Presynaptic paraneoplastic disorders of the neuromuscular junction: An update

The neuromuscular junction (NMJ) is the target of a variety of immune-mediated disorders, usually classified as presynaptic and postsynaptic, according to the site of the antigenic target and consequently of the neuromuscular transmission alteration. Although less common than the classical autoimmune postsynaptic myasthenia gravis, presynaptic disorders are important to recognize due to the frequent association with cancer. Lambert Eaton myasthenic syndrome is due to a presynaptic failure to release acetylcholine, caused by antibodies to the presynaptic voltage-gated calcium channels. Acquired neuromyotonia is a condition characterized by nerve hyperexcitability often due to the presence of antibodies against proteins associated with voltage-gated potassium channels. This review will focus on the recent developments in the autoimmune presynaptic disorders of the NMJ

A System for Optimizing Fertilizer Dosing in Innovative Smart Fertigation Pipelines: Modeling, Construction, Testing and Control

Smart fertigation is a topic of great interest in the effort to optimize different activities involved in local and extensive agriculture for assisting crops, optimizing production by using wireless technologies, data-processing electronic boards and sensors network. With the advent of Agriculture 4.0, similar to Industry 4.0, Information Communication Technology (ICT), associated with mechatronics, is giving an added value to this technique allowing optimization of water, fertilizers, control of water flow in pipes and period of irrigation. This paper intends to illustrate findings related to an innovative low cost system for assisting crops and achieving an accurate farming by investigating on the design, construction, testing and control of dosing system for liquid and granular fertilizers. Four different dosage systems have been designed, realized and tested with different granular and liquid fertilizers; the analysis of an extensive experimental campaign allows to define the characteristic and the mathematical expressions for each analyzed fertilizer and for each dosage system. The accurate modeling allows to control with extreme precision the realized dosing systems after estimating the quantity of fertilizer which the crop needs by means of the smart fertigation system. The obtained results permit the optimization of the fertilizer dosage in terms of quantity, which at the same time translates into lower production costs, greater environmental sustainability and optimization of production in terms of quantity and quality

Neural Surface Antibodies and Neurodegeneration: Clinical Commonalities and Pathophysiological Relationships

Autoimmune encephalitis and neurodegenerative disorders share several clinical features, including behavioural and psychiatric manifestations, cognitive impairment, sleep and movement disorders. Therefore, it is not surprising that autoimmune encephalitis is one of the main differential diagnoses of rapidly progressive dementia. However, more chronic presentations of autoimmune disorders have been reported and can lead to the misdiagnosis of a neurodegenerative disease. On the other hand, antibodies against neuronal proteins, such as those directed against NMDAR, can occur during established neurogenerative disorders, and their role in this context is still unclear. They might be simple bystanders or modify the disease course and phenotype. Indeed, autoimmune encephalitis can leave long-term cognitive sequelae and specific antibodies to neuronal surface antigens are associated with clinical and pathological neurodegenerative features. Here we review the link between these antibodies and neurodegeneration. In particular we discuss: (a) the possibility that autoimmune encephalitis presents as a neurodegenerative disease, identifying the red flags that can help in the differential diagnosis between antibody-mediated and neurodegenerative disorders; (b) the occurrence of antibodies against neuronal surface antigens in patients with neurodegenerative disorders and their possible role in the disease course; and (c) the long-term cognitive and neuroradiological changes associated with autoimmune encephalitis, as well as the biomarkers that can help to predict the cognitive outcome. Finally, we review the clinical and pathological features of IgLON5 antibodies-related encephalitis, a unique model of the relationship between antibodies and neurodegeneration

MEMS-based Micro-scale Wind Turbines as Energy Harvesters of the Convective Airflows in Microelectronic Circuits

As an alternative to conventional batteries and other energy scavenging techniques, this paper introduces the idea of using micro-turbines to extract energy from wind forces at the microscale level and to supply power to battery-less microsystems. Fundamental research efforts on the design, fabrication, and test of micro-turbines with blade lengths of just 160 μm are presented in this paper along with analytical models and preliminary experimental results. The proof-of-concept prototypes presented herein were fabricated using a standard polysilicon surface micro-machining silicon technology (PolyMUMPs) and could effectively transform the kinetic energy of the available wind into a torque that might drive an electric generator or directly power supply a micro-mechanical system. Since conventional batteries do not scale-down well to the microscale, wind micro-turbines have the potential for becoming a practical alternative power source for microsystems, as well as for extending the operating range of devices running on batteries

Solar-Powered Deep Learning-Based Recognition System of Daily Used Objects and Human Faces for Assistance of the Visually Impaired

This paper introduces a novel low-cost solar-powered wearable assistive technology (AT) device, whose aim is to provide continuous, real-time object recognition to ease the finding of the objects for visually impaired (VI) people in daily life. The system consists of three major components: a miniature low-cost camera, a system on module (SoM) computing unit, and an ultrasonic sensor. The first is worn on the user’s eyeglasses and acquires real-time video of the nearby space. The second is worn as a belt and runs deep learning-based methods and spatial algorithms which process the video coming from the camera performing objects’ detection and recognition. The third assists on positioning the objects found in the surrounding space. The developed device provides audible descriptive sentences as feedback to the user involving the objects recognized and their position referenced to the user gaze. After a proper power consumption analysis, a wearable solar harvesting system, integrated with the developed AT device, has been designed and tested to extend the energy autonomy in the dierent operating modes and scenarios. Experimental results obtained with the developed low-cost AT device have demonstrated an accurate and reliable real-time object identification with an 86% correct recognition rate and 215 ms average time interval (in case of high-speed SoM operating mode) for the image processing. The proposed system is capable of recognizing the 91 objects oered by the Microsoft Common Objects in Context (COCO) dataset plus several custom objects and human faces. In addition, a simple and scalable methodology for using image datasets and training of Convolutional Neural Networks (CNNs) is introduced to add objects to the system and increase its repertory. It is also demonstrated that comprehensive trainings involving 100 images per targeted object achieve 89% recognition rates, while fast trainings with only 12 images achieve acceptable recognition rates of 55%

Glycine Receptor Antibody-Associated Progressive Encephalomyelitis with Rigidity and Myoclonus (PERM) During SARS-CoV-2 Infection: a Video-Case Report

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New generation of optical robotic sensor applied to small notch detection

In this paper the experimental application of a new class of an optical pressure sensor based on polydimethylsiloxane (PDMS)-Au is shown. The sensor consists of a tapered bended optical fiber, where an optical signal goes across, embedded into a PDMS-gold nanocomposite material (GNM) and it is used for scanning surfaces while it is moved automatically by a controlled servomotor. The sensor data during the scanning may be used for detecting a small notch on a beam. The experimental results are very encouraging for foreseeing successful use of this new sensor in robotic applications

Head and neck cancer subtypes with biological and clinical relevance : meta-analysis of gene-expression data

Head and neck squamous cell carcinoma (HNSCC) is a disease with heterogeneous clinical behavior and response to therapies. Despite the introduction of multimodality treatment, 40-50% of patients with advanced disease recur. Therefore, there is an urgent need to improve the classification beyond the current parameters in clinical use to better stratify patients and the therapeutic approaches. Following a meta-analysis approach we built a large training set to whom we applied a Disease-Specific Genomic Analysis (DSGA) to identify the disease component embedded into the tumor data. Eleven independent microarray datasets were used as validation sets. Six different HNSCC subtypes that summarize the aberrant alterations occurring during tumor progression were identified. Based on their main biological characteristics and de-regulated signaling pathways, the subtypes were designed as immunoreactive, inflammatory, human papilloma virus (HPV)-like, classical, hypoxia associated, and mesenchymal. Our findings highlighted a more aggressive behavior for mesenchymal and hypoxia-associated subtypes. The Genomics Drug Sensitivity Project was used to identify potential associations with drug sensitivity and significant differences were observed among the six subtypes. To conclude, we report a robust molecularly defined subtype classification in HNSCC that can improve patient selection and pave the way to the development of appropriate therapeutic strategies

Integrative miRNA-Gene expression analysis enables refinement of associated biology and prediction of response to cetuximab in head and neck squamous cell cancer

This paper documents the process by which we, through gene and miRNA expression profiling of the same samples of head and neck squamous cell carcinomas (HNSCC) and an integrative miRNA-mRNA expression analysis, were able to identify candidate biomarkers of progression-free survival (PFS) in patients treated with cetuximab-based approaches. Through sparse partial least square-discriminant analysis (sPLS-DA) and supervised analysis, 36 miRNAs were identified in two components that clearly separated long-and short-PFS patients. Gene set enrichment analysis identified a significant correlation between the miRNA first-component and EGFR signaling, keratinocyte differentiation, and p53. Another significant correlation was identified between the second component and RAS, NOTCH, immune/inflammatory response, epithelial-mesenchymal transition (EMT), and angiogenesis pathways. Regularized canonical correlation analysis of sPLS-DA miRNA and gene data combined with the MAGIA2 web-tool highlighted 16 miRNAs and 84 genes that were interconnected in a total of 245 interactions. After feature selection by a smoothed t-statistic support vector machine, we identified three miRNAs and five genes in the miRNA-gene network whose expression result was the most relevant in predicting PFS (Area Under the Curve, AUC = 0.992). Overall, using a well-defined clinical setting and up-to-date bioinformatics tools, we are able to give the proof of principle that an integrative miRNA-mRNA expression could greatly contribute to the refinement of the biology behind a predictive model