Easy, efficient and low demanding separation of mRNA from biological material is needed to study gene expression and to use in chip technologies. It is common knowledge that each mRNA molecule contains sequence of 25 adenines. This feature can be used for binding mRNA on the surface of the particles coated by thymine chains. The present work reports on suggesting and optimizing of mRNA separation and detection from biological material via paramagnetic microparticles coupled with electrochemical detection. Primarily we optimized cyclic and square wave voltammetric conditions to detect poly(A), which was used as standard to mimic behaviour of mRNA. Under the optimized square wave voltammetric conditions (frequency 280 Hz, accumulation time 200 s, supporting electrolyte and its temperature: acetate buffer 4.6 and 35 degrees C) we estimated detection limit down to 1 ng of poly(A) per ml. To enhance effectiveness and repeatability of isolation of nucleic acid automated approach for rinsing and hybridizing was proposed. We optimized the whole procedure and experimental conditions. Using automated way of isolation and under optimized conditions the yield of poly(A) (isolated concentration of poly(A)/given concentration of poly(A)*100) was approximately 75%. The suggested and optimized method for poly(A) isolation and detection was utilized for the analysis of brain tissues of patients with traumatic brain injury. The total amount of isolated mRNA varied from 40 to 760 g of mRNA per g of brain tissue. The isolation of mRNA from six samples per run was not longer than 2.5h. Moreover, we applied the optimized procedure on fully automated pipetting instrument to isolate mRNA. The instrument was successfully tested on the analysis of extracts from roots of maize plants treated with cadmium(II) ions.
- MeSH
- Adenine MeSH
- Automation MeSH
- Electrochemical Techniques methods MeSH
- Nucleic Acid Hybridization MeSH
- Zea mays genetics MeSH
- Humans MeSH
- Magnetics MeSH
- RNA, Messenger isolation & purification MeSH
- Brain Chemistry MeSH
- Nucleic Acids isolation & purification MeSH
- Base Pairing MeSH
- Brain Injuries genetics MeSH
- Thymine MeSH
- Particle Size MeSH
- Check Tag
- Humans MeSH
- Publication type
- Research Support, Non-U.S. Gov't MeSH
For generations, the evaluation of speech abnormalities in neurodegenerative disorders such as Parkinson's disease (PD) has been limited to perceptual tests or user-controlled laboratory analysis based upon rather small samples of human vocalizations. Our study introduces a fully automated method that yields significant features related to respiratory deficits, dysphonia, imprecise articulation and dysrhythmia from acoustic microphone data of natural connected speech for predicting early and distinctive patterns of neurodegeneration. We compared speech recordings of 50 subjects with rapid eye movement sleep behaviour disorder (RBD), 30 newly diagnosed, untreated PD patients and 50 healthy controls, and showed that subliminal parkinsonian speech deficits can be reliably captured even in RBD patients, which are at high risk of developing PD or other synucleinopathies. Thus, automated vocal analysis should soon be able to contribute to screening and diagnostic procedures for prodromal parkinsonian neurodegeneration in natural environments.
- MeSH
- Biomarkers MeSH
- Adult MeSH
- Respiration MeSH
- Dysphonia physiopathology MeSH
- Middle Aged MeSH
- Humans MeSH
- Parkinson Disease diagnosis physiopathology MeSH
- REM Sleep Behavior Disorder physiopathology MeSH
- Articulation Disorders physiopathology MeSH
- Pattern Recognition, Automated methods MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
In the food industry, in the process of creating new agricultural plant products, and in the testing of anti-cancer drugs there is often a need to assay multiple samples of low molecular weight antioxidants, plant samples and foods rich in antioxidants, with minimal additional costs and low degrees of uncertainty. With these demands in mind, we decided to study the fully automated assay of antioxidants using not only automated sample measurements but also automated processing of samples and application of reagents. The automated pipetting system epMotion 5075 and the automated spectrophotometer BS 400 were chosen for the assay purposes. Five methods were introduced for the automation: 2-diphenyl-1-picrylhydrazyl (DPPH) test, ferric reducing antioxidant power (FRAP) method, 2,2’-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid (ABTS) based test, N,N-dimethyl-1,4-diaminobenzene (DMPD) based test and the free radicals method. Samples containing one of the four antioxidants (standard rutin, quercitrin, ferulic and gallic acid) in a range 1–1000 μg/ml were used throughout. All of the tested methods were found suitable for implementation in an automated assay. However, some of them, such as the ABTS test failed to assay all tested antioxidants. The coefficients of determination were also unequal. From the analytical point of view, FRAP methods provided the most reliable results in the automated assay; because of the capacity of the method, approximately 240 samples per hour (one sample per 15 seconds) can be assayed using the automated protocol. We were encouraged by the data received and we expect further interest in the practical performance of such automation. As a mean of testing the robustness of our method, in the next step of our study, oxidative status was assessed in model cell lines derived from prostate cancer (PC-3, PNT1A and 22RV1) that were cultured on ellipticine (0, 0.5, 1, 1.5, 2, 2.5, 5, 7.5, 10, 15 μmol/l) supplemented agar. Antioxidant activity was assessed (DPPH, ABTS, FRAP, DMPD, FR) and calculated on the phenolic antioxidant level (rutin, quercitrin, ferulic and gallic acid), and thus an estimation was formulated of the oxidative stress as a result of the impact of anti-cancer drugs. It can be demonstrated that the new method has wide applicability.
- MeSH
- Antioxidants analysis MeSH
- Chemistry Techniques, Analytical methods instrumentation statistics & numerical data MeSH
- Ellipticines analysis chemistry metabolism MeSH
- Financing, Organized MeSH
- Fluorescence Recovery After Photobleaching MeSH
- Calibration MeSH
- Gallic Acid analysis chemistry MeSH
- Coumaric Acids analysis chemistry MeSH
- Automation, Laboratory methods instrumentation MeSH
- Luminescent Measurements MeSH
- Cell Line, Tumor MeSH
- Oxidative Stress drug effects MeSH
- Antineoplastic Agents chemistry metabolism toxicity MeSH
- Quercetin analogs & derivatives analysis chemistry MeSH
- Reproducibility of Results MeSH
- Rutin analysis chemistry MeSH
- Drug Screening Assays, Antitumor methods statistics & numerical data MeSH
- Spectrophotometry methods instrumentation statistics & numerical data MeSH
- Cell Survival drug effects MeSH
- Free Radicals analysis MeSH
- Structure-Activity Relationship MeSH
- Publication type
- Evaluation Study MeSH
- Statistics MeSH
- Tables MeSH
In today's biometric and commercial settings, state-of-the-art image processing relies solely on artificial intelligence and machine learning which provides a high level of accuracy. However, these principles are deeply rooted in abstract, complex "black-box systems". When applied to forensic image identification, concerns about transparency and accountability emerge. This study explores the impact of two challenging factors in automated facial identification: facial expressions and head poses. The sample comprised 3D faces with nine prototype expressions, collected from 41 participants (13 males, 28 females) of European descent aged 19.96 to 50.89 years. Pre-processing involved converting 3D models to 2D color images (256 × 256 px). Probes included a set of 9 images per individual with head poses varying by 5° in both left-to-right (yaw) and up-and-down (pitch) directions for neutral expressions. A second set of 3,610 images per individual covered viewpoints in 5° increments from -45° to 45° for head movements and different facial expressions, forming the targets. Pair-wise comparisons using ArcFace, a state-of-the-art face identification algorithm yielded 54,615,690 dissimilarity scores. Results indicate that minor head deviations in probes have minimal impact. However, the performance diminished as targets deviated from the frontal position. Right-to-left movements were less influential than up and down, with downward pitch showing less impact than upward movements. The lowest accuracy was for upward pitch at 45°. Dissimilarity scores were consistently higher for males than for females across all studied factors. The performance particularly diverged in upward movements, starting at 15°. Among tested facial expressions, happiness and contempt performed best, while disgust exhibited the lowest AUC values.
- MeSH
- Algorithms * MeSH
- Automated Facial Recognition * methods MeSH
- Biometric Identification methods MeSH
- Adult MeSH
- Head Movements physiology MeSH
- Middle Aged MeSH
- Humans MeSH
- Young Adult MeSH
- Face anatomy & histology MeSH
- Image Processing, Computer-Assisted methods MeSH
- Posture physiology MeSH
- Facial Expression * MeSH
- Imaging, Three-Dimensional MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
Many small molecules require derivatization to increase their volatility and to be amenable to gas chromatographic (GC) separation. Derivatization is usually time-consuming, and typical batch-wise procedures increase sample variability. Sequential automation of derivatization via robotic liquid handling enables the overlapping of sample preparation and analysis, maximizing time efficiency and minimizing variability. Herein, a protocol for the fully automated, two-stage derivatization of human blood-based samples in line with GC-[Orbitrap] mass spectrometry (MS)-based metabolomics is described. The protocol delivers a sample-to-sample runtime of 31 min, being suitable for better throughput routine metabolomic analysis. Key features • Direct and rapid methoximation on vial followed by silylation of metabolites in various blood matrices. • Measure ~40 samples per 24 h, identifying > 70 metabolites. • Quantitative reproducibility of routinely measured metabolites with coefficients of variation (CVs) < 30%. • Requires a Thermo ScientificTM TriPlusTM RSH (or comparable) autosampler equipped with incubator/agitator, cooled drawer, and automatic tool change (ATC) station equipped with liquid handling tools. Graphical overview Workflow for profiling metabolites in human blood using automated derivatization.
- Publication type
- Journal Article MeSH
The aim of this study was to estimate the lowest concentration of pesticide residues in non-fatty food matrix at which the residues can be successfully identified by automatic spectral deconvolution software AMDIS. For GC-MS measurements fast GC with narrow capillary column was utilized. For a mixture of 18 pesticides, the identification was successful at concentration levels 4–0.4 mg kg-1 in real matrix samples (apples). With decreasing concentration, the number of identified pesticides and the quality of deconvoluted spectra decreased. The calculated limits of full-scan detection ranged from 0.20 ng for chlorpyrifos to 1.10 ng for captan. Software AMDIS with the used experimental set-up is not sufficiently sensitive for reliable identification of pesticide residues in non-fatty food matrices with low maximal residual limits (e.g. baby food 0.01 mg kg-1).
The aim of this work was to describe a fully automated system for the in vitro release testing of semisolid dosage forms based on SIA technique. The system was tested for monitoring release profiles of different ointments containing 3% of salicylic acid (Belosalic, Diprosalic, Triamcinolone S). The native fluorescence of salicylic acid was used for fluorimetric detection. Phosphate buffer pH 7.4 was the receptor medium; samples were taken at 10 min intervals during 6 h of the release test; and each test was followed by calibration with five standard solutions. The linear calibration range was 0.05-10 microg ml(-1) (r = 0.9996, six standards); the maximal SIA sample throughput for this system was 120 h(-1), sample volume being 50 microl and flow rate 50 microl s(-1). The detection limit for salicylic acid was 0.01 microg ml(-1).
Purpose The purpose of this research note is to provide a performance comparison of available algorithms for the automated evaluation of oral diadochokinesis using speech samples from patients with amyotrophic lateral sclerosis (ALS). Method Four different algorithms based on a wide range of signal processing approaches were tested on a sequential motion rate /pa/-/ta/-/ka/ syllable repetition paradigm collected from 18 patients with ALS and 18 age- and gender-matched healthy controls (HCs). Results The best temporal detection of syllable position for a 10-ms tolerance value was achieved for ALS patients using a traditional signal processing approach based on a combination of filtering in the spectrogram, Bayesian detection, and polynomial thresholding with an accuracy rate of 74.4%, and for HCs using a deep learning approach with an accuracy rate of 87.6%. Compared to HCs, a slow diadochokinetic rate (p < .001) and diadochokinetic irregularity (p < .01) were detected in ALS patients. Conclusions The approaches using deep learning or multiple-step combinations of advanced signal processing methods provided a more robust solution to the estimation of oral DDK variables than did simpler approaches based on the rough segmentation of the signal envelope. The automated acoustic assessment of oral diadochokinesis shows excellent potential for monitoring bulbar disease progression in individuals with ALS.
- MeSH
- Acoustics MeSH
- Algorithms MeSH
- Amyotrophic Lateral Sclerosis * MeSH
- Bayes Theorem MeSH
- Humans MeSH
- Speech MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
The City of The Hague monitors the use of Alcohol and Other Drugs (AOD) in the town‘s nightlife since 2002. Nightlife par ticipants, although not completely underground, are a „hidden“ or „hard to reach“ population for which there is no sampling framework available. Time–Location and Snowball Sampling are often used in studies of hidden populations, but these methods produce biased results and do not provide the basis for valid generalizations towards populations. Respondent Driven Sampling (RDS) was designed to overcome some of these limitations. Combining a modified form of snowball sampling with a mathematical system for weighting the sample, RDS produces unbiased estimators and standard errors or confidence intervals. RDS is successfully applied in various vulnerable (IDUs, non-IDUs, CSW, MSM) and less vulnerable populations (recreational drug users, nightlife participants, Jazz musicians) and has become the „Golden Standard“ for sampling hidden populations. As in the Netherlands internet connectivity is approaching 100% in our target popula- tion, we have developed a novel application to conduct RDS studies on the internet. Combining RDS with internet technology strengthens and automates the entire survey process. All steps in the research process (sampling, interviewing and distri- bution of incentives) are conducted online, producing analyzable net work and survey data instantly, minimizing e.g. interviewer bias, while the application of RDS greatly improves the reliability of internet survey data. In September 2008 the application will be used for the first time to draw a sample of 1500 nightlife participants. This presentation will discuss the internet appli- cation and methodology of Project Residence, the first community based WEB-RDS study. Jean-Paul C. Grund is a drug policy schol- ar, specialized in f ield studies of drug use, and its social, health and policy con- comitants. In the recent past, Dr. Grund conducted a study of drug use and HIV risks among the Roma/Gypsy popula- tion of Central and Eastern Europe and evaluation studies of needle exchange in Russia and Eastern Europe. He was the founding Director of the International Harm Reduction Development program at The Lindesmith Center, which fostered the development of practical harm reduc- tion programs in Central Eastern Europe and Russia. He also was the first Research Fellow in Residence at this New York based drug policy research center, which is part of the Open Society Institute. Dr. Grund holds an advanced degree in Clinical and Developmental Psychology from Utrecht University and received a Ph.D. in Social Science from the Medical and Health Sciences Faculty at Erasmus University in Rotterdam, The Netherlands. Dr. Grund is the author of numerous articles and two books on drug use culture, HIV, and their social-politi- cal determinants.
