Detecting Lung Diseases from Exhaled Aerosols: Non-Invasive Lung Diagnosis Using Fractal Analysis and SVM Classification

Background

Each lung structure exhales a unique pattern of aerosols, which can be used to detect and monitor lung diseases non-invasively. The challenges are accurately interpreting the exhaled aerosol fingerprints and quantitatively correlating them to the lung diseases.

Objective and Methods

In this study, we presented a paradigm of an exhaled aerosol test that addresses the above two challenges and is promising to detect the site and severity of lung diseases. This paradigm consists of two steps: image feature extraction using sub-regional fractal analysis and data classification using a support vector machine (SVM). Numerical experiments were conducted to evaluate the feasibility of the breath test in four asthmatic lung models. A high-fidelity image-CFD approach was employed to compute the exhaled aerosol patterns under different disease conditions.

Findings

By employing the 10-fold cross-validation method, we achieved 100% classification accuracy among four asthmatic models using an ideal 108-sample dataset and 99.1% accuracy using a more realistic 324-sample dataset. The fractal-SVM classifier has been shown to be robust, highly sensitive to structural variations, and inherently suitable for investigating aerosol-disease correlations.

Conclusion

For the first time, this study quantitatively linked the exhaled aerosol patterns with their underlying diseases and set the stage for the development of a computer-aided diagnostic system for non-invasive detection of obstructive respiratory diseases.     

Exhaled Breath Analysis Using Electronic Nose in Cystic Fibrosis and Primary Ciliary Dyskinesia Patients with Chronic Pulmonary Infections

The current diagnostic work-up and monitoring of pulmonary infections may be perceived as invasive, is time consuming and expensive. In this explorative study, we investigated whether or not a non-invasive exhaled breath analysis using an electronic nose would discriminate between cystic fibrosis (CF) and primary ciliary dyskinesia (PCD) with or without various well characterized chronic pulmonary infections. We recruited 64 patients with CF and 21 with PCD based on known chronic infection status. 21 healthy volunteers served as controls. An electronic nose was employed to analyze exhaled breath samples. Principal component reduction and discriminant analysis were used to construct internally cross-validated receiver operator characteristic (ROC) curves. Breath profiles of CF and PCD patients differed significantly from healthy controls p = 0.001 and p = 0.005, respectively. Profiles of CF patients having a chronic P. aeruginosa infection differed significantly from to non-chronically infected CF patients p = 0.044. We confirmed the previously established discriminative power of exhaled breath analysis in separation between healthy subjects and patients with CF or PCD. Furthermore, this method significantly discriminates CF patients suffering from a chronic pulmonary P. aeruginosa (PA) infection from CF patients without a chronic pulmonary infection. Further studies are needed for verification and to investigate the role of electronic nose technology in the very early diagnostic workup of pulmonary infections before the establishment of a chronic infection.     

Exhaled Aerosol Pattern Discloses Lung Structural Abnormality: A Sensitivity Study Using Computational Modeling and Fractal Analysis

Background

Exhaled aerosol patterns, also called aerosol fingerprints, provide clues to the health of the lung and can be used to detect disease-modified airway structures. The key is how to decode the exhaled aerosol fingerprints and retrieve the lung structural information for a non-invasive identification of respiratory diseases.

Objective and Methods

In this study, a CFD-fractal analysis method was developed to quantify exhaled aerosol fingerprints and applied it to one benign and three malign conditions: a tracheal carina tumor, a bronchial tumor, and asthma. Respirations of tracer aerosols of 1 µm at a flow rate of 30 L/min were simulated, with exhaled distributions recorded at the mouth. Large eddy simulations and a Lagrangian tracking approach were used to simulate respiratory airflows and aerosol dynamics. Aerosol morphometric measures such as concentration disparity, spatial distributions, and fractal analysis were applied to distinguish various exhaled aerosol patterns.

Findings

Utilizing physiology-based modeling, we demonstrated substantial differences in exhaled aerosol distributions among normal and pathological airways, which were suggestive of the disease location and extent. With fractal analysis, we also demonstrated that exhaled aerosol patterns exhibited fractal behavior in both the entire image and selected regions of interest. Each exhaled aerosol fingerprint exhibited distinct pattern parameters such as spatial probability, fractal dimension, lacunarity, and multifractal spectrum. Furthermore, a correlation of the diseased location and exhaled aerosol spatial distribution was established for asthma.

Conclusion

Aerosol-fingerprint-based breath tests disclose clues about the site and severity of lung diseases and appear to be sensitive enough to be a practical tool for diagnosis and prognosis of respiratory diseases with structural abnormalities.     

Analysis of Volatile Compounds in Exhaled Breath Condensate in Patients with Severe Pulmonary Arterial Hypertension

Background

An important challenge to pulmonary arterial hypertension (PAH) diagnosis and treatment is early detection of occult pulmonary vascular pathology. Symptoms are frequently confused with other disease entities that lead to inappropriate interventions and allow for progression to advanced states of disease. There is a significant need to develop new markers for early disease detection and management of PAH.

Methodolgy and Findings

Exhaled breath condensate (EBC) samples were compared from 30 age-matched normal healthy individuals and 27 New York Heart Association functional class III and IV idiopathic pulmonary arterial hypertenion (IPAH) patients, a subgroup of PAH. Volatile organic compounds (VOC) in EBC samples were analyzed using gas chromatography/mass spectrometry (GC/MS). Individual peaks in GC profiles were identified in both groups and correlated with pulmonary hemodynamic and clinical endpoints in the IPAH group. Additionally, GC/MS data were analyzed using autoregression followed by partial least squares regression (AR/PLSR) analysis to discriminate between the IPAH and control groups. After correcting for medicaitons, there were 62 unique compounds in the control group, 32 unique compounds in the IPAH group, and 14 in-common compounds between groups. Peak-by-peak analysis of GC profiles of IPAH group EBC samples identified 6 compounds significantly correlated with pulmonary hemodynamic variables important in IPAH diagnosis. AR/PLSR analysis of GC/MS data resulted in a distinct and identifiable metabolic signature for IPAH patients.

Conclusions

These findings indicate the utility of EBC VOC analysis to discriminate between severe IPAH and a healthy population; additionally, we identified potential novel biomarkers that correlated with IPAH pulmonary hemodynamic variables that may be important in screening for less severe forms IPAH.     

Integrating Ion Mobility Mass Spectrometry with Molecular Modelling to Determine the Architecture of Multiprotein Complexes

Current challenges in the field of structural genomics point to the need for new tools and technologies for obtaining structures of macromolecular protein complexes. Here, we present an integrative computational method that uses molecular modelling, ion mobility-mass spectrometry (IM-MS) and incomplete atomic structures, usually from X-ray crystallography, to generate models of the subunit architecture of protein complexes. We begin by analyzing protein complexes using IM-MS, and by taking measurements of both intact complexes and sub-complexes that are generated in solution. We then examine available high resolution structural data and use a suite of computational methods to account for missing residues at the subunit and/or domain level. High-order complexes and sub-complexes are then constructed that conform to distance and connectivity constraints imposed by IM-MS data. We illustrate our method by applying it to multimeric protein complexes within the Escherichia coli replisome: the sliding clamp, (β₂), the γ complex (γ₃δδ′), the DnaB helicase (DnaB₆) and the Single-Stranded Binding Protein (SSB₄).     

Validation of an Ion Torrent Sequencing Platform for the Detection of Gene Mutations in Biopsy Specimens from Patients with Non-Small-Cell Lung Cancer

Background

Treatment for patients with advanced non-small cell lung cancer (NSCLC) is often determined by the presence of biomarkers that predict the response to agents targeting specific molecular pathways. Demands for multiplex analysis of the genes involved in the pathogenesis of NSCLC are increasing.

Methods

We validated the Ion Torrent Personal Genome Machine (PGM) system using the Ion AmpliSeq Cancer Hotspot Panel and compared the results with those obtained using the gold standard methods, conventional PCR and Sanger sequencing. The cycleave PCR method was used to verify the results.

Results and Conclusion

The Ion Torrent PGM resulted in a similar level of accuracy in identifying multiple genetic mutations in parallel, compared with conventional PCR and Sanger sequencing; however, the Ion Torrent PGM was superior to the other sequencing methods in terms of increased ease of use, even when taking into account the small amount of DNA that was obtained from formalin-fixed paraffin embedded (FFPE) biopsy specimens.     

Circulating MicroRNAs as Non-Invasive Biomarkers for Early Detection of Non-Small-Cell Lung Cancer

BackgroundDetection of lung cancer at an early stage by sensitive screening tests could be an important strategy to improving prognosis. Our objective was to identify a panel of circulating microRNAs in plasma that will contribute to early detection of lung cancer.ResultsWe identified a panel of 24 microRNAs with optimum classification performance. The combination of these 24 microRNAs alone could discriminate lung cancer cases from non-cancer controls with an AUC of 0.92 (95% CI: 0.87-0.95). This classification improved to an AUC of 0.94 (95% CI: 0.90-0.97) following addition of sex, age and smoking status to the model. Internal validation of the model suggests that the discriminatory power of the panel will be high when applied to independent samples with a corrected AUC of 0.78 for the 24-miRNA panel alone.ConclusionOur 24-microRNA predictor improves lung cancer prediction beyond that of known risk factors.     

A Plasma Long Noncoding RNA Signature for Early Detection of Lung Cancer

The early detection of lung cancer is a major clinical challenge. Long noncoding RNAs (lncRNAs) have important functions in tumorigenesis. Plasma lncRNAs directly released from primary tumors or the circulating cancer cells might provide cell-free cancer biomarkers. The objective of this study was to investigate whether the lncRNAs could be used as plasma biomarkers for early-stage lung cancer. By using droplet digital polymerase chain reaction, we determined the diagnostic performance of 26 lung cancer–associated lncRNAs in plasma of a development cohort of 63 lung cancer patients and 33 cancer-free individuals, and a validation cohort of 39 lung cancer patients and 28 controls. In the development cohort, 7 of the 26 lncRNAs were reliably measured in plasma. Two (SNHG1 and RMRP) displayed a considerably high plasma level in lung cancer patients vs. cancer-free controls (all P?<?.001). Combined use of the plasma lncRNAs as a biomarker signature produced 84.13% sensitivity and 87.88% specificity for diagnosis of lung cancer, independent of stage and histological type of lung tumor, and patients' age and sex (all P?>?.05). The diagnostic value of the plasma lncRNA signature for lung cancer early detection was confirmed in the validation cohort. The plasma lncRNA signature may provide a potential blood-based assay for diagnosing lung cancer at the early stage. Nevertheless, a prospective study is warranted to validate its clinical value.     

Analysis of Detection Processes for Breast Cancer

Most existing records of detection processes for breast cancer are in the form of cancer registries or are results of large clinical trials. Statistical modelling can be applied to these data sets to study various properties of breast cancer. In particular we estimate the probability of cure given the size of the tumour at detection, the distribution of tumour growth rates and the distribution of the size of the tumour at detection. There has been a strong recent interest in early detection methods. These consist of giving regular examinations, called screenings. The effect of screening design on the probability of cure is considered. The results of an existing screening trial are used to derive another estimate of the tumour growth rate distribution which agrees well both with our earlier estimate and the most widely used empirical estimate in the literature. The calculation of lead time, which is the time gained in detection when screenings are given, is also discussed.     

Urinary Volatile Compounds as Biomarkers for Lung Cancer: A Proof of Principle Study Using Odor Signatures in Mouse Models of Lung Cancer

A potential strategy for diagnosing lung cancer, the leading cause of cancer-related death, is to identify metabolic signatures (biomarkers) of the disease. Although data supports the hypothesis that volatile compounds can be detected in the breath of lung cancer patients by the sense of smell or through bioanalytical techniques, analysis of breath samples is cumbersome and technically challenging, thus limiting its applicability. The hypothesis explored here is that variations in small molecular weight volatile organic compounds (“odorants”) in urine could be used as biomarkers for lung cancer. To demonstrate the presence and chemical structures of volatile biomarkers, we studied mouse olfactory-guided behavior and metabolomics of volatile constituents of urine. Sensor mice could be trained to discriminate between odors of mice with and without experimental tumors demonstrating that volatile odorants are sufficient to identify tumor-bearing mice. Consistent with this result, chemical analyses of urinary volatiles demonstrated that the amounts of several compounds were dramatically different between tumor and control mice. Using principal component analysis and supervised machine-learning, we accurately discriminated between tumor and control groups, a result that was cross validated with novel test groups. Although there were shared differences between experimental and control animals in the two tumor models, we also found chemical differences between these models, demonstrating tumor-based specificity. The success of these studies provides a novel proof-of-principle demonstration of lung tumor diagnosis through urinary volatile odorants. This work should provide an impetus for similar searches for volatile diagnostic biomarkers in the urine of human lung cancer patients.     

Rapid Diagnosis of Lung Tumors,a Feasability Study Using Maldi-Tof Mass Spectrometry

ObjectiveDespite recent advances in imaging and core or endoscopic biopsies, a percentage of patients have a major lung resection without diagnosis. We aimed to assess the feasibility of a rapid tissue preparation/analysis to discriminate cancerous from non-cancerous lung tissue.MethodsFresh sample preparations were analyzed with the Microflex LT^TM MALDI-TOF analyzer. Each main reference spectra (MSP) was consecutively included in a database. After definitive pathological diagnosis, each MSP was labeled as either cancerous or non-cancerous (normal, inflammatory, infectious nodules). A strategy was constructed based on the number of concordant responses of a mass spectrometry scoring algorithm. A 3-step evaluation included an internal and blind validation of a preliminary database (n = 182 reference spectra from the 100 first patients), followed by validation on a whole cohort database (n = 300 reference spectra from 159 patients). Diagnostic performance indicators were calculated.Results127 cancerous and 173 non-cancerous samples (144 peripheral biopsies and 29 inflammatory or infectious lesions) were processed within 30 minutes after biopsy sampling. At the most discriminatory level, the samples were correctly classified with a sensitivity, specificity and global accuracy of 92.1%, 97.1% and 95%, respectively.ConclusionsThe feasibility of rapid MALDI-TOF analysis, coupled with a very simple lung preparation procedure, appears promising and should be tested in several surgical settings where rapid on-site evaluation of abnormal tissue is required. In the operating room, it appears promising in case of tumors with an uncertain preoperative diagnosis and should be tested as a complementary approach to frozen-biopsy analysis.     

A Simple Determination Method for Herbicides,NIP and CNP in Soil Using Single Ion Monitoring Mass Spectrometry

A purified extracellular endo β-1,3-xylanase (EC 3.2.1.32) from an isolated strain, Aspergillus terreus A-07, was found to hydrolyze 1,3-xylosyl linkages only. When rhodymenan (β-1,4 and β-1.3-linked xylan) was hydrolyzed by β-1,3-xylanase (EF-6), four β-1,4-linked xylooligosaccharide fractions were produced. The main product was β-1,4-xylotriose, with trace amounts of other β-1,4-linked xylooligosaccharides. Successive degradation by β-l,4-xylosidase of the β,4-xylooligosaccharides that were produced from hydrolysis of β-1,3-xylanase on rhodymenan yielded only xylose as the final product.

We compared the action pattern of this enzyme with that of an extracellular endo β-l,4-xylanase (EC 3.2.1.8) of Streptomyces. From a mixture of products of β-1,4-xylanase hydrolysis on rhodymenan, an isomeric xylotriose was isolated by charcoal chromatography after treating with β-1.4-xylosidase. The structure of this isomeric xylotriose was elucidated by methylation analysis and its susceptibility to β-1,4-xylanase, β-1,3-xylanase, and β-1,4-xylosidase. The obtained isomeric xylotriose was identified as 3-O-β-xylopyranosyl-4-O-β-D-xylopyranosyl-D-xylose (X1→3X1→4X). It has a melting point of 224~225°C and ${\left[\alpha \right]}_{\text{D}}^{20}$(c = 1, H₂O)= —46°.     

肺结核合并肺癌患者的临床分析

目的：探讨肺结核合并肺癌的临床特点,提高临床医师诊断肺结核合并肺癌的警惕性。方法：对2000年1月至2009年12月在我院诊断为肺结核、肺结核合并肺癌、肺癌患者各40例进行回顾性分析。结果：三组之间的临床表现与实验室检查进行相互比较,发现其临床表现并无统计学差异,影像学与支气管镜检查有明显统计学差异外,其他实验室检查无差异。结论：结核合并肺癌与肺结核、肺癌等临床表现、实验室检查近似,易造成漏诊、误诊,临床医师应提高警惕,及时行影像学及支气管镜等检查。