Noninvasive Diagnosis Of Diabetes Research Articles

Ultrasensitive Acetone Gas Sensor Based on a K/Sn-Co3O4 Porous Microsphere for Noninvasive Diabetes Diagnosis.

The detection of acetone in human exhaled breath is crucial for the noninvasive diagnosis of diabetes. However, the direct and reliable detection of acetone in exhaled breath with high humidity at the parts per billion level remains a great challenge. Here, an ultrasensitive acetone gas sensor based on a K/Sn-Co3O4 porous microsphere was reported. The sensor demonstrates a detection limit of up to 100 ppb, along with excellent repeatability and selectivity. Remarkably, without the removal of water vapor from exhaled breath, the sensor can accurately distinguish diabetic patients and healthy individuals according to the difference in acetone concentrations, demonstrating its great potential for diabetes diagnosis. The enhanced sensitivity of the sensor is attributed to the increased oxygen adsorption on the material surface due to K/Sn codoping and the stronger coadsorption of Sn-K atoms to acetone molecules. These findings shed light on the mechanisms underlying the sensor's improved performance.

Machine-learning-based diabetes classification method using blood flow oscillations and Pearson correlation analysis of feature importance

Abstract Diabetes is a global health issue affecting millions of people and is related to high morbidity and mortality rates. Current diagnostic methods are primarily invasive, involving blood sampling, which can lead to infection and increased patient stress. As a result, there is a growing need for noninvasive diabetes diagnostic methods that are both accurate and fast. High measurement accuracy and fast measurement time are essential for effective noninvasive diabetes diagnosis; these can be achieved using diffuse speckle contrast analysis (DSCA) systems and artificial intelligence algorithms. In this study, we use a machine learning algorithm to analyze rat blood flow signals measured using a DSCA system with simple operation, easy fabrication, and fast measurement for helping diagnose diabetes. The results confirmed that the machine learning algorithm for analyzing blood flow oscillation data shows good potential for diabetes classification. Furthermore, analyzing the blood flow reactivity test revealed that blood flow signals can be quickly measured for diabetes classification. Finally, we evaluated the influence of each blood flow oscillation data on diabetes classification through feature importance and Pearson correlation analysis. The results of this study should provide a basis for the future development of hemodynamic-based disease diagnostic methods.

Efficient mixed-potential acetone sensor with yttria-stabilized zirconia and porous Co3O4 nanofoam sensing electrode for hazardous gas monitoring and breath analysis

For hazardous gas monitoring and non-invasive diagnosis of diabetes using breath analysis, porous foams assembled by Co3O4 nanoparticles were designed as sensing electrode materials to fabricate efficient yttria-stabilized zirconia (YSZ)-based acetone sensors. The sensitivity of the sensors was improved by varying the sintering temperature to regulate the morphology. Compared to other materials sintered at different temperatures, the porous Co3O4 nanofoams sintered at 800 °C exhibited the highest electrochemical catalytic activity during the electrochemical test. The response of the corresponding Co3O4-based sensor to 10 ppm acetone was –77.2 mV and it exhibited fast response and recovery times. Moreover, the fabricated sensor achieved a low detection limit of 0.05 ppm and a high sensitivity of –56 mV/decade in the acetone concentration range of 1–20 ppm. The sensor also exhibited excellent repeatability, acceptable selectivity, good O2/humidity resistance, and long-term stability during continuous measurements for over 30 days. Moreover, the fabricated sensor was used to determine the acetone concentration in the exhaled breaths of patients with diabetic ketosis. The results indicated that it could distinguish between healthy individuals and patients with diabetic ketosis, thereby proving its abilities to diagnose and monitor diabetic ketosis. Based on its excellent sensitivity and exhaled breath measurement results, the developed sensor has broad application prospects.

YSZ-based mixed-potential acetone sensor with LaBaCo2O5+δ sensitive electrode for diabetic diagnosis

The acetone concentration in exhaled breath can be utilized for the noninvasive diagnosis of diabetes. In this study, to fulfil the requirements of breath analysis for diabetes diagnosis, high-performance mixed-potential acetone sensors based on yttria-stabilized zirconia were successfully developed by employing a novel sensitive electrode (SE) material, namely, LaBaCo2O5+δ. Electrochemical measurement results revealed that the LaBaCo2O5+δ SE, sintered at 1000 ℃, exhibited outstanding electrochemical catalytic activity toward acetone. The device incorporating this SE demonstrated a peak response of −46 mV to 100 ppm acetone at 655 ℃. Sensitive performance assessments indicated the capability of the sensor to detect acetone concentrations ranging from 200 ppb to 100 ppm. Based on the mixed-potential model, the sensitivities of the current device were quantified as −1.3 and −53.7 mV/decade within the acetone concentration ranges of 0.2−5 and 10−100 ppm, respectively. Furthermore, the fabricated sensor exhibited exceptional selectivity, repeatability, humidity resistance, and long-term stability. In addition, simulated expiratory experiments showed that the developed sensor has substantial potential for application in breath analysis for diabetes diagnosis and noninvasive monitoring of a patient’s physical status.

Dual functionalized Co3O4 porous cages with Pd and Co-MOF for acetone gas sensing under high humidity

Metal oxide semiconductor (MOS) gas sensors play a crucial role in gas sensors. However, the effect of higher ambient humidity on the sensitivity of MOS gas sensors is still largely an unsolved problem, limiting the application areas of sensors. In this work, an ultra-stable and sensitive acetone gas sensor based on dual functionalized Co3O4 porous cages with Pd and Co-MOF at high humidity is reported. The dual functionalized porous cages are made of MOF-derived Co3O4 porous cages substrate, Pd particles and hydrophobic Co-MOF coatings. The prepared porous cages composite exhibits humidity-independent and highly sensitive gas-sensing behavior to acetone under 70–90 % relative humidity (RH). The results indicate that the hydrophobic Co-MOF coatings not only offer excellent anti-humidity, but also show the ability of enrichment of low concentration acetone gas molecules, thus improving the sensitivity and response time of acetone detection. Meanwhile, due to the electronic spillover effect of Pd, porous cages composite possesses remarkable properties of high sensitivity, short response and recovery time, as well as low working temperature. The unique sensing characteristic makes the dual functionalized porous cages composite sensor a potential candidate for reliable noninvasive diagnosis of diabetes via exhaled breath analyses.

Construction of mesoporous silica-implanted tungsten oxides for selective acetone gas sensing

As a key biomarker for noninvasive diagnosis of diabetes, the selective detection of trace acetone in exhaled gas using a portable and low-cost device remains a great challenge. Semiconductor metal oxide (SMO) based gas sensors have drawn signification attention due to their potential in miniaturization, user-friendliness, high cost-effectiveness and selective real-time detection for noninvasive clinical diagnosis. Herein, we propose a one-pot solvent evaporation induced tricomponent co-assembly strategy to design a novel ordered mesoporous SMO of silica-implanted WO3 (SiO2/WO3) as sensing materials for trace acetone detection. The controlled co-assembly of silicon and tungsten precursors and amphiphilic diblock copolymer poly(ethylene oxide)-block-polystyrene (PEO-b-PS), and the subsequent thermal treatment enable the local lattice disorder of WO3 induced by the amorphous silica and the formation of ordered mesoporous SiO2/WO3 hybrid walls with a unique metastable ε-phase WO3 framework. The obtained mesoporous SiO2/WO3 composites possess highly crystalline framework with large uniform pore size (12.0–13.3 nm), high surface area (99–113 m2/g) and pore volume (0.17–0.23 cm3/g). Typically, the as-fabricated gas sensor based on mesoporous 2.5%SiO2/WO3 exhibits rapid response/recovery rate (5/17 s), superior sensitivity (Rair/Rgas = 105 for 50 ppm acetone), as well as high selectivity towards acetone. The limit of detection is as low as 0.25 ppm, which is considerably lower than the thresh value of acetone concentration (>1.1 ppm) in the exhaled breath of diabetic patients, demonstrating its great prospect in real-time monitoring in diabetes diagnosis. Moreover, the mesoporous 2.5%SiO2/WO3 sensor is integrated into a wireless sensing module connected to a smart phone, providing a convenient real-time detection of acetone.

Automated Multi-Injection Gas Chromatography-Photoionization Detection (AMI-GC-PID) for Qualitative and Quantitative Determination of Urinary Acetone

Acetone is produced from fat metabolism which is increased in diabetic patients due to the disruption of glucose metabolism. Therefore, urinary acetone is expected to be a biomarker for noninvasive diagnosis of diabetes. Although several techniques have been developed for urinary acetone, none meet the clinical needs of automated multi-injection for numerous samples. In this study, we developed automated multi-injection gas chromatography-photoionization detection (AMI-GC-PID) to determine urinary acetone. First, we optimized the sample preprocessing during urine collection and storage before detection and established a standard protocol. The urine was immediately sealed in headspace bottles, stored at 20 °C for 1 h, and equilibrated for 20 min at 80 °C before analysis. Next we evaluated the repeatability of the method and the influence of the urine matrix. The relative standard deviations (RSDs) of the intra-day (nine measurements) and inter-day (three days) measurements were less than 5%. The recovery rate was 97.4% ± 4.6%. AMI-GC-PID was applied to determine urinary acetone in 44 diabetic patients and 29 healthy subjects. The median concentration of urinary acetone was much higher in diabetic patients than in healthy controls (2019 μg/L compared 699 μg/L). The results show that AMI-GC-PID possesses suitable analytical figures of merit for urinary acetone with broad applications in the noninvasive diagnosis of diabetes.

Unlocking Diabetic Acetone Vapor Detection by A Portable Metal-Organic Framework-Based Turn-On Optical Sensor Device.

Despite exhaled human breath having enabled noninvasive diabetes diagnosis, selective acetone vapor detection by fluorescence approach in the diabetic range (1.8-3.5ppm) remains a long-standing challenge. A set of water-resistant luminescent metal-organic framework (MOF)-based composites have been reported for detecting acetone vapor in the diabetic range with a limit of detection of 200 ppb. The luminescent materials possess the ability to selectively detect acetone vapor from a mixture comprising nitrogen, oxygen, carbon dioxide, water vapor, and alcohol vapor, which are prevalent in exhaled breath. It is noteworthy that this is the first luminescent MOF material capable of selectively detecting acetone vapor in the diabetic range via a turn-on mechanism. The material can be reused within a matter of minutes under ambient conditions. Industrially pertinent electrospun luminescent fibers are likewise fabricated alongside various luminescent films for selective detection of ultratrace quantities of acetone vapor present in the air. Ab initio theoretical calculations combined with in situ synchrotron-based dosing studies uncovered the material's remarkable hypersensitivity toward acetone vapor. Finally, a freshly designed prototype fluorescence-based portable optical sensor is utilized as a proof-of-concept for the rapid detection of acetone vapor within the diabetic range.

Vertically aligned mesoporous Ce doped NiO nanowalls with multilevel gas channels for high-performance acetone gas sensors

In recent years, the potential use and development of acetone gas sensors in health monitoring and self-diagnostic applications has attracted increasing attention. Thus, accurate sensing of acetone is of crucial importance in the non-invasive diagnosis of diabetes. However, challenges remain in the detection of acetone, such as high concentrations detection limit, high operating temperature and slow response dynamics. Herein, vertically aligned Ce-doped mesoporous NiO nanowalls are prepared on Ag-Pd interdigitated electrode substrate using an in-situ growth method. The gas sensing performances show that the as-made 4 mol% Ce-doped NiO nanowalls has excellent gas response value (3.2) to 10 ppm acetone at a lower operating temperature (175 ℃) with fast adsorption and desorption equilibrium (13 s/7 s). Vertically aligned mesoporous NiO nanowalls doped by Ce element could pave the way for ultra-sensitive and low power consumption acetone gas sensors for breath gas detection.

Black phosphorus nanosheets-sensitized Zn-doped α-Fe2O3 nanoclusters for trace acetone detection

Acetone vapor not only poses a significant threat to the ecological environment and human health but serves as a specific volatile organic compounds (VOCs) biomarker within the exhaled breath to distinguish individual diabetes, thereby urgently necessitating a sensitive and selective acetone detection. To this end, conductometric gas sensors featuring black phosphorus nanosheets (BP)-decorated Zn-doped hematite (α-Fe2O3) nanoclusters as the sensitive material were prepared. By subtly modulating the componential ratio and operation temperature, the optimal Zn-doped α-Fe2O3/BP sensor delivered a prominent response of 5.32 and short response/recovery times of 8/68 s toward 0.5 ppm acetone at 160 °C as well as a large sensitivity of 5.3/ppm over the concentration range of 0.1–1.5 ppm. Moreover, favorable selectivity, repeatability, and long-term stability were demonstrated. The superior sensing performance after incorporating a tiny but moderate amount of BP over Zn-doped α-Fe2O3 sensors could be attributed to the consequent enlarged specific surface area, increased oxygen vacancies, and numerous p-n heterojunctions. Furthermore, additional polylactic acid (PLA) encapsulation membrane endowed the as-prepared sensors with a remarkably improved humidity tolerance, showcasing a huge application potential in non-invasive diabetes diagnosis from exhaled breath featuring high-humidity conditions in the future.

Development of a portable spectrophotometer using artificial neural networks for non-invasive determination of glycated hemoglobin in blood by Raman spectroscopy

BACKGROUND: Non-invasive diagnosis of diabetes is one of the major problems of contemporary medicine. The system being planned could be a new technology for measuring hemoglobin A1c (HbA1c) accurately and non-invasively. Therefore, a series of studies are to be conducted to assess the efficiency of the method under study and determine its potential for medical diagnosis and monitoring of HbA1c.
 AIMS:
 
 Investigation of the feasibility of Raman spectroscopy for non-invasive measurement of HbA1c.
 Development and design of a portable analyzer using this technology.
 Assessment of the efficiency and accuracy of the developed device.
 
 METHODS: Neural network creation requires collecting a training sample of measurements for subsequent application of TensorFlow library tools and performing laboratory measurements to calibrate the system for determining HbA1c. The device will use a 785-nm laser to take spectra according to the Raman spectroscopy. The obtained data will be fed to the input of the neural network based on the architecture of convolutional neural networks. Experiments will be conducted to train the model to determine the accuracy and efficiency of the device. A two-step data collection procedure is planned. First, a preliminary test will be done on 50 patients to see how the proposed method handles different age and gender groups and different HbA1c levels. Later, the data will continue to be collected on a larger scale, including patients with different types of diabetes and healthy individuals. Data will be collected using a portable spectrophotometer and monitored by high-performance liquid chromatography. Various metrics will be used to assess the efficiency and accuracy of the device such as accuracy, precision, recall, and F1-score.
 RESULTS: An analysis of the available literature was conducted and the following conclusions were drawn. In addition, a neural network model was developed using HbA1c measurements. Currently, our model is optimized to improve the accuracy and reliability of the results.
 CONCLUSIONS: The non-invasive Raman spectroscopy-based method has several advantages in measuring HbA1c levels. The procedure is faster and non-traumatic, and HbA1c levels can be monitored continuously. In particular, the non-invasive method eliminates errors associated with protein leakage outside the bloodstream.

ZIF-8/MWCNT-nanocomposite based-resistive sensor for highly selective detection of acetone in parts-per-billion: Potential noninvasive diagnosis of diabetes

Zeolitic imidazolate framework-8 (ZIF-8) is a promising metal-organic framework for gas sensing applications in humid conditions due to its inherent hydrophobicity. However, its low sorption capacity for alcohols and acetone at room temperature and atmospheric pressure limits its applications. To overcome this, a novel composite of ZIF-8 and bare multiwall-carbon-nanotubes (MWCNTs) was developed, significantly enhancing its sorption capacity for alcohols and acetone. A ZIF-8/MWCNT-coated quartz crystal microbalance (QCM) sensor successfully detected these volatile organic compounds (VOCs) at 0.62 parts-per-million (ppm) under ambient conditions, a marked improvement over ZIF-8 alone. However, selectivity towards acetone, a crucial biomarker for diabetes, is lacking when other VOCs are present. This study investigates the effect of different sensing platforms with varying response mechanisms on achieving high selectivity for acetone using a ZIF-8/MWCNT-coated QCM and a chemiresistive sensor. The chemiresistive-sensor demonstrated excellent acetone detection performance, with high selectivity, low humidity response, good repeatability, complete reversibility, and rapid response. Remarkably, it achieved a limit of detection of 1.7 parts-per-billion (ppb) at ambient conditions, three orders of magnitude lower than the concentration in the exhaled breath of diabetic patients (1.8 ppm). This breakthrough highlights the high potential of the sensor for diabetes diagnosis and monitoring through breath analysis.

Acetone gas sensor based on PVA self-assembled WGM microbottle resonator coated with PDMS

In this paper, an acetone gas sensor based on a polyvinyl alcohol (PVA) self-assembled whispering gallery mode (WGM) microbottle resonator coated with polydimethylsiloxane (PDMS) is proposed and demonstrated. The preparation of the PVA self-assembled microresonator and the coating of PDMS are all finished by employing a dip coater. Compared with the PVA microresonator and the PDMS microresonator, the PVA microresonator coated with PDMS shows the highest acetone gas sensitivity, which is up to −6.34 pm/ppm. Besides, it can effectively reduce the effects of humidity on the detection of acetone gas compared to the PVA microresonator. Since the concentration of acetone in breathing gas is an indicator of diabetes, the proposed sensor with the benefits of small size, low cost, and simple manufacture using non-toxic materials presents promising applications for painless and non-invasive diagnosis of diabetes.

CuMoO4 nanorods-based acetone chemiresistor-enabled non-invasive breathomic-diagnosis of human diabetes and environmental monitoring

A nano-enabled low-trace monitoring system for acetone has the potential to revolutionize breath omics-based non-invasive diagnosis of human diabetes and environmental monitoring technologies. This unprecedented study presents the state-of-the-art facile and economic template-assisted hydrothermal route to fabricate novel CuMoO4 nanorods for room temperature breath and airborne acetone detection. Physicochemical attribute analysis reveals the formation of crystalline CuMoO4 nanorods with diameters ranging from 90 to 150 nm, and an optical band gap of approximately 3.87 eV. CuMoO4 nanorods-based chemiresistor demonstrates excellent acetone monitoring performance, with a sensitivity of approximately 33.85 at a concentration of 125 ppm. Acetone detection is rapid, with a response time of 23 s and fast recovery within 31 s. Furthermore, the chemiresistor exhibits long-term stability and selectivity towards acetone, compared to other interfering volatile organic compounds (VOCs) commonly found in human breath such as ethanol, propanol, formaldehyde, humidity, and ammonia. The linear detection range of acetone from 25 to 125 ppm achieved by the fabricated sensor is well-suited for human breath-based diagnosis of diabetes. This work represents a significant advancement in the field, as it offers a promising alternative to time-consuming and costly invasive biomedical diagnostics, with the potential for application in cleanroom facilities for indoor contamination monitoring. The utilization of CuMoO4 nanorods as sensing nanoplatform opens new possibilities for the development of nano-enabled, low-trace acetone monitoring technologies for non-invasive diabetes diagnosis and environmental sensing applications.

Some interesting insights into the acetone sensing characteristics of monoclinic WO3

Considering the importance of tungsten oxide (WO3) in fabricating acetone sensors for the non-invasive diagnosis of diabetes, we have tried to understand the sensing behaviour of this interesting material in detail.

Acid-treated carbon nanotubes/polypyrrole/fluorine-doped tin oxide electrodes with high sensitivity for saliva glucose sensing

To alleviate the discomfort caused by the blood sampling process, a noninvasive saliva glucose sensor is investigated in this study. Owing to the low glucose concentration in the body fluid, the fabricated sensor is requested to possess high sensitivity. Herein, a chemical polymerization method is used to deposit polypyrrole (PPy) on a fluorine-doped tin oxide (FTO) conductive glass substrate followed by modification with acid treated carbon nanotubes (acid-CNTs) via the drop-casting method in order to fabricate acid-CNTs/PPy/FTO electrodes. In addition, the sensing device uses glucose oxidase as an enzyme to catalyze the redox reaction and improve the electrode selectivity. The electron transfer resistance decreases with layer-by-layer CNTs modification, and the acid-treatment time of CNTs significantly affects the sensing performance. The acid-CNTs/PPy/FTO electrodes exhibit excellent sensing performance with a high sensitivity of 95.26 μA/mMcm2 and a broad linear range of 10–700 μM with a R2 of 0.9958. Moreover, the acid-CNTs/PPy/FTO electrodes show high selectivity to glucose along with excellent stability. Furthermore, the sensing capability of the acid-CNTs/PPy/FTO electrode is tested using artificial saliva, that further confirms high potential of acid-CNTs/PPy/FTO electrodes in the noninvasive diagnosis of diabetes.

Metal-organic framework-derived In-doped Fe2O3 spindles with enhanced acetone gas sensing performance

The rapid detection of acetone gas plays a crucial role in controlling air pollution and realizing the non-invasive diagnosis of diabetes. Here, the In-doped Fe2O3 spindles derived from metal-organic framework (MOF) were prepared by hydrothermal method, and the efficient detection of acetone gas was realized. Due to the In3+ doping, the surface of the In-doped Fe2O3 spindles nanomaterials is covered with cracks. The BET surface area also increased from 19.755 m2/g−1 to 30.872 m2/g−1, which provides more active sites for the adsorption of oxygen molecules and target gases, thereby enhancing its gas sensing performance. As the results show, the In-doped Fe2O3-based gas sensor has a response of 31.7 to 100 ppm acetone gas at 320 °C, and the response recovery time is 1 s/1 s. Compared with the pure Fe2O3-based gas sensor, the In-doped Fe2O3-based gas sensor maintains fast response-recovery while exhibiting excellent selectivity for acetone gas. Therefore, the In-doped Fe2O3 gas sensors have broad application prospects in acetone gas detection.

Fabrication of Fe doped reduced graphene oxide (rGO) decorated WO3 based low temperature ppm level acetone sensor: Unveiling sensing mechanism by impedance spectroscopy

Chemiresistive MOS-based acetone sensing device is a futuristic pathway for non-invasive diagnosis of diabetes. Although their potential deployment is restricted till now due to lack of selective, low temperature operated ppm-level sensors. In this work, we demonstrated synthesis of iron doped reduced graphene oxide (rGO) decorated WO3 nanocomposites in a facile, environment friendly wet chemical sol-gel process. The as-synthesized nanocomposites were comprehensively characterized by using different characterization techniques. A maximum ~78% sensing response was obtained for the optimized composition of ~10 wt% Fe doped 3 wt% rGO decorated WO3 based thin film (thickness ~700 nm) sensor towards ~10 ppm acetone gas. This sensing performance was observed at comparatively low working temperature of ~130 °C with fast response (~20 s) and recovery (~75 s) time. The efficacy of the fabricated sensors was established by their capabilities to sense a very low concentration of ~1 ppm acetone under similar working environment. Further illustration of versatility of the sensors revealed that, the sensors could be able to manifest a repeatable and reproducible sensing performance with prolong stability and superior selectivity for acetone over other interfering gases. The acetone sensing mechanism was illustrated with the help of electron depletion model and impedance spectroscopy study. Impedance spectroscopy quantifies different electrical properties and enlightens the smooth electronic transition mechanism between analyte and sensing material.

Fluoride-assisted highly-active tungsten oxide with modulating exposed facets and defect sites for efficient ppb-level acetone detection

Defect engineering is currently one of the most effective strategies to improve material sensing performance, which always need to be processed under harsh and complicated experimental conditions. Herein, a simple fluoride-assisted hydrothermal method is reported for preparing highly defective and active WO3. The obtained WO3 shows an urchin-like morphology and a mixed crystal structure. The optimized sensor based on the WO3 exhibits a high response, excellent selectivity, and good reproducibility to acetone at 200 °C. More precisely, the response to 0.5 ppm acetone attains 3.3 and the detection limit reaches the ppb-level, showing a potential application in noninvasive diagnosis of diabetes. The unique sensing performance can be attributed to the dominated h-WO3 with preferential exposed (002) facets and the increased structural defects in the synthesized WO3, resulting in an extent of local electric polarization on the surface and an easier and selective chemical adsorption to acetone molecules. The abundant oxygen vacancies act as preferential adsorption sites for absorbing oxygen species that can react with acetone molecules. The sensing mechanism for the unique sensing performance was further proved by a semi-in-situ controlled experiment. It is believed that the present work provides a simple and controllable approach to prepare highly active nanomaterials for application in various fields.

PVA-coated miniaturized flexible fiber optic sensor for acetone detection: a prospective study for non-invasive diabetes diagnosis

PVA-coated miniaturized flexible fiber optic sensor for acetone detection: a prospective study for non-invasive diabetes diagnosis