Extraction Step Research Articles

Extraction of Technical Indicators and Data Augmentation-Based Stock Market Prediction Using Deep LSTM Integrated Competitive Swarm Feedback Algorithm

In recent decades, time-series analysis and value forecasting have grown in research fields. Making a significant decision in the stock market prediction strategy requires knowledge that may be gained by forecasting with time-series data. Various prediction methods are developed to forecast future stock prices, but accurate prediction with the time-series data using external factors still results in a difficult task. An effective prediction approach is designed in this paper using the adopted Competitive Swarm Feedback Algorithm-based Deep Long Short-Term Memory (CSFA-based Deep LSTM) classifier for predicting the stock market data. The CSFA is the integration of the Competitive Swarm Optimizer (CSO) and Feedback Artificial Tree (FAT) algorithm. Then, technical indicators’ extraction, feature fusion and data augmentation steps are carried out. The Deep LSTM achieved higher prediction results than the other traditional classifiers. The proposed method achieved lower MAE, lower MSE, and minimum RMSE with the values of 0.1418, 0.1119, and 0.2557.

Corrosion of Molybdenum-Based and Ni–Mo Alloys in Liquid Bismuth–Lithium Alloy

Bismuth–lithium alloys are considered primary candidates for the reductive extraction step in the on-line reprocessing of molten salt reactor fuel. The corrosion behavior of molybdenum-based alloys and Hastelloy® B-3 alloy (taken for comparison) was examined here in a liquid Bi–Li (5 mol.%) alloy at 650 °C. MoW10, MoW30, and TZM corrosion-resistant alloys were studied as prospective construction materials for holding liquid bismuth–lithium alloy. Rates of corrosion were determined by the gravimetric method as well as by chemical analysis of corrosion products formed in liquid-phase Bi–Li alloy. The microstructure and chemical composition of samples of the materials and Bi–Li alloys containing the corrosion products after the tests were determined using inductively coupled plasma–atomic emission spectroscopy, X-ray fluorescence analysis, scanning electron microscopy, and energy dispersive spectroscopy. TZM molybdenum-based alloy corrodes in the bismuth-lithium alloy due to the formation of a zirconium–bismuth intermetallic compound, which passes into the liquid phase. The corrosion rates of MoW10, MoW30, and TZM alloys at 650 °C were 16, 16, and 23 µm/year, respectively. Hastelloy® B-3 alloy, despite its high molybdenum content, was subjected to severe corrosion in liquid Bi–Li alloys due to dissolution of nickel in liquid bismuth. The corrosion rate of this alloy was 14 mm/year.

Polyamide-coated paper-based sorptive phase applied in high-throughput thin film microextraction designed by 3D printing

A new method was developed for the determination of endocrine disrupting compounds (phthalates, alkylphenols and bisphenols) in water samples by using thin film microextraction technique (TFME) with high-performance liquid chromatography and diode-array detection (HPLC-DAD). This paper reports for the first time the use of a paper-based sorptive phase (PSP) in a high-throughput semi-automated system. This system designed by 3D printing technology allowed us to integrate the PSP with a multi-well plate format. Multivariate design of experiments and one-factor-at-a-time approaches were employed for optimization. The optimized PSP preparation required two dips of the filter paper into the nylon 66 solution followed by the evaporation of the solvent. The best conditions for the extraction step were NaCl 30 % (w/v) added to the sample at pH 2 for 180 min. The desorption showed best results with acetonitrile for 20 min. The calibration curves exhibited coefficients of determination higher than 0.9837. The limits of detection varied from 1.5 to 7.6 μg L-1. Six of ten analytes showed promising results with accuracy ranging from 82 to 117 % and precision (RSD) equal or inferior to 24 % for mineral water sample. We also evaluated a coconut water sample that obtained higher deviations than mineral water sample. The time consumed for each sample was about 8.3 min.

Deep learning-based end-to-end automated stenosis classification and localization on catheter coronary angiography.

Automatic coronary angiography (CAG) assessment may help in faster screening and diagnosis of stenosis in patients with atherosclerotic disease. We aimed to provide an end-to-end workflow that separates cases with normal or mild stenoses from those with higher stenosis severities to facilitate safety screening of a large volume of the CAG images. A deep learning-based end-to-end workflow was employed as follows: (1) Candidate frame selection from CAG videograms with Convolutional Neural Network (CNN) + Long Short Term Memory (LSTM) network, (2) Stenosis classification with Inception-v3 using 2 or 3 categories (<25%, >25%, and/or total occlusion) with and without redundancy training, and (3) Stenosis localization with two methods of class activation map (CAM) and anchor-based feature pyramid network (FPN). Overall 13,744 frames from 230 studies were used for the stenosis classification training and fourfold cross-validation for image-, artery-, and per-patient-level. For the stenosis localization training and fourfold cross-validation, 690 images with > 25% stenosis were used. Our model achieved an accuracy of 0.85, sensitivity of 0.96, and AUC of 0.86 in per-patient level stenosis classification. Redundancy training was effective to improve classification performance. Stenosis position localization was adequate with better quantitative results in anchor-based FPN model, achieving global-sensitivity for left coronary artery (LCA) and right coronary artery (RCA) of 0.68 and 0.70. We demonstrated a fully automatic end-to-end deep learning-based workflow that eliminates the vessel extraction and segmentation step in coronary artery stenosis classification and localization on CAG images. This tool may be useful to facilitate safety screening in high-volume centers and in clinical trial settings.

Non-invasive treatment of ischemia/reperfusion injury: Effective transmission of therapeutic near-infrared light into the human brain through soft skin-conforming silicone waveguides.

Noninvasive delivery of near-infrared light (IRL) to human tissues has been researched as a treatment for several acute and chronic disease conditions. We recently showed that use of specific IRL wavelengths, which inhibit the mitochondrial enzyme cytochrome c oxidase (COX), leads to robust neuroprotection in animal models of focal and global brain ischemia/reperfusion injury. These life-threatening conditions can be caused by an ischemic stroke or cardiac arrest, respectively, two leading causes of death. To translate IRL therapy into the clinic an effective technology must be developed that allows efficient delivery of IRL to the brain while addressing potential safety concerns. Here, we introduce IRL delivery waveguides (IDWs) which meet these demands. We employ a low-durometer silicone that comfortably conforms to the shape of the head, avoiding pressure points. Furthermore, instead of using focal IRL delivery points via fiberoptic cables, lasers, or light-emitting diodes, the distribution of the IRL across the entire area of the IDW allows uniform IRL delivery through the skin and into the brain, preventing "hot spots" and thus skin burns. The IRL delivery waveguides have unique design features, including optimized IRL extraction step numbers and angles and a protective housing. The design can be scaled to fit various treatment areas, providing a novel IRL delivery interface platform. Using fresh (unfixed) human cadavers and isolated cadaver tissues, we tested transmission of IRL via IDWs in comparison to laser beam application with fiberoptic cables. Using the same IRL output energies IDWs performed superior in comparison to the fiberoptic delivery, leading to an up to 95% and 81% increased IRL transmission for 750 and 940 nm IRL, respectively, analyzed at a depth of 4cm into the human head. We discuss the unique safety features and potential further improvements of the IDWs for future clinical implementation.

Simultaneous Determination of Neonicotinoid and Carbamate Pesticides in Freeze-Dried Cabbage by Modified QuEChERS and Ultra-Performance Liquid Chromatography-Tandem Mass Spectrometry.

Dehydrated vegetables are popular in instant foods, but few reports have focused on their pesticide residues. This research developed and validated a modified QuEChERS method combined with ultra-performance liquid chromatography-tandem mass spectrometry to determine 19 kinds of neonicotinoid and carbamate pesticides in freeze-dried cabbage. Herein, acetonitrile/water (v/v = 2:1) was selected in the extraction step. Meanwhile, 4 g anhydrous magnesium sulfate and 1 g sodium chloride were applied to the partitioning step. Dispersive solid-phase extraction sorbents were selected, and liquid chromatography conditions were further optimized for dealing with the matrix effect. The limits of quantification ranged from 1.0 to 10.0 μg/kg. The validation results were acceptable, with average recoveries of 78.7-114.0% and relative standard deviations below 14.2%. The method recoveries were closely related to the volume proportion of water in the extractant. Finally, the developed method was applied to real freeze-dried cabbages and four pesticides (propamocarb, imidacloprid, acetamiprid, and thiacloprid) were detected in six samples.

Development of an HPLC-MS/MS Method for the Determination of Alkaloids in Lupins

Lupin alkaloids (LAs) represent a class of toxic secondary metabolites in plants, in particular in Lupinus spp.; they are produced as a defense mechanism due to their strong bitter taste and are very dangerous for human and animals. In this work, a sensitive and reliable high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) analytical method for the identification and quantification of thirteen lupin alkaloids was developed and validated according to FDA guidelines. Efficient extraction and clean-up steps, carried out by solid-phase extraction, were finely tuned on the basis of the characteristics of the analytes and lupin samples, providing good selectivity with minimized matrix interference. The effectiveness of the method was proven by the satisfactory recovery values obtained for most of the analytes and a matrix effect ≤23% for all tested levels. In addition, a sensitive and reliable determination of the target compounds was obtained; LOQs were between 1 and 25 µg Kg-1, i.e., below the requested maximum levels (<200 mg Kg-1). The method was applied to evaluate the LAs profile in different batches of raw L. albus L. samples, varying in size and across farming treatments.

Multi-residue determination of 17 antibiotics in sea cucumbers (Holothuria tubulosa) by ultrahigh performance liquid chromatography-tandem mass spectrometry

Antibiotics are a class of pharmaceuticals routinely prescribed to prevent and treat infections caused by pathogenic bacteria in humans and animals. Their widespread consumption results in a gradual contamination of the aquatic environment, which is of particular concern due to the phenomenon of resistance to antibiotics. Quantitative analysis of antibiotics in marine ecosystems is critical to assess the potential risk of exposure of aquatic species and, consequently, of consumers of seafood. In this context, filter feeders, such as holothurians, are considered ideal bioindicators of pollution in marine waters. In this work, an ultrahigh-performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) method was developed and validated for the simultaneous determination of 17 multiclass antibiotic residues in Holothuria tubulosa specimens. The proposed methodology involved two steps of ultrasound-assisted extraction (UAE) with methanol, followed by a clean-up based on dispersive solid phase extraction (d-SPE) using C18 adsorbent. 3^2 factorial designs were used for the optimization of the most influential variables of the extraction procedure. Linearity of 84.1–99.4 %, precision in terms of RSD lower than 15 %, recoveries between 87.9 and 114.9 %, and detection limits between 0.3 and 3.0 ng g−1 were obtained. After validation, the method was used to analyze specimens of Holothuria tubulosa collected along the Mediterranean coast of Granada. The obtained results indicate the presence of antibiotics in the area, CIP being the most prevalent.

Multiclass method to determine emerging pollutants in bats using a non-invasive approach based on guano matrix

Emerging pollutants have been ubiquitously found in environmental compartments, while there is scarce information about these substances and their effects on health status in wild terrestrial mammals. Bat species are very sensitive animals to any changes in the environment and are considered one of the best bioindicators of the quality of the environment to terrestrial wildlife. To acquire a better knowledge of the environmental exposure to these animals, a multiclass method is proposed to determine 20 emerging pollutants (six perfluoroalkyl substances (PFAS), four parabens (PB), four benzophenones (BP), a plasticizer (BPA), and five surfactants (four linear alkylbenzene sulfonates (LAS) and nonylphenol (NP)) in bats using a non-invasive approach based on guano matrix. Sample treatment involved ultrasonic solvent extraction and dispersive solid phase extraction prior to analysis in a single run with liquid chromatography-tandem mass spectrometry. The main variables affecting the extraction and clean-up steps were evaluated using single and multivariate strategies. Under the optimized conditions, satisfactory analytical characteristics in terms of linearity, recoveries (>80 %), precision (RSD < 24 %) and method quantification limits (from 0.01 to 64 ng/g dry weight) were obtained. Furthermore, and as a proof of concept, guano samples from a bat reference population were collected from a colony located in Brenna village, in south Poland. The results confirm the exposure of wild bats to emerging pollutants (LAS, PFAS, and PB compounds were frequently detected in the samples) and the suitability of the bat guano matrix for understanding the environmental exposure in terrestrial mammals.

Quality of Life Prediction in Driving Scenes on Thailand Roads Using Information Extraction from Deep Convolutional Neural Networks

In the modern era, urban design and sustainable development are vital topics for megacities, as they are important for the wellbeing of its residents. One of the effective key performance indices (KPIs) measuring the city plan’s efficiency in quantity and quality factors is Quality of Life (QOL), an index that policymakers can use as a critical KPI to measure the quality of urbanscape design. In the traditional approach, the researchers conduct the questionnaire survey and then analyze the gathered data to acquire the QOL index. The conventional process is costly and time-consuming, but the result of the evaluation area is limited. Moreover, it is difficult to embed in an application or system; we proposed artificial intelligence (AI) approaches to solve the limitation of the traditional method in Bangkok as a case study. There are two steps for our proposed method. First, in the knowledge extraction step, we apply deep convolutional neural networks (DCNNs), including semantic segmentation and object detection, to extract helpful information images. Second, we use a linear regression model for inferring the QOL score. We conducted various state-of-the-art (SOTA) models and public datasets to evaluate the performance of our method. The experiment results show that our novel approach is practical and can be considered for use as an alternative QOL acquisition method. We also gain some understanding of drivers’ insights from the experiment result.

Quantifying 64 drugs, illicit substances, and D- and L- isomers in human oral fluid with liquid-liquid extraction

Although human oral fluid has become more routine for quantitative drug detection in pain management, detecting a large scope of medications and substances is costly and technically challenging for laboratories. This paper presents a quantitative assay for 64 pain medications, illicit substances, and drug metabolites in human oral fluid. The novelty of this assay is that it was developed on an older model AB SCIEX 4000 instrument and renders obscure the need for more technical and expensive laboratory equipment. This method includes addition of internal standard and a 2-step liquid-liquid extraction and dry-down step to concentrate and clean the samples. The samples were suspended in 50% MeOH in water and separation and detection was accomplished using triple quadrupole mass spectrometry (LC-MS/MS). Separation was achieved using reverse-phase liquid chromatography with detection by LC-MS/MS. A second injection was done in negative mode to determine THC-COOH concentration as an indicator of THC. An aliquot of the (already) extracted samples was analyzed for D- and L- isomers of amphetamine and methamphetamine using a chiral column. The standard curve spanned from 5 to 2000 ng/mL for most of the analytes (1 to 2000 ng/mL for fentanyl and THC-COOH) and up to 1000 ng/mL for 13 analytes. Pregabalin and gabapentin ranged from 25 to 2000 ng/mL. The result is a low-cost method for the sensitive detection of a wide-ranging oral fluid menu for pain management. This assay has a high sensitivity, and good precision and accuracy for all analytes with an older model mass spectrometer.

ASAP─Automated Sonication-Free Acid-Assisted Proteomes─from Cells and FFPE Tissues.

Formalin-fixed, paraffin-embedded (FFPE) tissues are an invaluable resource for retrospective studies, but protein extraction and subsequent sample processing steps have been shown to be challenging for mass spectrometry (MS) analysis. Streamlined high-throughput sample preparation workflows are essential for efficient peptide extraction from complex clinical specimens such as fresh frozen tissues or FFPE. Overall, proteome analysis has gained significant improvements in the instrumentation, acquisition methods, sample preparation workflows, and analysis pipelines, yet even the most recent FFPE workflows remain complex and are not readily scalable. Here, we present an optimized workflow for automated sonication-free acid-assisted proteome (ASAP) extraction from FFPE sections. ASAP enables efficient protein extraction from FFPE specimens, achieving similar proteome coverage as established methods using expensive sonicators, resulting in reduced sample processing time. The broad applicability of ASAP on archived pediatric tumor FFPE specimens resulted in high-quality data with increased proteome coverage and quantitative reproducibility. Our study demonstrates the practicality and superiority of the ASAP workflow as a streamlined, time- and cost-effective pipeline for high-throughput FFPE proteomics of clinical specimens.

Optimal transport features for morphometric population analysis.

Brain pathologies often manifest as partial or complete loss of tissue. The goal of many neuroimaging studies is to capture the location and amount of tissue changes with respect to a clinical variable of interest, such as disease progression. Morphometric analysis approaches capture local differences in the distribution of tissue or other quantities of interest in relation to a clinical variable. We propose to augment morphometric analysis with an additional feature extraction step based on unbalanced optimal transport. The optimal transport feature extraction step increases statistical power for pathologies that cause spatially dispersed tissue loss, minimizes sensitivity to shifts due to spatial misalignment or differences in brain topology, and separates changes due to volume differences from changes due to tissue location. We demonstrate the proposed optimal transport feature extraction step in the context of a volumetric morphometric analysis of the OASIS-1 study for Alzheimer's disease. The results demonstrate that the proposed approach can identify tissue changes and differences that are not otherwise measurable.

A Python-OpenCV based software for processing single-bacterium tracking microscopy videos.

Real-time single-bacterium tracking (SBT) is a powerful microscopy technique that allows for direct observation of the motility of individual bacteria. This technique requires microscopy video processing methods to localize individual bacteria from microscopy videos, followed by obtaining bacterial trajectories by linking the localizations in successive video frames. These analysis steps are usually labor-intensive. In addition, many existing analysis softwares are developed using commercial programming languages, which makes it difficult to be further developed and used for larger contexts of usage. In this regard, a Python-OpenCV based software is presented which is capable of obtaining bacterial trajectories from SBT microscopy videos. The developed software does not require additional commercial software licenses. Also, the software can perform video processing and bacterial trajectory extraction steps in an automated way. The developed open-source software could thus be listed as one of the useful tools for related quantitative investigations. This work is supported by the National Natural Science Foundation of China (NSFC) under grant number 12104405.

Using eco-friendly alternatives for the recovery of pyruvic acid by reactive extraction

Efficiency, cost and environmental impact are important parameters that must be addressed in the recovery and purification of pyruvic acid (PA) from fermentation media. In this study, twelve vegetable oils were evaluated with three extraction agents for the reactive extraction of PA. Recoveries with trioctylamine (TOA) were significantly higher than those with tributylphosphate (TBP) and trioctylmethylammonium chloride (TOMAC). The reactive extraction yields with vegetable oils were similar and comparable to that with 1-octanol; among the tested oils, the highest efficiencies were achieved with almond oil. Efficiencies increased with higher extractant amounts, but were negatively affected by higher pH and PA concentrations. Higher temperatures caused slight negative effects on yields. Loading ratios (z) showed that 1:1 PA:Ext complexes most probably occur with all extractants tested; moreover, 2:1 PA:TOA may form due to the z values > 1 with TOA. The PA was stripped and organic phase was regenerated after two consecutive back extraction steps using 2.5 M NaNO3. Organic phases could be re-used 5 times with only a 1–2% decline in efficiency. Based on the performance parameters evaluated and extraction yields obtained, vegetable oils have the potential for use as organic phase diluents during reactive extraction of PA.

CortexODE: Learning Cortical Surface Reconstruction by Neural ODEs.

We present CortexODE, a deep learning framework for cortical surface reconstruction. CortexODE leverages neural ordinary differential equations (ODEs) to deform an input surface into a target shape by learning a diffeomorphic flow. The trajectories of the points on the surface are modeled as ODEs, where the derivatives of their coordinates are parameterized via a learnable Lipschitz-continuous deformation network. This provides theoretical guarantees for the prevention of self-intersections. CortexODE can be integrated to an automatic learning-based pipeline, which reconstructs cortical surfaces efficiently in less than 5 seconds. The pipeline utilizes a 3D U-Net to predict a white matter segmentation from brain Magnetic Resonance Imaging (MRI) scans, and further generates a signed distance function that represents an initial surface. Fast topology correction is introduced to guarantee homeomorphism to a sphere. Following the isosurface extraction step, two CortexODE models are trained to deform the initial surface to white matter and pial surfaces respectively. The proposed pipeline is evaluated on large-scale neuroimage datasets in various age groups including neonates (25-45 weeks), young adults (22-36 years) and elderly subjects (55-90 years). Our experiments demonstrate that the CortexODE-based pipeline can achieve less than 0.2mm average geometric error while being orders of magnitude faster compared to conventional processing pipelines.

Deep Learning Algorithms for Forecasting COVID-19 Cases in Saudi Arabia

In the recent past, the COVID-19 epidemic has impeded global economic progress and, by extension, all of society. This type of pandemic has spread rapidly, posing a threat to human lives and the economy. Because of the growing scale of COVID-19 cases, employing artificial intelligence for future prediction purposes during this pandemic is crucial. Consequently, the major objective of this research paper is to compare various deep learning forecasting algorithms, including auto-regressive integrated moving average, long short-term memory, and conventional neural network techniques to forecast how COVID-19 would spread in Saudi Arabia in terms of the number of people infected, the number of deaths, and the number of recovered cases. Three different time horizons were used for COVID-19 predictions: short-term forecasting, medium-term forecasting, and long-term forecasting. Data pre-processing and feature extraction steps were performed as an integral part of the analysis work. Six performance measures were applied for comparing the efficacy of the developed models. LSTM and CNN algorithms have shown superior predictive precision with errors of less than 5% measured on available real data sets. The best model to predict the confirmed death cases is LSTM, which has better RMSE and R2 values. Still, CNN has a similar comparative performance to LSTM. LSTM unexpectedly performed badly when predicting the recovered cases, with RMSE and R2 values of 641.3 and 0.313, respectively. This work helps decisionmakers and health authorities reasonably evaluate the status of the pandemic in the country and act accordingly.

Characterization and Applicability of a Bone Spheroid Model for the Evaluation of Cytocompatibility of Bone Substitutes

In vitro cell-based tests are an important preclinical step for the safety assessment of biomaterials and drugs. Three-dimensional cell culture models (3D) may improve the limitations of the usual 2D models, as they better simulate a physiological environment. This work describes the characterization of a 3D spheroid model of MC3T3-E1 murine preosteoblasts for the testing of bone-substitute materials and investigates its adequacy to some of the most employed cell viability tests. The spheroids presented structural stability for 28 days in culture, with a regular spheroidal aspect, compact surface, and dense inner structure, with high potential for mineralization, but a time-dependent reduction in size. The use of colorimetric tests (MTT, XTT, and NRU) did not achieve satisfactory optical densities and did not correlate with cell density in the 3D model, as the aggregates remain strongly stained even after dye extraction steps. On the other hand, the LDH test achieved appropriate optical density and a high correlation with cell density (r2 = 0.77) and identified a dose–response for a well-known cytotoxic polymer (latex), while no toxicity was identified for biocompatible PLA wires. These results indicate that material testing with 3D bone cell models requires a careful choice of test methods and parameters.

Small for date preterm infants and risk of higher blood pressure in later life: A systematic review and meta-analysis.

Historical reports suggest that infants born small for gestational age (SGA) are at increased risk for high blood pressure (BP) at older ages after adjustment for later age body size. Such adjustment may be inappropriate since adiposity is a known cause of cardiovascular and metabolic disease. To assess the association between SGA births and later BP among preterm births, considering potential background confounders and over-adjustment for later body size. A database search of studies up to October 2022 included MEDLINE, EMBASE and CINAHL. Studies were included if they reported BP (systolic [SBP] or diastolic [DBP]) (outcomes) for participants born preterm with SGA (exposure) or non-SGA births. All screening, extraction steps, and risk of bias (using the Risk of Bias In Non-randomised Studies of Interventions [ROBINS-I] tool) were conducted in duplicate by two reviewers. Data were pooled in meta-analysis using random-effects models. We explored potential sources of heterogeneity. We found no meaningful difference in later BP between preterm infants with and without SGA status at birth. Meta-analysis of 25 studies showed that preterm SGA, compared to preterm non-SGA, was not associated with higher BP at age 2 and older with mean differences for SBP 0.01 mmHg (95% CI -0.10, 0.12, I2 = 59.8%, n= 20,462) and DBP 0.01 mm Hg (95% CI -0.10, 0.12), 22 studies, (I2 = 53.0%, n= 20,182). Adjustment for current weight did not alter the results, which could be due to the lack of differences in later weight status in most of the included studies. The included studies were rated to be at risk of bias due to potential residual confounding, with a low risk of bias in other domains. Evidence indicates that preterm infants born SGA are not at increased risk of developing higher BP as children or as adults as compared to non-SGA preterm infants.

Simple HPLC method for simultaneous quantification of nicotine and cotinine levels in rat plasma after exposure to two different tobacco products

AbstractPurposeDevelopment and validation of a selective analytical method to accurately and precisely quantify nicotine and cotinine levels in rat's plasma after exposure to tobacco cigarettes and tobacco water-pipe.MethodsAn easy HPLC-Photodiode-Array Detection (PDA) method was developed and validated for simultaneous determination of nicotine and cotinine levels in plasma of 15 rats (10 rats after tobacco products exposure and 5 control rats). Nicotine and cotinine were extracted in one step from plasma using acetonitrile and concentrated to lowest volume using nitrogen stream.ResultsThe developed method offered a rapid analysis time of 14 min with single step of analytes extraction from rat's plasma with recovery percentage range between 93 and 95% and excellent linearity with correlation factor more than 0.994 with analytical range between 50 and 1000 ng mL−1 and LOD of 25 ng mL−1 and 23 ng mL−1 for nicotine and cotinine, respectively. The analysis of rat's plasma after 28 days of exposure to tobacco cigarettes and tobacco water-pipe revealed that the average concentrations of 376 ng mL−1 for cotinine and 223 ng mL−1 for nicotine were obtained after tobacco cigarettes exposure, and 220 ng mL−1 for cotinine and 192 ng mL−1 for nicotine after tobacco water-pipe exposure.ConclusionHigher nicotine and cotinine levels were found in plasma after tobacco cigarettes exposure than water-pipe exposure which may have potential undesirable effects on passive smokers in both cases.