Search

The sensing of left ventricular (LV) activity is fundamental in the diagnosis and monitoring of cardiovascular health in high-risk patients after cardiac surgery to achieve better short- and long-term outcome. Conventional approaches rely on noninvasive measurements even if, in the latest years, invasive microelectromechanical systems (MEMS) sensors have emerged as a valuable approach for precise and continuous monitoring of cardiac activity. The main challenges in designing cardiac MEMS sensors are represented by miniaturization, biocompatibility, and long-term stability. Here, we present a MEMS piezoresistive cardiac sensor capable of continuous monitoring of LV activity over time following epicardial implantation with a pericardial patch graft in adult minipigs. In acute and chronic scenarios, the sensor was able to compute heart rate with a root mean square error lower than 2 BPM. Early after up to 1 month of implantation, the device was able to record the heart activity during the most important phases of the cardiac cycle (systole and diastole peaks). The sensor signal waveform, in addition, closely reflected the typical waveforms of pressure signal obtained via intraventricular catheters, offering a safer alternative to heart catheterization. Furthermore, histological analysis of the LV implantation site following sensor retrieval revealed no evidence of myocardial fibrosis. Our results suggest that the epicardial LV implantation of an MEMS sensor is a suitable and reliable approach for direct continuous monitoring of cardiac activity. This work envisions the use of this sensor as a cardiac sensing device in closed-loop applications for patients undergoing heart surgery.

Microelectrode recordings from human peripheral and cranial nerves provide a means to study both afferent and efferent axonal signals at different levels of detail, from multi- to single-unit activity. Their analysis can lead to advancements both in diagnostic and in the understanding of the genesis of neural disorders. However, most of the existing computational toolboxes for the analysis of microneurographic recordings are limited in scope or not open-source. Additionally, conventional burst-based metrics are not suited to analyze pathological conditions and are highly sensitive to distance of the microelectrode tip from the active axons. To address these challenges, we developed an open-source toolbox that offers advanced analysis capabilities for studying neuronal reflexes and physiological responses to peripheral nerve activity. Our toolbox leverages the observation of temporal sequences of action potentials within inherently cyclic signals, introducing innovative methods and indices to enhance analysis accuracy. Importantly, we have designed our computational toolbox to be accessible to novices in biomedical signal processing. This may include researchers and professionals in healthcare domains, such as clinical medicine, life sciences, and related fields. By prioritizing user-friendliness, our software application serves as a valuable resource for the scientific community, allowing to extract advanced metrics of neural activity in short time and evaluate their impact on other physiological variables in a consistent and standardized manner, with the final aim to widen the use of microneurography among researchers and clinicians., Competing Interests: The authors have no conflicts to disclose., (© 2024 Author(s).)

The continuous monitoring of an individual's breathing can be an instrument for the assessment and enhancement of human wellness. Specific respiratory features are unique markers of the deterioration of a health condition, the onset of a disease, fatigue and stressful circumstances. The early and reliable prediction of high-risk situations can result in the implementation of appropriate intervention strategies that might be lifesaving. Hence, smart wearables for the monitoring of continuous breathing have recently been attracting the interest of many researchers and companies. However, most of the existing approaches do not provide comprehensive respiratory information. For this reason, a meta-learning algorithm based on LSTM neural networks for inferring the respiratory flow from a wearable system embedding FBG sensors and inertial units is herein proposed. Different conventional machine learning approaches were implemented as well to ultimately compare the results. The meta-learning algorithm turned out to be the most accurate in predicting respiratory flow when new subjects are considered. Furthermore, the LSTM model memory capability has been proven to be advantageous for capturing relevant aspects of the breathing pattern. The algorithms were tested under different conditions, both static and dynamic, and with more unobtrusive device configurations. The meta-learning results demonstrated that a short one-time calibration may provide subject-specific models which predict the respiratory flow with high accuracy, even when the number of sensors is reduced. Flow RMS errors on the test set ranged from 22.03 L/min, when the minimum number of sensors was considered, to 9.97 L/min for the complete setting (target flow range: 69.231 ± 21.477 L/min). The correlation coefficient r between the target and the predicted flow changed accordingly, being higher (r = 0.9) for the most comprehensive and heterogeneous wearable device configuration. Similar results were achieved even with simpler settings which included the thoracic sensors (r ranging from 0.84 to 0.88; test flow RMSE = 10.99 L/min, when exclusively using the thoracic FBGs). The further estimation of respiratory parameters, i.e., rate and volume, with low errors across different breathing behaviors and postures proved the potential of such approach. These findings lay the foundation for the implementation of reliable custom solutions and more sophisticated artificial intelligence-based algorithms for daily life health-related applications., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Hydrothermal growth of ZnO nanorods has been widely used for the development of tactile sensors, with the aid of ZnO seed layers, favoring the growth of dense and vertically aligned nanorods. However, seed layers represent an additional fabrication step in the sensor design. In this study, a seedless hydrothermal growth of ZnO nanorods was carried out on Au-coated Si and polyimide substrates. The effects of both the Au morphology and the growth temperature on the characteristics of the nanorods were investigated, finding that smaller Au grains produced tilted rods, while larger grains provided vertical rods. Highly dense and high-aspect-ratio nanorods with hexagonal prismatic shape were obtained at 75 °C and 85 °C, while pyramid-like rods were grown when the temperature was set to 95 °C. Finite-element simulations demonstrated that prismatic rods produce higher voltage responses than the pyramid-shaped ones. A tactile sensor, with an active area of 1 cm 2 , was fabricated on flexible polyimide substrate and embedding the nanorods forest in a polydimethylsiloxane matrix as a separation layer between the bottom and the top Au electrodes. The prototype showed clear responses upon applied loads of 2-4 N and vibrations over frequencies in the range of 20-800 Hz., Competing Interests: The authors declare no conflict of interest.

Multiple myeloma is a plasma cell tumor that homes to and expands in the bone marrow and that, despite the new available drugs, remains incurable. Extramedullary plasmacytoma is a not frequent manifestation during the natural history of multiple myeloma and is frequently associated with plasma cell bone marrow infiltration. The most common locations for an EMP include the gastrointestinal tract, pleura, testis, skin, peritoneum, liver, endocrine glands, and lymph nodes. Primary involvement of the gallbladder fossa is exceedingly rare. In this report, we describe a patient with multiple myeloma who achieved a clinical and serological remission after autologous transplant but progressed rapidly at extramedullary site mimicking a second cancer (i.e., pancreatic or biliary cancer). In this case, the extramedullary localization was refractory to standard therapy, differently from bone marrow localization, but responded to lymphoma-like therapy. In this patient (i) the particular site of developing plasmacytoma is the gallbladder fossa, (ii) the timing of onset of this neoplasm is immediately after autologous transplant, and (iii) its disjunction from primary myeloma is that it appears in clinical and serological remission phase which may be confounding during the diagnostic approach simulating a different tumor (solid tumor).

Small-bowel neoplasms are the 3%-6% of all gastrointestinal tract neoplasms. Due to the rarity of these lesions, the low index of clinical suspicion, and the inadequate radiologic examinations or incorrect interpretation of radiologic findings, a delay in diagnosis of 6-8 months from the first symptoms often occurs. Even if conventional enteroclysis and capsule endoscopy are the most common procedures used to accurately depict the bowel lumen and mucosal surface, their use in evaluating the mural and extramural extents of small-bowel tumors is limited. Instead multidetector computed tomographic enteroclysis and magnetic resonance enteroclysis have the potential to simultaneously depict intraluminal, mural, and extraintestinal abnormalities. In particular MR enteroclysis has an excellent soft tissue contrast resolution and multiplanar imaging capability. It can provide anatomic, functional, and real time information without the need of ionizing radiation. MR findings, appearances of the lesions, combined with the contrast-enhancement behavior and characteristic of the stenosis are important to differentiate small-bowel neoplasm from other nonneoplastic diseases.

Although effective low-cost interventions exist, child mortality attributable to sickle cell disease (SCD) remains high in low-resource areas due, in large part, to the lack of accessible diagnostic methods. The presence of dense (ρ > 1.120 g/cm(3)) cells is characteristic of SCD. The fluid, self-assembling step-gradients in density created by aqueous multiphase systems (AMPSs) identifies SCD by detecting dense cells. AMPSs separate different forms of red blood cells by density in a microhematocrit centrifuge and provide a visual means to distinguish individuals with SCD from those with normal hemoglobin or with nondisease, sickle-cell trait in under 12 min. Visual evaluation of a simple two-phase system identified the two main subclasses of SCD [homozygous (Hb SS) and heterozygous (Hb SC)] with a sensitivity of 90% (73-98%) and a specificity of 97% (86-100%). A three-phase system identified these two types of SCD with a sensitivity of 91% (78-98%) and a specificity of 88% (74-98%). This system could also distinguish between Hb SS and Hb SC. To the authors' knowledge, this test demonstrates the first separation of cells by density with AMPSs, and the usefulness of AMPSs in point-of-care diagnostic hematology.