Rapid and non-invasive diagnosis of type 2 diabetes through sniffing urinary acetone by a proton transfer reaction mass spectrometry

Eu-COF-based fluorescence and portable detection of acetone in exhaled breath

Exhaled biomarker detection, as a non-invasive and portable analytical method, holds great potential in the field of point-of-care testing. Acetone exhibits higher concentrations in the exhaled breath of diabetic patients compared to healthy individuals. Therefore, the development of highly sensitive, easy-to-operate, and portable acetone detection technology is desirable for the rapid screening of diabetes. A covalent organic framework, Eu-COF, was synthesized, and its fluorescence can be specifically quenched by acetone based on the mechanism of energy competition. A flexible Eu-COF-based fluorescent film, prepared using polyvinyl alcohol (PVA) as the substrate, was found to be suitable for use in a portable 3D printing device for on-site rapid detection of acetone. The linearity of concentration ranged from 0.2-8.0 mg m-3, with a detection limit of 0.1 mg m-3 (RSD = 8.6%). The accuracy of the device was validated by comparison with the results of GC-MS. Based on the F-test, there were no significant differences between the results of the portable device and GC-MS. The Eu-COF-based fluorescent film also exhibited a comparable response in humid environments. The detection device integrates sample collection and testing in one step, directly providing the concentration of acetone exhaled by the patient. It can serve as a rapid screening tool for point-of care, non-invasive diabetes detection.

Increased Sensitivity in VOC Detection by Using a Novel Photoinduced Multiple Ionization Mass Spectrometry

Proton transfer reaction mass spectrometry (PTR-MS) is an important online monitoring technique for volatile organic compounds (VOCs). VOCs have the characteristics of many types, rapid change, and low concentration. Enhancing the detection sensitivity and expanding the detection range of VOCs have been key challenges in PTR-MS research. In this work, we have developed and characterized a novel photoinduced multiple ionizations (PMI) source, which consists of four direct current (DC) vacuum ultraviolet Kr lamps and a V-shaped focusing quadrupole ion funnel (FQ-IF) drift tube. The novel PMI source has four ionization processes: proton transfer reaction, charge transfer reaction, single photon ionization, and photoelectron impact ionization. It is capable of detecting VOCs detectable by conventional PTR-MS via the PTR, as well as VOCs (carbon disulfide and acetylene) that are difficult to detect by conventional PTR-MS through other ionization processes, thus effectively expanding the detection range. In further comparative experiments, the improvements in sensitivity for the PMI-MS in FQ-IF mode range from 122.5 to 647.7 times compared to the PTR-MS in DC mode (conventional PTR-MS) for 12 test VOCs. Notably, the sensitivities of four BTEXs in 12 VOCs were more than 10,000 cps/ppb. Moreover, compared with PTR-MS in DC mode, the LOD of PMI-MS in FQ-IF mode for 12 test VOCs increased by 26-128.6 times. PMI-MS not only expands the detection range but also improves the sensitivity by 2 orders of magnitude, which would provide an important tool for the detection of ultratrace VOCs.

The potential of circulating free DNA of methylated IGFBP as a biomarker for type 2 diabetes Mellitus: A Comprehensive review

T2DM detection methods are commonly used in teens and adults but are generally unsuitable to unborn fetuses in the context of non-invasive prenatal testing (NIPT). Biophysical and biochemical tests for fetuses are often invasive, carry risks, and have low sensitivity and specificity, with no direct method available to diagnose T2DM in utero. In contrast, cell-free DNA (cfDNA) is known have high sensitivity (93-98 %) and specificity (94-100 %) for cancer detection and fetal genetic disorders (trisomy 21, 8, and 13) making it applicable for fetal epigenetic and genetic analysis, including T2DM early detection. However, no study has explored its use for this purpose. Our review focuses on the potential of IGFBP methylation levels in cfDNA as biomarkers for NIPT of T2DM. Placental global hypomethylation in GDM may predict T2DM during the prenatal period, and a similar pattern potentially be detected in cfDNA. Targeted genes reliable for NIPT, such as IGFBPs are needed because their significant role in T2DM and GDM. Among these, IGFBP-1 and IGFBP-2 have shown potential as predictive genes, exhibiting hypermethylation in placental tissue from GDM cases. This hypermethylation reduces their expression and the formation of the IGF-1-IGFBP complex, leading to increased levels of free IGF-1, which is associated with T2DM in the fetus. Hypermethylation regions have longer fragment sizes in cfDNA, thus in T2DM cases, hypermethylation of IGFBP-1 and IGFBP-2 from fetus results in longer cfDNA fragments. Therefore, analyzing the methylation levels and fragment sizes of IGFBP-1 or IGFBP-2 cfDNA could be a promising biomarker for identifying fetal T2DM risk non-invasively.

Qualitative and quantitative rapid detection of VOCs differentially released by VAP-associated bacteria using PTR-MS and FGC-PTR-MS

Ventilator-associated pneumonia (VAP) is a prevalent disease caused by microbial infection, resulting in significant morbidity and mortality within the intensive care unit (ICU). The rapid and accurate identification of pathogenic bacteria causing VAP can assist clinicians in formulating timely treatment plans. In this study, we attempted to differentiate bacterial species in VAP by utilizing the volatile organic compounds (VOCs) released by pathogens. We cultured 6 common bacteria in VAP in vitro, including Acinetobacter baumannii, Enterobacter cloacae, Escherichia coli, Pseudomonas aeruginosa, Stenotrophomonas maltophilia, and Staphylococcus aureus, which covered most cases of VAP infection in clinic. After the VOCs released by bacteria were collected in sampling bags, they were quantitatively detected by a proton transfer reaction-mass spectrometry (PTR-MS), and the characteristic ions were qualitatively analyzed through a fast gas chromatography-proton transfer reaction-mass spectrometry (FGC-PTR-MS). After conducting principal component analysis (PCA) and analysis of similarities (ANOSIM), we discovered that the VOCs released by 6 bacteria exhibited differentiation following 3 h of quantitative cultivation in vitro. Additionally, we further investigated the variations in the types and concentrations of bacterial VOCs. The results showed that by utilizing the differences in types of VOCs, 6 bacteria could be classified into 5 sets, except for A. baumannii and E. cloacae which were indistinguishable. Furthermore, we observed significant variations in the concentration ratio of acetaldehyde and methyl mercaptan released by A. baumannii and E. cloacae. In conclusion, the VOCs released by bacteria could effectively differentiate the 6 pathogens commonly associated with VAP, which was expected to assist doctors in formulating treatment plans in time and improve the survival rate of patients.

Noninvasive Diabetes Detection through Human Breath Using TinyML-Powered E-Nose

Volatile organic compounds (VOCs) in exhaled human breath serve as pivotal biomarkers for disease identification and medical diagnostics. In the context of diabetes mellitus, the noninvasive detection of acetone, a primary biomarker using electronic noses (e-noses), has gained significant attention. However, employing e-noses requires pre-trained algorithms for precise diabetes detection, often requiring a computer with a programming environment to classify newly acquired data. This study focuses on the development of an embedded system integrating Tiny Machine Learning (TinyML) and an e-nose equipped with Metal Oxide Semiconductor (MOS) sensors for real-time diabetes detection. The study encompassed 44 individuals, comprising 22 healthy individuals and 22 diagnosed with various types of diabetes mellitus. Test results highlight the XGBoost Machine Learning algorithm's achievement of 95% detection accuracy. Additionally, the integration of deep learning algorithms, particularly deep neural networks (DNNs) and one-dimensional convolutional neural network (1D-CNN), yielded a detection efficacy of 94.44%. These outcomes underscore the potency of combining e-noses with TinyML in embedded systems, offering a noninvasive approach for diabetes mellitus detection.

Rapid Detection of Volatile Organic Metabolites in Urine by High-Pressure Photoionization Mass Spectrometry for Breast Cancer Screening: A Pilot Study

Despite surpassing lung cancer as the most frequently diagnosed cancer, female breast cancer (BC) still lacks rapid detection methods for screening that can be implemented on a large scale in practical clinical settings. However, urine is a readily available biofluid obtained non-invasively and contains numerous volatile organic metabolites (VOMs) that offer valuable metabolic information concerning the onset and progression of diseases. In this work, a rapid method for analysis of VOMs in urine by using high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS) coupled with dynamic purge injection. A simple pretreatment process of urine samples by adding acid and salt was employed for efficient VOM sampling, and the numbers of metabolites increased and the detection sensitivity was improved after the acid (HCl) and salt (NaCl) addition. The established mass spectrometry detection method was applied to analyze a set of training samples collected from a local hospital, including 24 breast cancer patients and 27 healthy controls. Statistical analysis techniques such as principal component analysis, partial least squares discriminant analysis, and the Mann-Whitney U test were used, and nine VOMs were identified as differential metabolites. Finally, acrolein, 2-pentanone, and methyl allyl sulfide were selected to build a metabolite combination model for distinguishing breast cancer patients from the healthy group, and the achieved sensitivity and specificity were 92.6% and 91.7%, respectively, according to the receiver operating characteristic curve analysis. The results demonstrate that this technology has potential to become a rapid screening tool for breast cancer, with significant room for further development.

Detection of volatile organic compounds in adulterated tea using Fourier transform infrared spectroscopy and Proton-transfer-reaction mass spectrometry

Aroma is a key factor used to evaluate tea quality. Illegal traders usually add essence to expired or substandard tea to improve its aroma so as to gain more profit. Traditional physical and chemical testing methods are time-consuming and costly. Furthermore, rapid detection techniques, such as near-infrared spectroscopy and machine vision, can only be used to detect adulterated powdered solid essences in tea. In this study, proton-transfer reaction mass spectrometry (PTR-MS) and Fourier-transform infrared spectroscopy (FTIR) were employed to detect volatile organic compounds (VOCs) in samples, and rapid detection of different tea adulterated liquid essence was achieved. The prediction accuracies of PTR-MS and FTIR reached over 0.941 and 0.957, respectively, and the minimum detection limits were lower than the actual used values in both. In this study, the different application scenarios of the two technologies are discussed based on their performance characteristics.