanding of the disorder.

Traumatic brain injury is a significant public health issue and represents the main contributor to death and disability globally among all trauma-related injuries. Martial arts practitioners, military veterans, athletes, victims of physical abuse, and epileptic patients could be affected by the consequences of repetitive mild head injuries (RMHI) that do not resume only to short-termed traumatic brain injuries (TBI) effects but also to more complex and time-extended outcomes, such as post-concussive syndrome (PCS) and chronic traumatic encephalopathy (CTE). These effects in later life are not yet well understood; however, recent studies suggested that even mild head injuries can lead to an elevated risk of later-life cognitive impairment and neurodegenerative disease. While most of the PCS hallmarks consist in immediate consequences and only in some conditions in long-termed processes undergoing neurodegeneration and impaired brain functions, the neuropathological hallmark of CTE is the deposition of p-tau immunoreactive pre-tangles and thread-like neurites at the depths of cerebral sulci and neurofibrillary tangles in the superficial layers I and II which are also one of the main hallmarks of neurodegeneration. Despite different CTE diagnostic criteria in clinical and research approaches, their specificity and sensitivity remain unclear and CTE could only be diagnosed post-mortem. In CTE, case risk factors include RMHI exposure due to profession (athletes, military personnel), history of trauma (abuse), or pathologies (epilepsy). Numerous studies aimed to identify imaging and fluid biomarkers that could assist diagnosis and probably lead to early intervention, despite their heterogeneous outcomes. Still, the true challenge remains the prediction of neurodegeneration risk following TBI, thus in PCS and CTE. Further studies in high-risk populations are required to establish specific, preferably non-invasive diagnostic biomarkers for CTE, considering the aim of preventive medicine.

Drug‐resistant epileptic patients make up approximately one‐third of the global epilepsy population. The pathophysiology of drug resistance has not been fully elucidated; however, current evidence suggests intestinal dysbiosis, as a possible etiopathogenic factor. Ketogenic diet, whose effect is considered to be mediated by alteration of gut microbiota synthesis, has long been administered in patients with medically refractory seizures, with positive outcomes. In this review, we present data derived from clinical studies regarding alterations of gut microbiome profile in drug‐resistant epileptic patients. We further attempt to describe the mechanisms through which the gut microbiome modification methods (including ketogenic diet, pre‐ or probiotic administration) improve drug‐resistant epilepsy, by reporting findings from preclinical and clinical studies. A comprehensive search of the published literature on the PubMed, Embase, and Web of science databases was performed. Overall, the role of gut microbiome in drug‐resistant epilepsy is an area which shows promise for the development of targeted therapeutic interventions. More research is required to confirm the results from preliminary studies, as well as safety and effectiveness of altering gut bacterial composition, through the above‐mentioned methods.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, associated with extensive neuronal loss, dendritic and synaptic changes resulting in significant cognitive impairment. An increased number of studies have given rise to the neuroinflammatory hypothesis in AD. It is widely accepted that AD brains show chronic inflammation, probably triggered by the presence of insoluble amyloid beta deposits and neurofibrillary tangles (NFT) and is also related to the activation of neuronal death cascade. In the present study we aimed to investigate the role of YKL-40 levels in the cerebrospinal fluid (CSF) in the diagnosis of AD, and to discuss whether there are further potential roles of this protein in the management and treatment of AD. We conducted an online search on PubMed, Web of Science, and the Cochrane library databases from 1990 to 2021. The quantitative analysis showed that the levels of YKL-40 were significantly higher in Alzheimer’s disease compared to controls, to mild cognitive impairment (MCI) AD (MCI-AD) and to stable MCI. They were also increased in MCI-AD compared to stable MCI. The present study shows that the CSF levels of YKL-40 could be potentially used as a biomarker for the prognosis of mild cognitive impairment and the likelihood of progression to AD, as well as for the differential diagnosis between AD and MCI.

Conventional biometrics have been employed in high-security user-authentication systems for over 20 years now. However, some of these modalities face low-security issues in common practice. Brainwave-based user authentication has emerged as a promising alternative method, as it overcomes some of these drawbacks and allows for continuous user authentication. In the present study, we address the problem of individual user variability, by proposing a data-driven Electroencephalography (EEG)-based authentication method. We introduce machine learning techniques, in order to reveal the optimal classification algorithm that best fits the data of each individual user, in a fast and efficient manner. A set of 15 power spectral features (delta, theta, lower alpha, higher alpha, and alpha) is extracted from three EEG channels. The results show that our approach can reliably grant or deny access to the user (mean accuracy of 95.6%), while at the same time poses a viable option for real-time applications, as the total time of the training procedure was kept under one minute.