PLoS ONE最新文献

{"title":"Identification of a novel chemotherapy benefit index for patients with advanced ovarian cancer based on Bayesian network analysis.","authors":"Shuxiao Ma, Lu Zhou, Yi Liu, Hui Jie, Min Yi, Chenglin Guo, Jiandong Mei, Chuan Li, Lei Zhu, Senyi Deng","doi":"10.1371/journal.pone.0322130","DOIUrl":"https://doi.org/10.1371/journal.pone.0322130","url":null,"abstract":"<p><strong>Background: </strong>This study aims to evaluate the efficacy of chemotherapy and optimize treatment strategies for patients with advanced ovarian cancer.</p><p><strong>Methods: </strong>Based on The Cancer Genome Atlas (TCGA) transcriptome data, we conducted correlation and Bayesian network analyses to identify key genes strongly associated with chemotherapy prognosis. Reverse Transcription Quantitative Polymerase Chain Reaction (RT-qPCR) was used to verify the expression of these key genes. The Chemotherapy Benefit Index (CBI) was developed using these genes via multivariable Cox regression analysis, and validated using both internal and external validation sets (GSE32062 and GSE30161) with a random forest model. Subsequently, we analyzed distinct molecular characteristics and explored additional immunotherapy in CBI-high and CBI-low subgroups.</p><p><strong>Results: </strong>Based on the network and machine learning analyses, CBI was developed from the following ten genes: COL6A3, SPI1, HSF1, CD3E, PIK3R4, MZB1, FERMT3, GZMA, PSMB9 and RSF1. Significant differences in overall survival were observed among the CBI-high, medium, and low subgroups (P < 0.001), which were consistent with the two external validation sets (P < 0.001 and P = 0.003). The AUC of internal validation and two external validation cohorts were 0.87, 0.71 and 0.70, respectively. Molecular function analysis indicated that the CBI-low subgroup is characterized by the activation of cancer-related signaling pathways, immune-related biological processes, higher TP53 mutation rate, particularly with a better response to immune checkpoint blockade (ICB) treatment, while the CBI-high subgroup is characterized by inhibition of cell cycle, less response to ICB treatment, and potential therapeutic targets.</p><p><strong>Conclusions: </strong>This study provided a novel CBI for patients with advanced ovarian cancer through network analyses and machine learning. CBI could serve as a prognostic prediction tool for patients with advanced ovarian cancer, and also as a potential indicator for immunotherapy.</p>","PeriodicalId":20189,"journal":{"name":"PLoS ONE","volume":"20 5","pages":"e0322130"},"PeriodicalIF":2.9,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144161288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A weakly supervised method for surgical scene components detection with visual foundation model.","authors":"Xiaoyan Zhang, Jingyi Feng, Qian Zhang, Liming Wu, Yichen Zhu, Ziyu Zhou, Jiquan Liu, Huilong Duan","doi":"10.1371/journal.pone.0322751","DOIUrl":"https://doi.org/10.1371/journal.pone.0322751","url":null,"abstract":"<p><strong>Purpose: </strong>Detection of crucial components is a fundamental problem in surgical scene understanding. Limited by the huge cost of spatial annotation, current studies mainly focus on the recognition of three surgical elements [Formula: see text]instrument, verb, target[Formula: see text], while the detection of surgical components [Formula: see text]instrument, target[Formula: see text] remains highly challenging. Some efforts have been made to detect surgical components, yet their limitations include: (1) Detection performance highly depends on the amount of manual spatial annotations; (2) No previous study has investigated the detection of targets.</p><p><strong>Methods: </strong>We introduce a weakly supervised method for detecting key components by novelly combining the surgical triplet recognition model and the foundation model of Segment Anything Model (SAM). First, by setting appropriate prompts, we used SAM to generate candidate regions for surgical components. Then, we preliminarily localize components by extracting positive activation areas in class activation maps from the recognition model. However, using instrument's class activation as a position attention guide for target recognition leads to positional deviations in the target's resulting positive activation. To tackle this issue, we propose RDV-AGC by introducing an Attention Guide Correction (AGC) module. This module adjusts the attention guidance for target according to the instrument's forward direction. Finally, we match the initial localization of instruments and targets with the candidate areas generated by SAM, achieving precise detection of components in the surgical scene.</p><p><strong>Results: </strong>Through ablation studies and comparisons to similar works, our method has achieved remarkable performance without requiring any spatial annotations.</p><p><strong>Conclusion: </strong>This study introduced a novel weakly supervised method for detecting surgical components by integrating the surgical triplet recognition model with visual foundation model.</p>","PeriodicalId":20189,"journal":{"name":"PLoS ONE","volume":"20 5","pages":"e0322751"},"PeriodicalIF":2.9,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144161309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of a grey wolf optimization-enhanced convolutional neural network and bidirectional gated recurrent unit model for credit scoring prediction.","authors":"Yetong Fang","doi":"10.1371/journal.pone.0322225","DOIUrl":"https://doi.org/10.1371/journal.pone.0322225","url":null,"abstract":"<p><p>With the digital transformation of the financial industry, credit score prediction, as a key component of risk management, faces increasingly complex challenges. Traditional credit scoring methods often have difficulty in fully capturing the characteristics of large-scale, high-dimensional financial data, resulting in limited prediction performance. To address these issues, this paper proposes a credit score prediction model that combines CNNs and BiGRUs, and uses the GWO algorithm for hyperparameter tuning. CNN performs well in feature extraction and can effectively capture patterns in customer historical behaviors, while BiGRU is good at handling time dependencies, which further improves the prediction accuracy of the model. The GWO algorithm is introduced to further improve the overall performance of the model by optimizing key parameters. Experimental results show that the CNN-BiGRU-GWO model proposed in this paper performs well on multiple public credit score datasets, significantly improving the accuracy and efficiency of prediction. On the LendingClub loan dataset, the MAE of this model is 15.63, MAPE is 4.65%, RMSE is 3.34, and MSE is 12.01, which are 64.5%, 68.0%, 21.4%, and 52.5% lower than the traditional method plawiak of 44.07, 14.51%, 4.25, and 25.29, respectively. In addition, compared with traditional methods, this model also shows stronger advantages in adaptability and generalization ability. By integrating advanced technologies, this model not only provides an innovative technical solution for credit score prediction, but also provides valuable insights into the application of deep learning in the financial field, making up for the shortcomings of existing methods and demonstrating its potential for wide application in financial risk management.</p>","PeriodicalId":20189,"journal":{"name":"PLoS ONE","volume":"20 5","pages":"e0322225"},"PeriodicalIF":2.9,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144161325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the level of metabolic reprogramming and the role of prognostic factor SF3A3 in hepatocellular carcinoma through integrated single-cell landscape analysis.","authors":"Wanshuo Wei, Yuan Gan, Xindan Zhang, Yumo Chen, Zengfeng Huang, Shuhan Wang, Xiaomei Xie, Yongle Li, Pengtao Qin, Lihe Jiang","doi":"10.1371/journal.pone.0323559","DOIUrl":"https://doi.org/10.1371/journal.pone.0323559","url":null,"abstract":"<p><p>This study aims to investigate metabolic reprogramming heterogeneity in hepatocellular carcinoma (HCC) cells and identify novel therapeutic targets for HCC treatment. Single-cell RNA sequencing data from public databases were used to analyze the TME of HCC and reveal the characteristics of different cell subsets, including mononuclear phagocytes, epithelial cells, endothelial cells, NK/T cells, B cells, and unknown cells. The analysis revealed that these cell subsets play their own unique roles in tumor progression and immune escape. Analysis of copy number variations (CNVs) was performed on tumor-derived epithelial cells, with the epithelial cells in Cluster 3 subgroup showing the highest CNV levels. Gene Ontology (GO) enrichment analysis revealed that these cell subsets were involved in a variety of biological processes such as immune response, cell communication, and metabolic pathways, which were consistent with their functional roles. Pseudotemporal analysis further delineated the malignant trajectory of HCC cells, with Cluster 3 exhibiting enhanced phosphatidylinositol metabolism, suggesting a critical role for metabolic reprogramming in tumor invasion and proliferation. Furthermore, a diagnostic model incorporating metabolic reprogramming-associated gene signatures was established, which effectively distinguished HCC from normal tissues. Among these signatures, splicing factor 3a subunit 3 (SF3A3) was identified as both diagnostic and independent prognostic biomarker. Mechanistically, SF3A3 knockdown in HCC cell lines significantly suppressed proliferation, migration, PI3K/AKT signaling, and EMT marker expression, thereby demonstrating its role in driving HCC aggressiveness. In conclusion, these findings elucidate novel molecular characteristics of HCC based on metabolic reprogramming, while establishing SF3A3 as a promising multi-faceted target for HCC diagnosis, prognostic assessment, and therapeutic intervention.</p>","PeriodicalId":20189,"journal":{"name":"PLoS ONE","volume":"20 5","pages":"e0323559"},"PeriodicalIF":2.9,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144161344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"InBRwSANet: Self-attention based parallel inverted residual bottleneck architecture for human action recognition in smart cities.","authors":"Yasir Khan Jadoon, Muhammad Attique Khan, Yasir Noman Khalid, Jamel Baili, Nebojsa Bacanin, MinKyung Hong, Yunyoung Nam","doi":"10.1371/journal.pone.0322555","DOIUrl":"https://doi.org/10.1371/journal.pone.0322555","url":null,"abstract":"<p><p>Human Action Recognition (HAR) has grown significantly because of its many uses, including real-time surveillance and human-computer interaction. Various variations in routine human actions make the recognition process of action more difficult. In this paper, we proposed a novel deep learning architecture known as Inverted Bottleneck Residual with Self-Attention (InBRwSA). The proposed architecture is based on two different modules. In the first module, 6-parallel inverted bottleneck residual blocks are designed, and each block is connected with a skip connection. These blocks aim to learn complex human actions in many convolutional layers. After that, the second module is designed based on the self-attention mechanism. The learned weights of the first module are passed to self-attention, extract the most essential features, and can easily discriminate complex human actions. The proposed architecture is trained on the selected datasets, whereas the hyperparameters are chosen using the particle swarm optimization (PSO) algorithm. The trained model is employed in the testing phase for the feature extraction from the self-attention layer and passed to the shallow wide neural network classifier for the final classification. The HMDB51 and UCF 101 are frequently used as action recognition standard datasets. These datasets are chosen to allow for meaningful comparison with earlier research. UCF101 dataset has a wide range of activity classes, and HMDB51 has varied real-world behaviors. These features test the generalizability and flexibility of the presented model. Moreover, these datasets define the evaluation scope within a particular domain and guarantee relevance to real-world circumstances. The proposed technique is tested on both datasets, and accuracies of 78.80% and 91.80% were achieved, respectively. The ablation study demonstrated that a margin of error value of 70.1338 ± 3.053 (±4.35%) and 82.7813 ± 2.852 (±3.45%) for the confidence level 95%,1.960σx̄ is obtained for HMDB51 and UCF datasets respectively. The training time for the highest accuracy for HDMB51 and UCF101 is 134.09 and 252.10 seconds, respectively. The proposed architecture is compared with several pre-trained deep models and state-of-the-art (SOTA) existing techniques. Based on the results, the proposed architecture outperformed existing techniques.</p>","PeriodicalId":20189,"journal":{"name":"PLoS ONE","volume":"20 5","pages":"e0322555"},"PeriodicalIF":2.9,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144161364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Key stakeholders' views on the causes of medicine stock-outs in Mauritania: A qualitative study.","authors":"Mohamed Ali Ag Ahmed, Issa Coulibaly, Raffaella Ravinetto, Verónica Trasancos Buitrago, Catherine Dujardin","doi":"10.1371/journal.pone.0304930","DOIUrl":"https://doi.org/10.1371/journal.pone.0304930","url":null,"abstract":"<p><p>The number of medicine stock-outs is increasing globally. In Mauritania, they are recurring, although, to our knowledge, no study has yet been conducted to determine the causes. Therefore, this qualitative study aims to explore the views of key stakeholders in the pharmaceutical sector to identify the main local or national causes of stock-outs. It will thus provide a common understanding and guide policy-makers towards corrective action. The study was carried out in five health districts and at the regional and central levels. The samples were purposive. Two focus groups and twenty semi-structured individual interviews were held with 38 participants, including health professionals, managers from the Central Purchasing Office for Essential Medicines and Consumables, the Pharmacy and Laboratory Department and the Ministry of Health. All interviews were recorded and transcribed. A thematic content analysis was carried out. Our findings indicate the national causes of medicine stock-outs at three healthcare system levels (operational, regional, and central). They were grouped into five categories: insufficient human resource capacity (number of staff, training, retention), communication and coordination problems between stakeholders, logistical constraints (transport, storage), financial constraints, inadequate forecasting of needs, and complex procurement procedures. These causes of medicine stock-outs are interconnected, and many could be addressed locally through solutions initiated and led by the Mauritanian authorities. To address medicine stock-outs sustainably, we suggest and discuss some possible actions, including reforms to improve Central Purchasing Office for Essential Medicines and Consumables's governance and accountability and, more broadly, to strengthen the various pillars of the local health and pharmaceutical system.</p>","PeriodicalId":20189,"journal":{"name":"PLoS ONE","volume":"20 5","pages":"e0304930"},"PeriodicalIF":2.9,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144161365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of the Bacterial communities associated with Anopheles gambiae larval habitats in Southern Ghana.","authors":"Akua Obeng Forson, Isaac Kwame Sraku, Idan Baah Banson, Jones Gyamfi, Kwabena Obeng Duedu, Yaw Asare Afrane","doi":"10.1371/journal.pone.0323464","DOIUrl":"https://doi.org/10.1371/journal.pone.0323464","url":null,"abstract":"<p><p>Mosquito breeding habitats are ecosystems that comprise a complex, intimately associated micro-organism. This study aimed to determine the bacteria communities associated with Anopheles larval habitats and correlate their prevalence to the absence or presence of mosquito larvae. The 16S rRNA profiles of bacterial communities in Anopheles-positive breeding habitats (productive and semi-productive habitats) and negative habitats (non-productive) from Southern Ghana were analyzed using the Oxford Nanopore's MinION platform with water and larval samples. A total of 15 bacterial taxa were identified across all habitats based on productivity. Significantly, mosquito-positive breeding habitats (productive and semi-productive) had more bacterial diversity compared to mosquito-negative habitats (non-productive). Comparison of the composition of bacteria in the different habitat types revealed that non-productive habitats had a higher prevalence of Epsilonproteobacteria (58.1%), while Gammaproteobacteria (33.2%) and Betaproteobacteria (30.5%) were dominant in the productive and semi-productive habitats. Gammaproteobacteria and Betaproteobacteria were the most abundant bacterial classes in Anopheles larvae. Comparing the water samples to larvae microbiomes revealed distinct composition. Betaproteobacteria (58.5%) and Cytophagia (10.7%) were predominately present in the water samples, whilst Betaproteobacteria (47.9%) and Gammaproteobacteria (21.6%) were dominant in the larval samples. This study revealed a higher bacterial composition may play a role in Anopheles mosquitoes' attractiveness to a breeding habitat. These findings contribute to the understanding of which bacteria, directly or indirectly, can be linked to the absence or presence of mosquito larvae in breeding habitats and set the basis for the identification of specific bacterial taxa that could be harnessed for vector control in the future.</p>","PeriodicalId":20189,"journal":{"name":"PLoS ONE","volume":"20 5","pages":"e0323464"},"PeriodicalIF":2.9,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144160568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From home to the screen: How parental rejection fuels cyberbullying in college students.","authors":"Shuaijie Lan, Yangan Wang, Jiaxu Zhao, Xujing Hou, Chao Li","doi":"10.1371/journal.pone.0323124","DOIUrl":"https://doi.org/10.1371/journal.pone.0323124","url":null,"abstract":"<p><p>Previous research has highlighted the impact of family environment on college students' cyberbullying behavior, yet the role of parenting styles, particularly negative ones, remains underexplored. This study, grounded in the interpersonal acceptance-rejection theory and social information processing model, investigates how parental rejection influences cyberbullying behavior among college students through cognitive and emotional mechanisms. We surveyed 1,567 college students (620 males, 947 females; average age: 19.34 ± 1.24 years) from several universities in Shandong and Jilin provinces, China. Participants completed questionnaires assessing cyberbullying, parental rejection, empathy, and moral disengagement. The findings reveal that 456 individuals (29.1%) had engaged in at least one instance of cyberbullying behavior, including 180 males and 276 females. Subsequently, an investigation into the cyberbullying behaviors of these individuals revealed that: (1) parental rejection is a significant predictor of cyberbullying behavior; (2) empathy and moral disengagement serve as partial mediators in the relationship between parental rejection and cyberbullying; (3) both empathy and moral disengagement act as sequential mediators in this relationship. These results underscore the importance of empathy and moral disengagement in understanding the link between parental rejection and cyberbullying among college students, offering a new theoretical perspective for future interventions.</p>","PeriodicalId":20189,"journal":{"name":"PLoS ONE","volume":"20 5","pages":"e0323124"},"PeriodicalIF":2.9,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144160571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A measure of event-related potentials (ERP) indices of motivation during cycling.","authors":"Rémi Renoud-Grappin, Damien Gabriel, Emmanuelle Broussard, Laurent Mourot, Julie Giustiniani, Lionel Pazart","doi":"10.1371/journal.pone.0312981","DOIUrl":"https://doi.org/10.1371/journal.pone.0312981","url":null,"abstract":"<p><p>Although motivation is a central aspect of the practice of a physical activity, it is a challenging endeavour to predict an individual's level of motivation during the activity. The objective of this study was to assess the feasibility of measuring motivation through brain recording methods during physical activity, with a specific focus on cycling. The experiment employed the Effort Expenditure for Reward Task (EEfRT), a decision-making task based on effort and reward, conducted under two conditions: one involving cycling on an ergometer at moderate intensity and the other without cycling. The P300, an event-related potential linked to motivation, was recorded using electroencephalography. A total of 20 participants were recruited to complete the EEfRT, which involved making effort-based decisions of increasing difficulty in order to receive varying levels of monetary reward. The results demonstrated that the P300 amplitude was influenced by the act of cycling, exhibiting a reduction during the cycling session. This reduction may be explained by a reallocation of cognitive resources due to the exertion of physical effort, which is consistent with the transient hypofrontality theory. In terms of behaviour, participants demonstrated a tendency to make more challenging choices when the potential rewards were higher or the probability of gaining them was lower. This pattern was observed in both the cycling and non-cycling conditions. A positive correlation was identified between P300 amplitude and the proportion of difficult choices, particularly under conditions of low reward probability. This suggests that P300 may serve as a neural marker of motivation. The study demonstrates the feasibility of using electroencephalography to monitor motivation during exercise in real-time, with potential applications in rehabilitation settings. However, further research is required to refine the design and explore the effects of different exercise types on motivation.</p>","PeriodicalId":20189,"journal":{"name":"PLoS ONE","volume":"20 5","pages":"e0312981"},"PeriodicalIF":2.9,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144161231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High prevalence of low-concentration antimicrobial residues in commercial fish: A public health concern in Bangladesh.","authors":"Md Raihan Khan Nayem, Md Rahim Badsha, Md Kaisar Rahman, Shahneaz Ali Khan, Md Mazharul Islam, Md Latiful Bari, John I Alawneh, Ricardo J Soares Magalhaes, Mohammad Mahmudul Hassan","doi":"10.1371/journal.pone.0324263","DOIUrl":"https://doi.org/10.1371/journal.pone.0324263","url":null,"abstract":"<p><p>Antibiotics are widely used in commercial fish farms in Bangladesh for therapeutic and prophylactic purpose, raising concerns about antimicrobial resistance (AMR) and environmental contamination. This study used Thin Layer Chromatography to detect antimicrobial residues in four commercially available fish species- Tilapia (Oreochromis aureus), Stinging catfish (Heteropneustes fossilis), Climbing perch (Anabas testudineus), and Pabda (Ompok pabda)-with 100 samples per species. Ultra High-Performance Liquid Chromatography quantified residues in a subset of 25 samples per species. The prevalence of Ciprofloxacin, Oxytetracycline, and Chlortetracycline residues varied significantly among fish species, with the highest prevalence observed for Ciprofloxacin in Tilapia (42%), Oxytetracycline in Pabda (41%), and Chlortetracycline in Tilapia (49%). Additionally, the prevalence of Levofloxacin and Chlortetracycline differed by sampling location, with the highest levels found in Jhawtala market, 27.5% for Levofloxacin and 53.8% for Chlortetracycline. Furthermore, residue concentrations were highest for Enrofloxacin in Climbing perch (69.32 µg/Kg) and Oxytetracycline in Pabda (88.73 µg/Kg). The highest Hazard Quotient (HQ) was for Enrofloxacin in Climbing perch (0.480), followed by Pabda (0.460), Stinging catfish (0.420), and Tilapia (0.387). While the HQ values were below 1.0, indicating no immediate toxicological risk, residues raise public health concerns due to the chance of potential AMR development. Further research is needed on antimicrobial bioaccumulation, indirect exposure sources, environmental contamination, and antimicrobial resistance in aquaculture and wild fish.</p>","PeriodicalId":20189,"journal":{"name":"PLoS ONE","volume":"20 5","pages":"e0324263"},"PeriodicalIF":2.9,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144161274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}