Terahertz metamaterial biosensor for diagnosis of hepatocellular carcinoma at early stage.

We propose a method for diagnosis of cirrhosis and hepatocellular carcinoma (HCC) by using a terahertz (THz) metamaterial (MM) biosensor. The biosensor has a resonance frequency at about 0.801 THz and can measure the concentration of alpha-fetoprotein (AFP) in serum. The sensitivity of the sensor is 124 GHz/refractive index unit (RIU), and the quality-factor (Q) is 6.913, respectively. When the surface of the biosensor is covered with healthy serum (AFP≤7ng/mL), the maximum resonance frequency shift is 50 GHz. However, when it is covered with serum from patients with cirrhosis and early HCC (AFP>7ng/mL), the resonance frequency shift is more than 59 GHz. Positive correlation exists between the frequency shift of the biosensor and serum levels of the AFP in the HCC patients. This study provides a method for quick diagnosis and prediction of cirrhosis and HCC.

Love wave based portable sensing system for on-line detection of carcinoembryonic antigen in exhaled breath condensate.

Lung cancer is continuously the leading cause of cancer related death. Noninvasive exhaled breath condensate (EBC) detection using portable devices may pave a new avenue for monitoring conditions of the high-risk population of lung cancer, thereby increasing the 5-year survival rate. Here, a portable system is proposed for on-line detection of carcinoembryonic antigen (CEA) in EBC. This miniaturized system employs an aptameric Love-wave sensor to provide a detection with advantages of low cost, operational simplicity and high sensitivity. In addition, the aptasensor can be used in label-free and real-time monitoring. Integrating with measurement circuits and iOS APP, the system realizes online transmission and processing of signals which reflect the CEA concentration. Artificial samples have been detected, and experimental results demonstrate that the present system is sensitive (limit of detection = 1 ng/ml) and accurate (relative accuracy = 0.995). Applying in detection of clinical EBC, the relative error is as low as 0.305% and the result can be used to distinguish lung cancer patients from healthy subjects. Therefore, this portable system offers a potential solution for noninvasive detection of lung cancer in its early stage.

Bionic 3D spheroids biosensor chips for high-throughput and dynamic drug screening.

To perform the drug screening, planar cultured cell models are commonly applied to test efficacy and toxicity of drugs. However, planar cultured cells are different from the human 3D organs or tissues in vivo. To simulate the human 3D organs or tissues, 3D spheroids are developed by culturing a small aggregate of cells which reside around the extracellular matrix and interact with other cells in liquid media. Here we apply lung carcinoma cell lines to engineer the 3D lung cancer spheroid-based biosensor using the interdigitated electrodes for drug efficacy evaluation. The results show 3D spheroid had higher drug resistance than the planar cell model. The anticarcinogen inhibition on different 3D lung cancer spheroid models (A549, H1299, H460) can be quantitatively evaluated by electric impedance sensing. Besides, we delivered combination of anticarcinogens treatments to A549 spheroids which is commonly used in clinic treatment, and found the synergistic effect of cisplatin plus etoposide had higher drug response. To simultaneously test the drug efficacy and side effects on multi-organ model with circulatory system, a connected multiwell interdigitated electrode arraywas applied to culture different organoid spheroids. Overall, the organization of 3D cancer spheroids-based biosensor, which has higher predictive value for drug discovery and personalized medicine screening, is expected to be well applied in the area of pharmacy and clinical medicine.

A point-of-care testing system with Love-wave sensor and immunogold staining enhancement for early detection of lung cancer.

It has been reported that detection of exhaled breath condensate (EBC) is available for studies of pulmonary diseases, especially lung disease. In order to detect lung cancer (LC) at early stage, a point-of-care testing system suitable for measurement of tumor markers in EBC is developed. The assay, based on gold nanoparticle sandwich immunoassay and subsequent gold staining, was performed on a Love-wave sensor packaged inside a chip cartridge. Benefit from high sensitivity of Love-wave sensor, oriented immobilization of coating antibodies and immunogold staining enhancement, the present immunosensor could provide a sensitive, specific and rapid measurement. Carcinoembryonic antigen (CEA), neuron specific enolase (NSE) and squamous cell carcinoma antigen (SCC) in EBC collected from 17 patients with LC and 13 healthy volunteers were detected by this system. Results were compared with commercial chemiluminescence immunoassay and showed high correlation between two methods. Additionally, it revealed significantly statistical differences existing between two groups of subjects. These results indicate that the present system is suitable for detection of tumor markers in EBC and could be used as assistant tools for early detection of LC.

Exhaled metabolic markers and relevant dysregulated pathways of lung cancer: a pilot study.

INTRODUCTION: The clinical application of lung cancer detection based on breath test is still challenging due to lack of predictive molecular markers in exhaled breath. This study explored potential lung cancer biomarkers and their related pathways using a typical process for metabolomics investigation. MATERIAL AND METHODS: Breath samples from 60 lung cancer patients and 176 healthy people were analyzed by GC-MS. The original data were GC-MS peak intensity removing background signal. Differential metabolites were selected after univariate statistical analysis and multivariate statistical analysis based on OPLS-DA and Spearman rank correlation analysis. A multivariate PLS-DA model was established based on differential metabolites for pattern recognition. Subsequently, pathway enrichment analysis was performed on differential metabolites. RESULTS: The discriminant capability was assessed by ROC curve of whom the average AUC and average accuracy in 100-fold cross validations were 0.871 and 0.787, respectively. Eight potential biomarkers were involved in a total of 18 metabolic pathways. Among them, 11 metabolic pathways have p-value smaller than .1. DISCUSSION: Some pathways among them are related to risk factors or therapies of lung cancer. However, more of them are dysregulated pathways of lung cancer reported in studies based on genome or transcriptome data. CONCLUSION: We believe that it opens the possibility of using metabolomics methods to analyze data of exhaled breath and promotes involvement of knowledge dataset to cover more volatile metabolites. CLINICAL SIGNIFICANCE: Although a series of related research reported diagnostic models with highly sensitive and specific prediction, the clinical application of lung cancer detection based on breath test is still challenging due to disease heterogeneity and lack of predictive molecular markers in exhaled breath. This study may promote the clinical application of this technique which is suitable for large-scale screening thanks to its low-cost and non-invasiveness. As a result, the mortality of lung cancer may be decreased in future.Key messagesIn the present study, 11 pathways involving 8 potential biomarkers were discovered to be dysregulated pathways of lung cancer.We found that it is possible to apply metabolomics methods in analysis of data from breath test, which is meaningful to discover convinced volatile markers with definite pathological and histological significance.

Breath profile as composite biomarkers for lung cancer diagnosis.

OBJECTIVES: Lung cancer is continuously the leading cause of cancer related death, resulting from the lack of specific symptoms at early stage. A large-scale screening method may be the key point to find asymptomatic patients, leading to the reduction of mortality. METHODS: An alternative method combining breath test and a machine learning algorithm is proposed. 236 breath samples were analyzed by TD-GCMS. Breath profile of each sample is composed of 308 features extracted from chromatogram. Gradient boost decision trees algorithm was employed to recognize lung cancer patients. Bootstrap is performed to simulate real diagnostic practice, with which we evaluated the confidence of our methods. RESULTS: An accuracy of 85 % is shown in 6-fold cross validations. In statistical bootstrap, 72 % samples are marked as "confident", and the accuracy of confident samples is 93 % throughout the cross validations. CONCLUSION: We have proposed such a non-invasive, accurate and confident method that might contribute to large-scale screening of lung cancer. As a consequence, more asymptomatic patients with early lung cancer may be detected.

Confounding effect of benign pulmonary diseases in selecting volatile organic compounds as markers of lung cancer.

Lung cancer (LC) is a leading cause of cancer-related morbidity and mortality globally, and exhaled breath testing has been considered as a fast, convenient and non-invasive way to diagnose LC in its early stages. Volatile organic compounds (VOCs), as markers of LC in exhaled breath, have been widely investigated for cancer diagnosis. However, few studies have reported on the interference of benign pulmonary diseases (BPD) in the selection of VOC markers for LC. During this study, 207 samples were analyzed using thermal desorption instrumentation/gas chromatography/mass spectrometry (TD-GCMS) to detect C6-C30 VOCs, and all samples were divided into four groups: LC group, BPD group, lung disease (LD) group (including LC group and BPD group) and healthy group. To make up for the deficiency of detecting low carbon hydrocarbons (

Rational lung tissue and animal models for rapid breath tests to determine pneumonia and pathogens.

OBJECTIVE: This study works to develop novel models that may be adopted for earlier non-invasive breathomics tests to determine pneumonia pathogens. METHODS: Two types of pneumonia models were created, both in vitro and in vivo. Paraneoplasm lung tissue and specific pathogen-free (SPF) rabbits were adopted and separately challenged with sterile saline solution control or three pathogens: Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa. After inoculation, headspace air or exhaled air were absorbed by solid phase micro-extraction (SPME) fibers and subsequently analyzed with gas chromatograph Mass Spectrometer (GCMS). RESULTS: Pneumonia and pathogen-specific discriminating VOC patterns (1H-Pyrrole-3-carbonitrile, Diethyl phthalate, Cedrol, Decanoic acid, Cyclohexane, Diisooctyl phthalate) were determined. CONCLUSION: Our study successfully generated nosocomial pneumonia models for pneumonia diagnosis and pathogen-discriminating breath tests. The tests may allow for earlier pneumonia and pathogen diagnoses, and may transfer empirical therapy to targeted therapy earlier, thus improving clinical outcomes.

A novel sensitive cell-based Love Wave biosensor for marine toxin detection.

A novel HepG2 cell-based biosensor using Love Wave sensor was developed to implement the real-time and sensitive detection of a diarrheic shellfish poisoning (DSP) toxin, Okadaic acid (OA). Detachable Love Wave sensor unit and miniaturized 8-channel recording instrument were designed for the convenient experimental preparation and sensor response signal measurement. The Love Wave sensor, whose synchronous frequency is around 160 MHz, was fabricated with ST-cut quartz substrate. To establish a cell-based biosensor, HepG2 cells as sensing elements were cultured onto the Love Wave sensor surface, and the cell attachment process was recorded by this biosensor. Results showed this sensor could monitor the cell attachment process in real time and response signals were related to the initial cell seeding densities. Furthermore, cell-based Love Wave sensor was treated with OA toxin. This biosensor presented a good performance to various OA concentrations, with a wide linear detection range (10-100 µg/L). Based on the ultrasensitive acoustic wave platform, this cell-based biosensor will be a promising tool for real-time and convenient OA screening.

Breath analysis for noninvasively differentiating Acinetobacter baumannii ventilator-associated pneumonia from its respiratory tract colonization of ventilated patients.

A number of multiresistant pathogens including Acinetobacter baumannii (A. baumannii) place a heavy burden on ventilator-associated pneumonia (VAP) patients in intensive care units (ICU). It is critically important to differentiate between bacterial infection and colonization to avoid prescribing unnecessary antibiotics. Quantitative culture of lower respiratory tract (LRT) specimens, however, requires invasive procedures. Nowadays, volatile organic compounds (VOCs) have been studied in vitro and in vivo to identify pathogen-derived biomarkers. Therefore, an exploratory pilot study was conceived for a proof of concept that the appearance and level of A. baumannii-derived metabolites might be correlated with the presence of the pathogen and its ecological niche (i.e. the infection and colonization states) in ICU ventilated patients. Twenty patients with A. baumannii VAP (infection group), 20 ventilated patients with LRT A. baumannii colonization (colonization group) and 20 ventilated patients with neurological disorders, but without pneumonia or A. baumannii colonization (control group) were enrolled in the in vivo pilot study. A clinical isolate of A. baumannii strains was used for the in vitro culture experiment. The adsorptive preconcentration (solid-phase microextraction fiber and Tenax(®) TA) and analysis technique of gas chromatography-mass spectrometry were applied in the studies. Breath profiles could be visually differentiated between A. baumannii cultivation in vitro and culture medium, and among in vivo groups. In the in vitro experiment, nine compounds of interest (2,5-dimethyl-pyrazine, 1-undecene, isopentyl 3-methylbutanoate, decanal, 1,3-naphthalenediol, longifolene, tetradecane, iminodibenzyl and 3-methyl-indene) in the headspace were found to be possible A. baumannii derivations. While there were eight target VOCs (1-undecene, nonanal, decanal, 2,6,10-trimethyl-dodecane, 5-methyl-5-propyl-nonane, longifolene, tetradecane and 2-butyl-1-octanol) exhibiting characteristics of A. baumannii VAP derivations. The selected VOC profile in vivo could be adopted to efficiently differentiate the presence of LRT A. baumannii from its absence, and LRT A. baumannii infection from its colonization (AUC = 0.89 and 0.88, respectively). It is not feasible to simply transfer the metabolic biomarkers from the in vitro condition to in vivo. The direct detection of exhaled A. baumannii-derived VOCs may be adopted for an early alert of the LRT bacterial presence in ventilated ICU patients, and even in different parasitic states of A. baumannii (i.e. infection and colonization). However, further refinement and validation are required before its clinical use.

A novel Love Wave biosensor for rapid and sensitive detection of marine toxins.

Marine toxins are produced by plankton and do a great harm to human through food chain by accumulating in shellfishes and fishes. It is highly required and favorable to develop novel methods for the rapid and efficient detection of marine toxins to avoid the poisoning cases that have occurred frequently in many countries. This study presents a real-time Love Wave biosensor for the rapid detection of okadaic acid (OA), which used HepG2 cell lines as the sensing elements. The results indicate that this cell-based biosensor can provide real-time information of cellular activities induced by okadaic acid and has a higher sensitivity than the conventional cell-based assay. It is suggested that this cell-based biosensor can be used as a convenient and efficient method for marine toxin detection, which has a great potential to contribute to avoid the harmful effects of marine toxins on the human health.

Optimization of volatile markers of lung cancer to exclude interferences of non-malignant disease.

BACKGROUND: Breath analysis became promising for noninvasive diagnosis of cancer with sophisticated spectrometry technology introduced. OBJECTIVE: This study aimed to select volatile markers for lung cancer detection, which exclude the influences from non-malignant lung diseases. METHODS: 171 subjects who were divided into three groups: patients with LC, patients with PNMD and healthy controls were enrolled in our studies as training cohort. The volatile organic compounds (VOCs) in their breath samples were analyzed with solid-phase micro-extraction/gas chromatography/mass spectrometry (SPME-GCMS). Markers were selected by receiver operating characteristic (ROC) curves. After that, 78 subjects with high morbidity of LC were employed as validation cohort. Their breath samples were analyzed by thermal desorption instrument/gas chromatography/mass spectrometry (TD-GCMS). RESULTS: Through a series of comparisons among lung cancer patients, pulmonary non-malignant diseases patients, and healthy participants in training cohort, Nonane,5-(2-methyl-)propyl-; phenol,2,6-di-tert-butyl-,4-methyl-; dodecane,2,6,11-trimethyl-; hexadecanal and pentadecane,8-hexyl- were selected as markers for lung cancer diagnosis. Principal component analysis was employ to process data from validation cohort. As results, satisfied distinctions have been obtained with detection of these five selected markers, although the detection method is not identical with that used for training cohort. CONCLUSIONS: In conclusion, with optimization method described in this paper, breath test could be an effective method for diagnosis of lung cancer and avoid the interference of pulmonary non-malignant diseases.

CEA, SCC and NSE levels in exhaled breath condensate--possible markers for early detection of lung cancer.

Lung cancer (LC) is the leading cause of cancer-related death. The sensitive and non-invasive diagnostic tools in the early stage are still poor. We present a pilot study on the early diagnosis of LC by detecting markers in exhaled breath condensate (EBC). EBC samples were collected from 105 patients with LC and 56 healthy controls. We applied chemiluminescence immunoassay to detect CEA (carcinoembryonic antigen), SCC (squamous cell carcinoma) antigen and NSE (neuron specific enolase) in EBC and serum. Concentrations of markers were compared between independent groups and subgroups. A significantly higher concentration level of each marker was found in patients with LC than healthy controls. The areas under curve of receiver operating characteristic (ROC) curves were 0.800, 0.771, 0.659, 0.679, 0.636 and 0.626 for EBC-CEA, serum-CEA, EBC-SCC, serum-SCC, EBC-NSE and serum-NSE, respectively. Markers in EBC had a higher positive rate (PR) and were more specific to histologic types than markers in serum. In addition, multivariate analysis was performed to evaluate the association of presenting markers with the stages of non-small cell lung cancer (NSCLC). EBC-CEA showed the best predictive characteristic (p < 0.006) of early-NSCLC. Our study suggested that tumor markers in EBC may have a better diagnostic performance for LC than those in serum. With further investigation on the combination of markers in EBC, detection of EBC could probably be a novel and non-invasive method to detect NSCLC earlier.

The analysis of volatile organic compounds biomarkers for lung cancer in exhaled breath, tissues and cell lines.

BACKGROUND: Volatile organic compounds (VOCs) biomarkers in breath provide a novel, noninvasive and quick approach to diagnosis lung cancer. The aim of the proposed study was to investigate the VOCs biomarkers in exhaled breath for lung cancer. METHOD: The VOCs in exhaled breath of 88 lung cancer patients, 70 lung benign disease and 85 healthy people were analyzed by Solid Phase Micro Extraction - Gas Chromatography Mass Spectrometry (SPME-GCMS). Three types of lung cancer cells and 18 lung cancer patients' tissues were cultured in vitro. The VOCs in the headspace of these cultivations were analyzed as an evidence of production mechanism of the VOCs in breath. Three lung cancer diagnosis models were constructed respectively in exhaled breath samples using Linear Discriminant Analysis (LDA). Leave one out cross validation was employed to evaluate these models. RESULTS: 23 VOCs, whose areas under curve (AUC) of receiver operating characteristic (ROC) > 0.60 and p < 0.01, were confirmed as the VOCs biomarkers for lung cancer. Three diagnostic models based on 23 VOCs could easily discriminate lung cancer patients from controls with 96.47% sensitivity and 97.47% specificity. However, the discrimination between early stage and later stage lung cancer was not very obvious.