AQP4-MOG Double-Positive Neuromyelitis Optica Spectrum Disorder: Case Report with Central and Peripheral Nervous System Involvement and Review of Literature

(1) The co-occurrence of AQP4 and myelin oligodendrocyte glycoprotein (MOG) antibodies in patients with demyelinating disorders is extremely rare. In addition, a concomitant involvement of the peripheral nervous system (PNS) has been described either in association with AQP4 antibodies-positive neuromyelitis optica spectrum disorder (NMOSD), or MOG-associated disease. We report on a case of NMOSD with co-occurrence of AQP4 and MOG antibodies and concomitant central and peripheral nervous system involvement. We also reviewed available cases of AQP4-MOG double-positive patients. (2) Brain and spine MRI, cerebrospinal fluid studies, and electrophysiological test were performed. Serum AQP4 and MOG positivity was assessed with live cell-based assay. (3) A 62-year-old woman presented with recurrent optic neuritis, myelitis, and radiculitis, tested positive for AQP4 and MOG antibodies, and was treated successfully with rituximab. (4) Although few cases of AQP4-MOG double-positive patients were already described mostly affecting females with a concomitant spinal cord and optical nerve involvement, we describe the first case of double-positive NMOSD with the peculiar involvement of both central and peripheral nervous system.     

Effectiveness of Semi-Supervised Active Learning in Automated Wound Image Segmentation

Appropriate wound management shortens the healing times and reduces the management costs, benefiting the patient in physical terms and potentially reducing the healthcare system’s economic burden. Among the instrumental measurement methods, the image analysis of a wound area is becoming one of the cornerstones of chronic ulcer management. Our study aim is to develop a solid AI method based on a convolutional neural network to segment the wounds efficiently to make the work of the physician more efficient, and subsequently, to lay the foundations for the further development of more in-depth analyses of ulcer characteristics. In this work, we introduce a fully automated model for identifying and segmenting wound areas which can completely automatize the clinical wound severity assessment starting from images acquired from smartphones. This method is based on an active semi-supervised learning training of a convolutional neural network model. In our work, we tested the robustness of our method against a wide range of natural images acquired in different light conditions and image expositions. We collected the images using an ad hoc developed app and saved them in a database which we then used for AI training. We then tested different CNN architectures to develop a balanced model, which we finally validated with a public dataset. We used a dataset of images acquired during clinical practice and built an annotated wound image dataset consisting of 1564 ulcer images from 474 patients. Only a small part of this large amount of data was manually annotated by experts (ground truth). A multi-step, active, semi-supervised training procedure was applied to improve the segmentation performances of the model. The developed training strategy mimics a continuous learning approach and provides a viable alternative for further medical applications. We tested the efficiency of our model against other public datasets, proving its robustness. The efficiency of the transfer learning showed that after less than 50 epochs, the model achieved a stable DSC that was greater than 0.95. The proposed active semi-supervised learning strategy could allow us to obtain an efficient segmentation method, thereby facilitating the work of the clinician by reducing their working times to achieve the measurements. Finally, the robustness of our pipeline confirms its possible usage in clinical practice as a reliable decision support system for clinicians.     

Quantitative Analysis of Plant Cytosolic Calcium Signals in Response to Water Activated by Low-Power Non-Thermal Plasma

Non-thermal plasma technology is increasingly being applied in the plant biology field. Despite the variety of beneficial effects of plasma-activated water (PAW) on plants, information about the mechanisms of PAW sensing by plants is still limited. In this study, in order to link PAW perception to the positive downstream responses of plants, transgenic Arabidopsis thaliana seedlings expressing the Ca²⁺-sensitive photoprotein aequorin in the cytosol were challenged with water activated by low-power non-thermal plasma generated by a dielectric barrier discharge (DBD) source. PAW sensing by plants resulted in the occurrence of cytosolic Ca²⁺ signals, whose kinetic parameters were found to strictly depend on the operational conditions of the plasma device and thus on the corresponding mixture of chemical species contained in the PAW. In particular, we highlighted the effect on the intracellular Ca²⁺ signals of low doses of DBD-PAW chemicals and also presented the effects of consecutive plant treatments. The results were discussed in terms of the possibility of using PAW-triggered Ca²⁺ signatures as benchmarks to accurately modulate the chemical composition of PAW in order to induce environmental stress resilience in plants, thus paving the way for further applications in agriculture.     

The concept of the gas turbine‐based hybrid vehicle: system,design and configuration issues

The object of this study is a theoretical and experimental analysis of a new hybrid propulsion system for a passenger sedan in which the thermal engine is a small gas turbine set. Some preliminary results on the possibility of replacing the conventional ICE of a hybrid ‘series’ configuration by a turbogas were presented and discussed in previous papers by the same authors: several possible designs were examined under both a thermodynamic and an operative point of view. This paper presents a summary of the project and constitutes an attempt to put things in a proper engineering perspective: the technical feasibility of the project is assessed via a calculation of the required mission loads, a preliminary design of the most important elements of the propulsive system, the choice of the mission control strategy and the implementation of a numerical system simulator. The experiments that provided a verification for the assumed component efficiencies were carried out, in cooperation with the Research Centre of ENEA‐Casaccia, on an ELLIOTT TA‐45 group. Our results, though only preliminary, allow for a direct comparison between a GT‐hybrid vehicle and a modern diesel car, and indicate that the GT‐hybrid may be actually a competitor for the FC‐powered vehicle concept. Our ‘optimal’ configuration is a combination of a 100 kg battery pack and two turbogas set of 5 and 16 kW, respectively. Copyright © 2005 John Wiley & Sons, Ltd.     

Examining the alliance using the Psychotherapy Process Q-Set.

The alliance between therapist and patient was investigated using the Psychotherapy Process Q-Set (PQS), an instrument that quantitatively describes therapy sessions in a manner that captures the complexity of the therapy process. More specifically, the PQS was used to examine the treatment processes being assessed by observer ratings on the California Psychotherapy Alliance Scales (CALPAS). Using data from 30 brief psychodynamic psychotherapy treatments (patients aged 20–81 yrs), three PQS factors were found to be associated with alliance: Patient–Therapist Interaction, Patient Capacity/Commitment, and Therapist Countertransference. In multiple regression analyses with scales constructed from these three factors, Patient–Therapist Interaction was found to predict alliance ratings, while the other two aspects of the treatment process did not account for any significant additional variance in the alliance ratings. Results suggest that Patient–Therapist Interaction plays a defining role in the alliance construct, as assessed by the observer version of the CALPAS. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Solving advanced network reliability problems by means of cellular automata and Monte Carlo sampling

This paper extends the cellular automata-based, Monte Carlo sampling methodology for computing network reliability, previously proposed by the first author. The extension regards the development of cellular automata for the solution of the all-terminal and k-terminal connection problems and the maximum unsplittable flow problem, which can be considered as a particular, simplified case of the maximum flow distribution problem. The algorithms developed are presented in detail and verified on literature cases.