Cribriform Adenocarcinoma of Minor Salivary Glands: Diagnostic Challenges—A Case Report and Review of the Current Literature

INTRODUCTIONTreacher Collins syndrome (TCS) is an autosomal dominant disorder that affects the development of structures originating from the first and second brachial arches during early embryonic development.1 The essential components of the syndrome were described by Edward Treacher Collins (1900), the ophthalmologist after whom the disease is named.2 Treacher Collins syndrome (Mandibulofacial dysostosis) is characterized by deafness, facial bone hypoplasia, chin and cheekbones), anti-Mongoloid tendency to clefts, lower eyelid coloboma and bilateral ear disorders.3 The quality of life of these people can be improved by early detection and treatment.4

Background: It is widely recognised that COVID ‘stay at home’ and wider ‘lockdown orders’ negatively impacted quality of life (QoL). Children with cancer and their families may have represented a particularly vulnerable group due to their complex medical treatment. Here we examined stability and change in child and primary carer QoL between 2020 and 2021 in Australia. Methods: Parents of children aged 0-18 years who were undergoing hospital-based cancer treatment during the pandemic completed an online survey at two time points, an average of 7.14 months apart, (between October 2020 and July 2021). Child QoL was assessed by parent report using the CHU9D and PedsQL (Cancer Module). Parent QoL was assessed using the EQ-5D-5L. Results: Seventy-five parents completed both survey time points. Child QoL was lower than population norms at both time points with no improvement from 2020 to 2021. However, parent QoL was not different from norms and was stable over time. Impacts of family life were negative and distress was high at both time points despite a substantially lowered COVID-19 prevalence in 2021. Parents’ perceptions of medical care remained favourable, including the hospital being a safe place and limited disruption to care (except for psychosocial care) and medical decision-making. Conclusion: Both child QoL and wider family distress were elevated across the pandemic in Australia. This effect persisted despite the relaxation of pandemic restrictions in 2021, maintenance of high medical standards and increased access to psychosocial care. Additional psychosocial supports, intervention and monitoring should be considered for this population.

The anti-IgE autoantibodies are biomarkers of early omalizumab response in patients with chronic spontaneous urticariaYusuke Niwa1,2), Koremasa Hayama1,2)*, Shota Toyoshima3), Keisuke Shimizu1,2), Maho Tagui1,2), Mana Ito1,2), Tomomi Sakamoto2), Tadashi Terui1,2), Hideki Fujita1,2), Yoshimichi Okayama2,4,5,6,7)*1) Division of Cutaneous Science, Department of Dermatology, Nihon University School of Medicine, Tokyo, Japan2) Center for Allergy, Nihon University School of Medicine, Tokyo, Japan3) Department of Biochemistry & Molecular Biology, Nippon Medical School, Tokyo, Japan4) Department of Allergy, Internal Medicine, Misato Kenwa Hospital, Saitama, Japan5) Department of Medicine, Division of Respiratory Medicine, Nihon University School of Medicine, Tokyo, Japan6) Department of Internal Medicine, Division of Respiratory Medicine and Allergology, Showa University School of Medicine, Tokyo, Japan7) Advanced Medical Science Research Center, Gunma Paz University, Graduate School of Health Sciences, Gunma, Japan*Corresponding authors:Koremasa Hayama, M.D, Ph.D.Division of Cutaneous Science, Department of Dermatology, Nihon University School of Medicine, 30-1 Oyaguchi-kamicho, Itabashi-ku, Tokyo 173-8610, JapanTel.: +81-3-3972-8111 (Ext. 8144): Fax: +81-3-5995-9841E-mail: hayama.koremasa@nihon-u.ac.jpYoshimichi Okayama, M.D, Ph.D.Department of Allergy, Internal Medicine, Misato Kenwa Hospital, 4-491-1 Takano, Misato-shi, Saitama 341-8555, JapanTel.: +81-48-955-7171: Fax: +81-48-955-5120E-mail: okayama.yoshimichi@nihon-u.ac.jp

The rapid deployment of satellite constellations has driven the rapid development of the satellite industry. Low-cost solutions for the entire industry chain will greatly accelerate the improvement of satellite Internet. On the user side, flexible phased array antennas have replaced reflector antennas, but the resulting cost issues will become increasingly prominent. Based on this, a low-cost phased array architecture based on two-stage phase shifters is proposed. On the basis of subarray division, high-precision and low-precision phase shifters are organically combined to reduce costs by reducing the number of high-precision phase shifters. The structure is simple and easy to implement, and simulation results show that the phased array of this structure has reliable performance in scanning accuracy and beam sidelobes.

In the fish industry up to 70% of the entire fish ends up as side-streams. In order to reduce the amount of side-streams and meet the demand of the growing population, a higher utilization degree of the fish is needed. The aim of this study was to use cod backbone for enzymatic production of bioactive hydrolysates with antioxidative and antimicrobial properties. Three different enzymes were applied (Alcalase, Neutrase and Protamex) and hydrolysis was carried out for ½ - 6h within the enzyme’s optima for pH and temperature. Alcalase treated samples had the highest protein extracted yield (PEY) followed by Protamex treated samples and lowest PEY was obtained with Neutrase. No correlation was observed between PEY and degree of hydrolysis (DH). All hydrolysates had antioxidant activities measured by radical scavenging and iron chelation in vitro assays. For radical scavenging activity, Protamex treated hydrolysate showed lowest IC50 and had a molecular weight < 10 kDa. Whereas for iron chelation activity, the control samples (no enzyme added, but heat treated) showed similar or lower IC50 with molecular weights of 200 – 10 kDa. Amino acid composition measured on selected hydrolysates suggested that not only composition of amino acid, also but sequence and size influence the properties. None of the hydrolysates showed antimicrobial activity measured by the Minimum Inhibitory and disk diffusion assays. In summary, the results showed that protein with antioxidant activity can be extracted from the cod backbone and make it possible to use the side-streams generated in the fish industry.

Objective: The aim of this study is to investigate the prognostic correlation between tumor budding and stage I-II cervical cancer, with the goal of providing guidance for postoperative diagnosis and treatment strategies for patients.Methods: A comprehensive search was conducted across 12 databases including Pubmed, Cochrane, Embase, Scopus, OVID, Web of Science, EBSCohost, CNKI, Wan-Fang, VIP, Dui-Xiu and CBM to identify relevant literature on the association between tumor budding and prognosis or clinicopathological features of cervical cancer.The quality of included studies was assessed using the Newcastle-Ottawa scale. Statistical analysis was performed using Review Manager.Results: Our findings demonstrate that high-grade tumor budding in stage I-II cervical cancer is associated with significantly poorer overall survival (P<0.0001) and disease-free survival (P<0.0001). Subgroup analyses revealed that irrespective of sample size and histological type, the overall survival in the high-grade tumor budding group is markedly lower than that in the low-grade tumor budding group; similarly regardless of stage inclusion criteria, budding type, field boundary value or sample size,the disease-free survival in the high-grade tumor budding group is significantly lower than that in the low-grade tumor budding group.Furthermore,high grade tumor budding is correlated with several adverse pathological features.Conclusion: In light of these results,it can be concluded that tumor budding serves as an unfavorable prognostic factor for stage I-II cervical cancer,and may inform I-II stage postoperative treatment planning for such patients.

Introduction: Reflection and learning about the use of virtual care in healthcare delivery has become a central goal for health systems internationally. Insights drawn in the aftermath of the COVID-19 pandemic have led to vast changes to embed virtual care in health care delivery. This study explored the methodologies used to manage change that encompasses virtual care and factors contributing to success. Methods: A systematic review and narrative synthesis was undertaken. Eligible articles were those reporting structured change management processes in the context of virtual care published between 1 st January 2019 –31 st December 2023, identified by searching four electronic databases (Scopus, MedLine, PsycInfo and Business Source Premier). Data were extracted and synthesised from the eligible studies. Results: A total of 17 articles were included; studies focused on changes occurring within hospital settings and in community health centres, often within a single organisational unit, institution, or hospital network. Studies described using various methodologies for managing change during the introduction of large digital systems, embedding new digital tools, the introduction of new technologies and workflow or process design changes resulting from virtual care. Discussion: Structured change methodologies were often integrated in a strategic change framework with process improvement methods utilised to support the change process. Managing change relating to the technology with attention to the clinical and people aspects of change was considered a key gap and challenge in the context of virtual care change. Change leadership and the integration of technical and clinical teams were identified as key enablers.

Data acquisition, modelling and management are the three vital components of smart agriculture. Drones play a major role in this regard by capturing the detailed data using high-resolution cameras and advanced sensors. It acts as a key element driving enhancement to crop productivity, agricultural precision and many more. The data collected from the drones are at higher risk of security concerns as the process of data collection in smart agriculture involves collaboration among several entities. There is a possibility that the intruders can intentionally get in to the system and grab the data for wrong reasons. This emphasis the greater requirement for advancing the security features associated with aerial surveillance in smart agriculture. This paper presents a blockchain assisted secure two factor mutual authentication scheme for aerial surveillance security. The major contributions of this paper involve twofold: first a blockchain based secure authentication framework is provided; second, an efficient and lightweight two factor mutual authentication scheme for aerial surveillance in smart agriculture is provided. The proposed protocol is evaluated using the simulation tool called AVISPA and it is assessed for both security and performance related features. The security analysis of the proposed protocol states that this approach remains more resistant to most challenging security threats that occurs across IoT based smart agricultural systems. This protocol is also providing reduced computational cost and complexity measures in comparison the conventional approaches. A detailed comparative analysis shows that this approach provides the better results with the total computational complexity of 1.11ms.

Human pose estimation based on heatmap regression has achieved significant success in recent years. However, the semantic ambiguity caused by traditional hand-crafted heatmaps seriously affects the model performance. Specifically, hand-crafted heatmaps generated with a fixed Gaussian kernel are semantically misaligned. Various Gaussian covered areas for keypoints with the same type may cause model learning confusion. In this paper, we focus on learnable heatmap generation and propose a refined heatmap generator (RHG) to boost human pose estimation. First, we propose a joint training framework to connect the human pose estimator and RHG for end-to-end training. It employs a joint loss function to learn intermediate representations of the network and dataset. Second, RHG takes annotated dotpoints as input and utilizes scale-aware heatmaps as regression targets to deal with the scale variation. Scale-aware heatmaps are generated by adjusting Gaussian covered areas with geometric priors. Experimental results show that our method achieves 72.0%AP on COCO test-dev2017 and 74.0%AP on CrowdPose dataset, respectively, outperforming state-of-the-art methods.

Behavioral attacks on cloud networks target the behaviors and actions of users within cloud computing environments. Attackers can obtain unauthorized access, interfere with services, steal confidential information, or jeopardize system integrity by taking advantage of flaws in the cloud network’s infrastructure, protocols, or apps. Given the growing number of organizations depending on cloud services for data processing and storage, it is imperative to spot behavioral assaults on cloud networks. In order to improve the detection of behavioral assaults in cloud network environments, a unique ensemble technique has been developed and presented in this study. The suggested model combines multiple features with cutting-edge machine-learning approaches to improve detection accuracy. We exhibit the efficacy of our methodology in detecting diverse behavioral attacks in cloud networks through testing and assessment, hence providing improved defense against ever-changing cyber threats. Our work sets a new standard for cloud security and offers a workable way to strengthen defenses against advanced assaults. The paper introduces an advanced ensemble method combining multiple features with cutting-edge machine-learning techniques to enhance the detection of behavioral attacks in cloud networks. By evaluating its effectiveness against various attack types, the model demonstrates significant improvements in detection accuracy, providing a robust solution for evolving cyber threats in cloud security.

Linking characteristics of parasites to host diet-induced shifts in host-parasite interactions is a critical step in predicting the abundance of specific parasites within an ecosystem. A parasite’s infection route reflects a suite of parasite characteristics that could mediate the effects of host diet quality on host-parasite interactions, but the effect of infection route on the response of host-parasite interactions to host diet quality has not been quantified. We quantified these effects for the first time by experimentally manipulating a parasite capable of transmitting itself via multiple pathways utilized by a wide variety of parasites. We altered the diet phosphorus (P) content of Daphnia infected by the mixed mode transmitter Hamiltosporidian tvaerminnensis via one of two infection routes: horizontal, via the ingestion of spores from dead hosts, or vertical, via the infection of host offspring. Then, we quantified the effect of infection route on host diet-induced shifts in parasite load and virulence (i.e. host fecundity and survival). Overall, we found that horizontal infections were more sensitive to host diet than vertical infections. Low P diets decreased the parasite load and fecundity of all hosts, but had stronger negative effects on the loads of horizontally infected individuals than vertically infected ones. Likewise, horizontal infection reduced the survival of hosts fed low P (but not high P) diets while vertical infection did not affect survival. This increased sensitivity of horizontal infections to diet quality empirically supports the intuitive hypothesis that host diet quality exerts stronger effects on parasites that transmit via ingestion into a host’s gut than on parasites that transmit via routes less directly related to host nutrient uptake. Through these effects, host diet quality may shape the ecology and evolution of parasite communities.

Phenotypic plasticity is the key adaptive mechanism behind annual adjustment of breeding time in response to temperature. In nature, organisms are not only subjected to variation in temperature but encounter multiple fluctuating environmental factors that affect phenotypic expression, including conspecific density, which affects individual performances through resource competition. We examined the interactive effects of temperature and conspecific density at different spatial scales (territory and patch level) on breeding time and success utilizing data obtained from a long-term monitoring of a wild great tit (Parus major) population in a fragmented woodland. As expected, we detected earlier breeding in response to warmer spring temperatures but surprisingly, we report earlier laying at low local density, but no evidence of density effects at patch level. Birds experiencing low local density throughout their life bred on average earlier, but this response was also seen at the within-individual level showing earlier laying in years when individuals experienced a lower density than average. In terms of breeding success, earlier laying decreased the risk of brood failure and increased the number of fledglings. The number of fledglings was higher at low density at the territory level, while higher patch-level density increased the probability of brood failure. Altogether, these results support our hypothesis that density-related responses were likely mediated by food competition rather than increased numbers of low-quality birds or increased occupation of poorer territories at higher densities. This study highlights the importance of examining parameters at different spatial scales, along with the study of individual responses to multivariate cues for a comprehensive understanding of the variations in phenological plasticity.

Speech production interferes with accurate measurement of cardiac vagal activity during acute stress, attenuating the expected drop in heart rate variability in the respiratory frequency band. Speech also induces sympathetic changes similar to those induced by psychological stress. In the laboratory, confounding of physiological stress reactivity by speech may be controlled experimentally. In ambulatory assessments, however, detection of speech episodes is necessary to separate the physiological effects of psychosocial stress from those of speech. Using supervised machine learning (https://osf.io/bk9nf), we trained and tested speech classification models on data obtained from 56 participants (ages 18-39). They were equipped with privacy-secure wearables measuring thoracoabdominal respiratory inductance plethysmography (RIP from a single and a dual-band set-up), thoracic impedance pneumography, and an upper-sternum positioned unit with triaxial accelerometers and gyroscopes. Following an 80/20 train-test set split, nested cross-validations were run with the machine learning algorithms XGBoost, Gradient Boosting, Random Forest, and Logistic Regression on the training set to get unbiased generalized performance estimates. Speech classification by the best model per method was then validated in the unseen test-set. Speech versus no-speech classification performance (AUC) for both nested cross-validation and test-set predictions was excellent for thorax-abdomen RIP (nested cross-validation: 96.6%, test-set prediction: 98.5%), thorax-only RIP (97.5%, 99.1%), impedance (97.0%, 97.8%) and accelerometry (99.3%, 99.6%). The sternal accelerometer outperformed the other methods. These novel open-access models that leverage privacy-secure biosignals will enable researchers to detect speech and control for its confounding effects in ambulatory recordings, thereby enhancing the trustworthiness of psychophysiological findings.

The αLβ2 integrin LFA-1 plays a key role in T-cell adhesion to the endothelial vasculature and migration into both secondary lymphoid organs and peripheral tissues via interactions with its target protein ICAM-1, but the pathways that regulate LFA-1-mediated T-cell polarity and migration are not fully understood. In this study we screened two RNAi libraries targeting G protein-coupled receptors (GPCR)/GPCR-associated proteins and kinases in a HuT-78 T-cell line model of LFA-1-stimulated T cell migration. Based on staining of the actin cytoskeleton, multiple parameters to measure cell morphology were used to assess the contribution of 1109 genes to LFA-1-mediated T cell polarity and migration. These RNAi screens identified a number of both novel and previously identified genes that either increased or decreased the polarity and migratory capacity of these cells. Following multiparametric analysis, hierarchical clustering and pathway analysis, three of these genes were characterised in further detail using primary human T-cells, revealing novel roles for the heterotrimeric G protein subunit Gβ1 and casein kinase 2 in LFA-1-mediated T-cell polarity and migration in-vitro. Our studies also highlighted a new role for ICAP-1, an adaptor protein previously described to be associated with β1 integrins, in β2 integrin LFA-1-directed migration in T-cells. Knockdown of ICAP-1 expression in primary T cells revealed a role in cell polarity, cell velocity and transmigration towards SDF-1 for this adaptor protein. This study therefore uncovers new roles for GPCR/GPCR-associated proteins and kinases in T-cell migration and provides potential novel targets for modulation of the T-cell immune response

Here, we present a 6-step gram-scale synthesis of (±)-gregatin A, a fungal polyketide characterized by an alkylated furanone core origi-nally isolated from Cephalosporium gregatum. The synthetic route features an intermolecular 1,3-dipolar cycloaddition, a Mo-mediated disconnection of isoxazole skeleton, and an acid-mediated desilylation/enamine hydrolysis and hemiketalization cascade. Notably, this synthetic approach establishes a versatile platform applicable to the synthesis of diverse gregatin congeners, allowing for the introduc-tion of varied side chains. Our method not only facilitates the scalable production of (±)-gregatin A but also opens avenues for the ex-ploration of structural analogs with unique biological activities.

Carbon, with its excellent conductivity and structural stability, plays a crucial role in energy storage and conversion technologies and is considered an ideal choice in electrochemical energy storage for electrode materials. To address these needs and safeguard the environment, scientists have been investigating novel approaches to synthesis. In this field, molten salts or salt melts have emerged as a powerful and environmentally friendly alternative for synthesizing porous carbon materials with tunable chemical and structural properties. These salt systems can serve not only as solvents and reaction media but also as templates. Similar to traditional templating strategies, porous carbon materials synthesized using salt templates do not sacrifice carbon yield, but what sets them apart from traditional methods is that template removal can be achieved through deionized water washing. This article introduces a self-activation method that is applied to the preparation of carbon nanomaterials with high porosity and enormous specific surface area. The working principle, process steps, preparation characteristics, and research progress of this process are summarized, and its potential applications in next-generation electrochemical energy storage devices are discussed and envisioned.

In this paper, we investigate affine formation control problem for a class of multi-agent systems (MASs) where the formation information is only accessible to a small group of agents. We propose a class of generic formation maneuver (GFM) functions and unified analysis method to study different convergence properties including finite-time, fixed-time and predefined-time strategy. We first propose a consensus-based formation control of leaders and then a stress-matrix-based formation control for followers. Different GFM functions are specified for finite-time, fixed-time and predefined-time formation control of leader and follower groups, respectively. Finally, a number of simulation examples are provided to prove the effectiveness of the proposed algorithm.

Industry burnout is interlinked with cultural, individual, physical, or emotional exhaustion, and social factors, the resolution of which requires the technology-driven trends in the workplace and the technologies such as work pattern monitoring and Artificial Intelligence that can deal with large amounts of data. Industries face a gigantic problem i.e., employee burnout which can charge a firm loss in numerous hours and thousands of dollars every year. The more advanced companies use work pattern monitoring using Artificial Intelligence to make their employees work more professionally. In this research my attempts to understand the development and leadership, on the effects of work pattern monitoring using Artificial Intelligence technology on information technology organizations. In this approach, the data of the employees will be stored on a cloud server with governance & compliances. The study discussed the development of methods which are configured as two different system interfaces, which are of minimum valuable product (MVP) and the results obtained from the two approaches. The system will provide work pattern monitoring via the ‘Real-Time Database – MongoDB Atlas’ which will synchronize the employee burnout data to improve the employee experience. This research also illustrates the advantages and disadvantages of the proposed solutions. “Burnout Detection Mechanism” that will help Industry management and Human Resource Management to manage the emotional state of the employees, understanding their real state. The study conducted a self-survey, and the outputs of the surveys are explained in this research paper. The sample data we are using is mainly focused on information technology employment perception.

This research focuses on the topic of using AI in color variant management in products to enhance the appeal and performance of the products in the marketplace, by incorporating artificial intelligence, deep learning, and neural network systems. Applying consumers and products information, preferences, buying history and sales history, WE created AI models to predict and change product colors in real-time. The complete workflow used comprises data gathering, processing, and feature extraction, model training, integration of the color adjustment software tools, and finally, testing and validation. The efficiency of such AI-driven interventions was analyzed through the consumer satisfaction indices, the sales results, and the engagement data based on the consumption of digital platforms. This study demonstrates valuable potential of AI to improve product design application and development while providing valuable suggestion for Business adapting and improving market outcomes according to the changing consumer trends. Such an application of AI implements a new best practice in the ways of enhancing futuristic consumer-oriented marketing approaches.

This article discusses the use of classification of web visitors and determination of branding quality using AI in effort to redesign digital marketing. AI in its various forms such as machine learning, natural language processing, and computer vision helps businesses to better understand their users' behavior, better understand the context of supplied content and improve user experience. The application of AI in the management of the websites offers features such as Real-time monitoring, automated content tuning, and Analytics for predictions. Automated tools can analyze who is visiting the site, what kind of work they are doing in terms of SEO, and how to assist with creating high-quality content. Also, AI helps with mass and targeted promotions like recommended products and services, variable rates of prices, etc. AI can help unlock significant benefits for businesses, across the board and lead to enhanced engagement in the digital space and thus gives a competitive advantage in the market. The article also discusses investment in training and education for consumers to ensure them remain relevant with emerging technologies in AI and the market. By way of illustration and analysis, the article clearly outlines how the incorporation of AI in organizational functions can bring about operational efficiencies and cost savings, as well as result in remarkable improvements in branding and marketing strategies.

Anxiety is an interactive disorder of the mind and body, characterized by excessive worry about uncertain future events and a dysfunction of the autonomic nervous system. Previous studies have shown that slow, deep breathing can affect the body’s internal organs by increasing the activity levels of the vagus nerve, reducing physical tension, and anxiety. Although we know that slow and deep breathing techniques can effectively regulate anxiety and other emotions, the psychological and neurophysiological mechanisms of slow breathing on anxiety have not been systematically explored. In the study, we combined the paced breathing task with the threat uncertainty task for the first time to investigate the role of slow breathing in regulating anxiety. Here we investigated this question, using Spectral analysis of EEG to assess brain activity relating respiratory rate and he mechanism of respiratory rate impact on the anxious. Twenty-seven individuals participated in the experiment, which followed a 2 (respiratory rate: fast breathing, slow breathing) × 2 (certainty: certain, uncertain). The results of showed that: (1) Slow breathing effectively reduced anxiety, the valence and arousal are lower under the slow breathing. (2) The EEG of fast and slow breathing showed different characteristics. The delta, theta and alpha EEG power are increased during the slow-paced breathing. (3) The EEG of Respiratory rate and certainty had a significant effect on the theta power. When individuals are faced with uncertain information the theta EEG power decreased during the slow-paced breathing, however, the theta EEG power increased during the fast-paced breathing.

Animal Assisted Interventions (AAIs) have been shown to have several effects in humans but the underlying cerebral mechanisms are still widely unknown. This research explored the neurological aspects of human–animal interactions. Specifically, we focused on frontal alpha asymmetry (FAA), a feature indicating differences in alpha power between the left and right frontal cortex, which is recognized as a correlate of approach motivation and positive affect. Twenty-nine healthy adults participated in this study, in which we used electroencephalography to measure their brain activity. The study comprised five phases: baseline measurements, interaction with a real dog, interaction with a replica dog, interaction with a plant, and a neutral phase. Participants had both physical and visual contact with all the stimuli, and the procedure was repeated three times for each participant. We also assessed participants’ subjective experiences of mental states and intrinsic motivation through the Multidimensional Well-Being and the Intrinsic Motivation Inventory questionnaires. The objective measurements of motivation and positive affect through FAA did not show a significant difference between interactions with a real dog and control conditions, but the subjective assessments differed. Participants reported significantly higher motivation and a more positive state of mind after interacting with a real dog compared to the control conditions. In summary, this study highlights the complexity of human–animal interactions and shows an intricate interplay between objective and subjective measurements. Our findings emphasize the importance of considering both neural markers and subjective experiences for understanding the nuanced mechanisms involved in the meaningful connections humans have with animals.

While eDNA metabarcoding holds promise as a holistic approach for assessing vegetation changes and community composition across diverse spatial and temporal scales, systematic investigations into its efficacy compared to conventional surveys remain scarce in the literature. The present study explores the differences in plant diversity retrieved with field surveys and that captured with a multi-marker eDNA metabarcoding approach (two nrDNA ITS1 and ITS2, and two cpDNA rbcL and trnL) from river water samples. The eDNA approach retrieved 46 aquatic plants (hydrophytes and helophytes) and 245 terrestrial plants, compared to 24 and 127 species identified from the field surveys. On average, eDNA samples collected immediately downstream of the survey sites retrieved 43% and 39% of the observed aquatic and terrestrial species , respectively. Discrepancies were explained by differences in the taxonomic resolution, the stochasticity of retrieving rare and elusive species, and the presence of reference sequences. We found a significant positive correlation between spatial and community distances at scales ranging 2-9 km, and identified turnover as the driving force of these differences. eDNA demonstrated sensitivity to community changes, and both approaches converge on a similar community structure. Interestingly, eDNA samples collected immediately upstream of the survey sites exhibited significant species overlap with the downstream samples (c. 100 m apart). Overall our results demonstrate that, with adequate sampling and a multi-marker approach, eDNA has the potential to approximate catchment gamma diversity while still being informative of the local flora.