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World Congress on Biosensors 2014
Biosensors 2014
Monday 7 January 2013
Just Published: Analytica Chimica Acta
A new issue of this journal has just
been published. To see abstracts of the papers it contains (with links through
to the full papers) click here:
Plasmid DNA
(pDNA)-based vaccines offer more rapid avenues for development and production if
compared to those of conventional virus-based vaccines. They do not rely on
time- or labour-intensive cell culture processes and allow greater flexibility
in shipping and storage. Stimulating antibodies and cell-mediated components of
the immune system are considered as some of the major advantages associated with
the use of pDNA vaccines. This review summarizes the current trends in the
purification of pDNA vaccines for practical and analytical applications. Special
attention is paid to chromatographic techniques aimed at reducing the steps of
final purification, post primary isolation and intermediate recovery, in order
to reduce the number of steps necessary to reach a purified end product from the
crude plasmid.
Graphical abstract
Highlights
Strategies for purifying supercoiled plasmid DNA.
► Current trends in the separation and purification
of Plasmid DNA vaccines. ► Practical large and analytical scale productions of
the plasmid DNA vaccine are discussed. ► Separation challenges. ► New
developments and process solutions. ► Future prospective in the field of plasmid
DNA vaccines.
Quantification
of the effect of antiretroviral drugs on the insulin aggregation process is an
important area of research due to the serious metabolic diseases observed in
AIDS patients after prolonged treatment with these drugs. In this work,
multivariate curve resolution alternating least squares (MCR-ALS) was applied to
infrared monitoring of the insulin aggregation process in the presence of three
antiretroviral drugs to quantify their effect. To evidence concentration
dependence in this process, mixtures at two different insulin:drug molar ratios
were used. The interaction between insulin and each drug was analysed by
1H NMR spectroscopy. In all cases, the aggregation process was
monitored during 45min by infrared spectroscopy. The aggregates were further
characterised by scanning electron microscopy (SEM). MCR-ALS provided the
spectral and concentration profiles of the different insulin–drug conformations
that are involved in the process. Their feasible band boundaries were calculated
using the MCR-BANDS methodology. The kinetic profiles describe the aggregation
pathway and the spectral profiles characterise the conformations involved. The
retrieved results show that each of the three drugs modifies insulin
conformation in a different way, promoting the formation of aggregates.
Ritonavir shows the strongest promotion of aggregation, followed by efavirenz
and zidovudine. In the studied concentration range, concentration dependence was
only observed for zidovudine, with shorter aggregation time obtained as the
amount of zidovudine increased. This factor also affected the aggregation
pathway.
Graphical abstract
Highlights
► The structure of
insulin can be changed via interaction with antiretroviral drugs. ► The chemical
interaction promotes the formation of aggregates. ► This drug effect was
evaluated by MCR-ALS coupled to IR spectroscopy. ► Formation of aggregates was
favourable if drugs were able to form hydrogen bonds. ► Higher drug
concentrations favoured formation of amorphous aggregates.
In
biospectroscopy, suitably annotated and statistically independent samples (e.g.
patients, batches, etc.) for classifier training and testing are scarce and
costly. Learning curves show the model performance as function of the training
sample size and can help to determine the sample size needed to train good
classifiers. However, building a good model is actually not enough: the
performance must also be proven. We discuss learning curves for typical small
sample size situations with 5–25 independent samples per class. Although the
classification models achieve acceptable performance, the learning curve can be
completely masked by the random testing uncertainty due to the equally limited
test sample size. In consequence, we determine test sample sizes necessary to
achieve reasonable precision in the validation and find that 75–100 samples will
usually be needed to test a good but not perfect classifier. Such a data set
will then allow refined sample size planning on the basis of the achieved
performance. We also demonstrate how to calculate necessary sample sizes in
order to show the superiority of one classifier over another: this often
requires hundreds of statistically independent test samples or is even
theoretically impossible. We demonstrate our findings with a data set of ca.
2550 Raman spectra of single cells (five classes: erythrocytes, leukocytes and
three tumour cell lines BT-20, MCF-7 and OCI-AML3) as well as by an extensive
simulation that allows precise determination of the actual performance of the
models in question.
Graphical abstract
Highlights
► We compare sample size
requirements for classifier training and testing. ► Number of training samples:
determine from learning curve. ► Test sample size: specify confidence interval
width or model to compare to. ► Classifier testing needs far more samples than
training. ► Start with at least 75 cases per class, then refine sample size
planning.
The calibration
performance of partial least squares regression for one response (PLS1) can be
improved by eliminating uninformative variables. Many variable-reduction methods
are based on so-called predictor-variable properties or predictive properties,
which are functions of various PLS-model parameters, and which may change during
the steps of the variable-reduction process. Recently, a new
predictive-property-ranked variable reduction method with final complexity
adapted models, denoted as PPRVR-FCAM or simply FCAM, was introduced. It is a
backward variable elimination method applied on the predictive-property-ranked
variables. The variable number is first reduced, with constant PLS1 model
complexity A, until A variables remain, followed by a further decrease in PLS
complexity, allowing the final selection of small numbers of variables. In this
study for three data sets the utility and effectiveness of six individual and
nine combined predictor-variable properties are investigated, when used in the
FCAM method. The individual properties include the absolute value of the PLS1
regression coefficient (REG), the significance of the PLS1 regression
coefficient (SIG), the norm of the loading weight (NLW) vector, the variable
importance in the projection (VIP), the selectivity ratio (SR), and the squared
correlation coefficient of a predictor variable with the response y (COR). The
selective and predictive performances of the models resulting from the use of
these properties are statistically compared using the one-tailed Wilcoxon signed
rank test. The results indicate that the models, resulting from variable
reduction with the FCAM method, using individual or combined properties, have
similar or better predictive abilities than the full spectrum models. After
mean-centring of the data, REG and SIG, provide low numbers of informative
variables, with a meaning relevant to the response, and lower than the other
individual properties, while the predictive abilities are similar or better. SIG
has the best selective ability of all individual and combined properties, while
the predictive ability is similar. REG is faster than SIG. This means that
variable reduction with the FCAM method is preferably conducted with properties
REG or SIG. The selective ability of REG can be improved by combining it with
NLW or VIP.
Graphical abstract
Highlights
Selected variables after variable reduction by the
PPRVR-FCAM method, using individual and combined predictor-variable properties.
► Variable reduction using the PPRVR-FCAM method is
investigated. ► Performance of individual and combined predictor-variable
properties is studied. ► Selective and predictive performances of resulting
models statistically compared. ► Absolute PLS1 regression coefficient and its
significance are most effective.
Validation of
analytical methods is required prior to their routine use. In addition, the
current implementation of the Quality by Design (QbD) framework in the
pharmaceutical industries aims at improving the quality of the end products
starting from its early design stage. However, no regulatory guideline or none
of the published methodologies to assess method validation propose decision
methodologies that effectively take into account the final purpose of developed
analytical methods. In this work a solution is proposed for the specific case of
validating analytical methods involved in the assessment of the content
uniformity or uniformity of dosage units of a batch of pharmaceutical drug
products as proposed in the European or US pharmacopoeias. This methodology uses
statistical tolerance intervals as decision tools. Moreover it adequately
defines the Analytical Target Profile of analytical methods in order to obtain
analytical methods that allow to make correct decisions about Content uniformity
or uniformity of dosage units with high probability. The applicability of the
proposed methodology is further illustrated using an HPLC-UV assay as well as a
near infra-red spectrophotometric method.
Graphical abstract
Highlights
► Methodology to
validate methods for uniformity of dosage units tests. ► Valid methods will
ensure to make the correct decisions with high probability. ► A Quality by
Design compliant validation methodology for UDU assays. ► Analytical Target
Profile is defined for UDU assays. ► Application to the validation of an HPLC-UV
and NIRS method.
In this work,
urinary nicotine was determined in the presence of the metabolite cotinine and
the alkaloid anabasine using surface enhanced Raman spectroscopy and colloidal
gold as substrate. Spectra were decomposed using the multivariate curve
resolution-alternating least squares method, and pure contributions were
recovered. The standard addition method was applied by spiking urine samples
with known amounts of the analyte and relative responses from curve resolution
were employed to build the analytical curves. The use of multivariate curve
resolution in conjunction with standard addition method showed to be an
effective strategy that minimized the need for reagent and time-consuming
procedures. The determination of the alkaloid nicotine was successfully
accomplished at concentrations 0.10, 0.20 and 0.30μgmL−1 and total
error values less than 10% were obtained.
Graphical abstract
Highlights
► Determination of
urinary nicotine in the presence of cotinine and anabasine. ► Surface enhanced
Raman spectroscopy for analysis of nicotine. ► Multivariate curve resolution in
conjunction with standard addition method. ► Determination of nicotine was
accomplished with error values less than 10%.
A single-step
extraction-cleanup method, including microwave-assisted extraction (MAE) and
micro-solid-phase extraction (μ-SPE), was developed for the extraction of ten
organophosphorus pesticides in vegetable and fruit samples. Without adding any
polar solvent, only one kind of non-polar solvent (hexane) was used as
extraction solvent in the whole extraction step. Absorbing microwave μ-SPE
device, was prepared by packing activated carbon with microporous polypropylene
membrane envelope, and used as not only the sorbent in μ-SPE, but also the
microwave absorption medium. Some experimental parameters effecting on
extraction efficiency was investigated and optimized. 1.0g of sample, 8mL of
hexane and three absorbing microwave μ-SPE devices were added in the microwave
extraction vessel, the extraction was carried out under 400W irradiation power
at 60°C for 10min. The extracts obtained by MAE-μ-SPE were directly analyzed by
GC–MS without any clean-up process. The recoveries were in the range of
93.5–104.6%, and the relative standard deviations were lower than 8.7%.
Graphical abstract
Highlights
► An absorbing microwave
μ-SPE device packed with activated carbon was used. ► Absorbing microwave μ-SPE
device was made and used to enrich the analytes. ► Absorbing microwave μ-SPE
device was made and used to heat samples directly. ► MAE-μ-SPE was applied to
the extraction of OPPs with non-polar solvent only.
Considering the
great significance of microRNAs (miRNAs) in cancer detection and typing, the
development of sensitive, specific, quantitative, and low-cost methods for the
assay of expression levels of miRNAs is desirable. We describe a highly
efficient amplification platform for ultrasensitive analysis of miRNA (taking
let-7a miRNA as a model analyte) based on a dumbbell probe-mediated cascade
isothermal amplification (DP-CIA) strategy. The method relies on the
circularization of dumbbell probe by binding target miRNA, followed by rolling
circle amplification (RCA) reaction and an autonomous DNA machine performed by
nicking/polymerization/displacement cycles that continuously produces
single-stranded G-quadruplex to assemble with hemin to generate a color signal.
In terms of the high sensitivity (as low as 1zmol), wide dynamic range (covering
9 orders of magnitude), good specificity (even single-base difference) and easy
operation (one probe and three enzymes), the proposed label-free assay is
successfully applied to direct detection of let-7a miRNA in real sample (total
RNA extracted from human lung tissue), demonstrating an attractive alternative
for miRNA analysis for gene expression profiling and molecular diagnostics,
particularly for early cancer diagnosis.
Graphical abstract
Highlights
► This assay relies on
the circularization of dumbbell probe by target microRNA. ► Rolling circle
amplification and autonomous DNA machine are then occurred. ► G-quadruplex is
continuously produced to bind hemin to generate color signal. ► High
sensitivity, wide dynamic range, and good specificity is achieved.
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