Electrochemical and Colorimetric Nanosensors for Detection of Heavy Metal Ions: A Review.

Human exposure to acute and chronic levels of heavy metal ions are linked with various health issues, including reduced children's intelligence quotients, developmental challenges, cancers, hypertension, immune system compromises, cytotoxicity, oxidative cellular damage, and neurological disorders, among other health challenges. The potential environmental HMI contaminations, the biomagnification of heavy metal ions along food chains, and the associated risk factors of heavy metal ions on public health safety are a global concern of top priority. Hence, developing low-cost analytical protocols capable of rapid, selective, sensitive, and accurate detection of heavy metal ions in environmental samples and consumable products is of global public health interest. Conventional flame atomic absorption spectroscopy, graphite furnace atomic absorption spectroscopy, atomic emission spectroscopy, inductively coupled plasma-optical emission spectroscopy, inductively coupled plasma-mass spectroscopy, X-ray diffractometry, and X-ray fluorescence have been well-developed for HMIs and trace element analysis with excellent but varying degrees of sensitivity, selectivity, and accuracy. In addition to high instrumental running and maintenance costs and specialized personnel training, these instruments are not portable, limiting their practicality for on-demand, in situ, field study, or point-of-need HMI detection. Increases in the use of electrochemical and colorimetric techniques for heavy metal ion detections arise because of portable instrumentation, high sensitivity and selectivity, cost-effectiveness, small size requirements, rapidity, and visual detection of colorimetric nanosensors that facilitate on-demand, in situ, and field heavy metal ion detections. This review highlights the new approach to low-cost, rapid, selective, sensitive, and accurate detection of heavy metal ions in ecosystems (soil, water, air) and consumable products. Specifically, the review highlights low-cost, portable, and recent advances in smartphone-operated screen-printed electrodes (SPEs), plastic chip SPES, and carbon fiber paper-based nanosensors for environmental heavy metal ion detection. In addition, the review highlights recent advances in colorimetric nanosensors for heavy metal ion detection requirements. The review provides the advantages of electrochemical and optical nanosensors over the conventional methods of HMI analyses. The review further provides in-depth coverage of the detection of arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), manganese (Mn), nickel (Ni), lead (Pb), and zinc (Zn) ions in the ecosystem, with emphasis on environmental and biological samples. In addition, the review discusses the advantages and challenges of the current electrochemical and colorimetric nanosensors protocol for heavy metal ion detection. It provides insight into the future directions in the use of the electrochemical and colorimetric nanosensors protocol for heavy metal ion detection.

Drug Repurposing to Identify Nilotinib as a Potential SARS-CoV-2 Main Protease Inhibitor: Insights from a Computational and In Vitro Study.

COVID-19, an acute viral pneumonia, has emerged as a devastating pandemic. Drug repurposing allows researchers to find different indications of FDA-approved or investigational drugs. In this current study, a sequence of pharmacophore and molecular modeling-based screening against COVID-19 Mpro (PDB: 6LU7) suggested a subset of drugs, from the Drug Bank database, which may have antiviral activity. A total of 44 out of 8823 of the most promising virtual hits from the Drug Bank were subjected to molecular dynamics simulation experiments to explore the strength of their interactions with the SARS-CoV-2 Mpro active site. MD findings point toward three drugs (DB04020, DB12411, and DB11779) with very low relative free energies for SARS-CoV-2 Mpro with interactions at His41 and Met49. MD simulations identified an additional interaction with Glu166, which enhanced the binding affinity significantly. Therefore, Glu166 could be an interesting target for structure-based drug design. Quantitative structural-activity relationship analysis was performed on the 44 most promising hits from molecular docking-based virtual screening. Partial least square regression accurately predicted the values of independent drug candidates' binding energy with impressively high accuracy. Finally, the EC50 and CC50 of 10 drug candidates were measured against SARS-CoV-2 in cell culture. Nilotinib and bemcentinib had EC50 values of 2.6 and 1.1 µM, respectively. In summary, the results of our computer-aided drug design provide a roadmap for rational drug design of Mpro inhibitors and the discovery of certified medications as COVID-19 antiviral therapeutics.

Molecular modelling guided design, synthesis and QSAR analysis of new small molecule non-lipid autotaxin inhibitors.

The lysophospholipase D autotaxin (ATX) generates lysophosphatidic acid (LPA) that activates six cognate G-protein coupled receptors (GPCR) in cancerous cells, promoting their motility and invasion. Four novel compounds were generated aided by molecular docking guided design and synthesis techniques to obtain new dual inhibitors of ATX and the lysophosphatidic acid receptor subtype 1 (LPAR1). Biological evaluation of these compounds revealed two compounds, 10 and 11, as new ATX enzyme inhibitors with potencies in the range of 218-220 nM and water solubility (>100 µg/mL), but with no LPAR1 inhibitory activity. A QSAR model was generated that included four newly designed compounds and twenty-one additional compounds that we have reported previously. The QSAR model provided excellent predictability of the pharmacological activity and potency among structurally related drug candidates. This model will be highly useful in guiding the synthesis of new ATX inhibitors in the future.

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs.

Quality checks, assessments, and the assurance of food products, raw materials, and food ingredients is critically important to ensure the safeguard of foods of high quality for safety and public health. Nevertheless, quality checks, assessments, and the assurance of food products along distribution and supply chains is impacted by various challenges. For instance, the development of portable, sensitive, low-cost, and robust instrumentation that is capable of real-time, accurate, and sensitive analysis, quality checks, assessments, and the assurance of food products in the field and/or in the production line in a food manufacturing industry is a major technological and analytical challenge. Other significant challenges include analytical method development, method validation strategies, and the non-availability of reference materials and/or standards for emerging food contaminants. The simplicity, portability, non-invasive, non-destructive properties, and low-cost of NIR spectrometers, make them appealing and desirable instruments of choice for rapid quality checks, assessments and assurances of food products, raw materials, and ingredients. This review article surveys literature and examines current challenges and breakthroughs in quality checks and the assessment of a variety of food products, raw materials, and ingredients. Specifically, recent technological innovations and notable advances in quartz crystal microbalances (QCM), electroanalytical techniques, and near infrared (NIR) spectroscopic instrument development in the quality assessment of selected food products, and the analysis of food raw materials and ingredients for foodborne pathogen detection between January 2019 and July 2020 are highlighted. In addition, chemometric approaches and multivariate analyses of spectral data for NIR instrumental calibration and sample analyses for quality assessments and assurances of selected food products and electrochemical methods for foodborne pathogen detection are discussed. Moreover, this review provides insight into the future trajectory of innovative technological developments in QCM, electroanalytical techniques, NIR spectroscopy, and multivariate analyses relating to general applications for the quality assessment of food products.

Investigation of the binding and simultaneous quantifications of propanil and bromoxynil herbicide concentrations in human serum albumin.

This study reported the use of UV-visible and fluorescence spectroscopy and partial-least-square (PLS) multivariate regression for accurate and simultaneous quantifications of two widely used herbicides, propanil, 3',4'-dichloropropionanilide (PPL) and bromoxynil, 3,5-dibromo-4-hydroxybenzonitrile (BXL) in human serum albumin (HSA) at physiological conditions. The binding affinity and thermodynamic properties of PPL-HSA and BXL-HSA complexes were also investigated. Partial-least-square (PLS) regression was used to collate the variability in the absorption or emission spectra of PPL-HSA and BXL-HSA complexes with PPL and/or BXL concentrations in HSA samples. The binding constants of 7.66× 108 M-1 for PPL-HSA and 4.88× 106 M-1 for BXL-HSA complexes were calculated at physiological conditions (temperature, 310 K; pH 7.4). Thermodynamic parameter values: enthalpy (ΔH) (13.99 kJ mol-1), entropy (ΔS) (0.078 kJ mol-1 K-1), and Gibbs free energy (ΔG) (-10.19 kJ mol-1) were determined for PPL-HSA complexation at physiological conditions. However, differences in thermodynamic property values of: ΔH (-214.3 kJ mol-1), ΔS (-0.563 kJ mol-1 K-1), and ΔG (-39.70 kJ mol-1) were observed for BXL-HSA complexes. The binding constants and negative ΔG values indicated strong binding affinity and thermodynamically favorability of PPL-HSA and BXL-HSA complex formation. Results of the PLS regression calibration showed good linearity (R2 ≥ 0.998289), high sensitivity, and impressive low limit-of-detections (LODs) of 1.38× 10-8 M for PPL and 1.68× 10-8 M for BXL that are comparable and/or lower than many previously reported LODs for herbicide and pesticide analyses. Most importantly, PLS regression is capable of simultaneous quantifications of PPL and BXL concentrations in HSA samples with good accuracy and low errors of 3.66%. UV-visible spectrophotometers and spectrofluorometers are fairly inexpensive, easy to use, and are readily available in almost every laboratory, making this protocol excellent and affordable for routine analysis of weed/pest control chemical residues in humans. The results of this study are significant and remarkable that will provide critical insight into the binding mechanism of herbicide toxicity in humans and non-target organisms, which are of special interest in the area of biomedical study, environmental risk assessment, and ecotoxicology.

Designing potent inhibitors against the multidrug resistance P-glycoprotein.

The multidrug transporter P-glycoprotein is an ATP binding cassette (ABC) exporter responsible for resistance to tumor cells during chemotherapy. This study was designed with computational approaches aimed at identifying the best potent inhibitors of P-glycoprotein. Although many compounds have been suggested to inhibit P-glycoprotein, however, their information on bioavailability, selectivity, ADMET properties, and molecular interactions has not been revealed. Molecular docking, ADMET analysis, molecular dynamics, Principal component analysis (PCA), and binding free energy calculations were performed. Two compounds D1 and D2 showed the best docking score against P-glycoprotein and both compounds have 4-thiazolidinone derivatives containing indolin-3 one moiety are novel anti-tumor compounds. ADMET calculation analysis predicted D1 and D2 to have acceptable pharmacokinetic properties. The MD simulation discloses that D1-P-glycoprotein and D2-P-glycoprotein complexes are in stable conformation as apo-form. Hydrophobic amino acid such as phenylalanine plays significant on the interactions of inhibitors. Principal component analysis shows that both complexes are relatively similar variables as apo-form except planarity and Columbo energy profile. In addition, Quantitative Structural Activity Relationship (QSAR) of the ligand candidates were subjected to the principal component analysis (PCA) for pattern recognition. Partial-least-square (PLS) regression analysis was further utilized to model drug candidates' QSAR for subsequent prediction of the binding energy of validated drug candidates. PCA revealed groupings of the drug candidates based on the similarity or differences in drug candidates QSAR. Moreover, the developed PLS regression accurately predicted the values of the binding energy of drug candidates, with low residual error of prediction.Communicated by Ramaswamy H. Sarma.

Virtual screening, molecular dynamics, density functional theory and quantitative structure activity relationship studies to design peroxisome proliferator-activated receptor-γ agonists as anti-diabetic drugs.

Type 2 diabetes (T2D) is generally characterized by elevated blood glucose levels, insulin resistance, and relative lack of insulin; however, insulin resistance is the predominant risk factor. Hence, the use of insulin sensitizer drugs to increase insulin sensitivity has gained immense interest as an attractive treatment option for T2D and their major target is a nuclear receptor PPAR-γ (peroxisome proliferator-activated receptor-γ). A wide range of synthetic insulin sensitizers such as thiazolidinedione act as PPAR-γ agonists thereby enhancing insulin action and improving hyperglycemia in patients. Nonetheless, they pose severe adverse effects for human, necessitating an emergent need to develop effective insulin sensitizer drugs. Herein, virtual screening of 10,000 ligands is performed and the best five ligands are identified. MET364, ILE341, CYS285, ALA292, PHE282, and LEU330 residues are found to play an important role in ligand binding. It is shown from the molecular dynamics simulations results of the top-ranked ligands that increased numbers of hydrogen bonds are formed with PPAR-γ catalytic residues. Quantum chemical calculations reveal that all the best ligands can demonstrate good thermodynamic stability and pharmacokinetic properties. Partial-least-square (PLS) regression of quantitative structural activity relationship (QSAR) is utilized to model and predict the binding energy for ligands. Principal component analysis is further explored for the best ligands' QSAR pattern recognition. Importantly, the predicted values of the binding energy of the potential ligands by the PLS regression is favourably compared with the values of binding energy obtained from molecular docking with incredible high accuracy of 98%.

X-ray Crystallography-Guided Design, Antitumor Efficacy, and QSAR Analysis of Metabolically Stable Cyclopenta-Pyrimidinyl Dihydroquinoxalinone as a Potent Tubulin Polymerization Inhibitor.

Small molecules that interact with the colchicine binding site in tubulin have demonstrated therapeutic efficacy in treating cancers. We report the design, syntheses, and antitumor efficacies of new analogues of pyridopyrimidine and hydroquinoxalinone compounds with improved drug-like characteristics. Eight analogues, 5j, 5k, 5l, 5m, 5n, 5r, 5t, and 5u, showed significant improvement in metabolic stability and demonstrated strong antiproliferative potency in a panel of human cancer cell lines, including melanoma, lung cancer, and breast cancer. We report crystal structures of tubulin in complex with five representative compounds, 5j, 5k, 5l, 5m, and 5t, providing direct confirmation for their binding to the colchicine site in tubulin. A quantitative structure-activity relationship analysis of the synthesized analogues showed strong ability to predict potency. In vivo, 5m (4 mg/kg) and 5t (5 mg/kg) significantly inhibited tumor growth as well as melanoma spontaneous metastasis into the lung and liver against a highly paclitaxel-resistant A375/TxR xenograft model.

Delivery of phytochemical thymoquinone using molecular micelle modified poly(D, L lactide-co-glycolide) (PLGA) nanoparticles.

Continuous efforts have been made in the development of potent benzoquinone-based anticancer drugs aiming for improved water solubility and reduced adverse reactions. Thymoquinone is a liposoluble benzoquinone-based phytochemical that has been shown to have remarkable antioxidant and anticancer activities. In the study reported here, thymoquinone-loaded PLGA nanoparticles were synthesized and evaluated for physico-chemical, antioxidant and anticancer properties. The nanoparticles were synthesized by an emulsion solvent evaporation method using anionic molecular micelles as emulsifiers. The system was optimized for maximum entrapment efficiency using a Box-Behnken experimental design. Optimum conditions were found for 100 mg PLGA, 15 mg TQ and 0.5% w/v poly(sodium N-undecylenyl-glycinate) (poly-SUG). In addition, other structurally related molecular micelles such as poly(sodium N-heptenyl-glycinate) (poly-SHG), poly(sodium N-undecylenyl-leucinate) (poly-SUL), and poly(sodium N-undecylenyl-valinate) (poly-SUV) were also examined as emulsifiers. All investigated molecular micelles provided excellent emulsifier properties, leading to maximum optimized TQ entrapment efficiency, and monodispersed particle sizes below 200 nm. The release of TQ from molecular micelle modified nanoparticles was investigated by dialysis and reached lower levels than the free drug. The antioxidant activity of TQ-loaded nanoparticles, indicated by IC50 (mg ml( - 1) TQ for 50% 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging activity), was highest for poly-SUV emulsified nanoparticles (0.030 +/- 0.002 mg ml( - 1)) as compared to free TQ. In addition, it was observed that TQ-loaded nanoparticles emulsified with poly-SUV were more effective than free TQ against MDA-MB-231 cancer cell growth inhibition, presenting a cell viability of 16.0 +/- 5.6% after 96 h.

Multicomponent analyses of chiral samples by use of regression analysis of UV-visible spectra of cyclodextrin guest-host complexes.

We report the first combined use of analytical spectroscopy, guest-host chemistry, and multivariate regression analysis for determination of enantiometric composition of multicomponent samples of chiral analytes. Sample solutions containing multicomponent analytes of ephedrine, tryptophan, propranolol, and proline of varying enantiomeric composition with beta-cyclodextrin (BCD) or methyl-beta-cyclodextrin (Me-BCD) as chiral host molecules were investigated using ultraviolet (UV)-visible spectroscopy. The interactions of enantiomers of chiral analytes with chiral hosts resulted in the formation of transient diastereomeric inclusion complexes with varying spectral properties. Multivariate analysis using partial-least-square (PLS) regression was used to correlate subtle changes in the UV-visible spectra of the guest-host complexes with the enantiomeric composition of the calibration samples. These PLS regressions were carefully optimized and then used to predict the enantiomeric composition of multicomponent chiral analytes of validation samples. The results of these validation studies demonstrate the predictive ability of the regression models for determination of future enantiomeric composition of samples. The accuracy of the models to correctly predict the enantiomeric composition of samples, evaluated by use of the root mean square percent relative error (RMS%RE) was analyte and chiral host dependent. In general, better prediction of enantiomeric composition of samples and low RMS%RE values were obtained when Me-BCD was used as the chiral host. The analyses procedure reported here is simple, rapid, and inexpensive. In addition, this approach does not require prior separation of chiral analytes, thus reducing analysis time and eliminating the need for expensive chiral columns.

A versatile chiral selector for determination of enantiomeric composition of fluorescent and nonfluorescent chiral molecules using steady-state fluorescence spectroscopy.

A fluorescent chiral molecular micelle (FCMM), poly (sodium N-undecanoyl-L-phenylalaninate) (poly-L-SUF), was developed as a chiral selector for enantiomeric recognition and determination of enantiomeric composition of four fluorescent and four nonfluorescent chiral molecules by use of steady-state fluorescence spectroscopy. The influence of FCMM concentration, buffer pH and complexation medium on FCMM-analyte host-guest complexation, and the emission spectral properties of the resulting complexes were investigated. The chiral interactions of the analytes,1,1'-binaphthyl-2,2'-diamine, 1-(9-anthryl)-2,2,2-trifluoroethanol, propranolol, naproxen, chloromethyl menthyl ether (CME), citramalic acid, tartaric acid, and limonene (LIM), in the presence of poly-L-SUF were based on diastereomeric complex formation. The figures of merit obtained from the partial-least-squares regression modeling of the calibration samples suggested good prediction ability for the validation of six of the eight chiral analytes. Better host-guest complexation of the more hydrophobic molecules, CME and LIM, were obtained in methanol/water mixtures, resulting in better predictability of the regression models. Prediction ability of the models was evaluated by use of the root-mean-square percent relative error (RMS%RE) and was found to range from 1.77 to 15.80% (buffer), 1.26 to 7.95% (25:75 methanol/water), and 1.21 to 4.28% (75:25 methanol/water).

Chiral recognition of amino acids by use of a fluorescent resorcinarene.

The spectroscopic properties of a chiral boronic acid based resorcinarene macrocycle employed for chiral analysis were investigated. Specifically, the emission and excitation characteristics of tetraarylboronate resorcinarene macrocycle (TBRM) and its quantum yield were evaluated. The chiral selector TBRM was investigated as a chiral reagent for the enantiomeric discrimination of amino acids using steady-state fluorescence spectroscopy. Chiral recognition of amino acids in the presence of the macrocycle was based on diastereomeric complexes. Results demonstrated that TBRM had better chiral discrimination ability for lysine as compared to the other amino acids. Partial least squares regression modeling (PLS-1) of spectral data for macrocycle-lysine guest-host complexes was used to correlate the changes in the fluorescence emission for a set of calibration samples consisting of TBRM in the presence of varying enantiomeric compositions of lysine. In addition, validation studies were performed using an independently prepared set of samples with different enantiomeric compositions of lysine. The results of multivariate regression modeling indicated good prediction ability of lysine, which was confirmed by a root mean square percent relative error (RMS%RE) of 5.8%.

Experimental design and multivariate analysis for optimizing poly(D,L-lactide-co-glycolide) (PLGA) nanoparticle synthesis using molecular micelles.

The utility of polymeric nanoparticles as drug delivery systems depends on effective control of synthetic parameters with a significant impact on their physico-chemical characteristics. In this study, a chemometric central composite experimental design (CCD) was used to optimize the synthesis of poly(D,L-lactide-co-glycolide) (PLGA) nanoparticles by emulsification solvent evaporation using anionic molecular micelles, such as poly(sodium N-undecylenic sulfate) (poly-SUS), poly(sodium N-undecanoyl-glycinate) (poly-SUG) and poly(sodium N-undecanoyl-L-leucyl-valinate) (poly-L-SULV) as well as conventional emulsifiers, such as anionic sodium dodecyl sulfate (SDS) and non-ionic poly(vinyl alcohol) (PVA). The individual and combined effects of PLGA concentration, emulsifier concentration, homogenization speed, and sonication time (design variables) on particle size and polydispersity index (responses) were investigated using multivariate analysis. The most significant design variables influencing the nanoparticle size and size distribution were PLGA concentration and emulsifier concentration (p < 0.05) in comparison to the other design variables. The quadratic model demonstrated the highest predictive ability when the molecular micelles were used as emulsifiers. The PLGA nanoparticles optimally synthesized according to the CCD were further purified by dialysis and then freeze-dried. Dried nanoparticles synthesized with molecular micelles and PVA were readily re-suspended in water, as compared with SDS for which nanoparticle aggregation occurred. The size of PLGA nanoparticles synthesized using molecular micelles increased after freeze-drying, but remained smaller than 100 nm when poly-L-SULV was used as emulsifier. The PDI values indicated monodisperse nanoparticle suspensions after purification and freeze-drying for all investigated molecular micelles (PDI < 0.100). The nanoparticle suspensions synthesized using molecular micelles were the most stable after dialysis and freeze-drying, having low negative zeta potential values ranging from -54 +/- 1.6 mV for poly-L-SULV to -63.2 +/- 0.4 mV for poly-SUS. Transmission electron microscopy (TEM) micrographs showed spherical shape and smooth surface for the PLGA nanoparticles synthesized using molecular micelles.

Detection, Purity Analysis, and Quality Assurance of Adulterated Peanut (Arachis Hypogaea) Oils.

The intake of adulterated and unhealthy oils and trans-fats in the human diet has had negative health repercussions, including cardiovascular disease, causing millions of deaths annually. Sadly, a significant percentage of all consumable products including edible oils are neither screened nor monitored for quality control for various reasons. The prospective intake of adulterated oils and the associated health impacts on consumers is a significant public health safety concern, necessitating the need for quality assurance checks of edible oils. This study reports a simple, fast, sensitive, accurate, and low-cost chemometric approach to the purity analysis of highly refined peanut oils (HRPO) that were adulterated either with vegetable oil (VO), canola oil (CO), or almond oil (AO) for food quality assurance purposes. The Fourier transform infrared spectra of the pure oils and adulterated HRPO samples were measured and subjected to a partial-least-square (PLS) regression analysis. The obtained PLS regression figures-of-merit were incredible, with remarkable linearity (R² = 0.994191 or better). The results of the score plots of the PLS regressions illustrate pattern recognition of the adulterated HRPO samples. Importantly, the PLS regressions accurately determined percent compositions of adulterated HRPOs, with an overall root-mean-square-relative-percent-error of 5.53% and a limit-of-detection as low as 0.02% (wt/wt). The developed PLS regressions continued to predict the compositions of newly prepared adulterated HRPOs over a period of two months, with incredible accuracy without the need for re-calibration. The accuracy, sensitivity, and robustness of the protocol make it desirable and potentially adoptable by health departments and local enforcement agencies for fast screening and quality assurance of consumable products.

Analyses of polycyclic aromatic hydrocarbon (PAH) and chiral-PAH analogues-methyl-ß-cyclodextrin guest-host inclusion complexes by fluorescence spectrophotometry and multivariate regression analysis.

The negative health impact of polycyclic aromatic hydrocarbons (PAHs) and differences in pharmacological activity of enantiomers of chiral molecules in humans highlights the need for analysis of PAHs and their chiral analogue molecules in humans. Herein, the first use of cyclodextrin guest-host inclusion complexation, fluorescence spectrophotometry, and chemometric approach to PAH (anthracene) and chiral-PAH analogue derivatives (1-(9-anthryl)-2,2,2-triflouroethanol (TFE)) analyses are reported. The binding constants (Kb), stoichiometry (n), and thermodynamic properties (Gibbs free energy (ΔG), enthalpy (ΔH), and entropy (ΔS)) of anthracene and enantiomers of TFE-methyl-ß-cyclodextrin (Me-ß-CD) guest-host complexes were also determined. Chemometric partial-least-square (PLS) regression analysis of emission spectra data of Me-ß-CD-guest-host inclusion complexes was used for the determination of anthracene and TFE enantiomer concentrations in Me-ß-CD-guest-host inclusion complex samples. The values of calculated Kb and negative ΔG suggest the thermodynamic favorability of anthracene-Me-ß-CD and enantiomeric of TFE-Me-ß-CD inclusion complexation reactions. However, anthracene-Me-ß-CD and enantiomer TFE-Me-ß-CD inclusion complexations showed notable differences in the binding affinity behaviors and thermodynamic properties. The PLS regression analysis resulted in square-correlation-coefficients of 0.997530 or better and a low LOD of 3.81×10-7M for anthracene and 3.48×10-8M for TFE enantiomers at physiological conditions. Most importantly, PLS regression accurately determined the anthracene and TFE enantiomer concentrations with an average low error of 2.31% for anthracene, 4.44% for R-TFE and 3.60% for S-TFE. The results of the study are highly significant because of its high sensitivity and accuracy for analysis of PAH and chiral PAH analogue derivatives without the need of an expensive chiral column, enantiomeric resolution, or use of a polarized light.

The variation of macro- and micro-minerals of tissues in diabetic and non-diabetic rats.

This study determined the levels of Ca, Mg, Fe, Zn, Cu, and Na in various tissues samples (liver, brain, kidney, intestines, muscle and hair) of diabetic and non-diabetic rats by flame atomic absorption spectroscopy, in order to assess the role of element levels during T2DM. The ratios of Ca/Mg, Zn/Cu, Ca/Zn, and Mg/Zn in diabetic and non-diabetic rat tissues were also calculated. The determined element levels were further subjected to a student-t test statistical analysis and multiple-linear-regression in order to evaluate similarities, differences, and an inter-element association in tissues of diabetic and non-diabetic rats. The results of the study showed high variability in element levels and Ca/Mg Zn/Cu Mg/Zn Ca/Zn ratios in the tissues of diabetic and non-diabetic rats, but are tissue- and element-dependent, suggesting differences in the accumulation of the elements in tissues of diabetics and non-diabetics. The obtained significant differences in the levels of elements and Ca/Mg Zn/Cu Mg/Zn Ca/Zn ratios in several tissues of diabetic and non-diabetic rats in this study suggest that the investigated elements play considerable roles in the T2DM disease process. Strong inter-element associations (R2≥0.9) were observed for some elements in tissues of diabetic and non-diabetics rats. However, poor inter-elemental associations were obtained for some elements in the tissues of diabetic and non-diabetic rats.

Environmental biomonitoring of essential and toxic elements in human scalp hair using accelerated microwave-assisted sample digestion and inductively coupled plasma optical emission spectroscopy.

Human scalp hair samples were collected and used to assess exposure to toxic elements and essential elements in the state of North Carolina, USA using accelerated microwave assisted acid digestion and inductively coupled plasma optical emission spectroscopy (ICP-OES). The figures-of-merit of the ICP-OES were appropriate for elemental analysis in scalp hair with detection limits as low as 0.0001 mg/L for Cd, good linearity (R2 > 0.9978), and percent recoveries that ranged from 96 to 106% for laboratory-fortified-blanks and 88-112% for sample spike recovery study. The concentrations of essential elements in scalp hair were larger than those of toxic elements, with Ca having the highest average concentration (3080 µg/g, s = 14,500, n = 194). Some of the maximum concentrations observed for As (65 µg/g), Ni (331 µg/g), Cd (2.96 µg/g), and Cr (84.6 µg/g) in individual samples were concerning, however. Samples were statistically analyzed to determine the influence of race, gender, smoking habits, or age on the elemental concentrations in scalp hair. Higher concentrations of essential elements were observed in the scalp hair of Caucasians, females, and non-smokers, and the differences were often significant at a 90% confidence level. Several pairs of essential elements, for example Ca-K, Ca-Mg, and Ca-Zn, were strongly correlated in Caucasian hair but uncorrelated in African-American hair. Similarly, essential elements were strongly correlated in female hair but weakly correlated in male hair. Toxic element pairs (As-Cd, As-Se, Pb-As, and Se-Cd) were strongly correlated in the hair of smokers but uncorrelated in that of non-smokers, suggesting that cigarette smoke is a common source of toxic elements in humans.