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Although the over-expression of angiogenic factors is reported in diffuse large B-cell lymphoma (DLBCL), the poor response to anti-VEGF drugs observed in clinical trials suggests that angiogenesis in these tumours might be driven by VEGF-independent pathways. We show that sphingosine kinase-1 (SPHK1), which generates the potent bioactive sphingolipid sphingosine-1-phosphate (S1P), is over-expressed in DLBCL. A meta-analysis of over 2000 cases revealed that genes correlated with SPHK1 mRNA expression in DLBCL were significantly enriched for tumour angiogenesis meta-signature genes; an effect evident in both major cell of origin (COO) and stromal subtypes. Moreover, we found that S1P induces angiogenic signalling and a gene expression programme that is present within the tumour vasculature of SPHK1-expressing DLBCL. Importantly, S1PR1 functional antagonists, including Siponimod, and the S1P neutralising antibody, Sphingomab, inhibited S1P signalling in DLBCL cells in vitro. Furthermore, Siponimod, also reduced angiogenesis and tumour growth in an S1P-producing mouse model of angiogenic DLBCL. Our data define a potential role for S1P signalling in driving an angiogenic gene expression programme in the tumour vasculature of DLBCL and suggest novel opportunities to target S1P-mediated angiogenesis in patients with DLBCL.

Le recenti innovazioni nel campo dell’elettronica e della tecnologia dell’informazione hanno reso disponibili sensori avanzati e a basso costo finalizzati al monitoraggio del territorio. In particolare, le reti di sensori wireless (Wireless Sensor Network, WSN) ed i sensori per il telerilevamento Lidar e iperspettrale (Airborne Terrain Mapping, ATM) stanno attirando l’interesse degli enti territoriali per la loro vocazione a monitorare in modo distribuito ambienti naturali ostili quali siti contaminati, zone sismicamente attive e aree in frana (Polastre, 2003; Martinez et al., 2005; Werner-Allen et al., 2006). Nell’ambito del progetto WISELAND (Ministero dell’Università e della Ricerca Scientifica, finanziamento PRIN2007; http://prin07.geomin.unibo.it/prin07/index.html) sono state combinate le tecnologie WSN e ATM per il monitoraggio di una zona franosa ad elevata pericolosità (frana di Silla-Montecchi, Gaggio Montano, BO). Il progetto prevede di restituire i dati della rete WSN (spostamento del corpo di frana, condizioni idrauliche del versante) su una piattaforma ArcSDE/ArcIMS utilizzando come base un DEM ad alta risoluzione ottenuto dal rilievo Lidar. L’integrazione tra sensori di nuova generazione e tecnologie GIS ha lo scopo di promuovere la diffusione dei dati di monitoraggio e di migliorarne la condivisione con i soggetti istituzionali.

Pulse waves (PWs) are mechanical waves that propagate from the ventricles through the whole vascular system as brisk enlargements of the blood vessels’ lumens, caused by sudden increases in local blood pressure. Photoplethysmography (PPG) is one of the most widespread techniques employed for PW sensing due to its ability to measure blood oxygen saturation. Other sensors and techniques have been proposed to record PWs, and include applanation tonometers, piezoelectric sensors, force sensors of different kinds, and accelerometers. The performances of these sensors have been analyzed individually, and their results have been found not to be in good agreement (e.g., in terms of PW morphology and the physiological parameters extracted). Such a comparison has led to a deeper comprehension of their strengths and weaknesses, and ultimately, to the consideration that a multimodal approach accomplished via sensor fusion would lead to a more robust, reliable, and potentially more informative methodology for PW monitoring. However, apart from various multichannel and multi-site systems proposed in the literature, no true multimodal sensors for PW recording have been proposed yet that acquire PW signals simultaneously from the same measurement site. In this study, a true multimodal PW sensor is presented, which was obtained by integrating a piezoelectric forcecardiography (FCG) sensor and a PPG sensor, thus enabling simultaneous mechanical–optical measurements of PWs from the same site on the body. The novel sensor performance was assessed by measuring the finger PWs of five healthy subjects at rest. The preliminary results of this study showed, for the first time, that a delay exists between the PWs recorded simultaneously by the PPG and FCG sensors. Despite such a delay, the pulse waveforms acquired by the PPG and FCG sensors, along with their first and second derivatives, had very high normalized cross-correlation indices in excess of 0.98. Six well-established morphological parameters of the PWs were compared via linear regression, correlation, and Bland–Altman analyses, which showed that some of these parameters were not in good agreement for all subjects. The preliminary results of this proof-of-concept study must be confirmed in a much larger cohort of subjects. Further investigation is also necessary to shed light on the physical origin of the observed delay between optical and mechanical PW signals. This research paves the way for the development of true multimodal, wearable, integrated sensors and for potential sensor fusion approaches to improve the performance of PW monitoring at various body sites.

There is a growing demand of comfortable and unobtrusive wearable systems for monitoring a variety of physiological parameters and in particular the respiratory frequency ( ${f}_{R}$ ). The most popular techniques for ${f}_{R}$ monitoring cannot be used in several clinical applications and in unstructured environment. These issues have fostered a dramatic growth of interest for wearable systems devoted to monitor ${f}_{R}$ . In this arena, fiber Bragg grating (FBG) sensors have gained due to a variety of benefits. In this work, we designed and fabricated an FBG-based wearable device for ${f}_{R}$ monitoring from the nasal airflow. The proposed design does not require a mask to improve the comfortability and acceptability of the system. The sensing element was functionalized by a hygroscopic coating material to make the FBG sensitive to relative humidity changes. This feature allows calculating ${f}_{R}$ starting from the discrimination between the inspiration and expiration phases. A pilot study on 6 volunteers was designed to assess the system during three different breathing stages (i.e., slow, normal and fast breathing). Results showed high performance of the proposed wearable device in detecting mean and breath-by-breath ${f}_{R}$ values (i.e., mean percentage errors ≤ 2.29 % and bias ≤ 0.31 breaths per minute) during slow breathing, normal breathing, and fast breathing.

Cardio Vascular Diseases (CVDs) represent one of the main burden that affected world population in the last and in the current decades. The early detection by means of wide screening population-wide may represent a good path to avoid the worsening of pre-existent situation. In this arena, the use of wearable devices in combination with deep learning to deliver edge computing system seems to be the most viable pathway to follow in order to fight the CVDs burden. Despite the fact that many studies have concentrated on edge computing techniques for CVDs, there is a limited literature on Atrial Fibrillation (AF) detection directly on-device. Due to limited availability of research on this topic, the feasibility assessment of an on-device edge computing wearable system is described in this work. Starting with an examination of the features to be considered, the study progresses through the building of a Neural Network (NN), the training of the model, and the on-cloud testing process to completion. The NN is composed of 4 hidden layer made up of respectively 5, 30, 20 and 10 node. The learning rate is 0.005 and the number of training cycle is 30. The training set consists of 3362 windows, and the testing set consists of 796 windows. The findings of the test are encouraging, with an output F1-score of 0.94 for AF recognition as a result of the test. The model is then deployed on-device and evaluated offline, without the need for any additional devices or an internet connection, in order to run the inference process. Finally, the system that will be used for future human trials is presented, together with a description of the factors that led to the selection of this particular system and the major characteristic of the sensors.

Musculoskeletal disorders are the most common form of occupational ill-health. Neck pain is one of the most prevalent musculoskeletal disorders experienced by computer workers. Wrong postural habits and non-compliance of the workstation to ergonomics guidelines are the leading causes of neck pain. These factors may also alter respiratory functions. Health and safety interventions can reduce neck pain and, more generally, the symptoms of musculoskeletal disorders and reduce the consequent economic burden. In this work, a multi-parametric wearable system based on two fiber Bragg grating sensors is proposed for monitoring neck movements and breathing activity of computer workers. The sensing elements were positioned on the neck, in the frontal and sagittal planes, to monitor: (i) flexion-extension and axial rotation repetitions, and (ii) respiratory frequency. In this pilot study, five volunteers were enrolled and performed five repetitions of both flexion-extension and axial rotation, and ten breaths of both quite breathing and tachypnea. Results showed the good performances of the proposed system in monitoring the aforementioned parameters when compared to optical reference systems. The wearable system is able to well-match the trend in time of the neck movements (both flexion-extension and axial rotation) and to estimate mean and breath-by-breath respiratory frequency values with percentage errors &le, 6.09% and &le, 1.90%, during quiet breathing and tachypnea, respectively.

Grasping is one of the first dominant motor behaviors that enable interaction of a newborn infant with its surroundings. Although atypical grasping patterns are considered predictive of neuromotor disorders and injuries, their clinical assessment suffers from examiner subjectivity, and the neuropathophysiology is poorly understood. Therefore, the combination of technology with functional magnetic resonance imaging (fMRI) may help to precisely map the brain activity associated with grasping and thus provide important insights into how functional outcomes can be improved following cerebral injury. This work introduces an MR-compatible device (i.e., smart graspable device (SGD)) for detecting grasping actions in newborn infants. Electromagnetic interference immunity (EMI) is achieved using a fiber Bragg grating sensor. Its biocompatibility and absence of electrical signals propagating through the fiber make the safety profile of the SGD particularly favorable for use with fragile infants. Firstly, the SGD design, fabrication, and metrological characterization are described, followed by preliminary assessments on a preterm newborn infant and an adult during an fMRI experiment. The results demonstrate that the combination of the SGD and fMRI can safely and precisely identify the brain activity associated with grasping behavior, which may enable early diagnosis of motor impairment and help guide tailored rehabilitation programs.