Hand movement classification via surface electromyographic (sEMG) signal is a well-established approach for advanced Human-Computer Interaction. However, sEMG movement recognition has to deal with the long-term reliability of sEMG-based control, limited by the variability affecting the sEMG signal. Embedded solutions are affected by a recognition accuracy drop over time that makes them unsuitable for reliable gesture controller design. In this paper, we present a complete wearable-class embedded system for robust sEMGbased gesture recognition, based on Temporal Convolutional Networks (TCNs). Firstly, we developed a novel TCN topology (TEMPONet), and we tested our solution on a benchmark dataset (Ninapro), achieving 49.6% average accuracy, 7.8%, better than current State-Of-the-Art (SoA). Moreover, we designed an energy-efficient embedded platform based on GAP8, a novel 8-core IoT processor. Using our embedded platform, we collected a second 20-sessions dataset to validate the system on a setup which is representative of the final deployment. We obtain 93.7% average accuracy with the TCN, comparable with a SoA SVM approach (91.1%). Finally, we profiled the performance of the network implemented on GAP8 by using an 8-bit quantization strategy to fit the memory constraint of the processor. We reach a 4× lower memory footprint (460 kB) with a performance degradation of only 3% accuracy. We detailed the execution on the GAP8 platform, showing that the quantized network executes a single classification in 12.84 ms with a power envelope of 0.9 mJ, making it suitable for a longlifetime wearable deployment.

Objective: To develop a prediction model for clinical outcomes after unilateral adrenalectomy for unilateral primary aldosteronism. Summary Background Data: Unilateral primary aldosteronism is the most common surgically curable form of endocrine hypertension. Surgical resection of the dominant overactive adrenal in unilateral primary aldosteronism results in complete clinical success with resolution of hypertension without antihypertensive medication in less than half of patients with a wide between-center variability. Methods: A linear discriminant analysis (LDA) model was built using data of 380 patients treated by adrenalectomy for unilateral primary aldosteronism to classify post-surgical clinical outcomes. The total cohort was then randomly divided into training (280 patients) and test (100 patients) datasets to create and validate a score system to predict clinical outcomes. An online tool (PASO [Primary Aldosteronism Surgical Outcome] predictor) was developed to facilitate the use of the predictive score. Results: Six presurgical factors associated with complete clinical success (known duration of hypertension, sex, antihypertensive medication dosage, body mass index, target organ damage and size of largest nodule at imaging) were selected based on classification performance in the LDA model. A 25-point predictive score was built with an optimal cutoff of greater than 16 points (accuracy of prediction = 79.2%; specificity = 84.4%; sensitivity = 71.3%) with an area under the curve of 0.839. Conclusions: The predictive score and the PASO predictor can be used in a clinical setting to differentiate patients who are likely to be clinically cured after surgery from those who will need continuous surveillance after surgery due to persistent hypertension.

This paper presents an efficient binarized algorithm for both learning and classification of human epileptic seizures from intracranial electroencephalography (iEEG). The algorithm combines local binary patterns with brain-inspired hyperdimensional computing to enable end-to-end learning and inference with binary operations. The algorithm first transforms iEEG time series from each electrode into local binary pattern codes. Then atomic high-dimensional binary vectors are used to construct composite representations of seizures across all electrodes. For the majority of our patients (10 out of 16), the algorithm quickly learns from one or two seizures (i.e., one-/few-shot learning) and perfectly generalizes on 27 further seizures. For other patients, the algorithm requires three to six seizures for learning. Overall, our algorithm surpasses the state-of-the-art methods [1] for detecting 65 novel seizures with higher specificity and sensitivity, and lower memory footprint.

Background Coronavirus-2 (SARS-CoV-2) infection causes an acute respiratory syndrome accompanied by multi-organ damage that implicates a prothrombotic state leading to widespread microvascular clots. The causes of such coagulation abnormalities are unknown. The receptor tissue factor, also known as CD142, is often associated with cell-released extracellular vesicles (EV). In this study, we aimed to characterize surface antigens profile of circulating EV in COVID-19 patients and their potential implication as procoagulant agents. Methods We analyzed serum-derived EV from 67 participants who underwent nasopharyngeal swabs molecular test for suspected SARS-CoV-2 infection (34 positives and 33 negatives) and from 16 healthy controls (HC), as referral. A sub-analysis was performed on subjects who developed pneumonia ( n = 28). Serum-derived EV were characterized for their surface antigen profile and tested for their procoagulant activity. A validation experiment was performed pre-treating EV with anti-CD142 antibody or with recombinant FVIIa. Serum TNF-α levels were measured by ELISA. Findings Profiling of EV antigens revealed a surface marker signature that defines circulating EV in COVID-19. A combination of seven surface molecules (CD49e, CD209, CD86, CD133/1, CD69, CD142, and CD20) clustered COVID (+) versus COVID (-) patients and HC. CD142 showed the highest discriminating performance at both multivariate models and ROC curve analysis. Noteworthy, we found that CD142 exposed onto surface of EV was biologically active. CD142 activity was higher in COVID (+) patients and correlated with TNF-α serum levels. Interpretation In SARS-CoV-2 infection the systemic inflammatory response results in cell-release of substantial amounts of procoagulant EV that may act as clotting initiation agents, contributing to disease severity. Funding Cardiocentro Ticino Institute, Ente ospedaliero Cantonale, Lugano-Switzerland.

The deployment of Deep Neural Networks (DNNs) on end-nodes at the extreme edge of the Internet-of-Things is a critical enabler to support pervasive Deep Learning-enhanced applications. Low-Cost MCU-based end-nodes have limited on-chip memory and often replace caches with scratchpads, to reduce area overheads and increase energy efficiency -requiring explicit DMA-based memory transfers between different levels of the memory hierarchy. Mapping modern DNNs on these systems requires aggressive topology-dependent tiling and double-buffering. In this work, we propose DORY (Deployment Oriented to memoRY ) -an automatic tool to deploy DNNs on low cost MCUs with typically less than 1MB of on-chip SRAM memory. DORY abstracts tiling as a Constraint Programming (CP) problem: it maximizes L1 memory utilization under the topological constraints imposed by each DNN layer. Then, it generates ANSI C code to orchestrate off-and on-chip transfers and computation phases. Furthermore, to maximize speed, DORY augments the CP formulation with heuristics promoting performance-effective tile sizes. As a case study for DORY, we target GreenWaves Technologies GAP8, one of the most advanced parallel ultra-low power MCU-class devices on the market. On this device, DORY achieves up to 2.5× better MAC/cycle than the GreenWaves proprietary software solution and 18.1× better than the state-of-the-art result on an STM32-H743 MCU on single layers. Using our tool, GAP-8 can perform end-to-end inference of a 1.0-MobileNet-128 network consuming just 63 pJ/MAC on

Context Primary aldosteronism (PA) comprises unilateral (lateralized, LPA) and bilateral disease (BPA). The identification of LPA is important to recommend potentially curative adrenalectomy. Adrenal venous sampling (AVS) is considered the gold standard for PA subtyping, but the procedure is available in few referral centers. Objective To develop prediction models for subtype diagnosis of PA using patient clinical and biochemical characteristics. Design, Patients and Setting Patients referred to a tertiary hypertension unit. Diagnostic algorithms were built and tested in a training (N=150) and in an internal validation cohort (N=65), respectively. The models were validated in an external independent cohort (N=118). Main outcome measure Regression analyses and supervised machine learning algorithms were used to develop and validate two diagnostic models and a 20-point score to classify patients with PA according to subtype diagnosis. Results Six parameters were associated with a diagnosis of LPA (aldosterone at screening and after confirmatory testing, lowest potassium value, presence/absence of nodules, nodule diameter, and computed tomography results) and were included in the diagnostic models. Machine learning algorithms displayed high accuracy at training and internal validation (79.1% to 93%), whereas a 20-point score reached an AUC of 0.896, and a sensitivity/specificity of 91.7/79.3%. An integrated flow-chart correctly addressed 96.3% of patients to surgery and would have avoided AVS in 43.7% of patients. The external validation on an independent cohort confirmed a similar diagnostic performance. Conclusions Diagnostic modelling techniques can be used for subtype diagnosis and guide surgical decision in patients with PA in centers where AVS is unavailable.

Unilateral primary aldosteronism (PA) is the most common surgically curable form of hypertension that must be accurately differentiated from bilateral PA for therapeutic management (surgical versus medical). Adrenalectomy results in biochemical cure (complete biochemical success) in almost all patients diagnosed with unilateral PA; the remaining patients with partial or absent biochemical success comprise those with persisting aldosteronism who were misdiagnosed as unilateral PA preoperatively. To identify determinants of postsurgical biochemical outcomes, we compared the adrenal histopathology and the peripheral venous steroid profiles of patients with partial and absent or complete biochemical success after adrenalectomy for unilateral PA. A large multicenter cohort of adrenals from patients with absent and partial biochemical success (n=43) displayed a higher prevalence of hyperplasia (49% versus 21%; P=0.004) and a lower prevalence of solitary functional adenoma (44% versus 79%; P<0.001) compared with adrenals from age- and sex-matched patients with PA with complete biochemical success (n=52). We measured the peripheral plasma steroid concentrations in a subgroup of these patients (n=43) and in a group of patients with bilateral PA (n=27). Steroid profiling was associated with histopathologic phenotypes (solitary functional adenoma, hyperplasia, and aldosterone-producing cell clusters) and classified patients according to biochemical outcome or diagnosis of bilateral PA. If validated, peripheral venous steroid profiling may be a useful tool to guide the decision to perform surgery based on expectations of biochemical outcome after the procedure.

Aims -Circulating extracellular vesicles (EV) are raising considerable interest as a non-invasive diagnostic tool as they are easily detectable in biological fluids and contain specific set of nucleic acids, proteins, and lipids reflecting pathophysiological conditions. We aimed to investigate differences in plasma-derived EV surface-protein profile as biomarker to be used in combination with endomyocardial biopsies (EMB) for the diagnosis of allograft rejection.Methods and results -Plasma was collected from 90 patients (53 training cohort, 37 validation cohort) prior to EMB. EV concentration was assessed by nanoparticle tracking analysis. EV surface antigens were measured using a multiplex flow cytometry assay comprising 37 fluorescently labelled capture bead populations coated with specific antibodies directed against respective EV surface epitopes. The concentration of EV was significantly increased and their diameter decreased in patients undergoing rejection as compared to negative ones. The trend was highly significant for both antibody-mediated rejection (AMR), and acute cellular rejection (P<0.001). Among EV-surface markers, CD3, CD2, ROR1, SSEA-4, HLA-I, and CD41b were identified as discriminants between controls and ACR, whereas HLA-II, CD326, CD19, CD25, CD20, ROR1, SSEA-4, HLA-I, and CD41b discriminated controls from patients with AMR. ROC curves confirmed a reliable diagnostic performance for each single marker (AUC range 0.727-0.939). According to differential EV-marker expression, a diagnostic model was built and validated in an external cohort of patients. Our model was able to distinguish patients undergoing rejection from those without rejection. The accuracy at validation in an independent external cohort reached 86.5%. Its application for patient management has the potential to reduce the number of EMBs.Further studies in a higher number of patients are required to validate this approach for clinical purpose.Conclusions -Circulating EV are highly promising as new tool to characterize cardiac allograft rejection and to be complementary to EMB monitoring. 2NARRATIVE ABSTRACT -Our study describes a method for detecting and characterising circulating extracellular vesicles (EV) as a minimally invasive, liquid biopsy for the diagnosis of cardiac allograft rejection, and as a complementary tool to EMB monitoring. EV obtained from peripheral blood were profiled to identify rejection and its types in cardiac transplant recipients. A standardized and rapid tool was established using a fluorescent bead-based multiplex assay. We built a diagnostic model based on machine learning algorithms to identify non-rejecting patients who potentially do not require EMBs. EV profiling could represent a tool for non-invasive monitoring of allograft rejection in cardiac transplant recipients.