Psychobiological effects of an eHealth psychoeducational intervention to informal caregivers of persons with dementia: a pilot study during the COVID-19 pandemic in Italy

Background: The workload associated with caring for a person with dementia (PwD) could negatively affect informal caregivers physical and mental health. According to the recent literature, there is a need for studies testing the implementation of affordable and accessible interventions for improving caregivers well-being. Aims: This study aimed to explore the feasibility and effectiveness of an 8 week eHealth psychoeducation intervention held during the COVID-19 pandemic in Italy in reducing the psychological burden and neuroendocrine markers of stress in caregivers of PwD. Methods: Forty-one informal caregivers of PwD completed the eHealth psychoeducation intervention. Self-reported (i.e., caregiver burden, anxiety symptoms, depressive symptoms, and caregiver self-efficacy) and cortisol measurements were collected before and after the intervention. Results: Following the intervention, the caregivers self-efficacy regarding the ability to respond to disruptive behaviours improved (t = − 2.817, p = 0.007), anxiety and burden levels decreased (state anxiety: t = 3.170, p = 0.003; trait anxiety: t = 2.327, p = 0.025; caregiver burden: t = 2.290, p = 0.027), while depressive symptoms and cortisol levels did not change significantly. Correlation analyses showed that the increase in self-efficacy was positively associated with the improvement of caregiver burden from pre- to post-intervention (r = 0.386, p = 0.014). The intervention had a low rate of dropout (n = 1, due to the patient’s death) and high levels of appreciation. Discussion: The positive evidence and participation rate support the feasibility and effectiveness of the proposed eHealth psychoeducational intervention to meet the need for knowledge of disease management and possibly reduce detrimental effects on caregivers’ psychological well-being. Conclusion: Further placebo-controlled trials are needed to test the generalizability and specificity of our results

Catch me if you can: how AML and its niche escape immunotherapy

In spite of the remarkable progress in basic and preclinical studies of acute myeloid leukemia (AML), the five-year survival rate of AML patients remains poor, highlighting the urgent need for novel and synergistic therapies. Over the past decade, increased attention has been focused on identifying suitable immunotherapeutic strategies for AML, and in particular on targeting leukemic cells and their progenitors. However, recent studies have also underlined the important contribution of the leukemic microenvironment in facilitating tumor escape mechanisms leading to disease recurrence. Here, we describe the immunological features of the AML niche, with particular attention to the crosstalk between the AML blasts and the cellular components of the altered tumor microenvironment (TME) and the mechanisms of immune escape that hamper the therapeutic effects of the most advanced treatments. Considering the AML complexity, immunotherapy approaches may benefit from a rational combination of complementary strategies aimed at preventing escape mechanisms without increasing toxicity

Neonatal umbilical cord blood transplantation halts skeletal disease progression in the murine model of MPS-I

Umbilical cord blood (UCB) is a promising source of stem cells to use in early haematopoietic stem cell transplantation (HSCT) approaches for several genetic diseases that can be diagnosed at birth. Mucopolysaccharidosis type I (MPS-I) is a progressive multi-system disorder caused by deficiency of lysosomal enzyme α-L-iduronidase, and patients treated with allogeneic HSCT at the onset have improved outcome, suggesting to administer such therapy as early as possible. Given that the best characterized MPS-I murine model is an immunocompetent mouse, we here developed a transplantation system based on murine UCB. With the final aim of testing the therapeutic efficacy of UCB in MPS-I mice transplanted at birth, we first defined the features of murine UCB cells and demonstrated that they are capable of multi-lineage haematopoietic repopulation of myeloablated adult mice similarly to bone marrow cells. We then assessed the effectiveness of murine UCB cells transplantation in busulfan-conditioned newborn MPS-I mice. Twenty weeks after treatment, iduronidase activity was increased in visceral organs of MPS-I animals, glycosaminoglycans storage was reduced, and skeletal phenotype was ameliorated. This study explores a potential therapy for MPS-I at a very early stage in life and represents a novel model to test UCB-based transplantation approaches for various diseases

Reactivity of Cortical Alpha Rhythms to Eye Opening in Mild Cognitive Impairment and Alzheimer's Disease: an EEG Study.

2010 Oct 7. [Epub ahead of print

Hierarchy Theory of Evolution and the Extended Evolutionary Synthesis: Some Epistemic Bridges, Some Conceptual Rifts

Contemporary evolutionary biology comprises a plural landscape of multiple co-existent conceptual frameworks and strenuous voices that disagree on the nature and scope of evolutionary theory. Since the mid-eighties, some of these conceptual frameworks have denounced the ontologies of the Modern Synthesis and of the updated Standard Theory of Evolution as unfinished or even flawed. In this paper, we analyze and compare two of those conceptual frameworks, namely Niles Eldredge’s Hierarchy Theory of Evolution (with its extended ontology of evolutionary entities) and the Extended Evolutionary Synthesis (with its proposal of an extended ontology of evolutionary processes), in an attempt to map some epistemic bridges (e.g. compatible views of causation; niche construction) and some conceptual rifts (e.g. extra-genetic inheritance; different perspectives on macroevolution; contrasting standpoints held in the “externalism–internalism” debate) that exist between them. This paper seeks to encourage theoretical, philosophical and historiographical discussions about pluralism or the possible unification of contemporary evolutionary biology

Heterogeneity of the bone marrow niche in patients with myeloproliferative neoplasms: ActivinA secretion by mesenchymal stromal cells correlates with the degree of marrow fibrosis

Mesenchymal stromal cells (MSCs) represent an essential component of the bone marrow (BM) niche and display disease-specific alterations in several myeloid malignancies. The aim of this work was to study possible MSC abnormalities in Philadelphia-negative myeloproliferative neoplasms (MPNs) in relationship to the degree of BM fibrosis. MSCs were isolated from BM of 6 healthy donors (HD) and of 23 MPN patients, classified in 3 groups according to the diagnosis and the grade of BM fibrosis: polycythemia vera and essential thrombocythemia (PV/ET), low fibrosis myelofibrosis (LF-MF), and high fibrosis MF (HF-MF). MSC cultures were established from 21 of 23 MPN patients. MPN-derived MSCs did not exhibit any functional impairment in their adipogenic/osteogenic/chondrogenic differentiation potential and displayed a phenotype similar to HD-derived MSCs but with a decreased expression of CD146. All MPN-MSC lines were negative for the patient-specific hematopoietic clone mutations (JAK2, MPL, CALR). MSCs derived from HF-MF patients displayed a reduced clonogenic potential and a lower growth kinetic compared to MSCs from HD, LF-MF, and PV/ET patients. mRNA levels of hematopoiesis regulatory molecules were unaffected in MSCs from HF-MF compared to HD. Finally, in vitro ActivinA secretion by MSCs was increased in HF-MF compared to LF-MF patients, in association with a lower hemoglobin value. Increased ActivinA immunolabeling on stromal cells and erythroid precursors was also observed in HF-MF BM biopsies. In conclusion, higher grade of BM fibrosis is associated with functional impairment of MSCs and the increased secretion of ActivinA may represent a suitable target for anemia treatment in MF patients

The emerging structure of the Extended Evolutionary Synthesis: where does Evo-Devo fit in?

The Extended Evolutionary Synthesis (EES) debate is gaining ground in contemporary evolutionary biology. In parallel, a number of philosophical standpoints have emerged in an attempt to clarify what exactly is represented by the EES. For Massimo Pigliucci, we are in the wake of the newest instantiation of a persisting Kuhnian paradigm; in contrast, Telmo Pievani has contended that the transition to an EES could be best represented as a progressive reformation of a prior Lakatosian scientific research program, with the extension of its Neo-Darwinian core and the addition of a brand-new protective belt of assumptions and auxiliary hypotheses. Here, we argue that those philosophical vantage points are not the only ways to interpret what current proposals to ‘extend’ the Modern Synthesis-derived ‘standard evolutionary theory’ (SET) entail in terms of theoretical change in evolutionary biology. We specifically propose the image of the emergent EES as a vast network of models and interweaved representations that, instantiated in diverse practices, are connected and related in multiple ways. Under that assumption, the EES could be articulated around a paraconsistent network of evolutionary theories (including some elements of the SET), as well as models, practices and representation systems of contemporary evolutionary biology, with edges and nodes that change their position and centrality as a consequence of the co-construction and stabilization of facts and historical discussions revolving around the epistemic goals of this area of the life sciences. We then critically examine the purported structure of the EES—published by Laland and collaborators in 2015—in light of our own network-based proposal. Finally, we consider which epistemic units of Evo-Devo are present or still missing from the EES, in preparation for further analyses of the topic of explanatory integration in this conceptual framework

Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample

Asymmetry, sex differences and age-related changes in the white matter in the healthy elderly: a tract-based study

<p>Abstract</p> <p>Background</p> <p>Hemispherical asymmetry, sex differences and age-related changes have been reported for the human brain. Meanwhile it was still unclear the presence of the asymmetry or sex differences in the human brain occurred whether as a normal development or as consequences of any pathological changes. The aim of this study was to investigate hemispherical asymmetry, sex differences and age-related changes by using a tract-based analysis in the nerve bundles.</p> <p>Methods</p> <p>40 healthy elderly subjects underwent magnetic resonance diffusion tensor imaging, and we calculated fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values along the major white matter bundles.</p> <p>Results</p> <p>We identified hemispherical asymmetry in the ADC values for the cingulate fasciculus in the total subject set and in males, and a sex difference in the FA values for the right uncinate fasciculus. For age-related changes, we demonstrated a significant increase in ADC values with advancing age in the right cingulum, left temporal white matter, and a significant decrease in FA values in the right superior longitudinal fasciculus.</p> <p>Conclusion</p> <p>In this study, we found hemispherical asymmetry, sex differences and age-related changes in particular regions of the white matter in the healthy elderly. Our results suggest considering these differences can be important in imaging studies.</p

EEG-Based Functional Brain Networks: Does the Network Size Matter?

Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks – whose nodes can vary from tens to hundreds – are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of graph metrics such as clustering coefficient, modularity, efficiency, economic efficiency, and assortativity. We showed that the estimates of these metrics significantly differ depending on the network size. Larger networks had higher efficiency, higher assortativity and lower modularity compared to those with smaller size and the same density. These findings indicate that the network size should be considered in any comparison of networks across studies