Quality Control Methodology Research Articles

Ensuring Data Accuracy and Compliance in Remote Healthcare Monitoring

Remote healthcare delivery and clinical trials have undergone a dramatic transformation, necessitating robust frameworks for ensuring data accuracy and regulatory compliance in virtual settings. This comprehensive article explores the intricate challenges healthcare organizations face when implementing remote monitoring systems while maintaining data integrity and adhering to stringent regulatory requirements. Through examination of current technological infrastructure, quality control methodologies, and regulatory frameworks, this article presents a structured approach to achieving reliable data collection in remote care environments. The article addresses critical aspects, including device validation protocols, HIPAA compliance in virtual settings, and standardized procedures for remote patient monitoring, while offering practical solutions for common challenges in technology adoption and data discrepancy management. This article indicates that the successful implementation of remote healthcare data systems requires a three-pronged approach: robust technical infrastructure, comprehensive staff training programs, and patient-centric education initiatives. Furthermore, this article identifies emerging trends in artificial intelligence and predictive analytics that promise to enhance data validation processes and compliance monitoring in remote healthcare settings. This article provides healthcare professionals and organizations with actionable insights for establishing and maintaining reliable remote data collection systems while ensuring regulatory compliance and optimal patient care outcomes.

Factors affecting microplastic pollution of sandboxes in urban residential areas: Simple methodology for quality control in the context of potential exposure assessment for children.

Occurrence of microplastics in the environment is well studied, but our knowledge of their distribution in specific locations, such as the sandboxes, which are integral parts of popular playgrounds for children, is limited. Pioneering research on the factors affecting the microplastic pollution of sandboxes in urban residential areas was conducted within three estates in Kielce, Poland. Sand samples (Σ27) were collected from nine sandboxes and examined for the presence of microplastics, using a simple quality control methodology proposed by the authors. Microplastics were found in each sample and their contents ranged from 60 to 5,540 items/kg of sand. Fragments and fibers were the most prevalent types of microplastics in the samples. They contributed to approximately 95% of all microplastics found. Transparent fibers were the most abundant among fibers (63%) and red particles (57%) among fragments. A strong, positive, and significant correlation (rSpearman = 0.90) was found between the number of microplastic items and the location of sandboxes (above sea level). There was no correlation between the population density of the estates (rSpearman = 0.03), the distance of the estates from the center (rSpearman = 0.02), and the distance of the estates from main roads (rSpearman = 0.43). Considering that sandboxes can be a potential source of microplastics for children, and assuming that sand ingestion by children is similar to the estimated daily soil ingestion rate, our results indicate that the number of microplastics ingested could be 1,106. Inhalation, dermal transport, and ingestion of microplastic particles from sandboxes pose a potential threat to children's health. However, more research is needed to better understand the health risks associated with this source of microplastics.

Sigma Metrics misconceptions and limitations.

This paper further explores the Sigma Metric (SM) and its application in clinical chemistry. It discusses the SM, assay stability, and control failure relationship. : SM is not a valid measure of assay stability or the likelihood of failure. When an out-of-control event occurs for an assay with a higher SM value, the same QC rule will have greater power to detect error than assays with a lower SM value. Thus, it is easier to prevent errors from happening for higher SM assays. This rationale encourages using more frequent QC events and more QC samples for a QC scheme of a low SM assay or simply more QC cost for low SM assays. A laboratory can have a high-precision instrument that frequently fails and a low-precision instrument that hardly ever fails. Parvin's patient risk model presumes the bracketed continuous mode (BCM) testing workflow. If overlooked when designing QC schemes, this leads to the common misconception of the SM that one can save the cost of QC since assays with high SM require less frequent QC to ensure patient risk. There is no evidence that an assay's precision is correlated with its failure rate. Schmidt etal., in a series of papers, showed that an assay with a higher Pf or shift in probability will have a higher expected number of unacceptable results. Incorporating Pf into the QC design process presents significant challenges despite the proactive quality control (PQC) methodology. Unfortunately, TEa Six Sigma, as widely practiced in Clinical Chemistry, is not based on classical Six Sigma mathematical statistics. Classical Six Sigma would facilitate comparing results across activities where the principles of Six Sigma are employed.

A Critical Systematic Scoping Review on the Applications of Additive Manufacturing (AM) in the Marine Industry

(1) Background: Additive manufacturing (AM), which has also become known as 3D printing, is rapidly expanding its areas of use in the marine industry. This study undertakes a historical development of AM in the marine industry. The study also criticises these developments to date and the future technological applications they will lead to, while considering the benefits for the industry and its segments. (2) Methods: This review followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and was registered in the Open Science Framework. The personalized search strategy was applied to Scopus, and Web of Science databases. The core emphasis was placed on two eligibility criteria throughout the evaluation process. Firstly, Criteria 1 sought to determine the paper’s relevance to AM. Secondly, Criteria 2 aimed to assess whether the paper delves into the implementation of AM or provides valuable insights into its utilisation within the marine industry. The risk of bias was analysed using a checklist of important parameters to be considered. (3) Results: In recent years, there has been a growing trend in studies related to the application of AM in the marine industry. While AM is widespread in industries such as automotive, aviation, and healthcare, it is relatively new for the marine industry. Almost only 5% of publications related to AM are related to the marine industry. There is a need for extensive research in many areas. It has been observed that classification societies and approval institutions, which largely drive the marine industry, have not yet taken AM into consideration sufficiently. (4) Conclusions: The studies show that AM is very promising for the marine industry. However, there are new studies at the experimental and theoretical level that need to be carried out to determine the right materials and AM methods to establish the quality control methodology and the necessary classification rules. This review also emphazises AM’s pivotal role in reshaping the marine industry, addressing the potential environmental and occupational safety effects of AM.

Isolation and Identification of Lumpy Skin Disease Virus.

Lumpy skin disease virus (LSDV), a member of the capripoxvirus genus, induces lumpy skin disease (LSD) in cattle and buffalo populations. The initial documentation of LSD dates back to 1929 in Zambia, with subsequent rapid dissemination within the cattle community in Africa and Asia. Described as the "smallpox" equivalent in cattle, LSD manifests through clinical features such as fever, extensive cutaneous nodules, body wasting, lymph node enlargement, and skin edema. Isolation of LSDV constitutes an important step in evaluating virulence and implementing disease control measures. Skin samples obtained from afflicted cattle serve as the primary source for LSDV isolation due to its tropism for epidermal cells. Other tissues, including blood, lung, spleen, and saliva samples, are also employed for viral isolation. This chapter presents protocols for the convenient isolation of LSDV, relying on the plaque formation phenotype and utilizing MDBK cells. Emphasis is placed on stringent quality control methodologies to identify isolated viruses through multiple techniques, facilitating further investigations such as the in vitro growth of isolated LSDV.

Performance Assessment and Quality Control of Fluorescence Molecular Endoscopy With a Multi-Parametric Rigid Standard.

Fluorescence molecular endoscopy (FME) is emerging as a "red-flag" technique with potential to deliver earlier, faster, and more personalized detection of disease in the gastrointestinal tract, including cancer, and to gain insights into novel drug distribution, dose finding, and response prediction. However, to date, the performance of FME systems is assessed mainly by endoscopists during a procedure, leading to arbitrary, potentially biased, and heavily subjective assessment. This approach significantly affects the repeatability of the procedures and the interpretation or comparison of the acquired data, representing a major bottleneck towards the clinical translation of the technology. Herein, we propose a robust methodology for FME performance assessment and quality control that is based on a novel multi-parametric rigid standard. This standard enables the characterization of an FME system's sensitivity through a single acquisition, performance comparison of multiple systems, and, for the first time, quality control of a system as a function of time and number of usages. We show the photostability of the standard experimentally and demonstrate how it can be used to characterize the performance of an FME system. Moreover, we showcase how the standard can be employed for quality control of a system. In this study, we find that the use of composite fluorescence standards before endoscopic procedures can ensure that an FME system meets the performance criteria and that components prone to performance degradation are replaced in time, avoiding disruption of clinical endoscopy logistics. This will help overcome a major barrier for the translation of FME into the clinics.

Present and Future Use of Exosomes in Dermatology.

Exposure to external environmental stimuli can lead to skin aging, pigmentation, hair loss, and various immune-mediated as well as connective tissue diseases. Although conventional treatments are routinely used and favoured, they fail to achieve an adequate balance between clinical and cosmetic outcomes. Exosomes are vesicles with a lipid bilayer released by several cell types. These bioactive vesicles play a crucial role in intercellular communication and in several other physiological and pathological processes. They serve as vehicles for bioactive substances including lipids, nucleic acids, and proteins, making them appealing as cell-free treatments. According to studies, exosomes play a vital role in preventing scarring, and senescence, and promoting wound healing. Moreover, research on the biology of exosomes is growing, which has enabled the creation of specific guidelines and quality control methodologies to support their potential implementation in the future. In this review, we have mainly focused on the role of exosomes in various dermatological diseases, their clinical applications, and the potential for further research pertaining to this.

Advancing the Harmonization of Biopredictive Methodologies through the Product Quality Research Institute (PQRI) Consortium: Biopredictive Dissolution of Dipyridamole Tablets.

Biorelevant dissolution and its concept have been widely accepted and further developed to meaningfully predict the bioperformance of oral drug products. Biorelevant methodologies have been applied to design and optimize oral formulations, to facilitate formulation bridging, and to predict the outcome of bioperformance by coupling the results with modeling. Yet, those methodologies have often been independently customized to align with specific aspects of the oral drug products being developed. Therefore, the evolution of biorelevant dissolution methodologies has taken slightly diverse pathways rather than being standardized like compendial quality control (QC) methodologies. This manuscript presents an effort through the Product Quality Research Institute (PQRI, https://pqri.org) consortium entitled: the standardization of "in vivo predictive dissolution methodologies and in silico bioequivalent study working group" to find the key parameters for biorelevant dissolution, to identify the best practices, and to move toward standardization of biorelevant dissolution methodologies. This working group is composed of members from 10 pharmaceutical companies and academic institutes. The consortium project will be accomplished in five phases, whereby the first two phases have already been completed and published. In this paper, the next two phases are addressed by reporting the biorelevant dissolution profiles of dipyridamole, a weak base model drug, then incorporating the dissolution results into physiologically based biopharmaceutics modeling (PBBM) to determine whether they would lead to bioequivalence (BE) or non-BE.

The cluster analysis of traditional Chinese medicine authenticity identification technique assisted by chemometrics

This study explore the authenticity identification technique of traditional Chinese medicine (TCM) using chemometrics in conjunction with cluster analysis. A clustering Gaussian mixture model was constructed and applied for the data clustering analysis of four types of TCM. Chemical measurements combined with discrete wavelet transform (DWT), Fourier transform infrared spectroscopy (FTIR), and Fourier self-deconvolution (FSD) were utilized for the detailed differentiation of Bupleurum scorzonerifolium, Bupleurum yinchowense, Bupleurum marginatum, and Bupleurum smithii Wolff var. parvifolium. Differences in the attenuated total reflection-FTIR (ATR-FTIR) spectra among the four TCMs were observed. Utilizing clustering algorithms, the one-dimensional DWT of the infrared spectra of samples was employed for the authentication of Chinese herbal medicines. The model demonstrates optimal performance throughout 2000 rounds of network training. The accuracy (88.6 %), sensitivity (86.5 %), and specificity (82.7 %) of the model constructed in this study significantly surpassed those of the CNN model: accuracy (67.7 %), sensitivity (70.4 %), and specificity (68.5 %) (P < 0.05). By setting the cluster size K = 5 and the number of Gaussian mixture model components to 5, the model effectively fits the actual number of categories within the dataset. Infrared spectroscopy analysis revealed distinct carbon-oxygen stretching vibration absorption peaks between 1025 and 1200 cm−1 for Bupleurum scorzonerifolium, Bupleurum yinchowense, Bupleurum marginatum, and Bupleurum smithii Wolff var. parvifolium, indicating strong absorption peaks of carbohydrates. A comprehensive structural information analysis revealed a similarity of above 0.982 among the four types of TCM. Combined with chemometrics and intelligent algorithm-based cluster analysis, successful and accurate authentication of TCM authenticity was achieved, providing an effective methodology for quality control in TCM.

Nugget and corona bond size measurement through active thermography and transfer learning model

Resistance spot welding (RSW) is considered a preferred technique for joining metal parts in various industries, mainly for its efficiency and cost-effectiveness. The mechanical properties of spot welds are pivotal in ensuring structural integrity and overall assembly performance. In this work, the quality attributes of resistance spot welding, such as both nugget and corona bond sizes, are assessed by analyzing the thermal behavior of the joint using a physical information neural network (PINN). Starting from the thermal signal phase gradient and amplitude gradient maps, a convolutional neural network (CNN) estimates the size of nuggets and corona bonds. The CNN architecture is based on the Inception V3 architecture, a state-of-the-art neural network that excels in image recognition tasks. This study suggests adopting a new methodology for automatic RSW quality control based on thermal signal analysis.

Fingerprint profiling for quality evaluation and the related biological activity analysis of polysaccharides from Liuweizhiji Gegen-Sangshen beverage.

Liuweizhiji Gegen-Sangshen beverage (LGS) is popular in China, which has been used for alleviating alcohol-mediated discomfort and preventing alcoholic liver disease (ALD). This beverage is consisted of six herbal components that are known as functional foods and fruits. LGS is rich in polysaccharides, however, the activity and quality evaluation of LGS-derived polysaccharides remain unexplored. The purpose of this study is thus to establish a comprehensive quality control methodology for the assessment of LGS polysaccharides (LGSP) and to further explore the anti-oxidant, anti-inflammatory as well as prebiotic effect of LGSP. LGSP was extracted, followed by analysis of molecular weight distribution, monosaccharide content and structural characterization via integrating the application of high-performance size exclusion chromatography (HPSEC), 1-phenyl-3-methyl-5-pyrazolone-HPLC (PMP-HPLC), fourier transform infrared spectroscopy (FT-IR) as well as nuclear magnetic resonance spectroscopy (NMR) techniques. The anti-oxidation activity of LGSP was determined by DPPH, ABTS, hydroxyl radical scavenging capacity and total antioxidant capacity. The anti-inflammation of LGSP were assessed on the RAW 264.7 cells. The effect of LGSP on growth of Lactobacillus, Bifidobacterium bifidum and Bifidobacterium adolescentis was evaluated. The results demonstrated that LGSP had two molecular weight distribution peaks, with the average molecular weights of (6.569 ± 0.12) × 104 Da and (4.641 ± 0.30) × 104 Da. LGSP was composed of 8 monosaccharides, with galacturonic acid, glucose rhamnose and galactose representing the highest molar ratios. Homogalacturonic acid (HG) type and rhamnosegalacturonic acid glycans I (RG-I) type and α-1,4-glucan were present in LGSP. LGSP concentration in LGS was 17.94 ± 0.28 mg/mL. Furthermore, fingerprint analysis combined with composition quantification of 10 batches of LGSP demonstrated that there was a high similarity among batches. Notably, LGSP exhibited anti-oxidant effect and inhibited expressions of pro-inflammatory factors (TNF-α and IL-6) in LPS-stimulated RAW 264.7 cells. In addition, LGSP remarkably promoted the proliferation of probiotics Lactobacillus, Bifidobacterium bifidum and Bifidobacterium adolescentis, showing good prebiotic activity. The results of present study would be of help to gain the understanding of structure-activity relationship of LGSP, provide a reference for quality evaluation of bioactive LGSP, and facilitate development of unique health and functional products in the future.

Recent Advancements in the Analyses of Natural Products.

Since ancient times, medicinal plants and their active ingredients have played a key role in treating a wide array of ailments. Notably, in recent years, there has been a burgeoning interest in treatments using products derived from natural sources, and some have been studied as clinical treatments for a variety of disorders. The use of natural products to treat illness has gained increasing scientific and public interest. Not only to ensure quality control but also to verify their efficacy as active ingredients in various pharmaceutical formulations, the evaluation of natural products is of paramount importance. In this field, analytical methods like spectroscopy, electrophoresis, and chromatography are essential and are constantly being improved upon. Chromatographic techniques are essential for determining the quality and authenticity of natural products. Moreover, strict adherence to internationally recognized norms in validating analytical procedures guarantees the accuracy and dependability of results. Continual research initiatives are essential for tackling issues like adulteration and enhancing customer trust in natural products. This review navigates through the complex field of quality control methodologies and analytical techniques applied in evaluating plant-derived sources, which are generally used as natural products, and focuses on the analysis of Hypericum, Curcuma, and Cannabis species.

An imbalance data quality monitoring based on SMOTE-XGBOOST supported by edge computing

Product assembly involves extensive production data that is characterized by high dimensionality, multiple samples, and data imbalance. The article proposes an edge computing-based framework for monitoring product assembly quality in industrial Internet of Things. Edge computing technology relieves the pressure of aggregating enormous amounts of data to cloud center for processing. To address the problem of data imbalance, we compared five sampling methods: Borderline SMOTE, Random Downsampling, Random Upsampling, SMOTE, and ADASYN. Finally, the quality monitoring model SMOTE-XGBoost is proposed, and the hyperparameters of the model are optimized by using the Grid Search method. The proposed framework and quality control methodology were applied to an assembly line of IGBT modules for the traction system, and the validity of the model was experimentally verified.

AAPM Task Group Report 261: Comprehensive quality control methodology and management of dental and maxillofacial cone beam computed tomography (CBCT) systems.

Cone-beam computed tomography (CBCT) systems specifically designed and manufactured for dental, maxillofacial imaging (MFI) and otolaryngology (OLR) applications have been commercially available in the United States since 2001 and have been in widespread clinical use since. Until recently, there has been a lack of professional guidance available for medical physicists about how to assess and evaluate the performance of these systems and about the establishment and management of quality control (QC) programs. The owners and users of dental CBCT systems may have only a rudimentary understanding of this technology, including how it differs from conventional multidetector CT (MDCT) in terms of acceptable radiation safety practices. Dental CBCT systems differ from MDCT in several ways and these differences are described. This report provides guidance to medical physicists and serves as a basis for stakeholders to make informed decisions regarding how to manage and develop a QC program for dental CBCT systems. It is important that a medical physicist with experience in dental CBCT serves as a resource on this technology and the associated radiation protection best practices. The medical physicist should be involved at the pre-installation stage to ensure that a CBCT room configuration allows for a safe and efficient workflow and that structural shielding, if needed, is designed into the architectural plans. Acceptance testing of new installations should include assessment of mechanical alignment of patient positioning lasers and x-ray beam collimation and benchmarking of essential image quality performance parameters such as image uniformity, noise, contrast-to-noise ratio (CNR), spatial resolution, and artifacts. Several approaches for quantifying radiation output from these systems are described, including simply measuring the incident air-kerma (Kair) at the entrance surface of the image receptor. These measurements are to be repeated at least annually as part of routine QC by the medical physicist. QC programs for dental CBCT, at least in the United States, are often driven by state regulations, accreditation program requirements, or manufacturer recommendations.

Product Quality Anomaly Recognition and Diagnosis Based on DRSN-SVM-SHAP

Conventional quality control methodologies are inadequate for fully elucidating the aberrant patterns of product quality. A multitude of factors influence product quality, yet the limited number of controlled quality characteristics is insufficient for accurately diagnosing quality abnormalities. Additionally, there are asymmetries in data collection, data pre-processing, and model interpretation. In this context, a quality anomaly recognition and diagnosis model for the complex product manufacturing process is constructed based on a deep residual network, support vector machine (SVM), and Shapley additive explanation (SHAP). Given the numerous complex product quality characteristic indexes and unpredictable accidental factors in the production process, it is necessary to mine the deep relationship between quality characteristic data and quality state. This mining is achieved by utilizing the strong feature extraction ability of the deep residual shrinkage network (DRSN) through self-learning. The symmetry of the data within the model has also been taken into account to ensure a more balanced and comprehensive analysis. The excellent binary classification ability of the support vector machine is combined with the DRSN to identify the quality anomaly state. The SHAP interpretable model is employed to diagnose the quality anomaly problem of a single product and to identify and diagnose quality anomalies in the manufacturing process of complex products. The effectiveness of the model is validated through case analysis. The accuracy of the DRSN-SVM quality anomaly recognition model reaches 99%, as demonstrated by example analysis, and the model exhibits faster convergence and significantly higher accuracy compared with the naive Bayesian model classification and support vector machine classification models.

Improving high throughput manufacture of laser-inscribed graphene electrodes via hierarchical clustering

Laser-inscribed graphene (LIG), initially developed for graphene supercapacitors, has found widespread use in sensor research and development, particularly as a platform for low-cost electrochemical sensing. However, batch-to-batch variation in LIG fabrication introduces uncertainty that cannot be adequately tracked during manufacturing process, limiting scalability. Therefore, there is an urgent need for robust quality control (QC) methodologies to identify and select similar and functional LIG electrodes for sensor fabrication. For the first time, we have developed a statistical workflow and an open-source hierarchical clustering tool for QC analysis in LIG electrode fabrication. The QC process was challenged with multi-operator cyclic voltammetry (CV) data for bare and metalized LIG. As a proof of concept, we employed the developed QC process for laboratory-scale manufacturing of LIG-based biosensors. The study demonstrates that our QC process can rapidly identify similar LIG electrodes from large batches (n ≥ 36) of electrodes, leading to a reduction in biosensor measurement variation by approximately 13% compared to the control group without QC. The statistical workflow and open-source code presented here provide a versatile toolkit for clustering analysis, opening a pathway toward scalable manufacturing of LIG electrodes in sensing. In addition, we establish a data repository for further study of LIG variation.

A new statistical-quality-control methodology for panel line monitoring in shipyards

The construction of cruise ship hulls is a complex process that involves various stages: it begins with the fabrication of panels, which involves marking, cutting, and shaping metal plates of considerable length, followed by the welding of rigid structural components onto these panels. To minimize delays and cost overruns, it is crucial to detect any anomalies early on and rectify them through appropriate repair work. Therefore, real-time conformity verifications and effective Statistical Quality Control (SQC) methodologies are necessary. This paper proposes a novel SQC methodology, specifically tailored for monitoring the panel line in cruise-ship shipyards. This methodology, while adopting a traditional standardized p control chart with samples of variable size, integrates two original aspects: (i) it accounts for the significant level of customization and specific quality characteristics inherent in the different panels and (ii) it rigorously considers the measurement uncertainty associated with the large-volume metrology instruments (such as state-of-the-art total stations, laser trackers or laser scanners) used for conformity verification, following the ISO 14253-1:2017 standard. The methodology is exemplified through a real-world case study, providing practical insights into its application.

Leveraging AI and Machine Learning in Six-Sigma Documentation for Pharmaceutical Quality Assurance.

The pharmaceutical industry must maintain stringent quality assurance standards to ensure product safety and regulatory compliance. A key component of the well-known Six Sigma methodology for process improvement and quality control is precise and comprehensive documentation. However, there are a number of significant issues with traditional documentation procedures, including as slowness, human error, and difficulties with regulatory standards. This review research looks at innovative ways to employ machine learning (ML) and artificial intelligence (AI) to enhance Six Sigma documentation processes in the pharmaceutical sector. AI and ML provide cutting-edge technologies that have the potential to drastically alter documentation processes by automating data entry, collection, and analysis. Natural language processing (NLP) and computer vision technologies have the potential to significantly reduce human error rates and increase the efficacy of documentation processes. By applying machine learning algorithms to support real-time data analysis, predictive analytics, and proactive quality management, pharmaceutical organizations may be able to identify potential quality issues early on and take proactive efforts to address them. Combining AI and ML improves documentation accuracy and reliability while also strengthening compliance with stringent regulatory criteria. The primary barriers and limitations to the current state of Six Sigma documentation in the pharmaceutical industry are identified in this study. It examines the fundamentals of AI and ML with an emphasis on their specific applications in quality assurance and potential benefits for Six Sigma processes. The report includes extensive case studies that highlight notable developments and explain how AI/ML enhanced documentation is used in the real world.

Enhancing sustainability in the production of cruise-ship modules through quality monitoring

Constructing cruise-ship hull is long, complex and requires relatively precise metal carpentry, welding and assembly operations. Timely detection and correction of anomalies is vital to the sustainability of the process, in terms of reducing production time and costs. This paper presents a novel statistical quality control (SQC) methodology for monitoring the workshop for the production of so-called “modules” within shipyards, i.e., roughly parallelepiped sub-assemblies of large dimensions (on the order of several tens of meters on each side), obtained through manual carpentry and welding operations. The proposed methodology adopts a standardized p control chart with samples of variable size, incorporating two elements: (i) it accounts for the high level of customization of modules, and (ii) it takes into consideration the measurement uncertainty associated with the large-volume metrology instrument employed for conformity verification (such as a state-of-the-art Leica RTC360 laser scanner), following the ISO 14253-1:2017 standard. A real-world case study at an Italian Fincantieri S.p.A. shipyard demonstrates its practical application.

Evaluating Pfeiffer Circular Chromatography as a Qualitative Methodology of Viscum album Homeopathic Mother Tinctures: A Multicentric Collaborative Project Between Brazil and Switzerland

Viscum album (VA) is a peculiar semiparasitic plant with important role in cancer therapy. The differences in phytochemical composition as well as the influence of host tree in the metabolome of VA subspecies are already described1. Our goal is to use the Pfeiffer Circular Chromatography (PCC) as a qualitative methodology to differentiate V. album mother tinctures (VAMT). The assays were conducted in two countries: Brazil (São Paulo and Rio de Janeiro) and Switzerland (Arlesheim). Three VA subspecies (abietis, album, austriacum) and five host trees (Malus domestica, Quercus sp., Ulmus carpinifolia, Pinus sylvestris, Abies alba) were harvested in Switzerland in summer and winter. The VAMTs were prepared following the Brazilian Homeopathic Pharmacopoeia and the PCC experiments were performed as previously published2. The preliminary analysis and the reproducibility of the three independent assays performed in each experimental set are being evaluated by Image J software. The qualitative standard features will be used to the VAMT differentiation regarding the following PCC characteristics: inner and outer-zones, coloration, size, the presence of radial lines, among others. Besides, the High-Performance Thin Layer Chromatography (HPTLC) was performed as standard quality control methodology using the CAMAG instruments to compare the phytochemical profile of V. album mother tinctures described in the French and German Homeopathic Pharmacopoeias. The samples have been characterized by the compounds retention factor (Rf) compared to the chemical standards (chlorogenic acid, caffeic acid, hyperoside and rutin). They present similar chemical markers when both methodologies were compared but with different intensities between season and host trees. Based on the hypothesis that the PCC patterns could be correlated with the chemical composition of VAMT, this multicentric research project will be useful to propose a low-cost and an easy methodology focused on the quality control parameters of these herbal raw materials.