Fast Analysis Research Articles

Development of an organic polymer monolith column for the nano liquid chromatography fast analysis of monoclonal antibody in infusion bags prepared in a hospital pharmacy.

Poly(butyl methacrylate-co-ethylene dimethacrylate) monolith was in situ prepared in a liquid chromatography capillary column with a 75 μm internal diameter. This monolith offered high permeability (5.3 ± 10-14m2) and good peak capacity (140 for a 15 cm column length at 300 nl/min with a 20 min gradient time). This is exemplified by its separation ability in reversed mode for subunit analysis of monoclonal antibodies after IdeS digestion (middle-up analysis). The potential of this column was also illustrated for the fast analytical control of therapeutic monoclonal antibodies in standardized infusion bags prepared in advance in a pharmacy department. Linearity analysis revealed the column's capability for accurate quantification analysis of the different dose bandings (in mg) of monoclonal antibodies in <2min. In addition, lifetime analysis data indicated that the column can be highly reproducible and has a long lifetime with stable and low back pressure. The variations observed on the peak shape and area between unstressed (intact) and stressed monoclonal antibodies indicated that our nano liquid chromatographic method was stability indicating. In addition, using a gradient elution mode, the presence of minor components in the infusion bags was visualized.

Fast vibrational analysis of molecular systems

AbstractThe development of infrared difference spectroscopy provides unprecedented insights on structures of complex molecules like metalloproteins. However, the relevant information can be hard to find among the many bands of the vibrational spectra. The ab initio modeling is very helpful to assign the frequencies to vibrational modes but it is a challenge to process the huge quantity of data into descriptors useful for experimentalists. To this end, we developed a new tool called VIBMOL allowing to analyze vibrational modes of molecules from hessian matrices calculated with common quantum chemistry codes. VIBMOL program runs on Unix machines. Through a new graphical interface, the users can calculate the normal modes of molecules, visualize them, simulate infrared spectra, and explore the Potential Energy Distribution of normal modes among any set of vibration coordinates. It is combined with an interface program (gosdmu) formatting relevant data from the GAUSSIAN program. VIBMOL code is available upon request to the authors. A discussion is provided to help the readers to choose between a large choice of different software and it shows how VIBMOL can make the IR assignment easier in the context of collaborations with experimentalists.

An Artificial Neural Network Model for a Comprehensive Assessment of the Production Performance of Multiple Fractured Unconventional Tight Gas Wells

The potential of unconventional hydrocarbon resources has been unlocked since the hydraulic fracturing technique in combination with long horizontal wells was applied to develop this type of reservoir economically. The design and optimization of the fracturing treatment and the stimulated reservoir volume and the forecasting of production performance are crucial for the development and management of such resources. However, the production performance of tight gas reservoirs is a complicated nonlinear problem, described by many parameters loaded with uncertainty. The complexity of the problem influences and inspires the sophistication of the solution to be used. This paper proposed an artificial network model that allows for fast, extended, and accurate analyses of the production performance of multiple fractured unconventional tight gas wells. In the comprehensive approach developed, the reservoir rock parameters, the drainage area, and the hydraulic fracture parameters are treated as a variable input to the model. The analysis is no longer constrained by fixed “shoes box” geometry, and the values of the parameters defining the reservoir and stimulated volume are not limited to a few discrete values. The numerical experiment used to construct a database for model development was designed using a genetically optimized Latin hypercube sampling technique. A special approach was used in the preparation of “blind data”, which are crucial for truly reliable model verification. In the result, a developed tool offers an extended rock-fluid description, flexible model, and stimulated reservoir volume dimensioning and parameterization, as well as a high degree of applicability in sensitivity analysis and/or optimization.

The Fluorescent Cell Line SW620-GFP Is a Valuable Model to Monitor Magnetic Hyperthermia.

In this work, the cell line SW620-GFP has been used in a complete magnetic hyperthermia assay, from the preparation of the ferrofluid with folate-coated iron oxide nanoparticles to in vivo experiments. The physical and chemical characterization of the nanoparticles evidenced their superparamagnetic behaviour, an average diameter of 12 ± 4 nm, a 2 nm coat thickness, and a high-power loss density. The main innovation of the work is the exclusive capability of viable SW620-GFP cells to emit fluorescence, enabling fast analysis of both, cell viability in vitro with an epifluorescence microscope and tumour size and shape in vivo in a non-invasive manner using the iBox technology. Moreover, with this imaging technique, it was possible to demonstrate the successful tumour size reduction in mice applying magnetic hyperthermia three times a week over 3 weeks.

Cluster analysis for systematic database extension to improve machine learning performance in double-sided incremental sheet forming

Incremental sheet forming is a process for the production of sheet metal parts in small batch sizes. Due to the relatively low geometrical accuracy and the lack of precise and fast finite element analysis simulations of the process, industrial use cases are rare. Recently, a vast amount of scientific approaches simulated the process by utilizing machine learning techniques. Their success is limited by the quantity and quality of the used process data. Research institutes are struggling to gather enough data without industrial cooperations. For maximizing the distribution of process data in an experimental series and therefore their applicability for machine learning, the authors present a novel cluster analysis approach to systematically extend an existing database. The whole established process database consisting of 70 forming experiments and their toolpaths and digitizations is published to be used as a foundation for similar research.

The development of an automated microscope image tracking and analysis system.

Microscopy image analysis plays a crucial role in understanding cellular behavior and uncovering important insights in various biological and medical research domains. Tracking cells within the time-lapse microscopy images is a fundamental technique that enables the study of cell dynamics, interactions, and migration. While manual cell tracking is possible, it is time-consuming and prone to subjective biases that impact results. In order to solve this issue, we sought to create an automated software solution, named cell analyzer, which is able to track cells within microscopy images with minimal input required from the user. The program of cell analyzer was written in Python utilizing the open source computer vision (OpenCV) library and featured a graphical user interface that makes it easy for users to access. The functions of all codes were verified through closeness, area, centroid, contrast, variance, and cell tracking test. Cell analyzer primarily utilizes image preprocessing and edge detection techniques to isolate cell boundaries for detection and analysis. It uniquely recorded the area, displacement, speed, size, and direction of detected cell objects and visualized the data collected automatically for fast analysis. Our cell analyzer provides an easy-to-use tool through a graphical user interface for tracking cell motion and analyzing quantitative cell images.

Innovative microfluidic thread electroanalytical device with automated injector: A simple, green and upgraded analytical platform for the fast and sensitive analysis of ferrous ions

Cotton thread-based microfluidic devices are attractive for analytical procedures due to advantages that include affordable cost, low consumption of reagents and minimal production of waste. The coupling of cotton-based devices to electrochemical detection, known as microfluidic thread electroanalytical device (µTED), has demonstrated vast potential. However, some aspects in the µTED concept can still be improved, such as sample injection, which, until now, has been performed exclusively with manual micropipettes. This conventional injection has some deficiencies that include the variability in the aspiration/dispensing rates and inconstancy in the position of the pipette during injections, which can impair the measurements. Here we propose a new approach to improve the injection step in µTEDs by using electronic pipettes, which we named as automated injection. This novel injection mode overcomes the aforementioned disadvantages of manual injection. Electronic pipettes control aspiration and dispensing volumes/rates electronically and offer better ergonomics. We developed a new 3D-printed platform that allows to attach the electronic pipette to the µTED and always perform injections at a fixed position, increasing reproducibility of measurements. To demonstrate the practical applicability, this approach was first utilized for analysis of ferrous ion in pharmaceutical supplements and environmental samples. The method presented a wide linear range (0.7 to 200 μmolL–1), low limit of detection (0.21 μmolL–1), good accuracy (recoveries between 96 and 103 %) and excellent precision (relative standard deviation (RSD) ≤ 3.3 %). Thus, automated injection can be a great advance and change the state-of-the-art regarding µTEDs.

Rapid single flax (Linum usitatissimum) seed phenotyping of oil and other quality traits using single kernel near infrared spectroscopy

AbstractThe growing interest in the rapid measurement of seed ingredients using single‐kernel NIR (SKNIR) spectroscopy as a nondestructive measurement technique allows fast analysis of sample seed variance that can have effects on breeding and end‐use processing. Flax (Linum usitatissimum), an oilseed crop grown in the Northwest United States and worldwide, is highly beneficial for human health, food, and fiber. Its health benefits include its high protein and omega‐3 fatty acids content. Therefore, seed composition profiles are an important aspect of breeding. The goals of this research were the development of single seed NIR calibration models for protein, oil, and weight of intact flax seeds. In this study, SKNIR spectroscopy was used on a diverse set of flax accessions comprising of 306 samples to create prediction models on a custom built SKNIR instrument. Spectra data and reference protein, oil, and weight were used to build partial least squares (PLS) models. Calibration models provided reasonable prediction of these traits and could be used for screening purposes. PLS statistics were oil (R2 = 0.82, SEP = 1.72), weight (R2 = 0.74, SEP = 0.71), and protein (R2 = 0.62, SEP = 0.96) for validation data sets comprising of one‐third of the total samples. In conclusion, prediction models showed that SKNIR spectroscopy could be a very beneficial nondestructive technique to determine oil and weight as well as rapid screening of protein in single flax seeds while not requiring extensive preparation as compared to traditional techniques.

NFC Smartphone-Based Electrochemical Microfluidic Device Integrated with Nanobody Recognition for C-Reactive Protein.

Point-of-care testing (POCT) devices play a crucial role as tools for disease diagnostics, and the integration of biorecognition elements with electronic components into these devices widens their functionalities and facilitates the development of complex quantitative assays. Unfortunately, biosensors that exploit large conventional IgG antibodies to capture relevant biomarkers are often limited in terms of sensitivity, selectivity, and storage stability, considerably restricting the use of POCT in real-world applications. Therefore, we used nanobodies as they are more suitable for fabricating electrochemical biosensors with near-field communication (NFC) technology. Moreover, a flow-through microfluidic device was implemented in this system for the detection of C-reactive protein (CRP), an inflammation biomarker, and a model analyte. The resulting sensors not only have high sensitivity and portability but also retain automated sequential flow properties through capillary transport without the need for an external pump. We also compared the accuracy of CRP quantitative analyses between commercial PalmSens4 and NFC-based potentiostats. Furthermore, the sensor reliability was evaluated using three biological samples (artificial serum, plasma, and whole blood without any pretreatment). This platform will streamline the development of POCT devices by combining operational simplicity, low cost, fast analysis, and portability.

Computing platform for fast analysis of experimental big data

A multi-purpose computing platform for processing (big) data collected from experiments and large research infrastructures is developed. The platform is validated in a case study on monitoring data aquired by the SCADA system of the High Power Laser System at ELI-NP.

Versatile DNA extraction from diverse plant taxa using ionic liquids and magnetic ionic liquids: a methodological breakthrough for enhanced sample utility

BackgroundThere is a growing demand for fast and reliable plant biomolecular analyses. DNA extraction is the major bottleneck in plant nucleic acid-based applications especially due to the complexity of tissues in different plant species. Conventional methods for plant cell lysis and DNA extraction typically require extensive sample preparation processes and large quantities of sample and chemicals, elevated temperatures, and multiple sample transfer steps which pose challenges for high throughput applications.ResultsIn a prior investigation, an ionic liquid (IL)-based modified vortex-assisted matrix solid phase dispersion approach was developed using the model plant, Arabidopsis thaliana (L.) Heynh. Building upon this foundational study, the present study established a simple, rapid and efficient protocol for DNA extraction from milligram fragments of plant tissue representing a diverse range of taxa from the plant Tree of Life including 13 dicots and 4 monocots. Notably, the approach was successful in extracting DNA from a century old herbarium sample. The isolated DNA was of sufficient quality and quantity for sensitive molecular analyses such as qPCR. Two plant DNA barcoding markers, the plastid rbcL and nuclear ribosomal internal transcribed spacer (nrITS) regions were selected for DNA amplification and Sanger sequencing was conducted on PCR products of a representative dicot and monocot species. Successful qPCR amplification of the extracted DNA up to 3 weeks demonstrated that the DNA extracted using this approach remains stable at room temperature for an extended time period prior to downstream analysis.ConclusionsThe method presented here is a rapid and simple approach enabling cell lysis and DNA extraction from 1.5 mg of plant tissue across a broad range of plant taxa. Additional purification prior to DNA amplification is not required due to the compatibility of the extraction solvents with qPCR. The method has tremendous potential for applications in plant biology that require DNA, including barcoding methods for agriculture, conservation, ecology, evolution, and forensics.

Thickness-Resolved Electrochemiluminescence Microscopy of Extracellular Matrix at Tumor Tissues for Rapid Cancer Diagnosis.

The traditional recognition of extracellular matrix (ECM) at tissue sections relies on the time-consuming immunofluorescence that could not meet the demand of rapid diagnosis. Herein, we introduce a thickness-resolved electrochemiluminescence (ECL) microscopy to image thin-layer ECM at tissue sections for fast histopathological analysis. The unique surface-confined ECL mechanism enables to unveil the diversity and complexity of multiple tissue structures with varying thicknesses. Notably, the short lifetimes and the limited diffusion of electrogenerated coreactant radicals combined with their chemical reactivity result in a 2-fold increase in ECL intensity on ECM structures compared to the remaining tissue, enabling ECM visualization without specific labeling. The further quantitation of the ECM localization within tissue sections furnishes crucial insights into tumor progression and, more importantly, differentiates carcinoma and paracancerous tissues from patients in less than 30 min. Moreover, the reported electrochemistry-based microscopy is a dynamic approach allowing to investigate the transport, tortuosity, and trafficking properties through the tissues. This thickness-resolved recognition strategy not only opens new avenues for imaging complex samples but also holds promise for expediting tissue pathologic diagnosis, offering a more automated protocol with enhanced quantitative data compared to current intraoperative pathology methods.

Efficacy of blood plasma spectroscopy for early liver cancer diagnostics in obese patients

Introduction and ObjectivesHepatocellular carcinoma (HCC) represents one of the most common cancers worldwide. A considerable proportion of HCC is caused by cirrhosis related to metabolic dysfunction-associated steatohepatitis (MASH). Due to the increasing prevalence of metabolic syndrome, it is estimated that MASH-related HCC will become the most prevalent etiology of HCC. Currently, HCC screening is based on liver ultrasonography; however, the sensitivity of ultrasonography for early HCC stages in obese patients only reaches 23 %. To date, no studied biomarker shows sufficient efficacy for screening purposes. Nevertheless, the usage of spectroscopic methods offers a new perspective, as its potential use would provide cheap, fast analysis of samples such as blood plasma. Material and MethodsWe employed a combination of conventional and chiroptical spectroscopic methods to study differences between the blood plasma of obese cirrhotic patients with and without HCC. We included 20 subjects with HCC and 17 without evidence of liver cancer, all of them with body mass index ≥ 30. ResultsSensitivities and specificities reached values as follows: 0.780 and 0.905 for infrared spectroscopy, 0.700 and 0.767 for Raman spectroscopy, 0.840 and 0.743 for electronic circular dichroism, and 0.805 and 0.923 for Raman optical activity. The final combined classification model based on all spectroscopic methods reached a sensitivity of 0.810 and a specificity of 0.857, with the highest area under the receiver operating characteristic curve among all models (0.961). ConclusionsWe suggest that this approach can be used effectively as a diagnostic tool in patients who are not examinable by liver ultrasonography. Clinical trial registrationNCT04221347

A Robust Chromatographic Method for Drug Release profiling of liposomal doxorubicin HCl

Liposomes are excellent drug delivery vehicles for chemotherapeutics as they may change the pharmacokinetics of therapeutic compounds, resulting in altered tissues distribution, and in some cases, reduced cytotoxicity and enhanced distribution and efficacy of the active pharmaceutical ingredient (API) at target tissues. Drug release profiles of liposomal formulations are crucial to support equivalence evaluation and quality control in pre- and post-approval stages. We developed an automated chromatographic method for quantifying the drug release profile of liposomal formulations containing doxorubicin to overcome the shortcomings of currently available methods. The newly developed method employs nanoparticle exclusion chromatography (nPEC), using a monolithic silica column coated with polyvinylpyrrolidone to separate the released drug from liposomal encapsulated drug. We evaluated the effects of pH, temperature, and ammonium formate concentration on the drug release rate. The optimized release buffer consisting of 5 % sucrose, 20 mM l-histidine, and 200 mM ammonium formate was selected for the drug release profiling of five liposomal formulations at 47 °C. The drug release profiles of five liposomal doxorubicin formulations were similar. Our automated method requires very small amounts of the sample and provides release profiles with high sensitivity and accuracy. In addition, this method can be applied to other liposomal products to allow for simple, fast, and accurate analysis of in vitro drug release profiling.

Colorimetric Glucose Biosensor Based on Chitosan Films and Its Application for Glucose Detection in Beverages Using a Smartphone Application.

Nowadays, biosensors are gaining increasing interest in foods' and beverages' quality control, owing to their economic production, enhanced sensitivity, specificity, and faster analysis. In particular, colorimetric biosensors can be combined with color recognition applications on smartphones for the detection of analytes, rendering the whole procedure more applicable in everyday life. Herein, chitosan (CS) films were prepared with the deep eutectic solvent (DES) choline chloride/urea/glycerol (ChCl:U:Gly). Glucose oxidase (GOx), a widely utilized enzyme in quality control, was immobilized within CS films through glutaraldehyde (GA), leading to the formation of CS/GOx films. The optimized GOx concentration and DES content were determined for the films. Moreover, the effect of the pH and temperature of the glucose oxidation reaction on the enzymatic activity of GOx was studied. The structure, stability, and specificity of the CS/GOx films as well as the Km values of free and immobilized GOx were also determined. Finally, the analytical performance of the films was studied by using both a spectrophotometer and a color recognition application on a smartphone. The results demonstrated that the films were highly accurate, specific to glucose, and stable when stored at 4 °C for 4 weeks and when reused 10 times, without evident activity loss. Furthermore, the films displayed a good linear response range (0.1-0.8 mM) and a good limit of detection (LOD, 33 μM), thus being appropriate for the estimation of glucose concentration in real samples through a smartphone application.

COELENTERAZINE-DEPENDENT LUCIFERASES: PROPERTIES AND APPLICATION

The review presents data on the structure of bioluminescent systems of marine animals that use coelenterazine-dependent luciferases as light-emitting proteins. It has been shown that due to the availability, stability and high quantum yield of reactions, these luciferases, as well as their genetic variants with new useful properties, are successfully applied as reporter molecules in a variety of analytical systems in vitro and in vivo. Their use provides high sensitivity, simple design and fast analysis.

Ambient ionization mass spectrometry provides screening of selective androgen receptor modulators

Ambient ionization mass spectrometry allows for analysis of samples in their natural state, i.e., with no sample pre-treatment. It can be viewed as a fast, simple, and economical analysis, but its main disadvantages include a lower analytical performance due to the presence of complex sample matrix and the lack of chromatographic separation prior to the introduction of the sample into the mass spectrometer. Here we present an application of two ambient ionization mass spectrometry techniques, i.e., Desorption Atmospheric Pressure Photoionization and Dielectric Barrier Discharge Ionization, for the analysis of known Selective Androgen Receptor Modulators, which represent common compounds of abuse in professional and semiprofessional sport. Eight real samples of illegal food supplements, seized by the local law enforcement, were used to test the performance of the ambient mass spectrometry and the results were validated against a newly developed targeted LC-UV-MS/MS method performed in multiple reaction monitoring mode with an external calibration for each analyte. In order to decide whether or not the compound can be declared as present, we proposed a system of rules for the interpretation of the obtained spectra. The criteria are based on mass spectrum matching (5–10 ppm accuracy from the theoretical exact mass and a correct isotopic pattern), duration of the mass signal (three or five consecutive scans, depending on the instrumentation used), and intensity above the background noise (threefold increase in intensity and absolute intensity above 5E4 or 1E5, depending on the instrumentation). When applying these criteria, good agreement was found between the tested methods. Ambient ionization techniques were effective at detecting SARMs at pharmacologically relevant doses, i.e., approximately above 1 mg per capsule, although they may fail to detect lower levels or isomeric species. It is demonstrated that when adhering to a set of clear and consistent rules, ambient mass spectrometry can be employed as a qualitative technique for the screening of illegal SARMs with sufficient confidence and without the necessity to perform a regular LC-MS analysis.

Evaluation and Comparison of Rock Bolting Versus Steel Arch Support Systems in Thick Coal Seam Underground Galleries: A Case Study

This study investigates the feasibility of rock bolting support in an underground coal mine gallery with a thick coal seam. The Ömerler underground coal mine working area, owned by the West Lignite Enterprise (GLI) of the Turkish Coal Enterprises (TKI), was selected for this purpose. Longwall top coal caving (LTCC) is implemented as the production method in the Ömerler underground coal mine. Field and laboratory studies were conducted to determine rock mass and rock material properties, followed by experimental, empirical, and numerical analyses based on the acquired data. The obtained design results were evaluated using a resin-grouted rebar (RBR) rock bolting system and steel arch support (SAS) pilot application areas. The numerical modeling results conducted using the Fast Lagrangian Analysis of Continua 3D (FLAC 3D) (v6.0) program indicated less displacement and secondary stress change in the RBR-supported zone compared to the SAS-supported zone. In situ measurements also demonstrated that RBR provided more successful support to the roof during coal production activities. The findings suggest that RBR is a more effective solution when evaluating the feasibility of rock bolting support systems in underground galleries with thick coal seams at the Ömerler underground coal mine. This study emphasizes the importance of more sustainable and safe support systems to enhance operational efficiency in the coal mining industry.

Chemical composition analysis of Qingyan dropping pills using ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry.

This study developed a method for the rapid classification and identification of the chemical composition of Qingyan dropping pills (QDP) to provide the theoretical basis and data foundation for further in-depth research on the pharmacological substance basis of the formula and the selection of quality control indexes. Ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) and data postprocessing technology were used to analyze the chemical composition of QDP. The fragmentation information on possible characteristic fragments and related neutral losses was summarized based on the literature and was compared with the MS data obtained from the assay, and thus a rapid classification and identification of chemical components in QDP could be achieved. A total of 73 compounds were identified, namely 24 flavonoids, 14 terpenoids, 30 organic acids and their esters, 3 alkaloids, and 2 phenylpropanoids. In this study, UHPLC-Q-TOF-MS and data postprocessing technology were used to realize the rapid classification and identification of the chemical constituents of QDP, which provided a comprehensive, efficient, and fast qualitative analysis method, a basis for further quality control and safe medication of QDP.

Structure–spectrum relationship in the calculated Raman spectra of silicates

AbstractSilicate minerals have a rich structural variety consisting of silicon oxide clusters or networks of any dimensionality interdispersed with different types of elements, which is reflected in marked changes in the Raman spectra. Understanding how the changes in the Raman spectra are correlated with the atomic structure would be highly desirable for fast material identification and analysis. Extracting such trends from experimental spectra can be difficult owing to the uncertainties in the structural details of the samples and in ensuring consistency between measurements from different sources. Simulated spectra, however, avoid these problems, making them a good candidate for systematic studies. Here, we study the correlation between the structure and Raman spectral features of 179 silicates derived from a database of Raman spectra simulated using first‐principles calculations. We investigate the spectral similarities with a specific emphasis on materials containing isolated 0D clusters in nesosilicate (SiO4), sorosilicate (Si2O7), and cyclosilicate (Si3O9 and Si4O12) configurations. While trends identified in the previous reports can be confirmed, we find that the variations within each group of similar structural motifs tend to be larger than the changes across groups, and therefore, developing a reliable automated classification algorithm is likely to be challenging.