Quality Considerations Research Articles

Whole Slide Imaging, Artificial Intelligence, and Machine Learning in Pediatric and Perinatal Pathology: Current Status and Future Directions

The integration of artificial intelligence (AI) into healthcare is becoming increasingly mainstream. Leveraging digital technologies, such as AI and deep learning, impacts researchers, clinicians, and industry due to promising performance and clinical potential. Digital pathology is now a proven technology, enabling generation of high-resolution digital images from glass slides (whole slide images; WSI). WSIs facilitates AI-based image analysis to aid pathologists in diagnostic tasks, improve workflow efficiency, and address workforce shortages. Example applications include tumor segmentation, disease classification, detection, quantitation and grading, rare object identification, and outcome prediction. While advancements have occurred, integration of WSI-AI into clinical laboratories faces challenges, including concerns regarding evidence quality, regulatory adaptations, clinical evaluation, and safety considerations. In pediatric and developmental histopathology, adoption of AI could improve diagnostic efficiency, automate routine tasks, and address specific diagnostic challenges unique to the specialty, such as standardizing placental pathology and developmental autopsy findings, as well as mitigating staffing shortages in the subspeciality. Additionally, AI-based tools have potential to mitigate medicolegal implications by enhancing reproducibility and objectivity in diagnostic evaluations. An overview of recent developments and challenges in applying AI to pediatric and developmental pathology, focusing on machine learning methods applied to WSIs of pediatric pathology specimens is presented.

Hospital Length-of-Stay Prediction Using Machine Learning Algorithms—A Literature Review

Predicting hospital length of stay is critical for efficient hospital management, enabling proactive resource allocation, the optimization of bed availability, and optimal patient care. This paper explores the potential of machine learning algorithms to revolutionize hospital length-of-stay predictions, contributing to healthcare efficiency and patient care. The main objective is to identify the most effective machine learning algorithm for building a predictive model capable of predicting hospital length of stay. The bibliographic search of the existing literature on machine learning algorithms applied to hospital length of stay predictions highlighted the most relevant papers within this area of research. The papers were analyzed in terms of model types and metrics that contributed to the considerable impact on healthcare decision making. We also discuss the challenges and limitations of machine learning algorithms for predicting length of stay, and the importance of data quality and ethical considerations.

Selective Deposition and Fusion of AISI 316L: An Additive Manufacturing Process for Space Environments via Direct Ink Writing and Laser Processing

Abstract Unlocking the potential of additive manufacturing (AM) for space exploration hinges on overcoming key challenges, notably the ability to manufacture or repair parts on-site during exploration missions with consideration of quality, feedstock utilization, and challenges involved in microgravity environments. While there are multiple efforts to investigate the use of existing metal AM processes such as powder bed fusion (PBF), directed energy deposition (DED), and filament-based material extrusion, each process comes with a different set of challenges in space environments. Here, we introduce a new AM method that integrates the benefits of Direct Ink Writing (DIW) to selectively deposit metallic pastes with laser-based processing to locally de-bind and subsequently melt and fuse metal powder, layer by layer, enabling the manufacturing of AISI 316L samples with densities exceeding 99.0%. The impact of process parameters on single-track dimensions, surface morphology, and porosity was characterized. The efficacy of laser de-binding was assessed via secondary-ion mass spectrometry, permitting the carbon content to be estimated at 0.0152%, which is safely below the acceptable limit (0.03 wt%) for AISI 316L.

Effects of fish meal replacement with composite proteins source on growth performance, antioxidant capacity and swim bladder quality of chu’s croaker (Nibea coibor)

Collagen, as a commercial functional protein, is widely applied in food, cosmetics, biomedicine and other industries, and swim bladder is regarded as an ideal source of raw materials due to its rich collagen content. Chu’s croaker (Nibea coibor), as an economical fish that can produce high-grade swim bladder, is popularly farmed, however, its high fishmeal demand limits the development of the feed industry. Therefore, it is worth thinking about how to reduce the use of fish meal without affecting the swim bladder quality. Given that, an 8-week feeding trial was conducted to investigate the effects of fish meal partially replaced (0.0%, 5.0%, 10.0%, 15.0%, 20.0%) with composite proteins (CPs, consisted of corn protein meal and hydrolyzed feather meal) on growth, antioxidant capacity and swim bladder quality (texture and collagen deposition) of chu’s croaker. Results showed that although growth performance decrease with the increase of CPs’ replacement level, it did not compromise the swim bladder quality. Specifically, 5% - 10% CPs did not affect the growth significantly, but can improve antioxidant capacity, protein content, collagen synthesis and texture quality of swim bladder. The enhancement of swim bladder texture, including hardness, chewiness, springiness, and adhesiveness may be caused by changes in the expression of related genes, such as colga1t1, col12a1, cyp27a1, etc. These results indicated that appropriate fish meal replaced by CPs could be applied during aquaculture practice after comprehensive consideration of feed cost, growth rate and quality of swim bladder. This study provides support for the development of cost-effective feed formulations that improve the sustainability and economic viability of chu’s croaker culture.

Review of Uncertainty, Carbon Emissions, Greenness Index, and Quality Issues in Green Supply Chains

The ability of closed-loop supply chains (CLSC) and reverse logistics (RL) to improve the triple bottom line (economic, social and environmental values) has increased the development of design and management models for CLSCs and RL. Consequently, there exists an extensive body of literature dedicated to exploring these supply and logistics issues. This paper reviews recent and relevant literature on CLSC and RL with an emphasis on uncertainty, carbon emissions, greenness index, return product quality and reliability considerations. The selected references are organized, reviewed, and analyzed to establish valuable mapping to highlight major findings. Finally, the outcomes are synthesized, and the primary research gaps are emphasized, pointing toward potential avenues for future investigation. These findings reveal that research efforts must be directed towards the development of multi-criteria greenness indices and multi-objective robust optimization models for uncertain quality and reliability of returns.

Automated and closed clinical-grade manufacturing protocol produces potent NK cells against neuroblastoma cells and AML blasts

Natural killer (NK) cells are a promising allogeneic immunotherapy option due to their natural ability to kill tumor cells, and due to their apparent safety. This study describes the development of a GMP-compliant manufacturing protocol for the local production of functionally potent NK cells tailored for high-risk acute myeloid leukemia (AML) and neuroblastoma (NBL) patients. Moreover, the quality control strategy and considerations for product batch specifications in early clinical development are described. The protocol is based on the CliniMACS Prodigy platform and Natural Killer Cell Transduction (NKCT) (Miltenyi Biotec). NK cells are isolated from leukapheresis through CD3 depletion and CD56 enrichment, followed by a 12-hour activation with IL-2 and IL-15 cytokines. Three CliniMACS Prodigy processes demonstrated the feasibility and consistency of the modified NKCT process. A three-step process without expansion, however, compromised the NK cell yield. T cells were depleted effectively, indicating excellent safety of the product. Characterization of the NK cells before and after cytokine activation revealed a notable increase in the expression of activation markers, particularly CD69, consistent with enhanced functionality. Intriguingly, the NK cells exhibited increased killing efficacy against patient-derived CD33 + AML blasts and NBL cells in vitro, suggesting a potential therapeutic benefit in AML and NBL.

Technical and Quality Considerations for Stereotactic Radiation Treatment Techniques.

Stereotactic radiosurgery (SRS) and stereotactic body radiation therapy (SBRT), collectively termed SRS-SBRT, are advanced treatment modalities delivering high doses of radiation in a single treatment or condensed treatment phase. Due to the small margins and steep dose gradient used in SRS-SBRT, the technical and safety considerations are more stringent than traditional radiation therapy and may include more advanced simulation, patient immobilization, treatment planning, and treatment delivery techniques. Respiratory motion management and intrafraction motion monitoring are often used during SRS-SBRT to ensure treatments are robust to both internal organ motion and patient movement during treatment. To ensure optimal treatment quality, SRS-SBRT programs should use multidisciplinary coordination of care to ensure patient-specific treatment strategies are used for optimal patient outcomes. Quality and safety considerations are presented, including peer review and external validation, for optimizing quality and adhering to national guidelines for stereotactic techniques.

AI in neurodegenerative disorders: A neurosurgical perspective

This paper investigates the application of artificial intelligence (AI) in diagnosing and managing neurodegenerative disorders that require neurosurgical intervention, such as Parkinson's disease (PD) and Alzheimer's disease (AD). A systematic review of the literature was conducted to identify studies that utilized AI techniques for diagnosis, treatment planning, or post-operative monitoring of these conditions. The results demonstrate the potential of AI to improve early detection, treatment outcomes, and patient care. However, challenges such as data quality, explainability, and ethical considerations must be addressed to ensure the responsible and effective integration of AI into clinical practice.

Multimodal Approaches Based on Microbial Data for Accurate Postmortem Interval Estimation.

Accurate postmortem interval (PMI) estimation is critical for forensic investigations, aiding case classification and providing vital trial evidence. Early postmortem signs, such as body temperature and rigor mortis, are reliable for estimating PMI shortly after death. However, these indicators become less useful as decomposition progresses, making late-stage PMI estimation a significant challenge. Decomposition involves predictable microbial activity, which may serve as an objective criterion for PMI estimation. During decomposition, anaerobic microbes metabolize body tissues, producing gases and organic acids, leading to significant changes in skin and soil microbial communities. These shifts, especially the transition from anaerobic to aerobic microbiomes, can objectively segment decomposition into pre- and post-rupture stages according to rupture point. Microbial communities change markedly after death, with anaerobic bacteria dominating early stages and aerobic bacteria prevalent post-rupture. Different organs exhibit distinct microbial successions, providing valuable PMI insights. Alongside microbial changes, metabolic and volatile organic compound (VOC) profiles also shift, reflecting the body's biochemical environment. Due to insufficient information, unimodal models could not comprehensively reflect the PMI, so a muti-modal model should be used to estimate the PMI. Machine learning (ML) offers promising methods for integrating these multimodal data sources, enabling more accurate PMI predictions. Despite challenges such as data quality and ethical considerations, developing human-specific multimodal databases and exploring microbial-insect interactions can significantly enhance PMI estimation accuracy, advancing forensic science.

Path Planning for Mobile Robots Based on the Improved DAPF-QRRT* Strategy

The rapidly exploring random tree star (RRT*) algorithm is widely used to solve path planning problems. However, the RRT* algorithm and its variants fall short of achieving a balanced consideration of path quality and safety. To address this issue, an improved discretized artificial potential field-QRRT* (IDAPF-QRRT*) path planning strategy is introduced. Initially, the APF method is integrated into the Quick-RRT* (Q-RRT*) algorithm, utilizing the attraction of goal points and the repulsion of obstacles to optimize the tree expansion process, swiftly achieving superior initial solutions. Subsequently, a triangle inequality-based path reconnection mechanism is introduced to create and reconnect path points, optimize the path length, and accelerate the generation of sub-optimal paths. Finally, by refining the traditional APF method, a repulsive orthogonal vector field is obtained, achieving the orthogonalization between repulsive and attractive vector fields. This places key path points within the optimized vector field and adjusts their positions, thereby enhancing path safety. Compared to the Q-RRT* algorithm, the DPF-QRRT* algorithm achieves a 37.66% reduction in the time taken to achieve 1.05 times the optimal solution, and the IDAPF-QRRT* strategy nearly doubles generated path safety.

Research on the Constructed-Wetland/Regulation-Tank System at Pontificia Universidad Javeriana's Bogotá Campus

The stormwater harvesting (SWH) project at Pontificia Universidad Javeriana (Bogotá) began in 2007, initiated by the Water and Environmental Engineering research group (Ciencia e Ingeniería del Agua y el Ambiente), as part of PUJB's Environmental Management Plan and in collaboration with the University's Office of Physical Resources. This process included assessments of water supply and demand, rainwater quality, and financial and construction considerations. The system—a constructed wetland/regulation tank (CWRT)—collects rainwater runoff from the university's parking building (3,776 m²), the soccer field, and surrounding areas (14,816 m²). Beyond its hydrological performance, especially during periods of water scarcity, the system also functions as a full-scale laboratory to study biodiversity and ecological impacts in hydrological and hydraulic processes. The monitoring program, focused on changes in rainwater quantity and quality, has inspired several master's and doctoral research projects. These studies explore critical topics in urban hydrology, including stormwater harvesting, urban sediment management, real-time decision-making systems, and environmental and human health. This paper outlines the development of the stormwater harvesting system, shares the main findings from research projects, and discusses ongoing projects.

Leveraging Business Analytics for Enhanced Decision-Making Navigating Challenges and Exploiting Opportunities

Business analytics (BA) is revolutionizing how businesses use data to gain a competitive advantage. It offers opportunities for deeper customer insights, targeted marketing campaigns, improved customer satisfaction, new product and service opportunities, and optimized business processes. However, challenges like data quality, talent acquisition, and ethical considerations exist. This study reviews BA literature and discusses the challenges faced by organizations in implementing business analytics, including data availability, quality, skills and training, and technology infrastructure considerations. We conducted a study on 133 analytics professionals in Lahore, Pakistan. Data availability and quality, skills and training, and technology infrastructure all had a positive and significant impact on business analytics challenges and opportunities. The findings aim to contribute to a better understanding of business analytics, implementation challenges, and opportunities.

Role of Lake Morphometric and Environmental Drivers of Ice Cover Formation and Occurrence on Temperate Lakes: A Case Study from the Eastern Baltic Lakeland, Poland

The presence of ice cover on temperate lakes is a crucial factor in determining the functioning of these ecosystems. The isolation of water from atmospheric influences significantly alters physical, chemical, and biological processes, and the intensity of this impact depends on the duration of the ice cover. This study analyzed the basic parameters of ice cover on several dozen lakes in Northeastern Poland. The aim of this study is to investigate the influence of morphometric parameters, alongside environmental factors, on the variation of ice cover characteristics in lakes located within the Eastern Baltic Lakeland. Characterization of ice conditions in the analyzed lakes was based on basic statistics such as minimum and maximum values, mean, standard deviation, coefficients of variation, skewness, and kurtosis. Given that the dataset contains variables describing ice phenomena in the studied lakes and data describing location, morphometric parameters, and land cover directly adjacent to the lake (treated as independent variables), a method of Spearman’s rank correlations and constrained ordination method were decided upon. Despite the relatively small study area, significant variability was observed, with average differences as follows: 26 days for the onset of ice cover, 17 days for the end date, 15 cm for ice thickness, and a 30-day difference in the average duration of ice cover. Key factors included parameters such as lake volume, average depth, and land use (urbanized and agricultural areas). Understanding parameters such as the onset and end of ice cover is essential for lake ecosystems, both from an ecological and economic perspective. This knowledge is crucial for interpreting the behavior of living organisms, water quality, and economic considerations.

Leadership essential to steer success within the retail sector

PurposeThe purpose of this investigation was to determine the leadership necessary for the retail sector which is having to embrace complexity induced through accelerating changes in consumer behaviour and expanding information accessibility.Design/methodology/approachSemi-structured interviews were used for across 10 disparate retail organisations deployed as case studies safeguarding all quality considerations were fully integrated.FindingsThe findings reveal brands are coping with greater complexity than before. Loyalty is eroding whilst social media increases the complexity for brands to uphold control of their image. Developing the market without sacrificing exclusivity or alienating core consumers necessitates effective leadership. The deductions revealed that despite some leadership styles seemingly more conducive, that specific qualities are more important for the retail sector.Research limitations/implicationsBy selecting ten organisations every effort was made to ensure a greater degree of credibility in the findings. It is acknowledged that this investigation is sector specific. Nonetheless, there exists considerable transferability value as the role of leadership within the retail sector can mirror expectations within other environments.Originality/valueIt was validated that leaders need to foster an organisational culture promoting continuous learning whilst introducing a consumer-first perspective across the business. The findings reinforce how specific traits, behaviours and styles are imperative amongst leaders preserving the success of the retail organisation with less focus on specific leadership styles.

GenAI for Scientific Discovery in Electrochemical Energy Storage: State-of-the-Art and Perspectives from Nano- and Micro-Scale.

The transition to electric vehicles (EVs) and the increased reliance on renewable energy sources necessitate significant advancements in electrochemical energy storage systems. Fuel cells, lithium-ion batteries, and flow batteries play a key role in enhancing the efficiency and sustainability of energy usage in transportation and storage. Despite their potential, these technologies face limitations such as high costs, material scarcity, and efficiency challenges. This research introduces a novel integration of Generative AI (GenAI) within electrochemical energy storage systems to address these issues. By leveraging advanced GenAI techniques like Generative Adversarial Networks, autoencoders, diffusion and flow-based models, and multimodal large language models, this paper demonstrates significant improvements in material discovery, battery design, performance prediction, and lifecycle management across different types of electrochemical storage systems. The research further emphasizes the importance of nano- and micro-scale interactions, providing detailed insights into optimizing these interactions for improved efficiency and longevity. Additionally, the paper discusses the challenges and future directions for integrating GenAI in energy storage research, highlighting the importance of data quality, model transparency, workflow integration, scalability, and ethical considerations. By addressing these aspects, this research sets a new benchmark for the use of GenAI in battery development, promoting sustainable, efficient, and safer energy solutions.

The Impact of Air Pollution Risk on the Sustainability of Crop Insurance Losses

Climate change poses significant risks to natural and economic environments, particularly through its interaction with air pollution. As agriculture is vital for national production, and crop insurance supports social security, it is crucial to examine how air pollution affects crop insurance. Here, we quantify the impact of air quality on crop insurance claims from an actuarial perspective and evaluate the implications for the industry. Utilizing claims data from the U.S., we explore the potential of particulate matter (PM2.5) as a predictor of insurance claims, building on literature that highlights its economic damage to crops. Through the application of a generalized additive model (GAM) and extreme gradient boosting, we found that PM2.5 is indeed a factor influencing crop insurance indemnity in both models, with the GAM demonstrating superior predictive performance. Furthermore, we employed Bai and Perron breakpoint analysis to elucidate the relationship between PM2.5 levels and crop insurance claims over time, alongside two-way fixed effects models to investigate its correlation with various crop types. Our findings highlight the need for crop insurance managers to integrate air quality considerations into their risk processes to ensure sustainability of the industry and pricing strategy in the face of evolving environmental challenges.

Development of a sustainable optimization model for planning regional wastewater systems with consideration of water quality

Development of a sustainable optimization model for planning regional wastewater systems with consideration of water quality

The Integration of Machine Learning in Central Banks: Implications and Innovations

The effectiveness of using artificial intelligence (AI) techniques to lessen the negative effects of economic cycles is examined in this study article. Economic cycles, which are defined by variations in the level of economic activity, present important difficulties for decision-makers in government, business, and society at large. This research investigates many approaches to reducing economic cycles, such as forecasting, policy creation, and adaptive decision-making, by utilizing AI, namely machine learning algorithms. The first section of the paper reviews the body of research on economic cycles and how artificial intelligence might be used to address them. After that, it explores certain AI methods including neural networks, time series analysis, and natural language processing, explaining how they might be used for risk management and economic forecasting. Additionally, the study investigates AI-driven policy interventions, examining how machine learning algorithms might be used to optimize monetary and fiscal policies in order to sustain growth and prevent economic downturns. The study also addresses the difficulties and constraints that come with using AI technologies in economic management, such as problems with algorithmic biases, data quality, and legal considerations. It highlights how crucial interdisciplinary cooperation and openness are to creating AI-driven solutions that are reliable, moral, and socially conscious. This research paper offers insights into the possible advantages and disadvantages of employing AI tools for damping economic cycles through empirical analysis and case studies. In order to fully realize AI's promise in fostering resilience and stability in the economy, it ends by presenting future research areas and policy suggestions. Keywords: artificial intelligence, trend, economic cycles, resilience

Machine learning in oncological pharmacogenomics: advancing personalized chemotherapy.

This review analyzes the application of machine learning (ML) in oncological pharmacogenomics, focusing on customizing chemotherapy treatments. It explores how ML can analyze extensive genomic, proteomic, and other omics datasets to identify genetic patterns associated with drug responses. This, in turn, facilitates personalized therapies that are more effective and have fewer side effects. Recent studies have emphasized ML's revolutionary role of ML in personalized oncology treatment by identifying genetic variability and understanding cancer pharmacodynamics. Integrating ML with electronic health records and clinical data shows promise in refining chemotherapy recommendations by considering the complex influencing factors. Although standard chemotherapy depends on population-based doses and treatment regimens, customized techniques use genetic information to tailor treatments for specific patients, potentially enhancing efficacy and reducing adverse effects.However, challenges, such as model interpretability, data quality, transparency, ethical issues related to data privacy, and health disparities, remain. Machine learning has been used to transform oncological pharmacogenomics by enabling personalized chemotherapy treatments. This review highlights ML's potential of ML to enhance treatment effectiveness and minimize side effects through detailed genetic analysis. It also addresses ongoing challenges including improved model interpretability, data quality, and ethical considerations. The review concludes by emphasizing the importance of rigorous clinical trials and interdisciplinary collaboration in the ethical implementation of ML-driven personalized medicine, paving the way for improved outcomes in cancer patients and marking a new frontier in cancer treatment.

Quality of life in patients with myelin oligodendrocyte glycoprotein antibody associated disease compared to patients with AQP4-IgG positive neuromyelitis optica spectrum disorders: A Korean multicenter study

BackgroundLittle is known about the quality of life (QOL) of patients with myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD). We compared QOL and associated factors in patients with MOGAD and aquaporin4 IgG (AQP4-IgG) positive neuromyelitis optica spectrum disorder (NMOSD). MethodsThis multicenter questionnaire study compared the QOL of 41 patients with MOGAD and 78 with AQP4-IgG positive NMOSD. Patients who were positive for AQP4-IgG or MOG antibodies were included. WHO Quality of Life Scale Brief Version was used to assess QOL in physical, psychological, social, and environmental domains. QOL, sleep quality, pain, fatigue, and depression were compared between the two groups. The factors associated with QOL in each group and the entire cohort were analyzed. ResultsThe proportion of patients with poor QOL was not significantly different between MOGAD (51.22 %) and AQP4-IgG positive NMOSD (58.97 %, p = 0.054). In the MOGAD group, the pain score (β=-1.032, p = 0.001) and depression score (β=-0.694, p = 0.007) were negatively associated with physical and psychological QOL, respectively. Sleep quality was negatively associated with physical (β=-1.506, p = 0.034) and psychological (β =-2.064, p = 0.033) QOL. When the entire cohort was analyzed, a positive MOG antibody was independently associated with worse psychological QOL (β=-8.998, p = 0.013) compared to positive AQP4-Ab after adjustment for sleep quality, depression, fatigue, and pain. ConclusionsThe overall QOL of the patients of MOGAD was comparable to that of AQP4-IgG positive NMOSD. Patients with MOGAD were experiencing sleep disorder, fatigue, and depression at similar degrees to those of patients with AQP4-IgG positive NMOSD. Further consideration of sleep quality and psychological QOL is required to improve QOL in patients with MOGAD.