Sensing gastric cancer via point-of-care sensor breath analyzer.

BACKGROUND: Detection of disease by means of volatile organic compounds from breath samples using sensors is an attractive approach to fast, noninvasive and inexpensive diagnostics. However, these techniques are still limited to applications within the laboratory settings. Here, we report on the development and use of a fast, portable, and IoT-connected point-of-care device (so-called, SniffPhone) to detect and classify gastric cancer to potentially provide new qualitative solutions for cancer screening. METHODS: A validation study of patients with gastric cancer, patients with high-risk precancerous gastric lesions, and controls was conducted with 2 SniffPhone devices. Linear discriminant analysis (LDA) was used as a classifying model of the sensing signals obatined from the examined groups. For the testing step, an additional device was added. The study group included 274 patients: 94 with gastric cancer, 67 who were in the high-risk group, and 113 controls. RESULTS: The results of the test set showed a clear discrimination between patients with gastric cancer and controls using the 2-device LDA model (area under the curve, 93.8%; sensitivity, 100%; specificity, 87.5%; overall accuracy, 91.1%), and acceptable results were also achieved for patients with high-risk lesions (the corresponding values for dysplasia were 84.9%, 45.2%, 87.5%, and 65.9%, respectively). The test-phase analysis showed lower accuracies, though still clinically useful. CONCLUSION: Our results demonstrate that a portable breath sensor device could be useful in point-of-care settings. It shows a promise for detection of gastric cancer as well as for other types of disease. LAY SUMMARY: A portable sensor-based breath analyzer for detection of gastric cancer can be used in point-of-care settings. The results are transferrable between devices via advanced IoT technology. Both the hardware and software of the reported breath analyzer could be easily modified to enable detection and monitirng of other disease states.

Analyte detection with Cu-BTC metal-organic framework thin films by means of mass-sensitive and work-function-based readout.

Metal-organic frameworks (MOFs) constitute a new generation of porous crystalline materials, which have recently come into focus as analyte-specific active elements in thin-film sensor devices. Cu-BTC--also known as HKUST-1--is one of the most theoretically and experimentally investigated members of the MOF family. Its capability to selectively adsorb different gas molecules renders this material a promising candidate for applications in chemical gas and vapor sensing. Here, we explore details of the host-guest interactions between HKUST-1 and various analytes under different environmental conditions and study the vapor adsorption mechanism by mass-sensitive and work-function-based readouts. These complementary transduction mechanisms were successfully applied for the detection of low ppm (2 to 50 ppm) concentrations of different alcohols (methanol, ethanol, 1-propanol, and 2-propanol) adsorbed into Cu-BTC thin films. Evaluation of the results allows for the comparison of the amounts of adsorbed vapors and the contribution of each vapor to the changes of the electronic properties of Cu-BTC. The influence of the length of the alcohol chain (C1-C3) and geometry (1-propanol, 2-propanol) as well as their polarity on the sensing performance was investigated, revealing that in dry air, short chain alcohols are more likely adsorbed than long chain alcohols, whereas in humid air, this preference is changed, and the sensitivity toward alcohols is generally decreased. The adsorption mechanism is revealed to differ for dry and humid atmospheres, changing from a site-specific binding of alcohols to the open metal sites under dry conditions to weak physisorption of the analytes dissolved in surface-adsorbed water reservoirs in humid air, with the signal strength being governed by their relative concentration.

Fractional exhaled nitric oxide measurement with a handheld device.

A sensing system for fractional exhaled nitric oxide (FeNO) measurement is presented, which is characterized by a compact setup and a cost potential to be made available for the patient at home. The sensing is based on the work function measurement of a phthalocyanine-type sensing material, which is shown to be sufficiently sensitive for NO(2) in the ppb range. The transducer used to measure the work function is a field effect transistor with a suspended gate electrode. Selectivity is given with respect to other breath components including typically metabolic by-products. The measurement system includes breath treatments in a simple setup, which essentially are dehumidification and a quantitative conversion of NO to NO(2) with a conversion rate of approx. 95%, using a disposable oxidation catalyst. The accomplishment of the correct exhalation maneuver and feeding of the suited portion of exhaled air to the sensor is provided by breath sampling means. The sensor is not gas consuming. This allows us to fill the measurement chamber once, instead of establishing a gas flow for the measurement. This feature simplifies the device architecture. In this paper, we report on sensor characteristics, system architecture and measurement with artificial breath-gas as well as with human breath with the device.