Classifying algorithms for SIFT-MS technology and medical diagnosis

Highly Sensitive and Selective Gas Sensors Based on NiO/MnO2 @NiO Nanosheets to Detect Allyl Mercaptan Gas Released by Humans under Psychological Stress

NiO nanosheets are synthesized in situ on gas sensor chips using a facile solvothermal method. These NiO nanosheets are then used as gas sensors to analyze allyl mercaptan (AM) gas, an exhaled biomarker of psychological stress. Additionally, MnO₂ nanosheets are synthesized onto the surfaces of the NiO nanosheets to enhance the gas-sensing performance. The gas-sensing response of the NiO nanosheet sensor is higher than that of the MnO₂ @NiO nanosheet sensor. The response value can reach 56.69, when the NiO nanosheet sensor detects 40 ppm AM gas. Interestingly, a faster response time (115 s) is obtained when the MnO₂ @NiO nanosheet sensor is exposed to 40 ppm of AM gas. Moreover, the selectivity toward AM gas is about 17-37 times greater than those toward confounders. The mechanism of gas sensing and the factors contributing to the enhance gas response of the NiO and MnO₂ @NiO nanosheets are discussed. The products of AM gas oxidized by the gas sensor are identified by gas chromatography-mass spectrometry (GC/MS). AM gas detection is an unprecedented application for semiconductor metal oxides. From a broader perspective, the developed sensors represent a new platform for the identification and monitoring of gases released by humans under psychological stress, which is increasing in modern life.

Optical Detection of Acetone Using "Turn-Off" Fluorescent Rice Straw Based Cellulose Carbon Dots Imprinted onto Paper Dipstick for Diabetes Monitoring

Persistent bad breath has been reported as a sign of serious diabetes health conditions. If an individual's breath has a strong odor of acetone, it may indicate high levels of ketones in the blood owing to diabetic ketoacidosis. Thus, acetone gas in the breath of patients with diabetes can be detected using the current easy-to-use fluorescent test dipstick. In another vein, rice straw waste is the most well-known solid pollutant worldwide. Thus, finding a simple technique to change rice straw into a valuable material is highly important. A straightforward and environmentally friendly approach for reprocessing rice straw as a starting material for the creation of fluorescent nitrogen-doped carbon dots (NCDs) has been established. The preparation process of NCDs was carried out via one-pot hydrothermal carbonization using NH₄OH as a passivation substance. A testing strip was developed on the basis of cellulose CD nanoparticles (NPs) immobilized onto cellulose paper assay. The NCDs demonstrated a quantum yield of 23.76%. A fluorescence wavelength was detected at 443 nm upon applying an excitation wavelength of 354 nm. NCDs demonstrated remarkable selectivity for acetone gas as their fluorescence was definitely exposed to quenching by acetone as a consequence of the inner filter effect. A linear correlation was observed across the concentration range of 0.5-150 mM. To detect and measure acetone gas, the present cellulose paper strip has a "switch off" fluorescent signal. A readout limit was accomplished for an aqueous solution of acetone as low as 0.5 mM under ambient conditions. The chromogenic fluorescence of the cellulose assay responsiveness depends on the fluorescence quenching characteristic of the cellulose carbon dots in acetone. A thin fluorescent cellulose carbon dot layer was deposited onto the surface of cellulose strips by a simple impregnation process. CDs were made using NP morphology and analyzed using infrared spectroscopy and transmission electron microscopy. The carbon dot distribution on the paper strip was evaluated by scanning electron microscope and energy-dispersive X-ray analysis. The absorption and fluorescence spectral analyses were investigated. The paper sheets' mechanical qualities were also examined.

Recent Advancements and Future Prospects on E-Nose Sensors Technology and Machine Learning Approaches for Non-Invasive Diabetes Diagnosis: A Review

Diabetes mellitus, commonly measured through an invasive process which although is accurate, has manifold drawbacks especially when multiple reading are required at regular intervals. Accordingly, there is a need to develop a dependable non-invasive diabetes detection technique. Recent studies have observed that other human serums such as tears, saliva, urine and breath indicate the presence of glucose in them. These parameters open quite a few ways for non-invasive blood glucose level prediction. The analysis of a persons breath poses as a good non-invasive technique to monitor the glucose levels. It is seen that in breath, there are many bio-markers and monitoring the levels of these bio-markers indicate the possibility of various chronic diseases. Among these bio-markers, acetone a volatile organic compound found in breath has shown a good correlation to the glucose levels present in blood. Therefore, by evaluating the acetone levels in breath samples it is possible to monitor diabetes non-invasively. This paper reviews the various approaches and sensory techniques used to monitor diabetes though human breath samples.

Proposition of deposition and bias conditions for optimal signal-to-noise-ratio in resistor- and FET-type gas sensors

In the field of gas sensor studies, most researchers are focusing on improving the response of the sensors to detect a low concentration of gas. However, factors that make a large response, such as abundant or strong adsorption sites, also work as a source of noise, resulting in a trade-off between response and noise. Thus, the response alone cannot fully evaluate the performance of sensors, and the signal-to-noise-ratio (SNR) should additionally be considered to design gas sensors with optimal performance. In this regard, thin-film-type sensing materials are good candidates thanks to their moderate response and noise level. In this paper, we investigate the effects of radio frequency (RF) sputtering power for deposition of sensing materials on the SNR of resistor- and field-effect transistor (FET)-type gas sensors fabricated on the same Si wafer. In the case of resistor-type gas sensors, the deposition conditions that improve the response also worsen the noise either by increasing the scattering at the bulk or damaging the interface of the sensing material. Among resistor-type gas sensors with sensing materials deposited with different RF powers, a sensor with low noise shows the largest SNR despite its small response. However, the noise of FET-type gas sensors is not affected by changes in RF power and thus there is no trade-off between response and noise. The results reveal different noise sources depending on the deposition conditions of the sensing material, and provide design guidelines for resistor- and FET-type gas sensors considering noise for optimal performance.

Kernel density estimates for sepsis classification

OBJECTIVE

Severe sepsis is a leading cause of intensive care unit (ICU) admission, length of stay, mortality, and cost. systemic inflammatory response syndrome (SIRS) and organ failure due to infection define it, but also make it hard to diagnose. Early diagnosis reduces morbidity, mortality and cost, and diagnosis is often significantly delayed due to a lack of effective biomarkers. This research employs kernel density estimation (KDE) methods fusing a personalized, model-based insulin sensitivity (SI) metric with standard bedside measures of: temperature, heart rate, respiratory rate, systolic and diastolic blood pressure, and SIRS, as these measures are available hourly or more frequently.

METHODS

Model-based SI is a derived metric, identified using clinical data and a clinically validated metabolic model. The KDE classifier discriminates severe sepsis and septic shock from moderate sepsis using accepted consensus sepsis scores. A best case in-sample estimate, a worst case independent cross validation estimate, and an accepted .632 bootstrap estimate are calculated to assess performance using multi-level likelihood ratios, and sensitivity and specificity. Performance is assessed against clinically and statistically defined thresholds denoted for the minimum acceptable level as: "high accuracy, often providing useful information, and clinical significance," and similar definitions for greater or lesser quality.

RESULTS

The .632 bootstrap estimate performs near clinically defined levels of high accuracy, often providing useful information, and clinical significance based on sensitivity, specificity, and multilevel likelihood ratios.

CONCLUSION AND SIGNIFICANCE

The classifier created and this overall approach is useful for clinical decision making in diagnosing severe sepsis and septic shock in real time, for both case and control hours. However, improvements could be made with larger clinical data sets.

Current breathomics--a review on data pre-processing techniques and machine learning in metabolomics breath analysis

We define breathomics as the metabolomics study of exhaled air. It is a strongly emerging metabolomics research field that mainly focuses on health-related volatile organic compounds (VOCs). Since the amount of these compounds varies with health status, breathomics holds great promise to deliver non-invasive diagnostic tools. Thus, the main aim of breathomics is to find patterns of VOCs related to abnormal (for instance inflammatory) metabolic processes occurring in the human body. Recently, analytical methods for measuring VOCs in exhaled air with high resolution and high throughput have been extensively developed. Yet, the application of machine learning methods for fingerprinting VOC profiles in the breathomics is still in its infancy. Therefore, in this paper, we describe the current state of the art in data pre-processing and multivariate analysis of breathomics data. We start with the detailed pre-processing pipelines for breathomics data obtained from gas-chromatography mass spectrometry and an ion-mobility spectrometer coupled to multi-capillary columns. The outcome of data pre-processing is a matrix containing the relative abundances of a set of VOCs for a group of patients under different conditions (e.g. disease stage, treatment). Independently of the utilized analytical method, the most important question, 'which VOCs are discriminatory?', remains the same. Answers can be given by several modern machine learning techniques (multivariate statistics) and, therefore, are the focus of this paper. We demonstrate the advantages as well the drawbacks of such techniques. We aim to help the community to understand how to profit from a particular method. In parallel, we hope to make the community aware of the existing data fusion methods, as yet unresearched in breathomics.

A sub-ppm acetone gas sensor for diabetes detection using 10 nm thick ultrathin InN FETs

An indium nitride (InN) gas sensor of 10 nm in thickness has achieved detection limit of 0.4 ppm acetone. The sensor has a size of 1 mm by 2.5 mm, while its sensing area is 0.25 mm by 2 mm. Detection of such a low acetone concentration in exhaled breath could enable early diagnosis of diabetes for portable physiological applications. The ultrathin InN epilayer extensively enhances sensing sensitivity due to its strong electron accumulation on roughly 5–10 nm deep layers from the surface. Platinum as catalyst can increase output current signals by 2.5-fold (94 vs. 37.5 μA) as well as reduce response time by 8.4-fold (150 vs. 1,260 s) in comparison with bare InN. More, the effect of 3% oxygen consumption due to breath inhalation and exhalation on 2.4 ppm acetone gas detection was investigated, indicating that such an acetone concentration can be analyzed in air.

Breath ammonia and trimethylamine allow real-time monitoring of haemodialysis efficacy

Non-invasive monitoring of breath ammonia and trimethylamine using Selected-ion-flow-tube mass spectroscopy (SIFT-MS) could provide a real-time alternative to current invasive techniques. Breath ammonia and trimethylamine were monitored by SIFT-MS before, during and after haemodialysis in 20 patients. In 15 patients (41 sessions), breath was collected hourly into Tedlar bags and analysed immediately (group A). During multiple dialyses over 8 days, five patients breathed directly into the SIFT-MS analyser every 30 min (group B). Pre- and post-dialysis direct breath concentrations were compared with urea reduction, Kt/V and creatinine concentrations. Dialysis decreased breath ammonia, but a transient increase occurred mid treatment in some patients. Trimethylamine decreased more rapidly than reported previously. Pre-dialysis breath ammonia correlated with pre-dialysis urea in group B (r(2) = 0.71) and with change in urea (group A, r(2) = 0.24; group B, r(2) = 0.74). In group B, ammonia correlated with change in creatinine (r(2) = 0.35), weight (r(2) = 0.52) and Kt/V (r(2) = 0.30). The ammonia reduction ratio correlated with the urea reduction ratio (URR) (r(2) = 0.42) and Kt/V (r(2) = 0.38). Pre-dialysis trimethylamine correlated with Kt/V (r(2) = 0.21), and the trimethylamine reduction ratio with URR (r(2) = 0.49) and Kt/V (r(2) = 0.36). Real-time breath analysis revealed previously unmeasurable differences in clearance kinetics of ammonia and trimethylamine. Breath ammonia is potentially useful in assessment of dialysis efficacy.

Targeted metabolomics and mass spectrometry

While a great emphasis has been placed on global metabolomic analysis in recent years, the application of metabolomic style analyses to specific subsets of compounds (targeted metabolomics) also has merits in addressing biological questions in a more hypothesis-driven manner. These analyses are designed to selectively extract information regarding a group of related metabolites from the complex mixture of biomolecules present in most metabolomic samples. Furthermore, targeted metabolomics can also be applied to metabolism within macromolecules, hence furthering the systems biology impact of the analysis. This chapter describes the difference between the global metabolomics approach and the undertaking of metabolomics in a targeted manner and describes the application of this type of analysis in a number of biologically and medically relevant fields.