Behavioral and psychological symptoms represent common complications in patients with different types of dementia. Predominantly, they comprise psychosis, agitation and mood disorders, disinhibited behavior, impairment of the sleep and wakefulness rhythm, wandering, perseveration, pathological collecting, or shouting. Their appearance is related to more rapid progression of the disease, earlier institutionalization, use of physical restraints, and higher risk of mortality. Consequently, appearance of behavioral and psychological symptoms of dementia leads to higher costs of care provided and greater distress for caregivers. Clinical guidelines recommend nonpharmacological approaches as the first choice in the treatment of behavioral and psychological symptoms. Pharmacological therapy should be initiated only if the symptoms were not the result of somatic causes, did not respond to nonpharmacological interventions, or were not caused by the prior medication. Acetylcholinesterase inhibitors, memantine, antipsychotic drugs, antidepressants, mood stabilizers, and benzodiazepines are used. This review summarizes the current findings about the efficacy and safety of the treatment of the neuropsychiatric symptoms in dementias with psychopharmaceuticals. Recommendations for treatment with antipsychotics for this indication are described in detail as this drug group is prescribed most often and, at the same time, is related to the highest risk of adverse effects and increased mortality.

Ataxic gait monitoring and assessment of neurological disorders belong to important multidisciplinary areas that are supported by digital signal processing methods and machine learning tools. This paper presents the possibility of using accelerometric data to optimise deep learning convolutional neural network systems to distinguish between ataxic and normal gait. The experimental dataset includes 860 signal segments of 16 ataxic patients and 19 individuals from the control set with the mean age of 38.6 and 39.6 years, respectively. The proposed methodology is based upon the analysis of frequency components of accelerometric signals simultaneously recorded at specific body positions with a sampling frequency of 60 Hz. The deep learning system uses all of the frequency components in a range of (0,30) Hz. Our classification results are compared with those obtained by standard methods, which include the support vector machine, Bayesian methods, and the two-layer neural network with features estimated as the relative power in selected frequency bands. Our results show that the appropriate selection of sensor positions can increase the accuracy from 81.2% for the foot position to 91.7% for the spine position. Combining the input data and the deep learning methodology with five layers increased the accuracy to 95.8%. Our methodology suggests that artificial intelligence methods and deep learning are efficient methods in the assessment of motion disorders and they have a wide range of further applications.

Background: Adjuvant acupuncture for the symptomatic treatment of migraine reduces the frequency of headaches and may be at least similarly effective to treatment with prophylactic drugs. Methods: This article describes an open-label randomized controlled clinical trial with two groups: the intervention group (n=42) and the waiting-list control group (n=44). This study occurred at the Czech-Chinese Center for Traditional Chinese Medicine at the University Hospital Hradec Kralove between October 2015 and April 2017. Results: After 12 weeks of acupuncture, the number of migraine days was reduced by 5.5 and 2.0 days in the acupuncture and the waiting-list control groups, respectively, with a statistically significant inter-group difference of 2.0 migraine days (95% CI: -4 to -1). A significantly greater reduction in the number of migraine days per 4 weeks was reached at the end of the 6-month follow-up period in the acupuncture vs. control groups (Δ -4.0; 95% CI: -6 to -2). A statistically significant difference was observed in the number of responders to treatment (response defined as at least a 50% reduction in average monthly migraine day frequency) in the acupuncture vs waiting-list control groups (50% vs 27%; p<0.05) at the end of the intervention. A significantly greater percentage of responders to treatment was noted in the intervention vs control groups at the 6-month follow-up (81% vs 36%; p<0.001). Conclusion: Acupuncture can reduce symptoms and medication use, both short term and long term, as an adjuvant treatment in migraine prophylaxis in Czech patients.

The role of the immune system as an integral component of the inflammatory response in the pathophysiology of migraine remains unclear. The aim of this study was to evaluate the differences in immune system parameters (acquired immunity parameters) in patients with episodic migraine (EM) and in healthy controls. In EM patients, we aimed to determine whether the changes found in peripheral blood parameters were related to migraine severity according to the standardised MIDAS and HIT-6 tests. Forty-nine patients with EM and 50 healthy controls were included in this study. The authors compared different lymphocyte parameters obtained by multicolor flow cytometry in the EM and control groups by performing statistical tests. The relationship between the changes in peripheral blood parameters and migraine severity in EM patients was investigated using correlation and regression analysis. EM patients showed higher values than healthy controls, especially in nine parameters: relative count of lymphocytes, relative and absolute counts of CD3 T cells, relative and absolute counts of CD8 suppressor cytotoxic T cells, relative and absolute counts of CD4 + T_EMRA (terminally differentiated helper T lymphocytes), absolute count of CD8 naïve T cells, and absolute count of CD19 switched memory B cells. Among the lymphocyte parameters, CD4 + T_EM (effector memory helper T lymphocytes) and CD8 + T_EMRA (terminally differentiated cytotoxic T lymphocytes) were statistically significantly associated with HIT-6. Patients with a CD4 + T_EM value below 15 had a high probability (90%) that the HIT-6 value would be higher than 60. The results of this study show that EM patients have changes in immune system parameters measured in the peripheral blood. Changes in the abundance of CD4 + T_EM could be used as a biomarker for disease severity.

Multiple sclerosis (MS) is a chronic neurodegenerative disease that affects the central nervous system (CNS). Currently, MS treatment is limited to several Food and Drug Administration (FDA)- and European Medicines Agency (EMA)-approved medications that slow disease progression by immunomodulatory action. Fingolimod and siponimod have similar mechanisms of action, and consequently, their therapeutic effects may be comparable. However, while fingolimod is mainly used for relapsing–remitting MS (RRMS), siponimod, according to EMA label, is recommended for active secondary progressive MS (SPMS). Clinicians and scientists are analysing whether patients can switch from fingolimod to siponimod and identifying the advantages or disadvantages of such a switch from a therapeutic point of view. In this review, we aim to discuss the therapeutic effects of these two drugs and the advantages/disadvantages of switching treatment from fingolimod to siponimod in patients with the most common forms of MS, RRMS and SPMS.

Early diagnosis and treatment of multiple sclerosis (MS) in the initial stages of the disease can significantly retard its progression. The aim of the present study was to identify changes in the cerebrospinal fluid proteome in patients with relapsing-remitting MS and clinically isolated MS syndrome who are at high risk of developing MS (case group) compared to healthy population (control) in order to identify potential new markers, which could ultimately aid in early diagnosis of MS. The protein concentrations of each of the 11 case and 15 control samples were determined using a bicinchoninic acid assay. Nanoscale liquid chromatography coupled with tandem mass spectrometry was used for protein identification. Proteomics data were processed using the Perseus software suite and R. The results were filtered using the Benjamini-Hochberg procedure for the false discovery rate (FDR) correction (FDR<0.05). The results showed that, 26 proteins were significantly dysregulated in case samples compared to the controls. Nine proteins were found to be significantly less abundant in case samples, while the abundance of 17 proteins was significantly increased in case samples compared to controls. Three of the proteins were previously linked to RR MS, including immunoglobulin (Ig) γ-1 chain C region, Ig heavy chain V–III region BRO and Ig κ chain C region. Three proteins that were uniquely expressed in patients with RR MS were identified and these proteins may serve as prognostic biomarkers for identifying patients with a high risk of developing RR MS.