Unraveling and overcoming hurdles in direct neuronal reprogramming

Discovering new approaches to replace lost neurons following brain damage, as for traumatic injury, is one of the major goals in the field of regenerative medicine. Direct neuronal conversion of glial cells into neurons is emerging as a powerful strategy to achieve neuronal replacement. Despite large progress in the field, major limitations still exist before bringing this approach toward clinical translation. Major hurdles encompass epigenetic, metabolic and environmental barriers, which impede the newly generated neurons to properly integrate into the injured brain parenchyma, to substitute the lost neuronal networks and to fully replace the endogenous neuronal counterpart. The pathological process includes a cascade of fast-occurring events, such as metabolic impairment, reactive oxygen species and inflammatory molecules production, cell death, reactive gliosis and recruitment of inflammatory cells, which can have devastating consequences for the survival of the endogenous and reprogrammed neurons. Thus, a deeper understanding of the interplay between these mechanisms and how key players in the injury environment regulate processes of cell fate decision is needed. An important aspect fundamental to functional glia-to-neuron conversion in the injured brain is the viral vector used, especially in regard to the inflammatory reaction elicited in the tissue. Indeed we could observe that different viral vectors, routinely used in neuronal reprogramming studies, could induce diverse responses in the environment, independently from the transgene expressed. In particular, we noticed that retrovirus and lentivirus-mediated reprogramming elicited a strong inflammatory reaction, characterized by microglia and astrocyte reactivity, and massive immune cells infiltration, still persisting at the time when neurons start appearing. Conversely, adeno-associated virus (AAV)-mediated neuronal conversion had much a milder impact on the activation of the glial cells, with minimal immune cells recruitment. As using AAV greatly improved the rate of neuronal conversion, specification, integration and survival, compared to retroviral approaches, the environment plays a critical role in this successful reprogramming. A secondary mechanism also associated with inflammation is reactive oxygen species (ROS) production. Indeed, astrocytes transitioning into neurons face a burst of ROS, which lead to drastic cell death by ferroptosis if not properly counteracted. Consequently, buffering ROS with scavengers and pro-survival genes could greatly ameliorate the conversion efficiency in vitro as well as in vivo. As ROS production is mostly related to functional metabolic changes, we investigated this so far neglected aspect of direct neuronal reprogramming. I first demonstrated that a metabolic switch from glycolysis to oxidative phosphorylation is an essential requirement for a successful conversion to occur, as inhibiting the function of the electron transport chain did not improve the process despite the decrease in ROS, but actually entirely blocked the conversion of glia into neurons. As we were further interested in understanding the roles played by the metabolism in the reprogramming paradigm, we decided to characterize the mitochondria proteome of astrocytes and neurons, to identify differences in the mito-proteome between these cell types. We identified proteins enriched to each cell type, highlighting metabolic pathways relevant for their specific physiological functions. Interestingly, some of the specific mitochondrial proteins analyzed were correctly up-regulated or down-regulated during the transition from astrocytes to neurons, but at a relatively late stage in the reprogramming process. This finding further confirmed that a remodeling in mitochondrial proteins, and consequently metabolic pathways, occurs during the reprogramming process, even if partial and temporally delayed compared to the burst of ROS which converting neurons face. Early dCas9-mediated overexpression of anti-oxidant proteins in converting astrocytes, specific to the neuronal mitochondria proteome, could greatly improve the speed and efficiency of astrocyte-to- neuron conversion. Thus, understanding how to properly modify converting glial cells into neurons, not only from an epigenetic, genetic and morphological point of view, is necessary. In fact evaluating the impact on direct neuronal reprogramming of extrinsic factors, such as viral vectors and the environmental inflammatory reaction, as well as intrinsic constraints, such as mitochondria remodeling, ROS production and metabolic switch, could greatly improve the quality of reprogrammed neurons. The aim of my thesis is thus to unravel mechanisms involving inflammation, mitochondria and metabolic remodeling, which could increase our understanding of the glia-to-neuron conversion process, overall improving direct neuronal reprogramming

The Financial Data Services Domain: From Taxonomies to Ontologies

There are many different types of instruments and hundreds of different markets for investment, leading to an extremely large and hard-to-define universe of financial data. The related commercial offer is extremely heterogeneous and complex. In this scenario, it is difficult to source the most appropriate financial services providers. In the past, eProcurement mainly focused on using ERP management tools to record and examine previous buying decisions and expenditure data. In recent years, machine learning and artificial intelligence have been applied to procurement workflows, introducing computation of external or thirdparty unstructured data to achieve a higher level of market knowledge and decision automation. To exploit the possibilities provided by these new technologies to the full extent possible, theoretical models for understanding large amounts of unstructured data are essential. In this research-in-progress paper, we propose a taxonomy of financial data services and depict the related prototype ontological model, providing a possible conceptualization and specification of the domain of interest potentially useful for the development of applications based on semantic technologies

Validation Procedure for Predictive Functions of Driver Behaviour on Two-Lane Rural Roads

The study presented here aims to validate some operating speed prediction models calibrated on two-lane rural roads by using speed data collected in Northern and Southern Italy. Operating speed is defined as the speed at which drivers of passenger cars travel on a dry road in free flow conditions during daylight hours and it is calculated using a specific percentile of speed distribution, typically the 85th. Speed measurements were carried out by using laser detectors in connection with previous environmental and traffic conditions. The study is addressed to emphasize the reliability and easy application of one predictive speed model working both on tangent segments and on circular curves. The calibration phase involved roads in the Northern Italy, while the validation phase involved roads in the Southern Italy. Three models were validated applying them on eight two-lane rural roads falling within the road network of the Province of Salerno with features that reflect those adopted in the calibration phase; the selected models to be validated present the simplest analytical structure for type and number of explanatory variables and for the performance diagram shape of the operating speed values. The validation procedure was to estimate some synthetic statistical parameters as mean absolute deviation, mean squared error and coefficient of variation. The results allow in a simple way to trace continuous operating speed profiles on two-lane rural roads and to carry out safety analyses on the horizontal alignment

Pain perception and migraine

Background: It is well-known that both inter-and intra-individual differences exist in the perception of pain; this is especially true in migraine, an elusive pain disorder of the head. Although electrophysiology and neuroimaging techniques have greatly contributed to a better understanding of the mechanisms involved in migraine during recent decades, the exact characteristics of pain threshold and pain intensity perception remain to be determined, and continue to be a matter of debate.Objective: The aim of this review is to provide a comprehensive overview of clinical, electrophysiological, and functional neuroimaging studies investigating changes during various phases of the so-called "migraine cycle" and in different migraine phenotypes, using pain threshold and pain intensity perception assessments.Methods: A systematic search for qualitative studies was conducted using search terms "migraine," "pain," "headache," "temporal summation," "quantitative sensory testing," and "threshold," alone and in combination (subject headings and keywords). The literature search was updated using the additional keywords "pain intensity," and "neuroimaging"to identify full-text papers written in English and published in peer-reviewed journals, using PubMed and Google Scholar databases. In addition, we manually searched the reference lists of all research articles and review articles.Conclusion: Consistent data indicate that pain threshold is lower during the ictal phase than during the interictal phase of migraine or healthy controls in response to pressure, cold and heat stimuli. There is evidence for preictal sub-allodynia, whereas interictal results are conflicting due to either reduced or no observed difference in pain threshold. On the other hand, despite methodological limitations, converging observations support the concept that migraine attacks may be characterized by an increased pain intensity perception, which normalizes between episodes. Nevertheless, future studies are required to longitudinally evaluate a large group of patients before and after pharmacological and non-pharmacological interventions to investigate phases of the migraine cycle, clinical parameters of disease severity and chronic medication usage

Evaluation of wound healing and postoperative pain after oral mucosa laser biopsy with the aid of compound with chlorhexidine and sodium hyaluronate: a randomized double blind clinical trial

OBJECTIVES: The aim of this study is to evaluate secondary intention healing process and postoperative pain of oral soft tissues after laser surgery with the use of a compound containing chlorhexidine and sodium hyaluronate. MATERIALS AND METHODS: This double-blind, randomized clinical study included 56 patients affected by benign oral lesions and subjected to excisional biopsy with diode laser and randomly divided into three groups. Study group (SG) received 0.2% chlorhexidine digluconate and 0.2% sodium hyaluronate treatment; control group (CG) received 0.2% chlorhexidine digluconate; and placebo group (PG) followed the same protocol, taking a neutral solution having the same organoleptic characteristics. Wound healing was evaluated using percentage healing index (PHI). Numeric rating scale (NRS) was used to evaluate postoperative pain. RESULTS: PHI (T1 = 7 days) was 67.25% for SG, 58.67% for CG, and 54.55% for PG. PHI (T2 = 14 days) was 94.35% for SG, 77.79% for CG, and 78.98% for PG. A statistically significant difference was between the groups for PHI at T2 p = 0.001. No difference was detectable for pain index. CONCLUSIONS: A solution containing sodium hyaluronate and chlorhexidine is a good support to increase wound healing by secondary intention after laser biopsy, but no differences were in postoperative perception of pain. CLINICAL RELEVANCE: The use of the tested solution can be recommended after laser oral biopsies, to achieve a healing without suture. About the postoperative pain, the compound has not showed the same results and did not have measurable effects

Numerical solution and bifurcation analysis of nonlinear partial differential equations with extreme learning machines

We address a new numerical method based on a class of machine learning methods, the so-called Extreme Learning Machines (ELM) with both sigmoidal and radial-basis functions, for the computation of steady-state solutions and the construction of (one-dimensional) bifurcation diagrams of nonlinear partial differential equations (PDEs). For our illustrations, we considered two benchmark problems, namely (a) the one-dimensional viscous Burgers with both homogeneous (Dirichlet) and non-homogeneous boundary conditions, and, (b) the one- and two-dimensional Liouville–Bratu–Gelfand PDEs with homogeneous Dirichlet boundary conditions. For the one-dimensional Burgers and Bratu PDEs, exact analytical solutions are available and used for comparison purposes against the numerical derived solutions. Furthermore, the numerical efficiency (in terms of numerical accuracy, size of the grid and execution times) of the proposed numerical machine-learning method is compared against central finite differences (FD) and Galerkin weighted-residuals finite-element (FEM) methods. We show that the proposed numerical machine learning method outperforms in terms of numerical accuracy both FD and FEM methods for medium to large sized grids, while provides equivalent results with the FEM for low to medium sized grids; both methods (ELM and FEM) outperform the FD scheme. Furthermore, the computational times required with the proposed machine learning scheme were comparable and in particular slightly smaller than the ones required with FE

Unraveling and overcoming hurdles in direct neuronal reprogramming

Discovering new approaches to replace lost neurons following brain damage, as for traumatic injury, is one of the major goals in the field of regenerative medicine. Direct neuronal conversion of glial cells into neurons is emerging as a powerful strategy to achieve neuronal replacement. Despite large progress in the field, major limitations still exist before bringing this approach toward clinical translation. Major hurdles encompass epigenetic, metabolic and environmental barriers, which impede the newly generated neurons to properly integrate into the injured brain parenchyma, to substitute the lost neuronal networks and to fully replace the endogenous neuronal counterpart. The pathological process includes a cascade of fast-occurring events, such as metabolic impairment, reactive oxygen species and inflammatory molecules production, cell death, reactive gliosis and recruitment of inflammatory cells, which can have devastating consequences for the survival of the endogenous and reprogrammed neurons. Thus, a deeper understanding of the interplay between these mechanisms and how key players in the injury environment regulate processes of cell fate decision is needed. An important aspect fundamental to functional glia-to-neuron conversion in the injured brain is the viral vector used, especially in regard to the inflammatory reaction elicited in the tissue. Indeed we could observe that different viral vectors, routinely used in neuronal reprogramming studies, could induce diverse responses in the environment, independently from the transgene expressed. In particular, we noticed that retrovirus and lentivirus-mediated reprogramming elicited a strong inflammatory reaction, characterized by microglia and astrocyte reactivity, and massive immune cells infiltration, still persisting at the time when neurons start appearing. Conversely, adeno-associated virus (AAV)-mediated neuronal conversion had much a milder impact on the activation of the glial cells, with minimal immune cells recruitment. As using AAV greatly improved the rate of neuronal conversion, specification, integration and survival, compared to retroviral approaches, the environment plays a critical role in this successful reprogramming. A secondary mechanism also associated with inflammation is reactive oxygen species (ROS) production. Indeed, astrocytes transitioning into neurons face a burst of ROS, which lead to drastic cell death by ferroptosis if not properly counteracted. Consequently, buffering ROS with scavengers and pro-survival genes could greatly ameliorate the conversion efficiency in vitro as well as in vivo. As ROS production is mostly related to functional metabolic changes, we investigated this so far neglected aspect of direct neuronal reprogramming. I first demonstrated that a metabolic switch from glycolysis to oxidative phosphorylation is an essential requirement for a successful conversion to occur, as inhibiting the function of the electron transport chain did not improve the process despite the decrease in ROS, but actually entirely blocked the conversion of glia into neurons. As we were further interested in understanding the roles played by the metabolism in the reprogramming paradigm, we decided to characterize the mitochondria proteome of astrocytes and neurons, to identify differences in the mito-proteome between these cell types. We identified proteins enriched to each cell type, highlighting metabolic pathways relevant for their specific physiological functions. Interestingly, some of the specific mitochondrial proteins analyzed were correctly up-regulated or down-regulated during the transition from astrocytes to neurons, but at a relatively late stage in the reprogramming process. This finding further confirmed that a remodeling in mitochondrial proteins, and consequently metabolic pathways, occurs during the reprogramming process, even if partial and temporally delayed compared to the burst of ROS which converting neurons face. Early dCas9-mediated overexpression of anti-oxidant proteins in converting astrocytes, specific to the neuronal mitochondria proteome, could greatly improve the speed and efficiency of astrocyte-to- neuron conversion. Thus, understanding how to properly modify converting glial cells into neurons, not only from an epigenetic, genetic and morphological point of view, is necessary. In fact evaluating the impact on direct neuronal reprogramming of extrinsic factors, such as viral vectors and the environmental inflammatory reaction, as well as intrinsic constraints, such as mitochondria remodeling, ROS production and metabolic switch, could greatly improve the quality of reprogrammed neurons. The aim of my thesis is thus to unravel mechanisms involving inflammation, mitochondria and metabolic remodeling, which could increase our understanding of the glia-to-neuron conversion process, overall improving direct neuronal reprogramming

Accounting for goodwill: the pioneering thought of Gino Zappa (1910)

The accounting treatment of goodwill is still debated in the academic and professional accounting environment in order to find the best approach applicable in a general ac-cepted accounting standard for this particular item. From an examination of the ac-counting research on the goodwill it is possible to observe a strong historical analysis on the topic with different lines of research. Following the framework of Ding et al (2008) that analyze the different phases of the accounting treatment for goodwill in a period running from 1880 to the new century, this paper aims to highlight how the is-sue of the accounting treatment of goodwill had already been widely discussed in the first scientific work by Gino Zappa (1910), who is considered the father of the modern Italian “Economia Aziendale”. Gino Zappa in his volume “Assessment of Financial Statements” presented concepts of the accounting treatment for goodwill basically in accordance with modern IAS 38, thus demonstrating the high level of progress of the first XX century Italian theoretical studies. This analysis shows the pioneeristic thoughts of the Author that influenced the Italian accounting up to today and how the social and economic environment have an impact on the lack of their application

The Ebola virus disease outbreak in Tonkolili district, Sierra Leone: a retrospective analysis of the Viral Haemorrhagic Fever surveillance system, July 2014–June 2015

In Sierra Leone, the Ebola virus disease (EVD) outbreak occurred with substantial differences between districts with someone even not affected. To monitor the epidemic, a community event-based surveillance system was set up, collecting data into the Viral Haemorrhagic Fever (VHF) database. We analysed the VHF database of Tonkolili district to describe the epi- demiology of the EVD outbreak during July 2014–June 2015 (data availability). Multivariable analysis was used to identify risk factors for EVD, fatal EVD and barriers to healthcare access, by comparing EVD-positive vs. EVD-negative cases. Key-performance indicators for EVD response were also measured. Overall, 454 EVD-positive cases were reported. At multivariable analysis, the odds of EVD was higher among those reporting contacts with an EVD-positive/ suspected case (odds ratio (OR) 2.47; 95% confidence interval (CI) 2.44–2.50; P < 0.01) and those attending funeral (OR 1.02; 95% CI 1.01–1.04; P < 0.01). EVD cases from Kunike chief- dom had a lower odds of death (OR 0.22; 95% CI 0.08–0.44; P < 0.01) and were also more likely to be hospitalised (OR 2.34; 95% CI 1.23–4.57; P < 0.05). Only 25.1% of alerts were gen- erated within 1 day from symptom onset. EVD preparedness and response plans for Tonkolili should include social-mobilisation activities targeting Ebola/knowledge-attitudes-practice dur- ing funeral attendance, to avoid contact with suspected cases and to increase awareness on EVD symptoms, in order to reduce delays between symptom onset to alert generation and consequently improve the outbreak-response promptness