0000000000549200

AUTHOR

Gidi Shani

showing 7 related works from this author

Ex vivo emission of volatile organic compounds from gastric cancer and non-cancerous tissue

2018

The presence of certain volatile organic compounds (VOCs) in the breath of patients with gastric cancer has been reported by a number of research groups; however, the source of these compounds remains controversial. Comparison of VOCs emitted from gastric cancer tissue to those emitted from non-cancerous tissue would help in understanding which of the VOCs are associated with gastric cancer and provide a deeper knowledge on their generation. Gas chromatography with mass spectrometric detection (GC-MS) coupled with head-space needle trap extraction (HS-NTE) as the pre-concentration technique, was used to identify and quantify VOCs released by gastric cancer and non-cancerous tissue samples c…

Pulmonary and Respiratory MedicineAdultMaleUrine01 natural sciencesGas Chromatography-Mass Spectrometry03 medical and health scienceschemistry.chemical_compound0302 clinical medicineLimit of DetectionStomach NeoplasmsmedicineHumansAgedDetection limitCarbon disulfideVolatile Organic CompoundsChromatography010401 analytical chemistryCancerReproducibility of ResultsMiddle Agedmedicine.diseaseToluene0104 chemical sciences3. Good healthchemistryBreath Tests030220 oncology & carcinogenesisFemaleGas chromatographyGas chromatography–mass spectrometryEx vivoBiomarkersJournal of Breath Research
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Repeatability Study on a Classifier for Gastric Cancer Detection from Breath Sensor Data

2019

The SNIFFPHONE device is a portable multichannel gas sensor, aiming to detect gastric cancer (GC) from breath samples. It employs gold nanoparticle (GNP) sensors reacting to volatile organic compounds (VOCs) in the exhaled breath, a non-invasive technique to support early diagnosis. This study evaluates the repeatability of the SNIFFPHONE classification result for measurements conducted on healthy subjects over a short period of time of less than 10 minutes. Due to the portable nature of the device, repeatability is studied with respect to varying measurement location. We find the classification results repeatable with a statistically significant 81 % Pearson correlation coefficient, even t…

business.industryBreath sensorHealthy subjects02 engineering and technologyCancer detectionRepeatability021001 nanoscience & nanotechnologyCancer detectionPearson product-moment correlation coefficient03 medical and health sciencessymbols.namesake0302 clinical medicineSDG 3 - Good Health and Well-beingVolatile organic compunds030220 oncology & carcinogenesisClassification resultsymbolsMedicine/dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_beingDecision support for health0210 nano-technologybusinessGastric cancerClassifier (UML)Biomedical engineering
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How do international gastric cancer prevention guidelines influence clinical practice globally?

2020

Clinical guidelines recommend particular approaches, including 'screen-and-treat' strategy for Helicobacter pylori, to prevent gastric cancer. However, little of this is implemented in clinical practice. The aim of the study was to identify barriers to implementation of international guidelines. A web-based questionnaire distributed globally to specialists in the field. Altogether 886 responses from 75 countries were received. Of the responders, 570 (64%) were men of mean age 47 years. There were 606 gastroenterologists and 65 epidemiologists among the responders. Altogether, 79.8% of the responders disagreed that the burden of gastric cancer is a diminishing problem. 'Screen-and-treat' str…

AdultMaleCancer Researchmedicine.medical_specialtyEpidemiologyPopulationMEDLINEHelicobacter Infections03 medical and health sciencesYoung Adult0302 clinical medicineSDG 3 - Good Health and Well-beingStomach NeoplasmsSurveys and QuestionnairesmedicineHumans030212 general & internal medicinePractice Patterns Physicians'educationEarly Detection of CancerAgedAged 80 and overeducation.field_of_studybiologyHelicobacter pyloribusiness.industryGastric cancer preventionUpper endoscopyPublic Health Environmental and Occupational HealthCancerInternational AgenciesHelicobacter pyloriMiddle Agedmedicine.diseasebiology.organism_classificationPrognosis3. Good healthClinical PracticeVaccinationSurvival RateOncology030220 oncology & carcinogenesisFamily medicinePractice Guidelines as Topic/dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_beingFemalebusinessEuropean journal of cancer prevention : the official journal of the European Cancer Prevention Organisation (ECP)
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Overview on SNIFFPHONE:A portable device for disease diagnosis

2019

We present SNIFFPHONE, a handy and easy-To-use device that allows the non-invasive detection of gastric diseases. It analyzes the user's exhaled breath using specifically developed gas sensors. The device is coupled to a smartphone, which governs the breath analysis process, sends the data measurements to an external data analysis server, and finally gives feedback to the user. In this work, we describe the SNIFFPHONE device and the general platform under development.

SNIFFPHONEComputer sciencegastrointestinal cancerProcess (computing)DiseaseGastric Diseasesgas sensorExternal dataBreath gas analysisSDG 3 - Good Health and Well-beingHuman–computer interaction/dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_beingbreath analysis
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Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection

2022

Background: Gastric cancer is one of the deadliest malignant diseases, and the non-invasive screening and diagnostics options for it are limited. In this article, we present a multi-modular device for breath analysis coupled with a machine learning approach for the detection of cancer-specific breath from the shapes of sensor response curves (taxonomies of clusters). Methods: We analyzed the breaths of 54 gastric cancer patients and 85 control group participants. The analysis was carried out using a breath analyzer with gold nanoparticle and metal oxide sensors. The response of the sensors was analyzed on the basis of the curve shapes and other features commonly used for comparison. These f…

gastric cancer; breath analysis; electronic nose; machine learning; screeningClinical BiochemistryDiagnostics
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Non-contact breath sampling for sensor-based breath analysis

2019

Breath analysis holds great promise for real-time and non-invasive medical diagnosis. Thus, there is a considerable need for simple-in-use and portable analyzers for rapid detection of breath indicators for different diseases in their early stages. Sensor technology meets all of these demands. However, miniaturized breath analyzers require adequate breath sampling methods. In this context, we propose non-contact sampling; namely the collection of breath samples by exhalation from a distance into a miniaturized collector without bringing the mouth into direct contact with the analyzing device. To evaluate this approach different breathing maneuvers have been tested in a real-time regime on a…

AdultMalePulmonary and Respiratory MedicineSpectrum analyzer:MEDICINE [Research Subject Categories]breath samplingDiaphragmatic breathingContext (language use)01 natural sciencesYoung Adult03 medical and health sciences0302 clinical medicinevolatile organic compoundsHumansbreath analysisVolatile Organic CompoundsRespiration010401 analytical chemistryBreath samplingExhalationSampling (statistics)Middle AgedPTR-MS0104 chemical sciencesBreath Tests030228 respiratory systemBreath gas analysisBreathingEnvironmental scienceFemaleBiomedical engineeringJournal of Breath Research
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Sensing gastric cancer via point‐of‐care sensor breath analyzer

2021

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 an…

AdultMaleCancer ResearchValidation studymedicine.medical_specialtyvolatile organic compoundPoint-of-Care SystemsBiosensing TechniquesSensitivity and Specificity03 medical and health sciences0302 clinical medicineSDG 3 - Good Health and Well-beingbreath analyzerStomach NeoplasmsCancer screeningmedicineHumansNanotechnology030212 general & internal medicinePoint of careAgedAged 80 and overbusiness.industrygastric cancerscreeningCancerpersonalizedDiscriminant AnalysisGastric lesionsMiddle Agedmedicine.diseaseLinear discriminant analysisprecancerous lesion3. Good healthBreath analyzerOncologyBreath Tests030220 oncology & carcinogenesisArea Under CurveCase-Control Studies/dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_beingFemaleRadiologyInternet of ThingsbusinessPrecancerous ConditionsCancer
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