Semi-automatic Extraction Research Articles

Comparison of DNA extraction procedures for detection of Mycoplasma bovis directly from extended bovine semen straw samples using a commercial M. bovis PCR

BackgroundMycoplasma bovis is a global pathogen of cattle but was detected for the first time in New Zealand in 2017, triggering a response under their Biosecurity Act as an “unwanted organism”. Following a lengthy eradication campaign, the Ministry of Primary Industries (MPI) now requires all bovine semen destined for export to New Zealand to be screened with an M. bovis-specific real-time PCR (rtPCR) compliant with amended import health standard (IHS) test requirements aimed at preventing the accidental importation of M. bovis. The standard stipulates that semen samples cannot be centrifuged prior to DNA extraction. To comply with these strict requirements, one of the listed tests was validated together with different DNA preparation steps and compared with existing in-house procedures. DNA was extracted from semen straws using the current in-house semi-automated platform procedures for processing culture, tissue and body fluid sample submissions and was compared with the stipulated test requirements. DNA from centrifuged and unspun semen samples spiked with M. bovis was also compared.ResultsThe rtPCR had a sensitivity and specificity of 100% (95% confidence interval = 79–100% and 74–100%, respectively) when testing DNA from other Mycoplasma species or bovine semen spiked with the latter, with a high level of repeatability for within- and between- run replicates. The consistent limit of detection was 0.001 pg/µl M. bovis DNA and between 5.3 × 102 and 7.5 × 102 CFU/ml M. bovis when artificially spiked in semen. DNA extracted using the KingFisher Flex was detected with lower Cq values than the Maxwell 16, but the comparable improvements in sensitivity were mainly associated with non-centrifuged samples (p < 0.001). None of the procedures tested impeded the detection sensitivity of M. bovis in the presence of competitor organisms Acholeplasma laidlawii, Mycoplasma bovigenitalium and Ureaplasma diversum, confirming M. bovis specificity of the polC target.ConclusionsUnder the experimental conditions applied, this rtPCR test efficiently detected M. bovis in extended bovine semen straw samples from DNA extracted using both semi-automated extraction platforms, irrespective of prior centrifugation of extended semen.

Evaluation of the Seegene Allplex™ RV master assay for one-step simultaneous detection of eight respiratory viruses in nasopharyngeal specimens

BackgroundThe Seegene Allplex™ RV Master (RVM) assay is a one-step multiplex real-time reverse transcription polymerase chain reaction (RT-PCR) system for detecting eight viral respiratory pathogens from nasopharyngeal swab, aspirate, and bronchoalveolar lavage specimens. The eight RVM targets are: severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Influenza A (Flu A), Influenza B (Flu B), Human respiratory syncytial virus (RSV), adenovirus (AdV), rhinovirus (HRV), parainfluenza virus (PIV), and metapneumovirus (MPV). The assay is based on Seegene’s multiple detection temperature (MuDT) technology and provides cycle threshold (Ct) values for each of its viral targets upon PCR completion. ObjectiveWe aimed to evaluate the diagnostic performance of the RVM assay by calculating sensitivity, specificity, accuracy, Positive Predictive Value (PPV), Negative Predictive Value (NPV), Positive Percent Agreement (PPA), Negative Percent Agreement (NPA), and Overall Percent Agreement (OPA) compared to definite diagnosis and analogous reference assays. Study designDiagnostic sensitivity, specificity, accuracy, PPV, and NPV were calculated by comparing the results of the RVM assay to that of definite diagnosis assays; while PPA, NPA, and OPA were calculated by comparing results of the RVM assay to that of analogous reference products. Definite diagnosis and reference methods comprised whole genome sequencing and PCR genotyping, the Allplex™ SARS-CoV-2/FluA/FluB/RSV and Respiratory Panels 1, 2, and 3 assays (Seegene), and the Xpert® Xpress SARS-CoV-2/FluA/FluB/RSV Plus assay (Cepheid). Reproducibility of the RVM assay using fully-automated and semi-automated nucleic acid (NA) extraction workflows and as performed by independent operators was also assessed. In total, 249 positive respiratory specimens and at least 50 negative specimens for each target tested were used for this evaluation study. ResultsSensitivity, specificity, accuracy, PPV, NPV, PPA, NPA, and OPA ranged from 95.7 % to 100 % for detecting all eight targets tested on the RVM assay. Reproducibility PPA, NPA, and OPA between automated and semi-automated NA extraction workflows were all >97.9 %, while the reproducibility PPA, NPA and OPA between independent operators were all 100 %. ConclusionThese results demonstrate a high level of sensitivity, specificity, accuracy and diagnostic predictive value of the RVM assay and high agreement with comparable reference assays in identifying all eight of its targets. Taken together, our study underscores the diagnostic utility of the RVM assay in detecting eight viral respiratory pathogens.

Rapid green analytical methodology for simultaneous monitoring of nitrosamines and semi-volatile organic compounds in water and human urine samples.

Nitrosamines and semi-volatile organic compounds (SVOCs) are carcinogenic contaminants in water and biological matrices. Conventional analytical methods often struggle to detect trace concentrations due to poor extraction efficacies. This study presents a novel, low-cost, in-syringe-assisted fast extraction cum cleanup technique coupled with GC-FID for monitoring four nitrosamines and two SVOCs in drinking water and human urine samples to measure the contamination and exposure levels. This extraction protocol combines a novel green in-syringe liquid-liquid extraction step using dimethyl carbonate as the green extraction solvent, coupled with a semi-automated solid-phase extraction cleanup process. Then, the final extractant is analyzed using gas chromatography-flame ionization detection (GC-FID) for monitoring. The method demonstrated excellent linearity (R2 > 0.998) between 1.5 and 500ngmL⁻1 for all six target compounds. Detection limits ranged from 1.0 to 2.0ngmL⁻1. Extraction recoveries were between 87 and 105% for both urine samples and water samples. Intra-day and inter-day precision were below 9% RSD. The blue applicability grade index evaluation scored 70.0, indicating good practical applicability. The developed analytical protocol offers a sensitive, accurate, low-cost, rapid, and environmentally friendly method for simultaneously quantifying multiple nitrosamines and SVOCs in environmental and human samples. Its performance characteristics and sustainability metrics suggest the potential for broad application in monitoring and exposure studies.

Automated Versus Semi-Automated Lab Value Extraction for the VA Cardiac Surgical Quality Improvement Program

IntroductionThe Veterans Affairs Surgical Quality Improvement Program (VASQIP) trains surgical quality nurses (SQNs) at each Veterans Affairs (VA) hospital to extract or verify 187 variables from the medical record for all cardiac surgical cases. For ten preoperative laboratory values, VASQIP has a semiautomated (SA) system in which local lab values are automatically extracted, verified by SQNs, and lab values recorded at other VA facilities are manually extracted. The objective of this study was to develop and validate a method to automate the extraction of these ten preoperative laboratory values and compare results with the current SA method. Materials and methodsWe developed methods to extract ten preoperative laboratory values and measurement dates from the VA Corporate Data Warehouse using Logical Observation Identifiers Names and Codes. Automated (A) versus SA information extraction was compared in terms of agreement, conformance to data definitions, proximity to surgery, and missingness. ResultsFor surgeries with both A and SA lab values, the intraclass correlation coefficients for the ten variables ranged from 0.90 to 0.98. For several variables, the A method resulted in much lower rates of missing data (e.g., 2.4% versus 22.5% missing data for high-density lipoprotein) and eliminated out-of-date-range entries. ConclusionsAlthough SQN-extracted data are widely considered the gold standard within National Surgical Quality Improvement Programs, there may be advantages to fully automating extraction of lab values, including high congruence with SA SQN-extracted or verified values and lower rates of missingness and out-of-date-range data.

Semi-automated geometric feature extraction for railway bridges

Semi-automated geometric feature extraction for railway bridges

A single-sample workflow for joint metabolomic and proteomic analysis of clinical specimens

Understanding the interplay of the proteome and the metabolome helps to understand cellular regulation and response. To enable robust inferences from such multi-omics analyses, we introduced and evaluated a workflow for combined proteome and metabolome analysis starting from a single sample. Specifically, we integrated established and individually optimized protocols for metabolomic and proteomic profiling (EtOH/MTBE and autoSP3, respectively) into a unified workflow (termed MTBE-SP3), and took advantage of the fact that the protein residue of the metabolomic sample can be used as a direct input for proteome analysis. We particularly evaluated the performance of proteome analysis in MTBE-SP3, and demonstrated equivalence of proteome profiles irrespective of prior metabolite extraction. In addition, MTBE-SP3 combines the advantages of EtOH/MTBE and autoSP3 for semi-automated metabolite extraction and fully automated proteome sample preparation, respectively, thus advancing standardization and scalability for large-scale studies. We showed that MTBE-SP3 can be applied to various biological matrices (FFPE tissue, fresh-frozen tissue, plasma, serum and cells) to enable implementation in a variety of clinical settings. To demonstrate applicability, we applied MTBE-SP3 and autoSP3 to a lung adenocarcinoma cohort showing consistent proteomic alterations between tumour and non-tumour adjacent tissue independent of the method used. Integration with metabolomic data obtained from the same samples revealed mitochondrial dysfunction in tumour tissue through deregulation of OGDH, SDH family enzymes and PKM. In summary, MTBE-SP3 enables the facile and reliable parallel measurement of proteins and metabolites obtained from the same sample, benefiting from reduced sample variation and input amount. This workflow is particularly applicable for studies with limited sample availability and offers the potential to enhance the integration of metabolomic and proteomic datasets.Graphical

Automated Liquid Handling Extraction and Rapid Quantification of Underivatized Amino Acids and Tryptophan Metabolites from Human Serum and Plasma Using Dual-Column U(H)PLC-MRM-MS and Its Application to Prostate Cancer Study.

Amino acids (AAs) and their metabolites are important building blocks, energy sources, and signaling molecules associated with various pathological phenotypes. The quantification of AA and tryptophan (TRP) metabolites in human serum and plasma is therefore of great diagnostic interest. Therefore, robust, reproducible sample extraction and processing workflows as well as rapid, sensitive absolute quantification are required to identify candidate biomarkers and to improve screening methods. We developed a validated semi-automated robotic liquid extraction and processing workflow and a rapid method for absolute quantification of 20 free, underivatized AAs and six TRP metabolites using dual-column U(H)PLC-MRM-MS. The extraction and sample preparation workflow in a 96-well plate was optimized for robust, reproducible high sample throughput allowing for transfer of samples to the U(H)PLC autosampler directly without additional cleanup steps. The U(H)PLC-MRM-MS method, using a mixed-mode reversed-phase anion exchange column with formic acid and a high-strength silica reversed-phase column with difluoro-acetic acid as mobile phase additive, provided absolute quantification with nanomolar lower limits of quantification within 7.9 min. The semi-automated extraction workflow and dual-column U(H)PLC-MRM-MS method was applied to a human prostate cancer study and was shown to discriminate between treatment regimens and to identify metabolites responsible for discriminating between healthy controls and patients on active surveillance.

Identifying Terms to Represent Concepts of a Work Process Ontology

Objectives: To identify ontology concepts from text documents for the construction of work process ontology. Methods: This study proposes a methodology to identify terms representing a work process ontology concept from a document. The methodology encompasses several key steps: document collecting, text pre-processing, term weighting and analysis, terms mapping, and domain expert relevance judgments. A comparison between the results of three different term weighting schemes, namely the Term Frequency (TF), Term Frequency-Inverse Document Frequency (TFIDF), and Mutual Information (MI) is made with the ontology concept that the domain expert has judged. Findings: The approaches adopted in this study have managed to extract ontological concepts from the targeted domain knowledge source. The findings of the comparison analysis suggest that the TFIDF term weighting scheme exhibits better results compared to the TF and MI weighting schemes. Novelty: A work process ontology is a structured knowledge describing daily operations in the government sector. However, there has been little to no effort in building the work process ontology. This study presents an integrated approach for identifying ontology concepts from documents within the domain of the work process. To the utmost extent of our understanding, this research initiative is the initial attempt to introduce a structured methodology for the semi-automatic extraction and evaluation of concepts and relationships within this domain. The findings can be utilised as a foundation for developing an ontology in the specific field. Keywords: Ontology, Work process, Text extraction, Natural language processing, Term weighting

Interfering factors in the diagnosis of Senecavirus A.

Senecavirus A (SV-A) is an RNA virus that belongs to the genus Senecavirus within the family Picornaviridae. This study aimed to analyze factors that can influence the molecular diagnosis of Senecavirus A, such as oligonucleotides, RNA extraction methods, and RT-qPCR kits. Samples from suspected cases of vesicular disease in Brazilian pigs were analyzed for foot-and-mouth disease, swine vesicular disease, and vesicular stomatitis. All tested negative for these diseases but positive for SV-A. RT-qPCR tests were used, comparing different reagent kits and RNA extraction methods. Sensitivity and repeatability were evaluated, demonstrating efficacy in detecting SV-A in clinical samples. In RNA extraction, significant reduction in Cq values was observed with initial dilutions, particularly with larger supernatant volumes. Trizol and Maxwell showed greater sensitivity in automated equipment protocols, though results varied in tissue tests. RT-qPCR kit comparison revealed differences in amplification using viral RNA but minimal differences with plasmid DNA. Sensitivity among methods was comparable, with slight variations in non-amplified samples. Repeatability tests showed consistent results among RT-qPCRs, demonstrating similarity between methods despite minor discrepancies in Cq values. Trizol, silica columns, and semi-automated extraction were compared, as well as different RT-qPCR kits. The study found significant variations that could impact the final diagnosis.

Evaluation of a New DNA Extraction Method on Challenging Bone Samples Recovered from a WWII Mass Grave.

Bones and teeth represent a common finding in ancient DNA studies and in forensic casework, even after a long burial. Genetic typing is the gold standard for the personal identification of skeletal remains, but there are two main factors involved in the successful DNA typing of such samples: (1) the set-up of an efficient DNA extraction method; (2) the identification of the most suitable skeletal element for the downstream genetic analyses. In this paper, a protocol based on the processing of 0.5 g of bone powder decalcified using Na2EDTA proved to be suitable for a semi-automated DNA extraction workflow using the Maxwell® FSC DNA IQ™ Casework Kit (Promega, Madison, WI, USA). The performance of this method in terms of DNA recovery and quality was compared with a full demineralisation extraction protocol based on Qiagen technology and kits. No statistically significant differences were scored according to the DNA recovery and DNA degradation index (p-values ≥ 0.176; r ≥ 0.907). This new DNA extraction protocol was applied to 88 bone samples (41 femurs, 19 petrous bones, 12 metacarpals and 16 molars) allegedly belonging to 27 World War II Italian soldiers found in a mass grave on the isle of Cres (Croatia). The results of the qPCR performed by the Quantifiler Human DNA Quantification kit showed values above the lowest Limit of Quantification (lLOQ; 23 pg/µL) for all petrous bones, whereas other bone types showed, in most cases, lower amounts of DNA. Replicate STR-CE analyses showed successful typing (that is, >12 markers) in all tests on the petrous bones, followed by the metacarpals (83.3%), femurs (52.2%) and teeth (20.0%). Full profiles (22/22 autosomal markers) were achieved mainly in the petrous bones (84.2%), followed by the metacarpals (41.7%). Stochastic amplification artefacts such as drop-outs or drop-ins occurred with a frequency of 1.9% in the petrous bones, whereas they were higher when the DNA recovered from other bone elements was amplified (up to 13.9% in the femurs). Overall, the results of this study confirm that petrous bone outperforms other bone elements in terms of the quantity and quality of the recovered DNA; for this reason, if available, it should always be preferred for genetic testing. In addition, our results highlight the need for accurate planning of the DVI operation, which should be carried out by a multi-disciplinary team, and the tricky issue of identifying other suitable skeletal elements for genetic testing. Overall, the results presented in this paper support the need to adopt preanalytical strategies positively related to the successful genetic testing of aged skeletal remains in order to reduce costs and the time of analysis.

Identifying excessive length of antibiotic treatment duration for hospital-acquired infections: a semi-automated approach to support antimicrobial stewardship

BackgroundAvoiding excessive antibiotic treatment duration is a fundamental goal in antimicrobial stewardship. Manual collection of data is a time-consuming process, but a semi-automated approach for data extraction has been shown feasible for community-acquired infections (CAI). Extraction of data however may be more challenging in hospital-acquired infections (HAI). The aim of this study is to explore whether semi-automated data extraction of treatment duration is also feasible and accurate for HAI.MethodsData from a university-affiliated hospital over the period 1-6-2020 until 1-6-2022 was used for this study. From the Electronic Health Record, raw data on prescriptions, registered indications and admissions was extracted and processed to define treatment courses. In addition, clinical notes including prescription instructions were obtained for the purpose of validation. The derived treatment course was compared to the registered indication and the actual length of treatment (LOT) in the clinical notes in a random sample of 5.7% of treatment courses, to assess the accuracy of the data for both CAI and HAI.ResultsIncluded were 10.564 treatment courses of which 73.1% were CAI and 26.8% HAI. The registered indication matched the diagnosis as recorded in the clinical notes in 79% of treatment courses (79.2% CAI, 78.5% HAI). Higher error rates were seen in urinary tract infections (UTIs) (29.0%) and respiratory tract infections (RTIs) (20.5%) compared to intra-abdominal infections (7.4%), or skin or soft tissue infections (11.1%), mainly due to incorrect specification of the type of UTI or RTI. The LOT was accurately extracted in 98.5% of courses (CAI 98.2%, HAI 99.3%) when compared to prescriptions in the EHR. In 21% of cases however the LOT did not match with the clinical notes, mainly if patients received treatment from other health care providers preceding or following the present course.ConclusionSemi-automatic data extraction can yield reliable information about the indication and LOT in treatment courses of hospitalized patients, for both HAI and CAI. This can provide stewardship programs with a surveillance tool for all in-hospital treated infections, which can be used to achieve stewardship goals.

Extracting the winter wheat using the decision tree based on time series dual-polarization SAR feature and NDVI.

Winter wheat is one of the most important crops in the world. It is great significance to obtain the planting area of winter wheat timely and accurately for formulating agricultural policies. Due to the limited resolution of single SAR data and the susceptibility of single optical data to weather conditions, it is difficult to accurately obtain the planting area of winter wheat using only SAR or optical data. To solve the problem of low accuracy of winter wheat extraction only using optical or SAR images, a decision tree classification method combining time series SAR backscattering feature and NDVI (Normalized Difference Vegetation Index) was constructed in this paper. By synergy using of SAR and optical data can compensate for their respective shortcomings. First, winter wheat was distinguished from other vegetation by NDVI at the maturity stage, and then it was extracted by SAR backscattering feature. This approach facilitates the semi-automated extraction of winter wheat. Taking Yucheng City of Shandong Province as study area, 9 Sentinel-1 images and one Sentinel-2 image were taken as the data sources, and the spatial distribution of winter wheat in 2022 was obtained. The results indicate that the overall accuracy (OA) and kappa coefficient (Kappa) of the proposed method are 96.10% and 0.94, respectively. Compared with the supervised classification of multi-temporal composite pseudocolor image and single Sentinel-2 image using Support Vector Machine (SVM) classifier, the OA are improved by 10.69% and 5.66%, respectively. Compared with using only SAR feature for decision tree classification, the producer accuracy (PA) and user accuracy (UA) for extracting the winter wheat are improved by 3.08% and 8.25%, respectively. The method proposed in this paper is rapid and accurate, and provide a new technical method for extracting winter wheat.

Semi-automatic Knowledge Graph Construction Based on Deep Learning

The paper studied course knowledge graph in teaching resources and curriculum knowledge management tasks from the perspective of knowledge management. Considering that the course of Python Language Programming itself has formed a relatively complete knowledge system and knowledge point structure, the paper adopted a top-down approach to build the knowledge graph. Firstly, the paper obtained different types of course-related corpus and data from different sources, and then constructed the ontology layer of Python programming course. At the ontology level, the paper defined the concept type, relation type and attribute type of the course domain respectively. Considering the completeness of knowledge points in the curriculum domain knowledge graph, the paper extracted all entities, relationships, attributes, and its values from the curriculum corpus using a semi-automatic extraction method that takes into account both accuracy and efficiency based on the modeling results of the ontology layer. Then they were transformed into triples in the form of &lt; entity, relationship, entity &gt; or &lt; entity, attribute, attribute value &gt; to build data layer of knowledge graph. Finally, visualization of triplet data was realized through Neo4j graph database.

Lexiques et corpus au service de la littératie océanique : propriétés et relations lexicales dans le domaine de la faune marine

Lexicons and Corpora Serving Ocean Literacy: Properties and Lexical Relations in the Field of Marine Fauna. In this article, we present the first steps of the Ocean Literacy project, which aims to promote the creation and processing of a comparable medium-sized specialised corpus (French-Italian) in the field of marine fauna as well as the creation of a lexicographic resource to describe the lexical relations between the terminological units (TUs) of the field in question, using the theoretical framework of Explanatory and Combinatorial Lexicology. After describing the methodological and theoretical choices made when compiling and processing the ZooCor (fr-it) corpus, we will proceed to the analysis of the French term candidates resulting from semi-automatic extraction using TermoStat software. We will then describe the general architecture of the lexicographic resource ZooTerm which is currently under development, and explain, with the help of a terminographical data model and concrete examples, why we chose to use lexical functions to systematically encode the relations between terms associated with the marine fauna domain. Finally, we will discuss the results and the main theoretical issues raised from this exploratory work. Keywords: lexicon, corpus, ocean literacy, marine fauna, lexical relations

Semi-automated approaches for interrogating spatial heterogeneity of tissue samples

Tissues are spatially orchestrated ecosystems composed of heterogeneous cell populations and non-cellular elements. Tissue components’ interactions shape the biological processes that govern homeostasis and disease, thus comprehensive insights into tissues’ composition are crucial for understanding their biology. Recently, advancements in the spatial biology field enabled the in-depth analyses of tissue architecture at single-cell resolution, while preserving the structural context. The increasing number of biomarkers analyzed, together with whole tissue imaging, generate datasets approaching several hundreds of gigabytes in size, which are rich sources of valuable knowledge but require investments in infrastructure and resources for extracting quantitative information. The analysis of multiplex whole-tissue images requires extensive training and experience in data analysis. Here, we showcase how a set of open-source tools can allow semi-automated image data extraction to study the spatial composition of tissues with a focus on tumor microenvironment (TME). With the use of Lunaphore COMET platform, we interrogated lung cancer specimens where we examined the expression of 20 biomarkers. Subsequently, the tissue composition was interrogated using an in-house optimized nuclei detection algorithm followed by a newly developed image artifact exclusion approach. Thereafter, the data was processed using several publicly available tools, highlighting the compatibility of COMET-derived data with currently available image analysis frameworks. In summary, we showcased an innovative semi-automated workflow that highlights the ease of adoption of multiplex imaging to explore TME composition at single-cell resolution using a simple slide in, data out approach. Our workflow is easily transferrable to various cohorts of specimens to provide a toolset for spatial cellular dissection of the tissue composition.

Semi-automated filter micro-solid phase extraction based on urea-formaldehyde microsphere followed by corona discharge-ion mobility spectrometry for simultaneous detection of fentanyl and nor-fentanyl in urine

In this work, semi-automated filter micro-solid phase extraction (SA-FμSPE) packed by urea–formaldehyde (UF) microspheres coupled with corona discharge ionization-ion mobility spectrometry (CD-IMS) was developed for the extraction and determination of fentanyl and nor-fentanyl in urine samples. UF microspheres, synthesized by aqueous polymerization, can be used to achieve effective extraction of analytes through multiple adsorption interactions such as hydrophobic, dipole–dipole and hydrogen bonding interactions. UF microspheres dispersed in methanol were packed in the filter by syringe, and the extraction process was performed by a portable automatic filter extraction device. The parameters affecting extraction efficiency (i.e., extraction time, the flow rate of extraction, pH value, amount of sorbent, ionic strength, cycle times of desorption, and type of eluent) were investigated. Then, the eluate containing fentanyl and nor-fentanyl was simultaneously analyzed by CD-IMS in positive mode. Under the optimal extraction conditions, the detection limits for the target compounds were 5.00 ng mL−1 and 8.00 ng mL−1, respectively. The spiked recoveries were in the range of 81.97 % −112.37 % by testing the real samples and validated by UHPLC-MS. Thus, the satisfactory results reveal that SA-FμSPE coupled with CD-IMS has great potential for on-site extraction and detection of fentanyl and nor-fentanyl in urine samples.

Selection of an Algorithm for Assessing the Verticality of Complex Slender Objects Using Semi-Automatic Point Cloud Analysis

Remote technologies, including laser scanning, are frequently employed to acquire data describing the geometric condition of engineering objects. The automation of point cloud processing becomes essential for promptly and reliably monitoring changes in their current shape. The article introduces a methodology for generating point clouds, focusing on detecting the shape of the object’s cross profiles and subsequently determining its inclination through simulations and real data recorded using terrestrial laser scanning technology. The simulations enabled the identification of variations in the characteristics of changes in the course of the axis of a slender structure, depending on the adopted calculation method. Point clouds derived from measurements of complex engineering objects facilitated the validation of the assumptions of the proposed methodology. The suggested solution enables the semi-automatic extraction of data from point clouds and the assessment of the geometric state of engineering object axes based on multi-temporal point clouds.

A Semi-Automated Molecularly Imprinted Polymer Solid-Phase Extraction Method for Catechin Analysis from Jaboticaba (Plinia sp.) Peel Extract Samples by UHPLC-DAD

Jaboticaba (Plinia sp.), a nutritionally significant Brazilian fruit, is rich in phenolic compounds, such as catechin, known for its biological and pharmaceutical properties. Extracting and studying these compounds is an economic and environmental strategy to fully explore the great potential that these chemical compounds have. Therefore, this study focuses on developing a semi-automated solid-phase extraction method using molecularly imprinted polymer for catechin analysis in jaboticaba peel extracts, coupled with an ultra-high-performance liquid chromatography with a diode array detector. The reported polymer demonstrated superior extractor capacity compared to the non-imprinted polymer. Different extraction parameters were optimized, and the method exhibited a linear range of 10 to 100 µg mL-1 for catechin. The obtained precision with the coefficient of variation was below 7.3%, and the limits of quantification and detection were 12.4 and 4.1 µg mL-1 , respectively. The developed sorbent maintained analytical performance through approximately 40 injections. Results suggest that the reported method could efficiently extract catechin from jaboticaba peels in less than 10 min, providing a promising tool for the rapid investigation of natural products. This selective and sustainable approach demonstrated here contributes to the economic and environmental aspects of catechin extraction and analysis in jaboticaba fruit.

Automated Protocol for Monitoring Droplets and Fomites on Surfaces

During the COVID-19 pandemic, extensive efforts focused on developing a better understanding of indirect transmission routes, environmental monitoring of fomites, and suitable surveillance strategies, providing new perspectives to also face other communicable diseases. Rapid methods for monitoring environmental contamination are strongly needed to support risk assessment, epidemiological surveillance and prevent infections from spreading. We optimized and automatized a protocol based on fomite detection by qPCR, using a microbial-signature approach based on marker genes belonging to the microbiota of droplets or different biological fluids. The procedure was implemented by exploiting the available tools developed for SARS-CoV-2 tracing, such as flocked swab sampling, real-time PCR equipment and automatic extraction of nucleic acids. This approach allowed scaling up, simplifying, and speeding up the extraction step of environmental swabs, processing at least 48 samples within 45 min vs. 90 min for about 24 samples by manual protocols. A comparison of microflora data by Next-Generation Sequencing (NGS) strongly supports the effectiveness of this semiautomated extraction procedure, providing good quality DNA with comparable representation of species as shown by biodiversity indexes. Today, equipment for qPCR is widely available and relatively inexpensive; therefore this approach may represent a promising tool for hospital hygiene in surveilling fomites associated with SARS-CoV-2 or other pathogen’s transmission.

Comparative Study of Two Semi-automated Forensic DNA Extraction Methods

Automation in forensic DNA analysis is crucial for analysts to reduce time, improve results, and decrease risk of contamination. With the variety of commercially available automated DNA extraction systems, comes the need for end-users to be informed of what they provide and what they might lack. Thus, this study aimed to evaluate the efficiency of two semi-automated DNA extraction systems used for forensic DNA analysis: Automate Express™ and Hamilton Microlab STAR™ system, for four parameters; reproducibility, stability, sensitivity and contamination. Overall, the results indicated that both semi-automated systems performed similarly in providing robust and reproducible DNA results while maintaining good capability to overcome PCR inhibition with low risk of contamination. The two semi-automated systems showed higher DNA recovery than organic extraction using phenol-chloroform by 22% for semen and 7% for blood samples. In addition, three sample types, blood, saliva, semen were tested to compare the two systems (total samples n=100). Overall, the data showed the average DNA recovery for Hamilton was higher than the DNA recovery by Automate Express™ for the blood and semen sample types indicating better performance of the Hamilton Microlab STAR™ in terms of recovery and sensitivity level.