Multiple Platforms Research Articles

Comprehensive assessment of long-read sequencing platforms and calling algorithms for detection of copy number variation.

Copy number variations (CNVs) play pivotal roles in disease susceptibility and have been intensively investigated in human disease studies. Long-read sequencing technologies offer opportunities for comprehensive structural variation (SV) detection, and numerous methodologies have been developed recently. Consequently, there is a pressing need to assess these methods and aid researchers in selecting appropriate techniques for CNV detection using long-read sequencing. Hence, we conducted an evaluation of eight CNV calling methods across 22 datasets from nine publicly available samples and 15 simulated datasets, covering multiple sequencing platforms. The overall performance of CNV callers varied substantially and was influenced by the input dataset type, sequencing depth, and CNV type, among others. Specifically, the PacBio CCS sequencing platform outperformed PacBio CLR and Nanopore platforms regarding CNV detection recall rates. A sequencing depth of 10x demonstrated the capability to identify 85% of the CNVs detected in a 50x dataset. Moreover, deletions were more generally detectable than duplications. Among the eight benchmarked methods, cuteSV, Delly, pbsv, and Sniffles2 demonstrated superior accuracy, while SVIM exhibited high recall rates.

New Generation Environmental In Situ TEM Holder for Gas Cell Research Across Multiple Platforms

New Generation Environmental In Situ TEM Holder for Gas Cell Research Across Multiple Platforms

Second-level agenda-setting effects of news media and public policy on social media discourse across platforms: immigration during the COVID-19 pandemic in the U.S.

ABSTRACT Integrating intermedia, second-level agenda-setting, and cross-platform research, this study examines the role of news media attention and public policy decisions in driving the salience of issue attributes on multiple social media platforms. We collected three immigration-related datasets (i.e., social media posts, news media headlines, and policy documents covering the first year of the COVID-19 pandemic that also marked the last year of the Trump presidency) and applied manual coding, automated text analysis, and time series modeling to analyze the relationships. The results show that news media set the public agenda across Facebook, Twitter, and YouTube at the second level, with conservative media elevating travel-related discussion and liberal media driving border- and court-rulings-related discussion on those platforms. Compared to news media, the second-level agenda-setting effect of policy announcements was small and platform-specific. Among the three platforms, public discourse on YouTube was the most responsive to media and policy agendas. These findings advance second-level agenda-setting research, demonstrate partisan differences in news media’s impact, reveal the limited role of public policy, and provide a nuanced portrayal of cross-platform public discourse dynamics.

Retrogradation of carbonate platforms on a rifted margin: the late Ediacaran record of the northwestern Yangtze Craton (SW China)

The Late Ediacaran Dengying Formation, located in the Sichuan Basin of the northwestern Yangtze Craton, is of significant interest in oil and gas exploration due to its abundant pores and vugs within microbial mound-shoal complexes. However, there is still uncertainty regarding the spatiotemporal distribution and controlling factors of the platform margin. This study comprehensively analyzes the retrogradation pattern of the Dengying Formation platform margin using seismic data, well logs, field outcrops, and petrological characteristics. Our findings reveal that the Dengying Formation strata surrounding the rift basin at the northwestern of the Yangtze Craton can be divided into three main depositional facies: basin facies, slope facies, and platform margin facies. Additionally, based on the integration of lithological, log, and seismic characteristics, the Dengying Formation is subdivided into four third-order sequences, with five sequence boundaries and three seismic facies identified. Supported by sequence stratigraphy and geophysical data, we have reconstructed the tectono-sedimentary evolution of the multiple platform margins on the eastern side of the Deyang-Anyue rift in the Sichuan Basin during the late Ediacaran. Our findings indicate that the platform underwent two phases of retrogradation. The second-stage platform margin underwent retrogradation towards the interior, spanning a distance between 10 and 80 km, based on the initial configuration established by the first-stage platform margin. The main controls for progradation and retrogradation of carbonate platforms are eustatic sea-level changes and tectonic activity. Eustatic sea-level changes can be divided into constructive and destructive phases. Constructive phases are commonly observed in highstand systems tracts, while destructive phases are often associated with transgressive systems tracts and are related to platform retrogradation processes. However, sea-level changes alone cannot fully control the process of platform retrogradation. The thermal subsidence following mantle plume events likely played a significant role in the retrogradation of the platform in the study area. During this period, tectonic processes controlled the geometry of the platform and the deposition of carbonates in the platform margin-slope-basin environment. Additionally, karst-related mound-shoal complexes developed extensively along the platform margin of the Dengying Formation in the northwestern Yangtze Craton. The Lower Cambrian dark shales represent high-quality hydrocarbon source rocks, while the Dengying Formation exhibits an optimal source-reservoir configuration.

Real-time Multi-platform Route Planning in ridesharing

The increasing availability of mobile Internet and portable devices has led to the popularity of spatial crowdsourcing recently. The significant burgeoning of ridesharing services in spatial crowdsourcing has transformed urban mobility. However, due to the imbalance between supply and demand, some requests may be rejected or workers may keep idle for a long time. Fortunately, sharing tasks and workers across multiple platforms in a collaborative manner can diminish the negative effects of non-uniform distribution. In this paper, we propose a Multi-platform Route Planning in ridesharing problem (MPRP), which integrates route planning and task assignment in spatial crowdsourcing. We study how to improve the total revenue of platforms through coordination with multiple ridesharing platforms. To solve the problem, we propose the Greedy Multi-platform Route Planning algorithm (G-MPRP), which extends the dynamic programming insertion operation and assigns workers in a greedy manner. To overcome the shortcoming of G-MPRP, we further propose the Pack-based Multi-platform Route Planning algorithm (P-MPRP), which packs requests through a ranking-based function. Extensive experimental results shows the proposed algorithms can improve the revenues significantly.

The relational forms of cultural-creative crowdfunding: A typology of practices through mapping platforms in Europe and Latin America

Cultural-creative crowdfunding (CCCF) intersects the culture sector production chain and alternative finance technology as a global web-enabled phenomenon for funding cultural-creative activities. Yet, busking or aspects of patronage are not new to artists and cultural-creative agents; the novelty is doing so through a virtual intermediator space, a crowdfunding platform (CFP). CFPs have proliferated worldwide but the literature is embryonic and lacks further elaboration on how platform dynamics can impact the funding/financing patterns of specific sectors. In the case of the culture sector, given its unique attributes, specificities, and relational structuring, the impact of crowdfunding requires even more conceptual development, systematization, and potential policy instrumentalization. Hence, this study explores how CCCF has evolved and what different models (and channels) within multiple platforms were developed under the CCCF umbrella. Based on a combination of methods (tracking and trawling, Delphi, and categoric analysis), the current research maps the CFPs focusing on culture-creative projects throughout Europe and Latin America. The aim is to conceptualize a broader typology of CCCF practices that can better serve the cultural-creative circuit. This work is among the first to pursue such CCCF typology bridging cross-disciplinary understanding and real-world practices. This research, therefore, offers implications for interdisciplinary academics, practitioners, and policymakers by enabling the nuanced comprehension of the relational forms of CCCF as multiple-practices, expanding its boundaries amid a vaster umbrella of possible web-enabled genre (sub-)models to be adopted, legitimized, and systematized in (and by) the culture sector.

Local read haplotagging enables accurate long-read small variant calling

Long-read sequencing technology has enabled variant detection in difficult-to-map regions of the genome and enabled rapid genetic diagnosis in clinical settings. Rapidly evolving third-generation sequencing platforms like Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) are introducing newer platforms and data types. It has been demonstrated that variant calling methods based on deep neural networks can use local haplotyping information with long-reads to improve the genotyping accuracy. However, using local haplotype information creates an overhead as variant calling needs to be performed multiple times which ultimately makes it difficult to extend to new data types and platforms as they get introduced. In this work, we have developed a local haplotype approximate method that enables state-of-the-art variant calling performance with multiple sequencing platforms including PacBio Revio system, ONT R10.4 simplex and duplex data. This addition of local haplotype approximation simplifies long-read variant calling with DeepVariant.

Evaluating airborne, mobile and terrestrial laser scanning for urban tree inventories: A case study in Ghent, Belgium

In urban tree inventories, structural measurements such as diameter at breast height (DBH), tree height (H), crown projection area (CPA) and crown volume (CV) are essential for diverse applications, including accurate ecosystem service estimation and management decisions. Traditionally, tree measurements are obtained using range finders and diameter tape. These measurements can be integrated into urban tree inventories through 3D laser scanning (also known as LiDAR). Multiple platforms for laser scanning exist, each with its own advantages and disadvantages, which have not yet been compared explicitly for urban tree inventories. We collected terrestrial laser scanning (TLS), mobile laser scanning (MLS) and airborne laser scanning (ALS) in leaf-on (TLS, MLS, ALS) and leaf-off conditions (TLS, MLS) in Ghent, Belgium. We evaluated the DBH, H, CPA and CV of 95 individual trees acquired from each acquisition platform, benchmarking against TLS. Our results show accurate DBH derivation from both TLS and MLS (bias < 2 cm, concordance correlation coefficient (CCC) ≈ 1). However, during leaf-on conditions, occlusion from shrubs and ivy is observed. For leaf-on MLS, point clouds of large trees exhibited occlusion in the top canopy, impacting crown volume (CV MLS leaf-on: bias = −116 m², CCC=0.85) and, to a lesser extent, tree height (H MLS leaf-on: bias = −0.38 m, CCC=0.99). Crown projected area was less affected (bias = 0.49 m², CCC=0.99), with differences more attributed to varying point precision among sensors. The difference between the metric and benchmark increased with tree size and the structural complexity of the surroundings (e.g. buildings), especially for MLS, for which limited GNSS coverage, traffic, and suboptimal walking patterns impeded ideal data collection. Our results will help city councils and tree managers choose the most optimal LiDAR platform for urban tree inventories, accounting for their purpose, site complexity and budget.

Early-life manipulation of the serotonergic system exacerbates the harmful effects of sleep deprivation on cognitive functions.

Serotonin is a monoamine neurotransmitter that plays a main role in regulating physiologicaland cognitive functions. Serotonergic system dysfunction is involved in the etiology of various psychiatric and neurological disorders. Therefore, the present study was designed to investigate the effects of early-life serotonin depletion on cognitive disorders caused by sleep deprivation. Serotonin was depleted by para-chlorophenylalanine (PCPA, 100 mg/kg, s.c.) at postnatal days 10-20, followed by sleep deprivation-induced through the multiple platform apparatus for 24 h at PND 60. After the examination of the novel object recognition and passive avoidance memories, the hippocampi and prefrontal cortex were dissected to examine the brain-derived neurotrophic factor (BDNF) mRNA expression by PCR. Our findings showed that postnatal serotonin depletion and sleep deprivation impaired the novel object recognition and passive avoidance memories and changed the BDNF levels. In the same way, the serotonin depletion in early life before sleep deprivation exacerbated the harmful effects of sleep deprivation on cognitive function and BDNF levels. It can be claimed that the serotonergic system plays a main role in the modulation of sleep and cognitive functions.

Studying the Impact of Different TCP DoS Attacks on the Parameters of VoIP Streams

In today’s digital world, no one and nothing is safe from potential cyberattacks. There is also no 100% protection from such attacks. Therefore, it is advisable to carry out various studies related to the effects of the different cyberattacks on the performance of the specific devices under attack. In this work, a study was carried out to determine how individual TCP DoS attacks affect the parameters of VoIP (Voice over IP) voice and video streams. For the purpose of this work, a model of a simple IP network has been created using the GNS3 IP network-modeling platform. The VoIP platform used was Asterisk Free PBX. Tools from Kali Linux were used to implement the individual TCP DoS attacks; IP-network-monitoring tools and round-trip-delay-measurement tools were also used. The proposed study is applicable to multiple VoIP platforms wherein voice and video traffic are passed/processed by the VoIP server. From the obtained results, it was found that Asterisk Free PBX is very well secured against TCP DoS attacks, which do not affect the platform performance or the parameters of the voice and video streams. The values of the observed parameters, such as jitter, packet loss, round-trip delay, etc., are very far from the maximum allowable values. We also observed a low load on the CPU and RAM of the system during the whole study.

Rank Matrix Approach for Endometriosis: Integrating Data and Constructing Diagnostic Models

Background: Endometriosis is a debilitating gynecological disorder characterized by chronic pain, infertility, and the growth of endometrial tissue outside the uterus. Accurate and early detection of this condition is crucial for effective management and treatment. Methods: We developed a gene rank matrix-based model to integrate endometriosis cohorts across multiple platforms. After removing batch effects, we identified 83 genes associated with endometriosis and further refined a diagnostic model using 11 of these genes. The model was trained on two platforms and validated on two others using SVM, Random Forest, Logistic Regression, and gradient-boosting machine learning algorithms. Results: The integration via the gene rank matrix effectively mitigated batch effects. Utilizing a gradient boosting classifier with a subset of 11 genes, the model demonstrated commendable diagnostic efficacy, achieving an Area Under the Curve (AUC) of 0.77, an accuracy of 0.72, and an F1 score of 0.72 for the training dataset. When subjected to validation, the model maintained its performance, yielding an AUC of 0.769, an accuracy of 0.719, and an F1 score of 0.732. These 11 genes were found to be associated with immunosuppression. Conclusion: Our approach to integrating gene rank matrices effectively consolidates endometriosis data across diverse platforms. The diagnostic model, harnessing the predictive power of 11 specific genes, surpasses alternative models, thereby offering promising prospects for aiding clinical diagnosis of endometriosis. Further validation is imperative to elucidate the functional significance of these 11 genes. Our study underscores the potential of data integration coupled with machine learning techniques in advancing the diagnosis of intricate diseases, such as endometriosis.

Identification of intracellular bacteria from multiple single-cell RNA-seq platforms using CSI-Microbes.

The study of the tumor microbiome has been garnering increased attention. We developed a computational pipeline (CSI-Microbes) for identifying microbial reads from single-cell RNA sequencing (scRNA-seq) data and for analyzing differential abundance of taxa. Using a series of controlled experiments and analyses, we performed the first systematic evaluation of the efficacy of recovering microbial unique molecular identifiers by multiple scRNA-seq technologies, which identified the newer 10x chemistries (3' v3 and 5') as the best suited approach. We analyzed patient esophageal and colorectal carcinomas and found that reads from distinct genera tend to co-occur in the same host cells, testifying to possible intracellular polymicrobial interactions. Microbial reads are disproportionately abundant within myeloid cells that up-regulate proinflammatory cytokines like IL1Β and CXCL8, while infected tumor cells up-regulate antigen processing and presentation pathways. These results show that myeloid cells with bacteria engulfed are a major source of bacterial RNA within the tumor microenvironment (TME) and may inflame the TME and influence immunotherapy response.

Integrated Resonant Electro‐Optic Comb Enabled by Platform‐Agnostic Laser Integration

AbstractThe field of integrated photonics has significantly impacted numerous fields including communication, sensing, and quantum physics owing to the efficiency, speed, and compactness of its devices. However, the reliance on off‐chip bulk lasers compromises the compact nature of these systems. While silicon photonics and III‐V platforms have established integrated laser technologies, emerging demands for ultra‐low optical loss, wider bandgaps, and optical nonlinearities necessitate other platforms. Developing integrated lasers on less mature platforms is arduous and costly due to limited throughput or unconventional process requirements. In response, a novel platform‐agnostic laser integration technique is proposed, utilizing a singular design and process flow, applicable without modification to a diverse range of platforms. Leveraging a two‐step micro‐transfer printing method, nearly identical laser performance is achieved across platforms with refractive indices between 1.7 and 2.5. Experimental validation demonstrates strikingly similar laser characteristics between devices processed on lithium niobate and silicon nitride platforms. Furthermore, the integration of a laser with a resonant electro‐optic comb generator on the thin‐film lithium niobate platform is showcased, producing over 80 comb lines spanning 12 nm. This versatile technique transcends platform‐specific limitations, facilitating applications like microwave photonics, handheld spectrometers, and cost‐effective Lidar systems, across multiple platforms.

COMPARISON OF MESSAGE PASSING SYSTEMS IN CONTEXT OF ADAPTIVE LOGGING METHOD

Computer software is an important part of technological progress. As it becomes more and more complex and sophisticated, so does the need to protect it. Apart from typical information security aspects of integrity, availability and confidentiality, the scale and complexity of modern computer systems require a high level of control and observability. The main goal of this research is to build upon the foundations laid by the general idea of an adaptive logging method and introduce the next iteration of its design in the form of an appropriate message passing system to be used to propagate required changes to corresponding implementation in an effective and performant manner. Four different message passing system models are introduced, based on different technologies such as RabbitMQ message broker, communication channels in PostgreSQL database management system, general web server architecture and Linux-based process signaling interface. For each of those an overview description and graphical model is presented. Finally, the resulting comparison is conducted, comparing aspects such as reliance on third-party software, communication medium, error surface increase and authentication related considerations. As a result, the design based on process signaling approach is determined to be the most suitable for adaptive logging method, as it does not introduce any third-party software (and as such affects error surface in a somewhat negligible manner), binds directly to an observed application, is built using low level concepts that should be present in multiple different platforms and programming languages and should be able to reuse authentication logic that is already used when accessing computational machine where observed program is executed.

New approach to measuring topological phase transitions utilizing Floquet technology

Abstract The Floquet technique provides a novel anomalous topological phase for non-equilibrium phase transitions. Based on the high symmetry of the quantum anomalous Hall model, the findings suggest a one-to-one correspondence between the average spin texture and the Floquet quasi-energy spectrum. A new approach is proposed to directly measure the quasi-energy spectrum, replacing previous measurements of the average spin texture. Finally, we proposed a reliable experimental scheme based on ion trap platforms. This scheme markedly reduces the measurement workload, improves the measurement fidelity, and is applicable to multiple platforms such as cold atoms and nuclear magnetic resonance.

Exploration and verification a 13-gene diagnostic framework for ulcerative colitis across multiple platforms via machine learning algorithms

Ulcerative colitis (UC) is a chronic inflammatory bowel disease with intricate pathogenesis and varied presentation. Accurate diagnostic tools are imperative to detect and manage UC. This study sought to construct a robust diagnostic model using gene expression profiles and to identify key genes that differentiate UC patients from healthy controls. Gene expression profiles from eight cohorts, encompassing a total of 335 UC patients and 129 healthy controls, were analyzed. A total of 7530 gene sets were computed using the GSEA method. Subsequent batch correction, PCA plots, and intersection analysis identified crucial pathways and genes. Machine learning, incorporating 101 algorithm combinations, was employed to develop diagnostic models. Verification was done using four external cohorts, adding depth to the sample repertoire. Evaluation of immune cell infiltration was undertaken through single-sample GSEA. All statistical analyses were conducted using R (Version: 4.2.2), with significance set at a P value below 0.05. Employing the GSEA method, 7530 gene sets were computed. From this, 19 intersecting pathways were discerned to be consistently upregulated across all cohorts, which pertained to cell adhesion, development, metabolism, immune response, and protein regulation. This corresponded to 83 unique genes. Machine learning insights culminated in the LASSO regression model, which outperformed others with an average AUC of 0.942. This model's efficacy was further ratified across four external cohorts, with AUC values ranging from 0.694 to 0.873 and significant Kappa statistics indicating its predictive accuracy. The LASSO logistic regression model highlighted 13 genes, with LCN2, ASS1, and IRAK3 emerging as pivotal. Notably, LCN2 showcased significantly heightened expression in active UC patients compared to both non-active patients and healthy controls (P < 0.05). Investigations into the correlation between these genes and immune cell infiltration in UC highlighted activated dendritic cells, with statistically significant positive correlations noted for LCN2 and IRAK3 across multiple datasets. Through comprehensive gene expression analysis and machine learning, a potent LASSO-based diagnostic model for UC was developed. Genes such as LCN2, ASS1, and IRAK3 hold potential as both diagnostic markers and therapeutic targets, offering a promising direction for future UC research and clinical application.

Developing an Avatar-Based Framework for Unified Client Identification in Banking Systems Using Generative AI and Graph Neural Networks

In today’s data-driven world, the accurate and consistent identification of clients across multiple platforms is a critical challenge for financial institutions, government comptroller departments, and third-party service providers. Fragmented and inconsistent data across various systems pose significant risks, including regulatory non-compliance, fraud, and operational inefficiencies. This thesis presents the development of an innovative avatar-based framework for unified client identification in banking systems, leveraging the power of Generative AI and Graph Neural Networks (GNNs). The framework synthesizes disparate client data into a single, cohesive representation in the virtual world, effectively addressing the challenges of data fragmentation and inconsistency. Generative AI models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), are employed to enhance data quality by generating realistic synthetic data and imputing missing values. These models augment the existing datasets, ensuring completeness and accuracy of client profiles. Simultaneously, GNNs are utilized to model the complex relationships and interactions within the client data, capturing intricate dependencies and enhancing the accuracy of client identification. The proposed framework offers substantial benefits, including improved regulatory compliance, enhanced operational efficiency, and superior customer experience. By providing a unified view of client data, financial institutions can better detect and prevent fraudulent activities, meet stringent regulatory requirements, and deliver personalized services. Government comptroller departments can ensure more effective public fund management and maintain transparency. Third-party service providers can leverage accurate client profiles for better service delivery and risk management. The race to implement such advanced frameworks is a pivotal factor in determining leadership in the financial and administrative sectors. Institutions that adopt and integrate this technology swiftly will gain a significant competitive advantage, setting new standards in client identification and data management. This research underscores the transformative potential of combining Generative AI and GNNs in creating a robust, scalable, and efficient system for unified client identification, paving the way for future advancements in this critical field.

Collaborative Prediction in Anti-Fraud System Over Multiple Credit Loan Platforms

Collaborative Prediction in Anti-Fraud System Over Multiple Credit Loan Platforms

Turning Night into Day: The Creation and Validation of Synthetic Nighttime Visible Imagery Using the Visible Infrared Imaging Radiometer Suite (VIIRS) Day–Night Band (DNB) and Machine Learning

Abstract Meteorological satellite imagery is a critical asset for observing and forecasting weather phenomena. The Joint Polar Satellite System (JPSS) Visible Infrared Imaging Radiometer Suite (VIIRS) Day–Night Band (DNB) sensor collects measurements from moonlight, airglow, and artificial lights. DNB radiances are then manipulated and scaled with a focus on digital display. DNB imagery performance is tied to the lunar cycle, with the best performance during the full moon and the worst with the new moon. We propose using feed-forward neural network models to transform brightness temperatures and wavelength differences in the infrared spectrum to a pseudo-lunar reflectance value based on lunar reflectance values derived from observed DNB radiances. JPSS NOAA-20 and Suomi National Polar-Orbiting Partnership (SNPP) satellite data over the North Pacific Ocean at night for full moon periods from December 2018 to November 2020 were used to design the models. The pseudo-lunar reflectance values are quantitatively compared to DNB lunar reflectance, providing the first-ever lunar reflectance baseline metrics. The resulting imagery product, Machine Learning Nighttime Visible Imagery (ML-NVI), is qualitatively compared to DNB lunar reflectance and infrared imagery across the lunar cycle. The imagery goal is not only to improve upon the consistent performance of DNB imagery products across the lunar cycle, but ultimately to lay the foundation for transitioning the algorithm to geostationary sensors, making global continuous nighttime imagery possible. ML-NVI demonstrates its ability to provide DNB-derived imagery with consistent contrast and representation of clouds across the full lunar cycle for nighttime cloud detection. Significance Statement This study explores the creation and evaluation of a feed-forward neural network to generate synthetic lunar reflectance values and imagery from VIIRS infrared channels. The model creates lunar reflectance values typical of full moon scenes, enabling quantifiable comparisons for nighttime imagery evaluations. Additionally, it creates imagery that highlights low clouds better than its infrared counterparts. Results indicate the ability to create visually consistent nighttime visible imagery across the full lunar cycle for the improved nighttime detection of low clouds. Wavelengths chosen are available on both polar and geostationary satellite sensors to support the utilization of the algorithm on multiple sensor platforms for improved temporal resolution and greater simultaneous geographic coverage over polar orbiters alone.

The Role of Healthcare Leaders in Promoting Vaccine Acceptance in Saudi Arabia.

Several vaccines have been recommended by the health authorities in recent years and have been opposed by debates, lack of public trust, and variable levels of hesitance that resulted in increased anti-vaccination advocacy and a subsequent reduction in vaccination rates worldwide. This study aimed to explore the community's perceptions of the role of healthcare leadership in promoting vaccine acceptance. This cross-sectional study used a validated questionnaire designed according to the study's objectives for a heterogeneous purposive sample of individuals over 18 years old in Saudi Arabia. Participants completed the questionnaire online via a link provided by multiple social media platforms. The study included 7159 participants with various demographical features. On a five-point Likert scale, the average level of agreement on the role of healthcare executives in promoting vaccines was 3.76. The average level of agreement about the role of healthcare leaders in promoting vaccines was 3.76 out of five. Men were more likely than women to agree on healthcare worker's influence, 63.6% and 58.6%, respectively (P < 0.001). The ages of participants showed a favorable correlation with their level of agreement on the role of healthcare leaders in promoting vaccines (P < 0.001). The level of agreement on healthcare leader's role in promoting vaccines was inversely proportional to the education level (P < 0.001). The retired group reported the highest score, followed by the employed ones (P < 0.001). Unlike other political and religious leaders, this study indicates that healthcare professionals significantly impact vaccine hesitancy and uptake. Despite rising vaccine hesitancy, healthcare leaders remain more trustworthy providers of guidance and influence over vaccination decisions than others. In addition, the health leader's factual message boosts people's self-esteem and helps them decide to be vaccinated.