Use of Sensor Array Analysis to Detect Ovarian Cancer through Breath, Urine, and Blood: A Case-Control Study.

Ovarian cancer (OC) is the eighth most common cancer in women. Since screening programs do not exist, it is often diagnosed in advanced stages. Today, the detection of OC is based on clinical examination, transvaginal ultrasound (US), and serum biomarker (Carbohydrate Antigen 125 (CA 125) and Human Epididymis Protein 4 (HE4)) dosage, with a sensitivity of 88% and 95%, respectively, and a specificity of 84% for US and 76% for biomarkers. These methods are clearly not enough, and OC in its early stages is often missed. Many scientists have recently focused their attention on volatile organic compounds (VOCs). These are gaseous molecules, found in the breath, that could provide interesting information on several diseases, including solid tumors. To detect VOCs, an electronic nose was invented by a group of researchers. A similar device, the e-tongue, was later created to detect specific molecules in liquids. For the first time in the literature, we investigated the potential use of the electronic nose and the electronic tongue to detect ovarian cancer not just from breath but also from urine, blood, and plasma samples.

Development of a Universal Validation Protocol and an Open-Source Database for Multi-Contextual Facial Expression Recognition.

Facial expression recognition (FER) poses a complex challenge due to diverse factors such as facial morphology variations, lighting conditions, and cultural nuances in emotion representation. To address these hurdles, specific FER algorithms leverage advanced data analysis for inferring emotional states from facial expressions. In this study, we introduce a universal validation methodology assessing any FER algorithm's performance through a web application where subjects respond to emotive images. We present the labelled data database, FeelPix, generated from facial landmark coordinates during FER algorithm validation. FeelPix is available to train and test generic FER algorithms, accurately identifying users' facial expressions. A testing algorithm classifies emotions based on FeelPix data, ensuring its reliability. Designed as a computationally lightweight solution, it finds applications in online systems. Our contribution improves facial expression recognition, enabling the identification and interpretation of emotions associated with facial expressions, offering profound insights into individuals' emotional reactions. This contribution has implications for healthcare, security, human-computer interaction, and entertainment.

Detecting Dementia by Saliva Analysis: A Fingerprinting Unobtrusive Method Based on a Fast and Cheap Sensor System.

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.

Microsystem Nodes for Soil Monitoring via an Energy Mapping Network: A Proof-of-Concept Preliminary Study.

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.

Micromachined Tools Using Acoustic Wave Triggering for the Interaction with the Growth of Plant Biological Systems.

A plant biological system is exposed to external influences. In general, each plant has its characteristics and needs with specific interaction mechanisms adapted to its survival. Interactions between systems can be examined and modeled as energy exchanges of mechanical, chemical or electrical variables. Thus, each specific interaction can be examined by triggering the system via a specific stimulus. The objective of this work was to study a specific stimulus (mechanical stimulation) as a driver of plants and their interaction with the environment. In particular, the experimental design concerns the setting up and testing of an automatic source of mechanical stimuli at different wavelengths, generated by an electromechanical transducer, to induce a micro-interaction in plants (or in parts of them) that produces a specific behavior (hypothesis) of plants. Four different experimental setups were developed for this work, each pursuing the same objective: the analysis of the germination process induced by stimulation by sound waves in the audible range. It can be said that the introduction of sound waves as a stimulant or a brake for the growth of plants can offer significant advantages when used on a large scale in the primary sector, since these effects can be used instead of polluting chemical solutions.

Gut Microbiota and Related Electronic Multisensorial System Changes in Subjects With Symptomatic Uncomplicated Diverticular Disease Undergoing Rifaximin Therapy.

Background: Intestinal dysbiosis might play a pathogenetic role in subjects with symptomatic uncomplicated diverticular disease (SUDD), but the effect of rifaximin therapy has been scantly explored with regard to gut microbiota variations in patients with SUDD. Aims: To verify to which extent rifaximin treatment affects the gut microbiota and whether an electronic multisensorial assessment of stools and breath has the potential for detecting these changes. Methods: Breath and stool samples were collected from consecutive patients with SUDD before and after a 7 days' therapy with rifaximin. Stool microbiota was assessed, and the electronic multisensorial assessment was carried out by means of the BIONOTE electronic (e-)tongue in stools and (e-)nose in breath. Results: Forty-three subjects (female 60%, median age 66 years) were included, and 20 (47%) reported clinical improvement after rifaximin therapy. Alpha and beta diversity of stool microbiota did not significantly change after treatment, while a significant variation of selected taxa was shown (i.e., Citrobacter, Coprococcus, Anaerotruncus, Blautia, Eggerthella lenta, Dehalobacterium, SMB53, and Haemophilus parainfluenzae). Overall, the electronic multisensorial system suboptimally mirrored microbiota changes, but it was able to efficiently predict patients' clinical improvement after rifaximin with accuracies ranging from 0.81 to 0.98. Conclusions: In patients with SUDD, rifaximin administration is associated with significant variation of selected taxa. While inaccurate in predicting gut microbiota change, an electronic multisensorial system, made up of e-tongue and e-nose, was able to predict clinical improvement, thus potentially qualifying as an easy and cheap tool to forecast subjects taking most likely benefit from rifaximin therapy.

A Multi-Sensor System for Sea Water Iodide Monitoring and Seafood Quality Assurance: Proof-of-Concept Study.

Iodine is a trace chemical element fundamental for a healthy human organism. Iodine deficiency affects about 2 billion people worldwide causing from mild to severe neurological impairment, especially in children. Nevertheless, an adequate nutritional intake is considered the best approach to prevent such disorders. Iodine is present in seawater and seafood, and its common forms in the diet are iodide and iodate; most iodide in seawater is caused by the biological reduction of the thermodynamically stable iodate species. On this basis, a multisensor instrument which is able to perform a multidimensional assessment, evaluating iodide content in seawater and seafood (via an electrochemical sensor) and discriminating when the seafood is fresh or defrosted quality (via a Quartz Micro balance (QMB)-based volatile and gas sensor), is strategic for seafood quality assurance. Moreover, an electronic interface has been opportunely designed and simulated for a low-power portable release of the device, which should be able to identify seafood over or under an iodide threshold previously selected. The electrochemical sensor has been successfully calibrated in the range 10-640 µg/L, obtaining a root mean square error in cross validation (RMSECV) of only 1.6 µg/L. Fresh and defrosted samples of cod, sea bream and blue whiting fish have been correctly discriminated. This proof-of-concept work has demonstrated the feasibility of the proposed application which must be replicated in a real scenario.

Design of an Innovative Methodology for Cerebrospinal Fluid Analysis: Preliminary Results.

Cerebrospinal fluid (CSF) analysis supports diagnosis of neurodegenerative diseases (NDs), however a number of issues limits its potentialities in clinical practice. Here, a newly developed technique for fluid voltammetry, relying on a simple sensor (BIOsensor-based multisensorial system for mimicking Nose, Tongue and Eyes, BIONOTE), was used to test the applicability for CSF analysis. BIONOTE was initially calibrated on an artificial CSF-like solution and then applied on human CSF, either immediately after collection or after refrigerated storage. Following optimization, it was used to evaluate 11 CSF samples correlating the electrochemical dataset with CSF routine parameters and biomarkers of neurodegeneration. Multivariate data analysis was performed for model elaboration and calibration using principal component analysis and partial least squares discriminant analysis. BIONOTE presented a high capacity to predict both physiological and pathological constituents of artificial CSF. It differentiated distinct fresh human CSF samples well but lost accuracy after refrigerated storage. The electrochemical analysis-derived data correlated with either CSF routine cytochemical indexes or a biomarker of neurodegeneration. BIONOTE resulted as being a reliable system for electrochemical analysis of CSF. The CSF fingerprint provided by the sensor has shown itself to be sensitive to CSF modification, thus it is potentially representative of CSF alteration. This result opens the way to its testing in further study addressed at assessing the clinical relevance of the methodology. Because of its advantages due to the ease and rapidity of the methodology, a validation study is now required to translate the technique into clinical practice and improve diagnostic workup of NDs.

Development and Test of a Portable ECG Device with Dry Capacitive Electrodes and Driven Right Leg Circuit.

The use of wearable sensors for health monitoring is rapidly growing. Over the past decade, wearable technology has gained much attention from the tech industry for commercial reasons and the interest of researchers and clinicians for reasons related to its potential benefit on patients' health. Wearable devices use advanced and specialized sensors able to monitor not only activity parameters, such as heart rate or step count, but also physiological parameters, such as heart electrical activity or blood pressure. Electrocardiogram (ECG) monitoring is becoming one of the most attractive health-related features of modern smartwatches, and, because cardiovascular disease (CVD) is one of the leading causes of death globally, the use of a smartwatch to monitor patients could greatly impact the disease outcomes on health care systems. Commercial wearable devices are able to record just single-lead ECG using a couple of metallic contact dry electrodes. This kind of measurement can be used only for arrhythmia diagnosis. For the diagnosis of other cardiac disorders, additional ECG leads are required. In this study, we characterized an electronic interface to be used with multiple contactless capacitive electrodes in order to develop a wearable ECG device able to perform several lead measurements. We verified the ability of the electronic interface to amplify differential biopotentials and to reject common-mode signals produced by electromagnetic interference (EMI). We developed a portable device based on the studied electronic interface that represents a prototype system for further developments. We evaluated the performances of the developed device. The signal-to-noise ratio of the output signal is favorable, and all the features needed for a clinical evaluation (P waves, QRS complexes and T waves) are clearly readable.

BIONOTE as an Innovative Biosensor for Measuring Endocannabinoid Levels.

In this study, a novel approach was developed to quantify endocannabinoids (eCBs), and was based on the liquid biosensor BIONOTE. This device is composed of a probe that can be immersed in a solution, and an electronic interface that can record a current related to the oxy-reductive reactions occurring in the sample. The two most representative members of eCBs have been analysed in vitro by BIONOTE: anandamide (N-arachidonoylethanolamine, AEA) and 2-arachidonoylglycerol (2-AG). Bovine serum albumin was used to functionalize the probe and improve the sensibility of the whole analytical system. We show that BIONOTE is able to detect both AEA and 2-AG at concentrations in the low nanomolar range, and to discriminate between these eCBs and their moieties arachidonic acid, ethanolamine and glycerol. Notably, BIONOTE distinguished these five different molecules, and it was also able to quantify AEA in human plasma. Although this is just a proof-of-concept study, we suggest BIONOTE as a cheap and user-friendly prototype sensor for high throughput quantitation of eCB content in biological matrices, with an apparent diagnostic potential for tomorrow's medicine.

Integration of voltammetric analysis, protein electrophoresis and pH measurement for diagnosis of pleural effusions: a non-conventional diagnostic approach.

Pleural effusion is very common, but an etiologic diagnosis is often difficult. We used three unconventional diagnostic techniques (voltammetric analysis, protein electrophoresis and pH measurement) performed on pleural effusion to do a preliminary distinction between a neoplastic and a non-neoplastic origin. Pleural fluid samples were collected through thoracentesis, thoracoscopy, or post-surgery pleural drainage of 116 patients admitted to acute care wards. Samples were analyzed with the three unconventional techniques: voltammetric analysis using the BIONOTE system, capillary electrophoresis and pH measurement using a potentiometric method. The BIONOTE system is an innovative system that performs a cyclic voltammetric analysis of a biological liquid sample. The final output of the electrochemical analysis is an electrical pattern that represents a fingerprint of the analyzed sample and each sample has a different fingerprint. Data from the three unconventional diagnostic techniques were analyzed using partial least squares discriminant analysis to discriminate neoplastic from non-neoplastic effusions; we also evaluated sensitivity, specificity and percentage of correct classification. The mean age was 68 years (SD: 12); 78 (67.24%) participants were men. Results obtained from all the unconventional techniques employed showed that neoplastic and non-neoplastic pleural effusions were correctly classified in 80.2% of cases, with a sensitivity of 77% and specificity of 83%. The combined use of voltammetric analysis, protein electrophoresis and pH measurement of pleural fluid can easily and quickly distinguish a neoplastic from a non-neoplastic pleural effusion with reliable accuracy and represents an innovative diagnostic approach. In fact, this protocol can be executed in just few minutes directly in the patient's bed and it holds great promise to improve the prognosis and therapeutic chances.

Characterization of inflammatory profile by breath analysis in chronic coronary syndromes.

AIMS: Exhaled breath contains thousands of volatile organic compounds (VOCs) produced during various metabolic processes both in health and disease.Analysis of breath with electronic nose BIONOTE-V allows modifications of exhaled VOCs to be studied, which are clinically recognized to be a marker for several disorders, including heart failure. New noninvasive tests based on VOCs analysis might be a useful tool for early detection of chronic coronary syndromes (CCS). METHODS: Exhaled air was collected and measured in individuals with an indication to perform invasive coronary angiography (ICA). All patients' samples were obtained before ICA. RESULTS: Analysis with BIONOTE-V was performed in a total cohort of 42 patients consecutively enrolled, of whom 19 did not require myocardial revascularization and 23 with indication for myocardial revascularization. BIONOTE-V was able to correctly identify 18 out of 23 patients affected by severe coronary artery disease (sensitivity = 78.3% and specificity = 68.4%). Our predicted model had a tight correlation with SYNTAX score (error of the BIONOTE-V = 15). CONCLUSION: CCS patients have a distinctive fingerprint of exhaled breath, and analysis by BIONOTE-V has the potential for identifying these patients. Moreover, it seems that this technique can correctly identify patients according to anatomical disease severity at ICA. If the preliminary data of this proof of concept study will be confirmed, this rapid and noninvasive diagnostic tool able to identify CCS might have an impact in routine clinical practice.

Voltammetric analysis for distinguishing portal hypertension-related from malignancy-related ascites: A proof of concept study.

BACKGROUND: Serum-ascites albumin gradient (SAAG) remains the most sensitive and specific marker for the differentiation of ascites due to portal hypertension from ascites due to other causes. SAAG has some limitations and may fail in selected conditions. Voltammetric analysis (VA) has been used for the detection of electroactive species of biological significance and has proven effective for detection infections in biological fluids. AIMS: In this study, we compared the accuracy of voltammetric analysis (VA) with that of SAAG to differentiate ascites due to portal hypertension from that having a different origin. METHODS: 80 ascites samples were obtained from patients undergoing paracentesis at the Campus Bio-Medico Hospital of Rome. VA was performed using the BIONOTE device. The ability of VA to discriminate ascitic fluid etiology and biochemical parameters was evaluated using Partial Least Square Discriminant Analysis (PLS-DA), with ten-fold cross-validations. RESULTS: Mean age was 68.6 years (SD 12.5), 58% were male. Ascites was secondary to only portal hypertension in 72.5% of cases (58 subjects) and it was secondary to a baseline neoplastic disease in 27.5% of cases (22 subjects). Compared to SAAG≥1.1, e-tongue predicted ascites from portal hypertension with a better accuracy (92.5% Vs 87.5%); sensitivity (98.3% Vs 94.8%); specificity (77.3% Vs 68.2%); predictive values (PPV 91.9% Vs 88.7% and NPV 94.4% Vs 83.3%). VA correctly classified ascites etiology in 57/58 (98.2%) of cases with portal hypertension and in 17/22 (77.2%) of cases with malignancy. Instead, VA showed poor predictive capacities towards total white blood count and polymorphonuclear cell count. CONCLUSIONS: According to this proof of concept study, VA qualifies as a promising low-cost and easy method to discriminate between ascites secondary to portal hypertension and ascites due to malignancy.

CO2 and O2 Detection by Electric Field Sensors.

In this work an array of chemical sensors for gas detection has been developed, starting with a commercial sensor platform developed by Microchip (GestIC), which is normally used to detect, trace, and classify hand movements in space. The system is based on electric field changes, and in this work, it has been used as mechanism revealing the adsorption of chemical species CO2 and O2. The system is composed of five electrodes, and their responses were obtained by interfacing the sensors with an acquisition board based on an ATMEGA 328 microprocessor (Atmel MEGA AVR microcontroller). A dedicated measurement chamber was designed and prototyped in acrylonitrile butadiene styrene (ABS) using an Ultimaker3 3D printer. The measurement cell size is 120 × 85 mm. Anthocyanins (red rose) were used as a sensing material in order to functionalize the sensor surface. The sensor was calibrated using different concentrations of oxygen and carbon dioxide, ranging from 5% to 25%, mixed with water vapor in the range from 50% to 90%. The sensor exhibits good repeatability for CO2 concentrations. To better understand the sensor response characteristics, sensitivity and resolution were calculated from the response curves at different working points. The sensitivity is in the order of magnitude of tens to hundreds of µV/% for CO2, and of µV/% in the case of O2. The resolution is in the range of 10-1%-10-3% for CO2, and it is around 10-1% for O2. The system could be specialized for different fields, for environmental, medical, and food applications.

A Sensor Platform for Athletes' Training Supervision: A Proof of Concept Study.

One of the basic needs of professional athletes is the real-time and non-invasive monitoring of their activities. The use of these kind of data is necessary to develop strategies for specific tailored training in order to improve performances. The sensor system presented in this work has the aim to adopt a novel approach for the monitoring of physiological parameters, and athletes' performances, during their training. The anaerobic threshold is herein identified with the monitoring of the lactate concentration and the respiratory parameters. The data collected by the sensor are used to build a model using a supervised method (based on the partial least squares method, PLS) to predict the values of the parameters of interest. The sensor is able to measure the lactate concentration from a sample of saliva and it can estimate a respiratory parameter, such as maximal oxygen consumption, maximal carbon dioxide production and respiratory rate from a sample of exhaled breath. The main advantages of the device are the low power; the wireless communication; and the non-invasive sampling method, which allow its use in a real context of sport practice.

Use of voltammetric analysis for fast and objective discrimination of the etiology, evolution, and bacterial infection of lower limb ulcers.

The timely recognition of leg ulcers (LU) etiology and infection is pivotal to optimize management and accelerate healing. The objective of this proof-of-concept study was to test the diagnostic performance of voltammetric analysis (VA) on ulcer exudate to identify LU etiology, infection, and predict clinical course. We enrolled 25 patients aged ≥60 years, affected by 42 venous/arterial LU. Clinical examination (Leg Ulcer Measurement Tool score, LUMT), swab culture, and VA were performed at baseline and 30 days. The ability of VA to predict outcomes was tested using partial least square-discrimination analysis. Mean age was 75 years (SD 11.1), 9/25 were male. The accuracy, sensitivity, and specificity vs. etiology were 97.4, 100%, and 94.1%, respectively; the corresponding figures were 95.2%, 100%, 88.9%, for infection and 94%, 84.6%, 100% for predicted objective LUMT worsening. VA is a promising diagnostic/prognostic tool for management of LU that may allow a more timely targeted therapy.

Identification and prospective stability of electronic nose (eNose)-derived inflammatory phenotypes in patients with severe asthma.

BACKGROUND: Severe asthma is a heterogeneous condition, as shown by independent cluster analyses based on demographic, clinical, and inflammatory characteristics. A next step is to identify molecularly driven phenotypes using "omics" technologies. Molecular fingerprints of exhaled breath are associated with inflammation and can qualify as noninvasive assessment of severe asthma phenotypes. OBJECTIVES: We aimed (1) to identify severe asthma phenotypes using exhaled metabolomic fingerprints obtained from a composite of electronic noses (eNoses) and (2) to assess the stability of eNose-derived phenotypes in relation to within-patient clinical and inflammatory changes. METHODS: In this longitudinal multicenter study exhaled breath samples were taken from an unselected subset of adults with severe asthma from the U-BIOPRED cohort. Exhaled metabolites were analyzed centrally by using an assembly of eNoses. Unsupervised Ward clustering enhanced by similarity profile analysis together with K-means clustering was performed. For internal validation, partitioning around medoids and topological data analysis were applied. Samples at 12 to 18 months of prospective follow-up were used to assess longitudinal within-patient stability. RESULTS: Data were available for 78 subjects (age, 55 years [interquartile range, 45-64 years]; 41% male). Three eNose-driven clusters (n = 26/33/19) were revealed, showing differences in circulating eosinophil (P = .045) and neutrophil (P = .017) percentages and ratios of patients using oral corticosteroids (P = .035). Longitudinal within-patient cluster stability was associated with changes in sputum eosinophil percentages (P = .045). CONCLUSIONS: We have identified and followed up exhaled molecular phenotypes of severe asthma, which were associated with changing inflammatory profile and oral steroid use. This suggests that breath analysis can contribute to the management of severe asthma.

Resonant Directly Coupled Inductors⁻Capacitors Ladder Network Shows a New, Interesting Property Useful for Application in the Sensor Field, Down to Micrometric Dimensions.

The study of ladder networks made by sequences of directly coupled inductor⁻capacitor single cells has led us to discover a new property, which may be of certain interest in the sensor field. In the case of n cells, the n-frequencies vector characterizing each node may allow for the identification of that capacitor (sensor), which has experienced a variation of its nominal value. This localization is possible independently from the observable node of the ladder network as proven by the application of the following multivariate data analysis techniques: principal component analysis and partial least square discriminant analysis. This property can be applied on a large scale down to micrometric dimensions in agreement with the technologic ability to shrink the capacitive sensor dimensions.

Methodological considerations for large-scale breath analysis studies: lessons from the U-BIOPRED severe asthma project.

Methods for breath sampling and analysis require robust quality assessment to minimise the risk of false discoveries. Planning large-scale multi-site breath metabolite profiling studies also requires careful consideration of systematic and random variation as a result of sampling and analysis techniques. In this study we use breath sample data from the recent U-BIOPRED cohort to evaluate and discuss some important methodological considerations such as batch variation and correction, variation between sites, storage and transportation, as well as inter-instrument analytical differences. Based on this we provide a summary of recommended best practices for new large scale multi-site studies.

Advances in the Electronics for Cyclic Voltammetry: the Case of Gas Detection by Using Microfabricated Electrodes.

This paper presents an advanced voltammetric system to be used as electronic tongue for liquid and gas analysis. It has been designed to be more flexible and accurate with respect to other existing and similar systems. It features improved electronics and additional operative conditions. Among others these include the possibility to optically excite the solution and to treat the output signal by a differentiation process in order to better evidence the existence of small details in the response curve. Finally by the same type of tongue preliminary results are shown dealing with O2 and CO2 concentration measurements in appropriate solutions.