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The potential of a voltametric E-tongue coupled with a custom data pre-processing stage to improve the performance of machine learning techniques for rapid discrimination of tomato purées between cultivars of different economic value has been investigated. To this aim, a sensor array with screen-printed carbon electrodes modified with gold nanoparticles (GNP), copper nanoparticles (CNP) and bulk gold subsequently modified with poly(3,4-ethylenedioxythiophene) (PEDOT), was developed to acquire data to be transformed by a custom pre-processing pipeline and then processed by a set of commonly used classifiers. The GNP and CNP-modified electrodes, selected based on their sensitivity to soluble monosaccharides, demonstrated good ability in discriminating samples of different cultivars. Among the different data analysis methods tested, Linear Discriminant Analysis (LDA) proved to be particularly suitable, obtaining an average F1 score of 99.26%. The pre-processing stage was beneficial in reducing the number of input features, decreasing the computational cost, i.e., the number of computing operations to be performed, of the entire method and aiding future cost-efficient hardware implementation. These findings proved that coupling the multi-sensing platform featuring properly modified sensors with the custom pre-processing method developed and LDA provided an optimal tradeoff between analytical problem solving and reliable chemical information, as well as accuracy and computational complexity. These results can be preliminary to the design of hardware solutions that could be embedded into low-cost portable devices.

Liquid biopsy is a non-invasive and efficient technique for the detection of tumor biomarkers in biological fluids, which currently represents a new frontier in theranostics and precision medicine. Among mutations of the KRAS oncogene, p.G12D single nucleotide variation in the KRAS gene plays a central role in the early diagnosis and therapeutic treatment of colorectal cancer. In this context, we developed a highly sensitive magneto-genosensing assay based on PNA capture probes immobilized on magnetic microbeads. To detect the mutation of the KRAS oncogene, two capture probe sequences recognizing wild-type and p.G12D tumor DNA were used in association with a DNA signaling probe allowing for electrochemical detection using an enzyme conjugate. The assay conditions were optimized using a 32 full-factorial design, obtaining an outstanding specificity, evidenced by a remarkably lower (>95%) signal of the singly-mismatched- compared to that of fully-complementary-DNA. Ultra-high sensitivity was achieved in 10-fold diluted human plasma reaching detection limits of 818 fM for the p.G12D and 1.8 pM for the wild-type targets. The genosensing assay was tested on genomic tumor DNA samples provided by IRCCS-Regina Elena National Cancer Institute and finally integrated into a smart portable multichannel potentiostat capable of performing up to four simultaneous acquisitions. The developed magneto-genosensing assay demonstrated portability, simplicity, and high sensitivity, showing good potential as theranostic tool for personalized medicine in oncology.

Acute cowperitis, which was previously known as a common complication of sexually transmitted infections (STIs), is now commonly associated with bacterial urinary tract infections, particularly Escherichia coli. Patients often have a history of STIs, and the symptoms resemble other male accessory gland infections (MAGIs). Recent cases associated with sepsis have been managed with percutaneous drainage and/or surgery. We present a case of acute cowperitis with sepsis and an abscess in the right small gland. The diagnosis was made using transperineal ultrasound, and the patient was successfully treated only with a long-term antibiotic therapy.

This article presents the flipped (F)-2T2R resistive random access memory (RRAM) compute cell enhancing the performance of RRAM-based mixed-signal accelerators for deep neural networks (DNNs) in machine-learning (ML) applications. The F-2T2R cell is designed to exploit the features of the FD-SOI technology and it achieves a large increase in cell output impedance, compared to the standard 1-transistor 1-resistor (1T1R) cell. The article also describes the modeling of an F-2T2R-based accelerator and its transistor-level implementation in a 22-nm FD-SOI technology. The modeling results and the accelerator performance are validated by simulation. The proposed design can achieve an energy efficiency of up to 1260 1 bit-TOPS/W, with a memory array of 256 rows and columns. From the results of our analytical framework, a ResNet18, mapped on the accelerator, can obtain an accuracy reduction below 2%, with respect to the floating-point baseline, on the CIFAR-10 dataset.

Live imaging of organoid growth remains a challenge: it requires long-term imaging of several samples simultaneously and dedicated analysis pipelines. Here the authors report an experimental and image processing framework to turn long-term light-sheet imaging of intestinal organoids into digital organoids.

The Internet of Things paradigm has expanded the possibility of using sensors ubiquitously, particularly if connected to a cloud service for data sharing. There are several ways to connect sensors to the cloud: wearable or portable devices often lean on a smartphone that acts as a gateway, while other sensors, such as smart sensors for continuous monitoring (e.g. fall detectors) are connected through wireless networks covering a limited area (e.g. ZigBee or Wi-Fi). Their functionality can be improved using them in both outdoor and indoor environments without other devices. NB-IoT is a recently introduced wide-range protocol with a good compromise between low power, low deployment costs, payload length, and data rate. Traditionally, sensor nodes rely on only one type of radio: an innovative solution could be a sensor node exploiting a combination of different transmission technologies with the aim of achieving higher portability. In this paper, a hybrid solution based on NB-IoT/Wi-Fi is presented. The Wi-Fi connection is primarily selected due to its lower power consumption (compared to NB-IoT), while NB-IoT is activated only when a Wi-Fi network is not available. This study aims to evaluate the power consumption of the proposed solution with respect to single radio NB-IoT technology. Test boards have been implemented, and several data transmission tests have been carried out with both NB-IoT and Wi-Fi radios. Different received powers and payload lengths have been considered to analyze the impact on the energy profile of smart sensors. It has been demonstrated that using NB-IoT for both indoor and outdoor leads to an acceptable battery discharge time, with a strong dependence on the payload length. Under certain conditions, the proposed hybrid solution results in a battery duration up to two times higher than single-radio NB-IoT.

Recent advances in Internet-of-Things technology have opened the doors to new scenarios for biosensor applications. Flexibility, portability, and remote control and access are of utmost importance to move these devices to people’s homes or in a Point-of-Care context and rapidly share the results with users and their physicians. In this paper, an innovative portable device for both quantitative and semi-quantitative electrochemical analysis is presented. This device can operate autonomously without the need of relying on other devices (e.g., PC, tablets, or smartphones) thanks to built-in Wi-Fi connectivity. The developed hardware is integrated into a cloud-based platform, exploiting the cloud computational power to perform innovative algorithms for calibration (e.g., Machine Learning tools). Results and configurations can be accessed through a web page without the installation of dedicated APPs or software. The electrical input/output characteristic was measured with a dummy cell as a load, achieving excellent linearity. Furthermore, the device response to five different concentrations of potassium ferri/ferrocyanide redox probe was compared with a bench-top laboratory instrument. No difference in analytical sensitivity was found. Also, some examples of application-specific tests were set up to demonstrate the use in real-case scenarios. In addition, Support Vector Machine algorithm was applied to semi-quantitative analyses to classify the input samples into four classes, achieving an average accuracy of 98.23%. Finally, COVID-19 related tests are presented and discussed.

Innovative and highly performing smart voltammetric immunosensors for rapid and effective serological tests aimed at the determination of SARS-CoV-2 antibodies were developed and validated in human serum matrix. Two immunosensors were developed for the determination of immunoglobulins directed against either the nucleocapsid or the spike viral antigen proteins. The immunosensors were realized using disposable screen-printed electrodes modified with nanostructured materials for the immobilization of the antigens. Fast quantitative detection was achieved, with analysis duration being around 1 h. Signal readout was carried out through a smart, compact and battery-powered potentiostat, based on a Wi-Fi protocol and devised for the Internet of Things (IoT) paradigm. This device is used for the acquisition, storage and sharing of clinical data. Outstanding immunosensors’ sensitivity, specificity and accuracy (100%) were assessed, according to the diagnostic guidelines for epidemiological data. The overall performance of the sensing devices, combined with the portability of the IoT-based device, enables their suitability as a high-throughput diagnostic tool. Both of the immunosensors were validated using clinical human serum specimens from SARS-CoV-2 infected patients, provided by IRCCS Ospedale San Raffaele.

An IoT-WiFi smart and portable electrochemical immunosensor for the quantification of SARS-CoV-2 spike protein was developed with integrated machine learning features. The immunoenzymatic sensor is based on the immobilization of monoclonal antibodies directed at the SARS-CoV-2 S1 subunit on Screen-Printed Electrodes functionalized with gold nanoparticles. The analytical protocol involves a single-step sample incubation. Immunosensor performance was validated in a viral transfer medium which is commonly used for the desorption of nasopharyngeal swabs. Remarkable specificity of the response was demonstrated by testing H1N1 Hemagglutinin from swine-origin influenza A virus and Spike Protein S1 from Middle East respiratory syndrome coronavirus. Machine learning was successfully used for data processing and analysis. Different support vector machine classifiers were evaluated, proving that algorithms affect the classifier accuracy. The test accuracy of the best classification model in terms of true positive/true negative sample classification was 97.3%. In addition, the ML algorithm can be easily integrated into cloud-based portable Wi-Fi devices. Finally, the immunosensor was successfully tested using a third generation replicating incompetent lentiviral vector pseudotyped with SARS-CoV-2 spike glycoprotein, thus proving the applicability of the immunosensor to whole virus detection.

Objectives: To assess the diagnostic potential of bidirectional axial transmission (BDAT) ultrasound, and high-resolution peripheral quantitative computed tomography (HR-pQCT) in X-linked hypophosphatemia (XLH, OMIM #307800), a rare genetic disorder of phosphate metabolism caused by mutations in the PHEX gene.Methods: BDAT bone ultrasound was performed at the non-dominant distal radius (33% relative to distal head) and the central left tibia (50%) in eight XLH patients aged between 4.2 and 20.8 years and compared to twenty-nine healthy controls aged between 5.8 and 22.4 years. In eighteen controls, only radius measurements were performed. Four patients and four controls opted to participate in HR-pQCT scanning of the ultradistal radius and tibia.Results: Bone ultrasound was feasible in patients and controls as young as 4 years of age. The velocity of the first arriving signal (νFAS) in BDAT ultrasound was significantly lower in XLH patients compared to healthy controls: In the radius, mean νFAS of XLH patients and controls was 3599 ± 106 and 3866 ± 142 m/s, respectively (−6.9%; p < 0.001). In the tibia, it was 3578 ± 129 and 3762 ± 124 m/s, respectively (−4.9%; p = 0.006). HR-pQCT showed a higher trabecular thickness in the tibia of XLH patients (+16.7%; p = 0.021).Conclusions: Quantitative bone ultrasound revealed significant differences in cortical bone quality of young XLH patients as compared to controls. Regular monitoring of XLH patients by a radiation-free technology such as BDAT might provide valuable information on bone quality and contribute to the optimization of treatment. Further studies are needed to establish this affordable and time efficient method in the XLH patients.

Abstract Background The management of metastatic Renal Cell Carcinoma (RCC) has changed dramatically in the last 20 years, and the role of surgery in the immunotherapy’s era is under debate. Metastatic lesions interesting pancreas are infrequent, but those harbouring from RCC have an high incidence. If metachronous resections are not rare, synchronous resection of primary RCC and its pancreatic metastasis is uncommonly reported, and accounts for a bad prognosis. Case presentation We report the case of a 68 years old woman, who presented hematuria at hospital incoming, with radiological appearance of a 13 cm left renal mass, with a 2.5 cm single pancreatic tail metastasis. Work-up of staging ruled out other distant metastases, urothelial cancer and there was no evidence of inferior vena cava thrombosis. We choose a 5-port trans-peritoneal robotic approach using lazy right lateral decubitus. Synchronous robotic radical nephrectomy and spleen-sparing pancreatic resection was performed. The pancreatic mass was completely enucleated from pancreatic parenchyma using a latero-medial dissection. Peri-operative hemoglobine loss was 2.4 g/dL. Total operative time was 213 min. No post-operative complications were recorded and patient was discharged in 7th post-operative day. Histopathological examination showed a pT2b N0 M1 RCC, Fuhrman grade II, with pancreatic tail metastasis; both, primary and metastatic lesions had the same histological characteristics with negative surgical margins. After 9 months patient had no evidence of disease recurrence at radiological studies. Conclusions The rationale for surgical removal of disseminated tumor, followed by immunotherapy, includes improving prognosis and enhancing the potential of an immune-mediated response to systemic treatment. A spleen-sparing procedure can adequately preserve post-operative immunologic capabilities. In our experience, the correct assessment of pre-operative imaging data and surgeon skills in robotic surgery seem to play a key role in the success of these procedures. Robotic surgery seems to enhance the possibility to control multiple vessels encountered during dissection. Such a conservative approach may be helpful in future research aimed at uncovering biological features, and also leading to better targeted preventive interventions and more individualized and effective treatments.

Abstract There is emerging evidence that microRNAs (miRNAs) dysregulation is involved in the genesis and the progression of Prostate Cancer (PCa), thus potentially increasing their use in urological clinical practice. This is the first pilot study which utilizes Illumina Deep Sequencing to examine the entire miRNAs spectrum existent in urine exfoliated prostate cells (UEPCs) of PCa patients. A total of 11 male patients with histological diagnosis of PCa were enrolled in the present study. First-catch urine (30 mL) was collected following a prostate massage. Total RNA was extracted from urine and sequenced using an HiSeq2500 System (Illumina). QPCR assay was used to validate the highest NGS results in PCA patients and in age-matched, caucasian men. Remarkably, PCA let-7 family was down-regulated (P

Accurate lineage reconstruction of mammalian pre-implantation development is essential for inferring the earliest cell fate decisions. Lineage tracing using global fluorescence labeling techniques is complicated by increasing cell density and rapid embryo rotation, which hampers automatic alignment and accurate cell tracking of obtained four-dimensional imaging data sets. Here, we exploit the advantageous properties of primed convertible fluorescent proteins (pr-pcFPs) to simultaneously visualize the global green and the photoconverted red population in order to minimize tracking uncertainties over prolonged time windows. Confined primed conversion of H2B-pr-mEosFP-labeled nuclei combined with light-sheet imaging greatly facilitates segmentation, classification, and tracking of individual nuclei from the 4-cell stage up to the blastocyst. Using green and red labels as fiducial markers, we computationally correct for rotational and translational drift, reduce overall data size, and accomplish high-fidelity lineage tracing even for increased imaging time intervals – addressing major concerns in the field of volumetric embryo imaging.

The paper describes the design and implementation of power management circuits for RF energy harvesters suitable for integration in wireless sensor nodes. In particular, we report the power management circuits used to provide the voltage supply of an integrated temperature sensor with analog-to-digital converter. A DC-DC boost converter is used to transfer efficiently the energy harvested from a generic radio-frequency rectifier into a charge reservoir, whereas a linear regulator scales the voltage supply to a suitable value for a sensing and conversion circuit. Implemented in a 65 nm CMOS technology, the power management system achieves a measured overall efficiency of 20%, with an available power of 4.5 μW at the DC-DC converter input. The system can sustain a temperature measurement rate of one sample/s with an RF input power of −28 dBm, making it compatible with the power levels available in generic outdoor environments.

Nowadays, analytical techniques are moving towards the development of smart biosensing strategies for the point-of-care accurate screening of disease biomarkers, such as human epididymis protein 4 (HE4), a recently discovered serum marker for early ovarian cancer diagnosis. In this context, the present work represents the first implementation of a competitive enzyme-labelled magneto-immunoassay exploiting a homemade IoT Wi-Fi cloud-based portable potentiostat for differential pulse voltammetry readout. The electrochemical device was specifically designed to be capable of autonomous calibration and data processing, switching between calibration, and measurement modes: in particular, firstly, a baseline estimation algorithm is applied for correct peak computation, then calibration function is built by interpolating data with a four-parameter logistic function. The calibration function parameters are stored on the cloud for inverse prediction to determine the concentration of unknown samples. Interpolation function calibration and concentration evaluation are performed directly on-board, thus reducing the power consumption. The analytical device was validated in human serum, demonstrating good sensing performance for analysis of HE4 with detection and quantitation limits in human serum of 3.5 and 29.2 pM, respectively, reaching the sensitivity that is required for diagnostic purposes, with high potential for applications as portable and smart diagnostic tool for point-of-care testing.