Frontiers in Artificial Intelligence最新文献

{"title":"Endophytic actinomycetes as a reservoir of novel chemotherapeutic leads against multidrug-resistant cancer.","authors":"Dhanvee Ganatra, Mahendrapal Singh Rajput, Gaurav Sanghvi","doi":"10.3389/frai.2026.1974583","DOIUrl":"10.3389/frai.2026.1974583","url":null,"abstract":"<p><p>Multidrug resistance (MDR) has emerged as a major barrier to effective cancer control, undermining the efficacy of chemotherapy, targeted agents and, increasingly, immunotherapies. This review synthesizes current knowledge on the molecular and cellular bases of MDR in cancer, including ABC transporter-mediated drug efflux, enhanced DNA damage responses, survival-signaling rewiring, tumor-microenvironmental protection and cancer stem cell-driven plasticity. Particular emphasis is placed on endophytic actinomycetes and their metabolites as an underexploited reservoir of MDR modulating chemotypes with potent cytotoxic, resistance-reversal and microenvironment-modulating activities. By integrating recent mechanistic and pharmacological data, the review first delineates the crisis of drug-resistant cancer, then catalogs actinomycete-derived metabolites with documented efficacy against resistant cell lines, including those that inhibit efflux pumps, restore pro-apoptotic signaling or interfere with angiogenesis, epithelial-mesenchymal transition and stemness traits. Finally, translational challenges, such as suboptimal pharmacokinetics, off-target toxicity, herb-drug interactions and the paucity of biomarker-driven clinical trials, are critically examined alongside emerging strategies involving semi-synthetic optimization, nanocarrier delivery and rational combination regimens. A forward-looking discovery pipeline is proposed that couple's genome mining of endophytic actinomycetes with systematic MDR-mechanism profiling and medicinal chemistry, aiming to prioritize the most promising metabolites for preclinical and clinical development as next-generation MDR-targeted therapeutics.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1974583"},"PeriodicalIF":6.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13668826/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UniPET: a unified approach for whole-body CT to PET translation.","authors":"Francesco Di Feola, Valerio Guarrasi, Mikael Johansson, Paolo Soda","doi":"10.3389/frai.2026.1919564","DOIUrl":"10.3389/frai.2026.1919564","url":null,"abstract":"<p><p>Positron emission tomography (PET) provides critical metabolic information for oncological imaging, yet its use is constrained by radiation exposure, cost, and limited availability. Synthesizing PET-like images from computed tomography (CT) has been proposed as a way to approximate metabolic information; however, existing approaches either fail to capture region-specific variability or rely on multiple organ-specific models that do not scale to whole-body imaging. In this work, we introduce UniPET, a unified approach for whole-body CT-to-PET translation based on curriculum learning. By progressively incorporating anatomical regions of increasing complexity, UniPET enables a single network to learn coherent morpho-metabolic relationships across different regions. We evaluate UniPET on a public dataset of 900 patients and an external cohort of 579 patients. The model achieves image quality and metabolic consistency comparable to region-specific approaches, while improving over conventional whole-body models and maintaining stable performance under distribution shifts. By capturing region-specific variability within a single model, UniPET provides a promising approach for augmenting CT-based workflows with synthetic metabolic information, with potential applications in screening triage, in low-resource environments where PET is unavailable, and as an additional input for multimodal analysis pipelines. The code is available at: https://github.com/arco-group/UniPET.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1919564"},"PeriodicalIF":6.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13668879/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large-scale conversational data auditability via cryptographic commitments and hash-chained audit-ledger verification.","authors":"Iván Ortiz-Gárces, Pablo Palacios, Washington Loza-Herrera, William Villegas-Ch","doi":"10.3389/frai.2026.1929127","DOIUrl":"10.3389/frai.2026.1929127","url":null,"abstract":"<p><p>The auditability of large language model-based systems requires verifiable evidence without exposing the sensitive content of interactions. This article proposes a hash-chained audit-ledger framework to transform public LLM conversations into canonical off-chain units, generate cryptographic commitments using SHA-256 and HMAC-SHA-256, build a lightweight ledger with Merkle roots, and verify integrity against induced tampering. The study integrates three heterogeneous sources, LMSYS-Chat-1M, WildChat, and Chatbot Arena, and produces 3,971,887 auditable units. In the full-corpus HMAC-SHA-256 experiment, the 11,899.55 MiB normalized repository was represented by 1,473.44 MiB of hash-only audit records, 2.03 MiB of ledger block headers, and 380.52 MiB for the operational ledger plus membership structures required for record-level verification. Five induced tampering categories, Merkle and decision-hash integrity, hash-chain linkage integrity, block ordering integrity, membership completeness, and dataset-membership consistency, were detected without false acceptance; a separate clean-ledger baseline validated the untampered ledger with zero error signals. Load experiments showed near-linear growth of mean total pipeline time with respect to the number of audit records. Operational ledger-stage throughput increased across the intermediate workloads but declined at the largest evaluated load, while full record-set verification throughput remained comparatively stable up to 250,000 interactions before decreasing in the 500,000-interaction scenario. This contribution demonstrates that the traceability of decisions generated by LLM can be formulated as a measurable, private, and cryptographically verifiable experimental chain. The framework provides a documented numerical-audit basis for independent consistency assessment, regulatory compliance, and forensic analysis when ledger states are evaluated against preserved artifacts, trusted checkpoints, or externally anchored commitments.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1929127"},"PeriodicalIF":6.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13668802/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149217033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stock health analysis and forecasting using risk-adjusted metrics and LSTM-guided Monte Carlo simulation.","authors":"Balakrishnan P, A Anny Leema, Ananaey Garg, Shakti Patidar","doi":"10.3389/frai.2026.1811451","DOIUrl":"10.3389/frai.2026.1811451","url":null,"abstract":"<p><p>The complex and nonlinear behaviors of financial markets make it exceedingly challenging for average investors to evaluate the performance of a specific asset or make meaningful predictions about its future behavior. Investing in stocks involves various evaluation techniques. However, many of these techniques are restricted to a small set of variables or measurements that are static and historical, limiting their ability to assess the evolving risk-return tradeoff. The objective of this research is to develop a more sophisticated technique that integrates stock evaluation and forecasting by combining traditional financial performance indicators and machine learning time-series forecasting techniques. The result is a system that provides a more organized, simplified, and transparent representation of asset performance and prospective trends. The system provides risk-adjusted performance health scores based on traditional financial metrics such as annualized return, volatility, Sharpe ratio, and drawdown, and supplements this assessment with a Long Short-Term Memory (LSTM) network, which is trained on historical return patterns to generate the predictive signal. Monte Carlo simulation technique is implemented in this work to expect future price paths which, on one hand, are mathematically constrained by the financial limits and, on the other hand, are statistically based on the past distributions. Besides, the framework applies explainable AI methods to interpret the forecasted variations in terms of publicly available market and macroeconomic data, ultimately aiming at opening the \"black box\" to the users and building their trust. The suggested method offers consistent performance evaluation, stable long-term forecasts, and comprehensible insights, which makes it appropriate for decision-support applications in retail investment analysis, according to experimental evaluation using Indian equity and mutual fund data.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1811451"},"PeriodicalIF":6.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13669191/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149217047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Nuchal Translucency analysis for early prenatal risk stratification.","authors":"Smita S Pawar, Mangesh D Nikose, Prakash G Burade","doi":"10.3389/frai.2026.1914775","DOIUrl":"10.3389/frai.2026.1914775","url":null,"abstract":"<p><p>Nuchal Translucency (NT) is a measurement taken from ultrasound images described as the gathering of fluid behind the fetus's neck, during 11-13 weeks. Increased Nuchal Translucency can lead to high risk of chromosomal abnormalities like Down Syndrome. The existing AI model may struggle to detect the subtle difference due to low resolution fetal movement, which can lead to errors in measurements and diagnosis. In addition, traditional NT measurement methods rely heavily on manual caliper placement, making the process operator-dependent and reducing consistency and automation. To address these challenges, this paper proposes an Enhanced Multi-Scale Spatial Temporal Graph Attention Network (EMSSTGAN) to predict the risk of chromosomal abnormalities in NT. Initially, the input images of NT are pre-processed using feature refinement attention network (FRANet) for denoising and Multi-Scale Attention Retinex Network (MARN) for contrast enhancement. NT detection is then performed by localizing the nuchal translucency region using bounding box annotations, followed by U-Net-based segmentation for precise extraction of the NT area. The Active Contour Model (ACM) is subsequently applied to measure the NT thickness based on boundary geometry. The thickness when compared with threshold values is used to identify the risk of chromosomal abnormalities as low, medium and high. The proposed model achieves an accuracy of 98.15%, an error of 1.85%, and a FDR of 2.44%, respectively.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1914775"},"PeriodicalIF":6.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13668809/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid neuro-symbolic graph explanation framework for fraud detection in financial transaction networks.","authors":"Neeraj S Kumar, Idhikash J, Jannath Nisha O S, Shreenidhi S R","doi":"10.3389/frai.2026.1904459","DOIUrl":"10.3389/frai.2026.1904459","url":null,"abstract":"<p><strong>Introduction: </strong>This research focuses on the development of a hybrid neuro-symbolic system for fraud detection in rapidly expanding digital financial systems.</p><p><strong>Methods: </strong>A bipartite customer-merchant graph is constructed from the Nigerian Financial Transactions dataset, and a 3-layer Graph Attention Network (FraudGAT) is used to model structural dependencies while a symbolic reasoning layer and constrained LLM prompting generate auditor-readable explanations.</p><p><strong>Results: </strong>On the curated evaluation split, FraudGAT achieves AUC 0.874, AUPRC 0.771, and F1 0.609. We additionally benchmark against GCN, GraphSAGE, GIN, a graphtransformer baseline, and tabular baselines (Random Forest, XGBoost, LightGBM): tabular ensembles perform best on this sampled split, while FraudGAT remains stronger than several graph baselines and supports an interpretable detect-explain workflow.</p><p><strong>Discussions: </strong>Therefore, we position the contribution as an auditable neuro-symbolic graph framework and an empirical foundation for scaling to larger, richer transaction graphs.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1904459"},"PeriodicalIF":6.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13668811/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exhaustive feature-subset search for data-driven post-correction of satellite atmospheric processors: a case study on chlorophyll-a retrieval in inland reservoirs.","authors":"Victoria Amores-Chaparro, Fernando Broncano, Jesús A Torrecilla-Pinero, Aurora Cuartero","doi":"10.3389/frai.2026.1947948","DOIUrl":"https://doi.org/10.3389/frai.2026.1947948","url":null,"abstract":"<p><p>Satellite retrieval of chlorophyll-a (Chl-a) is central to monitoring eutrophication in inland reservoirs, yet the atmospheric-correction processors it depends on, trained on oceanic and large-lake conditions, are systematically biased over small water bodies. A data-driven post-correction layer is increasingly used to reduce this bias; for such a layer over the joint output of the C2-Net processors (C2RCC, C2X, and C2XC), we ask which engineered features robustly carry the corrective signal rather than chasing an accuracy headline. Enumerating all 2¹⁷-1 = 131, 071 subsets of a symmetric 17-feature pool exposes a broad performance plateau on which three mainstream heuristics pick inequivalent \"best\" subsets at near-identical error, so any single optimum is an artifact of selection on the cross-validated statistic. Stability selection instead isolates a small robust core (led by <math> <msqrt><mrow><mo>|</mo> <mtext>C</mtext> <mn>2</mn> <mtext>X</mtext> <msub><mrow><mtext>C</mtext></mrow> <mrow><mi>r</mi></mrow> </msub> <mo>|</mo></mrow> </msqrt> </math> , chosen in 98% of subsamples at <i>k</i> = 6), the transformations that dominate the lowest-error subset being selected near chance. On 252 satellite-<i>in-situ</i> pairs from five Guadiana-Basin reservoirs (Spain), the correction improves pooled RMSE over raw C2RCC modestly but, for the Huber model, significantly (~10-16%, <i>p</i> = 0.047 under a cross-validation-corrected test), with the three estimators statistically indistinguishable from one another; under Leave-One-Reservoir-Out validation the degradation runs from ~15% (pooled) to ~34% (per-reservoir mean), both within the wide bootstrap intervals and thus approximate rather than precise. We distill a compact reporting template: an RMSE-vs.-<i>k</i> curve with variance bands, stability selection in place of a single \"best\" subset, a relative-error metric (MdAPE) alongside RMSE, and LORO as a minimum transferability check. All code, data, and models are released openly.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1947948"},"PeriodicalIF":6.7,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13662142/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149196917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust multi-class DDoS and fraud detection using a Context-Adaptive Deep Evolutionary Game Framework with CNN-BiGRU.","authors":"Ashwini V, Priya V","doi":"10.3389/frai.2026.1928178","DOIUrl":"10.3389/frai.2026.1928178","url":null,"abstract":"<p><p>Distributed Denial of Service (DDoS) attacks have become a serious threat in modern computer networks, which can commonly be integrated with other types of attacks to evade detection by conventional devices. Existing deep learning-based intrusion detection methods have high identification capability. However, they are also highly susceptible to adversarial attacks, in which even slight perturbations to features can fool the model into misclassification. To address these issues, this study introduced a hybrid deep learning framework. It integrates Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Units (BiGRU) to detect diverse types of attacks in real time. CNN extracts spatial interactions to model traffic characteristics, while BiGRU, a recurrent neural network, models temporal dependencies in data flows. This combination enables the model to capture complex spatiotemporal behavioral patterns in network traffic. To address class imbalance, we propose a new Neuro-Evolutionary Resampling Algorithm (NERA). This method uses neural density estimation and evolutionary optimization to generate balanced, noise-filtered training samples. Additionally, a Context-Adaptive Deep Evolutionary Game Framework (CADEGF) is designed to improve resistance to attacks. It does this by simulating interactions between attackers and defenders. It also adjusts detection thresholds using game-theoretic ideas. Experimental results show that this approach achieves high classification accuracy, effectively detects minority classes, and highlights trade-offs between minority-class detection and adversarial robustness, indicating contexts where NERA benefits or harms resilience. The integrated synergy of NERA, CNN-BiGRU, and CADEGF establishes a context-aware, adaptive defense model that advances intelligent intrusion detection with balanced performance and adversarial robustness.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1928178"},"PeriodicalIF":6.7,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13661905/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149184622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deepfake anomaly detection using kernel density estimation based convolutional autoencoder.","authors":"Manas Ranjan Prusty, Sahil Amritkar, Ananthakrishnan Balasundaram, Subhra Rani Patra","doi":"10.3389/frai.2026.1834939","DOIUrl":"10.3389/frai.2026.1834939","url":null,"abstract":"<p><p>The rise of deepfake technology has fuelled the need for robust detection methods to counter the increasing sophistication of manipulated multimedia content. Research has shown that humans struggle to identify deepfakes, highlighting the pressing need for AI-based solutions to safeguard the integrity of the information we consume daily. This paper proposes a novel approach CAE-DAD (Convolutional Autoencoder Deepfake Anomaly Detector) which facilitates deepfake detection through visual anomaly identification using convolutional autoencoders (CAEs). Methods like Kernel Density Estimation are used in the latent space to widen the anomaly score gap between real and fake images. The model is trained only on real face images. Existing models like VGG16 which are known for their feature extraction capabilities as incorporated as part of our encoder. The model is benchmarked against state-of-the-art methods and establish a comprehensive set of evaluation metrics, including accuracy, precision, recall, F1 score. The model achieves comparable performance to existing two-class training methods, and achieves 91% accuracy. The proposed anomaly identification using CAEs serves as a valuable addition to the arsenal of tools aimed at mitigating the risks associated with the proliferation of deepfake content.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1834939"},"PeriodicalIF":6.7,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13662156/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149184978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence adoption and organizational efficiency: the mediating role of organizational learning capability and moderating role of digital literacy.","authors":"Hayford Asare Obeng, Richard Arhinful, Leviticus Mensah, Abdullah Sarwar","doi":"10.3389/frai.2026.1891713","DOIUrl":"https://doi.org/10.3389/frai.2026.1891713","url":null,"abstract":"<p><strong>Introduction: </strong>Strategic and organizational solutions are becoming essential due to the global demand for digital transformation, sustained operational efficiency, and the rapid advancement of artificial intelligence (AI) technologies. This study examines the influence of artificial intelligence adoption on organizational efficiency, with digital literacy as a boundary condition and organizational learning capability serving as a mediator.</p><p><strong>Methods: </strong>The study utilized a quantitative cross-sectional survey-based design. A multistage sampling technique was used to collect data from employees working in the Ghanaian banking sector. Data collected from 388 employees from the banks were utilized in an empirical study employing structural equation modeling. Both direct and indirect relationships were assessed.</p><p><strong>Results: </strong>The findings revealed that the level of artificial intelligence adoption has a significant and positive influence on both organizational efficiency and organizational learning capability. The results further showed that organizational learning capability has a significant and positive influence on organizational efficiency and partially mediates the relationship between artificial intelligence adoption and organizational efficiency. Additionally, the results revealed that digital literacy moderates the relationships between artificial intelligence adoption and organizational efficiency, as well as the relationships between organizational learning capability and organizational efficiency.</p><p><strong>Conclusion: </strong>This study introduces a novel theoretical framework that connects the hitherto overlooked linkages between artificial intelligence adoption, organizational learning capability, digital literacy, and organizational efficiency within the banking sector of an emerging economy. By integrating these constructs into a unified model, the study clarifies the mechanisms and boundary conditions through which AI adoption translates into organizational efficiency, thereby extending the Resource-Based View in an emerging-market context.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1891713"},"PeriodicalIF":6.7,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13661937/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149196983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}