Real-world Impact Research Articles

Glucagon-like peptide-1 receptor agonist therapy effects on glycemic control and weight in a primary care clinic population.

Glucagon-like peptide-1 receptor agonist (GLP-1a) medications have been shown in randomized controlled trials (RCTs) to have consistent and impressive effectiveness in lowering hemoglobin A1c (HbA1c) and weight, but limited data exist on the efficacy of GLP-1a medications in clinical practice. We studied the association between GLP-1a therapy and changes in weight and HbA1c in a real-world patient population. In this retrospective cohort study of patients seen in a primary care clinic between 2012 and 2021, we examined the change in weight and HbA1c over 12 months in a cohort of patients with at least one prescription for a GLP-1a. Within this cohort, treatment was defined as having ≥2 GLP-1a prescriptions at a therapeutic dosage separated by ≥10 months. The cohort included 693 patients of whom 393 (57%) were treated with GLP-1a therapy. The treatment group had a mean change in body mass index (BMI) of -0.83 kg/m2 (±2.88) compared to -0.70 kg/m2 (±2.99) in the without GLP-1a group (p = 0.57). Treated patients had a mean change in HbA1c of -1.00% (±2.07) compared to -0.83% (±1.92) in the without GLP-1a group (p = 0.27). For treated and without GLP-1a patients, respectively, the proportion of patients with a decrease in BMI was 65 versus 64% (p = 0.86), and the proportion with a decrease in HbA1c was 73 versus 69% (p = 0.28). In clinical practice, GLP-1a therapy was associated with more modest reductions in weight and HbA1c than shown in prior RCTs. As GLP-1a use continues to expand throughout primary care, the real-world impact of this pharmacotherapy will require further evaluation.

Clinical impact of metagenomic next-generation sequencing of bronchoalveolar lavage fluids for the diagnosis of pulmonary infections in respiratory intensive care unit

BackgroundThe real-world clinical impact of mNGS on BALF in the respiratory intensive care unit (RICU) is not yet fully understood. MethodsWe investigated the clinical impact of mNGS on BALF samples obtained from 92 patients admitted to the RICU over a 2-year period. We utilized both mNGS and culture methods to evaluate the effectiveness of mNGS in diagnosing pulmonary infections. The clinical impact of mNGS were evaluated by the clinician committees. ResultsAmong the 92 diagnosed patients, 78 cases (84.7 %) were determined to have infectious diseases caused by pathogenic microorganisms, and the bacterial infections constituted the most prevalent diagnostic category. For mixed infection, the most common type was the Pneumocystis jironecii and cytomegalovirus co-infection. The mNGS results had a positive impact on the clinical management of 43 cases (46.7 %). Moreover, 19 cases (44.2 %) of positive clinical impacts were solely based on new diagnoses made possible by mNGS results. These new diagnoses were particularly helpful for identifying rare pathogens, which could not be detected by conventional diagnostic methods. ConclusionsThe BALF mNGS has a positive real-world impact in RICU. Clinician committee play a critical role in ensuring the appropriate use of mNGS.

Using Machine Learning to Catch Bogus Firms

We investigate the use of a machine learning (ML) algorithm to identify fraudulent non-existent firms that are used for tax evasion. Using a rich dataset of tax returns in an Indian state over several years, we train an ML-based model to predict fraudulent firms. We then use the model predictions to carry out field inspections of firms identified as suspicious by the ML tool. We find that the ML model is accurate in both simulated and field settings in identifying non-existent firms. Withholding a randomly selected group of firms from inspection, we estimate the causal impact of ML driven inspections. Despite the strong predictive performance, our model driven inspections do not yield a significant increase in enforcement as evidenced by the cancellation of fraudulent firm registrations and tax recovery. We provide two explanations for this discrepancy based on a close analysis of the tax department’s operating protocols: overfitting to proxy-labels, and institutional friction in integrating the model into existing administrative systems. Our study serves as a cautionary tale for the application of machine learning in public policy contexts and of relying solely on test set performance as an effectiveness indicator. Field evaluations are critical in assessing the real-world impact of predictive models.

Utilization and impact of esophageal string testing in children with eosinophilic esophagitis: A 1 year experience.

The 1-h esophageal string test (EST) is a minimally invasive test that can be used to monitor eosinophilic esophagitis (EoE) disease activity and guide treatment without endoscopy. We aimed to describe the real-world utilization and impact of EST on the care of children with EoE over the first year this was used at our center. Between 12/1/2022 and 11/30/2023, 39 ESTs were successful in 45 attempts (87% completion rate) in 31 patients. Five patients underwent multiple ESTs. Adverse events during the EST included vomiting. Reasons for failure to complete the EST (13%, n = 6) were patients could not swallow the capsule (n = 5) and vomiting (n = 1). EST was used to assess EoE without the need for endoscopy in 95% (n = 37) of cases. Treatment approach varied based on whether the EST indicated active (38.5%) or inactive (61.5%) EoE. The EST is a well-tolerated minimally invasive disease monitoring tool for patients with EoE.

Exploring Legal and Technical Challenges of Deep Fakes in India

The rapid advancement of deep fake technology has introduced significant challenges in the realms of data security, privacy, and intellectual property rights. Deep fakes, which involve the creation of highly realistic but manipulated digital content using artificial intelligence, have been misused for various malicious purposes, including political manipulation, identity theft, and defamation. This article examines the current state of deep fake regulation in India, highlighting the inadequacies of existing laws and the urgent need for specific legislation to address these issues. Despite the presence of broader laws under the Information Technology Act and the Indian Penal Code, there is a conspicuous absence of precise legal frameworks targeting the unique threats posed by deep fakes. Through an interdisciplinary approach that integrates legal analysis and technical insights, this study evaluates the effectiveness of current detection technologies and explores their limitations. The article also provides case studies illustrating the misuse of deep fakes in political and personal contexts, underscoring the real-world impact of this technology. In conclusion, the study advocates for comprehensive legal reforms and enhanced technological measures to mitigate the risks associated with deep fakes. By proposing targeted amendments to existing laws and recommending the development of advanced detection tools, this research aims to contribute to a more robust and effective regulatory environment for deep fakes in India.

How pre-analytical conditions impact glucose measurement and (gestational) diabetes diagnosis: A real-world stability study and a call for harmonization

Background and aimsSince 2023, guidelines of the AACC/ADA recommend the use of citrate buffer-containing tubes as a first option for glucose measurement. This study aims to assess the pre-analytical stability of glucose under various conditions (room temperature (RT) or at 4 °C) and the potential real-world impact of introducing these tubes on (gestational) diabetes and IFG prevalence. Materials and Methods25 healthy volunteers were sampled to assess glucose stability across time, at 4 °C and at RT, before and following centrifugation. 701 patients undergoing fasting plasma glucose analysis and 109 women having OGTT were collected according to current procedures (NaFl K2C2O4 (NaFl) tubes) as well as with citrate-containing tubes (FC Mix). ResultsThe mean glucose concentration bias between FC Mix and NaFl tubes when centrifugation occurred within 5 min was 0.53 % and this difference raised slowly to reach 2.3 %, six-hours post-centrifugation. When centrifugation was delayed, a rapid decrease in glucose concentrations was observed for NaFl tubes (4.9 % at 30 min) and this trend was only partially reduced by placing samples at 4 °C (3.1 %). The decrease reached 10.8 % (RT) and 7.8 % (4 °C) at 2 h, before reaching a plateau. Samples collected on citrate remained stable during 24 h. In real-life conditions, the mean bias between FC Mix and NaFl tubes increased progressively over time and reached 8.59 % for samples centrifuged between two- and four-hours following sampling. Compared to widespread practices, the use of citrate-containing tubes increased IFG, DM and GDM prevalences by 84.0 %, 36.7 % and 150 %, respectively. ConclusionsGlucose concentrations rapidly decrease in NaFl tubes following collection and placing samples at 4 °C reduces only marginally the decay. Citrate-containing tubes offer a valuable solution for direct and long-lasting glucose stabilization but, before wider adoption, large epidemiologic studies should confirm or redefine current diabetes diagnostic thresholds.

Current type 2 diabetes guidelines: Individualized treatment and how to make the most of metformin.

Evidence-based guidelines provide the premise for the delivery of quality care to preserve health and prevent disabilities and premature death. The systematic gathering of observational, mechanistic and experimental data contributes to the hierarchy of evidence used to guide clinical practice. In the field of diabetes, metformin was discovered more than 100 years ago, and with 60 years of clinical use, it has stood the test of time regarding its value in the prevention and management of type 2 diabetes. Although some guidelines have challenged the role of metformin as the first-line glucose-lowering drug, it is important to point out that the cardiovascular-renal protective effects of sodium-glucose co-transporter-2 inhibitors and glucagon-like peptide-1 receptor agonists were gathered from patients with type 2 diabetes, the majority of whom were treated with metformin. Most national, regional and international guidelines recommend metformin as a foundation therapy with emphasis on avoidance of therapeutic inertia and early attainment of multiple treatment goals. Moreover, real-world evidence has confirmed the glucose-lowering and cardiovascular-renal benefits of metformin accompanied by an extremely low risk of lactic acidosis. In patients with type 2 diabetes and advanced chronic kidney disease (estimated glomerular filtration rate 15-30 mL/min/1.73m2), metformin discontinuation was associated with an increased risk of cardiovascular-renal events compared with metformin persistence. Meanwhile, it is understood that microbiota, nutrients and metformin can interact through the gut-brain-kidney axis to modulate homeostasis of bioactive molecules, systemic inflammation and energy metabolism. While these biological changes contribute to the multisystem effects of metformin, they may also explain the gastrointestinal side effects and vitamin B12 deficiency associated with metformin intolerance. By understanding the interactions between metformin, foods and microbiota, healthcare professionals are in a better position to optimize the use of metformin and mitigate potential side effects. The United Kingdom Prospective Diabetes Study and the Da Qing Diabetes Prevention Program commenced 40 years ago provided the first evidence that type 2 diabetes is preventable and treatable. To drive real-world impact fromthis evidence, payors, practitioners and planners need to co-design and implement an integrated, data-driven, metformin-based programme to detect people with undiagnosed diabetes and prediabetes (intermediate hyperglycaemia), notably impaired glucose tolerance, for early intervention. The systematic data collection will create real-world evidence to bring out the best of metformin and make healthcare sustainable, affordable and accessible.

Survival and patient-reported outcomes of real-world high-risk stage II and stage III colon cancer patients after reduction of adjuvant CAPOX duration from 6 to 3 months

AimAdjuvant chemotherapy has been advised for high-risk stage II and III colon cancer since 2004. After the IDEA study showed no clinically relevant difference in outcome, reduction of adjuvant CAPOX duration from 6 to 3 months was rapidly adopted in the Dutch treatment guideline in 2017. This study investigates the real-world impact of the guideline change on overall survival (OS) and patient-reported outcomes (PROs). MethodsPatients with high-risk stage II (pT4 +) and III (pN+) colon cancer were selected from the Netherlands Cancer Registry, based on surgical resection and adjuvant CAPOX before (2015–2016) versus after (2018–2019) the guideline change. Both groups were compared on OS, using multivariable Cox regression, and on PROs. ResultsPatients treated before (n = 2330) and after (n = 2108) the guideline change showed similar OS (HR 1.02; 95 %CI [0.89–1.16]), also in high-risk stage III (pT4/N2, HR 1.06 [0.89–1.26]). After the guideline change, 90 % of patients were treated for 3 months with no inferior OS to those still receiving 6 months (HR 0.89 [0.66–1.20]). PROs 2 years after CAPOX completion, available for a subset of patients, suggest a lower neuropathy (n = 366; 26.2 [21.3–31.1] to 16.5 [14.4–18.6]) and better quality of life (n = 396; 80.9 [78.6–83.2] to 83.9 [82.8–84.9]), but no significant difference in workability (n = 120; 31.5 [27.9–35.1]) to 35.3 [33.8–36.7]), with reduction from 6 to 3 months of CAPOX. ConclusionThis real-world study confirmed that shorter adjuvant CAPOX did not compromise OS and may improve PROs, complementing the IDEA study and supporting 3 months of adjuvant CAPOX in daily clinical practice.

Survey of short-term traffic flow prediction based on LSTM

Short-time traffic flow prediction can not only help urban traffic management to complete the control and induce but also reduce the degree of urban congestion and improve the efficiency of urban operation. At the same time, improving the effect of short-time traffic flow prediction is one of the key points of traffic control and guidance development. Currently, there are many different models for short-term traffic flow prediction, and different prediction models have their different advantages and disadvantages. In this paper, we review the current status of research on short-time traffic flow prediction based on Long Short-Term Memory (LSTM) neural network: first, we analyze the research method in this paper; second, we briefly review the research and application effects of common prediction methods in traffic flow prediction; finally, the optimization method based on LSTM and its application effect are summarized and analyzed from the aspect of LSTM network and its combination model. In addition, short-time traffic flow prediction still faces challenges due to the problems of the data itself and the complexity of real-world traffic flow impact factors.

Leveraging existing mid-end ultrasound machine for point-of-care intestinal ultrasound in low-resource settings: Prospective, real-world impact on clinical decision-making.

Point-of-care ultrasound (POCUS) has transformed inflammatory bowel disease (IBD) management, but the cost to purchase high-end equipment can be prohibitive. To assess prospectively the feasibility of POCUS using pre-existing mid-end ultrasound equipment without incurring additional cost. Consecutive IBD patients underwent POCUS with or without faecal calprotectin (FCP) using a mid-end ultrasound machine. If POCUS with or without FCP could not guide management, we performed additional ileocolonoscopy or cross-sectional imaging. We evaluated the impact of POCUS on IBD management and its correlation with ileocolonoscopy or cross-sectional imaging. We analysed pregnant, paediatric and post-operative patients separately. Among 508 patients with IBD, we analysed 419 (60.4% Crohn's disease [CD]; 61.3% male, age [years]: 36 [18-78]) undergoing 556 POCUS sessions. POCUS with or without FCP independently influenced clinical management in 42.8% of patients with CD and 49.7% with ulcerative colitis (UC). POCUS helped avoid colonoscopy in 51.4% of patients with CD and 51.8% with UC, and cross-sectional imaging in 38.1% of suspected active small bowel CD. In patients with additional diagnostics, POCUS-based decisions remained unchanged in 81.2% with CD and 85% with UC. Sensitivity and specificity of POCUS compared to ileocolonoscopy were 80% and 94.4% for CD and 80.8% and 92.8% for UC, respectively. Sensitivity and specificity compared to cross-sectional imaging were 87.2% and 87.5%, respectively. POCUS using existing mid-end ultrasound equipment in low-resource settings influenced IBD clinical decision-making with excellent accuracy, often avoiding colonoscopy and cross-sectional imaging.

Multidimensional Position And Displacement Estimation Using Periodic Coded Optical Apertures In A Single Camera Imaging System

Accurately estimating multidimensional positions and displacements in fluid dynamic experiments is essential for understanding complex flow phenomena. This paper presents a novel optical measurement setup capable of determining velocity fields using periodic coded optical apertures in a single-camera imaging system. Our method involves placing a periodic transmission grating in front of the optical system and aligning a laser beam along the system’s optical axis. When particles intersect the laser beam, they scatter light, producing out-of-focus images on the imaging sensor resembling the periodic grating. The size and position of these images vary with the distance to the imaging system and the offset from the optical axis, allowing for accurate determination of particle positions. We derive mathematical formulas describing the imaging system and the relationship between image properties and particle positions and verify them through simulations. Additionally, we develop a calibration method enabling arbitrary optical imaging systems to be used with this technique. Finally, we demonstrate the effectiveness of our approach by measuring velocity fields near a ducted ship propeller, a practical application that underscores the real-world impact of our research in fluid dynamics.

Universal Refugee Social Protection Guidelines: A Discussion into the Challenges to Their Successful Adoption

AbstractThis paper discusses some of the impediments to the realization of refugee-specific conventions and the international bill of human rights content, i.e., the International Covenant on Civil and Political Rights; International Covenant on Economic, Social and Cultural Rights and the UDHR. The discrepancy highlights a broader failure in the diffusion of the global refugee regime and underscores the disparity between the intentions of global policy and its real-world impact. Challenges to global social policy diffusion (and the resultant implementation gaps in Africa) are examined through the use of secondary data on refugees in South Africa and the region. Also examined is the content of human rights conventions and their adoption. The challenges noted include (i) compulsory forms of power within the global governance system and constraints on the global refugee regime; (ii) protracted social conflict situations, funding gaps, geopolitical reordering and an ensuing lack of cohesion amongst stakeholders in the transference of global social policy and (iii) limitations in the content of refugee rights and a poor alignment of the said content at the national and local contexts.

Machine learning to promote translational research: predicting patent and clinical trial inclusion in dementia research.

Projected to impact 1.6 million people in the UK by 2040 and costing £25 billion annually, dementia presents a growing challenge to society. This study, a pioneering effort to predict the translational potential of dementia research using machine learning, hopes to address the slow translation of fundamental discoveries into practical applications despite dementia's significant societal and economic impact. We used the Dimensions database to extract data from 43 091 UK dementia research publications between the years 1990 and 2023, specifically metadata (authors, publication year, etc.), concepts mentioned in the paper and the paper abstract. To prepare the data for machine learning, we applied methods such as one-hot encoding and word embeddings. We trained a CatBoost Classifier to predict whether a publication will be cited in a future patent or clinical trial. We trained several model variations. The model combining metadata, concept and abstract embeddings yielded the highest performance: for patent predictions, an area under the receiver operating characteristic curve of 0.84 and 77.17% accuracy; for clinical trial predictions, an area under the receiver operating characteristic curve of 0.81 and 75.11% accuracy. The results demonstrate that integrating machine learning within current research methodologies can uncover overlooked publications, expediting the identification of promising research and potentially transforming dementia research by predicting real-world impact and guiding translational strategies.

Evaluation of Explainable Artificial Intelligence using TOPSIS Method

Explainable Artificial Intelligence (XAI) refers to the development of AI systems are transparent, explainable and their comprehensible for results can provide explanations or predictions. As AI technologies, particularly machine learning models, become more complex and sophisticated, there is a growing need to ensure that their decisions can be comprehended and trusted by humans, especially health, Finance and such as criminal justice in important domains. Evaluating Explainable Artificial Intelligence (XAI) is essential to ensure transparency, accountability, and user trust in AI systems. Interpretability is a key factor, examining how easily the model's internal mechanisms can be understood. Model transparency, feature importance, and the clarity of visualizations contribute to this aspect. Differentiate between post-hoc and intrinsic explanations, considering whether the model inherently provides interpretable insights. The distinction between local and global explanations is crucial, as it determines whether explanations focus on individual predictions or the overall model behavior. Robustness and consistency are assessed through stability and sensitivity analysis, ensuring that explanations remain reliable across similar instances. Additionally, ethical considerations, such as fairness and transparency in decision-making, must be addressed to uncover and mitigate biases. User feedback and the relevance of explanations to the specific use case contribute to a comprehensive evaluation, fostering the development of XAI systems that are not only technically robust but also ethically sound and user-friendly. The significance of research in Explainable Artificial Intelligence (XAI) lies in addressing critical challenges associated with the adoption and deployment of AI systems in various domains. As AI technologies, particularly complex machine learning models, become integral to decision-making processes in areas such as healthcare, finance, and criminal justice, the need for transparency and interpretability becomes paramount. Topsis involves optimizing from an advantageous standpoint by simultaneously minimizing the distance to and maximizing the distance from a reference point, which is defined in relation to solutions within a set of alternative options and numerous identification criteria. The importance of Topsis criteria lies in the potential to integrate comparative weights. This study conducts a comprehensive review of Topsis, exploring various weighing schemes and employing different distance measurements. Numerous applications of Topsis are examined, particularly its utilization in comparing results for a diverse set of multiple criteria data with varying weights. Interpretable Machine Learning Models, Human-Centric Design in XAI, Ethical Implications of XAI, Industry-specific Applications of XAI and Hybrid Approaches for Model Interpretability. Interpretability Metrics, Human-Subjective Evaluation, Algorithmic Robustness and Real-world Impact. the Ranking of Evaluation Explainable Artificial Intelligence. Industry-specific Applications of XAI is got the first rank whereas is the Ethical Implications of XAI is having the Lowest rank.

Osimertinib-Related QTc Prolongation: Real-World Incidence and Impact of Drug Dosing on Recurrence Risk

Background: Osimertinib is associated with increased risk of QTc interval prolongation compared to other epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs). However, real-world data on its incidence, risk factors, and association with drug dosing is limited. Methods: This retrospective study analysed data from 970 Chinese patients with EGFR-positive non-small cell lung cancer (NSCLC) who received osimertinib between 1 November 2017 and 18 November 2023. Patients underwent pre-osimertinib (baseline) and regular electrocardiogram (ECG) monitoring while on osimertinib. QTc prolongation was graded according to Common Terminology Criteria for Adverse Events guidelines version 5.0. Clinical outcomes of patients with grade 3 or above QTc prolongation were collated. Results: Out of 970 patients, 9 (0.9%) developed grade 3 QTc prolongation. Median onset time from drug initiation was 49 days (range 14 to 371). No patients experienced arrhythmic events (grade 4) or death (grade 5). Notably, baseline QTc was above 450 ms (grade 1 or 2) in 7 of them (78%), comparing to 21% (102 out of 497) in patients without grade 3 QTc prolongation (p<0.001). Rechallenging osimertinib at 80mg daily dose resulted in recurrent grade 3 QTc prolongation in 3 out of 4 patients, while only 1 out of 9 experienced recurrences with dose reduction. Conclusion: The real-world incidence of osimertinib-induced grade 3 QTc prolongation was similar to that reported in clinical trials. Baseline QTc prolongation may be a potential risk factor. Dose reduction mitigated recurrence risk, while rechallenging osimertinib at same dose conferred high recurrence risk.

Industrial Metaverse: A Comprehensive Review, Environmental Impact, and Challenges

The Industrial Metaverse paradigm can be broadly described as a virtual environment that integrates various technologies such as augmented reality and mixed reality to enhance business operations and processes. It aims to streamline workflows, reduce error rates, improve efficiency, and provide a more engaging experience for employees. The promise of the Industrial Metaverse to drive sustainability and resource efficiency is compelling. Using advanced technologies such as the Industrial Metaverse is vital in an endeavor to have a competitive edge in a rapidly evolving business environment. However, the environmental impact of the technologies underpinning the Industrial Metaverse, like data centers and network infrastructure, should not be overlooked. The ecological footprint of these technologies must be considered in the sustainability equation. Researchers have warned that, by 2025, without sustainable artificial intelligence (AI) practices, AI will consume more energy than the human workforce, significantly offsetting zero carbon gains. As the Metaverse persists in evolving and gaining momentum, it will be necessary for companies to prioritize sustainability and explore new ways to balance technological advancements with environmental stewardship. However, recent studies have conjectured that the Metaverse holds the potential to reduce carbon emissions, as digital replacements for physical goods become more prevalent and physical activities like mobility and construction are reduced. Moreover, the specific extent to which this substitution can alleviate environmental concerns remains an open issue, presenting a knowledge gap in understanding the real-world impact of digital replacements. Thus, the objective of this paper is to provide a comprehensive review of the Industrial Metaverse, as well as explore the environmental impact of the Industrial Metaverse. The integrative literature review design and methodological approach involved multiple sources from the Web of Science and databases such as the ACM library, IEEE Library, and Google Scholar, which were analyzed to provide a comprehensive understanding of the developments in the Industrial Metaverse. Firstly, by considering the Industrial Metaverse’s architecture, we elucidate the Industrial Metaverse concept and the associated enabling technologies. Secondly, we performed an exploration through a discussion of the prevalent use cases and the deployment of the emerging Industrial Metaverse. Thirdly, we explored the impact of the Industrial Metaverse on the environment. Lastly, we address novel security and privacy risks, as well as upcoming research challenges, keeping in mind that the Industrial Metaverse is based on a strong data fabric. The results point to the Industrial Metaverse as having both positive and negative environmental effects in terms of energy consumption, e-waste, and pollution. Research, however, indicates that most Industrial Metaverse applications have a positive environmental impact and subsequently trend toward sustainability. Finally, for sustainability in the Industrial Metaverse, enterprises may consider utilizing renewable energy sources and cloud services. Furthermore, we examined the effects of products on the environment, as well as in the creation of a circular economy.

Health-Related Quality of Life and Perceived Stigma in Eosinophilic Esophagitis: A Real-World, US, Web-Based Survey

BACKGROUND AND AIMSEosinophilic esophagitis (EoE) is associated with impaired health-related quality of life (HRQoL) and stigma perceptions. Therefore, we examined the real-world impact of EoE on the daily life and ability to function in adolescents (caregiver-reported) and adults with EoE in the United States of America in a non-interventional, cross-sectional, web-based survey. METHODSHRQoL was assessed using the Short Form Health Survey (domains: vitality and social functioning) and the European Health Interview Survey (domain: sleep). Scores for the survey responses were on a scale of 0 to 100; higher scores indicated better performance in the HRQoL domain. Anxiety and depression were assessed using the Patient-Reported Outcomes Measurement Information System (PROMIS) short forms. Higher PROMIS scores indicated higher levels of anxiety and depression; a mean score of 50.0 was representative of the general population (individuals without EoE). The sources and impact of EoE-associated perceived stigma were also examined. RESULTSOverall, 211 caregivers and 184 adults completed the survey. HRQoL scores were slightly higher for adolescents than adults with EoE (adolescent and adult scores, respectively: vitality, 50.3 and 36.1; social functioning, 64.0 and 62.4; and sleep, 55.7 and 52.0). Anxiety scores (adolescent and adult scores, respectively: 54.8 and 59.7) and depression scores (54.5 and 56.3) were higher in those with EoE than in the general population. Most participants reported experiencing perceived stigma, which was most commonly from family, friends, classmates, or healthcare professionals. CONCLUSIONPatients with EoE had poor HRQoL, which was demonstrated by the high EoE-associated emotional and psychological burdens and perceived stigma they experience.

IMPACT OF GREEN INNOVATION (GI) PERFORMANCE ON COMPETITIVE ADVANTAGE OF FIRMS IN INDIA

Green Innovation (GI) is increasingly recognized as a critical factor in enhancing the competitive advantage of firms globally, and this trend is evident in India as well. This article explores how the performance of Green Innovation contributes to a firm’s competitive edge in the Indian context. Key drivers such as regulatory compliance, market demand, cost-reduction opportunities, and corporate social responsibility (CSR) mandates are examined. The article highlights various GI performance metrics, including resource efficiency and green product development, and discusses how these can translate into competitive advantages such as brand reputation, differentiation, cost leadership, and access to new markets. Case studies of Indian companies like Tata Motors, ITC Limited, and Mahindra &amp; Mahindra demonstrate the real-world impact of GI on business success. Finally, the challenges that firms face in implementing Green Innovation, including high initial costs and supply chain constraints, are considered.

Methods for Learning in Tripartite Collaboration – A Study Inspired by Action Research

Both private and public organizations are in need of employees with sustainable health, to be effective and productive. Mobile health applications have now started to be implemented for health promotion for individuals and groups at work. Such applications can motivate participation and engagement for health activities, and support managers in suggesting appropriate activities for the individuals and for the working groups, to promote the health of the employees in the production industry. The aim of this research is to analyze how research activities can be designed in collaboration with industrial companies and a software company to support learning for improved work-related health, in implementing digital applications. Action research is used as an approach to knowledge production within this field of work. This paper sheds light on the underlying mechanisms that make the collaborative endeavor a dynamic and impactful process. The active involvement of researchers, private industrial companies, and the software company is not merely a facilitator for data collection; it is the driving force that propels the research forward, ensuring its relevance, innovation, and real-world impact.

Brain region-specific alterations in gene expression trajectories in the offspring born from influenza A virus infected mice

Influenza A virus (IAV) infection during pregnancy can increase the risk for neurodevelopmental disorders in the offspring, however, the underlying neurobiological mechanisms are largely unknown. To recapitulate viral infection, preclinical studies have traditionally focused on using synthetic viral mimetics, rather than live IAV, to examine consequences of maternal immune activation (MIA)-dependent processes on offspring. In contrast, few studies have used live IAV to assess effects on global gene expression, and none to date have addressed whether moderate IAV, mimicking seasonal influenza disease, alters normal gene expression trajectories in different brain regions across different stages of development. Herein, we show that moderate IAV infection during pregnancy, which causes mild maternal disease and no overt foetal complications in utero, induces lasting effects on the offspring into adulthood. We observed behavioural changes in adult offspring, including disrupted prepulse inhibition, dopaminergic hyper-responsiveness, and spatial recognition memory deficits. Gene profiling in the offspring brain from neonate to adolescence revealed persistent alterations to normal gene expression trajectories in the prefronal cortex, hippocampus, hypothalamus and cerebellum. Alterations were found in genes involved in inflammation and neurogenesis, which were predominately dysregulated in neonatal and early adolescent offspring. Notably, late adolescent offspring born from IAV infected mice displayed altered microglial morphology in the hippocampus. In conclusion, we show that moderate IAV during pregnancy perturbs neurodevelopmental trajectories in the offspring, including alterations in the neuroinflammatory gene expression profile and microglial number and morphology in the hippocampus, resulting in behavioural changes in adult offspring. Such early perturbations may underlie the vulnerability in human offspring for the later development of neurodevelopmental disorders, including schizophrenia. Our work highlights the importance of using live IAV in developing novel preclinical models that better recapitulate the real-world impact of inflammatory insults during pregnancy on offspring neurodevelopmental trajectories and disease susceptibility later in life.