Future Research Directions Research Articles

Mapping socioeconomic factors driving antimicrobial resistance in humans: An umbrella review.

Mapping socioeconomic factors driving antimicrobial resistance in humans: An umbrella review.

Liver-derived Exosomal miRNA in NAFLD: Mechanisms of Action, Biomarkers, and Therapeutic Applications.

Nonalcoholic fatty liver disease (NAFLD) is of global concern due to its high prevalence worldwide. NAFLD, as one of the most common causes of liver function abnormalities, is associated with obesity, insulin resistance, and type 2 diabetes mellitus, and there are no medications available to treat NAFLD. Extracellular vesicles (EVs) are nanosized, membrane-bound vesicles that deliver biomolecules between cells. Exosomes are a subtype of EVs that mediate intercellular communication by delivering proteins and RNAs. MicroRNAs (miRNAs) are a highly conserved class of small tissue-specific non-coding RNAs that influence the expression of many functionally interacting genes. Hepatic-derived exosomal miRNAs are tightly associated with NAFLD occurrence and progression through multiple mechanisms. In addition, the characterization of miRNAs suggests that they may serve as multifunctional biomarkers for NAFLD, be used as clinical therapeutic targets for NAFLD, and be significant predictors of patient prognosis. Here, we review recent advances in the regulation and function of exosome-derived miRNAs in NAFLD, focusing on miRNAs specifically expressed or enriched in hepatocytes (HCs), hepatic macrophages, hepatic stellate cells (HSCs), and other immune cells in the liver. Finally, we discuss future research directions on exosomal miRNAs as biomarkers for NAFLD's diagnosis and clinical therapeutic targets.

A review of the literature on emotional labor based on CiteSpace visualization

Emotional labor plays a crucial role in modern service industries, serving as the core factor driving customer satisfaction, employee well-being, and long-term corporate competitiveness. While research on emotional labor has made some progress, issues such as vague research subjects, lack of generalizable situational analysis, fragmented findings, and insufficient systematic organization remain. This paper selects two widely representative databases, Web of Science (WOS), to systematically review and conduct bibliometric analysis of 2,392target doc-uments using CiteSpace software. It reconstructs the literature from both cogni-tive regulation and affective management perspectives, revealing the current state of research on emotional labor. The study finds that the anteced-ents of emotional labor involve four dimensions: individual, organizational, customer, and socio-cultural. Its outcomes have profound impacts on employ-ees, organi-zations, and customers. The research on emotional labor presents three levels of exploration: dimensions, specific manifestations, and situational contexts. After reviewing the outcome variables and influencing factors at each level, it inte-grates an integrated analysis framework for emotional labor, ex-plores its dy-namic evolution and feedback loop mechanisms, and summarizes the short-comings of existing research. Based on this, it discusses future research direc-tions. This paper approaches from the dual perspectives of cognitive regu-lation and emotion management, revealing research blind spots through Citespace visualization. By employing multi-level research threads such as key-word clus-tering, co-occurrence analysis, and time series analysis, it systemati-cally re-views previously scattered related studies, providing scientific evidence for management. This promotes employee well-being and organizational devel-op-ment, offers pathways for theoretical integration and practical optimization.

Strategies for integrating animal social learning and culture into conservation translocation practice.

Conservation translocations are increasingly used in species' recovery. Their success often depends upon maintaining or restoring survival-relevant behaviour, which is socially learned in many animals. A lack of species- or population-appropriate learning can lead to the loss of adaptive behaviour, increasing the likelihood of negative human interactions and compromising animals' ability to migrate, exploit resources, avoid predators, integrate into wild populations, reproduce and survive. When applied well, behavioural tools can address deficiencies in socially learned behaviours and boost survival. However, their use has been uneven between species and translocation programmes, and behaviour commonly contributes to translocation failure. Critically, current international guidance (e.g. the International Union for Conservation of Nature's translocation guidelines) does not directly discuss social learning or its facilitation. We argue that linking knowledge about social learning to appropriate translocation strategies will enhance guidance and direct future research. We offer a framework for incorporating animal social learning into translocation planning, implementation, monitoring and evaluation across wild and captive settings. Our recommendations consider barriers practitioners face in contending with logistics, time constraints and intervention cost. We emphasize that stronger links between researchers, translocation practitioners and wildlife agencies would increase support for social learning research, and improve the perceived relevance and feasibility of facilitating social learning.This article is part of the theme issue 'Animal culture: conservation in a changing world'.

Awareness of bone strength in patients with neuromuscular disorders: ERN EURO-NMD clinician survey and European patient survey.

Awareness of bone strength in patients with neuromuscular disorders: ERN EURO-NMD clinician survey and European patient survey.

Immune checkpoint inhibitor-associated pneumonitis: focus on diagnosis and underlying mechanisms.

This review aims to provide an updated overview of the diagnosis, risk factors, and treatment strategies for immune checkpoint inhibitor (ICI) pneumonitis, with a particular emphasis on its underlying pathophysiology. Recent advances, such as single-cell RNA sequencing of bronchoalveolar lavage fluid and the identification of biomarkers, including autoantibodies, are enhancing our understanding of ICI-related pneumonitis. These findings suggest that both cell-mediated and humoral mechanisms contribute to the pathophysiology of the condition. Pneumonitis can significantly limit the efficacy of life-saving cancer treatments, such as ICIs. Although corticosteroids are the first-line treatment according to guidelines, steroid-refractory pneumonitis remains common and is associated with high mortality. Emerging data is providing a more detailed understanding of the dysregulated immune response responsible for pneumonitis, which may guide the development of targeted therapies and direct future research efforts.

Pressure Injury Prediction in Intensive Care Units Using Artificial Intelligence: A Scoping Review.

Background/Objetives: Pressure injuries pose a significant challenge in healthcare, adversely impacting individuals' quality of life and healthcare systems, particularly in intensive care units. The effective identification of at-risk individuals is crucial, but traditional scales have limitations, prompting the development of new tools. Artificial intelligence offers a promising approach to identifying and preventing pressure injuries in critical care settings. This review aimed to assess the extent of the literature regarding the use of artificial intelligence technologies in the prediction of pressure injuries in critically ill patients in intensive care units to identify gaps in current knowledge and direct future research. Methods: The review followed the Joanna Briggs Institute's methodology for scoping reviews, and the study protocol was prospectively registered on the Open Science Framework platform. Results: This review included 14 studies, primarily highlighting the use of machine learning models trained on electronic health records data for predicting pressure injuries. Between 6 and 86 variables were used to train these models. Only two studies reported the clinical deployment of these models, reporting results such as reduced nursing workload, decreased prevalence of hospital-acquired pressure injuries, and decreased intensive care unit length of stay. Conclusions: Artificial intelligence technologies present themselves as a dynamic and innovative approach, with the ability to identify risk factors and predict pressure injuries effectively and promptly. This review synthesizes information about the use of these technologies and guides future directions and motivations.

Systematic Review of In Vitro Studies in Dentistry: How to Conduct It?

Systematic reviews (SRs) are pivotal in synthesizing evidence across primary studies to address specific research questions. In dentistry, particularly in dental materials science, in vitro studies are crucial for evaluating material performance before clinical trials. This article provides a comprehensive guide to conducting SRs focused on in vitro studies, offering step-by-step instructions on formulating research questions, developing search strategies, assessing bias, and synthesizing data. Adherence to PRISMA guidelines is emphasized to ensure transparency and reproducibility. This guide aims to simplify SR processes, fostering the production of high-quality evidence to inform new approaches proposed for clinical use and direct future research.

Current Progress of Hederagenin and Its Derivatives for Disease Therapy (2017-Present).

Natural products have emerged as crucial sources of biologically active compounds, holding promise for applications in drug development. Among the extensively researched pentacyclic triterpenes, hederagenin (HG) stands out for its diverse biological activities and serves as a valuable scaffold for synthesizing novel derivatives. These derivatives hold significant promise for the development of novel therapeutic agents aimed at treating a wide range of diseases. Over the past years, a multitude of HG derivatives with varied bioactivities have been synthesized through chemical modifications. This review article consolidates the most recent findings (since 2017) on HG derivatives, emphasizing their biological effects and mechanisms of action in both in vitro and in vivo models. The objective of this compilation is to offer insights and direct future research endeavors in the realm of HG.

Understanding interactive effects between habitat configuration and pesticide use for pollination: towards better informed landscape management

BackgroundThe restoration of natural landscape elements is a frequently adopted pathway to improve wild pollinator abundance, diversity, and their pollination services in intensively used agricultural landscapes. However, pollinators in the intended refuges can become exposed to agrochemicals when foraging in surrounding agricultural fields. In order to effectively design pollinator conservation measures such as habitat restoration or pesticide reduction schemes, the effect of land use configuration on pesticide exposure and pollination service requires further investigation.MethodsWe developed a pollination model that extends existing approaches by simulating both pollination flights and concurrent pollinator exposure to toxic pesticides, enabling the estimation of pesticide impacts on pollination services. We calculated pollination service and pollinator health for a set of artificial landscapes, which varied in the percentage of pollinator habitat and agriculture, in the clustering of these land uses, as well as in the pollinator mortality hazard arising from the pesticides applied on agriculture.ResultsOur results show that in landscapes with less than 10% habitat and highly toxic pesticides, pollination services are mostly safeguarded by compact patches of habitat, as this configuration shelters more habitat from pesticide exposure. With increasing habitat amount or with pesticide applications causing less than 50% mortality in pollinators, more dispersed patches of habitat achieve a better pollination service for the landscape. We further tested the effect of pesticide application for different foraging ranges in a more realistic land use scenario. For pollinators with shorter foraging ranges, pesticide exposure from the immediate surroundings determines the achieved pollination. For species with longer foraging ranges, the availability of resources and the application of pesticides at landscape scale controls the pollination.ConclusionOur study highlights the importance of assessing spatial configuration effects on pesticide exposure for local pollinators. By applying these insights, land managers can devise land use arrangements to protect pollinator habitats and establish buffer zones to support pollinator activity in pesticide-intensive landscapes. As current guidelines largely lack spatially-explicit measures, we suggest to direct future research and policies towards the underlying spatial processes and their facilitation on parcel, farm, and landscape scale.

Current practices of Portuguese speech-language pathologists with preschool-age children with pragmatic impairment: A cross-sectional survey.

This study aims to investigate the practice patterns used by Portuguese speech-language pathologists (SLPs) with preschool-age children with pragmatic impairment and to identify the actual need(s) perceived by SLPs in this field. A total of 351 SLPs responded. The results reveal that 81.5 per cent of the respondents (n=286) reported working or had previously worked with preschool-age children with pragmatic impairment arising from autism spectrum disorder, developmental language disorder, or both. Considering the clinical practice, similarities and differences were found, many of which are due not to the inherent characteristics of each disorder but to the scarcity of research in clinical pragmatics. These results are also reflected in the needs perceived by SLPs and the degree of confidence with which they work with these children. Implications for clinical practice and directions for future research are discussed.

Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance

Emerging Distributed AI systems are revolutionizing big data computing and data processing capabilities with growing economic and societal impact. However, recent studies have identified new attack surfaces and risks caused by security, privacy, and fairness issues in AI systems. In this article, we review representative techniques, algorithms, and theoretical foundations for trustworthy distributed AI through robustness guarantee, privacy protection, and fairness awareness in distributed learning. We first provide a brief overview of alternative architectures for distributed learning, discuss inherent vulnerabilities for security, privacy, and fairness of AI algorithms in distributed learning, and analyze why these problems are present in distributed learning regardless of specific architectures. Then, we provide a unique taxonomy of countermeasures for trustworthy distributed AI, covering (1) robustness to evasion attacks and irregular queries at inference, and robustness to poisoning attacks, Byzantine attacks, and irregular data distribution during training; (2) privacy protection during distributed learning and model inference at deployment; and (3) AI fairness and governance with respect to both data and models. We conclude with a discussion on open challenges and future research directions toward trustworthy distributed AI, such as the need for trustworthy AI policy guidelines, the AI responsibility-utility co-design, and incentives and compliance.

The Safety of Digital Mental Health Interventions: Findings and Recommendations From a Qualitative Study Exploring Users' Experiences, Concerns, and Suggestions.

The literature around the safety of digital mental health interventions (DMHIs) is growing. However, the user/patient perspective is still absent from it. Understanding the user/patient perspective can ensure that professionals address issues that are significant to users/patients and help direct future research in the field. This qualitative study aims to explore DMHI users' experiences, views, concerns, and suggestions regarding the safety of DMHIs. We included individuals aged 18 years old or older, having experience in using a DMHI, and can speak and understand English without the need for a translator. Fifteen individual interviews were conducted. Deductive thematic analysis was used to analyze the data. The analysis of the interview transcripts yielded 3 main themes: Nonresponse: A Concern, a Risk, and How Users Mitigate It, Symptom Deterioration and Its Management, and Concerns Around Data Privacy and How to Mitigate Them. The results of this study led to 7 recommendations on how the safety of DMHIs can be improved: provide "easy access" versions of key information, use "approved by..." badges, anticipate and support deterioration, provide real-time feedback, acknowledge the lack of personalization, responsibly manage access, and provide genuine crisis support. These recommendations arose from users' experiences and suggestions. If implemented, these recommendations can improve the safety of DMHIs and enhance users' experience.

Integrating sustainability into PMM systems of small businesses: some future research directions

PurposeThis work aims to provide, from the perspective of small businesses, a state-of-the-art of management and accounting literature concerning the development of performance measurement and management (PMM) systems addressing sustainability and traditional measures of business performance.Design/methodology/approachA systematic review of the literature was developed by using Scopus and ISI Web of Science as sources of data. After a careful screening of the results, 61 articles have been selected and analysed in depth.FindingsThe article highlights the key theoretical frameworks adopted by articles focussing on PMM and sustainability in SMEs, as well as the methods used, and the key re-sults obtained. Moreover, the article provides several insights for future studies on this emerging topic.Practical implicationsThis contribution provides important elements to design PMM systems aimed at ad-dressing sustainability in SMEs. As such, the results obtained may supports SMEs’ managers in implementing such systems as well as in putting in place reporting ac-tivitiesOriginality/valueThe topic of measuring and managing sustainability performance in SMEs has re-mained largely underexplored both in theory and in practice. This contribution offers a comprehensive exploration of this theme, while also outlining future research direc-tions for scholars interested in analyzing this research area from the perspective of small businesses.

Probiotics interventions modulating gut microbiota composition in individuals with intestinal constipation: Protocol of a systemic review and meta-analysis of randomized controlled trials.

Intestinal constipation is a substantive global health concern, significantly impairing patient quality of life. An emerging view is that the gut microbiota plays a critical role in intestinal function, and probiotics could offer therapeutic benefits. This study aims to consolidate evidence from randomized controlled trials (RCTs) that assess the effectiveness of probiotics in modulating microbiota and ameliorating symptoms of constipation. We will execute a systematic evidence search across Medline (via PubMed), Embase, Cochrane CENTRAL, Web of Science, Scopus, and CINAHL, employing explicit search terms and further reference exploration. Two independent reviewers will ensure study selection and data integrity while assessing methodological quality via the Cochrane Collaboration's Risk of Bias-2 tool. Our primary goal is to outline changes in microbiota composition, with secondary outcomes addressing symptom relief and stool characteristics. Meta-analyses will adopt a random-effects model to quantify the effects of interventions, supplemented by subgroup analyses and publication bias assessments to fortify the rigor of our findings. This study endeavors to provide a rigorous, synthesized overview of the probiotics interventions evidence for modulating gut microbiota in individuals with intestinal constipation. The insights derived could inform clinical guidelines, nurture the creation of novel constipation management strategies, and direct future research in this field. As this study aggregates and analyzes existing data without direct human subject involvement, no ethical approval is required. We will disseminate the study's findings through scientific forums and seek publication in well-regarded, peer-reviewed journals. OSF registration number: 10.17605/OSF.IO/MEAHT.

Acne vulgaris: advances in pathogenesis and prevention strategies.

The aim is to encourage the creation of innovative prevention and treatment measures and to help readersin selecting the most effective ones. Acne vulgaris is the most prevalent skin condition of adolescents, affecting approximately 9% of theglobal population. Patients becomemore prone to mental and psychological problems because of it. Severalstrategies have been established to effectively improve acne vulgaris. However, the complexity of its pathogenesisand the limitations of the existing strategies to control it in terms of bacterial resistance, patient compliance, andsafety have made the development of new control strategies a hot topic in skin health research. This review systematically summarizes the pathogenesis and prevention strategies of acne vulgarisaccording to the most recent studies. The limitations of the current research on acne vulgaris and future researchdirections are presented based on the analysis of the strengths and weaknesses of the existing prevention andtreatment strategies.

Creating Value Since 2015: A Review of the Journal of Creating Value

The Journal of Creating Value (JCV) is a Scopus-indexed, peer-reviewed journal that has been published since 2015. Over the last decade, JCV has played a pivotal role in enhancing knowledge creation and sharing among academia and practitioners in its chosen field. This study is an attempt to map the contribution of the journal in terms of classifying impactful authors, current and evolving trends and themes, and set a direction for future researchers and practitioners. The review of the articles published was performed using bibliometric analysis and techniques. A total of 165 articles published during the years 2015 up to 2023 were part of the scope of the study using the Scopus database. Open-source visualization software VOSviewer was used for analysis. Performance analysis and science mapping techniques were employed, and the findings indicate five major themes/clusters. Thirty per cent of the publications are from the USA. Other contributing countries include India, Germany, the Netherlands and the UK. Overall, there are significant global contributions as well. Insightful trends and themes in the field of creating value are documented, which could be a useful resource for prospective researchers.

A longitudinal examination of parallel growth and reciprocal changes in teacher-student relationships and academic achievement.

There is a compendium of research to support the premise that positive teacher-student relationships (TSR) set the stage for children's success via classroom engagement, social functioning, and academic skills development. Although studies have demonstrated reciprocal associations between TSR and academic achievement, inferences that stem from prior study results are limited due to methodological designs that fall short in capturing directionality in developmental change processes. To address gaps in the literature and improve our understanding of the complex associations between TSR and academic achievement, we analyzed the codevelopment of TSR and achievement in reading and mathematics using dual change score models (DCSM), a type of latent change score model, focusing on the associations between longitudinal trajectories of TSR-achievement pairs and on the reciprocal prediction of latent changes between each wave of measurement. We examined data from a large-scale, nationally representative study (Early Childhood Longitudinal Study Kindergarten Cohort of 2010-2011). Results of our DCSM, contrary to prior findings, demonstrate that variability in the ratings of TSR did not predict subsequent latent changes in reading or mathematics achievement. Likewise, the variability in achievement scores did not predict subsequent latent changes in ratings of TSR. Limitations and future directions for research are discussed. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Multi-task Deep Learning in Medical Image Processing: A Systematic Review

Purpose—Multi-task learning (MTL) is a deep learning approach that aims to jointly learn two or more tasks with the goal of leveraging shared knowledge among the tasks. This study aimed to review existing MTL models in medical image processing to understand the current state of research, evaluate major breakthroughs, and analyze open gaps and future research direction. Methodology—The study conducted a systematic literature review employing a search of peer-reviewed journal articles and conference proceedings. The articles were sourced from IEEE, ScienceDirect, PubMed, and Google Scholar databases. 52 primary papers published between 2016 and 2024 were considered in this study. Results—The study's findings reveal that breakthroughs have been made in increasing the scope of task combinations in both homogenous and heterogenous tasks. Additionally, innovative architectural designs and learning methods have emerged. Although MTL has emergedas a panacea for medical image processing, some grey areas in research need to be addressed. They include task relatedness, scope of task combinations, generative MTL, and longitudinal MTL. Conclusion—The study conducted a comprehensive analysis of multi-task models in medical image processing. The findings reveal breakthroughsin architecture, task combinations, and learning methods,and open gaps in this field. Metrics variability and proprietary datasets were the major limitations of this study. Recommendations—Future researchers should focus on addressing the gaps identified in this study especially increasing the scope of MTL and designing more robust and highly generalizable neural networks for longitudinal MTL. Research Implications—The review evaluates the current state of medical image processing using MTL, offering insights into both theoretical and practical aspects. These insights provide direction for future researchers to advance the field and for policymakers to support ethical data collection and sharing. Keywords—multi-task learning, deep learning, medical image processing, computer vision, neural networks

USOD10K: A New Benchmark Dataset for Underwater Salient Object Detection.

Underwater salient object detection (USOD) is an emerging research area that has great potential for various underwater visual tasks. However, USOD research is still in its early stage due to the lack of large-scale datasets within which salient objects are well-defined and pixel-wise annotated. To address this issue, this paper introduces a new dataset named USOD10K. It contains 10,255 underwater images, covering 70 categories of salient objects in 12 different underwater scenes. Moreover, the USOD10K provides salient object boundaries and depth maps of all images. The USOD10K is the first large-scale dataset in the USOD community, making a significant leap in diversity, complexity, and scalability. Secondly, a simple but strong baseline termed TC-USOD is proposed for the USOD10K. The TC-USOD adopts a hybrid architecture based on an encoder-decoder design that leverages transformer and convolution as the basic computational building block of the encoder and decoder, respectively. Thirdly, we make a comprehensive summarization of 35 state-of-the-art SOD/USOD methods and benchmark them on the existing USOD dataset and the USOD10K. The results show that our TC-USOD achieves superior performance on all datasets tested. Finally, several other use cases of the USOD10K are discussed, and future directions of USOD research are pointed out. This work will promote the development of the USOD research and facilitate further research on underwater visual tasks and visually-guided underwater robots. To pave the road in the USOD research field, the dataset, code, and benchmark results are publicly available: https://github.com/Underwater-Robotic-Lab/USOD10K.