Noninvasive Strategy Based on Real-Time in Vivo Cataluminescence

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A Non-Invasive Strategy Based on Real-time in Vivo Cataluminescence Monitoring for Clinical Breath Analysis Runkun Zhang, Wanting Huang, Gongke Li, and Yufei Hu Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.6b03898 • Publication Date (Web): 20 Feb 2017 Downloaded from http://pubs.acs.org on February 24, 2017

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Analytical Chemistry

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A Non-Invasive Strategy Based o n Real-time in Vivo

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Cataluminescence Monitoring for Clinical Breath Analysis

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Runkun Zhang, Wanting Huang, Gongke Li*, Yufei Hu*

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School of Chemistry, Sun Yat-sen University, Guangzhou 510275, China

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Runkun Zhang and Wanting Huang contributed equally to this work

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* Corresponding Authors: Gongke Li, Yufei Hu

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Tel. : +86-20-84110922

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Fax. : +86-20-84115107

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E-mail: [email protected] [email protected]

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ACS Paragon Plus Environment


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Abstract: The development of non-invasive methods for real-time in vivo analysis is of great

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significant, which provides powerful tools for medical research and clinical diagnosis. In the

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present work, we described a new strategy based on cataluminescence (CTL) for real-time in

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vivo clinical breath analysis. To illustrate such strategy, a homemade real-time CTL

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monitoring system characterized by coupling an on-line sampling device with a CTL sensor

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for sevoflurane (SVF) was designed, and a real-time in vivo method for the monitoring of

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SVF in exhaled breath was proposed. The accuracy of the method was evaluated by analyzing

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the real exhaled breath samples, and the results were compared with those obtained by

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GC-MS. The measured data obtained by the two methods were in good agreement.

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Subsequently, the method was applied to real-time monitoring of SVF in exhaled breath from

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rat models of control group to investigate elimination pharmacokinetics. In order to further

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probe the potential of the method for clinical application, the elimination pharmacokinetics of

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SVF from rat models of control group, liver fibrosis group alcohol liver group and

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non-alcoholic fatty liver group were monitored by the method. The raw data of

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pharmacokinetics of different groups were normalized and subsequently subjected to linear

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discriminant analysis (LDA). These data were transformed to canonical scores which were

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visualized as well-clustered with the classification accuracy of 100%, and the overall accuracy

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of leave-one-out cross-validation procedure is 88%, thereby indicating the utility of the

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potential of the method for liver disease diagnosis. Our strategy undoubtedly opens up a new

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door for real-time clinical analysis in a pain-free and non-invasive way, and also guides a

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promising development direction for CTL.

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2


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■ INTRODUCTION

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Direct measurement of chemical compounds in live organisms remains technical

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challenge and ethical issue.1 Because its ability to measure the chemical compounds of

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interest to non-invasively observe the biochemical processes in living organism, in vivo

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analysis is recognized as a powerful and widely applicable tool for biological and medical

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research, as well as clinical diagnosis.2,3 Over the last several decades, a great deal of

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endeavor has been done toward developing such field, and many innovative methods based on

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different principles including infrared spectroscopy,4,5 fluorescence spectroscopy,6-10

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chemiluminescence,11-16

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spectrometry,22-24 and nuclear magnetic resonance25-27 have been developed for in vivo

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analysis. Recently, intensive research has been focused on the development of rapid,

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inexpensive, and real-time in vivo analytical methods.

Raman

spectroscopy,17-19

electrochemistry,20,21

mass

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Exhaled breath analysis represents one of the most accessible in vivo tools for the

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diagnosis and study of medical diseases.28-30 Breath analysis falls into two basic categories:

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analysis that quantifies molecules in breath without any prior administration of a drug or

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substrate; and analysis that quantifies molecules in breath after administration of a drug or

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substrate.31 The first group of breath analysis is based on human breath contains various

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volatile organic compounds (VOCs) produced endogenously as a result of normal or

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abnormal physiologies.31,32 For example, breath acetone concentrations are increased in

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diabetes patients.33 In lung cancer patients, iso-prene levels are significantly lower in

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comparison to patients without lung cancer.32,34,35 Breath pentane is linked to heart

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disease,32,36 and exhaled 2-propanol in human breath is a important biomarker of breast 3



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cancer.37,38 For the second group of breath analysis, exogenous substance including

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therapeutic drugs, abused drugs, anesthetic agents etc. and their metabolites also can be

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detected in exhaled breath after intake,31, 39 it can be used to diagnose diseases, monitor new

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drug therapy or to detect potential adverse effects. For example, C-labelled breath tests were

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proposed as attractive tools for human liver function assessment, which is founded on the

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intravenous injection or oral administration of exogenous carbon-labelled compounds such as

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*C-labelled aminopyrine, phenacetin, caffeine etc. that undergo metabolic processes in liver

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up to labelled CO2 production, the information of *CO2 in breath after *C-substrate

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administration means that the administered substance has been metabolized by the liver, thus

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reflecting the function investigated.39 However, clinical breath analysis still remains in its

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infancy, despite that its potential has been recognized for centuries. It was stated that the

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wider availability of real-time, portable analytical instrumentation will represent a

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breakthrough in the application of clinical breath analysis.31

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Cataluminescence (CTL) is a kind of chemiluminescence (CL) produced on the surface

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of solid catalysts.40-43 It has the advantages of rapid response, good selectivity, high sensitivity,

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and long-term stability, its development provides a powerful tool to design robust sensor for

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rapid and on-line sensing of VOCs.44-46 However, previous researches are mainly focused on

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the detection of VOCs in environment, only very limited research articles on exhaled breath

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analysis could be located that possibly applied to for disease diagnosis.47-49 What is more,

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there are off-line methods that do not accord with the mainstream of clinical breath analysis.

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Our strategy is that CTL is a powerful tool for rapid analysis, and breath samples can be

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collected readily and non-invasively as often as required, even during surgery or sleep, 4


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therefore, real-time analysis with breath-to-breath resolution would be easily achieved when

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on-line coupling sampling device with CTL-based detecting device. To demonstrate this

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strategy, we developed an inexpensive CTL monitoring system for real-time analysis of

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exhaled breath. As a proof of principle work, a method for rapid real-time monitoring of

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sevoflurane (SVF) was proposed, which allows us facilely investigate the elimination

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pharmacokinetics of SVF in exhaled breath. In order to further probe the potential application

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of the new method in clinical application, we applied the method to real-time investigation of

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the elimination pharmacokinetics of SVF in exhaled breath from rat models of control group,

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liver fibrosis group, alcohol liver group and non-alcoholic fatty liver group, respectively. The

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data of pharmacokinetics of different groups were analyzed using linear discriminant analysis

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(LDA), and then LDA classified the rat models into four distinct clusters with 100% accuracy,

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which points to the promising application of the present method in pain-free and non-invasive

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diagnosis of liver disease. The value of our work is not limit to the development of a new

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strategy for inexpensive, real-time and non-invasive clinical breath analysis. To our

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knowledge, this is the first attempt to apply CTL to the real-time in vivo clinical breath

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analysis, undoubtedly, it will open up a new development direction for CTL.

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■ EXPERIMENTAL SECTION

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Chemical Reagents. All chemicals used were of analytical reagent grades. Acetone,

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methanol, ethanol, n-hexane, ammonia, iso-octane, cyclo-hexane, ethyl acetate, ethyl

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propionate were obtained from Guangzhou Chemical Reagent Co. Ltd. (Guangzhou, China).

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Formic acid, acetic acid, iso-propanol, formaldehyde, acetaldehyde, benzaldehyde, benzene,

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toluene, p-xylene, o-xylene, m-xylene, iso-prene and dimethyl sulfide were purchased from 5



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Aladdin Reagent Co. Ltd. (Shanghai, China). Carbon dioxide was purchased from XiCheng

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Industrial Gases and Equipment Co. Ltd. (Guangzhou, China). Hydrogen sulfide was supplied

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by JinHua Industrial Gases Co. Ltd. (Changzhou, China). Sevoflurane (SVF) and iso-flurane

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were obtained from Shenzhen Ward Life Science and Technology Co. Ltd (Shenzhen, China).

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All of the nano-materials including SrO, SrCO3, ZrO2, SrSO4, CeO2, ZnO, CaCO3, SiO2

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and TiO2 were purchased from Aladdin Reagent Co. Ltd. (Shanghai, China).

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Fabrication of Real-Time CTL Monitoring System for Exhaled Breath

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Analysis. The real-time cataluminescence (CTL) monitoring system for analysis of SVF in

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exhaled breath is shown in Figure 1. The system mainly comprises three parts: a

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laboratory-made cataluminescence (CTL) sensor, a dual-channel sampling pump, and a

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miniaturized anesthesia machine. The CTL sensor was constructed by placing a ceramic

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heater element into a quartz tube (length = 8.5 cm, diameter = 1.0 cm) with two gas inlets and

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one gas outlet, solid nano-material was sintered onto the surface of the heater element to form

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a catalyst layer. A direct-current electrical source was used to control the temperature of the

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heater element. The reaction temperature was controlled by adjusting the output voltage of the

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direct-current voltage controller. The two gas inlets of the quartz tube were connected to the

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two channels that were controlled by the same flow meter in the sampling pump, respectively.

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One of the channels of the pump was designed for on-line sampling of exhaled breath;

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another channel was used to introduce compensatory air flow for supplying enough oxygen

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for CTL reaction, and indoor air was direct used as compensatory air. A commercial

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anesthesia machine for SVF (Model: R530SE, Shenzhen Ward Life Science and Technology

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Co. LTD, Shenzhen, China) was employed to administer the anesthesia protocol on the rat 6


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models. In order to realize on-line and real-time operation, a three way switching valve was

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used to connect sampling pump, anesthesia machine and rats. The normally-opened valve port

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a was connected to rats via a facemask, the valve port b and c were connected to anesthesia

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machine and sampling pump, respectively. A predefined anesthetic protocol was performed

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for induction of anesthesia. At this stage, the valve port c was closed, valve port a and b were

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connected when the handle of the three way switching valve was in the position ofⅠ, 2% SVF

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delivered in 100% oxygen was continuously conducted to anesthetize rats. The flow of

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oxygen was set at 4 L/min with the help of the anesthesia machine. The supply of SVF was

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terminated when the rats lose consciousness, and then the handle of the three way valve was

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switched to the position of Ⅱ. At this stage, the valve port b was closed, and valve port a was

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connected to c. Subsequently, exhaled breaths of the rats were on-linely and continuously

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pumped into the CTL sensor, a BPCL ultra-weak luminescence analyzer (Institute of

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Biophysics, Academia Sinica, Beijing, China) equipped with a photomultiplier (PMT) was

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used for real-time monitoring of CTL signals. The high voltage of the PMT was set as 800 V,

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and the data acquisition time for each signal point of was set as 5 s. The detection

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wavelengths could be selected from 350 nm to 620 nm by changing the interference optical

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filters.

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The power source of the system is provided by the sampling pump, that is SVF samples

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(both in exhaled breath and sampling bag) and compensatory air are pumped into the sensor

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together by the same sampling pump. We found that when exhaled breaths and sampling bags

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were direct connected to the sensor without external driving force provided by the sampling

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pump, no CTL signals were detected, perhaps because the analyte could not pass through the 7



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sensor quickly, indicating that the pressures of the exhaled breaths and sampling bags can be

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negligible compared with the sampling pump. In this regard, SVF samples both in exhaled

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breaths and sampling bags are pumped into the sensor by the sampling pump instead of the

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driving forces from the samples, and the pressure of the system (pressure in gas outlet) is

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depended on the flow rate of sampling pump. Therefore, the sampling procedures of SVF

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both in exhaled breaths and sampling bags can be unified under the same system with the

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same measuring conditions include working temperature, detecting wavelength and flow rate

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(or pressure). The pressure of the system is proportional to flow rate of the sampling pump, as

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shown in Figure S1 of the Supporting Information, the linear regression equation is described

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as P = 0.1776F+ 3.487 (r = 0.9988), where P is the pressure of system, F is the flow rate of

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sampling pump, r is the linear regression coefficient. In the present work, both the sample and

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compensatory air are pumped into the sensor at an optimum rate of 400 mL/min (p=74 hPa)

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by the sampling pump with dual-channel, which simplifies the system and eliminates the

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effect of the difference between sampling flow rate and compensatory flow rate on CTL

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analysis.

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Animals and Experimental Design. The animal experiments were conducted with the

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approval of the Animal Research Committee at Sun Yat-sen University. A reliable and

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well-established method for preparing modeling animals was provided by Laboratory Animal

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Center of Sun Yat-sen University. In brief, sixteen specific pathogen free (SPF) male Wistar

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rats (Animal Permit Number: SYXK (Yue) 2011-0112) weighting 170-200 g were housed in

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barrier system at 20-26°C with a 12 h light–dark cycle and free access to food and water. The

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rats were divided into four groups with four in each group (n=4): control group was fed 8


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standard laboratory diet; alcohol liver group was fed Lieber-DeCarli ethanol diet;

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non-alcoholic fatty liver group was fed choline-deficient L-amino acid-defined (CDAA) diet;

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liver fibrosis group was fed methionine- and choline-deficient (MCD) diet. These rats were

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fed corresponding diet for 12 weeks, during this period, some hematologic and blood

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biochemical variables related to liver function were tested at the indicated weeks. However,

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the SPF animal is frail, unfortunately and regretfully, one of the rats in the control group and

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liver fibrosis died during the period of feeding, resulting in there were only three rats left in

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control group (n=3) and liver fibrosis group (n=3) for the subsequent experiments. After

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finishing the necessary experiments, livers tissue of the rats were frozen-sectioned and stained

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with oil red O lipid stain for microscopic examination to observe the degree of pathological

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change in liver structure.

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Methods for GC-MS and Data Processing. A Shimadzu QP-2010 gas

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chromatography-mass spectrometry (GC-MS) instrument equipped with a capillary column

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(Agilent J&W DB-5ms, 30 m, 0.25 mm inner diameter, and 0.25 µm film thickness) were

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used together with the present method to analyze exhaled breath samples containing SVF. The

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instrument conditions were as follows: SIM mode at m/z 131; injector temperature, 180 °C;

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ion source temperature, 200 °C; interface temperature, 230 °C; oven temperature: 180 °C. The

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calibration curve of peak area versus SVF concentration was linear in the range of 0.1 to 75

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µmoL/L (2.2 to 1680 ppm) with a detection limit of 0.02 µmoL/L (0.4 ppm). The linear

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equation was characterized by A=4068C+157.8 (r=0.9990), where A is the peak area and C is

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the concentration of SVF.

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The data obtained by BPCL ultra-weak luminescence analyzer were further processed 9



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with the OriginPro 8.0 (OriginLab) software. The linear discriminant analysis (LDA),

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covariance analysis, one-way analysis of variance and leave-one-out cross-validation were

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performed with SPSS 19.0 for window.

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Flow Chart of the Strategy. CTL has many inherent advantages that are suitable for

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real-time breath analysis, in order to further guide the readers through the strategy, a flow

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chart as shown in Figure S2 was depicted to elaborate the strategy. In brief, CTL sensor is

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on-line coupled to sampling device to construct real-time CTL monitoring system with

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necessary advantages for breath analysis. Based on the system, we can develop rapid method

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after optimizing the working conditions and establishing calibration curve, and then the

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method can be used for pharmacokinetics study, disease diagnosis or monitor therapy etc. via

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data analysis such as fitting analysis and statistical analysis. The following work was also

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performed according to this flow chart.

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■ RESULTS AND DISCUSSION

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Development of Rapid Method Based on CTL for Real-time Monitoring of

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Sevoflurane. Real-time clinical breath analysis requires the detecting methods must have

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the advantages of high sensitivity, good specificity, rapid response capacity, good stability and

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moisture resistance etc. In the present study, sevoflurane (SVF) was selected as mode

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compound to demonstrate the feasibility of the proposed strategy. First, we use in total of 9

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kinds of nano-material as sensing elements to construct cataluminescence (CTL) sensor for

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rapid detection of SVF. As Figure 2A shows, SVF produces strong CTL emission on

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nano-SrO surface, medium strong signal on SrCO3, and low signals on ZrO2 and SrCO3, but it 10


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can not produce CTL on other nano-materials such as CeO2 and ZnO etc. The limit of

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detection (LOD) of SVF using SrCO3, ZrO2 and SrCO3 as sensing elements are found to be

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2.4 µmoL/L (54 ppm), 3.3 µmoL/L (74 ppm), 12 µmoL/L (269 ppm), respectively. As

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detailedly described below, the LOD of SVF using SrO as sensing element is 0.08 µmoL/L

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(1.8 ppm). It was well known that luminescent efficiencies of the CTL are different for a

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given compound on different catalysts, and the same catalyst exhibits different CTL

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properties upon exposure to different analytes, even for the same catalyst, the size,

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morphology and crystal form of catalysts also have great effects on the CTL

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performances.40,48 Over the last several decades, analytical scientists have made full use of the

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CTL phenomenon to develop new analytical methods, unfortunately, the details of the CTL

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reaction mechanism are not yet known, which is a multiple-subject intersectional field

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involving materials science, physical chemistry, quantum chemistry etc. The study on the

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possible reaction mechanism of SVF is still under way in our lab, although it is difficult for us

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to clarify the reaction mechanism, the phenomenon of strong CTL emission from SVF on

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nano-SrO surface provides a promising method for sensing of SVF.

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Specificity is a very important performance indicator for a method because poor

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specificity will result in false positives, then SVF at the concentrations of 15 µmoL/L (336

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ppm), and 22 kinds of other common volatile organic compounds at the concentration of 60

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µmoL/L (1344 ppm), as well as ammonia, carbon dioxide and hydrogen sulfide at 150

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µmoL/L (3360 ppm) were tested to investigate the specificity of nano-SrO. As Figure 2B

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shows, SVF can produce strong CTL emission on nano-SrO, although acetone and ethanol

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can also produce CTL emissions, their CTL intensities are much weaker than that of SVF 11



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(less than 3.0% of SVF). In addition, their concentrations are four times than that of SVF, and

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no CTL emissions were detected when reduced their concentrations to 15 µmoL/L (336 ppm).

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It was reported that the levels of acetone in breath of patients with diabetes, and ethanol in

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breath of healthy volunteers are much lower than 336 ppm,50,51 which means that the

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endogenic acetone and ethanol have insignificant influence on the analysis of SVF in exhaled

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breath. For other common gases in breath including carbon dioxide, iso-prene, and

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acetaldehyde etc., no emissions were detected, indicating the nano-SrO has a good specificity

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to SVF.

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The stability of the CTL sensor using nano-SrO were investigated by detecting the CTL

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intensities of SVF three times a day for 7 day, the results are shown in Figure S3A of the

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Supporting Information. The relative standard deviation (RSD) of the measured data is 3.6%,

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indicating the good stability of the sensor, which can be attributed to the fact that the sensing

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material are solid catalyst and is essentially not consumed during the sensing process.52

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During the CTL detection process, indoor air was direct pumped into the sensor without

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pre-drying operation to provide CTL reaction with enough oxygen. In order to investigate

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whether the indoor humidity has influence on the CTL detection, we recorded the relative

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humidity according to a psychrometer for each experiment. As Figure S3B of the Supporting

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Information shows, the RSD of CTL intensities measured under different humidity is 4.5%,

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which indicates the good moisture resistance of the sensor. This phenomenon is in accordance

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with the previous report revealing that the humidity has no significant effect on the CTL

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detection.53

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In order to realize real-time analysis, the response speed of the detecting method must 12


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fast enough to produce corresponding signals when the signal molecules pass the detector.

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CTL method enjoy a superior reputation in rapid response speed, in order to investigate the

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response capacity of the CTL sensor using nano-SrO, 1 L sampling bags containing SVF at

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concentrations of 9, 15 and 30 µmoL/L were connected to the pump and subsequent were

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continuously pumped into the sensor, the corresponding dynamic CTL response profiles are

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shown in Figure 2C. The CTL response profiles reach their maximum signal immediately and

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remain stable for dozens of seconds once SVF is pumped into the sensor from sampling bags,

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subsequently the CTL signals decrease as SVF in sampling bags further consumed, indicating

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the sensor has rapid response capacity to reflect the change trend of SVF passing through the

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sensor in real-time, which is the foundation of the real-time monitoring.

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The above results demonstrate that the nano-SrO is a satisfactory sensing element for

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SVF. Thus, nano-SrO is used as sensing element for the CTL determination of SVF. The

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transmission electron microscope pattern and X-ray power diffraction pattern of nano-SrO are

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shown Figure S4 of the Supporting Information. According to the standard X-ray power

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diffraction pattern (Powder Diffraction File: 06-0520), the phase of nano-SrO was identified

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as cubic with specific sharp, intense diffraction peaks at 2θ of 31.7°, 34.9°, 50.1°, 59.5°.54

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Working temperature, detecting wavelength and flow rate (or pressure) have great influences

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on the CTL signal and signal to noise ratio (SNR) of CTL analysis, these working parameters

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were optimized. The results are shown in Figure S5 of the Supporting Information, and 210 oC,

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440 nm and 400 mL/min (or pressure = 74 hPa) were chosen as the optimum working

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temperature, detecting wavelength and flow rate (pressure) for CTL detection of SVF,

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respectively. 13



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We found that during the sensing process, although the SVF concentration in sampling

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bag decrease inevitably with time results in decrease of CTL signal (Figure 2C), the

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maximum CTL signal is depend on the initial SVF concentration, which can be used to

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construct calibration curve. Therefore, calibration curve for SVF were prepared by plotting

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the CTL intensity versus the concentration under the optimized conditions, and the results are

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shown in Figure 2D. A well linear relationship between the CTL intensity and the

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concentration of SVF in the range of 0.2–75 µmoL/L (4.5-1680 ppm) with a correlation

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coefficient of 0.9933 was observed. The linear regression equation is characterized by

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I=181.1C+118.8, where I is the CTL intensity and C is the concentration of SVF. The limit of

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detection (LOD) is found to be 0.08 µmoL/L (1.8 ppm) at an SNR of 3, the CTL signals of

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LOD and blank sample are shown in Figure S6 of the Supporting Information. Therefore, a

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simple method for rapid and sensitive analysis of SVF was established based on its CTL

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characteristics on nano-SrO. The analytical characteristics of the present method were

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compared with the previously reported methods for SVF. As shown in Table S1 of the

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Supporting Information, although LOD for SVF obtained by the present method is higher than

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methods based on mass spectrometric detection, the present method has the advantages of

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inexpensive cost, simplified construction, rapid response capacity and easy operation.

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Quantitative Analysis of SVF in Exhaled Breath Samples. GC and GC-MS have

309

long been choice for detection of SVF.55,56 To demonstrate the practical abilities of the

310

method, exhaled breath samples were analyzed by the present method and GC-MS method at

311

the same time. The procedure are as follows: a selected model animal was anesthetized by

312

SVF thrice in one day, exhaled breaths in different periods after cessation of SVF were 14


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313

continuously pumped into sampling bags of 1 L at a flow rate of 400 mL/min for 2.5 min,

314

then 200 µL of the samples were extracted from the sampling bags and subsequently injected

315

into the GC-MS instrument, and the residual samples were direct introduced into the sensor

316

for CTL analysis by connecting sampling bags to the pump. The results are shown in Table 1,

317

although no reproducible samples available for recovery test because large volumes of the

318

samples were consumed during the on-line CTL analysis process, the measured values of the

319

SVF in the exhaled breath samples obtained by the two methods are in good agreement,

320

which also demonstrates the accuracy of the method for routine monitoring of SVF. However,

321

GC-MS instrument is expensive, bulky and the measuring procedure is relatively slow, which

322

does not benefit the continuous in-field monitoring of SVF, while our method holds great

323

promise for real-time, in-field monitoring of SVF due to its outstanding advantages as

324

mentioned above.

325

Study of Elimination Pharmacokinetics. After successfully quantitative analysis of

326

SVF in real exhaled breath samples, we next explore the potential of the method for clinical

327

application. Precise information about the pharmacokinetics is important for determining

328

effective dosage regimens.57,58 SVF is an inhaled anesthetic routinely administered during

329

surgery, is mainly eliminated via exhaled breath.55 The study of the pharmacokinetics of SVF

330

is very important for its extensive use in clinical anesthesia. In order to probe the utility value

331

of the new method in this dimension, we administered a predefined anesthetic protocol

332

described in experimental section to three rat models of control group. After cessation of SVF,

333

exhaled breaths of the rats were on-linely and continuously pumped into the CTL sensor for

334

real-time monitoring the concentration-time curve of SVF elimination in respiratory gas. 15



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335

Since CTL signal is depended on the concentration of SVF, as mentioned in the experimental

336

section, the sampling procedures of SVF both in exhaled breaths and sampling bags can be

337

unified under the same system with the same measuring conditions include working

338

temperature, detecting wavelength and flow rate (or pressure), and the concentrations of SVF

339

in exhaled breath at different times (concentration–time curve) can be calculated according to

340

the linear regression equation in Figure 2D. The concentration–time curves of SVF

341

elimination in exhaled breath are shown in Figure 3, the fitting analysis shows that the

342

obtained data are well fitted to a bi-exponential decay function, which is in accordance with

343

the reported medical research.56,59 The bi-exponential decay function describes the elimination

344

law of SVF can be presented by the following form: C = A1e − k1t + A2e − k2t

345 346

Where k1 and k2 represent the time constants of the fast and slow components of

347

elimination, and A1 and A2 are constants representing the relative volumes of the fast and slow

348

components of elimination.59 These results not only demonstrate the utility of the method for

349

pharmacokinetics study, but also show its potential for guiding the reliable administration of

350

anesthetic dosage and calculation of subsequent dosage requirements during operation via

351

real-time, in-field monitoring.

352

Diagnosis of Liver Disease. Liver disease emerges as the 21st century's looming public

353

health threat because of its high prevalence worldwide and poor long-term clinical

354

outcome.60,61 Function tests in liver are extreme importance in diagnose and monitor liver

355

disease or damage, however, since the liver performs huge number of functions so no single

356

test is sufficient to provide complete information for the management of patients with liver 16


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357

disease.39 Liver slices have been used extensively as an in vitro tool for assessing the degree

358

of impaired liver function. Unfortunately, slices are invasive and time-consuming procedures

359

that suffer disadvantages of inadequate penetration of the medium, damaged cells on the outer

360

edges.62,63 Therefore, looking for rapid and non-invasive method for diagnosis of liver disease

361

has attracted a considerable amount of scientific and clinical interest. Inhaled SVF is mainly

362

metabolized by the liver, and the residual SVF is quickly eliminated via exhaled breath. Pilot

363

study showed that SVF can be as a promising probe molecule for liver function assessment.55

364

We wonder whether our method has the potential to diagnose liver disease according to the

365

elimination pharmacokinetics of SVF.

366

As an exploratory work, the same anesthetic protocol was administered to 14 rat models

367

of control group (n=3), liver fibrosis group (n=3), alcohol liver group (n=4) and non-alcoholic

368

fatty liver group (n=4) for probing the potential of the method for diagnosis of liver diseases.

369

The representative pharmacokinetics curves expressed in CTL intensity–time of the four

370

groups are shown in Figure S7 of the Supporting Information. Linear discriminate analysis

371

(LDA), a statistic method for recognizing the linear combination of features that differentiate

372

two or more classes of object, was used to quantitatively indentify the discrimination between

373

the data sets on curves of elimination pharmacokinetics. We first conducted three independent

374

assays on three different days, the raw data of the CTL intensity–time curves of different

375

groups were normalized and subsequently direct subjected to LDA (the normalized data of

376

CTL intensities for LDA are listed in Table S2 of the Supporting Information), the normalized

377

CTL intensities were converted to canonical patterns and visualized in the canonical score

378

plots. Figure S8 of the Supporting Information stands for the 2D canonical score plots of the 17



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

379

three independent assays analyzed by LDA independently, which classified the rat models

380

into four respective clusters with 100% accuracy. One-way analysis of variance shows that

381

there is no significant statistical difference among the replicates conducted in three different

382

days (Table S3 of the Supporting Information). We next subjected the 42 data sets (14 rat

383

models × 3 assays) of the three independent assays to LDA together, the plot of the three

384

factors is presented in Figure 4A. The results show that the 42 data sets were also separated

385

into four respective groups with 100% accuracy, which further demonstrate the uniformity of

386

the different assays. The three canonical factors are 78.6%, 18.7%, and 2.7%, and the detailed

387

parameters for LDA are shown in Table S4 and Table S5 of the Supporting Information.

388

We then performed leave-one-out cross-validation (LOOCV) procedure to assess the

389

predictive accuracy of the classification algorithm. In LOOCV nearly all the data except for a

390

single observation are used for training and the model is tested on that single observation.

391

Therefore, the accuracy estimate obtained using LOOCV is known to be almost unbiased. As

392

shown in Table 2, all of the control group (100% sensitivity, 90% specificity), 7 of 9 liver

393

fibrosis groups (78% sensitivity, 78% specificity), 10 of 12 alcohol liver groups (83%

394

sensitivity, 91% specificity), and 11 of 12 non-alcoholic fatty liver groups (92% sensitivity,

395

92% specificity) were correctly classified with an overall accuracy of 88%, which indicates

396

the good performance of the present classification algorithm.

397

We finally performed frozen-section examination to visualize the degree of pathological

398

changes in liver structure, and the pathological pictures are shown in Figure 4B. Very small fat

399

particles were observed in liver structure of control group, and high-density fat particles

400

distribute in the livers of alcohol group, while medium fat particles and large fat areas were 18


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401

observed for non-alcoholic fatty liver group and liver fibrosis group, indicating the different

402

degrees of pathological change in liver structure for the four groups of rat model. Although 3

403

to 4 samples in each groups is less than ideal in clinical diagnosis, and more experimental

404

samples with different liver diseases are required for the further research, our pilot work has

405

demonstrated the potential of the method for diagnosing liver disease in a pain-free and

406

non-invasive window. Significantly, our method does not need to accurately measure the

407

concentration of SVF, but just needs to obtain its relative change trend (the CTL

408

intensity–time curves) for liver disease diagnosis, which reduces the influence of fluctuation

409

factors on the diagnostic result and simplifies diagnostic procedure for the dispensable

410

calibration curve.

411 412

■ CONCLUSION

413

In summary, we describe a non-invasive strategy based on cataluminescence (CTL) for

414

real-time breath analysis, and a new method for real-time monitoring of sevoflurane (SVF)

415

was developed to demonstrate this strategy. The method allows real-time monitoring of

416

elimination pharmacokinetics of SVF in exhaled breath. Although further research on human

417

subjects in the use of reported technology to diagnose liver disease is required, our pilot

418

animal experiments demonstrate the great potential of the method for rapid diagnosis of liver

419

disease in a pain-free and non-invasive way. As a further development, we expect the present

420

real-time CTL monitoring system can be miniaturized to develop integrate and inexpensive

421

diagnostic device for non-invasive diagnosis, severity estimation, prognosis assessment and

422

therapy evaluation in patients with liver disease.

19



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423

It is worth mentioning that the inherent advantages of CTL make it quite suitable for

424

real-time breath analysis, although the present work is focuses on the monitoring of

425

exogenous SVF in exhaled breath, various chemical compounds such as alcohols, aldehydes

426

and ketones etc. that are identified as endogenic biomarkers of diseases, can produce CTL

427

signals on different catalysts, in theory, our strategy also can be used for the real-time CTL

428

monitoring of these endogenic markers via selection of appropriate catalysts, and further work

429

in this direction is under way in our lab. Thus, we anticipate our strategy will attract intensive

430

research interest in both CTL and breath analysis, and push forward the development of

431

related fields.

432

433

■ ASSOCIATED CONTENT

434

Supporting Information

435

Additional information as noted in text. This material is available free of charge via the

436

Internet at http://pubs.acs.org.

437

438

■ AUTHOR INFORMATION

439

Runkun Zhang and Wanting Huang contributed equally to this work

440

Corresponding Author

441

*E-mail: [email protected]

442

*E-mail: [email protected]

443

20


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444

Notes

445

The authors declare no competing financial interest.

446

447

■ ACKNOWLEDGMENT

448

The work were supported by the National Natural Science Foundation of China

449

(Nos.21475153, 21605163 and 21675178), the Guangdong Provincial Natural Science

450

Foundation of China (No. 2015A030311020), the Special Funds for Public Welfare Research

451

and Capacity Building in Guangdong Province of China (No.2015A030401036), the

452

Guangzhou Science and Technology Program of China (No. 201604020165), and the

453

Cultivation Project of Young Teacher in University (No.2016-31000-31610743) respectively.

454

Thanks for the kind help of the senior engineers Mr. Liang and Mr. Qiu, veterinarian Mrs.

455

Zhang and technician Mr. Wu in Laboratory Animal Center of Sun Yat-sen University for

456

animal experiment.

457 458

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Tables

Table 1. Determination of SVF in exhaled breaths by the CTL and GC-MS methods

Sample number

CTL method (µmoL/L)

GC-MS (µmoL/L)

Relative error (%)

1

25.9

25.6

+1.1

2

13.9

12.9

+7.8

3

2.32

2.43

-4.5

25



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Page 26 of 31

Table 2. The results of leave-one-out cross-validation Predicted group Actual group

Ⅰ

Ⅰ

Ⅰ

Ⅰ

Total

Sensitivity (%)

Ⅰ

9

0

0

0

9

100

Ⅰ

0

7

1

1

9

78

Ⅰ

1

1

10

0

12

83

Ⅳ

0

1

0

11

12

92

Total

10

9

11

12

42

88

Specificity (%)

90

78

91

92

88

-

Ⅰ: Control group Ⅱ: Liver fibrosis group Ⅲ: Alcohol liver group Ⅳ: Non-alcoholic fatty liver group

26


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Figures

Figure 1. Schematic illustration of the real-time CTL monitoring system for analysis of SVF in exhaled breath.

27



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 2. (A) CTL emissions of SVF on different nano-material surfaces. (B) CTL emissions of different chemical compounds on nano-SrO surface. (C) CTL response profiles of SVF at different concentrations. (D) Calibration curve between CTL intensity and concentration of SVF. Working temperature, 210 °C; wavelength, 440 nm; flow rate, 400 mL/min (pressure = 74 hPa).

28


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Figure 3. The concentration–time curves of SVF elimination in exhaled breath of three rat models of control group. Working temperature, 210 °C; wavelength, 440 nm; flow rate, 400 mL/min (pressure = 74 hPa).

29



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 4. (A) Canonical score plots for the first three factors of CTL intensity–time curves on three different days analyzed by LDA together. (B) The pathological pictures of the four

groups of rat model.Ⅰ: Control group, Ⅰ: Liver fibrosis group, Ⅰ: Alcohol liver group, Ⅰ: Non-alcoholic fatty liver group. The Arabic numerals (1, 2, 3, 4) in parentheses stand for different individuals of the group.

30


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Noninvasive Strategy Based on Real-Time in Vivo Cataluminescence

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