Exhaled breath diagnostics of lung and gastric cancers in China using nanosensors

This article reports on a noninvasive approach in detecting and following-up individuals who are at-risk or have an existing COVID-19 infection, with a potential ability to serve as an epidemic control tool. The proposed method uses a developed breath device composed of a nanomaterial-based hybrid sensor array with multiplexed detection capabilities that can detect disease-specific biomarkers from exhaled breath, thus enabling rapid and accurate diagnosis. An exploratory clinical study with this approach was examined in Wuhan, China, during March 2020. The study cohort included 49 confirmed COVID-19 patients, 58 healthy controls, and 33 non-COVID lung infection controls. When applicable, positive COVID-19 patients were sampled twice: during the active disease and after recovery. Discriminant analysis of the obtained signals from the nanomaterial-based sensors achieved very good test discriminations between the different groups. The training and test set data exhibited respectively 94% and 76% accuracy in differentiating patients from controls as well as 90% and 95% accuracy in differentiating between patients with COVID-19 and patients with other lung infections. While further validation studies are needed, the results may serve as a base for technology that would lead to a reduction in the number of unneeded confirmatory tests and lower the burden on hospitals, while allowing individuals a screening solution that can be performed in PoC facilities. The proposed method can be considered as a platform that could be applied for any other disease infection with proper modifications to the artificial intelligence and would therefore be available to serve as a diagnostic tool in case of a new disease outbreak.

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“…A more detailed description is provided in the Materials and Methods section , the Supporting Information , and previous work. 21 …”

Section: Resultsmentioning

confidence: 99%

Multiplexed Nanomaterial-Based Sensor Array for Detection of COVID-19 in Exhaled Breath

et al. 2020

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“…[ 11 , 55 , 57 ] Essentially, the instrument consists of head‐space sampling, sensor array, and pattern recognition modules, to generate signal patterns that are used for detecting the VOC fingerprint in disease. Haick and coworkers have developed chemiresistors based on monolayer‐capped metal nanoparticles or single‐wall carbon nanotubes to determine and classify a number of diseases from exhaled breath, including lung cancer [ 58 , 59 , 60 ] and gastric cancer [ 59 , 61 , 62 , 63 ] metabolic diseases as Crohn's disease, [ 64 ] ulcerative colitis, [ 64 ] irritable bowel syndrome, [ 65 ] and neurological diseases as idiopathic Parkinson, [ 64 , 66 ] atypical Parkinsonism, [ 64 , 66 , 67 ] multiple sclerosis, [ 68 , 69 ] as well as different infectious diseases including tuberculosis (TB) [ 70 ] and COVID‐19. [ 71 ] Using the same approach, they have shown that a breath‐based system can be used for diagnosis of infectious disease and pandemics.…”

Section: Materials and Sensing Technologiesmentioning

confidence: 99%

Biodiagnostics in an era of global pandemics—From biosensing materials to data management

Broza

Haick

2021

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The novel corona virus SARS‐CoV‐2 (COVID‐19) has exposed the world to challenges never before seen in fast diagnostics, monitoring, and prevention of the outbreak. As a result, different approaches for fast diagnostic and screening are made and yet to find the ideal way. The current mini‐review provides and examines evidence‐based innovative and rapid chemical sensing and related biodiagnostic solutions to deal with infectious disease and related pandemic emergencies, which could offer the best possible care for the general population and improve the approachability of the pandemic information, insights, and surrounding contexts. The review discusses how integration of sensing devices with big data analysis, artificial Intelligence or machine learning, and clinical decision support system, could improve the accuracy of the recorded patterns of the disease conditions within an ocean of information. At the end, the mini‐review provides a prospective on the requirements to improve our coping of the pandemic‐related biodiagnostics as well as future opportunities.

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“…Therefore, it is important to identify novel, more economical, and easily accessible prognostic and predictive markers to improve the patient outcomes in GC patients. 7,8 Many studies have demonstrated the clinical significance of serum tumor markers, particularly carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), and carbohydrate antigen 72-4 (CA72-4), in patients with GC. The most important function of these markers is to reflect tumor load to allow the prediction of cancer recurrence.…”

Section: Introductionmentioning

confidence: 99%

“…Besides this, there are no biomarkers proven to be predictive or prognostic for GC patients after receiving adjuvant chemotherapy, and current treatment selections are mainly based on postoperative pathological staging. Therefore, it is important to identify novel, more economical, and easily accessible prognostic and predictive markers to improve the patient outcomes in GC patients 7,8 …”

Section: Introductionmentioning

confidence: 99%

Association of preoperative and postoperative CA72‐4 with gastric cancer outcome

Wang

et al. 2021

Journal of Surgical Oncology

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Background and Objectives: Carbohydrate antigen 72-4 (CA72-4) is widely used and has been associated with poor prognosis in gastric cancer (GC), but the prognostic significance of elevated preoperative CA72-4 that normalizes after resection remains unknown.Methods: This retrospective cohort analysis was conducted at the Sun Yat-Sen University Cancer Center (SYSUCC). Consecutive patients (n = 1179) with GC who had undergone curative resection for stage Ⅰto Ⅲ gastric adenocarcinoma. The patients were grouped into three cohorts: normal preoperative CA72-4 (C1), elevated preoperative but normalized postoperative CA72-4 (C2), and elevated preoperative and postoperative CA72-4 (C3).Results: In total, 1179 patients were identified. Kaplan-Meier analysis showed that patients with normal preoperative CA72-4 had a longer overall survival (OS) (p < .001) and recurrence-free survival (RFS) (p < .001) than those with elevated preoperative CA72-4. Patients with C1 had a longer OS and RFS than those with C2 or C3. Moreover, patients with C3 had the lowest OS, but had similar RFS to patients with C2. Multivariate Cox regression analysis showed that elevated pre-or postoperative CA72-4 was independently associated with shorter OS (hazard ratio [HR] = 1.273; 95% confidence interval [CI], 1.026-1.580; p = .029) and RFS (HR = 1.333; 95% CI, 1.064-1.668; p = .012).Conclusions: Both elevated preoperative and postoperative CA72-4 can well predict the poor prognosis of patients with GC. Therefore, routine measurement of both postoperative and preoperative CA72-4 is warranted.

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Exhaled breath diagnostics of lung and gastric cancers in China using nanosensors

Cited by 23 publications

References 13 publications

Multiplexed Nanomaterial-Based Sensor Array for Detection of COVID-19 in Exhaled Breath

Multiplexed Nanomaterial-Based Sensor Array for Detection of COVID-19 in Exhaled Breath

Biodiagnostics in an era of global pandemics—From biosensing materials to data management

Association of preoperative and postoperative CA72‐4 with gastric cancer outcome

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