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Background: Artificial Intelligence (AI) and Machine Learning (ML) technologies, such as conversational agents, are becoming increasingly essential tools across multiple industries, particularly in healthcare. This paper presents a scoping review (PRISMA-ScR) of conversational agents (CAs) in mobile health coaching systems (MHCS). It examines existing applications of MHCS, focusing on development strategies, usage contexts, impacts on users, benefits, and research gaps, emphasizing the ability of explainable artificial intelligence (XAI) in making health guidance and decision-support recommendations transparent, trustworthy, and interpretable, if properly integrated. This scoping review identifies opportunities to maximize the use of conversational agents, explainable AI, and mobile technologies to make mobile health coaching systems more accessible and trustworthy, as well as further research gaps worth exploring. Objective: This scoping review maps the evidence on CAs and XAI-enabled technologies in MHCS, identifies trust-related design criteria, categorizes reported outcomes, and highlights opportunities for explainable conversational agents (XCA) in a mobile health context, especially in tackling general medical conditions pertinent in underserved settings. Eligibility criteria: Reported eligible resources evaluated, designed, or conceptually analyzed existing CAs, XAI techniques, and MHCS, AI-supported medical dialogue systems, e-coaching systems, and mobile health applications. We considered sources only relevant to healthcare, health coaching, trust, explainability, or patient engagement that were published between 2006 and 2025. Sources of Evidence: Searches were conducted in IEEE Xplore, Google Scholar, Springer, ScienceDirect/Elsevier, ProQuest, and ACM Digital Library, supplemented by targeted web searches and backward citation checks. Charting methods: Data were charted by system type, communication mode, health context, operational mode, technology used, XAI/trust features, degree of automation, study designs and outcome classification. We applied a revised outcome classification: generated desired outcome (GDO) and partially generated desired outcome (P-GDO), and did not generate desired outcome (DN-GDO). Results: A total of 201 resources were collected. Charted studies clustered around CAs in health, MHCS for chronic diseases and stress management, XAI methods such as LIME, SHAP, Prospector, and counterfactual explanations, and trust-related elements such as voice quality, communication style, appearance, social intelligence, privacy, and performance quality. Most health CAs and MHCS addressed chronic diseases, mental health, or behavior change; fewer addressed general medical diagnosis or autonomous mobile-based primary care support. Conclusions: Existing evidence suggests that CAs and MHCSs can support engagement, coaching, education, and selected decision-support tasks, but evidence for safe, autonomous, explainable general practice functionality remains limited. Future work should prioritize clinically supervised XCA designs, core safety assessment, interfaces with transparent explanation, data protection, culturally and linguistically responsive implementation, and future-oriented review in underserved mobile health settings. Full article

Eukaryotic organisms play a critical role in maintaining agricultural ecosystem functions and crop health. Irrigation practices and water salinity significantly affect eukaryotic communities, yet the interactive effects of drip irrigation depth and water salinity on these communities remain unclear. This study aimed to investigate the interactive effects of drip irrigation depth and water salinity on the diversity, community structure, and functional groups of winter wheat rhizosphere eukaryotes, and to examine their relationships with soil environmental factors. A two-year field experiment was conducted in Cangzhou, Hebei Province, with two drip irrigation depths (5 cm shallow, 25 cm deep) and two irrigation water salinity levels (2 g·L⁻¹, 3 g·L⁻¹). High-throughput sequencing was used to analyze rhizosphere microbial communities, and α/β diversity, species composition, LEfSe differential analysis, and redundancy analysis (RDA) were performed to assess the effects of environmental factors. Results showed that both irrigation depth and water salinity significantly influenced α/β diversity and community structure of soil eukaryotes. The 5 cm shallow + 2 g·L⁻¹ salinity treatment favored species richness, while the 25 cm deep + 3 g·L⁻¹ treatment promoted community evenness. Dominant taxa responded selectively, with Annelida markedly suppressed and groups such as Streptophyta and Chytridiomycota enriched under different treatments. Network analysis revealed that key microbial taxa occupied central positions in interspecies interactions. RDA indicated that soil pH, nitrogen, potassium, and organic matter were important drivers of community structure. In conclusion, drip irrigation depth and water salinity synergistically shape soil eukaryotic community structure. These findings provide a scientific basis for optimizing drip irrigation depth, utilizing brackish water, and enhancing agricultural ecosystem functions. Full article

To scientifically evaluate the health of river aquatic ecosystems in the Qianxinan Buyi and Miao Autonomous Prefecture, southwestern Guizhou, systematic surveys of benthic macroinvertebrate and periphytic algal communities were conducted in representative rivers during October 2024 (autumn) and April 2025 (spring), coupled with concurrent water quality monitoring. Reference sites were selected based on water quality indicators and habitat conditions. Core parameters were identified through correlation analysis, discriminatory ability analysis, and distribution range analysis to construct a Benthic Index of Biotic Integrity (B-IBI) and a Periphytic Algae Index of Biotic Integrity (P-IBI) suitable for the region. These indices were then applied to assess the ecological health of the rivers. Additionally, stepwise regression analysis was employed to investigate the key environmental drivers influencing the two biotic integrity indices. The results indicated that: (1) In terms of species composition, the benthic macroinvertebrate community structure was relatively simple, dominated by arthropods, particularly chironomid larvae. Bacillariophyta and Cyanophyta consistently dominated the periphytic algae community. (2) Assessments using both B-IBI and P-IBI showed that the overall river health in spring was slightly better than in autumn. However, more than half of the sampling sites were rated as “fair” or worse in both seasons. The reference sites (S2, S10) consistently exhibited “excellent” or “good” health, while the impaired sites showed significant spatial heterogeneity. Discrepancies between B-IBI and P-IBI ratings at some sites revealed differential responses of the two biological communities to environmental stressors. (3) Stepwise regression analysis unveiled a seasonal shift in key environmental drivers. The primary factor affecting the B-IBI in autumn was biochemical oxygen demand (BOD₅), which shifted to total phosphorus (TP) and ammonia nitrogen (NH₄⁺-N) in spring. For the P-IBI, the main factor changed from dissolved oxygen (DO) in autumn to chemical oxygen demand (COD) in spring. These findings confirm the applicability of the B-IBI and P-IBI systems in this region, and indicate that multi-assemblage integrated assessments can contribute to understanding the health status of river ecosystems in the Qianxinan Prefecture. This study could serve as a scientific reference for the protection, management, and restoration of local river ecosystems. Full article

Background: Hypertension is one of the most common noncommunicable diseases. Objectives: This research assessed the magnitude of hypertension among young adults, identified its key determinants, and explored potential strategies adopted for prevention. Methods: A cross-sectional design was employed, including 1606 participants aged 18 years and older, recruited through convenience sampling from universities and community settings. Data were collected using a content-validated questionnaire covering sociodemographic information, personal and family medical history, and lifestyle habits. Results: Of the participants, 993 (61.8%) reported hypertension, nearly double previous national estimates. Male gender, age ≥ 30 years, and family history were significant risk factors, along with smoking, alcohol use, sedentary lifestyle, and unhealthy diet, while physical activity and dietary modification were protective. Despite high prevalence, only 22.1% had controlled blood pressure and 17.8% adhered to medication, with 51.5% relying on herbal remedies. Conclusions: These findings highlight the urgent need for early screening, youth-focused awareness, and culturally tailored interventions to reduce hypertension and prevent long-term cardiovascular complications. Hypertension among young adults in the UAE is a major public health concern, requiring integrated strategies combining education, lifestyle modification, and medical management to improve outcomes. Full article

As microplastic pollution intensifies, its impact on soil microbial communities has drawn widespread attention. This study treated soil samples with five microplastics, including polystyrene (PS), polyethylene (PE), polypropylene (PP), polyvinyl chloride (PVC), and polyethylene terephthalate (PET), to assess effects on soil properties. High-throughput sequencing was used to analyze soil fungal community structure and functional diversity. Results showed that microplastic treatments significantly altered pH, total carbon (TC), ammonium nitrogen (NH₄⁺-N), nitrate nitrogen (NO₃⁻-N), and available phosphorus (AP). Notably, all treatments reduced NO₃⁻-N levels. Fungal community composition was affected, particularly Mortierellomycota and the genera Mortierella, Plectosphaerella, Pseudogymnoascus, Penicillium, Tuber, and Stachybotrys. Functional analysis revealed decreases in certain groups, especially Endophyte–Plant Saprotroph–Undefined Saprotroph and Endophyte–Plant Pathogen–Plant Saprotroph, in PE, PS, and PVC treatments. Mantel analysis further indicated that soil pH, NH₄⁺-N, and NO₃⁻-N significantly influenced fungal communities. These results highlight that microplastic pollution alters soil properties, thereby affecting fungal communities in a microplastic-type dependent manner, providing a theoretical basis for soil health management and pollution mitigation. Full article

Background: Primary healthcare systems continue to face patient safety challenges, particularly misdiagnosis and medication errors, which contribute to preventable harm and reduced quality of care. Electronic Medical Records (EMRs) have the potential to improve clinical documentation, support decision-making, and reduce risks; however, these benefits depend on effective utilization in routine clinical practice. This study examined factors influencing EMR utilization in primary healthcare settings. Methods: A sequential explanatory mixed-methods design was conducted across 42 community health centers in one Indonesian city. Quantitative data from general practitioners were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the relationships among clinical workflow fit, digital health competency, governance, system capabilities, interprofessional collaboration, perceived patient engagement, and EMR utilization. Qualitative interviews were subsequently conducted to provide a contextual explanation of the quantitative findings. Results: Clinical workflow fit and digital health competency emerged as the strongest factors associated with EMR utilization. Their effects operated through interprofessional collaboration and perceived patient engagement, indicating the importance of integrating EMRs into everyday clinical workflows. Governance structures and system capabilities primarily functioned as enabling conditions rather than direct determinants of utilization. Qualitative findings further highlighted the importance of practical workflow integration, communication processes, and user competency in supporting meaningful system use. Conclusions: EMR utilization may contribute to improved care coordination, patient engagement, and service efficiency in primary healthcare settings. Strengthening workflow alignment and digital competency may help support safer and more reliable care delivery, particularly in resource-constrained environments where risks of misdiagnosis and medication errors remain significant. Full article

Major depressive disorder (MDD) is a mental illness with high mortality, suicide, and relapse rates that could become the leading cause of health problems worldwide by 2030. The microbiota–gut–brain axis involves bidirectional communication between the human gut microbiota and the central nervous system (CNS). The gut microbiome is a complex ecosystem of approximately 100 trillion microorganisms, including viruses, bacteria, and fungi. The gut microbiota has recently been recognized for its impact on various diseases and health concerns. Several factors influence the composition and structure of gut microbes, ultimately affecting human physiology, with the nervous system being particularly vulnerable. The gut–brain–microbiota axis influences several important brain functions through numerous pathways, including vagus nerve signaling, gut microbial synthesis of metabolites, and immune-related chemicals. These factors can influence neurotransmitter activity, neuroinflammation, behavior, and mental health. Despite increased interest, the possibility of modifying the gut microbiota as a therapeutic approach remains unclear. Although numerous studies suggest that microbiota play an important role in many illnesses, the precise mechanisms are yet to be elucidated, and there are currently no evidence-based, microbiota-focused treatments for these illnesses. Recent research indicates that gut dysbiosis (GD) causes increased intestinal permeability (leaky gut), initiates systemic inflammation, and contaminates the blood. Opportunistic microbial metabolites cross the blood–brain barrier, triggering a neuroinflammatory cascade and apoptotic pathways while affecting neurogenesis and neurotransmitters, ultimately resulting in the development of MDD and anxiety. This review examined the factors influencing normal gut microbiota and GD-mediated MDD, as well as possible therapeutic options. The study outlines its objectives and methodological approaches, including the screening and filtering of research on GD-induced depression. Furthermore, it explored the daily use of dietary supplements, revealing new paths for clinical and preclinical research. Full article

In recent years, there has been an unprecedented upsurge in opioid prescriptions for pain management. Consequently, the widespread availability of these medicines has led to an increase in misuse and abuse. This has led to a greater number of overdose-related deaths. The high prevalence of drug misuse was born of multiple and complex societal factors. However, from a medical perspective, critical contributors to the dire consequences of the crisis have been the need for chronic pain relief, as well as mental health issues within communities. Chronic pain coupled with psychological distress exacerbates patients’ predicaments and thus further fuels the crisis. Anxiety and depression have bidirectional and complex relationships with pain. The somatic symptoms associated with anxiety potentially worsen pain, whilst pain emanating from a chronic condition worsens anxiety. The same relational dynamic applies to depression and pain. Thus, these psychopathological states may be major contributors to the opioid abuse epidemic. Thus, psychosocial management as a first-line treatment instead of starting with drug treatments seems an enlightened approach to this problem. Cognitive behavioral therapy (CBT) has been proven to be effective in managing specific symptoms associated with chronic pain. Similarly, patient education has been shown to be a viable alternative to drugs for certain aspects of chronic pain treatment. We consider that the opioid crisis could be addressed with a greater reliance and emphasis on non-pharmacological approaches to managing chronic pain patients. This mini-review examines non-pharmaceutical and monitoring-based interventions to reduce opioid misuse risk among adults prescribed opioids for chronic non-cancer pain. Studies were identified through PubMed/MEDLINE, Scopus, and Google Scholar using terms related to chronic pain, prescription opioid misuse, opioid use disorder, cognitive behavioral therapy, patient education, prescription drug monitoring programs, digital health, telehealth, and non-pharmacological interventions. Studies were included if they focused on adults with chronic pain who were prescribed opioids or at risk of misuse, and evaluated interventions aimed at reducing unsafe opioid use, misuse risk, or opioid-related harm. Evidence was synthesized narratively to identify key intervention approaches, limitations, and clinical implications. Full article

Underutilized and abundant in the Danish coastal area, Carcinus maenas has become a threat to the environment and to the local fisheries. In this study we investigated the microbiota and presence of microbial hazards of interest for human health in crabs caught over the period of one year, to investigate its potential for human consumption. Between 2023 and 2024, four seasonal samples of live specimens (n = 5) were caught off the Lillebælt (DK) coastal area. To characterize the microbiota of the crabs, a culture-dependent approach was used to determine total aerobic mesophilic count, total aerobic psychrotrophic count, Bacillus spp., Enterobacteriaceae, lactic acid bacteria, fungi, Salmonella spp., Listeria monocytogenes and Bacillus cereus. MALDI-TOF was used to corroborate results and further identify isolated microorganisms. The results were then compared with data obtained from amplicon sequencing of community 16S rRNA genes to compare family-level compositions of the microbiota. Of the pathogens of interest, B. cereus was detected during summer/autumn, reaching a maximum of 2.5 log cfu/g. Salmonella spp., and L. monocytogenes were below the limit of detection (<1 cfu/0.1 g). Spoilage bacteria were detected (e.g., Brochothrix spp., Carnobacterium spp., Photobacterium spp., Pseudomonas spp., Psychrobacter spp. and Shewanella spp.). The study highlighted significant seasonal changes (PERMANOVA, FDR-adjusted p = 0.00003) in the microbial composition. The gathered evidence suggests that with proper handling, the crabs could represent a safe resource. Full article

Background/Objectives: The dental setting has been suggested as a location for opportunistic diabetes screenings. Diabetes screening is a pathway consisting of several steps that must be completed to reach a diagnosis. Previous research has found that most patients in the dental setting, when offered the opportunity to screen for diabetes, are willing to do so; however, amongst those who are referred for medical follow-up, there is low compliance. If diabetes screening in the dental setting is to be effective, strategies are required to maximise uptake and ensure completion of the screening pathway. Methods: This qualitative study examined participants in a diabetes screening trial held at dental clinics in Victoria, Australia. Semi-structured interviews were conducted by telephone, transcribed and analysed thematically. The themes identified were then deductively mapped onto the Capability, Opportunity, Motivation, Behaviour (COM-B) model and Theoretical Domains Framework (TDF). Results: Ten individuals who were screened for diabetes and referred to their general medical practitioner (GP) for a diabetes diagnosis were interviewed. The themes identified from the interviews were mapped to five COM-B domains: reflective motivation and automatic motivation, social and physical opportunity and psychological capability. These were linked to eight TDF domains associated with issues related to knowledge, environmental context and resources, memory, attention and decision processes, social influences, beliefs about consequences, emotions, and beliefs about capability. Conclusions: This study investigated the determinants influencing individuals’ decision to participate in diabetes screening and comply with referral advice. The results demonstrate the need to increase community knowledge around diabetes and screening for the condition, facilitate risk interpretation, and streamline the referral pathway between oral health professionals (OHP) and GPs. The study provides evidence that can be utilised for the development of future interventions that promote diabetes screening participation and maximise medical follow-up of referred individuals. Full article

The gut–brain axis is a central regulatory network linking dietary habits, metabolic homeostasis, and brain function through bidirectional communication among the intestine, microbiota, and central nervous system. Acting as a key mediator, the gut microbiota translates environmental and nutritional factors into systemic outcomes that influence both health and disease. Within this context, serine and glycine metabolism emerges as a critical yet underexplored hub connecting microbial activity with brain regulation. Changes in gut microbial composition can profoundly affect host one-carbon metabolism and amino acid availability, shaping systemic physiology and neural processes. In this review, we outline a biochemical framework in which gut microbiota alterations influence brain and liver serine/glycine (ser/gly) metabolism, driving the hepatic production of secondary metabolites, including taurine-conjugated bile acids. We delineate how gut–brain axis pathways orchestrate systemic and neural functions, and highlight glioblastoma (GBM) as a pathological context where hijacked serine–glycine metabolism fuels tumor growth, stemness, and therapy resistance. By focusing on the interplay between gut microbiota, ser/gly metabolism, and brain tumor biology, this review offers a cohesive perspective on translational interventions. Glycine-centered pathways emerge as promising targets to modulate the gut–brain–tumor axis, opening new avenues to influence GBM progression and enhance therapeutic strategies. Full article

Bridge structural health monitoring (SHM) systems often suffer from large-scale data missing due to sensor faults, communication interruptions and other reasons during long-term operation, which seriously restricts the reliability of structural state assessment and maintenance decision-making. Compared with conventional single-channel independent modeling strategies commonly used for data imputation, their inherent neglect of spatial correlations and cross-modal causal associations among multi-source heterogeneous monitoring data such as displacement, wind speed, and temperature constrain the imputation capability, particularly when the target channel suffers from long-term continuous data loss. To address the above problems, this paper proposes a collaborative imputation framework integrating a graph attention network (GAT), a modal-aware cross-attention (MACA) mechanism and temporal encoder–decoder architecture (ITimeGAN). Firstly, the sensor feature topological graph is constructed based on the Pearson correlation coefficient, and the spatial dependency among multi-source features is adaptively learned through GAT. Then, the MACA module is introduced, which takes the target displacement as Query and environmental loads as Key/Value, and dynamically aggregates cross-modal driving information through multi-head attention. Finally, a bidirectional LSTM encoder and a unidirectional LSTM decoder are adopted to capture long-range temporal dependencies, so as to realize the accurate reconstruction of missing displacement data. Validated on the 9-dimensional real-world monitoring data from the GeoSHM system of the Forth Road Bridge (UK) under both random missing (10–50%) and continuous long-term missing (1–10 days) scenarios, ITimeGAN achieves an R² of 0.9950 (MAE = 4.25 mm) for longitudinal displacement and 0.9759 (MAE = 6.70 mm) for vertical displacement even under 10 consecutive days of complete data absence. Ablation analysis further reveals that the incorporation of graph attention and cross-modal attention modules reduces the longitudinal displacement MAE by 57% over the baseline, with the imputation performance ranking across three displacement directions being fully consistent with the underlying physical correlation strengths, thereby confirming the effectiveness of the proposed cross-modal collaborative strategy. Full article

Inflation, recognized as a social determinant of health (SDOH), significantly affects the daily lives of individuals through the rising costs of food, housing, and other basic needs, all of which are public health concerns. Since the COVID-19 pandemic, inflation has become a prominent concern in the U.S. and has been linked to increased stress and poor mental health among adults. While data on inflation is tracked routinely, how it is discussed publicly is understudied. Social media platforms provide insights into how inflation is framed and experienced by the public, and these assessments may be used to determine public health needs and policy advocacy. In this study, we conducted a time-bound, platform-specific case study of inflation-related discourse on X (formerly Twitter). Analysis revealed a predominance of negative sentiments (68.5%) including frustration and distrust. Posts primarily concerned monetary policy/government spending (31.6%), Federal Reserve interest rates/financial markets (24.5%), and U.S. presidential politics (12.9%). The users did not explicitly discuss personal-level hardships, and the discussions largely focused on macro-level issues framed in polarized political perspectives. These patterns matter for public health because institutional trust shapes support for social and health policies. Our study findings suggest a fragmented social environment that may exacerbate community-wide anxiety and challenge health promotion efforts and the need for public health surveillance through surveys or personal interviews to identify and address the psychological burden of inflation. Full article

Background: Mobile health has been increasingly integrated into tuberculosis care to support patient education, communication, and treatment engagement. However, evidence remains limited regarding whether positive engagement with mHealth is associated with knowledge, attitudes, and practices among patients with multidrug-resistant tuberculosis. This study aimed to examine the association between positive mHealth engagement and knowledge, attitude, practice, and total KAP among patients with multidrug-resistant tuberculosis, and to evaluate the psychometric properties of the engagement score used as the primary exposure variable. Methods: A cross-sectional study was conducted among patients with multidrug-resistant tuberculosis. A positive mHealth engagement score was constructed from 12 mHealth-related items after harmonizing item directionality so that higher scores indicated more favorable engagement. The 12 items reflected five behavioural domains: intensity of use, ease and acceptability of use, functional engagement (communication with providers, access to health information, and perceived benefit for disease self-management), continuity of use, and barriers to sustained engagement. The composite score was computed as the mean of the 12 standardised items, with higher values indicating more positive engagement. Internal consistency was assessed using Cronbach’s alpha and corrected item–total correlations, and structural validity was explored using principal component analysis. Adjusted linear regression models were used to examine associations between the engagement score and Knowledge, Attitude, Practice, and total KAP scores, controlling for age, sex, and occupation. Sensitivity analyses were performed after excluding a poorly performing item, and tertile analyses were used to assess dose–response patterns. Results: The positive mHealth engagement score showed good internal consistency, with a Cronbach’s alpha of 0.852. One item demonstrated poor psychometric performance, and Cronbach’s alpha increased to 0.864 after its exclusion. The data were suitable for dimensionality assessment, with a Kaiser–Meyer–Olkin value of 0.870 and a significant Bartlett’s test. Principal component analysis identified a dominant first component explaining 43.29% of the total variance. Using the refined score, higher positive mHealth engagement was significantly associated with higher Knowledge scores (β = 2.06; 95% CI: 1.28–2.85; p < 0.001), higher Attitude scores (β = 4.68; 95% CI: 3.30–6.06; p < 0.001), and higher total KAP scores (β = 6.68; 95% CI: 4.62–8.74; p < 0.001), whereas no significant association was observed for the Practice score (β = −0.07; 95% CI: −0.63 to 0.49; p = 0.804). In tertile analyses, Knowledge, Attitude, and total KAP scores increased significantly across engagement levels, while Practice scores did not. Conclusions: Positive mHealth engagement was associated with better knowledge, attitudes, and overall KAP among patients with multidrug-resistant tuberculosis, but not with practice. These findings are associative; the cross-sectional design does not permit causal conclusions. The engagement score demonstrated good reliability and acceptable structural validity and may be a useful summary measure for evaluating patient interaction with mHealth interventions in tuberculosis care. Integrated strategies combining mHealth with clinical follow-up, adherence counseling, and structural support may be needed to translate informational and attitudinal gains into practice change. Full article

Poplar (Populus) trees are indispensable to various industries and environmental sustainability efforts. They are widely utilized for paper production, timber, and windbreaks, while also playing a significant role in carbon sequestration. Given their economic and ecological importance, the effective management of diseases is crucial. Convolutional Neural Networks (CNNs), renowned for their ability to process visual data, are pivotal in accurately detecting and classifying plant diseases. This study presents a domain-specific dataset of manually collected images of diseased poplar leaves from Uzbekistan and South Korea, ensuring geographic diversity and broader applicability. The dataset includes four disease classes, i.e., “Parsha (Scab),” “Brown spotting,” “White-Gray spotting,” and “Rust,” which represent common afflictions in these regions. To advance research efforts, this dataset will be made publicly accessible, providing a valuable resource for the scientific community. Leveraging the cutting-edge YOLOv9c model, a state-of-the-art CNN architecture, we applied the Histogram Equalization technique as a preprocessing step to enhance the image quality to increase the accuracy of disease detection. This method not only improves the diagnostic performance of the model but also provides a scalable solution for monitoring and managing poplar diseases. By ensuring the health of poplar trees, this approach supports the sustainability of these critical resources. To our knowledge, this is the first publicly available dataset specifically focused on diseased poplar leaves, making it a significant contribution to global research efforts. It offers an invaluable resource for researchers and practitioners, enabling further advancements in early disease detection and sustainable forestry management. Full article