Real World Research Articles

Some educational uses of EDXRF for technologists in health science

Abstract Physicists teach physics to students of various academic disciplines such as physics, chemistry, health sciences, biology, geology, environmental sciences, etc. Energy dispersive X-ray fluorescence spectroscopy (EDXRF) is a case of application of atomic physics in the real world. Students, through applications of EDXRF, may find out how the basic concepts of atomic physics can be used for elemental analysis of samples of their scientific interest and everyday life. This paper presents some educational implementations of EDXRF and highlights the importance of incorporating this spectroscopic method in undergraduate or postgraduate labs or lectures in the field of health sciences. Five experimental activities, a general one for understanding X-ray spectra and the others for the analysis of substances/materials related to chemistry/toxicology, dentistry, optics, and cosmetics, are presented and discussed.

Oxygen‐Stable Electrochemical CO2 Capture using Redox‐Active Heterocyclic Benzodithiophene Quinone

AbstractElectrochemical carbon capture offers a promising alternative to thermal amine technology, which serves as the traditional benchmark method for CO2 capture. Despite its technological maturity, the widespread deployment of thermal amine technologies is hindered by high energy consumption and sorbent degradation. In contrast, electrochemical methods, with their inherently isothermal operation, address these challenges, offering enhanced energy efficiency and robustness. Among emerging strategies, electrochemical carbon capture systems using redox‐active materials such as quinones stand out for their potential to capture CO2. However, their practical application is currently limited by their low stability in the presence of oxygen. We demonstrate that benzodithiophene quinone (BDT‐Q), a heterocyclic quinone, exhibits high stability in electrochemical carbon capture processes with oxygen‐containing feed gas. Conducted in a cyclic flow system with a simulated flue gas mixture containing 13 % CO2 and 3.5 % O2 for over 100 hours, the process demonstrates high oxygen stability with an electron utilization of 0.83 without significant degradation, indicating a promising approach for real world applications. Our study explores the potential of new heterocyclic quinone compounds in the context of carbon capture technologies.

Oxygen-Stable Electrochemical CO2 Capture using Redox-Active Heterocyclic Benzodithiophene Quinone.

Electrochemical carbon capture offers a promising alternative to thermal amine technology, which serves as the traditional benchmark method for CO2 capture. Despite its technological maturity, the widespread deployment of thermal amine technologies is hindered by high energy consumption and sorbent degradation. In contrast, electrochemical methods, with their inherently isothermal operation, address these challenges, offering enhanced energy efficiency and robustness. Among emerging strategies, electrochemical carbon capture systems using redox-active materials such as quinones stand out for their potential to capture CO2. However, their practical application is currently limited by their low stability in the presence of oxygen. We demonstrate that benzodithiophene quinone (BDT-Q), a heterocyclic quinone, exhibits high stability in electrochemical carbon capture processes with oxygen-containing feed gas. Conducted in a cyclic flow system with a simulated flue gas mixture containing 13 % CO2 and 3.5 % O2 for over 100 hours, the process demonstrates high oxygen stability with an electron utilization of 0.83 without significant degradation, indicating a promising approach for real world applications. Our study explores the potential of new heterocyclic quinone compounds in the context of carbon capture technologies.

Patient and public involvement in the design of an international clinical trial: real world experience

BackgroundThe value of patient and public involvement (PPI) during the earliest stages of clinical trial development, and prior to the award of substantive funding, is widely recognised. However, it is often under-resourced and PPI processes during this phase are rarely reported in detail. Having benefitted from seed funding to develop an international clinical trial proposal, we sought to describe and appraise PPI activities and processes that support pre-award co-development.MethodsA 12-month “accelerator” award facilitated development of a substantive funding application to deliver the Rheumatoid Arthritis Prevention PlatfORm Trial (RAPPORT), conceived to prioritise preventative interventions for people at risk of RA. PPI partners, including individuals at risk of rheumatoid arthritis (RA), RA patients, relatives and members of the public, provided feedback on key trial design issues through online meetings, a feedback form and emails. PPI processes employed during the one-year accelerator project were thereafter evaluated by PPI partners using an anonymous online feedback form with reference to National Institute of Health and Care Research (NIHR) UK standards for public involvement in research.ResultsSixteen out of the 25-strong PPI partner panel completed an online feedback form (64%). Respondents perceived PPI processes positively in relation to all NIHR standard domains. Several key facilitators and challenges were identified, including the need for adequate PPI funding during pre-award phases of research, strategies for creating an inclusive environment, flexibility around levels of involvement, and challenges in achieving representatively diverse participation, and the importance of communicating transparent processes for role-assignment and time-reimbursement.ConclusionsIn general, RAPPORT was considered an example of PPI well done, and in line with UK standards for public involvement in research. Facilitators and challenges of relevance for the development of future translational and clinical trial funding applications are highlighted.

Emotive Narrative: Thinking and Writing Experimental Animation through Things’ Perspective

The purpose of this study is to explore the magical nature of things, to investigate nonhuman entities, and to study from the philosophy of Object-Oriented Ontology. Rethinking Paul Wells’ scripted artefact based on the ontography concept of Alien Phenomenology, specifically for the transition of objects from real world objects to cinematic objects, Animators go through the found or made object and object as narrative stages. To explore the potential of things, we engage creators in co-speculation through Evocative Object Cards activity that evokes experience. By thinking about the theme of the secret life of things and experimenting with writing from thing’s perspective, we can realize the emotive narrative of things. Using the concept of “human-object” experience entanglement, this study conducted an experimental animation writing workshop to stimulate the tactile memory of the creators, so that they can understand how objects reveal their unique agency in the animation, and then understand the satisfaction of the participants through semi-structured qualitative interviews. The research results show that this activity has a positive impact on the creators’ creative writing of animation scripts, which not only puts forward alternative views beyond the mainstream animation stories, but also enriches the audience’s viewing experience.

Promoting students' autonomous motivation for innovative teaching project in the "Real World".

Promoting students' autonomous motivation for innovative teaching project in the "Real World".

Optimizing Microservice Deployment in Edge Computing with Large Language Models: Integrating Retrieval Augmented Generation and Chain of Thought Techniques

Large Language Models (LLMs) have demonstrated impressive capabilities in autogenerating code based on natural language instructions provided by humans. We observed that in the microservice models of edge computing, the problem of deployment latency optimization can be transformed into an NP-hard mathematical optimization problem. However, in the real world, deployment strategies at the edge often require immediate updates, while human-engineered code tends to be lagging. To bridge this gap, we innovatively integrated LLMs into the decision-making process for microservice deployment. Initially, we constructed a private Retrieval Augmented Generation (RAG) database containing prior knowledge. Subsequently, we employed meticulously designed step-by-step inductive instructions and used the chain of thought (CoT) technique to enable the LLM to learn, reason, reflect, and regenerate. We decomposed the microservice deployment latency optimization problem into a collection of granular sub-problems (described in natural language), progressively providing instructions to the fine-tuned LLM to generate corresponding code blocks. The generated code blocks underwent integration and consistency assessment. Additionally, we prompted the LLM to generate code without the use of the RAG database for comparative analysis. We executed the aforementioned code and comparison algorithm under identical operational environments and simulation parameters, conducting rigorous result analysis. Our fine-tuned model significantly reduced latencies by 22.8% in handling surges in request flows, 37.8% in managing complex microservice types, and 39.5% in processing increased network nodes compared to traditional algorithms. Moreover, our approach demonstrated marked improvements in latency performance over LLMs not utilizing RAG technology and reinforcement learning algorithms reported in other literature. The use of LLMs also highlights the concept of symmetry, as the symmetrical structure of input-output relationships in microservice deployment models aligns with the LLM’s inherent ability to process and generate balanced and optimized code. Symmetry in this context allows for more efficient resource allocation and reduces redundant operations, further enhancing the model’s effectiveness. We believe that LLMs hold substantial potential in optimizing microservice deployment models.

Survey of learning-based approaches for robotic in-hand manipulation

Human dexterity is an invaluable capability for precise manipulation of objects in complex tasks. The capability of robots to similarly grasp and perform in-hand manipulation of objects is critical for their use in the ever changing human environment, and for their ability to replace manpower. In recent decades, significant effort has been put in order to enable in-hand manipulation capabilities to robotic systems. Initial robotic manipulators followed carefully programmed paths, while later attempts provided a solution based on analytical modeling of motion and contact. However, these have failed to provide practical solutions due to inability to cope with complex environments and uncertainties. Therefore, the effort has shifted to learning-based approaches where data is collected from the real world or through a simulation, during repeated attempts to complete various tasks. The vast majority of learning approaches focused on learning data-based models that describe the system to some extent or Reinforcement Learning (RL). RL, in particular, has seen growing interest due to the remarkable ability to generate solutions to problems with minimal human guidance. In this survey paper, we track the developments of learning approaches for in-hand manipulations and, explore the challenges and opportunities. This survey is designed both as an introduction for novices in the field with a glossary of terms as well as a guide of novel advances for advanced practitioners.

Societal Attitudes Toward Service Robots: Adore, Abhor, Ignore, or Unsure?

Societal or population-level attitudes are aggregated patterns of different individual attitudes, representing collective general predispositions. As service robots become ubiquitous, understanding attitudes towards them at the population (vs. individual) level enables firms to expand robot services to a broad (vs. niche) market. Targeting population-level attitudes would benefit service firms because: (1) they are more persistent, thus, stronger predictors of behavioral patterns and (2) this approach is less reliant on personal data, whereas individualized services are vulnerable to AI-related privacy risks. As for service theory, ignoring broad unobserved differences in attitudes produces biased conclusions, and our systematic review of previous research highlights a poor understanding of potential heterogeneity in attitudes toward service robots. We present five diverse studies (S1–S5), utilizing multinational and “real world” data (Ntotal = 89,541; years: 2012–2024). Results reveal a stable structure comprising four distinct attitude profiles (S1–S5): positive (“adore”), negative (“abhor”), indifferent (“ignore”), and ambivalent (“unsure”). The psychological need for interacting with service staff, and for autonomy and relatedness in technology use, function as attitude profile antecedents (S2). Importantly, the attitude profiles predict differences in post-interaction discomfort and anxiety (S3), satisfaction ratings and service evaluations (S4), and perceived sociability and uncanniness based on a robot’s humanlikeness (S5).

Comparison of Multi-Agent Platform Usability for Industrial-Grade Applications

Modern systems often employ decentralised and distributed approaches. This can be attributed, among others, to the increasing complexity of system processes, which go beyond the capabilities of singular components. Additionally, with the growth in demand for system automation and high-level coordination, solutions belonging to the decentralised Artificial Intelligence and collaborative decision-making are often applied. It can be observed that these concerns fall within the domain of multi-agent systems. However, even though MAS concepts emerged more than 40 years ago, despite their obvious advantages and continuous efforts of the scientific community, agents remain rarely used in industrial-grade applications. In this context, the goal of this contribution is to analyse the reasons for the lack of adoption of agent solutions in the real world. During the analysis, all pertinent aspects of the modern software development life cycle are examined and compared to what is currently available in the agent system domain. Specifically, the study focuses on identifying gaps that are often overlooked when it comes to scientific applications of MAS, but are critical in terms of potential for large-scale system development in practice.

NEUTROSOPHIC EXTENSION OF THE NEGATIVE BINOMIAL DISTRIBUTION: PROPERTIES AND APPLICATIONS TO INTERVAL-VALUED DATA

In real world while addressing the challenges of ambiguity and vagueness, the problems call for a scientific approach to quantify the uncertainties. The negative binomial distribution, a cornerstone in classical probability theory, traditionally examines the probability of achieving the kth success in n trials, following failures, with precise observations. However, the available literature does not address the occurrence of observations in the intervals and hence the handling of interval-valued data remained unattended. This study introduces the neutrosophic negative binomial distribution which is extended from the classical version by incorporating both indeterminacy and crisp intervals. We derive various properties of this generalized distribution which includes; characteristic function, probability function and moment generating function. This study also presents the reliability analysis metrics like cumulative hazard rate, Mills ratio, survival hazard rate, odds ratio and reversed hazard rate. This study explores the order statistics for neutrosophic negative binomial distribution, encompassing with joint, maximum, minimum and median order statistics. Special case studies are discussed which demonstrate the application of the proposed distribution, paving the way for addressing complex problems that combine conventional approaches with non-precisely determined information.

Benefits of combining SGLT2 inhibitors and pioglitazone on risk of MASH in type 2 diabetes-A real-world study.

Both pioglitazone and glucagon-like peptide 1 receptor agonists (GLP1RA) alone improve metabolic dysfunction-associated steatohepatitis (MASH) in randomized clinical trials, whereas preclinical studies suggested MASH benefits with sodium glucose co-transporter 2 inhibitors (SGLT2i). In the real world, patients with type 2 diabetes often require multiple agents for glycaemic control. Here, we investigated the benefits of combining these agents on risks of MASH. Longitudinal changes in FibroScan-aspartate aminotransferase (FAST) score were measured in 888 patients with type 2 diabetes. Use of pioglitazone, GLP1RA and/or SGLT2i was defined as continuous prescriptions of ≥180 days prior to their last reassessment FibroScan. Multivariable logistic regression analysis was conducted to evaluate the associations between use of these agents and FAST score changes. Over a median follow-up of 3.9 years, the increasing number of these agents used was significantly associated with more reductions in FAST score (p for trend <0.01). Dual combination was independently associated with a higher likelihood of achieving low FAST score at reassessment than single use of any of these agents (odds ratio [OR] 2.84, p = 0.01). Among the different drug combinations, using SGLT2i and pioglitazone (median dose 15 mg daily) together, as compared to not using any of these three agents, was associated with a higher likelihood of both low FAST score at reassessment (OR 6.51, p = 0.008) and FAST score regression (OR 12.52, p = 0.009), after adjusting for changes in glycaemic control and body weight during the study. Combining SGLT2i and pioglitazone is a potentially useful strategy to ameliorate 'at-risk' MASH in patients with type 2 diabetes.

Real-World HbA1c Changes Among Type 2 Diabetes Mellitus Patients Initiating Treatment With a 1.0 Mg Weekly Dose of Semaglutide for Diabetes.

Background: Medical management of patients with type 2 diabetes mellitus (T2DM) is complex because of the chronic nature of the disease and its associated comorbidities. Injectable once-weekly semaglutide for diabetes (OW sema T2D) is a type of glucagon-like peptide-1 receptor agonist approved for the treatment of patients with T2DM. Objectives: To describe patient characteristics and HbA1c changes for patients prescribed 1.0 mg maintenance dose OW sema T2D. Methods: This retrospective study included adult patients with T2DM with a pre-index glycated hemoglobin (HbA1c) of at least 7%, initiating treatment with OW sema T2D between January 1, 2018, and December 31, 2019, and prescribed a 1.0 mg maintenance dose. Patients were identified in the Optum Research Database and were included if they had continuous health plan enrollment for at least 12 months prior to (pre-index) and at least 12 months following (post-index) the date of the first OW sema T2D claim (index). Dose at initiation and prescriber specialty were captured. Change in HbA1c between the latest post-index and pre-index HbA1c measurement was calculated among all patients and among those with at least 90 days of continuous treatment (persistent patients). Results: A total of 2168 patients were included in this study. On average, patients were taking 13.5 different classes of medications. The majority of patients had lipid metabolism disorder (90.8%), hypertension (86.6%), diabetes with complications (86.8%), or other nutritional/endocrine/metabolic disorders (72.5%). The mean HbA1c reduction was 1.2% (P < .001). Patients persistent with OW sema T2D (n =1280) had a mean HbA1c reduction of 1.4% (P < .001). The mean (SD) days covered with a 1.0 mg maintenance dose was 236.1 (94.1) days. Discussion: Despite being medically complex, the patients in this real-world study experienced significant reductions in HbA1c following initiation of OW sema T2D. Conclusions: A 1.0 mg maintenance dose of OW sema T2D is an effective treatment for T2DM in the real world.

An actuator space optimal kinematic path tracking framework for tendon-driven continuum robots: Theory, algorithm and validation

Path tracking of continuum robots is a fundamental and crucial problem across various applications. In this article, we address this problem by focussing on three aspects. Firstly, we propose an efficient multi-solution inverse kinematics solver for three-section constant curvature robots by bridging the theoretical reduction and the numerical correction. Secondly, we derive a linear tendon-driven actuation model, establishing the connection between the robot configuration space and the actuator space. With this model, we achieve optimal distance planning and optimal time allocation considering the constraints of actuator velocity and acceleration, generating a continuous trajectory directly in the actuator space. Finally, we present our kinematic path tracking framework, which includes offline optimal trajectory planning and online feedforward and feedback control. Experiments are conducted both in simulations and in the real world on our three-section tendon-driven continuum robot. The experiments validate the increased efficiency, higher success rates, and accessibility of multiple solutions offered by our inverse kinematics solver, as well as the optimality in distance planning and time allocation in the actuator space. Performance improvements in tracking accuracy are demonstrated through comparative experiments and the application of our framework in path tracking tasks with obstacles is presented through a case study.

Clinical Characteristics and Treatment of Patients Diagnosed with Pulmonary Arterial Hypertension: A Real-World Study in the USA, Europe and Japan.

This study aimed to describe the clinical characteristics of patients with pulmonary arterial hypertension, treatment received, and factors predicting initial or earlier combination therapy. The Adelphi Real World Pulmonary Arterial Hypertension (PAH) Disease Specific Programme™ is a cross-sectional survey with retrospective data collection conducted in the USA, Europe (France, Germany, Italy, Spain, and the UK), and Japan from March to August 2022. Physicians reported patient characteristics, treatment history, and reasons for treatment selection. Descriptive statistics were grouped by country and World Health Organization functional classification. A multivariable Cox regression analysis investigated factors predicting initial or earlier combination therapy use. Data for 1173 patients was provided by 293 physicians. Patients' mean (standard deviation) age was 58.7 (13.8) years and 54.6% were female. Overall, 91.2% of patients were receiving, or had previously received, PAH-specific treatment. About three-quarters of the cohort were still taking the initial treatment strategy: for this group, 54% were prescribed monotherapy and 32% combination therapy; 15% of patients received supportive therapy alone. The proportion of patients receiving PAH-specific treatment was lowest in the USA (82.0%) and highest in France (94.6%). The proportion of patients receiving PAH on combination therapy was lowest in the USA (23.8%) and highest in Germany (36.5%). Treatment was prescribed for PAH in 87.6%, 89.8%, 89.3%, and 75.0% of patients who were functional classI, II, III, and IV, respectively, and combination therapy usage was more likely for those with more advanced functional class. Higher risk status, care by a pulmonologist, Japanese residence, more complete assessments, and hospitalization in the past 12months were statistically associated with decreased time to combination therapy for PAH. Older age was statistically associated with increased time to combination therapy. In this real-world, geographically diverse sample, monotherapy treatment was common, even among patients with advanced disease.

Translating player monitoring into training prescriptions: Real world soccer scenario and practical proposals

Data-driven training prescription based on previous training or match data is thought to be associated with better training outcome, compared to prescription without considering any monitoring data. Understanding the complex relationship between training load, physical performance, fitness status, fatigue and injury risk represents a challenge for health and performance practitioners and researchers. Although studies have revealed a positive correlation between training load and injury risk, this cause-effect relation cannot be determined given the multifactorial nature of injuries. Additionally, conflicting findings have been published explaining the relationship between training load and injuries, underlining the importance of training load management, prescription, and communication within the multidisciplinary team to improve physical performance and reduce injury risk. In this sense, practitioners may benefit from practical examples based on training load data to make informed decisions for prescribing training. This narrative review provides real-world examples of training decisions based on training load data in soccer, including training prescription, drill design and multidisciplinary team communication. Finally, a framework was provided to make informed training prescription from a physiological standpoint and elucidate the relationship between training load and injury risk.

Efficacy of 8-week daclatasvir-sofosbuvir regimen in chronic hepatitis C: a systematic review and meta-analysis

BackgroundThe high rates of the sustained virologic response 12 weeks after treatment (SVR12) in real world settings provoked the adoption of shortened courses of the costly direct-acting antivirals (DAAs) regimens. This study provides, to our knowledge, the first systematic review and meta-analysis for the efficacy of the shortened 8-week course of sofosbuvir (SOF) plus daclatasvir (DCV), the most accessible DAAs in the low-middle income countries (LMICs).MethodsWe performed a proportion meta-analysis to determine a reliable rate of SVR12 by pooling all studies that evaluated the results of the 8-week regimen of DCV + SOF. In addition, we applied sensitivity analyses using two imputation paradigms: a conservative approach, and a pragmatic approach to avoid overestimating the efficacy of the 8-week regimen in studies that followed a response-guided treatment (RGT) approach.ResultsSix studies with a total of 159 patients were included. The pooled SVR12 rate ranged from 91 to 97% in the included scenarios. The pragmatic scenario showed that the pooled SVR12 was 97% (95% confidence interval (CI) 91%; 100%) with lower variability as assessed by the prediction interval. The conservative approach revealed an SVR12 of 93% (95% CI 84%; 95%).ConclusionThe 8-week course of 60 mg DCV with SOF provided a comparable SVR12 to the standard 12-week regimen in treatment-naïve, non-HIV co-infected patients with a minimum estimated efficacy of 90%.

Augmented Reality Adoption: Exploring Security Concerns and Consumer Resourcefulness in the Era of Society 5.0

ABSTRACTSociety 5.0, building its foundation on industry 4.0, defines the concept of super smart cities as places where humans can adapt novel technologies such as augmented reality (AR) in their daily lives. AR brought its consumers an immersive and hybrid experience, blurring the boundaries between the real and virtual worlds. This study explores the user adoption of AR, an interactive technology, in the e‐commerce domain in the present era of Society 5.0. This study seeks to address the question of what factors drive the consumer's adoption of AR while shopping online and how security concern and technostress (TS) impact their intention towards its use and its actual usage behaviour. This research offers a novel perspective and fills a gap in the existing literature by integrating consumers' security concerns and TS, previously unexplored factors, in the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The ability of the UTAUT model, over the earlier technology adoption models, to incorporate context‐specific factors makes it significantly relevant for studying AR adoption, a technology blending the virtual and real world. A modified UTAUT‐based questionnaire assessed relevant factors influencing this behaviour. This study employed a quantitative, cross‐sectional survey design to apprehend a broad range of users and to investigate consumer perceptions and adoption of AR in online shopping. Purposive sampling ensured that respondents were aligned with the research context and were knowledgeable about the use of AR in the e‐commerce domain and its relevant applications, thereby strengthening the validity of the study findings. A two‐staged partial least square structural equation modelling–artificial neural network (PLS‐SEM–ANN) data analysis approach was used to determine the study's findings. The meaningful responses collected from 457 respondents were analysed using this targeted approach for in‐depth insights. The synthesis of the study's analysis suggests that personal innovativeness towards information technology and security concerns are critical drivers of intention and actual usage. Interestingly, TS and social influence emerged as non‐significant, indicating a nuanced view of the UTAUT model in this context. A possible explanation for their insignificance lies in the present generation's ability to rapidly adapt to new technologies and make autonomous decisions, reducing reliance on peer influence. By understanding these factors, the research seeks to pave the way for the efficacious integration of AR within the e‐commerce domain. It further emphasizes the significance of consumer resourcefulness, a gap in current research, for bridging the intention‐usage gap. This focus on consumers’ personal innovativeness towards IT, security concerns and consumer resourcefulness provides valuable insights for e‐commerce platforms seeking to drive successful AR adoption. The study explores how well AR will be accepted and used, informing scholarly researchers and businesses on making AR‐integrated online shopping successful.

A PBL strategy for incorporating 3D cell culture into the classrooms of undergraduate biotechnology engineering students

ABSTRACT Background In Mexico, the undergraduate curriculum in biological, biomedical and related sciences only gives hands-on experience in 2D cell culture due to its low cost and ease of implementation. These traditional lecture-based cell culture courses are often limited to routine learning methods and fail to demonstrate alternative culture strategies that would be needed to realistically mimic the processes occurring within the human body and be used to solve relevant medical problems. The use of 3D cell culture, on the other hand, makes it possible to recreate a microenvironment similar to that of living tissue, which is extremely useful for physicochemical studies. Moreover, the incorporation of problem-based learning (PBL) techniques to teach 3D cell culture has the potential to greatly enhance student learning. Purpose The objective of the work herein was to evaluate the success of implementing a 3D cell culture technique using the PBL method of teaching. Sample In this pilot study, eight students attended a traditionally structured cell culture course to learn cell culture techniques (control group), and six students were taught using a PBL model. Design and methods The activities were carried out either with the guidance of an instructor (traditional learning) or through an autonomous search for information (PBL).3D cell culture and scaffold printing exercises were incorporated into the course schedule, fostering in our students the ability to solve real world problems within a team. Results With the PBL teaching method, students displayed a slight tendency to better interpret and discuss their results (P = 0.436). Conclusion This pilot study highlights the usefulness of self-learning through a PBL system and warrants larger-scale research to better examine the potential benefits of incorporating a PBL strategy for teaching 3D cell culture in undergraduate biotechnology engineering laboratories.

Comparison of safety profile in patients with atopic dermatitis treated with dupilumab or conventional systemic treatment: real world data from the US network

Background Safety of dupilumab in atopic dermatitis (AD) was investigated in randomized controlled trials (RCT). However, head-to-head trials comparing with conventional systemic drugs are lacking and large real-world data on the long-term safety profile as compared are scarce. Objective To compare long-term safety profile of dupilumab with conventional systemic drugs used in the management of moderate to severe AD. Methods Data from electronic health records of AD patients treated with either dupilumab, azathioprine, Cyclosporine A, mycophenolate mofetil, methotrexate, or oral glucocorticoids were retrieved from the TriNetX US Collaborative Network. Risks of adverse events and new onset of type-2-inflammatory diseases within 5 years after treatment initiation was investigated. Results 5 propensity-matched cohorts, up to 18,708 individuals per cohort, were created. Dupilumab treatment displayed reduced risk for diseases of the circulatory, the upper respiratory, and the musculoskeletal system, infections, and type 2 diseases as compared to all other treatment options. In contrast risk for conjunctivitis was increased in dupilumab treated patients as compared to mycophenolate mofetil and methotrexate. Conclusion Here presented data indicates that treatment with dupilumab for AD has reduced risk for adverse effects of conventional systemic drugs and thus might be safer. Obtained data should be verified in prospective studies.