Quality Control Tool Research Articles

Dual-mode detection for the total antioxidant capability of skincare products based on porous CuS@CdS@Au nanoshells.

The antioxidants in skincare products play a crucial role in delaying the aging process of the skin. With the growing variety of cosmetic products, it is essential to develop effective methods for measuring their total antioxidant capability (TAC). This study introduces a novel nanoenyzme, CuS@CdS@Au nanoshells (NSs), characterized by porous morphologies and composite materials, which demonstrate remarkable localized surface plasmon resonance (LSPR) effects, thereby enhancing their photocatalytic and photothermal properties. Under 808 nm laser irradiation, these nano-enzymes exhibited superior catalytic ability for TMB oxidation and temperature increases compared to CuS or CuS @Au NSs. The TMB absorption response and temperature increase showed high sensitivity to antioxidants such as ascorbic acid, glutathione, and ferulic acid, enabling the development of a dual-mode detection strategy for quantifying the TAC in skincare products without the need for complex pretreatments. Furthermore, the temperature response-based detection results proved to be more accurate than those derived from absorption response in recovery experiments. This research not only improves the reliability of antioxidant assessments but also provides a valuable tool for quality control in the skincare industry.

Quality Control of an Isogenic H/N/KRAS-Less Mouse Embryonic Fibroblast Cell Line Panel.

Isogenic H/N/KRAS-less mouse embryonic fibroblast (MEF) cell lines have been developed to assist in cell-based assays of RAS inhibitors. The quality control assessment of a panel of these isogenic MEFs is described here, with a focus on ensuring the proper insertion of the desired mutant RAS transgene, a determination of gene copy number, and an investigation of potential off-target mutations which could lead to phenotypes which are undesired in downstream experiments. Using this suite of quality control tools, a MEF cell line can be readily validated, and researchers can be assured of the rationale for an observed phenotype.

Determination of phytochemical markers andrographolide, eugenol and zingerone in nilavembu kudineer by RP-HPLC method

In the present research, the reverse phase (RP)-high performance liquid chromatography (HPLC) method was developed and validated to determine phytochemical markers andrographolide, eugenol, and zingerone in nilaveembu kudineer (NK) a Siddha polyherbal formulation. There are no RP-HPLC methods reported for the simultaneous estimation of these marker compounds in any herbal formulation. In this study, the optimized chromatographic conditions were C18 Column (150 × 4.6 × 5 μm) mobile phase containing Acetonitrile and 0.1% Ortho Phosphoric acid 30:70, flow rate 1.0 ml/minutes, at 254 nm using a ultravoilet detector, column temperature of 40°C and run time of 20 minutes. In the linearity study, a linear relation was observed from 25 to 200 μg/ml with a correlation coefficient of 0.9990. The optimized method is successfully applied for the estimation of these phytochemical markers in NK formulation. The suggested approach is an excellent quality control tool for the simultaneous quantitative assessment and detection of phytochemical markers present in polyherbal formulations and in traditional medicines such as ayurveda, siddha, unani, and homeopathic formulations.

Development and Validation of Dissolution Test Method for Herbal Medicine Containing Ginsenosides

Background and objective: This study aimed to develop a dissolution test method for quality control of commercial 40% ethanol extract of ginseng-containing dosage forms.Methods: Through preliminary dissolution testing of the commercial dosage forms, suitable parameters for HPLC (high performance liquid chromatography) analysis were determined, including an injection volume of 50 μL and a dissolution time of 1 hour. Additionally, the stability testing of the solution enabled the selection of an appropriate dissolution medium, pH 4.0 buffer solution. Method validation was carried out using reference standard of eight ginsenosides (Rg1, Re, Rf, Rg2, Rb1, RC, Rb2, Rd), indicative components of ginseng, under the selected testing conditions. The evaluation of method validation encompassed specificity, accuracy, precision, and linearity as assessment criteria.Results: The accuracy assessment revealed that the average recovery rates within a 95% confidence interval were appropriate for all ginsenosides, ranging from 98.25% to 101.17%. The precision evaluation, conducted at an intermediate concentration of 8 μg/mL (n = 6), demonstrated relative standard deviations of 0.25% to 1.08%, which met the criterion of being below 2%. The linearity assessment showed that the coefficient of determination (R²) values for all ginsenosides exceeded 0.999, indicating excellent linearity. The quantification limits ranged from 0.32 to 1.09 μg/mL, confirming their suitability for dissolution assessment of the respective formulation. Employing the selected dissolution test method, the dissolution profiles of most ginsenosides in the commercially available ginseng-containing dosage forms reached their maximum values within 60 minutes. No significant differences were observed in results beyond 120 minutes, confirming that a dissolution time of 60 minutes is appropriate for this dosage form.Conclusion: The established method holds promise as a valuable tool for quality control of pharmaceuticals containing ginseng, including those previously unassessed, contributing to future applications in the pharmaceutical industry.

Development of a loop-mediated isothermal amplification assay for the rapid detection of Styphnolobium japonicum (L.) Schott as an adulterant of Ginkgo biloba (L.)

BackgroundSpecies adulteration is a concern in herbal products, especially when plant substitutes of lower economic value replace valuable botanicals. Styphnolobium japonicum is well known as a potential adulterant of Ginkgo biloba, which is one of the most demanded medicinal plants due to its wide use in pharmaceuticals, food supplements, and traditional medicine. Despite bearing some resemblance to ginkgo's flavonol composition, S. japonicum lacks many of G. biloba's desired therapeutic properties. To prevent adulteration practices, it is crucial to implement rigorous quality control measures, including fast and simple diagnostic tools that can be used on-field. PurposeThis study aims to develop for the first time a species-specific loop-mediated isothermal amplification (LAMP) method for the fast identification of S. japonicum in ginkgo-containing products. MethodsA set of four specific primers (SjF3, SjB3, SjFIP, and SjBIP) and loop primers (SjLF and SjLB) were designed for a LAMP based assay using the 5.8S partial sequence and the internal transcribed spacer 2 of nuclear ribosomal DNA of S. japonicum. ResultsThe successful amplification of the LAMP assay was inspected through visual detection, with the highest intensity recorded at the optimal conditions set at 68 °C for 40 min. The primers showed high specificity and were able to accurately discriminate S. japonicum from G. biloba and 49 other species of medicinal plants. Furthermore, the proposed LAMP assay proved to be fast, selective, and highly sensitive, as demonstrated by the absolute and relative limits of detection, which were reached at 0.5 pg for S. japonicum DNA and 0.01 % S. japonicum in G. biloba, respectively. ConclusionsThis novel approach allows easy identification and discrimination of S. japonicum as a potential adulterant of G. biloba, thus being a useful tool for quality control. Compared to chromatographic or PCR-based methods, the assay proved to be fast, sensitive and did not require expensive equipment, thus offering the possibly usage in field analysis.

Are you sure it’s an artifact? Artifact detection and uncertainty quantification in histological images

Modern cancer diagnostics involves extracting tissue specimens from suspicious areas and conducting histotechnical procedures to prepare a digitized glass slide, called Whole Slide Image (WSI), for further examination. These procedures frequently introduce different types of artifacts in the obtained WSI, and histological artifacts might influence Computational Pathology (CPATH) systems further down to a diagnostic pipeline if not excluded or handled. Deep Convolutional Neural Networks (DCNNs) have achieved promising results for the detection of some WSI artifacts, however, they do not incorporate uncertainty in their predictions. This paper proposes an uncertainty-aware Deep Kernel Learning (DKL) model to detect blurry areas and folded tissues, two types of artifacts that can appear in WSIs. The proposed probabilistic model combines a CNN feature extractor and a sparse Gaussian Processes (GPs) classifier, which improves the performance of current state-of-the-art artifact detection DCNNs and provides uncertainty estimates. We achieved 0.996 and 0.938 F1 scores for blur and folded tissue detection on unseen data, respectively. In extensive experiments, we validated the DKL model on unseen data from external independent cohorts with different staining and tissue types, where it outperformed DCNNs. Interestingly, the DKL model is more confident in the correct predictions and less in the wrong ones. The proposed DKL model can be integrated into the preprocessing pipeline of CPATH systems to provide reliable predictions and possibly serve as a quality control tool.

Unsupervised Machine Learning Clustering of Seismic and Infrasound Data Quality Metrics

Abstract Developing techniques for improving quality control (QC) schemes to catch seismic and infrasound data defects continues to be an area of active research. Selecting universal thresholds for the automation of data quality (DQ) checks is an efficient way to find QC issues, but these thresholds may not apply well to multiple stations with varying DQ characteristics. In addition, these thresholds may not catch subtle changes in DQ parameters that still indicate problems. Machine learning can be an alternative way of diagnosing QC issues. K-means clustering, an unsupervised machine learning clustering algorithm, has been effectively used in the past for geophysical pattern exploration. This study furthers k-means applications to DQ analysis through clustering on DQ metrics derived from day-long segments of nuclear explosion monitoring data. Our k-means implementation on broadband seismometer DQ metrics separately clustered mass recenters, calibrations lasting at least one hour, and days without either. Applying this technique to infrasound DQ metrics revealed clusters related to physical issues at the stations, such as missing back volume screws and the flooding of ported pipe inlets. These are both examples of QC issues that are difficult to diagnose or detect through the thresholding of metrics or by inspecting waveforms and spectra. Our results show that k-means clustering can be a useful QC tool in exploring DQ patterns to assist analyst review of station operation and maintenance. The learned knowledge from this exploration can then inform a thresholding workflow on how to tailor to individual stations, or the k-means model could classify data directly.

A Novel HPLC Method for Rapid Determination of HMF in Dulce de Leche: A Potential Tool for Quality Control

A validated isocratic method utilizing reverse-phase high-performance liquid chromatography (RP-HPLC) was developed for the quantification of hydroxymethylfurfural (HMF) in five distinct commercially available Dulce de Leche samples that are sold in Turkish markets. The X-Terra RP-18 column, which has dimensions of 150 × 4.60 mm i.d. and a particle size of 5 μm, was selected for the stationary phase. The separation process was conducted isocratically by utilizing a methanol-water mixture (10:90, v/v) containing 1% TCA as the mobile phase. The separation was carried out at a temperature of 25 oC and a flow rate of 0.5 mL/min. The detection was performed at a wavelength of 284 nm. The methodology exhibited favorable selectivity and linearity, as evidenced by a correlation coefficient (r) exceeding 0.999. The obtained values for the limit of detection (LOD) and limit of quantification (LOQ) were 0.0019 μg/mL and 0.0059 μg/mL, respectively. Additionally, the recovery rate was determined to be 95.45%. Furthermore, it has been reported that the levels of HMF in commercially available Dulce de Leche originating from Turkey vary between 50.55 and 90.18 mg/kg for the total HMF content.

Comparison of Platelet Counts Estimated by Peripheral Blood Smear Examination and Automated Haematology Analyzer

Introduction: Platelet count estimation is one of the common as well as important investigations in clinical practice to diagnose many diseases like dengue, malaria, etc. Different methods for platelet estimations are available. These methods are manual counting semi-automated and automated hematology analyzer counting, platelet count estimation by peripheral blood smear (PBS) method, etc. Semi-automated or automated analyzers may produce erroneous results in the presence of particles or light scatter like giant platelets, fragmented red blood cells, and platelet clumps, so alternative methods like PBS examination can be used for validation. Aim: To compare platelet count estimation performed by the automated cell counter method and the PBS examination method. Objective: Peripheral Blood smear examination acts as a good quality control tool to validate the results produced by the automated cell counter. Materials and Methods: The present study was carried out in the Department of Pathology, Akshaya Health Centre, Urban Bangalore. The study included 100 random blood samples collected into ethylenediaminetetraacetic acid (EDTA) vacutainers over 3 months. These were analyzed by both peripheral blood smear and automated cell counter for platelet estimation. The statistical analysis was done for test performances and their comparisons by using the coefficient of variation (CV), linear regression, and mean differences with SPSS software. Results: No significant difference (p = 0.06635) was observed between the manual peripheral blood smear (PBS) method (platelets average per 100x, multiplied by 15000/ µl) of platelet estimation (207.13 ±15.898 x 1000/ µl) and that of automated cell counter platelet value (206.53 ±16.278 x 1000/ µl). A significant positive correlation was observed between the results of both methods (r=0.9995, p < 0.001) when analyzed by the Pearson correlation test. Conclusion: The peripheral blood smear platelet estimation results are comparable with automated analyzer results. Hence, the PBS examination serves as a quality control tool in assessing the results of the automated cell counters.

Construction of natural population cohort on telephone follow-up management quality control system and discussion regarding critical issues by REDCap system

With completing a baseline survey of a large natural population cohort, conducting regular follow-up has become a key factor in further improving the quality of cohort construction and ensuring its sustainable development. Typical cohort follow-up methods include repeat surveys, routine monitoring, and community-oriented surveillance. However, in practical applications, there are often issues such as high costs, difficulty, and high error rates. Telephone follow-up is an important supplementary method to the methods mentioned above, as it has the characteristics of low cost, fast response, and high quality. However, the with difficult organization, quality control is challenging, response rates are low, and management levels vary widely, which limits its widespread use in large-scale population cohort studies. Given the above problems, this study draws on customer relationship management based on the actual needs of the China Northwest Cohort follow-up. It relies on the REDCap electronic data collection platform to build a telephone follow-up management and quality control system. Targeted solutions are provided for key issues in telephone follow-up implementation, including organizational structure, project management, data collection, and process quality control, to improve the quality control level of telephone follow-up comprehensively and thereby enhance the quality and efficiency of follow-up. We hope to provide standardized follow-up programs and efficient quality control tools for newly established and existing cohort studies.

PENERAPAN SQC DAN FMEA DALAM ANALISIS PRODUK CACAT DI CV. KONVEKSI 3 DARA BANDUNG

In this day and age, business competition is increasingly fierce in responding to diverse consumer demands, strategic action is needed in carrying out company operations. CV. Konveksi 3 Dara Bandung is a micro, small and medium enterprise (MSME) which operates in the field of clothing production including clothes, jackets, robes, shirts and the like. Based on the observations that have been made, the quality of the products produced still does not meet consumer satisfaction, because the products produce many product defects including rejected clothes, broken seams, loose seams and wrinkles on clothes. Therefore, quality control tools are needed to identify the causes of products that do not meet standards, and then provide solutions to correct these problems so that the quality of the products produced can reach the maximum level. The combination of methods between Statistical Quality Control (SQC) and FMEA is expected to help companies in optimizing product quality control. Based on the analysis of the Statistical Quality Control method combined with the FMEA concept, it was concluded that the defects that occurred included rejected clothes (19%), broken stitches (37%), loose stitches (24%), and wrinkles (20%).

Early detection of acrolein precursors in vegetable oils by using proton transfer reaction – mass spectrometry

Acrolein is a toxic volatile compound derived from oxidative processes, that can be formed in foods during storage and cooking. This study employs proton transfer reaction mass spectrometry (PTR-MS) to detect acrolein precursors in vegetable oils by focusing on the m/z (mass-to-charge ratio) 57. To this purpose, hempseed, sesame, walnut, olive and linseed oils were stored for 168 h at 60 °C in presence of 2,2′-azobis(2-metilpropionitrile) (3 mM) radicals initiator. The evolution of m/z 57 by PTR-MS was also compared with traditional lipid oxidation indicators such as peroxide value, conjugated diene, oxygen consumption and, isothermal calorimetry. The obtained results were explained by the fatty acid composition and antioxidant capacity of the oils. Hempseed fresh oil presented a very low total volatile organic compounds (VOCs) intensity (5.6 kncps). Nonetheless, after storage the intensity increased ∼70 times. A principal component analysis (PCA) confirmed the potential of m/z 57 to differentiate fresh versus rancid hempseed oil sample. During an autoxidation experiment oils high in linolenic and linoleic acids showed higher m/z 57 emissions and shorter induction times: linseed oil (38 h) > walnut oil (47 h) > hempseed oil (80 h). The m/z 57 emission presented a high correlation coefficient with the total VOC signal (r > 0.95), conjugated dienes and headspace oxygen consumption. A PCA analysis showed a complete separation of the fresh oils on the first component (most significant) with the exception of olive oil. Walnut, hempseed and linseed oil were placed on the extreme right nearby total VOCs and m/z 57. The results obtained highlight the potential of PTR-MS for the early detection of oil autoxidation, serving as a quality control tool for potential acrolein precursor emissions, thereby enhancing food safety in the industry.

Investigation on the roller-pavement interaction and asphalt pavement compactness based on marginal spectrum of vibration signal energy

Compaction is a critical assurance for the quality and performance of asphalt pavement in terms of durability and serviceability. At present, there is a knowledge gap in the roller-pavement interaction in terms of energy variation during the process of asphalt pavement compaction. This paper aims to reveal the interaction and energy variation between the vibrating drum and pavement surface course during compaction process. Based on field tests, vibration signals of both the vibrating drum and pavement (i.e., the top, middle and bottom surface layers) were captured by acceleration sensors, and then the Hilbert marginal spectrum of the signals was obtained by the Hilbert-Huang Transform (HHT) for interpreting the signal features. Based on the marginal spectrum, the energy variation of the vibrating system (drum and pavement) during compaction was obtained. A predictive model of pavement compactness was further established according to the observed linear relationships between the pavement compactness and the energies of the vibrating system, and was then validated in the field application. The findings show that the energy-based approach adopted herein is able to not only characterize the compactness of asphalt pavement, but also reflect the uniformity of compaction. The developed relationships have a good potential as a useful tool for quality control in the field compaction practice.

Rapid and simultaneous quantitative and discriminative analyses of liquor quality parameters with machine learning-assisted batch Raman spectroscopy: Synergistic instrumental upgrade and chemometric optimization

This study investigates the rapid and simultaneous assessments of two key liquor quality parameters – alcohol content and overall sensory quality – utilizing a batch Raman spectroscopic system and chemometrics, with a commercial baijiu (Chinese liquor) as the subject. An in-house designed motorized 12-cuvette tray facilitated stable and efficient spectral acquisition from 34 production batches of standard baijiu, supplemented by alcohol content-adjusted and overall sensory-disqualified samples, to form two separate datasets for the optimization and evaluation of chemometric approaches based on multivariate analysis and machine learning. The combination of dimension reduction with principal component analysis (PCA) and support vector regression (SVR) with a nonlinear kernel showed superior performances for predicting alcohol content and identifying sensory-disqualified samples. Expanding the alcohol content range of the training set enhanced the quantification capacity of the PCA-SVR model and yielded a relatively accurate alcohol content prediction (±0.15 % v/v) for the tested standard baijiu. The PCA-SVR model built for sensory quality grading achieved an average precision of 93% for identifying disqualified baijiu samples that compositionally resemble standard ones. Based on the synergistic instrumental and chemometric optimization, the proposed machine learning-assisted batch Raman spectroscopic system offers a rapid, reliable, and integrated quality control tool for liquor production.

Leaning out the Small Component Paint Process: Using Lean Six Sigma for Process Improvement at Tobyhanna Army Depot

TYAD repairs and refurbishes a variety of Department of Defense systems that are essential for our military effectiveness and daily operations. Employees at TYAD have noticed a tall number of overrun hours for projects in their small component paint branch. These high number of overrun hours have led to TYAD steadily losing revenue and profitability on their projects. This capstone group utilized Lean Six Sigma’s (LSS) DMAIC (Define, Measure, Analyze, Improve, and Control) process to decrease the number of overrun hours experienced and hopefully allow a way for greater throughput to go through the system. The efforts of this group led to discoveries of issues with the implementation of quality control tools. Our discoveries led to a revamped quality control protocol that will decrease the number of overrun hours experienced by TYAD and ultimately save them an estimated $478,000.00 over the course of the next three years.

Automated quality control of structural brain MRI scans from aging and dementia datasets

AbstractBackgroundDementias platform UK (DPUK) currently hosts over 40 dementia cohorts and provides a remote analysis environment for multimodal cross‐cohort data analysis. Nineteen cohorts include brain MRI data, hence automated quality control (QC) becomes crucial for the reliability of image analyses. This work aims to provide a framework for quality prediction of T1‐weighted (T1w) scans from aging and dementia datasets: we compared manual quality ratings with the output of two existing automated QC pipelines and propose a new QC classifier.MethodWe used 2438 T1w scans (including patients and controls, 1.5T and 3T scanners, 3 manufacturers, 11 sites) from 4 different aging and dementia datasets (ADNI, Whitehall II, Oxford Parkinson’s Disease Centre, Oxford Brain Health Clinic). Manual quality ratings for all images were performed by the dataset owners. Each image was processed in MRIQC (Esteban et al. 2017, PLoS ONE) and CAT12 (Computational Anatomy Toolbox, Gaser et al. 2022, bioRxiv). We compared the quality agreement for MRIQC predictions and CAT12’s weighted image quality ratings (IQR) with manual QC using inter‐rater reliability. Further, we explored the effect of changing the accept‐reject threshold from automated QC tools on the reliability measure. Finally, we designed a custom QC classifier (manual QC as target) by combining MRIQC and CAT12’s quality metrics as features and training linear support vector machines in a nested cross‐validation framework.ResultWe found overall agreement between automated QC methods and manual QC when applied to dementia datasets (MRIQC: Kappa = 0.30, p = 0.001; CAT12: Kappa = 0.28, p = 0.001). Adjusting the acceptance thresholds of the automated tools within dataset improved the agreement. On the combined data (training N = 1951; test N = 487), the proposed classifier showed 94.2% accuracy (sensitivity 94.3%, specificity 85.7%) on the test data. Leaving one site out (training with 10 sites, N = 2055), we found 86.4% accuracy (sensitivity 86.2%, specificity 100%) on the test site (N = 383).ConclusionThe performance of the proposed classifier appears promising on these heterogeneous datasets from different scanners. In the future, we aim to utilise this framework for improving generalisability of prediction and release the classifier on the DPUK data portal to robustly QC T1w scans for other aging and dementia cohorts.

Nanopore Cas9-Targeted Long-Read Sequencing - a Fast and Flexible Diagnostic Tool for the Identification of B-Cell Acute Lymphoblastic Leukemia Associated Gene Rearrangements

B-cell acute lymphoblastic leukemia (B-ALL) is characterized by a multitude of recurrent genetic alterations, which drive malignant transformation. Besides their essential role in leukemia biology these aberrations also serve as predictors of treatment response and survival, making their reliable detection a prerequisite for risk stratification in state-of-the-art clinical trials. Currently, the genetic characterization of B-ALL in most diagnostic laboratories still relies on an integrative approach combining conventional cytogenetics, reverse transcription polymerase chain reaction (RT-PCR) and fluorescence in situ hybridization (FISH) assays as well as single nucleotide polymorphism (SNP) array analysis. However, these conventional diagnostic tools fail to identify a considerable number of genetic alterations, particularly cryptic rearrangements such as IGH::DUX4, IGH::EPOR or MEF2D involving fusions. Although next generation DNA- and RNA-based sequencing (NGS) assays have significantly improved the reliable detection of fusion genes, the need for large sample numbers to achieve acceptable turnaround times and cost effectiveness impairs their application in small- to medium-sized diagnostic laboratories. To address these issues, we developed a targeted sequencing assay, based on the commercially available combination of Cas9-mediated target enrichment and Nanopore long-read sequencing, which facilitates the identification of B-ALL associated gene rearrangements on the DNA level in a single sample format. The targeted panel design included an initial review of the literature to define the breakpoint-spanning regions involved in fusion gene formation followed by the design of specific guide RNAs (gRNA) to facilitate Cas9-mediated enrichment of these regions of interest (ROIs). We first conducted ROI enrichment and sequencing with a limited number of gRNAs targeting only ETV6, KMT2A and DUX4 fusions. Then we progressively extended the pool of gRNAs with each sequencing run until it comprised 91 gRNAs covering most B-ALL-associated rearrangements. This stepwise approach allowed us to identify negative effects of increasing panel size on overall enrichment efficacy and simplified the identification and replacement of poor performing gRNAs. Our initial cohort consisted of 12 well-characterized samples harboring 13 gene rearrangements, including ETV6::RUNX1, KMT2A::AFF1, MEF2D::BCL9, ZMIZ1::ABL1, SSBP2::CSF1R, PDGFRB::EBF1, IGH::EPOR, IGH::DUX4, IGH::CRLF2, IGH::CEBPE, IGH::MYC, and IGH::BCL2. To ensure easy implementation in diagnostic workflows, we used genomic DNA, isolated according to standard protocols. Cas9-based target enrichment and long-read sequencing on MinION flow cells (v9.4.1) were performed according to protocols provided by Oxford Nanopore and each sample was sequenced for 72 hours. On average, each run yielded 3.72±1.21 Gb, a mean read length of 7.40±1.00 kb with an N50 value of 12.87±1.71 kb and an average read quality (Q) score of 14.72±0.52. For data processing and analysis, we adapted the existing nextflow nf-core bioinformatics pipeline, nanoseq, to include publicly available structural variation callers and quality control tools. Our approach correctly identified 92.3% (12/13) of the rearrangements with a mean of 42 (range 2-97) breakpoint-supporting reads (BSR) per alteration. Only the IGH::MYC translocation was missed as the breakpoint was located outside of our predefined ROIs for MYC. To corroborate these results, we sequenced an additional cohort of 14 B-ALL samples, including ZNF384::DUX4, KMT2A::MLLT10, KMT2A::USP2, PAX5::SOX5, EP300::ZNF384, P2RY8::CRLF2, and IGH::DUX4 positive cases and were able to correctly identify 100% (14/14) of the fusion genes with an average of 83 (range 7-367) BSR. In conclusion, our newly developed targeted sequencing panel correctly identified 96.3% (26/27) analyzed gene rearrangements in B-ALL, including alterations evading standard diagnostic methodologies such as IGH::DUX4 or IGH::EPOR. Importantly, the assay allows individual analysis in a single sample format, thereby representing a valuable tool for B-ALL diagnostics in small to medium-sized laboratories.

Rapid assessment of changes in phage bioactivity using dynamic light scattering.

Extensive efforts are underway to develop bacteriophages as therapies against antibiotic-resistant bacteria. However, these efforts are confounded by the instability of phage preparations and a lack of suitable tools to assess active phage concentrations over time. In this study, we use dynamic light scattering (DLS) to measure changes in phage physical state in response to environmental factors and time, finding that phages tend to decay and form aggregates and that the degree of aggregation can be used to predict phage bioactivity. We then use DLS to optimize phage storage conditions for phages from human clinical trials, predict bioactivity in 50-y-old archival stocks, and evaluate phage samples for use in a phage therapy/wound infection model. We also provide a web application (Phage-Estimator of Lytic Function) to facilitate DLS studies of phages. We conclude that DLS provides a rapid, convenient, and nondestructive tool for quality control of phage preparations in academic and commercial settings.

Benchmarking MicrobIEM – a user-friendly tool for decontamination of microbiome sequencing data

BackgroundMicrobiome analysis is becoming a standard component in many scientific studies, but also requires extensive quality control of the 16S rRNA gene sequencing data prior to analysis. In particular, when investigating low-biomass microbial environments such as human skin, contaminants distort the true microbiome sample composition and need to be removed bioinformatically. We introduce MicrobIEM, a novel tool to bioinformatically remove contaminants using negative controls.ResultsWe benchmarked MicrobIEM against five established decontamination approaches in four 16S rRNA amplicon sequencing datasets: three serially diluted mock communities (108–103 cells, 0.4–80% contamination) with even or staggered taxon compositions and a skin microbiome dataset. Results depended strongly on user-selected algorithm parameters. Overall, sample-based algorithms separated mock and contaminant sequences best in the even mock, whereas control-based algorithms performed better in the two staggered mocks, particularly in low-biomass samples (≤ 106 cells). We show that a correct decontamination benchmarking requires realistic staggered mock communities and unbiased evaluation measures such as Youden’s index. In the skin dataset, the Decontam prevalence filter and MicrobIEM’s ratio filter effectively reduced common contaminants while keeping skin-associated genera.ConclusionsMicrobIEM’s ratio filter for decontamination performs better or as good as established bioinformatic decontamination tools. In contrast to established tools, MicrobIEM additionally provides interactive plots and supports selecting appropriate filtering parameters via a user-friendly graphical user interface. Therefore, MicrobIEM is the first quality control tool for microbiome experts without coding experience.

Improving the Gradation of Aggregate used in Asphalt Mixtures Using Bailey Method

The bailey method of gradation analysis it is one of the techniques used in pavement engineering for evaluating and designing asphalt mixtures based on the particle size distribution. This paper presents a comprehensive review of research conducted on the Bailey technique, focusing on its application and impact in the field of pavement engineering. The review encompasses studies that have investigated the effectiveness of the Bailey technique as a mix design tool, a quality control tool, and a performance assessment tool. Researchers have explored its ability to optimize asphalt mixtures for desired performance characteristics, such as rutting resistance, stability, and durability. They have also examined its effectiveness in ensuring consistency and conformity to specified requirements during asphalt production. Furthermore, the review highlights the correlation between the Bailey technique's gradation data and various performance properties of asphalt mixtures. Researchers have investigated the relationship between particle size distribution and properties like rutting resistance, moisture susceptibility, drainage, and particle packing. These studies have provided valuable insights into the influence of gradation on the performance of asphalt pavements.