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In the past, little or no attention was paid to cognitive disorders associated with depression (a condition sometimes termed pseudodementia). However, recent years have seen a growing interest in these changes, not only because of their high frequency in acute-stage depression, but also because they have been found to persist, as residual symptoms (in addition to affective and psychomotor ones), in many patients who respond well to antidepressant treatment. These cognitive symptoms seem to impact significantly not only on patients' functioning and quality of life, but also on the risk of recurrence of depression. Therefore, over the past decade, pharmacological research in this field has focused on the development of new agents able to counteract not only depressive symptoms, but also cognitive and functional ones. In this context, novel antidepressants with multimodal activity have emerged. This review considers the different issues, in terms of disease evolution, raised by the presence of cognitive disorders associated with depression and considers, particularly from the neurologist's perspective, the ways in which the clinical approach to cognitive symptoms, and their interpretation to diagnostic and therapeutic ends, have changed in recent years. Finally, after outlining the pharmacodynamics and pharmacokinetics of the first multimodal antidepressant, vortioxetine, it reports the main results obtained with the drug in depressed patients, also in consideration of the ever-increasing evidence on its different mechanisms of action in animal models.

Despite the fact that in recent years Portugal has not seen the occurrence of high-magnitude earthquakes, it remains threatened by these events due to its geographic location. Since the 1960s, reinforced concrete has been the most used material for new constructions; however, the historic urban centers are dominated by old unreinforced masonry (URM) buildings, which techniques and construction materials have evolved since the Great Lisbon earthquake that occurred in 1755 (Mw = 8.5). Given the presence of these buildings in areas of significant seismicity, extensive research is needed to assess the seismic risk and define mitigation policies. This kind of studies is often supported by empirical methods and based on expert judgment due to the high variability of the building stock and lack of information. The main purpose of this work is: (i) to provide analytical fragility curves, supported by nonlinear static analysis, for the entire population of old masonry buildings, built before the introduction of the first design code for building safety against earthquakes (RSSCS) in 1958; (ii) define vulnerability curves to be used by the technical community for seismic assessment of pre code URM buildings. The characterization of the building stock geometry and material properties is based on information previously collected, which was essential to define representative archetypes and typologies.

This paper describes a shake table test on a one-storey full-scale unreinforced masonry structure, which complements an earlier testing of a two-storey structure with similar characteristics. The building specimen was meant to represent the upper floors of the end-unit of a terraced house, built with cavity walls and without any particular seismic design or detailing. In these specimens, the masonry walls were composed of two leaves: a load-bearing inner one made of calcium silicate bricks sustaining a reinforced concrete floor and an external leaf made of clay-bricks connected to the inner leaf by means of metallic ties. A pitched timber roof was supported by two triangular gable walls. Floor acceleration response histories of the previously tested two-storey specimen were used as input motions. An incremental dynamic test, with vertical and horizontal inputs, was carried out up to the explicit collapse of some bearing elements of the structure. In particular, a two-way bending out-of-plane collapse of a load-bearing wall was observed and described.

Neurofilament light chains (NfL) are neuron-specific cytoskeletal proteins whose plasmatic concentrations have been explored as a clinically useful marker in several types of dementia. Plasma concentrations of NfL are extremely low, and just two assays are commercially available for their study: one based on the SiMoA technology and one based on Ella. We thus studied plasma levels of NfL with both platforms to check the correlation between them and to assess their potential in the diagnosis of neurodegeneration. Plasma NfL levels were measured on 50 subjects: 18 healthy controls, 20 Alzheimer’s disease, and 12 frontotemporal dementia patients. Ella returned plasmatic NfL levels significantly higher than SiMoA, however the results were strongly correlated (r = 0.94), and a proportional coefficient of 0.58 between the two assays was calculated. Both assays detected higher plasma NfL levels in patients with dementia than in the control group ( p  < 0.0001) and allowed their discrimination with excellent diagnostic performance (AUC > 0.95). No difference was found between Alzheimer’s and Frontotemporal dementia either using SiMoA or Ella. In conclusion, both the analytical platforms resulted effective in analysing plasma levels of NfL. However, the correct interpretation of results requires the precise knowledge of the assay used.

Migraine is an extremely common disorder. The underlying mechanisms of this chronic illness interspersed with acute symptoms appear to be increasingly complex. An important aspect of migraine heterogeneity is comorbidity with other neurological diseases, cardiovascular disorders, and psychiatric illnesses. Depressive disorders are among the leading causes of disability worldwide according to WHO estimation. In this review, we have mainly considered the findings from general population studies and studies on clinical samples, in adults and children, focusing on the association between migraine and psychiatric disorders (axis I of the DSM), carried over after the first classification of IHS (1988). Though not easily comparable due to differences in methodology to reach diagnosis, general population studies generally indicate an increased risk of affective and anxiety disorders in patients with migraine, compared to non-migrainous subjects. There would also be a trend towards an association of migraine with bipolar disorder, but not with substance abuse/dependence. With respect to migraine subtypes, comorbidity mainly involves migraine with aura. Patients suffering from migraine, however, show a decreased risk of developing affective and anxiety disorders compared to patients with daily chronic headache. It would also appear that psychiatric disorders prevail in patients with chronic headache and substance use than in patients with simple migraine. The mechanisms underlying migraine psychiatric comorbidity are presently poorly understood, but this topic remains a priority for future research. Psychiatric comorbidity indeed affects migraine evolution, may lead to chronic substance use, and may change treatment strategies, eventually modifying the outcome of this important disorder.

Background In Brazil, many public hospitals face constant problems related to high demand vis-à-vis an overall scarcity of resources, which hinders the operations of different sectors such as the surgical centre, as it is considered one of the most relevant pillars for the proper hospital functioning, due to its complexity, criticality as well as economic and social importance. Proper asset management based on well-founded decisions is, therefore, a sine-qua-non condition for addressing such demands. However, subjectivity and other difficulties present in decisions make the management of hospital resources a constant challenge. Methods Thus, the present work proposes the application of a hybrid approach, formed by the QFD tools, fuzzy logic and SERVQUAL as a decision support tool for the quality planning of the surgical centre of the Onofre Lopes Teaching Hospital ( Hospital Universitário Onofre Lopes —HUOL). To accomplish such objective, it was necessary to discover and analyse the main needs of the medical team working in the operating room, through the application of the SERVQUAL questionnaire, associated with fuzzy logic. Results Then, the most relevant deficiencies were transformed into entries for the QFD-fuzzy, where they were translated into project requirements. Soon after, the analysis of the existing relationships between the inputs and these requirements was carried out, generating the ranking of actions with the greatest impact on the improvement of the surgical centre overall quality. Conclusions As a result, it was found that the proposed methodology can optimize the decision process to which hospital managers are submitted, improving the surgical centre operation efficiency.

Among dementia-like diseases, Alzheimer disease (AD) and Vascular Dementia (VD) are two of the most frequent. AD and VD may share multiple neurological symptoms that may lead to controversial diagnoses when using conventional clinical and MRI criteria. Therefore, other approaches are needed to overcome this issue. Machine learning (ML) combined with magnetic resonance imaging (MRI) has been shown to improve diagnostic accuracy of several neurodegenerative diseases, including dementia. To this end, in this study we investigated, first, whether different kind of ML algorithms, combined with advanced MRI features, could be supportive in classifying VD from AD; secondly, whether the developed approach might help in predicting the prevalent disease in subjects with an unclear profile of AD or VD. Three ML categories of algorithms were tested: artificial Neural Network (ANN), support vector machine (SVM), and Adaptive Neuro-Fuzzy Inference System (ANFIS). Multiple regional metrics from resting-state fMRI (rs-fMRI) and Diffusion Tensor Imaging (DTI) of 60 subjects (33 AD, 27 VD) were used as input features to train the algorithms and find the best feature pattern to classify VD from AD. We then used the identified VD-AD discriminant feature pattern as input for the most performant ML algorithm to predict the disease prevalence in 15 dementia patients with a “mixed VD-AD dementia” (MXD) clinical profile using their baseline MRI data. ML predictions were compared with the diagnosis evidence from a three years clinical follow-up. ANFIS emerged as the most efficient algorithm in discriminating AD from VD reaching a classification accuracy greater than 84% using a small feature pattern. Moreover, ANFIS showed improved classification accuracy when trained with a multimodal input feature dataset (e.g. DTI + rs-fMRI metrics) rather than a unimodal feature dataset. When applying the best discriminant pattern to the MXD group, ANFIS achieved a correct prediction rate of 77.33%. Overall, results showed that our approach have a high discriminant power to classify AD and VD profiles. Moreover, the same approach showed also potential in predicting earlier the prevalent underlying disease in dementia patients whose clinical profile is uncertain between AD and VD, therefore suggesting its usefulness in supporting physicians’ diagnostic evaluations.

Evidence indicates that patients with Alzheimer’s dementia (AD) show signs of copper (Cu) dyshomeostasis. This study aimed at evaluating the potential of Cu dysregulation as an AD susceptibility factor. We performed a meta-analysis of 56 studies investigating Cu biomarkers in brain specimens (pooled total of 182 AD and 166 healthy controls, HC) and in serum/plasma (pooled total of 2929 AD and 3547 HC). We also completed a replication study of serum Cu biomarkers in 97 AD patients and 70 HC screened for rs732774 and rs1061472 ATP7B, the gene encoding for the Cu transporter ATPase7B. Our meta-analysis showed decreased Cu in AD brain specimens, increased Cu and nonbound ceruloplasmin (Non-Cp) Cu in serum/plasma samples, and unchanged ceruloplasmin. Serum/plasma Cu excess was associated with a three to fourfold increase in the risk of having AD. Our replication study confirmed meta-analysis results and showed that carriers of the ATP7B AG haplotype were significantly more frequent in the AD group. Overall, our study shows that AD patients fail to maintain a Cu metabolic balance and reveals the presence of a percentage of AD patients carrying ATP7B AG haplotype and presenting Non-Cp Cu excess, which suggest that a subset of AD subjects is prone to Cu imbalance. This AD subtype can be the target of precision medicine-based strategies tackling Cu dysregulation.

Background Several risk factors are associated with the chronic evolution of migraine. Clinical and preclinical studies have provided data about the role of hypertension (HT) as one of the potential modifiable risk factors of chronic migraine (CM). This review is focused on the biological and clinical evidence supporting common mechanisms underlying HT and migraine and the potential role of HT in the transition from episodic to chronic migraine. Methods We conducted a narrative review from a literature search covering the available evidence from studies investigating: i) the role of HT in the transition to CM in clinical practice; ii) the biological mechanisms potentially underpinning the association between HT and evolution to CM; iii) the role of antihypertensive medications in migraine prophylaxis. Results HT proved to be at the base of multiple mechanisms underlying migraine and migraine chronicization. Endothelial dysfunction, blood–brain barrier alterations, calcitonin gene-related peptide signaling, and renin–angiotensin–aldosterone system dysregulation are involved in the worsening effect of HT on migraine frequency, and the role of HT in the transition to CM is supported by clinical observations. Conclusions The observed evidence supports HT contribution to CM evolution due to shared pathophysiologic mechanisms. While a bidirectional influence appears to be ascertained, data are still lacking about the one-way role of HT as direct risk factor for CM transition. Further research is needed to confirm a causal role of HT in this process.

The present study was aimed to cluster sub-groups of patients with varying degrees of cognitive impairment (Subjective Cognitive Decline, mild or Major Neurocognitive Disorder) based on their modifiable risk factors and cognitive reserve with k-means analysis. As a secondary analysis, we described the identified clusters from different perspectives, i.e., socio-demographic characteristics, cognitive functioning, and mental health. The analysis revealed two clusters, which were composed by 27 and 43 patients characterized by protective (Cluster 1) and unprotective (Cluster 2) everyday life habits, respectively. The two groups showed significant differences across all examined dimensions, with Cluster 1 demonstrating a more favourable profile compared to Cluster 2. Specifically, Cluster 1 exhibited advantages in: (1) sociodemographic (education, technological skills, and occupation), (2) cognitive (global cognitive functioning, executive functioning, and working memory), and (3) mental health (mood state and quality of life) characteristics. Such a finding is representative of a more positive individual wellbeing for people who adopt protective behaviours. In the field of dementia prevention, these results support the importance to intervene proactively and simultaneously in the management of multiple risk factors during the entire lifespan.