Variety Of Features Research Articles

STRUCTURAL INSIGHTS AND ANTICANCER POTENTIAL OF MELITTIN IN CD147 INTERACTION

Objective: This study investigates the interaction between melittin (PDB ID: 2MLT), a bioactive peptide from honeybee venom, and CD147 (PDB ID: 5XF0), a glycosylated transmembrane protein implicated in tumor progression. Material and Method: Employing molecular docking and bioinformatics tools, our structural analysis reveals diverse binding features, including hydrogen bonds, salt bridges, and non-bonded contacts, between the CD147 complex and melittin. Result and Discussion: Non-bonded interactions between 2MLT and specific amino acids (Gly181 and Arg201) of CD147 are highlighted, resembling aspects of the CypA/CD147 binding mechanism (Pro180-Gly181 and Arg201). The elevated anticancer potential of 2MLT was substantiated by utilizing the AntiCP 2.0 server and the ENNAACT server, employing machine learning and artificial neural network algorithms. Additionally, hydrophobicity analysis aligns with characteristics associated with anticancer peptides. Notably, thermodynamic stability variations with temperature underscore the robust binding affinity of 2MLT to the 5XF0 receptor. While our study comprehensively explores molecular interactions and predictive analyses, further in vitro and in vivo investigations are crucial to validate these findings for potential therapeutic applications.

SMIAltmetric: A comprehensive metric for evaluating social media impact of scientific papers on Twitter (X)

The rise of social media has significantly influenced scholarly communication, knowledge dissemination, and research evaluation, leading to the enrichment of alternative metrics (altmetrics) for evaluating academic papers’ social impact, which assesses the social impact of academic papers through online activities, including reading, bookmarking, downloading, and commenting. However, these altmetrics often focus on the number of mentions on social media rather than thoroughly evaluating the source, content, and dissemination of these mentions. To address this gap, this study introduces the social media impact altmetric (SMIAltmetric), which is based on 44,087 publications and 860,680 tweets (now “posts”), a comprehensive scoring system for evaluating scientific papers on Twitter (now “X”), using diverse features, including literature-related, social media engagement-related, user-related, and content-related features. Employing Altmetric Attention Acores (AAS) as labels, we tested eight machine learning algorithms, with XGBoost demonstrating the highest accuracy at 0.8672. Crucial factors influencing SMIAltmetric, as identified by the SHAP value, were followers, retweets, mentions, and citation. Furthermore, consistency analysis and convergent validation between the proposed SMIAltmetric and AAS confirm the reliability and finer differentiation of SMIAltmetric. The proposed SMIAltmetric provides a more comprehensive understanding of a paper’s social media impact, enhancing the evaluation of scientific discourse and its engagement with society.

Role of Magnetic Resonance Imaging in evaluating T2-weighted hyperintensities in spinal cord in a tertiary care hospital in central India

Background Hyperintensities on T2-weighted images in the spinal cord are a complex and diagnostically challenging entity that can present with diverse clinical features. This study protocol outlines a comprehensive investigation to understand the causes, clinical and imaging characteristics, and correlation with pathological findings of hyperintensities on T2-weighted images in the spinal cord. By establishing a systematic assessment approach, this study seeks to provide valuable insights into these abnormalities’ diagnostic and prognostic implications. Methods The study will be conducted as a prospective observational design. Patients with clinically diagnosed or suspected spinal cord lesion presenting with intramedullary T2-weighted hyperintensity and referred for MRI evaluation will be included. Data collection will encompass patient demographics, clinical features, and extensive imaging parameters. Pathological data, when available, will be correlated with imaging findings. Various statistical methods will be employed to analyse the data, including frequency analysis, comparative tests, logistic regression, and survival analysis. Expected Results The study anticipates elucidating the spectrum of etiologies underlying hyperintensities on T2-weighted images in the spinal cord and their clinical and imaging profiles. The systematic approach will offer a structured diagnostic method, while correlations with pathological data will provide an enhanced understanding of these conditions. The results are expected to provide clinicians with valuable insights into diagnosing, treating, and prognosticating patients with spinal cord hyperintensities on T2-weighted images.

SwinDefNet: A Novel Surface Water Mapping Model in Mountain and Cloudy Regions Based on Sentinel-2 Imagery

Surface water plays a pivotal role in the context of climate change, human activities, and ecosystems, underscoring the significance of precise monitoring and observation of surface water bodies. However, the intricate and diverse nature of surface water distribution poses substantial challenges to accurate mapping. The extraction of water bodies from medium-resolution satellite remote sensing images using CNN methods is constrained by limitations in receptive fields and inadequate context modeling capabilities, resulting in the loss of boundary details of water bodies and suboptimal fusion of multi-scale features. The existing research on this issue is limited, necessitating the exploration of novel deep-learning network combinations to overcome these challenges. This study introduces a novel deep learning network combination, SwinDefNet, which integrates deformable convolution and Swin Transformer for the first time. By enhancing the effective receptive field and integrating global semantic information, the model can effectively capture the diverse features of water bodies at various scales, thereby enhancing the accuracy and completeness of water extraction. The model was evaluated on Sentinel-2 satellite images, achieving an overall accuracy of 97.89%, an F1 score of 92.33%, and, notably, an accuracy of 98.03% in mountainous regions. These findings highlight the promising potential of this combined approach for precise water extraction tasks.

Pengembangan LKPD Berbasis Masalah untuk Meningkatkan Minat Belajar Peserta Didik Kelas IV Tahun Pelajaran 2023/2024

This research aims to determine the characteristics of LKPD based on mathematical sentences, determine the feasibility of LKPD products, and determine students' interest in learning after implementing LKPD based on math sentence problems in chapter 11 class IV. This type of research is Development Research (Research and Development) using the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model and the resulting product is a mathematical sentence-based LKPD. This research involved 22 class IV students at SD Negeri 1 Pasung. The feasibility of the LKPD is tested with validation from expert practitioners, media and materials. Data collection used in this research was interviews, observations, questionnaires, tests and documentation. The interview sheet is used to analyze the needs of educators and students before preparing the product. The results of this research concluded that the LKPD has attractive design characteristics, diverse character traits, problem presentation in the form of dialogue, problem presentation stages that are in accordance with the PBL model, presentation of material in the form of videos and games. The feasibility of this LKPD is declared appropriate and not revised, because it is in accordance with the quality indicators of the LKPD content, media, materials and illustrations. Students' interest in learning increased by 41% after using this mathematical sentence problem-based worksheet.

Infrared Image Super-Resolution Network Utilizing the Enhanced Transformer and U-Net.

Infrared images hold significant value in applications such as remote sensing and fire safety. However, infrared detectors often face the problem of high hardware costs, which limits their widespread use. Advancements in deep learning have spurred innovative approaches to image super-resolution (SR), but comparatively few efforts have been dedicated to the exploration of infrared images. To address this, we design the Residual Swin Transformer and Average Pooling Block (RSTAB) and propose the SwinAIR, which can effectively extract and fuse the diverse frequency features in infrared images and achieve superior SR reconstruction performance. By further integrating SwinAIR with U-Net, we propose the SwinAIR-GAN for real infrared image SR reconstruction. SwinAIR-GAN extends the degradation space to better simulate the degradation process of real infrared images. Additionally, it incorporates spectral normalization, dropout, and artifact discrimination loss to reduce the potential image artifacts. Qualitative and quantitative evaluations on various datasets confirm the effectiveness of our proposed method in reconstructing realistic textures and details of infrared images.

Regulating the cationic vacancy structure of NiO to optimize its d band center and accelerate oxygen evolution reaction

In order to study the influence of cationic vacancy structure on the oxygen evolution reaction (OER) activity of catalysts. A series of cationic vacancy NiO catalysts (d-Mx-NiO) with different vacancy structure were prepared by alkali-etching the doping amphoteric oxide element in this paper. Due to diverse intrinsic characteristic of doping amphoteric oxide element, the electronic configuration of cationic vacancy in d-Mx-NiO is changed after alkali-etching. Density functional theory (DFT) calculations reveal that the d band center of Ni element is increased gradually with the increasing valence state of doping vacancy element, which is beneficial to optimize the adsorption energy for the OER intermediate products. Among the d-Mx-NiO catalysts, d-Al0.2-NiO catalyst possesses the highest d band center and oxidation state of Ni, resulting in the superior OER performance with only 285 mV overpotential to achieve the current density of 10 mA cm−2. Additionally, d-Al0.2-NiO also demonstrates admirable stability after 100 h OER test with a ∼99% faradaic efficiency. The current finding would offer a valuable insights to design and fabricate the cationic vacancy OER catalysts with high efficiency.

On the Interpretation of National Cultural Elements in Visual Communication

With the development of market economy and the progress of scientific progress, visual communication design has also entered a new period of modern development. At the same time, visual communication design also presents diversified characteristics. Entering a new era, the cultural construction has been promoted to a new historical height by the Party Central Committee. China s cultural construction has also made relatively high achievements, showing a vivid picture of more prosperous and vigorous development of culture. This requires the full combination of local national culture and visual communication design, which can not only provide better services for local urban development and brand publicity, but also play an important role in driving the local market economy.

Digital Humanities and Cultural Identification of Prefec-ture-Level Cities in China and Sustainable Development: A 3-Year Empirical Study (2021-2023) on Digital-Based Literary Narration in Short Videos

The advent of technology has revolutionized the methods by which modern citizens access information and engage in literary narration (through storytelling), transforming it into a multidimensional and intricately interwoven tapestry that encompasses various disciplines such as communications and media. As an exciting frontier of study, numerous scholars are dedicated to theoretical analyses within the realm of digital humanities, presenting vast opportunities for its practical application. This article explores the digital transformation of literary narration within the realm of short videos, which not only serves as a primary means of acquiring information (which engages with a large amount of demographic information) but also facilitates cultural identification with prefecture-level cities in China. The major cities enjoy the “say” in the media landscape; hence, seeking outlets in short videos (which do not need advanced infrastructure) serves as a “life-saving straw” for prefecture-level cities. The dominance of short videos in terms of extensive coverage surpasses that of print media, television, and other traditional media. This new form of literary narration incorporates elements such as pictures, animations, sound effects, and catering to diverse audience preferences and features the grand vitality of digital humanities. This article conducts comprehensive research from 2021 to 2023, aiming to unveil an innovative approach for the cultural identification of prefecture-level cities and the effectiveness of digital humanities applications with regard to literary transformation in the era of short video. Moreover, it endeavors to capture precise data directly from local residents, thereby assessing the efficacy of technology-based literary narration in short video creation and dissemination. Ultimately, these efforts aim to fortify the construction of prefecture-level cities against the challenges posed by metropolises. After 3 years of the experiment, we found that digitally transformed literary narration in short video helps the cultural identification of prefecture-level cities and brings cohesion to their citizens. Therefore, the survival of prefecture-level cities in China necessitates the “snatch” of opportunity for this new form of literary narration and sheds light on the further application of digital humanities.

Clinicopathologic and Molecular Characterization of Inflammatory Bowel Disease–Associated Neuroendocrine Carcinomas and Mixed Neuroendocrine-Non-Neuroendocrine Neoplasms

The pathogenesis of neuroendocrine carcinomas (NECs) and mixed neuroendocrine-non-neuroendocrine neoplasms (MiNENs) in the gastrointestinal tract remains poorly understood. This study aims to characterize the clinicopathologic and molecular features of NEC/MiNEN in patients with inflammatory bowel disease (IBD). Eighteen surgically resected IBD-associated intestinal carcinomas with a minimum of 30% neuroendocrine component were collected from 6 academic centers and compared with a control group of 12 IBD-associated carcinomas lacking neuroendocrine differentiation. Both groups exhibited a male predominance and similar age distribution. The NEC/MiNEN group was more likely to have a higher percentage of Crohn disease (9/18 vs 1/12; P = .024), occur in the rectum (9/18 vs 3/12) and small intestine (4/18 vs 0/12) (P < .01), be diagnosed on resection without a preceding biopsy (6/18 vs 0/12; P = .057), and have unidentifiable precursor lesions (10/18 vs 1/12; P = .018) than the control group. Synchronous carcinoma, advanced tumor stage (pT3 and pT4), and lymph node metastasis occurred at similar rates; however, the NEC/MiNEN group had a higher incidence of angiovascular invasion (14/18 vs 4/12; P = .024), distant metastasis (8/18 vs 1/12; P = .049), mortality (8/18 vs 2/12; P = .058), and worse survival (Kaplan–Meier; P = .023) than the control group. All tested cases were mismatch repair proficient. A Ki-67 proliferation index ranged from 25% to 100%. Next-generation sequencing in 11 NEC/MiNEN cases revealed low tumor mutational burdens but complex genetic abnormalities commonly involving TP53 (9/11; 82%), FBXW7 (4/11; 36%), and APC (3/11; 27%) genes, with the other genetic alterations randomly occurring in 1 or 2 cases. The neuroendocrine component, which shared similar molecular alterations as the nonneuroendocrine component, was subcategorized into intermediate (G3a) and high grade (G3b); the higher grade correlated with more genetic alterations. In conclusion, IBD-associated NEC/MiNEN shows diverse histologic features, variable precursor lesions, intricate genetic abnormalities, and aggressive biologic behavior. The classification and grading of gastrointestinal NEC/MiNEN may be refined for better clinical management.

MACCoM: A multiple attention and convolutional cross-mixer framework for detailed 2D biomedical image segmentation

The UNet architecture, which is widely used for biomedical image segmentation, has limitations like blurred feature maps and over- or under-segmented regions. To overcome these limitations, we propose a novel network architecture called MACCoM (Multiple Attention and Convolutional Cross-Mixer) – an end-to-end depthwise encoder-decoder fully convolutional network designed for binary and multi-class biomedical image segmentation built upon deeperUNet. We proposed a multi-scope attention module (MSAM) that allows the model to attend to diverse scale features, preserving fine details and high-level semantic information thus useful at the encoder-decoder connection. As the depth increases, our proposed spatial multi-head attention (SMA) is added to facilitate inter-layer communication and information exchange, enabling the network to effectively capture long-range dependencies and global context. MACCoM is also equipped with a convolutional cross-mixer we proposed to enhance the feature extraction capability of the model. By incorporating these modules, we effectively combine semantically similar features and reduce artifacts during the early stages of training. Experimental results on 4 biomedical datasets crafted from 3 datasets of varying modalities consistently demonstrate that MACCoM outperforms or matches state-of-the-art baselines in the segmentation tasks. With Breast Ultrasound Image (BUSI), MACCoM recorded 99.06 % Jaccard, 77.58 % Dice, and 93.92 % Accuracy, while recording 99.50 %, 98.44 %, and 99.29 % respectively for Jaccard, Dice, and Accuracy on the Chest X-ray (CXR) images used. The Jaccard, Dice, and Accuracy for the High-Resolution Fundus (HRF) images are 95.77 %, 74.35 %, and 95.95 % respectively. The findings here highlight MACCoM's effectiveness in improving segmentation performance and its valuable potential in image analysis.

A novel facial expression recognition model based on harnessing complementary features in multi-scale network with attention fusion

This paper presents a novel method for facial expression recognition using the proposed feature complementation and multi-scale attention model with attention fusion (FCMSA-AF). The proposed model consists of four main components: the shallow feature extractor module, parallel structured two-branch multi-scale attention module (MSA), feature complementing module (FCM), and attention fusion and classification module. The MSA module contains multi-scale attention modules in a cascaded fashion in two paths to learn diverse features. The upper and lower paths use left and right multi-scale blocks to extract and aggregate the features at different receptive fields. The attention networks in MSA focus on salient local regions to extract features at granular levels. The FCM uses the correlation between the feature maps in two paths to make the multi-scale attention features complementary to each other. Finally, the complementary features are fused through an attention network to form an informative holistic feature which includes subtle, visually varying regions in similar classes. Hence, complementary and informative features are used in classification to minimize information loss and capture the discriminating finer aspects of facial expression recognition. Experimental evaluation of the proposed model carried out on AffectNet and CK+ datasets achieve accuracies of 64.59% and 98.98%, respectively, outperforming some of the state-of-the-art methods.

DEM super-resolution guided by shaded relief using attention-based fusion

Deep-learning based approaches have been proven effective for Digital Elevation Model (DEM) super-resolution (SR) tasks. Previous networks typically treat DEM elevation values as single-channel image for input. However, DEM images alone cannot fully capture spatial and terrain features. Shaded relief images (SRIs), derived from DEMs, serve as crucial visual cues that intuitively convey terrain characteristics, addressing the limitations of DEM images and providing synergistic benefits for training DL models. The primary challenge in utilizing SRIs for guiding DEM SR lies in accurately selecting a consistent structure to extract and effectively integrate features from SRIs and DEMs. In this study, we propose an Attention-based Hierarchical Terrain Fusion (AHTF) framework for guided DEM SR. Specifically, an Attention-based Feature Fusion Module (AFFM) is designed to efficiently fuse relevant information from LR DEM and SRI, which includes a feature enhancement block to select valuable features and a feature recalibration block to fuse diverse terrain features. Additionally, we optimize the loss function from the perspectives of terrain analysis and visual effects. We validate AHTF on our newly constructed real-world Shade-DEM SR dataset and two open-source DEM SR datasets. Compared to the current state-of-the-art methods, our AHTF achieves the best results in terms of root mean square error (RMSE) for elevation, slope, and aspect. Furthermore, the extracted stream networks are closer to real-world conditions. This study offers new insights and methods for further research and application in the field of DEM super-resolution. Our dataset can be obtained at https://doi.org/10.6084/m9.figshare.25590945.

Acute Leukemia Subtype Recognition in Blood Smear Images with Machine Learning

Acute leukemia is a swiftly progressing blood cancer affecting white blood cells which poses a significant threat to the immune system and often leads to fatal outcomes if not detected and treated promptly. The current manual diagnostic method, being time-consuming and prone to errors, necessitates an urgent shift toward a comprehensive automated system. This paper presents an innovative approach to automatically identify acute leukemia cells and their subtypes by analyzing microscopic blood smear images. The proposed methodology involves the segmentation of clustered lymphocytes, isolation of nuclei, and extraction of diverse features from each nucleus. A random forest classifier is then trained to categorize nuclei into healthy or cancerous, with further precision in classifying cancerous nuclei into specific subtypes. The method achieves an impressive 97% accuracy across all evaluations, holding profound implications for pathologists and medical practitioners in their decision-making processes

The Origin and Development of Vietnamese People’s Belief in Worshipping Mother Goddesses Three Palaces and Four Palaces: A Case Study in Thua Thien Hue Province

Vietnamese people's belief of the worship Mother Goddesses of the Three Palaces and Four Palaces formed from the worship of women in traditional culture combined with the elements "Mother Goddess Lieu Hanh ", "the Mother Goddesses of Three Realms" was “molded” from a number of discrete worship activities into a systematic belief. Currently, Vietnamese mother-worshiping custom is divided into three different forms, namely Mother-worshiping in the North, Central and South, associated with the worshiping forms, the spaces of shrines, and different ritual practices of mediumship. This distinction is shaped on the basis of historical factors and the cultural acculturation and exchange in the process of the nation's southern advance. This is quite obvious through the form of the worship of the Mother Goddesses in the Central region especially in Hue. In this article, the origin of the custom of the worship of the Mother Goddesses of the Four Palaces in Hue is clarified. In addition, the development of this belief which bears many imprints of regional cultural interference and is one of the outstanding features of Vietnamese cultural diversity is explained through each historical period.

Versatile Framework for Drug-Target Interaction Prediction by Considering Domain-Specific Features.

Predicting drug-target interactions (DTIs) is one of the crucial tasks in drug discovery, but traditional wet-lab experiments are costly and time-consuming. Recently, deep learning has emerged as a promising tool for accelerating DTI prediction due to its powerful performance. However, the models trained on limited known DTI data struggle to generalize effectively to novel drug-target pairs. In this work, we propose a strategy to train an ensemble of models by capturing both domain-generic and domain-specific features (E-DIS) to learn diverse domain features and adapt them to out-of-distribution data. Multiple experts were trained on different domains to capture and align domain-specific information from various distributions without accessing any data from unseen domains. E-DIS provides a comprehensive representation of proteins and ligands by capturing diverse features. Experimental results on four benchmark data sets in both in-domain and cross-domain settings demonstrated that E-DIS significantly improved model performance and domain generalization compared to existing methods. Our approach presents a significant advancement in DTI prediction by combining domain-generic and domain-specific features, enhancing the generalization ability of the DTI prediction model.

Deep-Learning-Based detection of recreational vessels in an estuarine soundscape in the May River, South Carolina, USA.

This paper presents a deep-learning-based method to detect recreational vessels. The method takes advantage of existing underwater acoustic measurements from an Estuarine Soundscape Observatory Network based in the estuaries of South Carolina (SC), USA. The detection method is a two-step searching method, called Deep Scanning (DS), which includes a time-domain energy analysis and a frequency-domain spectrum analysis. In the time domain, acoustic signals with higher energy, measured by sound pressure level (SPL), are labeled for the potential existence of moving vessels. In the frequency domain, the labeled acoustic signals are examined against a predefined training dataset using a neural network. This research builds training data using diverse vessel sound features obtained from real measurements, with a duration between 5.0 seconds and 7.5 seconds and a frequency between 800 Hz to 10,000 Hz. The proposed method was then evaluated using all acoustic data in the years 2017, 2018, and 2021, respectively; a total of approximately 171,262 2-minute.wav files at three deployed locations in May River, SC. The DS detections were compared to human-observed detections for each audio file and results showed the method was able to classify the existence of vessels, with an average accuracy of around 99.0%.

Enhancing brain tumor segmentation in MRI images using the IC-net algorithm framework

Brain tumors, often referred to as intracranial tumors, are abnormal tissue masses that arise from rapidly multiplying cells. During medical imaging, it is essential to separate brain tumors from healthy tissue. The goal of this paper is to improve the accuracy of separating tumorous regions from healthy tissues in medical imaging, specifically for brain tumors in MRI images which is difficult in the field of medical image analysis. In our research work, we propose IC-Net (Inverted-C), a novel semantic segmentation architecture that combines elements from various models to provide effective and precise results. The architecture includes Multi-Attention (MA) blocks, Feature Concatenation Networks (FCN), Attention-blocks which performs crucial tasks in improving brain tumor segmentation. MA-block aggregates multi-attention features to adapt to different tumor sizes and shapes. Attention-block is focusing on key regions, resulting in more effective segmentation in complex images. FCN-block captures diverse features, making the model more robust to various characteristics of brain tumor images. Our proposed architecture is used to accelerate the training process and also to address the challenges posed by the diverse nature of brain tumor images, ultimately leads to potentially improved segmentation performance. IC-Net significantly outperforms the typical U-Net architecture and other contemporary effective segmentation techniques. On the BraTS 2020 dataset, our IC-Net design obtained notable outcomes in Accuracy, Loss, Specificity, Sensitivity as 99.65, 0.0159, 99.44, 99.86 and DSC (core, whole, and enhancing tumors as 0.998717, 0.888930, 0.866183) respectively.

The Perizonium Ultrastructure, Divided Apical Pore Fields, Various Pore Occlusions and Visible Intermissio of Cymbella (Bacillariophyceae) with Descriptions of Four New Species.

The initial valves of two Cymbella species are observed under a scanning electron microscope, and the perizonium ultrastructure of Cymbella is revealed for the first time. The perizonium is composed of alternate nodes and internodes and lacks transverse perizonium bands. Four new species, Cymbella apiculatophora sp. nov., C. hunanensis sp. nov., C. juglandis sp. nov. and C. menyuanensis sp. nov., are described using light and scanning electron microscopy based on epilithon samples collected from rivers in Hunan and Qinghai Provinces, China. Cymbella menyuanensis is a typical Cymbella species that closely resembles species in the group around C. cymbiformis Agardh, the type species of the genus. Cymbella apiculatophora is similar to C. sinensis Metzeltin & Krammer, while Cymbella hunanensis is closer to the C. hustedtii Krasske group. The last species, C. juglandis, has a cymbelloid valve outline, an obscured intermissio, internal occlusions of the areolae, dorsally deflected distal raphe fissures and a divided apical pore field at each apex, and it does not appear to belong to any group. In addition, new observations on C. cf. excisiformis Krammer and C. hustedtii are reported. The current concept of the genus Cymbella does not represent a monophyletic group as shown by molecular phylogenetic analyses. However, these analyses are still at the preliminary stage and are not yet sufficient to support a complete revision of the genus. Thus, although extremely diverse ultrastructural features are observed in the six Cymbella species investigated in this paper, we prefer to keep them within Cymbella at this moment for the sake of nomenclatural stability.

Biodiversity and Parasitological Characteristics of Myxozoa (Cnidaria) Infecting European Seabass, Dicentrarchus labrax (Linnaeus, 1758) In the Aegean Sea Coasts of Türkiye

In this study, diversity and parasitological features of Myxozoan parasites infecting Dicentrarchus labrax (Linnaeus, 1758) grown in sea cages located in the coasts of İzmir and Muğla districts of Türkiye were investigated. For this aim, a total of 240 host specimens were examined and 147 of them (61.25%) determined to be infected by Myxozoan parasites. Based on the morphology of the myxospores examined, the presence of one species belonging to the genus Kudoa Meglitsch, 1947 and two different species within the genus Ceratomyxa Thélohan, 1892 were determined. Depending on phylogenetic relationships obtained from 18S rDNA nucleotide sequence data, Kudoa specimens were assigned to K. dicentrarchi Sitjà-Bobadilla &amp; Álvarez-Pellitero, 1992 and Ceratomyxa species were assigned to C. diplodae Lubat, Radujkovic, Marques &amp; Bouix, 1989 and C. labracis Sitjà-Bobadilla &amp; Álvarez-Pellitero, 1993. The K. dicentrarchi specimens were found in the gallbladder and gonads of the European seabass with 38.75% prevalence of infection. Ceratomyxa diplodae and C. labracis were only found in the bile with 37.92% and 2.5% prevalence of infections, respectively. This study presents the first record of C. diplodae and C. labracis in D. labrax from the Turkish marine environment. Moreover, this research contributes the initial molecular record for K. dicentrarchi identified in D. labrax from the Turkish coastline.