Experimental design optimization for the ICP-AES determination of Li, Na, K, Al, Fe, Mn and Zn in human serum

Dried Blood Spots and Miniaturized Ultrasonic Nebulization Microplasma Optical Emission Spectrometry for Point-of-Care Testing of Blood Lithium

Despite the significant importance of blood lithium (Li) detection in the treatment of bipolar disorder (BD), its point-of-care testing (POCT) remains a great challenge due to tedious sample preparation and the use of large-footprint atomic spectrometers. Herein, a system coupling dried blood spots (DBS) with a point discharge optical emission spectrometer equipped with a miniaturized ultrasonic nebulizer (MUN-μPD-OES) was developed for POCT of blood Li. Three microliters of whole blood were used to prepare a dried blood spot on a piece of filter paper to which 10 μL of eluent (1% (v/v) formic acid and 0.05% (v/v) Triton-X) was added. Subsequently, the paper was placed onto the vibrating steel membrane of the ultrasonic nebulizer and powered on to generate aerosol. The aerosol was directly introduced to the μPD-OES for quantification of Li by monitoring its atomic emission line at 670.8 nm. The proposed method minimized matrix interference caused by high levels of salts and protein. It is worth noting that the MUN suitably matches the needs of DBS sampling and can provide aerosolized introduction of Li into the assembled μPD-OES, thus eliminating all tedious sample preparation and the need for a commercial atomic spectrometer. Calibration response is linear in the therapeutic range and a limit of detection (LOD) of 1.3 μg L-1 is well below the Li minimum therapeutic concentration (2800 μg L-1). Li in mouse blood was successfully detected in real-time using MUN-μPD-OES after intraperitoneal injection of lithium carbonate, confirming that the system holds great potential for POCT of blood Li for patients with BD.

A novel highly selective FRET sensor for Fe(III) and DFT mechanistic evaluation

A novel FRET-based sensor has been designed and developed through the conjugation of naphthyl and rhodamine via propylamine spacer, Naph-Rh. The naphthyl moiety serves as a FRET donor due to its emission spectrum overlapping with the rhodamine B absorption band. Naph-Rh exhibited a selectivity for sensing Fe³⁺ over other metal ions with a visual color change and fluorescent enhancement. The ratio of the Naph-Rh and Fe³⁺ was determined to be 1:1 based on Job's plot analysis with a detection limit of 83 nM. The probe exhibited a linear response to Fe³⁺ in the range of 0-120 μM. Furthermore, the density functional theory (DFT) calculations of Naph-Rh were carried out to rationalize the design and portray the plausible Fe³⁺ sensing mechanism.

Determination of lithium in human serum by isotope dilution atomic absorption spectrometry

The therapeutic dose of lithium (Li) compounds, which are widely used for the treatment of psychiatric and hematologic disorders, is close to its toxic level; therefore, drug monitoring protocols are mandatory. Herein, we propose a fast, simple, and low-cost analytical procedure for the traceable determination of Li concentration in human serum, based on the monitoring of the Li isotope dilution through the partially resolved isotope shift in its electronic transition around 670.80 nm using a commercially available high-resolution continuum source graphite furnace atomic absorption spectrometer. With this technique, serum samples only require acidic digestion before analysis. The procedure requires three measurements-an enriched 6Li spike, a mixture of a certified standard solution and spike, and a mixture of the sample and spike with a nominal 7Li/6Li ratio of 0.82. Lanthanum has been used as an internal spectral standard for wavelength correction. The spectra are described as the linear superposition of the contributions of the respective isotopes, each consisting of a spin-orbit doublet, which can be expressed as Gaussian components with constant spectral position and width and different relative intensity, reflecting the isotope ratio in the sample. Both the spectral constants and the correlation between isotope ratio and relative band intensity have been experimentally obtained using commercially available materials enriched with Li isotopes. The Li characteristic mass (mc) obtained corresponds to 0.6 pg. The procedure has been validated using five human serum certified reference materials. The results are metrologically comparable and compatible to the certified values. The measurement uncertainties are comparable to those obtained by the more complex and expensive technique, isotope dilution mass spectrometry.

Development of a Portable Method for Serum Lithium Measurement Based on Low-Cost Miniaturized Ultrasonic Nebulization Coupled with Atmospheric-Pressure Air-Sustained Discharge

An accurate, rapid but cheap, and portable method for monitoring of serum lithium (Li) is highly desirable for mental patients who take Li medicine for treatment. Conventional techniques are usually bulky, costly, and cannot provide on-site real-time measurements. Herein, a miniaturized, reliable, cost-effective, and portable optical emission method for rapid and sensitive determination of serum Li was developed based on a combination of miniaturized ultrasonic nebulization (MUN) and a low-power (≈22 W) atmospheric-pressure air-sustained discharge (APAD) excitation source. The proposed method eliminates the use of any compressed gas or pump and can achieve serum Li detection within 40 s with low sample consumption (less than 20 μL serum). Except for dilution with water, no extra treatment is needed for serum Li analysis by MUN-APAD-OES. In addition, it offers a significant advantage of good tolerance to the coexisting high concentration of Na, K, Ca, and Mg, which is in contrast with the obvious matrix effect encountered in conventional inductively coupled plasma optical emission spectrometry (ICP-OES). Different operating parameters affecting the performance of MUN-APAD-OES were evaluated. Under optimized conditions, the detection limit of Li (670.8 nm) was calculated to be 0.6 μg L^-1 (6 μg L^-1 in serum). Finally, the accuracy of the proposed method was validated by the analysis of two certified reference materials (Seronorm serum L-1 and L-2 RUO), six real human serum samples, and eight real animal serum samples. All of the results indicate that the low-cost and low-power MUN-APAD-OES provides a promising reliable method for on-site serum Li measurement and may also be extended to other elements.

The assessment of the usability of selected instrumental techniques for the elemental analysis of biomedical samples

The fundamental role of major, minor and trace elements in different physiological and pathological processes occurring in living organism makes that elemental analysis of biomedical samples becomes more and more popular issue. The most often used tools for analysis of the elemental composition of biological samples include Flame and Graphite Furnace Atomic Absorption Spectroscopy (F-AAS and GF-AAS), Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) and Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Each of these techniques has many advantages and limitations that should be considered in the first stage of planning the measurement procedure. Their reliability can be checked in the validation process and the precision, trueness and detection limits of elements belong to the most frequently determined validation parameters. The main purpose of this paper was the discussion of selected instrumental techniques (F-AAS, GF-AAS, ICP-OES and ICP-MS) in term of the achieved validation parameters and the usefulness in the analysis of biological samples. The focus in the detailed literature studies was also put on the methods of preparation of the biomedical samples. What is more based on the own data the usefulness of the total reflection X-ray fluorescence spectroscopy for the elemental analysis of animal tissues was examined. The detection limits of elements, precision and trueness for the technique were determined and compared with the literature data concerning other of the discussed techniques of elemental analysis. Reassuming, the following paper is to serve as a guide and comprehensive source of information concerning the validation parameters achievable in different instrumental techniques used for the elemental analysis of biomedical samples.

Extraction of Al, Cu, Fe, Mn, Ni and Zn--polyphenol complexes from black tea infusions by Amberlite resins

BACKGROUND

Metals can be present in tea infusion as ions or as complexes with endogenous bioligands naturally occurring in tea, especially with polyphenols. Their chemical form, i.e. free or metal complexes, can affect their actual bioavailability, gastrointestinal absorption and possible toxicity.

RESULTS

The total amount of Al, Cu, Fe, Mn, Ni and Zn in black tea infusions was determined by inductively coupled plasma--atomic emission spectroscopy after mineralization and compared with their fractions occurring as polyphenol complexes, extracted using two different polymeric resins: Amberlite XAD-2 and Amberlite XAD-7.

CONCLUSION

The obtained results demonstrated the suitability of Amberlite resins for the extraction of Al, Cu, Fe, Mn, Ni and Zn–polyphenol complexes from black tea infusions and for the evaluation of the actual distribution of the considered metals among different chemical species, i.e. as free metals and as polyphenol complexes.

The diagnostics of diabetes mellitus based on ensemble modeling and hair/urine element level analysis

The aim of the present work focuses on exploring the feasibility of analyzing the relationship between diabetes mellitus and several element levels in hair/urine specimens by chemometrics. A dataset involving 211 specimens and eight element concentrations was used. The control group was divided into three age subsets in order to analyze the influence of age. It was found that the most obvious difference was the effect of age on the level of zinc and iron. The decline of iron concentration with age in hair was exactly consistent with the opposite trend in urine. Principal component analysis (PCA) was used as a tool for a preliminary evaluation of the data. Both ensemble and single support vector machine (SVM) algorithms were used as the classification tools. On average, the accuracy, sensitivity and specificity of ensemble SVM models were 99%, 100%, 99% and 97%, 89%, 99% for hair and urine samples, respectively. The findings indicate that hair samples are superior to urine samples. Even so, it can provide more valuable information for prevention, diagnostics, treatment and research of diabetes by simultaneously analyzing the hair and urine samples.

Solid-phase extraction and separation procedure for trace aluminum in water samples and its determination by high-resolution continuum source flame atomic absorption spectrometry (HR-CS FAAS)

In the present study, a separation/preconcentration procedure for determination of aluminum in water samples has been developed by using a new atomic absorption spectrometer concept with a high-intensity xenon short-arc lamp as continuum radiation source, a high-resolution double-echelle monochromator, and a charge-coupled device array detector. Sample solution pH, sample volume, flow rate of sample solution, volume, and concentration of eluent for solid-phase extraction of Al chelates with 4-[(dicyanomethyl)diazenyl] benzoic acid on polymeric resin (Duolite XAD-761) have been investigated. The adsorbed aluminum on resin was eluted with 5 mL of 2 mol L(-1) HNO(3) and its concentration was determined by high-resolution continuum source flame atomic absorption spectrometry (HR-CS FAAS). Under the optimal conditions, limit of detection obtained with HR-CS FAAS and Line Source FAAS (LS-FAAS) were 0.49 μg L(-1) and 3.91 μg L(-1), respectively. The accuracy of the procedure was confirmed by analyzing certified materials (NIST SRM 1643e, Trace elements in water) and spiked real samples. The developed procedure was successfully applied to water samples.

Diagnostic Assay of Toxic Zinc in an Ex vivo Cell Using Voltammetry

Voltammetric detection of the toxic Zn ion was investigated using a fluorine-doped graphite pencil electrode (FPE). It is notable from the study that pencils were used as reference and working electrodes. In all the experiments, a clean seawater electrolyte solution was used to yield good results. The analytical working range was attained to 10 μgL(-1). The optimized voltammetric condition was examined to maximize the effect of the detection of trace Zn. The developed sensor was applied to an earthworm's tissue cell. It was found that the methods can be applicable to in vivo fluid or agriculture soil and plant science.

Prediction of type-2 diabetes based on several element levels in blood and chemometrics

The present study was designed to evaluate the levels of eight elements including lithium, zinc, chromium, copper, iron, manganese, nickel and vanadium in whole blood of type-2 diabetes patients, to compare them with age-matched healthy controls and to investigate the feasibility of combining them with an ensemble model for diagnosing purpose. A dataset involving 158 samples, among which 105 were taken from healthy adults and the remaining 53 from patients with type-2 diabetes, was collected. All samples were split into the training set and the test set with the equal size. Based on a simple variable selection, two elements, i.e., chromium and iron, are also picked out as the most important elements. Three kinds of algorithms, i.e., fisher linear discriminate analysis (FLDA), support vector machine (SVM) and decision tree (DT), were used for constructing member models. The best ensemble classifiers constructed on the training set were validated on the independent test set, and the prediction results were compared with those from clinical diagnostics on the same subjects. The results reveal that almost all ensemble classifiers exhibit similar performance, implying that these elements coupled with an appropriate ensemble classifier can serve as a valuable tool of diagnosing diabetes type-2.

Classification models for detection of lung cancer based on nine element distribution of urine samples

The detection of lung cancer has a special value in the diagnosis of cancer diseases. Based on nine elemental concentrations (i.e., chromium, iron, manganese, aluminum, cadmium, copper, zinc, nickel, and selenium) in urine samples and an ensemble linear discriminant analysis (ELDA), a detection method for lung cancer has been developed. A dataset containing 30 healthy samples and 27 lung cancer samples is used for experiment. The whole dataset was first split into a training set with 29 samples and a test set with 28 samples. The prediction results from the ELDA classifier were compared with those from single Fisher's discriminate analysis (FDA). On the test set, the ELDA classifier achieved better performance, that is, a sensitivity of 100%, a specificity of 86.7%, and an overall accuracy of 92.9%, while the FDA classifier had a sensitivity of 92.3%, a specificity of 93.3%, and an overall accuracy of 92.9%. The superiority of ELDA to FDA is ascribed to the fact that ELDA can model more nonlinear relationships through the cooperation of several single models, suggesting that ensemble modeling is more advisable in such a task.

Application of boosting classification and regression to modeling the relationships between trace elements and diseases

The study on the relationship between trace elements and diseases often need to build a classification/regression model. Furthermore, the accuracy of such a model is of particular importance and directly decides its applicability. The goal of this study is to explore the feasibility of applying boosting, i.e., a new strategy from machine learning, to model the relationship between trace elements and diseases. Two examples are employed to illustrate the technique in the applications of classification and regression, respectively. The first example involves the diagnosis of anorexia according to the concentrations of six elements (i.e. classification task). Decision stump and support vector machine are used as the weak/base algorithm and reference algorithm, respectively. The second example involves the prediction of breast cancer mortality based on the intake of trace elements (i.e. a regression task). In this regard, partial least squares is not only used as the weak/base algorithm, but also the reference algorithm. The results from both examples confirm the potential of boosting in modeling the relationship between trace elements and diseases.

The prediction of cardiovascular disease based on trace element contents in hair and a classifier of boosting decision stumps

The early discovery of cardiovascular disease (CVD) is crucial for performing successful treatments. This study aims at exploring the feasibility of Adaboost (ensemble from machining learning) using decision stumps as weak classifier, combined with trace element analysis of hair, for accurately predicting early CVD. A total of 124 hair samples composed of two groups of samples (one is healthy group from 100 healthy persons aged 24-72 while the other is patient group from 24 cardiovascular disease patients aged 36-81) were used. Nine kinds of trace elements, i.e., chromium (Cr), manganese (Mn), cadmium (Cd), copper (Cu), zinc (Zn), selenium (Se), iron (Fe), aluminum (Al), and nickel (Ni), were selected. In a preliminary analysis, no obvious linear correlations between elements can be observed and the concentration of Cr, Fe, Al, Cd, Ni, or Se for healthy group is higher than that for patient group while the opposite is true for Mn, Cu, or Zn, indicating that both low Se/Fe and high Mn/Cu can be identified as major risk factors. Based on the proposed approach, the final ensemble classifier, constructed on the training set and contained only four decision stumps, achieved an overall identification accuracy of 95.2%, a sensitivity of 100% and a specificity of 94% on the independent test set. The results suggested that integrating Adaboost and trace element analysis of hair sample can serve as a useful tool of diagnosing CVD in clinical practice.

Early prediction of lung cancer based on the combination of trace element analysis in urine and an Adaboost algorithm

Early detection of cancer is the key to effective treatment and long-term survival. Lung cancer is one of the most frequently occurring cancers and its early detection is particularly of interest. This work investigates the feasibility of a combination of Adaboost (ensemble from machining learning) using decision stumps as weak classifier and trace element analysis for predicting early lung cancer. A dataset involving the determination of 9 trace elements of 122 urine samples is used for illustration. Kennard and Stone (KS) algorithm coupled with an alternate re-sampling was used to realize sample set partitioning. The whole dataset was split into equally sized training and test set, which were then reversed to yield a second operating case, we called them case A and case B, respectively. The prediction results based on the Adaboost were compared with those from Fisher discriminant analysis (FDA). On the test set, the final Adaboost classifiers achieved a sensitivity of 100% for both cases, a specificity of 93.8%, 95.7%, and an overall accuracy of 95.1%, 96.7%, for case A and case B, respectively. In either case, Adaboost always achieves better performance than FDA; also, it is less sensitive to the composition of the training set compared to FDA and easy to control over-fitting. It seems that Adaboost is superior to FDA in the present task, indicating that integrating Adaboost and trace element analysis of urine can serve as a useful tool for diagnosing early lung cancer in clinical practice.