Microbiological contamination may cause microbial proliferation and consequently additional problems for pharmaceutical companies through production stoppage, product contamination, investigations of process deviations, out-of-specification results and product disposal. This is one of the major concerns of the regulatory health agencies. Microbiological load (bioburden) may represent a potential risk for patients if the sterilization process is not effective and/or due to the production of toxins. Although bioburden can be eliminated by terminal sterilization or filtration processes, it is important to monitor the amount and determine the identity and characteristics of the microorganisms present prior to final processing. The application of microorganism identification systems is crucial for identifying the type of contamination, which can be extremely useful for investigating. The aim of this study was to evaluate the profiles of microorganisms identified in bioburden assays from solutions, culture medias, and products (SCP) from a pharmaceutical industry facility. From 2018-2020, a total of 1,078 samples from 857 different lots of SCP were analyzed and isolated microorganisms were identified. A prefiltering step was included after March 2020, in order to reduce the bioburden before sterilizing filtration. Criteria for the definition and management of microorganisms identified were evaluated after an integrative bibliographic review, and three groups were proposed (critical, objectionable, and nonobjectionable microorganisms). For the samples that did not include prefiltering (n=636), 227 (35.7%) presented microbial growth. For those that included prefiltering, before prefiltering (n=221), 60.6% presented microbial growth, and after prefiltering, this value was reduced to 4.1%, which can be attributed to a contamination during the sampling or a wrong filtering. From the samples that presented microbial growth, 678 microorganisms were identified as bacteria and 59 as molds and yeasts. A total of 120 microorganisms (56 and 27 Gram-positive and negative bacteria, respectively, 31 yeasts, and six filamentous molds) could not be identified, and the remaining microorganisms were classified as objectionable (n=507; 82.2%), nonobjectionable (n=103; 16.7%) and critical (n=7; 1.1%). Most of the bioburden species (>80.0%) were considered objectionable microorganisms. A process for classification and management of bioburden analysis results based on a literature review of pathogenic and physiological characteristics of the microorganisms was proposed.

The Risk Knowledge Infinity (RKI) Cycle Framework was featured as part of the ICH-sanctioned training materials supporting the recent issuance of ICH Q9(R1) Quality Risk Management To support ICH Q9(R1) understanding and adoption, this paper presents a case study on the application of the RKI Cycle, based on an underlying out-of-specification investigation. This case study provides a stepwise walk-through of the cycle to illustrate how key concepts within the ICH Q9(R1) revision can be achieved through better connecting quality risk management and knowledge management with a framework such as the RKI Cycle.

Integrating machine learning (ML) models into clinical practice presents a challenge of maintaining their efficacy over time. While existing literature offers valuable strategies for detecting declining model performance, there is a need to document the broader challenges and solutions associated with the real-world development and integration of model monitoring solutions. This work details the development and use of a platform for monitoring the performance of a production-level ML model operating in Mayo Clinic. In this paper, we aimed to provide a series of considerations and guidelines necessary for integrating such a platform into a team\'s technical infrastructure and workflow. We have documented our experiences with this integration process, discussed the broader challenges encountered with real-world implementation and maintenance, and included the source code for the platform. Our monitoring platform was built as an R shiny application, developed and implemented over the course of 6 months. The platform has been used and maintained for 2 years and is still in use as of July 2023. The considerations necessary for the implementation of the monitoring platform center around 4 pillars: feasibility (what resources can be used for platform development?); design (through what statistics or models will the model be monitored, and how will these results be efficiently displayed to the end user?); implementation (how will this platform be built, and where will it exist within the IT ecosystem?); and policy (based on monitoring feedback, when and what actions will be taken to fix problems, and how will these problems be translated to clinical staff?). While much of the literature surrounding ML performance monitoring emphasizes methodological approaches for capturing changes in performance, there remains a battery of other challenges and considerations that must be addressed for successful real-world implementation.

Prostate magnetic resonance imaging (MRI) stands as the cornerstone in diagnosing prostate cancer (PCa), offering superior detection capabilities while minimizing unnecessary biopsies. Despite its critical role, global disparities in MRI diagnostic performance persist, stemming from variations in image quality and radiologist expertise. This manuscript reviews the challenges and strategies for enhancing image quality in prostate MRI, spanning patient preparation, MRI unit optimization, and radiology team engagement. Quality assurance (QA) and quality control (QC) processes are pivotal, emphasizing standardized protocols, meticulous patient evaluation, MRI unit workflow, and radiology team performance. Additionally, artificial intelligence (AI) advancements offer promising avenues for improving image quality and reducing acquisition times. The Prostate-Imaging Quality (PI-QUAL) scoring system emerges as a valuable tool for assessing MRI image quality. A comprehensive approach addressing technical, procedural, and interpretative aspects is essential to ensure consistent and reliable prostate MRI outcomes.

Calibration of an analytical measurement procedure is an important basis for the reliability of patient results. Many publications and as well as procedures on how to estimate quality control and interpret those results have been become available over the years. In this publication we are focusing on the critical part of the calibration as there are no clear communication or guidelines on how to perform it. Usually only the recommendation of the reagent or instrument manufacturer is available. We would like to point out this gap to invite for a discussion and improvement of the current situation.

Small RNA (sRNA) profiling of Extracellular Vesicles (EVs) by Next-Generation Sequencing (NGS) often delivers poor outcomes, independently of reagents, platforms or pipelines used, which contributes to poor reproducibility of studies. Here we analysed pre/post-sequencing quality controls (QC) to predict issues potentially biasing biological sRNA-sequencing results from purified human milk EVs, human and mouse EV-enriched plasma and human paraffin-embedded tissues. Although different RNA isolation protocols and NGS platforms were used in these experiments, all datasets had samples characterized by a marked removal of reads after pre-processing. The extent of read loss between individual samples within a dataset did not correlate with isolated RNA quantity or sequenced base quality. Rather, cDNA electropherograms revealed the presence of a constant peak whose intensity correlated with the degree of read loss and, remarkably, with the percentage of adapter dimers, which were found to be overrepresented sequences in high read-loss samples. The analysis through a QC pipeline, which allowed us to monitor quality parameters in a step-by-step manner, provided compelling evidence that adapter dimer contamination was the main factor causing batch effects. We concluded this study by summarising peer-reviewed published workflows that perform consistently well in avoiding adapter dimer contamination towards a greater likelihood of sequencing success.

The extraction process plays a crucial role in the production of Tibetan medicines. This study focused on assembling a set of online near-infrared (NIR) spectroscopy detection devices for the extraction of medicinal herbs. The original infrared device was transformed into an online detection system. After evaluating the stability of the system, we applied online NIR spectroscopy monitoring to the flavonoid contents (total flavonoids, quercetin-3-O-sophoroside, and luteolin) of Meconopsis quintuplinervia Regel. during the ultrasonic extraction process and determined the extraction endpoint. Nine batches of samples were employed to construct quantitative and discriminant models, half of the remaining two batches of samples are used for external verification. Our research shows that the residual predictive deviation (RPD) values of total flavonoids, quercetin-3-O-sophoroside and luteolin models exceeded 2.5. The R values for external verification of the three ingredients were above 0.9, with RPD values generally exceeding 2 and RSEP values within 10 %, demonstrating the model\'s strong predictive performance. Most of the extraction endpoints of the flavonoid components in M. quintuplinervia ranged from 18 to 58 min, with high consistency between the predicted extraction endpoints of the external validation, suggesting accurate determination of extraction endpoints based on predicted values. This study can provide a reference for the online NIR spectroscopy quality monitoring of the extraction process of Chinese and Tibetan herbs.

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Implementing and deploying advanced technologies are principal in improving manufacturing processes, signifying a transformative stride in the industrial sector. Computer vision plays a crucial innovation role during this technological advancement, demonstrating broad applicability and profound impact across various industrial operations. This pivotal technology is not merely an additive enhancement but a revolutionary approach that redefines quality control, automation, and operational efficiency parameters in manufacturing landscapes. By integrating computer vision, industries are positioned to optimize their current processes significantly and spearhead innovations that could set new standards for future industrial endeavors. However, the integration of computer vision in these contexts necessitates comprehensive training programs for operators, given this advanced system\'s complexity and abstract nature. Historically, training modalities have grappled with the complexities of understanding concepts as advanced as computer vision. Despite these challenges, computer vision has recently surged to the forefront across various disciplines, attributed to its versatility and superior performance, often matching or exceeding the capabilities of other established technologies. Nonetheless, there is a noticeable knowledge gap among students, particularly in comprehending the application of Artificial Intelligence (AI) within Computer Vision. This disconnect underscores the need for an educational paradigm transcending traditional theoretical instruction. Cultivating a more practical understanding of the symbiotic relationship between AI and computer vision is essential. To address this, the current work proposes a project-based instructional approach to bridge the educational divide. This methodology will enable students to engage directly with the practical aspects of computer vision applications within AI. By guiding students through a hands-on project, they will learn how to effectively utilize a dataset, train an object detection model, and implement it within a microcomputer infrastructure. This immersive experience is intended to bolster theoretical knowledge and provide a practical understanding of deploying AI techniques within computer vision. The main goal is to equip students with a robust skill set that translates into practical acumen, preparing a competent workforce to navigate and innovate in the complex landscape of Industry 4.0. This approach emphasizes the criticality of adapting educational strategies to meet the evolving demands of advanced technological infrastructures. It ensures that emerging professionals are adept at harnessing the potential of transformative tools like computer vision in industrial settings.

Objective  Our objective was to investigate the basic information of the personnel and facilities of nuclear medicine in Beijing. Methods  This survey was performed by the Beijing Quality Control Center in 2018. The investigation included personnel, equipment, and clinical applications, and data were then compared with previous surveys. The paper questionnaires were used for the survey, which required information about the personnel, devices, and clinical applications. Results  About 38 nuclear medicine departments in Beijing were involved in the survey. The number of nuclear medicine staff was 531 in 2018, showing an increase of 58.7% over the past decade. Positron emission tomography/computed tomography (PET/CT), single-photon emission computed tomography (SPECT), and single-photon emission computed tomography/computed tomography (SPECT/CT) represented the main nuclear medicine facilities, and the total number of surveyed departments was 18, 24, and 34, respectively. The quality control results showed significant improvement from the 2005 levels. The total number of scintigraphy procedures was estimated at 199,607 (153,185 SPECT and 46,422 PET/CT). The estimated annual number of scintigraphy images was 8.9 per 1,000 population for SPECT and 2.7 per 1,000 population for PET/CT during 2018. The most frequent radioiodine-targeted therapy was 131 I-targeted therapy for hyperthyroidism in 2018. Conclusion  Nuclear medicine has experienced rapid growth in the past 10 years in Beijing, either in personnel, equipment, and scintigraphy. Future efforts will focus on the use of new isotopes in the diagnosis, implementing quality strategy, and enhancing training.