Quality Control Tool Research Articles

Delta-S-Cys-Albumin as a Marker of Pediatric Biospecimen Integrity.

Blood plasma storage is a crucial element of pediatric biobanking. Improperly stored or handled specimens (e.g., at > -30°C) can result in altered biomolecular compositions that no longer reflects invivo reality. We report application of a previously developed assay in adults-the ΔS-Cys-Albumin assay, which facilitates estimation of plasma and serum exposure to thawed conditions-to a population of pediatric EDTA plasma samples from patients aged 3-18 years to determine the assay's applicability, estimate its reference range for pediatric samples, and assess the impact of pre-centrifugation delay at 0°C. In addition, the effect of plasma thawed-state exposure to a range of times at 23°C, 4°C, and -20°C on ΔS-Cys-Albumin was evaluated. Using 98 precollected and processed pediatric EDTA plasma specimens, no difference was found in ΔS-Cys-Albumin under conditions of pre-centrifugation delay for up to 10 hours at 0°C. This lack of change allowed us to estimate a pediatric reference range for ΔS-Cys-Albumin of 7.0%-22.5% (mean of 12.8%) with a modest Pearson correlation between ΔS-Cys-Albumin and age (p = 0.0037, R2 = 0.29). ΔS-Cys-Albumin stability in six specimens at 23°C, 4°C, and -20°C was also evaluated. Plateaus in the decay curves were reached by 1 day, 7 days, and 14-28 days at these respective temperatures. The estimated pediatric reference range observed in children was lower than that previously observed in 180 adults of 12.3%-30.6% (mean of 20.0%), and the slope of the age correlation in children was twice as steep as that from adults. ΔS-Cys-Albumin decay curves at 23°C, 4°C, and -20°C were similar to those previously observed in adults. The data reported here support the use of ΔS-Cys-Albumin in evaluating the integrity and overall exposure of pediatric EDTA plasma specimens to thawed conditions. In doing so, they add an important quality control tool to the biobanker's arsenal.

ProSeq4: A user-friendly multiplatform program for preparation and analysis of large-scale DNA polymorphism datasets.

Preparation of DNA polymorphism datasets for analysis is an important step in evolutionary genetic and molecular ecology studies. Ever-growing dataset sizes make this step time consuming, but few convenient software tools are available to facilitate processing of large-scale datasets including thousands of sequence alignments. Here I report "processor of sequences v4" (proSeq4)-a user-friendly multiplatform software for preparation and evolutionary genetic analyses of genome- or transcriptome-scale sequence polymorphism datasets. The program has an easy-to-use graphic user interface and is designed to process and analyse many thousands of datasets. It supports over two dozen file formats, includes a flexible sequence editor and various tools for data visualization, quality control and most commonly used evolutionary genetic analyses, such as NJ-phylogeny reconstruction, DNA polymorphism analyses and coalescent simulations. Command line tools (e.g. vcf2fasta) are also provided for easier integration into bioinformatic pipelines. Apart of molecular ecology and evolution research, proSeq4 may be useful for teaching, e.g. for visual illustration of different shapes of phylogenies generated with coalescent simulations in different scenarios. ProSeq4 source code and binaries for Windows, MacOS and Ubuntu are available from https://sourceforge.net/projects/proseq/.

Intact cell mass spectrometry coupled with machine learning reveals minute changes induced by single gene silencing

Intact (whole) cell MALDI TOF mass spectrometry is a commonly used tool in clinical microbiology for several decades. Recently it was introduced to analysis of eukaryotic cells, including cancer and stem cells. Besides targeted metabolomic and proteomic applications, the intact cell MALDI TOF mass spectrometry provides a sufficient sensitivity and specificity to discriminate cell types, isogenous cell lines or even the metabolic states. This makes the intact cell MALDI TOF mass spectrometry a promising tool for quality control in advanced cell cultures with a potential to reveal batch-to-batch variation, aberrant clones, or unwanted shifts in cell phenotype. However, cellular alterations induced by change in expression of a single gene has not been addressed by intact cell mass spectrometry yet. In this work we used a well-characterized human ovarian cancer cell line SKOV3 with silenced expression of a tumor suppressor candidate 3 gene (TUSC3). TUSC3 is involved in co-translational N-glycosylation of proteins with well-known global impact on cell phenotype. Altogether, this experimental design represents a highly suitable model for optimization of intact cell mass spectrometry and analysis of spectral data. Here we investigated five machine learning algorithms (k-nearest neighbors, decision tree, random forest, partial least squares discrimination, and artificial neural network) and optimized their performance either in pure populations or in two-component mixtures composed of cells with normal or silenced expression of TUSC3. All five algorithms reached accuracy over 90 % and were able to reveal even subtle changes in mass spectra corresponding to alterations of TUSC3 expression. In summary, we demonstrate that spectral fingerprints generated by intact cell MALDI-TOF mass spectrometry coupled to a machine learning classifier can reveal minute changes induced by alteration of a single gene, and therefore contribute to the portfolio of quality control applications in routine cell and tissue cultures.

Evaluating the relationship between magnetic resonance image quality metrics and deep learning-based segmentation accuracy of brain tumors.

Magnetic resonance imaging (MRI) scans are known to suffer from a variety of acquisition artifacts as well as equipment-based variations that impact image appearance and segmentation performance. It is still unclear whether a direct relationship exists between magnetic resonance (MR) image quality metrics (IQMs) (e.g., signal-to-noise, contrast-to-noise) and segmentation accuracy. Deep learning (DL) approaches have shown significant promise for automated segmentation of brain tumors on MRI but depend on the quality of input training images. We sought to evaluate the relationship between IQMs of input training images and DL-based brain tumor segmentation accuracy toward developing more generalizable models for multi-institutional data. We trained a 3D DenseNet model on the BraTS 2020 cohorts for segmentation of tumor subregions enhancing tumor (ET), peritumoral edematous, and necrotic and non-ET on MRI; with performance quantified via a 5-fold cross-validated Dice coefficient. MRI scans were evaluated through the open-source quality control tool MRQy, to yield 13 IQMs per scan. The Pearson correlation coefficient was computed between whole tumor (WT) dice values and IQM measures in the training cohorts to identify quality measures most correlated with segmentation performance. Each selected IQM was used to group MRI scans as "better" quality (BQ) or "worse" quality (WQ), via relative thresholding. Segmentation performance was re-evaluated for the DenseNet model when (i) training on BQ MRI images with validation on WQ images, as well as (ii) training on WQ images, and validation on BQ images. Trends were further validated on independent test sets derived from the BraTS 2021 training cohorts. For this study, multimodal MRI scans from the BraTS 2020 training cohorts were used to train the segmentation model and validated on independent test sets derived from the BraTS 2021 cohort. Among the selected IQMs, models trained on BQ images based on inhomogeneity measurements (coefficient of variance, coefficient of joint variation, coefficient of variation of the foreground patch) and the models trained on WQ images based on noise measurement peak signal-to-noise ratio (SNR) yielded significantly improved tumor segmentation accuracy compared to their inverse models. Our results suggest that a significant correlation may exist between specific MR IQMs and DenseNet-based brain tumor segmentation performance. The selection of MRI scans for model training based on IQMs may yield more accurate and generalizable models in unseen validation.

How to control product quality at an industrial enterprise?

The article discusses a coordinate measuring machine as a quality control tool in an industrial enterprise. The history of creation, structure, operating principle of coordinate measuring machines, their classification by design, controlled parameters, and areas of application in various industries are described. The factors influencing the quality of control of measured products are given. The results of measurements on coordinate measuring machines are considered from the point of view of their influence on the optimization of production processes, and arguments are also given in favor of a coordinate measuring machine as one of the profit centers of the enterprise.

Influence of the Drying Process on the Volatile Profile of Different Capsicum Species.

Chili is a globally significant spice used fresh or dried for culinary, condiment, and medicinal purposes. Growing concerns about food safety have increased the demand for high-quality products and non-invasive tools for quality control like origin tracing and safety assurance. Volatile analysis offers a rapid, comprehensive, and safe method for characterizing various food products. Thus, this study aims to assess the impact of the drying process on the aromatic composition of various Capsicum species and to identify key compounds driving the aromatic complexity of each genetic makeup. To accomplish these objectives, the aroma was examined in fruits collected from 19 different pepper accessions (Capsicum sp.) belonging to four species: one ancestral (C. chacoense) and three domesticated pepper species (C. annuum, C. baccatum and C. chinense). Fresh and dried samples were analyzed using a headspace PTR-TOF-MS platform. Our findings reveal significant changes in the composition and concentration of volatile organic compounds (VOCs) from fresh to dried Capsicum. Notably, chili peppers of the species C. chinense consistently exhibited higher emission intensity and a more complex aroma compared to other species (both fresh and dried). Overall, the data clearly demonstrate that the drying process generally leads to a reduction in the intensity and complexity of the aromatic compounds emitted. Specifically, fresh peppers showed higher volatile organic compounds content compared to dried ones, except for the two sweet peppers studied, which exhibited the opposite behavior. Our analysis underscores the variability in the effect of drying on volatile compound composition among different pepper species and even among different cultivars, highlighting key compounds that could facilitate species classification in dried powder. This research serves as a preliminary guide for promoting the utilization of various pepper species and cultivars as powder, enhancing product valorization.

Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells

T cells have the ability to eliminate infected and cancer cells and play an essential role in cancer immunotherapy. T cell activation is elicited by the binding of the T cell receptor (TCR) to epitopes displayed on MHC molecules, and the TCR specificity is determined by the sequence of its α and β chains. Here, we collect and curate a dataset of 17,715 αβTCRs interacting with dozens of class I and class II epitopes. We use this curated data to develop MixTCRpred, an epitope-specific TCR-epitope interaction predictor. MixTCRpred accurately predicts TCRs recognizing several viral and cancer epitopes. MixTCRpred further provides a useful quality control tool for multiplexed single-cell TCR sequencing assays of epitope-specific T cells and pinpoints a substantial fraction of putative contaminants in public databases. Analysis of epitope-specific dual α T cells demonstrates that MixTCRpred can identify α chains mediating epitope recognition. Applying MixTCRpred to TCR repertoires from COVID-19 patients reveals enrichment of clonotypes predicted to bind an immunodominant SARS-CoV-2 epitope. Overall, MixTCRpred provides a robust tool to predict TCRs interacting with specific epitopes and interpret TCR-sequencing data from both bulk and epitope-specific T cells.

Potentiometric Surfactant Sensor with a Pt-Doped Acid-Activated Multi-Walled Carbon Nanotube-Based Ionophore Nanocomposite.

Two new surfactant sensors were developed by synthesizing Pt-doped acid-activated multi-walled carbon nanotubes (Pt@MWCNTs). Two different ionophores using Pt@MWCNTs, a new plasticizer, and (a) cationic surfactant 1,3-dihexadecyl-1H-benzo[d]imidazol-3-ium-DHBI (Pt@MWCNT-DHBI ionophore) and (b) anionic surfactant dodecylbenzenesulfonate-DBS (Pt@MWCNT-DBS ionophore) composites were successfully synthesized and characterized. Both surfactant sensors showed a response to anionic surfactants (dodecylsulfate (SDS) and DBS) and cationic surfactants (cetylpyridinium chloride (CPC) and hexadecyltrimethylammonium bromide (CTAB)). The Pt@MWCNT-DBS sensor showed lower sensitivity than expected with the sub-Nernstian response of ≈23 mV/decade of activity for CPC and CTAB and ≈33 mV/decade of activity for SDS and DBS. The Pt@MWCNT-DHBI surfactant sensor had superior response properties, including a Nernstian response to SDS (59.1 mV/decade) and a near-Nernstian response to DBS (57.5 mV/decade), with linear response regions for both anionic surfactants down to ≈2 × 10-6 M. The Pt@MWCNT-DHBI was also useful in critical micellar concentration (CMC) detection. Common anions showed very low interferences with the sensor. The sensor was successfully employed for the potentiometric titration of a technical grade cationic surfactant with good recoveries. The content of cationic surfactants was measured in six samples of complex commercial detergents. The Pt@MWCNT-DHBI surfactant sensor showed good agreement with the ISE surfactant sensor and classical two-phase titration and could be used as an analytical tool in quality control.

Screening and identification of fetal alcohol spectrum disorder in criminal legal settings: A realist review.

Screening for fetal alcohol spectrum disorder (FASD) has been identified as a promising approach to improve recognition, understanding and effective response to the unique needs of those with FASD in criminal legal settings. However, to date, there has been limited synthesis of relevant screening tools, indicators, or implementation considerations in this context. The present review aimed to synthesise evidence and develop a conceptual framework for understanding how, when, why, for whom and by whom FASD screening tools, items and/or indicators and characteristics serve to accurately identify people with FASD in criminal legal contexts, with consideration of individual and system needs relevant to effective implementation and response. A preregistered search was conducted using a modified realist review framework for both peer-reviewed articles and grey literature. Included sources were available in English, which focused on individuals with prenatal alcohol exposure and/or FASD with criminal legal involvement and offered new empirical evidence. Sources were reviewed using the Quality Control Tool for Screening Titles and Abstracts by Second Reviewer framework, extracted using a structured coding form and narratively synthesised. The search yielded 52 sources, 11 FASD screening tools designed for or applied in criminal legal settings and 38 potential FASD indicators or characteristics relevant to identifying people who may have FASD in criminal legal settings, organised into six conceptually related domains. There was limited evidence supporting the psychometric properties of screening tools across populations or settings, though growing evidence highlights the promise of some instruments. Although few studies characterised potential considerations to be made when implementing a screening tool or approach, both system and individual level needs related to recognising and effectively responding to FASD in criminal legal contexts were identified, and findings revealed strong support among legal and clinical professionals regarding the need for FASD screening in these settings. Findings of this review can be used to inform the development, selection, implementation and evaluation of FASD screening tools in criminal legal settings and underscore a continued need for enhanced resources, policy and cross-sectoral response to better support the needs of people with FASD in the criminal legal contexts.

TANDEM MASS SPECTROMETRIC METHOD FOR THE TRACE LEVEL DETERMINATION OF 2-AMINOPYRIDINE: A POTENTIAL GENOTOXIC IMPURITY IN TENOXICAM API

Objective: This study aimed to develop a highly sensitive method for the determination of the genotoxic impurity 2-amino pyridine in Tenoxicam, employing hyphenated techniques. Methods: The determination of 2-amino pyridine was carried out using a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method in Selected Ion Monitoring mode (SIM). A LiChrospher RP-18 (100×4.6 mm) 5.0 µm column was utilized for the separation. A gradient elution technique was employed with acetonitrile (mobile phase A) and 0.01M ammonium acetate buffer (mobile phase B) in varying ratios. The gradient program (T/%B) was set as 0/5, 2.50/15, 5.00/30, 10.00/50, 15.00/95, 20.00/95. The developed method was validated according to the International Conference on Harmonization guidelines. Results: The limits of detection (LOD) and quantification (LOQ) for 2-amino pyridine were found to be 0.09 ppm and 0.3 ppm, respectively. The method demonstrated accuracy within the range of 89.1% to 106.6% for the analyte. The method's linearity was confirmed through a six-point calibration graph spanning 6 ppm to 75 ppm, corresponding to a concentration of 20 mg/ml of Tenoxicam. Conclusion: Developed hyphenated LC-MS/MS method presented in this study offers a highly sensitive and accurate means for the determination of the genotoxic impurity 2-amino pyridine in Tenoxicam. With validated LOD and LOQ values, as well as demonstrated accuracy, this method proves to be a robust quality control tool suitable for the quantitation of 2-amino pyridine at very low concentrations in the pharmaceutical compound Tenoxicam.

Phytochemical Investigation of Alysicarpus vaginalis by Gas Chromatography-mass Spectrometry and High-performance Thin-layer Chromatography

Introduction: Alysicarpus vaginalis belongs to the Fabaceae family, commonly known as buffalo clover and one-leaf clover. Alysicarpus vaginalis is medicinally used in disease-related kidney, diuretics, leprosy, pulmonary problems, jaundice, skin problems, respiratory difficulties, and as a hepatoprotective. Alysicarpus vaginalis is a medicinally important plant according to our review of literature on pharmacological activities, modern pharmacological activities, ethnomedicinal surveys, and traditional systems. However, very limited research related to phytochemical investigation by chromatographic techniques was studied. Methods: So, to investigate the phytochemicals of Alysicarpus vaginalis methanol extract by High-performance thin-layer chromatography and gas chromatography-mass spectrometry. Result: Phytochemical screening revealed the presence of steroids, flavonoids, polyphenols, terpenoids, carbohydrates, alkaloids, and tannins. GC-MS revealed, the presence of six out of nine phytochemicals found medicinally important. 4-O-Methylmannose, Undecane, Neophytadiene, 2-amino-5-[(2-carboxy) vinyl]- Imidazole, Lup-20(29)-en-3-one and stigmasterol are some of the important phytochemical useful as anticancer or as anti-inflammatory. A solvent system was developed by thin-layer chromatography, followed by the HPTLC analysis using the mobile phase toluene: methanol: ethyl acetate (7:3:1). High-performance thin-layer chromatography fingerprinting detected total of 8 peaks with Rf value of 0.007, 0.089, 0.142, 0.211, 0.468, 0.585, 0.772, and 0885 at 254nm and total of 9 peaks with Rf value of 0.007, 0.089, 0.140, 0.208, 0.468, 0.661, 0.772, 0887, and 0.940 at 366nm, respectively. Conclusion: Chemoprofiling by sophisticated spectral techniques such as GC-MS and HPTLC fingerprinting can serve as an important tool for quality control of assuring purity, safety, quality, and efficacy of herbal formulation, and herbal extracts from Alysicarpus vaginalis.

DEVELOPMENT AND SUBSTANTIATION OF PROMISING MODELS OF MEDICAL CARE FOR DISEASES OF CIRCULATORY SYSTEM IN INDUSTRIAL REGION. 2010–2023 RESULTS

HighlightsWe have identified regional peculiarities of traditional and non-traditional cardiovascular risk factors.We have defined and characterized clinical and organizational characteristics of the specialized care, prevention, and treatment of diseases of the circulatory system in the Kemerovo region - Kuzbass.Moreover, we have described the principles of innovative organizational models for improving care for patients with diseases of the circulatory system, focused on the needs of outpatient organizations. AbstractThe review presents the main research findings obtained at the Department of Optimization of Medical Care for Cardiovascular Diseases of the Research Institute for Complex Issues of Cardiovascular Diseases over the past 13 years of work. The uniqueness of the obtained data lies in the fact that we have identified regional peculiarities of cardiovascular risk factors in residents of an industrial region (using the Kemerovo region data). Data on monitoring of health and risk factors served as the basis for the development of innovative organizational models for improving care for patients with diseases of the circulatory system from the stage of primary care to tertiary care using a systematic and integrated approach, and situational analysis. Management decisions within models are based on international quality standards that reaffirm that risk-based management, the role of personnel and the work environment are the integral to the improvement of performance and the achievement of the main goals of medical organization. Using the project approach, we have implemented management tools for internal quality control and safety of patients and personnel.

Evaluation of a Strain Sweep Tests Protocol as a Tool for Assessing the Homogeneity of SBR Copolymer Composition

Global competition in products and services demands constant improvement in production systems. Similarly, it is essential for industries to have quality control tools capable of assessing the compliance of a given product at a lower cost and with greater effectiveness. In the rubber and composite industry, where complex formulation and mixing systems can affect not only the quality of the final product but also the processing of rubber, the development of a protocol that assumes this function is important. The present study developed samples of elastomeric blends based on styrene-butadiene copolymer (SBR) with different filler concentrations (30, 60, and 90 phr of ground tire rubber as filler), which were analyzed using an oscillating cavity rheometer (MDpt-TechPro). Based on a proprietary testing protocol, the degree of homogeneity of the composites was evaluated, which was compared with scanning electron microscopy studies. The implemented protocol, which provides results within 30 minutes, proved to be promising for quality control of these blends, and it can be used by rubber processing industries, saving both time and cost, being an unprecedented study on the 'homogeneity-rheology' relationship.

Integrating cine EPID, dynamic delivery, and the off-axis Winston-Lutz test to enhance quality control in multiple brain metastasis stereotactic radiotherapy

PurposeStereotactic radiotherapy (SRT) has transformed cancer treatment, especially for brain metastases. Ensuring accurate SRT delivery is crucial, with the Winston-Lutz test being an important quality control tool. Off-axis Winston-Lutz (OAWL) tests are designed for accuracy assessment, but most are limited to fixed angles and hampered by local-field shifts caused by suboptimal Multi-Leaf Collimator (MLC) positioning. This study introduces a new OAWL approach for quality control in multi-brain-metastasis SRT. Utilizing cine Electronic Portal Imaging Device (EPID) images, it can be used with dynamic conformal arc (DCA) therapy. However, dynamic OAWL (DOAWL) is prone to more local-field shifts due to dynamic MLC movements. A two-step DOAWL is proposed: step 1 calculates local-field shifts using dynamic MLC movements in the beam-eye view data from the Treatment Planning System (TPS), while step 2 processes cine EPID images with an OAWL algorithm to isolate true deviations. MethodsValidation involved an anthropomorphic head phantom with metallic ball-bearings, Varian TrueBeam STx accelerator delivering six coplanar/non-coplanar DCA beams, cine EPID, and ImageJ's OAWL analysis algorithm. ResultsInherent local-field shifts ranged from 0.11 to 0.49 mm; corrected mean/max EPID-measured displacement was 0.34/1.03 mm. Few points exceeded 0.75/1.0-mm thresholds. ConclusionsThis two-step DOAWL test merges cine-EPID acquisitions, DCA, OAWL, and advanced analysis and offers effective quality control for multi-brain-metastasis SRT. Its routine implementation may also improve physicist knowledge of the treatment precision of their machines.

Neural Network Based Determination of the Degree of Fiber Mixing in Hybrid Yarns and Composites

A deep understanding on the intermixing of components in hybrid yarn or composite structures is decisive in order to develop hybrid structures with desired properties. This paper presents the development of a versatile procedure for the determination of the degree of fiber mixing in yarns and composites based on microscopy images auto-segmented by a neural network. The procedure is based on the quantification of blend irregularity values and blend homogeneity. For this purpose, functions of spatial point patterns analysis have been used to investigate the blend uniformity of yarn and composite cross sectional areas. The results show that the trained neural network model for segmentation of images has an accuracy of 92 %, indicating that the method is capable of accurately assessing the location of fibers in hybrid struc-tures. The results of the spatial point patterns analysis reveals a correlation between the blend value and the properties of yarns and composites. The proposed method provides a fast and reliable way to evaluate the hybrid structures, which could be used as a tool for quality control and process optimization.

Enhancing quality control in emulsion-type sausage production: Predicting chemical composition of intact samples with near infrared spectroscopy

This research actively explores the potential of near infrared spectroscopy (NIR) for analyzing the chemical composition of emulsion-type sausages, focusing on critical factors like residual nitrite, moisture, protein, and fat content. To establish robust and generalizable models, we utilized a dataset of 100 experimentally prepared sausages encompassing a wide range of pork back fat replacement levels (5%, 15%, 30%, 45%, and 60%) and added sodium nitrite amounts (0, 80, 125, 250, and 375 ppm). An external validation set of 20 commercially sourced sausages further assessed the model’s real-world applicability. Partial least squares (PLS) regression calibration models with multiplicative scatter correction (MSC) pre-treatment demonstrated impressive accuracy for moisture (RMSECV = 0.57%, RPD = 9.8), fat (RMSECV = 1.17%, RPD = 9.5), and protein (RMSECV = 0.30%, RPD = 7.6). While residual nitrite prediction presented challenges due to its inherent complexity, the external validation yielded a competitive root mean square error of prediction (RMSEP) of 12.02 ppm, surpassing the average performance reported in similar studies (RMSEP ∼15 ppm) by 3 ppm. Importantly, sample homogenization did not significantly affect parameter prediction, highlighting the robustness of the NIR-based approach. These findings suggest that NIR spectroscopy, with its non-destructive, rapid, and cost-effective nature, could provide valuable tools for quality control and monitoring in the emulsion-type sausage industry. More importantly, improved nitrite prediction could pave the way for enhanced precision and control in sausage production, ultimately contributing to improved food safety and sustainability.

Comprehensive analysis of vitamin D3 impurities in oily drug products using supercritical fluid chromatography-mass spectrometry.

Vitamin D3, an essential micronutrient, often requires supplementation via medicines or food supplements, which necessitate quality control (QC). This study presents the development of a method for detecting and quantifying seven impurities of vitamin D3 in oily drug products using supercritical fluid chromatography-mass spectrometry (SFC-MS). Targeted impurities include two esters of vitamin D3 and five non-esters including four that are isobaric to vitamin D3. Firstly, a screening study highlighted the Torus 1-AA column and acetonitrile modifier as adequate for the separation, followed by optimization of the SFC conditions. Secondly, make-up solvent composition and MS settings were optimized to reach high sensitivity. For both the separation and MS response, the screening design of experiments proved useful. Lastly, a fast saponification and liquid-liquid extraction method was developed, enabling efficient sample cleanup and impurities recovery from the complex oily matrix. The SFC-MS method suitability was assessed in two validation studies. The first study employed the ICH Q2 guideline for impurity limit test to demonstrate method specificity and establish a limit of detection (LOD) and a limit of quantification (LOQ) at 0.2% and 0.5%, respectively, for ester impurities. The second study conducted a comprehensive quantitative assessment for three non-ester impurities using a total error approach, determining method validity through accuracy profiles. The validated method exhibited reliable performance across impurity concentrations from 0.1% to 2.0%, with estimated LODs ranging from 2 to 7ng/mL. This study further promotes SFC-MS as a valuable, versatile, and green tool for routine pharmaceutical QC.

Disposables used cumulatively in routine IVF procedures could display toxicity.

Is there a cumulative toxicity of disposables used in IVF procedures? A toxicity may be detected when consumables are used cumulatively, while no toxicity is detected when the same consumables are used and tested individually. Many components of items used in IVF laboratories may impair human embryonic development. Consequently, it is necessary to screen all reagents and materials which could be in contact with gametes and embryos. Toxicity tests, such as the mouse embryo assay and the human sperm motility assay (HSMA), are used by manufacturers as quality control tools to demonstrate the safety of their products. This evaluation is currently individually performed for each single consumable. However, during an IVF cycle, several devices are used sequentially, potentially creating a cumulative exposure to chemical contaminants, which could not be detected for individually tested consumables. The objective of this observational study conducted from March 2021 to October 2022 was to evaluate with the HSMA methodology if there was a cumulative toxicity when several disposables are sequentially used. Fourteen categories of consumables currently used in routine IVF procedures were studied, which included devices used for sperm and oocyte collection (cups, condoms, and oocyte aspiration needles), manipulation (flasks, tubes, tips, pipettes, embryo transfer catheters, syringes, and gloves), culture (dishes), and storage (straws). After obtaining patient consent, the surplus semen assessed as having normal parameters according to the World Health Organization 2010 criteria were used to perform the HSMAs. First, each consumable was tested individually. Then, associations of three, four, and five consumables, previously validated as non-toxic when tested individually, were analyzed. HSMAs were conducted three times to ensure reproducibility, with a defined toxicity threshold of a sperm motility index (SMI) below 0.85 in at least two of three tests. Thirty-six references of disposables were first individually tested across 53 lots. Forty-nine (92%) demonstrated compliance. However, four (8%) devices revealed toxicity: one lot of 1 ml syringes, two lots of sperm cups, and one lot of 25 cm2 flasks. These four references were excluded from the IVF routine procedures. A total of 48 combinations of consumables were assessed, involving 41 lots from 32 references that were previously individually tested. Among the evaluated combinations, 17 out of 48 (35%) associations exhibited toxicity with a SMI below 0.85 for two of the three tests (n = 8) or all the three tests (n = 9). Notably, three out of 17 (18%) of the three-consumable associations, five out of 16 (31%) of the four-consumable associations, and nine out of 15 (60%) of the five-consumable associations were found not compliant. The toxicity did not originate from a single consumable, because only consumables that were individually pre-validated as non-toxic were included in the combinations, but the toxicity had a cumulative origin. The risk of cumulative toxicity increased with the number of consumables included in the association (Cochran-Mantel-Haenszel statistic, P = 0.013). The high proportion of non-compliant combinations of disposables can be attributed directly to the extreme rigorous extraction conditions employed during the tests, which could deviate from the conditions encountered in routine clinical use. Also, the methodology employed in the HSMAs (e.g. toxicity extraction duration, sperm concentrations, and protein supplementation of the medium) can influence the sensitivity of the tests. This study highlights the significance of performing toxicity testing on devices before introducing them into clinical practice. Disposables should be tested individually to detect immediate toxicities and also in combination. Our results advocate rationalizing the number of consumables used in each IVF procedure and re-evaluating the use of glass consumables. This study received fundings from GCS Ramsay Santé pour l'Enseignement et la Recherche (Paris, France) and the Centre de Biologie Médicale BIOGROUP(Le Chesnay-Rocquencourt, France). The authors declare that they have no conflict of interest that could be perceived as prejudicing the impartiality of the reported research. N/A.

UV-Visible Spectroscopy: A Review on its Pharmaceutical and Bio-allied Sciences Applications

Abstract: Ultraviolet-Visible (UV-Vis) spectroscopy has emerged as a powerful analytical tool with diverse applications in pharmaceutical and bio-allied sciences. This article provides a comprehensive overview of the extensive utility of UV-Vis spectroscopy, emphasizing its pivotal role in characterizing and analyzing various compounds critical for drug development and bio-allied research. In the pharmaceutical sector, UV-Vis spectroscopy is a fundamental technique for quantifying the concentrations of active pharmaceutical ingredients (APIs) in formulations. Its non-destructive nature and high sensitivity make it an indispensable tool for quality control, ensuring the consistency and potency of pharmaceutical products. Furthermore, this technique has been employed in the study of drug-receptor interactions to elucidate the molecular mechanisms underlying therapeutic effects. In bio-allied applications, UV-Vis spectroscopy is used to analyze biomolecules like proteins, nucleic acids, and enzymes. This technique allows for the study of protein conformational changes, DNA structure, and enzymatic activity, offering crucial insights into fundamental biological processes. Additionally, UV-Vis spectroscopy aids in determining biomarker concentrations, assisting in the early diagnosis and monitoring of various diseases. This article also explores recent advancements in UV-Vis spectroscopy, including the integration of nanomaterials and computational approaches to enhance sensitivity and selectivity. Moreover, it discusses the potential of UV-Vis spectroscopy in emerging areas such as personalized medicine and point- of-care diagnostics. As technology continues to evolve, UV-Visible spectroscopy is poised to significantly contribute to the ever-expanding landscape of pharmaceutical and bio-related research.

Variational inference-based transfer learning for profile monitoring with incomplete data

Profile monitoring is a widely used tool in quality control. The rapid development of sensor technology has created unprecedented opportunities for multi-channel profile data collection, which motivates the modeling and transfer learning for multi-profile data. The Multi-output Gaussian Process (MGP) is often used for multi-profile data, due to its flexible modeling capability and elegant mathematical properties. However, two practical concerns limit the broader application of MGP for transfer learning and monitoring of multi-profile data: high computational complexity and data incompleteness. In this article, we propose a Variational Inference (VI)-based MGP framework to facilitate transfer learning and profile monitoring using incomplete profile data. The proposed framework features a specially designed convolutional structure for constructing an explicit covariance relationship between the inducing variables in VI and the MGP in multi-profile data. This structure inspires a comprehensive solution to both computational complexity and data incompleteness in modeling multi-profile data, which facilities the transfer learning for profile monitoring. Various numerical studies and one real case study are conducted to demonstrate and compare the transfer learning and monitoring performance of the proposed method. The results show the method can achieve superior monitoring performance while maintain a very low level of computational complexity.