Sensors used for collecting high-value physiological and biochemical data strongly support a precision medicine approach, by enabling the integration of these complex datasets with routine clinical outcomes to provide more accurate diagnostic and prognostic evaluations. Building on extensive experience in this field, the authors have developed multi-sensor technologies designed to analyze multiple biological fluids, with a particular focus on exhaled breath both in as it is and processed in liquid media. These technologies have been implemented in a large-scale clinical study involving 863 patients affected by seven different diseases, allowing for the acquisition of heterogeneous data suitable for computational modeling and the identification of disease-related characteristics. By integrating multiple sensors and analyzing diverse breath samples, this work aims to generate a comprehensive reference library of breathprints and thereby advance the clinical applicability of breath analysis. The study demonstrates the potential of this multi-omic, multisensory approach to differentiate healthy individuals from patients with various respiratory, cardiovascular, and metabolic disorders, while pioneering investigations of exhaled breath in liquid media—although conducted on a smaller patient cohort—highlight promising opportunities for technological innovation in multisensory diagnostics. While the overall results support the feasibility and potential impact of this methodology, further research will be required to refine the technique, enlarge patient cohorts, and improve the accuracy and specificity of disease detection.

Diabetes mellitus represents one of the most widespread chronic diseases globally, characterized by alterations in glucose metabolism that require constant monitoring of blood glucose levels. Traditionally, blood testing has been the standard for glucose monitoring; however, interstitial fluid has emerged as a viable alternative, due to its less invasive nature which enhances user comfort. Despite improvements in technology, the accuracy of currently available continuous glucose monitors remains a concern, particularly when the rate of change is higher, such as in hypoglycemic and hyperglycemic ranges. Effective management of hypoglycemia relies on the monitor’s ability to provide precise and specific readings when blood glucose levels drop dangerously low. In this context, the demand for heightened accuracy is paramount to timely alert users to impending hypoglycemic events. The inaccuracies of these sensors are attributed to the dynamics of the sample analysis. Specifically the interstitial fluid experiences a delay in concentration due to the diffusion process from capillary blood to interstitial fluid. In this study, we developed a microfluidic device that simulates the diffusion dynamics from capillary glucose to interstitial fluid. We demonstrate the reduction of lag time diffusion from 20 min to 5 min by increasing dermal electro-osmotic flow, which generates convection that transports glucose faster than diffusion, thus resulting in lower lag times. These findings highlight the potential of inciting electro-osmotic flow for improving the responsiveness and accuracy of CGMs, ultimately enhancing diabetes management for users.

The BIONOTE-L voltametric sensor was used in this work to exploit cyclic voltammetry as an operating principle to interact with the plant/soil system. Indeed, in bacteria, the metabolic process oxidizes organic compounds from the soil organic matter with extracellular electron transfer. Plants can also release electrons into the rhizosphere—in what are known as plant microbial fuel cells (PMFCs). The electrons obtained, when appropriately conveyed, generate an electric current. On this basis, the innovation of a sensor system “interrogating” the plant/soil system via cyclic voltammetry is proposed. This work tries to place a new paradigm: Rather than exploiting the energy autonomously produced, the plant-microorganism system and the interaction that occurs between them can be analyzed in energetic terms, which is novel with respect to the standard approach measuring other parameters related to plant growth. An external input is provided, and the response of the system to this solicitation is observed. The energy extracted does not have the purpose of powering other devices or being stored. Instead, it is treated as data on the state of health of the plant-microorganism system. This test has been structured as a step-by-step experimental investigation, with a gradual increase in complexity. The BIONOTE-L was tested to analyze the behavior of four different genera of bacteria in solution: Rhizobium, Bacillus, Enterobacter, and Kosakonia. The behavior of Bacillus was then analyzed in interaction with the soil and with a corn plant (Zea mays L.). The gradual growth of the bacteria in the solution was assessed using a spectrophotometer (optical density: O.D.) and predicting O.D. with BIONOTE-L with root mean square errors in cross validation from 0.004 to 0.2 (depending on the different bacteria). Specific fingerprints were detected for each hour of bacterial growth (therefore specific for the concentration of bacteria in solution) and for each different genus of bacteria. Finally, the electrical response of Bacillus in interaction with the soil and plant was characterized, thus providing a specific electrical signature to be monitored in a real-world scenario.

Bioactive glasses (BGs) are widely investigated for biomedical applications due to their bioactivity, ion release capability, and potential antimicrobial behavior. In this work, a novel zinc/potassium-doped BG, designated as Gaia-GN, was synthesized using the melt-quench technique. The thermal behavior was characterized through differential thermal analysis (DTA), thermogravimetry (TG), and heating microscopy (HM), which revealed a glass transition temperature of 513 degrees C and a broad processing window suitable for viscous-flow sintering. Subsequently, Gaia-GN powders were thermally processed to obtain sintered specimens. XRD analysis showed that sintering at relatively low temperatures, namely 600 degrees C, produced a compact and predominantly amorphous structure, minimizing the formation of crystalline phases. In addition, mechanical properties were evaluated by Vickers micro-indentation, determining Vickers hardness, Young's modulus, and fracture toughness using four different theoretical models. The results suggest that the combined presence of Zn and K improves thermal stability and mechanical performance compared with conventional formulations such as 45S5 Bioglass and S53P4. Finally, antimicrobial assays were conducted, revealing a strong antimicrobial action of the glass against Gram-negative bacteria. Overall, these findings underscore the potential of the new K/Zn-doped BG not only for its antibacterial properties but also for applications requiring thermal processing, such as the fabrication of scaffolds or coatings on metallic substrates.

Capacitive sensors based on interdigitated electrodes (IDCs) represent a solution for highly sensitive detection of environmental gases, thanks to their strong dependence on dielectric variations induced by analyte adsorption. This work presents an IDC-based gas sensor functionalized with a hydrogel-based sensitive layer, together with a dedicated analog readout circuit. The sensing mechanism relies on the modulation of the effective permittivity of the functional layer upon gas exposure, resulting in capacitance variations in the sub-picofarad range. These variations (to 27pF, from a baseline of 9pF) are converted into measurable voltage signals through a differential phase-shift architecture, based on the Second-Generation Voltage Conveyor (VCII), allowing inherent rejection of common-mode electrical disturbances and reduced sensitivity to parasitic effects. Thanks to the intrinsic linear phase response around the operating point, the sensor system achieves a highly linear capacitance-phase-voltage transduction over the experimentally investigated range, with a coefficient of determination of approximately R2=0.997, simplifying calibration and improving accuracy. Parametric simulations of a CMOS VCII implementation show a capacitance-to-voltage sensitivity of 1.81mV/pF and a resolution of 0.019pF. Experimental results on a PCB prototype, based on the AD844, configured as a VCII, confirm the approach, with sensitivity around 103.31mV/pF and a minimum detectable capacitance variation of approximately 0.33pF. Gas sensing experiments demonstrate promising performance for H₂O, O₂, and CO₂ detection, under controlled laboratory conditions, in terms of sensitivity and resolution, confirming the feasibility of the proposed approach as a highly linear laboratory-scale platform for capacitive gas-sensing investigations.

User-friendliness of exhaled breath sampling procedure and portability of the collected samples are strategic keypoints for disease monitoring in unstructured environments. These issues have been addressed by collecting exhaled breath on adsorbing cartridges; many samplers on cartridge have been used in clinical experiments through exhalate collection, including our patented Pneumopipe (European patent 12425057.2). Increasing the operative functionality of the sampling device enhances collection effectiveness and the representativity of the specimen. These features become of paramount importance when a minimal amount of disease needs to be detected with preventive approach in screening and follow-up. The primary endpoint was aimed at achieving Pneumopipe optimization (Pneumopipe II). The secondary endpoint was to investigate through a pilot longitudinal study focused on monitoring recurrence after lung cancer surgical resection and the evolution of the breath fingerprint before and after surgery. A quartz crystal-based gas sensor array, named BIONOTE-V was used to measure the breath fingerprint. Cross-validated Partial Least Square Discriminant Analysis and Principal component analysis were used to study the time evolution of the breath fingerprint before and after surgery. Thirty-five patients had the breath fingerprint collected before and after surgery, with fifteen having at least two more measurements for each timepoint (at least two before and two after surgery). (Primary endpoint) At isoprene lab tests, Pneumopipe has shown to preserve its effectiveness in collecting volatile mixture in a representative sample. (Secondary endpoint) Cancer recurrence was observed in 8 patients. Postoperative breath fingerprint detected lung cancer recurrence with an accuracy of 91 %, a sensitivity of 75 %, a specificity of 96 %, a PPV of 86 % and a NPV of 93 %. Effectiveness of exhalate collection and portability of the sampler are preserved with Pneumopipe II.

Quality control is mandatory in the food industry and chemical sensors play a crucial role in this field. Coffee is one of the most consumed and commercialized food products globally, and its quality is of the utmost importance. Many scientific papers have analyzed coffee quality using different approaches, such as analytical and sensor analyses, which, despite their good performance, are limited to structured lab implementation. This study aims to evaluate the capability of a smart electrochemical sensor to discriminate among different beverages prepared using coffee beans with different moisture content (0%, 2%, >4%) and ground in three sizes (fine, medium and coarse). These parameters reflect real scenarios where coffee is produced and its quality influenced. The possibility of optimizing coffee quality in real time by tuning these parameters could open the way to intelligent coffee machines. A specific experimental setup has been designed, and the data has been analyzed using machine learning techniques. The results obtained from Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) show the sensor’s capability to distinguish between samples of different quality, with a percentage of correct classification of 86.6%. This performance underscores the potential benefits of this sensor for coffee quality assessment, enabling time and resource savings, while facilitating the development of analytical methods based on smart electrochemical sensors.

Human-Plant Interaction (HPI) has emerged as a subfield of Human-Computer Interaction, encouraging the exploration of communication pathways between humans and plants through sensing and interactive technologies. Ultrasonic Acoustic Emissions (AEs) - transient high-frequency sounds caused by cavitation events in the xylem - represent an unexplored yet potentially rich source of data about plant stress, especially about their hydration level. AEs have been recorded extensively with high-end systems but their cost and complexity hinder their use in design and bioart. This study evaluates the self-noise and frequency response of two cost-effective alternatives using an ultrasound emitter as a test source. Moreover, the characteristics of an acoustically insulated box are evaluated for future testing with plants. While both systems showed potential in capturing ultrasound signals, results are incomplete due to the lack of sensitivity information in the calibration data. The ULTRAMIC384K EVO provided higher fidelity and better overall capabilities, while the contact microphone’s performance and evaluation was limited by the audio interface characteristics. Despite limitations in the datasheets, the findings support the feasibility of low-cost AE detection in HPI applications, offering a basis for future bio-sonification tools, plant-responsive media, and novel interaction paradigms in eco-centric design.

Background & AimsObeticholic acid (OCA) is the only licensed second-line therapy for primary biliary cholangitis (PBC). With novel therapeutics in advanced development, clinical tools are needed to tailor the treatment algorithm. We aimed to derive and externally validate the OCA response score (ORS) for predicting the response probability of individuals with PBC to OCA.MethodsWe used data from the Italian RECAPITULATE (N 441) and the IBER-PBC (N 244) OCA real-world prospective cohorts to derive/validate a score including widely available variables obtained either pre-treatment (ORS), or also after 6 months of treatment (ORS+). Multivariable Cox’s regressions with backward selection were applied to obtain parsimonious predictive models. The predicted outcomes were biochemical response according to POISE (ALP/ULN<1.67 with a reduction of at least 15%, and normal bilirubin), or ALP/ULN<1.67, or NORMAL RANGE criteria (NR: normal ALP, ALT and bilirubin) up to 24 months.ResultsDepending on the response criteria, ORS included age, pruritus, cirrhosis, ALP/ULN, ALT/ULN, GGT/ULN and bilirubin. ORS+ also included ALP/ULN and bilirubin after 6 months of OCA therapy. Internally validated c-statistics for ORS were of 0.75, 0.78 and 0.72 for POISE, ALP/ULN<1.67 and NR response, which raised to 0.83, 0.88, 0.81 with ORS+, respectively. The respective performances in validation were of 0.70, 0.72 and 0.71 for ORS, and 0.80, 0.84, 0.78 for ORS+. Results were consistent across groups with mild/severe disease.ConclusionsWe developed and externally validated a scoring system capable to predict OCA response according to different criteria. This tool will enhance a stratified second-line therapy model to streamline standard care and trial delivery in PBC.

Electrical stimulation can be used in several applications such as fatigue reduction, muscle rehabilitation, neurorehabilitation, neuro-prosthesis and pain relief. Moreover, electrical stimulation can be used for drug delivery applications or body fluids extraction (e.g., sweat and interstitial fluid) to successively monitor several parameters, such as glucose, lactate, etc. All these applications are performed using electrical stimulator devices capable of applying constant voltage pulses or constant current pulses via electrodes to human tissues. Usually, constant current stimulators are most widely used because of their safety, stability, and repeatability. Thus, the aim of this work was to design, realize and test a mixed-signal electronic interface capable of producing current pulses with custom amplitude, duration, frequency, polarity and symmetry with extended voltage compliance. To achieve this result, we developed a high-voltage current stimulator suitable for iontophoresis applications. Current stimuli can be applied setting the intensity, frequency and duty cycle of the stimulation patterns through a µC. A custom electronic interface was designed to allow the control of the injected current in real time and to prevent electrical injuries to the patient by avoiding potential unwanted short circuits. Moreover, the system was tested in a simulated environment demonstrating its effectiveness and applicability for transdermal monitoring applications. The obtained results show that the device is able to apply monophasic and biphasic pulses, ranging from 0.1 to 10 mA, with a maximum error of about 10% at the minimum intensity; in addition, current stimuli can be applied up to a maximum frequency of 100 kHz with a voltage compliance of 120 V.

The availability and quality of water have become major concerns worldwide due to the impact of climate change and pollution.To ensure safe water consumption, a monitoring and filtration system comprising a Miniwell Filter 360 passive filter, a multisensorial system with an electrochemical sensor and spectrophotometer, was designed.Water samples were collected in two locations on the north coast of Rome, and analyses were conducted before and after filtration.Multivariate statistical analyses were performed to assess the instruments' ability.The results indicate that the implemented system can increase the availability of safe water.

The production of “Nocciola Romana” hazelnuts in the province of Viterbo, Italy, has evolved into a highly efficient and profitable agro-industrial system. Our approach is based on a hierarchical framework utilizing aggregated data from multiple temporal data and sources, offering valuable insights into the spatial, temporal, and phenological distributions of hazelnut crops To achieve our goal, we harnessed the power of Google Earth Engine and utilized collections of satellite images from Sentinel-2 and Sentinel-1. By creating a dense stack of multi-temporal images, we precisely mapped hazelnut groves in the area. During the testing phase of our model pipeline, we achieved an F1-score of 99% by employing a Hierarchical Random Forest algorithm and conducting intensive sampling using high-resolution satellite imagery. Additionally, we employed a clustering process to further characterize the identified areas. Through this clustering process, we unveiled distinct regions exhibiting diverse spatial, spectral, and temporal responses. We successfully delineated the actual extent of hazelnut cultivation, totaling 22,780 hectares, in close accordance with national statistics, which reported 23,900 hectares in total and 21,700 hectares in production for the year 2022. In particular, we identified three distinct geographic distribution patterns of hazelnut orchards in the province of Viterbo, confined within the PDO (Protected Designation of Origin)-designated region. The methodology pursued, using three years of aggregate data and one for SAR with a spectral separation clustering hierarchical approach, has effectively allowed the identification of the specific perennial crop, enabling a deeper characterization of various aspects influenced by diverse environmental configurations and agronomic practices.The accurate mapping and characterization of hazelnut crops open opportunities for implementing precision agriculture strategies, thereby promoting sustainability and maximizing yields in this thriving agro-industrial system.

INTRODUCTION:Electronic nose (E-nose) technology has reported excellent sensitivity and specificity in the setting of lung cancer screening. However, the performance of E-nose specifically for early-stage tumors remains unclear. Therefore, the aim of our study was to assess the diagnostic performance of E-nose technology in clinical stage I lung cancer. METHODS:This phase IIc trial (NCT04734145) included patients diagnosed with a single greater than or equal to 50% solid stage I nodule. Exhalates were prospectively collected from January 2020 to August 2023. Blinded bioengineers analyzed the exhalates, using E-nose technology to determine the probability of malignancy. Patients were stratified into three risk groups (low-risk, [<0.2]; moderate-risk, [≥0.2-0.7]; high-risk, [≥0.7]). The primary outcome was the diagnostic performance of E-nose versus histopathology (accuracy and F1 score). The secondary outcome was the clinical performance of the E-nose versus clinicoradiological prediction models. RESULTS:Based on the predefined cutoff (<0.20), E-nose agreed with histopathologic results in 86% of cases, achieving an F1 score of 92.5%, based on 86 true positives, two false negatives, and 12 false positives (n = 100). E-nose would refer fewer patients with malignant nodules to observation (low-risk: 2 versus 9 and 11, respectively; p = 0.028 and p = 0.011) than would the Swensen and Brock models and more patients with malignant nodules to treatment without biopsy (high-risk: 27 versus 19 and 6, respectively; p = 0.057 and p < 0.001). CONCLUSIONS:In the setting of clinical stage I lung cancer, E-nose agrees well with histopathology. Accordingly, E-nose technology can be used in addition to imaging or as part of a "multiomics" platform.

Soil use and its proper management are key elements of sustainable development. However, given the complexity of the issue, it is necessary to address it using an interdisciplinary approach. The proposed work aims to analyze the consequences, in terms of damage assessment, of two different soil management systems of a cereal crop through the use of Life Cycle Assessment (LCA) methodology. One system follows a traditional approach and the other utilizes a Decision Support System (DSS). The long-term impacts on human health, ecosystems, and resource availability are calculated by employing the ReCiPe 2016 endpoint method. The results show notable reductions in resource use and environmental impacts with DSS, with a 41% decrease in damage to human health, a 24% reduction in ecosystem damage, and a 23% reduction in resource use. Hence, implementing new technologies and new management strategies in agriculture can lead to more sustainable management choices and can avoid long-term burdens compared to a traditional approach.

Active life monitoring via chemosensitive sensors could hold promise for enhancing athlete monitoring, training optimization, and performance in athletes. The present work investigates a resistive flex sensor (RFS) in the guise of a chemical sensor. Its carbon ‘texture’ has shown to be sensitive to CO2, O2, and RH changes; moreover, different bending conditions can modulate its sensitivity and selectivity for these gases and vapors. A three-step feasibility study is presented including: design and fabrication of the electronic read-out and control; calibration of the sensors to CO2, O2 and RH; and a morphological study of the material when interacting with the gas and vapor molecules. The 0.1 mm−1 curvature performs best among the tested configurations. It shows a linear response curve for each gas, the ranges of concentrations are adequate, and the sensitivity is good for all gases. The curvature can be modulated during data acquisition to tailor the sensitivity and selectivity for a specific gas. In particular, good results have been obtained with a curvature of 0.1 mm−1. For O2 in the range of 20–70%, the sensor has a sensitivity of 0.7 mV/%. For CO2 in the range of 4–80%, the sensitivity is 3.7 mV/%, and for RH the sensitivity is 33 mV/%. Additionally, a working principle, based on observation via scanning electron microscopy, has been proposed to explain the chemical sensing potential of this sensor. Bending seems to enlarge the cracks present in the RFS coverage; this change accounts for the altered selectivity depending on the sensor’s curvature. Further studies are needed to confirm result’s reliability and the correctness of the interpretation.

Background: Recent studies have shown that oxidative stress plays a relevant role in Alzheimer’s disease (AD), and in the pathogenesis of vascular dementia (VaD). New diagnostic methods look for biological samples with non-invasive sampling methods. Among these, saliva shows an increase in oxidative stress products, thus a corresponding reduction in antioxidant products were found in dementia cases compared to healthy controls. Compounds identified in saliva include some hydrocarbons whose production has been related to the presence of reactive oxygen species. Objective: The hypothesis is that the voltammetric analysis performed on saliva could be a useful test for diagnosing dementia, potentially discriminating between AD and VaD. Methods: A single-center observational study was conducted on patients referred to the dementia clinic in the Neurology area and healthy controls recruited in the Orthopedics area of the Campus Bio-Medico Hospital in Rome. The study was aimed at evaluating the discriminative properties of salivary voltammetric analysis between healthy subjects and patients with dementia and, as a secondary outcome, between AD and VaD. A total of 69 subjects were enrolled, including 29 healthy controls, 20 patients with AD, and 20 patients with VaD. The degree of cognitive impairment was classified on the basis of the Mini-Mental State Examination score. Results: The results obtained are promising, with an accuracy of 79.7%, a sensitivity of 82.5%, and a specificity of 75.8%, in the discrimination of dementia versus controls. Conclusions: The methods tested demonstrate to be relevant in the discrimination between dementia and controls. A confirmatory study is already running.

The need for accurate information and the availability of novel tool and technological advances in agriculture have given rise to innovative autonomous systems. The aim is to monitor key parameters for optimal water and fertilizer management. A key issue in precision agriculture is the in situ monitoring of soil macronutrients. Here, a proof-of-concept study was conducted that tested two types of sensors capable of capturing both the electrochemical response of the soil and the electrical potential generated by the interaction between the soil and plants. These two sensors can be used to monitor large areas using a network approach, due to their small size and low power consumption. The voltammetric sensor (BIONOTE-L) proved to be able to characterize different soil samples. It was able, indeed, to provide a reproducible voltammetric fingerprint specific for each soil type, and to monitor the concentration of CaCl2 and NaCl in the soil. BIONOTE-L can be coupled to a device capable of capturing the energy produced by interactions between plants and soil. As a consequence, the functionality of the microsystem node when applied in a large-area monitoring network can be extended. Additional calibrations will be performed to fully characterize the instrument node, to implement the network, and to specialize it for a particular application in the field.

Ladder networks are typically used for passive filters, and they also represent a good equivalent model for mechanical, chemical and thermal system. These networks could be also used in the study of sensor networks, in order to control their parameters (and their static and dynamic behavior) by interrogating each point of the network; in particular, each node of the network brings the information of each cells of the network. The aim of this work is to provide an automatic model for the monitoring of a sensor network. For this purpose, a network of distributed sensors evaluating the characteristic of the network when the sensors change their features has been studied. This approach was achieved by using Quartz Micro Balances (QMB), a type of sensors generally applied in environmental and medical field.

Pneumopipe is a patented device for breath collection on cartridge. Its evolution towards a portable device for mobile breath analysis in points-of-care or at home, pass through the embedding of the device with sensors for the improvement of the collection process and for a ‘preview’ breath analysis. Here cyclic voltammetry has been designed and tested, with a specific low-power and low-size electronics, in order to monitor carbon dioxide and oxygen in exhaled breath during collection. The results are promising and the sensors will be integrated in the Pneumopipe, also allowing electrochemical breath analysis in liquids, as already demonstrated in a previous work.