Ground calcium carbonate as a low cost and biosafety excipient for solubility and dissolution improvement of praziquantel

Calcium carbonate is an abundant mineral with several advantages to be a successful carrier to improve oral bioavailability of poorly water-soluble drugs, such as praziquantel. Praziquantel is an antiparasitic drug classified in group II of the Biopharmaceutical Classification System hence characterized by high-permeability and low-solubility. Therefore, the dissolution rate is the limiting factor for the gastrointestinal absorption that contributes to the low bioavailability. Consequently, the therapeutic dose of the praziquantel must be high and big tablets and capsules are required, which are difficult to swallow, especially for pediatric and elderly patients. Mixtures of praziquantel and calcium carbonate using solid-solid physical mixtures and solid dispersions were prepared and characterized using several techniques (X-ray diffraction differential scanning calorimetry, thermogravimetric analysis, scanning electron microscopy, laser diffraction, Fourier transform infrared and Raman spectroscopies). Solubility of these formulations evidenced that the solubility of praziquantel-calcium carbonate interaction product increased in physiological media. In vitro dissolution tests showed that the interaction product increased the dissolution rate of the drug in acidic medium. Theoretical models were studied to understand this experimental behavior. Cytotoxicity and cell cycle studies were performed, showing that praziquantel-calcium carbonate physical mixture and interaction product were biocompatible with the HTC116 cells, because it did not produce a decrease in cell viability or alterations in the cell cycle

Lessons learned from the ATLAS performance studies of the Iberian Cloud for the first LHC running period

In this contribution we describe the performance of the Iberian (Spain and Portugal) ATLAS cloud during the first LHC running period (March 2010-January 2013) in the context of the GRID Computing and Data Distribution Model. The evolution of the resources for CPU, disk and tape in the Iberian Tier-1 and Tier-2s is summarized. The data distribution over all ATLAS destinations is shown, focusing on the number of files transferred and the size of the data. The status and distribution of simulation and analysis jobs within the cloud are discussed. The Distributed Analysis tools used to perform physics analysis are explained as well. Cloud performance in terms of the availability and reliability of its sites is discussed. The effect of the changes in the ATLAS Computing Model on the cloud is analyzed. Finally, the readiness of the Iberian Cloud towards the first Long Shutdown (LS1) is evaluated and an outline of the foreseen actions to take in the coming years is given. The shutdown will be a good opportunity to improve and evolve the ATLAS Distributed Computing system to prepare for the future challenges of the LHC operation.Peer Reviewe

Cortical thickness modeling and variability in Alzheimer's disease and frontotemporal dementia

Alzheimer's disease (AD) and frontotemporal dementia (FTD) show different patterns of cortical thickness (CTh) loss compared with healthy controls (HC), even though there is relevant heterogeneity between individuals suffering from each of these diseases. Thus, we developed CTh models to study individual variability in AD, FTD, and HC.We used the baseline CTh measures of 379 participants obtained from the structural MRI processed with FreeSurfer. A total of 169 AD patients (63 ± 9 years, 65 men), 88 FTD patients (64 ± 9 years, 43 men), and 122 HC (62 ± 10 years, 47 men) were studied. We fitted region-wise temporal models of CTh using Support Vector Regression. Then, we studied associations of individual deviations from the model with cerebrospinal fluid levels of neurofilament light chain (NfL) and 14-3-3 protein and Mini-Mental State Examination (MMSE). Furthermore, we used real longitudinal data from 144 participants to test model predictivity.We defined CTh spatiotemporal models for each group with a reliable fit. Individual deviation correlated with MMSE for AD and with NfL for FTD. AD patients with higher deviations from the trend presented higher MMSE values. In FTD, lower NfL levels were associated with higher deviations from the CTh prediction. For AD and HC, we could predict longitudinal visits with the presented model trained with baseline data. For FTD, the longitudinal visits had more variability.We highlight the value of CTh models for studying AD and FTD longitudinal changes and variability and their relationships with cognitive features and biomarkers.© 2023. The Author(s)

Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross-sectional and longitudinal magnetic resonance imaging data

Alzheimer's disease (AD) and frontotemporal dementia (FTD) are common causes of dementia with partly overlapping, symptoms and brain signatures. There is a need to establish an accurate diagnosis and to obtain markers for disease tracking. We combined unsupervised and supervised machine learning to discriminate between AD and FTD using brain magnetic resonance imaging (MRI). We included baseline 3T-T1 MRI data from 339 subjects: 99 healthy controls (CTR), 153 AD and 87 FTD patients; and 2-year follow-up data from 114 subjects. We obtained subcortical gray matter volumes and cortical thickness measures using FreeSurfer. We used dimensionality reduction to obtain a single feature that was later used in a support vector machine for classification. Discrimination patterns were obtained with the contribution of each region to the single feature. Our algorithm differentiated CTR versus AD and CTR versus FTD at the cross-sectional level with 83.3% and 82.1% of accuracy. These increased up to 90.0% and 88.0% with longitudinal data. When we studied the classification between AD versus FTD we obtained an accuracy of 63.3% at the cross-sectional level and 75.0% for longitudinal data. The AD versus FTD versus CTR classification has reached an accuracy of 60.7%, and 71.3% for cross-sectional and longitudinal data respectively. Disease discrimination brain maps are in concordance with previous results obtained with classical approaches. By using a single feature, we were capable to classify CTR, AD, and FTD with good accuracy, considering the inherent overlap between diseases. Importantly, the algorithm can be used with cross-sectional and longitudinal data.© 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC

Multifocal Transcranial Direct Current Stimulation in Primary Progressive Aphasia Does Not Provide a Clinical Benefit Over Speech Therapy

Primary progressive aphasia (PPA) is a group of neurodegenerative disorders including Alzheimer's disease and frontotemporal dementia characterized by language deterioration. Transcranial direct current stimulation (tDCS) is a non-invasive intervention for brain dysfunction.To evaluate the tolerability and efficacy of tDCS combined with speech therapy in the three variants of PPA. We evaluate changes in fMRI activity in a subset of patients.Double-blinded, randomized, cross-over, and sham-controlled tDCS study. 15 patients with PPA were included. Each patient underwent two interventions: a) speech therapy + active tDCS and b) speech therapy + sham tDCS stimulation. A multifocal strategy with anodes placed in the left frontal and parietal regions was used to stimulate the entire language network. Efficacy was evaluated by comparing the results of two independent sets of neuropsychological assessments administered at baseline, immediately after the intervention, and at 1 month and 3 months after the intervention. In a subsample, fMRI scanning was performed before and after each intervention.The interventions were well tolerated. Participants in both arms showed clinical improvement, but no differences were found between active and sham tDCS interventions in any of the evaluations. There were trends toward better outcomes in the active tDCS group for semantic association and reading skills. fMRI identified an activity increase in the right frontal medial cortex and the bilateral paracingulate gyrus after the active tDCS intervention.We did not find differences between active and sham tDCS stimulation in clinical scores of language function in PPA patients

Cognitive and motivational monitoring during enriched sport activities in a sample of children living in Europe. The ESA program

Enriched Sport Activities (ESA) Program is an Evidence-based Practice Exercise Program cofounded by the Erasmus + Programme of the European Union (Key action: Sport-579661-EPP-1-2016-2-IT-SPO-SCP). It aims to enhance social inclusion, equal opportunities and psycho-physical wellbeing in children with typical development and special needs. This aim will be pursued through two ways: (1) Children and preadolescents’ participation in Enriched Sport Activities (ESA) Program; (2) Parents’ involvement and education on cognitive, motivational and social benefits of Physical Activities (PA) in their children. Recent research showed that high-level cognitive processes, such as inhibition, shifting, working memory and planning, can be improved by aerobic exercise programs following both single bouts of exercise and longer trainings from moderate to vigorous intensity [1]. Nevertheless, in the developmental age, structured sport activities, such as martial arts, basketball, soccer, rowing and dancing, act by delivering both physical and psychological benefits. The former involve physical fitness such as cardiorespiratory fitness, muscular strength, muscular endurance, flexibility and motor skills such as coordination, whilst the latter concern enjoyment, self-confidence and self-esteem, a sense of belonging and social support [2–4]. The effectiveness of PMA (Programma Motorio Arricchito), a structured motor program on coordination and executive functioning in kindergarten children, has been demonstrated [5]

Early-onset Alzheimer's disease shows a distinct neuropsychological profile and more aggressive trajectories of cognitive decline than late-onset

Early- and late-onset Alzheimer's disease (EOAD and LOAD) share the same neuropathological traits but show distinct cognitive features. We aimed to explore baseline and longitudinal outcomes of global and domain-specific cognitive function in a well characterized cohort of patients with a biomarker-based diagnosis.In this retrospective cohort study, 195 participants were included and classified according to their age, clinical status, and CSF AD biomarker profile: 89 EOAD, 37 LOAD, 46 young healthy controls (age???65?years), and 23 old healthy controls (>65?years). All subjects underwent clinical and neuropsychological assessment, neuroimaging, APOE genotyping and lumbar puncture.We found distinct neuropsychological profiles between EOAD and LOAD at the time of diagnosis. Both groups showed similar performances on memory and language domains, but the EOAD patients displayed worsened deficits in visual perception, praxis, and executive tasks (p?<?0.05). Longitudinally, cognitive decline in EOAD was more pronounced than LOAD in the global outcomes at the expense of these non-amnestic domains. We found that years of education significantly influenced the decline in most of the neuropsychological tests. Besides, the APOE ?4 status showed a significant effect on the decline of memory-related tasks within the EOAD cohort (p?<?0.05).Age of onset is a main factor shaping the cognitive trajectories in AD patients, with younger age driving to a steeper decline of the non-memory domains. Years of education are related to a transversal decline in all cognitive domains and APOE ?4 status to a specific decline in memory performance in EOAD.© 2022 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association

Cortical microstructure in primary progressive aphasia: a multicenter study

Cortical mean diffusivity is a novel imaging metric sensitive to early changes in neurodegenerative syndromes. Higher cortical mean diffusivity values reflect microstructural disorganization and have been proposed as a sensitive biomarker that might antedate macroscopic cortical changes. We aimed to test the hypothesis that cortical mean diffusivity is more sensitive than cortical thickness to detect cortical changes in primary progressive aphasia (PPA).In this multicenter, case-control study, we recruited 120 patients with PPA (52 non-fluent, 31 semantic, and 32 logopenic variants; and 5 GRN-related PPA) as well as 89 controls from three centers. The 3-Tesla MRI protocol included structural and diffusion-weighted sequences. Disease severity was assessed with the Clinical Dementia Rating scale. Cortical thickness and cortical mean diffusivity were computed using a surface-based approach.The comparison between each PPA variant and controls revealed cortical mean diffusivity increases and cortical thinning in overlapping regions, reflecting the canonical loci of neurodegeneration of each variant. Importantly, cortical mean diffusivity increases also expanded to other PPA-related areas and correlated with disease severity in all PPA groups. Cortical mean diffusivity was also increased in patients with very mild PPA when only minimal cortical thinning was observed and showed a good correlation with measures of disease severity.Cortical mean diffusivity shows promise as a sensitive biomarker for the study of the neurodegeneration-related microstructural changes in PPA.© 2022. The Author(s)