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HbA1c is a pivotal biomarker in diabetes management, reflecting long-term glycaemic control. HbA1c is often measured with capillary electrophoresis, which generally is a very precise technique, but there can be interference, especially in the case of haemoglobin diseases. Thus, in patients with underlying conditions, the accurate measurement of HbA1c can be challenging. We present a case of special interference in a 74-year-old female patient referred to a HbA1c test, in whom the measurement was thought to be significantly affected by hyperleukocytosis and led to an unexpected diagnosis of leukemic low-grade lymphoma. This case report highlights the underrecognized potential interference of leukocytosis in HbA1c measurement. It underscores the importance of clinical vigilance when interpreting HbA1c results in patients with underlying haematological conditions. [ABSTRACT FROM AUTHOR]

There is a rising need for rapid and reliable analytical methods for separating microorganisms in clinical and biomedical applications. Microscale-insulator-based electrokinetic (iEK) systems have proven to be robust platforms for assessing a wide variety of microorganisms. Traditionally, iEK systems are usually stimulated with direct-current (DC) potentials. This work presents a comparison between using DC potentials and using DC-biased alternating-current (AC) potentials in iEK systems for the separation of microorganisms. The present study, which includes mathematical modeling and experimentation, compares the separation of bacterial and yeast cells in two distinct modes by using DC and DC-biased AC potentials. The quality of both separations, assessed in terms of separation resolution ( R s ), showed a complete separation ( R s = 1.51) with the application of a DC-biased low-frequency AC signal but an incomplete separation ( R s = 0.55) with the application of an RMS-equivalent DC signal. Good reproducibility between experimental repetitions (<10%) was obtained, and good agreement (~18% deviation) was observed between modeling and experimental retention times. The present study demonstrates the potential of extending the limits of iEK systems by employing DC-biased AC potentials to perform discriminatory separations of microorganisms that are difficult to separate with the application of DC potentials. [ABSTRACT FROM AUTHOR]

Ensuring food safety of the quality of sold products is one of the urgent problems of the wine industry. The trend of falsification of well-known brands of domestic and imported wines is a characteristic feature of the modern market of wine products. Known methodological bases for assessing the quality of wine products in most cases do not have the potential to recognize many modern «technological solutions». To solve this problem an analytical method can be proposed on modern equipment "FTIR BACCHUS 3". This analyzer allows you to determine the chemical substances and the range characteristics of these substances. This method has been chosen for the identification of wine samples and not by chance, when using it, the analysis time is significantly reduced, and the range of chemical elements determined by the analyzer is quite wide, and reagents are not needed for this equipment. The method does not require preliminary preparation of samples; it is also quite new, not popular in Russia. The article presents a comparative review of classical wine identification methods. The presented material indicates the relevance of creating a database on the chemical composition of wines that determine their authenticity using new instrumental techniques. The proposed multi-parameter wine analyzer FTIR BACCHUS 3 is a system for the analysis of worts and wines using chemometric methods, based on the acquisition of the mid-IR spectrum (FTIR). The method is based on chemometrics, i.e. obtaining chemical data using mathematical methods of data processing and extraction, which will increase the reliability of assessing the authenticity and place of origin of wine products.

There is a rising need for rapid and reliable analytical methods for separating microorganisms in clinical and biomedical applications. Microscale-insulator-based electrokinetic (iEK) systems have proven to be robust platforms for assessing a wide variety of microorganisms. Traditionally, iEK systems are usually stimulated with direct-current (DC) potentials. This work presents a comparison between using DC potentials and using DC-biased alternating-current (AC) potentials in iEK systems for the separation of microorganisms. The present study, which includes mathematical modeling and experimentation, compares the separation of bacterial and yeast cells in two distinct modes by using DC and DC-biased AC potentials. The quality of both separations, assessed in terms of separation resolution (Rs), showed a complete separation (Rs = 1.51) with the application of a DC-biased low-frequency AC signal but an incomplete separation (Rs = 0.55) with the application of an RMS-equivalent DC signal. Good reproducibility between experimental repetitions (

Proteinograms, a semiquantitative analytical method that separates proteins into multiple bands, have not been explored in teleosts for diagnostic or prognostic purposes. This study aimed to establish reference values for proteinograms in the serum of gilthead seabream (Sparus aurata) and European sea bass (Dicentrarchus labrax), two important farmed fish species in the Mediterranean region. Serum proteins were studied using SDS-PAGE, electropherogram, and HPLC-mass spectrometry. SDS-PAGE analysis revealed four major bands of proteins around 11, 25, 70, and 100 kDa in the serum of gilthead seabream and European sea bass. Electropherogram results showed that a protein with a molecular weight of 76.8 kDa was the most abundant protein in the serum of gilthead seabream, while a peak of 75.5 kDa was the most abundant in European sea bass. HPLC-mass spectrometry detected 87 proteins and 119 proteins in the serum of gilthead seabream and European sea bass, respectively, including α1-globulins, α2-globulins, β-globulins, and γ-globulins. Notably, the albumin sequence was not detected in either of the two species. These results help to characterize the serum protein profile and to establish reference proteinograms for these two fish species. They also provide a basis for the development of novel approaches for the rapid detection of loss of haemostasis due to stress, health disorders or disease in farmed fish. [Display omitted] • Electropherograms improve fish serum protein data precision over SDS-PAGE. • SDS-PAGE showed 4 major protein bands in seabream and sea bass sera. • Seabream and sea bass had 4 and 5 fractions, respectively, based on Protein80 and Protein230 results. • Protein80 and Protein230 kits found 24 and 17 peaks in seabream serum, with 76.8 kDa protein as the most abundant. • HPLC-mass spectrometry found 87 proteins in seabream and 119 in sea bass serum, including globulins. [ABSTRACT FROM AUTHOR]

Ricinus communis L. (Euphorbiaceae), the local name jarak kepyar, is a tropical plant that is widely planted in East Java, Indonesia. The leaves and seeds of this plant have been traditionally utilized to cure liver disease, stomach disorders, inflammation, fever, headache, etc. Ricin, one of the most toxic substances known isolated from R. communis L. seeds, is a heterodimeric two-domain polypeptide protein that includes chain A (30 kDa) and a slightly bigger chain B (35 kDa). The technique to distinguish and separate ricin is not much reported. In this study, we analyzed the crude ricin extracted from R. communis L. seeds originated from Nganjuk, East Java, Indonesia. The techniques used were (1) liquid chromatography (LC); (2) column liquid chromatography (CLC); and (2) fast protein liquid chromatography (FPLC), followed by SDS-PAGE. Results show that all techniques positively confirm the presence of ricin protein indicated by a doublet peak in all chromatograms. This study might contribute to understanding the biological and chemical properties of the ricin protein of R. communis L. seeds. [ABSTRACT FROM AUTHOR]

The accurate assessment of antibody glycosylation during bioprocessing requires the high-throughput generation of large amounts of glycomics data. This allows bioprocess engineers to identify critical process parameters that control the glycosylation critical quality attributes. The advances made in protocols for capillary electrophoresis-laser-induced fluorescence (CE-LIF) measurements of antibody N-glycans have increased the potential for generating large datasets of N-glycosylation values for assessment. With large cohorts of CE-LIF data, peak picking and peak area calculations still remain a problem for fast and accurate quantitation, despite the presence of internal and external standards to reduce misalignment for the qualitative analysis. The peak picking and area calculation problems are often due to fluctuations introduced by varying process conditions resulting in heterogeneous peak shapes. Additionally, peaks with co-eluting glycans can produce peaks of a non-Gaussian nature in some process conditions and not in others. Here, we describe an approach to quantitatively and qualitatively curate large cohort CE-LIF glycomics data. For glycan identification, a previously reported method based on internal triple standards is used. For determining the glycan relative quantities our method uses a clustering algorithm to ‘divide and conquer’ highly heterogeneous electropherograms into similar groups, making it easier to define peaks manually. Open-source software is then used to determine peak areas of the manually defined peaks. We successfully applied this semi-automated method to a dataset (containing 391 glycoprofiles) of monoclonal antibody biosimilars from a bioreactor optimization study. The key advantage of this computational approach is that all runs can be analyzed simultaneously with high accuracy in glycan identification and quantitation and there is no theoretical limit to the scale of this method.

Human identification using forensic DNA profiling has made enormous advancement over the past two-and-half decades. Forensic DNA profiling provides enormous genetic data from a variety of biological materials and individuals to help solve many important criminal and civil cases that confront society. Under certain environmental conditions, the total deterioration of soft-tissue leaves skeletal remains as the only available sample for DNA testing to identify missing persons, victims of natural disasters, or exonerate suspect(s) in a criminal case. We report the findings of a case involving the human remains of a missing person submitted to the Forensic Science Laboratory of the Ghana Police Service for forensic DNA profiling in comparison to an alleged living relative of the deceased. DNA from the femur bone and buccal swabs of alleged relative of the deceased were extracted, quantified, and short tandem repeat (STR) profiled using Qiagen's Investigator kit, Applied Biosystem's Quantifiler trio, and GlobalFiler kits. Full STR profiles were generated for both the femur bone from the salty environment and the buccal swabs from the alleged relative. The femur bone was genetically identified to be that of the missing person. The remains were thus handed over to the relatives for final funeral rites and burial to bring closure to the long search for the missing person.

Single stranded RNA molecules quickly fold due to hydrogen bonding mechanism if they are left in their environment. Helices which are made from the folding process known as stem. Only six (AU, GU, GC, UA, UG & CG) are stable to form base pairs among 16 possible ones. The nucleotide sequence of stems can vary and made variable RNA helical regions. The substitution of RNA bases are important in maintaining the secondary structure of RNA. DNA structure is not important to study the evolutionary models because that is double stranded due to which the base pairs in DNA does not give accurate results. So, secondary structure of RNA gives validate consequences in evolution of parasitic nematodes of Periplaneta americana. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • κ-Carrageenan hydrolysates were analyzed by capillary electrophoresis (CE). • Precise and robust quantitative CE method to detect sulfated oligosaccharides. • CE allowed the detection of up to eight oligosaccharides in crude hydrolysates. • Hexasaccharide alditol derivative isolated and characterized for the first time. • Different hydrolysis conditions produced distinct pools of oligosaccharides. Abstract Different hydrolysis conditions to produce κ-carrageenan oligosaccharide alditols were studied and the depolymerization process monitored by capillary electrophoresis (CE). Semisynthesis, ion-exchange and exclusion chromatography were used to obtain and isolate sulfated di-, tetra- and hexasaccharide alditols, the last being fully characterized for the first time. Those derivatives were used as standards to validate a new quantitative CE analytical method which was used to compare two different partial hydrolysis methodologies: an acid hydrolysis followed by reduction and a one-pot reductive hydrolysis using 4-methylmorpholine borane. The resulting depolymerization profiles were quite different from each other. Optimal hydrolysis conditions to produce high yields of specific sulfated oligosaccharides as well as particular mixtures of oligosaccharide alditols were determined. Moreover, using the novel CE method, we were able to distinguish up to eight different oligosaccharides in the hydrolysate mixtures. [ABSTRACT FROM AUTHOR]

Highlights • We apply Artificial Neural Networks to electrophotographic data to identify features of STR DNA profiles. • DNA profiles can be produced under different conditions with respect to laboratory hardware, protocols used and template source. • We trialled the ability of neural networks to generalise across the different factors involved in generation of profiles. • A single neural network was able to be trained on, and applied to, data produced under all different conditions. Abstract Previous work has shown that artificial neural networks can be used to classify signal in an electropherogram into categories that have interpretational meaning (such as allele, baseline, pull-up or stutter). The previous work trained the neural networks on a single data type, produced under a single laboratory condition and applied it to data that was matched in these factors. In this work we investigate the ability of neural networks to be trained on data of different types (i.e. single sourced profiles or mixed DNA profiles) and from different laboratory conditions (specifically the model of electrophoresis instrument) to determine whether a set of neural networks is required for each different type of data produced or whether a single neural network can be used for a broad range of data and still achieve the same level of performance. The results of our study have implications as to how a laboratory would choose to train and apply neural networks to classify data in electropherograms produced in their laboratory. [ABSTRACT FROM AUTHOR]

Lab-on-a-chip technology is an emerging and convenient system to easily and quickly separate proteins of high molecular weight. The current study established a high-molecular-weight glutenin subunit (HMW-GS) identification system using Lab-on-a-chip for three, six, and three of the allelic variations at the Glu-A1, Glu-B1, and Glu-D1 loci, respectively, which are commonly used in wheat breeding programs. The molecular weight of 1Ax1 and 1Ax2* encoded by Glu-A1 locus were of 200 kDa and 192 kDa and positioned below 1Dx subunits. The HMW-GS encoded by Glu-B1 locus were electrophoresed in the following order below 1Ax1 and 1Ax2*: 1Bx13 ≥ 1Bx7 = 1Bx7OE > 1Bx17 > 1By16 > 1By8 = 1By18 > 1By9. 1Dx2 and Dx5 showed around 4-kDa difference in their molecular weights, with 1Dy10 and 1Dy12 having 11-kDa difference, and were clearly differentiated on Lab-on-a-chip. Additionally, some of the HMW-GS, including 1By8, 1By18, and 1Dy10, having different theoretical molecular weights showed similar electrophoretic mobility patterns on Lab-on-a-chip. The relative protein amount of 1Bx7OE was two-fold higher than that of 1Bx7 or 1Dx5 and, therefore, translated a significant increase in the protein amount in 1Bx7OE. Similarly, the relative protein amounts of 8 & 10 and 10 & 18 were higher than each subunit taken alone. Therefore, this study suggests the established HMW-GS identification system using Lab-on-a-chip as a reliable approach for evaluating HMW-GS for wheat breeding programs.

Current models for single source and mixture samples, and probabilistic genotyping software based on them used for analysing STR electropherogram data, assume simple probability distributions, such as the gamma distribution, to model the allelic peak height variability given the initial amount of DNA prior to PCR amplification. Here we illustrate how amplicon number distributions, for a model of the process of sample DNA collection and PCR amplification, may be efficiently computed by evaluating probability generating functions using discrete Fourier transforms. [ABSTRACT FROM AUTHOR]

Electropherograms are produced in great numbers in forensic DNA laboratories as part of everyday criminal casework. Before the results of these electropherograms can be used they must be scrutinised by analysts to determine what the identified data tells them about the underlying DNA sequences and what is purely an artefact of the DNA profiling process. This process of interpreting the electropherograms can be time consuming and is prone to subjective differences between analysts. Recently it was demonstrated that artificial neural networks could be used to classify information within an electropherogram as allelic (i.e. representative of a DNA fragment present in the DNA extract) or as one of several different categories of artefactual fluorescence that arise as a result of generating an electropherogram. We extend that work here to demonstrate a series of algorithms and artificial neural networks that can be used to identify peaks on an electropherogram and classify them. We demonstrate the functioning of the system on several profiles and compare the results to a leading commercial DNA profile reading system. [ABSTRACT FROM AUTHOR]

Electropherograms are produced in great numbers in forensic DNA laboratories as part of everyday criminal casework. Before the results of these electropherograms can be used they must be scrutinised by analysts to determine what the identified data tells us about the underlying DNA sequences and what is purely an artefact of the DNA profiling process. A technique that lends itself well to such a task of classification in the face of vast amounts of data is the use of artificial neural networks. These networks, inspired by the workings of the human brain, have been increasingly successful in analysing large datasets, performing medical diagnoses, identifying handwriting, playing games, or recognising images. In this work we demonstrate the use of an artificial neural network which we train to ‘read’ electropherograms and show that it can generalise to unseen profiles. [ABSTRACT FROM AUTHOR]

Albania continues to be a significant supplier of wild Medicinal and Aromatic Plants to the world markets of which Sage remains the major export item accounting for about 70% of the total sage imports to the US in 2013. Sage plants were randomly picked from different cultivation sites in Albania (North/Koplik; Southeast/Skrapar and South/Libohove) in order to screen genetic diversity amongst them employing Randomly Amplified Polymorphic DNA markers using twenty decameric oligonucleotide primers. A total of 2132 DNA bands were generated of which notably clear and scorable were 1555 (from 150 to 1999bp). Primers produced between 63 and 156 bands per Sage plant with an average of 107 bands per primer. Cultivated Sage plant generated between 112 to166 DNA bands with an average of 143 bands per plant. DNA banding patterns, obtained from the Shimadzu Multina PCR-RAPD analysis, were quite polymorphic and were used to carry out hierarchical cluster analysis using the average linkage between groups method of SPSS version 22. The dendrogram showed splitting of the North cultivated Sage from the Southern (southeast and south) group due to (dis)similarity in climate and soil structure/texture. Southeast cultivated Sage plants exhibited some genetic diversity within the group (intrinsic factors driven). This study indicates that RAPDs were fast and easy to use and proved to be efficient discriminatory tools detecting a high level of polymorphism within the same species (intraspecific level) which is explained with ecological variation and the genetic make-up of each individual. [ABSTRACT FROM AUTHOR]

The use of procedures for the automated scoring of amplified fragment length polymorphisms ( AFLP) fragments has recently increased. Corresponding software does not only automatically score the presence or absence of AFLP fragments, but also allows an evaluation of how different settings of scoring parameters influence subsequent population genetic analyses. In this study, we used the automated scoring package rawgeno to evaluate how five scoring parameters influence the number of polymorphic bins and estimates of pairwise genetic differentiation between populations ( Fst). Steps were implemented in r to automatically run the scoring process in rawgeno for a set of different parameter combinations. While we found the scoring parameters minimum bin width and minimum number of samples per bin to have only weak influence on pairwise Fst values, maximum bin width and bin reproducibility had much stronger effects. The minimum average bin fluorescence scoring parameter affected Fst values in an only moderate way. At a range of scoring parameters around the default settings of rawgeno, the number of polymorphic bins as well as pairwise Fst values stayed rather constant. This study thus shows the particularities of AFLP scoring, be it either manual or automatical, can have profound effects on subsequent population genetic analysis. [ABSTRACT FROM AUTHOR]

DNA profiles are generated in forensic biology laboratories around the world. It is possible that these profiles are assessed by two independent people in order for the profiles to be 'read'. Recent work has been carried out to develop a neural network model to classify fluorescence in a DNA profile electropherogram and potentially replace one, or both human readers. The ability to use neural networks for this function has been programmed into the software FaSTR™ DNA, which has been validated for use in at least one laboratory in Australia. The work that previously developed a neural network system had a number of limitations, specifically it was computer intensive, did not make the best use of available data, and consequently the performance of this model was sub-optimal in some conditions (particularly for low-intensity peaks). In the current work a new neural network model is developed that makes various improvements on the old model, by using convolutional layers, a multi-head architecture and data augmentation. Results indicate that an improved performance can be expected for low-intensity profiles. • Previous work showed Neural Networks identifying DNA profile features. • These 10 NNs, in FaSTR DNA, performed well over a range of profiles. • The NNs would sometimes mis-classify low level peaks as baseline. • A single model, using multi-head NN is developed to classify an entire profile. • The training data is also expanded using data augmentation using existing profile. [ABSTRACT FROM AUTHOR]

It has been shown that electropherograms of DNA sequences can be modeled with hidden Markov models. Basecalling, the procedure that determines the sequence of bases from the given eletropherogram, can then be performed using the Viterbi algorithm. A training step is required prior to basecalling in order to estimate the HMM parameters. In this paper, we propose a Bayesian approach which employs the Markov chain Monte Carlo (MCMC) method to perform basecalling. Such an approach not only allows one to naturally encode the prior biological knowledge into the basecalling algorithm, it also exploits both the training data and the basecalling data in estimating the HMM parameters, leading to more accurate estimates. Using the recently sequenced genome of the organism Legionella pneumophila we show that the MCMC basecaller outperforms the state-of-the-art basecalling algorithm in terms of total errors while requiring much less training than other proposed statistical basecallers. [ABSTRACT FROM PUBLISHER]

Lab-on-a-chip technology is an emerging and convenient system to easily and quickly separate proteins of high molecular weight. The current study established a high-molecular-weight glutenin subunit (HMW-GS) identification system using Lab-on-a-chip for three, six, and three of the allelic variations at the Glu-A1, Glu-B1, and Glu-D1 loci, respectively, which are commonly used in wheat breeding programs. The molecular weight of 1Ax1 and 1Ax2* encoded by Glu-A1 locus were of 200 kDa and 192 kDa and positioned below 1Dx subunits. The HMW-GS encoded by Glu-B1 locus were electrophoresed in the following order below 1Ax1 and 1Ax2*: 1Bx13 ≥ 1Bx7 = 1Bx7OE > 1Bx17 > 1By16 > 1By8 = 1By18 > 1By9. 1Dx2 and Dx5 showed around 4-kDa difference in their molecular weights, with 1Dy10 and 1Dy12 having 11-kDa difference, and were clearly differentiated on Lab-on-a-chip. Additionally, some of the HMW-GS, including 1By8, 1By18, and 1Dy10, having different theoretical molecular weights showed similar electrophoretic mobility patterns on Lab-on-a-chip. The relative protein amount of 1Bx7OE was two-fold higher than that of 1Bx7 or 1Dx5 and, therefore, translated a significant increase in the protein amount in 1Bx7OE. Similarly, the relative protein amounts of 8 & 10 and 10 & 18 were higher than each subunit taken alone. Therefore, this study suggests the established HMW-GS identification system using Lab-on-a-chip as a reliable approach for evaluating HMW-GS for wheat breeding programs. [ABSTRACT FROM AUTHOR]

Rapid DNA analysis is an ultrafast and fully automated DNA-typing system, which can produce interpretable genetic profiles from biological samples within 90 minutes. This "swab in—profile out" method comprises DNA extraction, amplification by PCR multiplex, separation and detection of DNA fragments by capillary electrophoresis. The aim of study was the validation of the Accelerated Nuclear DNA Equipment (ANDE) 6C system as a typing method for reference samples according to the ISO/IEC 17025 standard. Here, we report the evaluation of the validity and reproducibility of results by the comparison of the genetic profiles generated by the ANDE 6C System with those generated by standard technologies. A quantity of 104 buccal swabs were analyzed both through the ANDE 6C technology and the traditional method (DNA extraction and quantification, amplification and separation by capillary electrophoresis). Positive typing was observed in 97% of cases for ANDE 6C technology with only three buccal swabs failing to reveal interpretable signals. Concordance was determined by comparing the allele calls generated by ANDE 6C and conventional technology. Comparison of 2800 genotypes revealed a concordance rate of 99.96%. These results met the ISO/IEC 17025 requirements, enabling us to receive the accreditation for this method. Finally, rapid technology has certainly reached a level of reliability which has made its use in laboratories of forensic genetics a reality. [ABSTRACT FROM AUTHOR]