IF 16.4 1区化学

Accounts of Chemical Research Pub Date : 2025-08-01 Epub Date: 2021-06-21 DOI: 10.23736/S2724-5276.21.06303-5

Ayşegül Erdoğan, Deniz Tuncel, Sedat Işikay, Ramazan A Okyay

{"title":"Restless legs syndrome and headache cause sleepiness and consequent poor school performance: a community-based study from Turkey.","authors":"Ayşegül Erdoğan, Deniz Tuncel, Sedat Işikay, Ramazan A Okyay","doi":"10.23736/S2724-5276.21.06303-5","DOIUrl":"10.23736/S2724-5276.21.06303-5","url":null,"abstract":"Background: Disturbance of sleep habits leads to excessive daytime sleepiness (EDS), which may affect learning abilities and consequently academic performance. Therefore, the main aim of current paper was to determine the prevalence of headache and Restless legs syndrome (RLS) in school-aged adolescents and to evaluate the type of headache in adolescents, with a secondary aim to determine the effect of daytime sleepiness on academic success.Methods: This cross-sectional study was conducted on adolescents aged between 15 and 19 years of age, who were in high school education in the 2016-2017 academic years in Kahramanmaraş province. A comprehensive interview form including questions on demographic data, RLS diagnostic criteria, headache, and Epworth Sleepiness Scale (ESS) was applied to a total of 4151 students.Results: RLS was found in 3.2% of the participants in all age groups. The mean ESS scores in adolescents with RLS were significantly higher than in those without RLS. Headache was reported by 46.9% of the adolescents in the study, with a frequency of TTH type headache of 17.7% and migraine frequency of 5.2%. RLS frequency was determined to be significantly higher in adolescents with headache and migraine. The academic success rate was significantly lower in those with higher ESS scores.Conclusions: Migraine and RLS often coexist as comorbid conditions. EDS is an important factor affecting academic success in children. Headache and RLS should not be forgotten, among other reasons for increased daytime sleepiness and its etiology.","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":" ","pages":"314-319"},"PeriodicalIF":16.4,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39252949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PPARγ modulator predictor (PGMP_v1): chemical space exploration and computational insights for enhanced type 2 diabetes mellitus management. PPARγ调节因子预测因子（PGMP_v1）：化学空间探索和2型糖尿病强化管理的计算见解

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-08-01 Epub Date: 2025-02-01 DOI: 10.1007/s11030-025-11118-5

Sk Abdul Amin, Lucia Sessa, Shovanlal Gayen, Stefano Piotto

{"title":"PPARγ modulator predictor (PGMP_v1): chemical space exploration and computational insights for enhanced type 2 diabetes mellitus management.","authors":"Sk Abdul Amin, Lucia Sessa, Shovanlal Gayen, Stefano Piotto","doi":"10.1007/s11030-025-11118-5","DOIUrl":"10.1007/s11030-025-11118-5","url":null,"abstract":"Peroxisome proliferator-activated receptor gamma (PPARγ) plays a critical role in adipocyte differentiation and enhances insulin sensitivity. In contemporary drug discovery, in silico design strategies offer significant advantages by revealing essential structural insights for lead optimization. The study is guided by two main objectives: (i) a ligand-based approach to explore the chemical space of PPARγ modulators followed by molecular docking ensembles (MDEs) to investigate ligand-binding interactions, (ii) the development of a supervised ML model for a large dataset of compounds targeting PPARγ. Additionally, the combination of chemical space networks with ML models enables the rapid screening and prediction of PPARγ modulators. These modeling analyses will assist medicinal chemists in designing more potent PPARγ modulators. To further enhance accessibility for the scientific community, we developed an online tool, \"PGMP_v1,\" aimed at prospective screening for PPARγ modulators. The tool \"PGMP_v1\" is available at the provided link https://github.com/Amincheminfom/PGMP_v1 . The integration of these computational methods has uncovered crucial structural motifs that are essential for PPARγ activity, advancing the development of more effective modulators in the future.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3305-3321"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143073424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classification models and SAR analysis of anaplastic lymphoma kinase (ALK) inhibitors using machine learning algorithms with two data division methods. 使用两种数据分割方法的机器学习算法对无性淋巴瘤激酶 (ALK) 抑制剂进行分类模型和 SAR 分析。

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-08-01 Epub Date: 2024-11-12 DOI: 10.1007/s11030-024-10990-x

Dan Qu, Aixia Yan

{"title":"Classification models and SAR analysis of anaplastic lymphoma kinase (ALK) inhibitors using machine learning algorithms with two data division methods.","authors":"Dan Qu, Aixia Yan","doi":"10.1007/s11030-024-10990-x","DOIUrl":"10.1007/s11030-024-10990-x","url":null,"abstract":"Anaplastic lymphoma kinase (ALK) plays a critical role in the development of various cancers. In this study, the dataset of 1810 collected inhibitors were divided into a training set and a test set by the self-organizing map (SOM) and random method, respectively. We developed 32 classification models using Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF), and Extreme Gradient Boosting (XGBoost) to distinguish between highly and weakly active ALK inhibitors, with the inhibitors represented by MACCS and ECFP4 fingerprints. Model 7D which was built by the RF algorithm using training set 1/test set 1 divided by the SOM method, provided the best performance with a prediction accuracy of 90.97% and a Matthews correlation coefficient (MCC) value of 0.79 on the test set. We clustered the 1810 inhibitors into 10 subsets by K-Means algorithm to find out the structural characteristics of highly active ALK inhibitors. The main scaffolds of highly active ALK inhibitors were also analyzed based on ECFP4 fingerprints. It was found that some substructures have a significant effect on high activity, such as 2,4-diarylaminopyrimidine analogues, pyrrolo[2,1-f][1,2,4]triazin, indolo[2,3-b]quinoline-11-one, benzo[d]imidazol and pyrrolo[2,3-b]pyridine. In addition, the subsets were summarized into several clusters, among which four clusters showed a significant relationship with ALK inhibitory activity. Finally, Shapley additive explanations (SHAP) was also used to explain the influence of modeling features on model prediction results. The SHAP results indicated that our models can well reflect the structural features of ALK inhibitors.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"2919-2943"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142611847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A multitask interpretable model with graph attention mechanism for activity prediction of low-data PIM inhibitors. 低数据PIM抑制剂活动预测的多任务可解释图注意机制模型。

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-08-01 Epub Date: 2024-11-30 DOI: 10.1007/s11030-024-11060-y

Zixiao Wang, Lili Sun, Yu Chang, Fang Yang, Kai Jiang

{"title":"A multitask interpretable model with graph attention mechanism for activity prediction of low-data PIM inhibitors.","authors":"Zixiao Wang, Lili Sun, Yu Chang, Fang Yang, Kai Jiang","doi":"10.1007/s11030-024-11060-y","DOIUrl":"10.1007/s11030-024-11060-y","url":null,"abstract":"The aberrant expression of proviral integration site for Moloney murine leukemia virus (PIM) kinases is closely related to various tumors and chemotherapy resistance, making them attractive targets for cancer therapy. However, due to the extremely high homology among the three PIM isoforms (PIM1, PIM2, PIM3) and the limited availability of existing bioactivity data, screening and designing selective PIM inhibitors remain a daunting challenge. To address this issue, this study constructed a multitask regression model that can simultaneously predict the half-maximal inhibitory concentration (IC50 values). The model utilizes an attention mechanism to capture effects within local atomic groups and the interactions between different groups of atoms. Through weight sharing, the model enhances the accuracy of predicting PIM3 inhibitors by leveraging the rich and highly correlated data from PIM1 and PIM2 isoforms. Additionally, visualizing the weights of nodes (atoms in the molecule) in the model helps us to intuitively understand the relationship between molecular features and prediction outcomes, thereby enhancing the interpretability of the model. In summary, this work provides new insights and methods for performing activity prediction tasks for multiple similar targets in low-data scenarios.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3101-3112"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142765266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning approaches for predicting the small molecule-miRNA associations: a comprehensive review. 预测小分子- mirna关联的机器学习方法：综合综述。

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-08-01 Epub Date: 2025-05-20 DOI: 10.1007/s11030-025-11211-9

Ashish Panghalia, Vikram Singh

{"title":"Machine learning approaches for predicting the small molecule-miRNA associations: a comprehensive review.","authors":"Ashish Panghalia, Vikram Singh","doi":"10.1007/s11030-025-11211-9","DOIUrl":"10.1007/s11030-025-11211-9","url":null,"abstract":"MicroRNAs (miRNAs) are evolutionarily conserved small regulatory elements that are ubiquitous in cells and are found to be abnormally expressed during the onset and progression of several human diseases. miRNAs are increasingly recognized as potential diagnostic and therapeutic targets that could be inhibited by small molecules (SMs). The knowledge of SM-miRNA associations (SMAs) is sparse, mainly because of the dynamic and less predictable 3D structures of miRNAs that restrict the high-throughput screening of SMs. Toward augmenting the costly and laborious experiments determining the SM-miRNA interactions, machine learning (ML) has emerged as a cost-effective and efficient platform. In this article, various aspects associated with the ML-guided predictions of SMAs are thoroughly reviewed. Firstly, a detailed account of the SMA data resources useful for algorithms training is provided, followed by an elaboration of various feature extraction methods and similarity measures utilized on SMs and miRNAs. Subsequent to a summary of the ML algorithms basics and a brief description of the performance measures, an exhaustive census of all the 32 ML-based SMA prediction methods developed so far is outlined. Distinctive features of these methods have been described by classifying them into six broad categories, namely, classical ML, deep learning, matrix factorization, network propagation, graph learning, and ensemble learning methods. Trend analyses are performed to investigate the patterns in ML algorithms usage and performance achievement in SMA prediction. Outlining key principles behind the up-to-date methodologies and comparing their accomplishments, this review offers valuable insights into critical areas for future research in ML-based SMA prediction.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3825-3856"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144109420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

First report on analysis of chemical space, scaffold diversity, critical structural features of HDAC11 inhibitors. 首次报道HDAC11抑制剂的化学空间、支架多样性、关键结构特征分析。

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-08-01 Epub Date: 2025-05-17 DOI: 10.1007/s11030-025-11217-3

Rinki Prasad Bhagat, Jyotisha, Indrasis Dasgupta, Sk Abdul Amin, Pranay Jakkula, Arijit Bhattacharya, Insaf Ahmed Qureshi, Shovanlal Gayen

{"title":"First report on analysis of chemical space, scaffold diversity, critical structural features of HDAC11 inhibitors.","authors":"Rinki Prasad Bhagat, Jyotisha, Indrasis Dasgupta, Sk Abdul Amin, Pranay Jakkula, Arijit Bhattacharya, Insaf Ahmed Qureshi, Shovanlal Gayen","doi":"10.1007/s11030-025-11217-3","DOIUrl":"10.1007/s11030-025-11217-3","url":null,"abstract":"In the histone deacetylase (HDAC) family, HDAC11 is the smallest and a single member under the class IV subtype. It is important as a drug target mainly in cancer, inflammatory and autoimmune diseases. The design and development of selective HDAC11 inhibitors is quite a challenge for the chemist community due to the unavailability of the crystal structure of HDAC11. Ligand-based drug design (LBDD) strategies are the hope to speed up the development of its inhibitors. Here, an in-depth analysis of 712 HDAC11 inhibitors is performed through compound space networks and various cheminformatics approaches. The analyses demonstrated significant clustering of similar compounds based on their chemical structures, offering valuable insights into the chemical space occupied by HDAC11 inhibitors. Furthermore, the current work aimed to develop robust classification-based QSAR models that deliver the essential structural fingerprints. This study highlighted that the compounds bearing scaffolds such as isoindoline, benzimidazole, carboxamide/hydroxamate moieties, etc., are important for HDAC11 inhibitors. Molecular docking and MD simulations further provide an in-depth analysis of the binding interaction of the identified fingerprints in the catalytic site of HDAC11. In brief, our study delivers some important structural attributes that will aid medicinal chemists in designing and developing future potent HDAC11 inhibitors.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3679-3702"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144085631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

QMGBP-DL: a deep learning and machine learning approach for quantum molecular graph band-gap prediction. QMGBP-DL：量子分子图带隙预测的深度学习和机器学习方法。

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-08-01 Epub Date: 2025-04-19 DOI: 10.1007/s11030-025-11178-7

Outhman Abbassi, Soumia Ziti

{"title":"QMGBP-DL: a deep learning and machine learning approach for quantum molecular graph band-gap prediction.","authors":"Outhman Abbassi, Soumia Ziti","doi":"10.1007/s11030-025-11178-7","DOIUrl":"10.1007/s11030-025-11178-7","url":null,"abstract":"Predicting molecular and quantum material properties, especially the band gap, is crucial for accelerating discoveries in drug design and material science. Although graph neural networks and probabilistic encoders are well established in molecular data analysis, their targeted integration and application for band-gap prediction remain an active research area. This paper introduces QMGBP-DL, a deep learning approach that combines a molecular graph encoder with machine learning models to improve the prediction accuracy of molecular and material band-gap energy. The encoder uses graph convolutional networks to derive latent representations of chemical structures from SMILES strings, optimized via Kullback-Leibler divergence loss. These representations serve as inputs for training various machine learning models to predict properties. QMGBP-DL's effectiveness is assessed using the QM9, PCQM4M, and OPV datasets, demonstrating significant improvements, particularly with a random forest model for property prediction. A comparative analysis against established approaches DenseGNN, MEGNet, and ALIGNN reveals that QMGBP-DL excels in predicting HOMO, LUMO, and band gap, achieving notably lower MAE values. The integration of GCN-derived latent spaces with traditional machine learning models, especially Random Forest, provides a powerful approach for band-gap prediction. The results highlight the efficacy of our integrated approach, showcasing that graph-based molecular encoding combined with machine learning, particularly Random Forest, is highly effective for accurate band-gap prediction, thereby facilitating material discovery and design.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3501-3515"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143954943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparison of cartilage and temporalis fascia grafts in type 1 tympanoplasty: A meta-analysis. 1型鼓室成形术中软骨与颞筋膜移植的比较：荟萃分析

IF 16.4 1区化学

Accounts of Chemical Research Pub Date : 2025-08-01 Epub Date: 2022-10-27 DOI: 10.1177/01455613221137122

Kai Chen, Rui Zhao

{"title":"Comparison of cartilage and temporalis fascia grafts in type 1 tympanoplasty: A meta-analysis.","authors":"Kai Chen, Rui Zhao","doi":"10.1177/01455613221137122","DOIUrl":"10.1177/01455613221137122","url":null,"abstract":"ObjectiveTo systematically review the results of type 1 tympanoplasties with temporalis fascia (TF) vs cartilage grafts in patients with chronic otitis media.MethodsEligible studies were identified from PubMed, Ovid, and EMBASE databases prior to November 2021. We analyzed the pure tone audiometry (PTA) and air-bone gap (ABG) data as continuous variables, and the success rate was analyzed as a dichotomous variable.ResultsForty-four studies, including 4582 patients, were eligible. The cartilage graft overall morphologic success rate was higher than that of the TF grafts (P < .001). In the palisade (P < .004) and island grafts (P < .001) subgroups, the analysis was significantly different. However, there was no significant difference in the inlay butterfly grafts subgroup. For hearing outcomes, the analysis revealed that TF grafts had a smaller mean post-operative ABG (P = .009). However, the subgroup analysis showed no significant difference in the mean post-operative ABG. For PTA, there was no significant difference in hearing improvement. However, the palisade cartilage graft subgroup resulted in a better hearing outcome than the TF graft subgroup in terms of the mean post-operative PTA (P = .007). There was no significant difference in the functional success rate or mean ABG gain.ConclusionCartilage grafts have a better success rate than TF grafts in tympanoplasty. Both cartilage and TF tympanoplasty provided similar improvements in hearing outcome, while TF grafts generated a better outcome in post-operative ABG and palisade cartilage grafts in post-operative PTA. This may be related to the biological characteristics of the grafts. Further thorough studies need to be conducted.","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":" ","pages":"NP476-NP489"},"PeriodicalIF":16.4,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40428086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual-Gas Sensor Employing Wavelength-Stabilized Tunable Diode Laser Absorption Spectroscopy and H-Infinity Filtering Algorithm. 采用波长稳定可调谐二极管激光吸收光谱和h-∞滤波算法的双气体传感器。

IF 2.2 3区化学

Applied Spectroscopy Pub Date : 2025-08-01 Epub Date: 2025-01-10 DOI: 10.1177/00037028241310463

Dingli Xu, Qiannan Cai, Gang Zhang, Qiang Ge, Linguang Xu

引用次数: 0

Bioanalysis of monodisperse HO-PEG₈-OH polymers in MCF-7 cells by UHPLC-MS/MS coupled with μ-SPE to improve greenness and sensitivity. 采用UHPLC-MS/MS联合μ-SPE对MCF-7细胞中单分散HO-PEG8-OH聚合物进行生物分析，提高绿色度和灵敏度。

IF 3.8 2区化学

Analytical and Bioanalytical Chemistry Pub Date : 2025-08-01 Epub Date: 2025-06-25 DOI: 10.1007/s00216-025-05954-5

Luyao Yu, Meichen Liu, Ziyi Niu, Jiarui Zhang, Yue Deng, Xinyue Zhou, Jiansong You, Hongyu Xue, Lei Yin, Meiyun Shi

{"title":"Bioanalysis of monodisperse HO-PEG8-OH polymers in MCF-7 cells by UHPLC-MS/MS coupled with μ-SPE to improve greenness and sensitivity.","authors":"Luyao Yu, Meichen Liu, Ziyi Niu, Jiarui Zhang, Yue Deng, Xinyue Zhou, Jiansong You, Hongyu Xue, Lei Yin, Meiyun Shi","doi":"10.1007/s00216-025-05954-5","DOIUrl":"10.1007/s00216-025-05954-5","url":null,"abstract":"Monodisperse polyethylene glycol (PEG) derivatives offer significant advantages over conventional polydisperse PEGs for biomedical applications due to their precisely defined molecular structures. This study establishes an eco-efficient, selective, and sensitive analytical assay integrating microscale solid-phase extraction (μ-SPE) with ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) to investigate the cellular uptake of HO-PEG8-OH polymers in MCF-7 cells. The method achieved greater than 91% recovery from 20 μL lysates using M-PEG6-OH as the internal standard, with validated linearity (10-1000 ng/mL, R > 0.997), accuracy (relative error < ± 7.49%), and precision (RSD < 7.50%). The novelty of the assay is the harmonization of analytical performance with green analytical chemistry principles. The validated method provides a robust platform for studying monodisperse PEG derivatives while addressing growing demands for sustainable analytical technologies. Green analytical chemistry metric assessments confirmed the environmental sustainability of the method. Cellular pharmacokinetic analysis revealed time-/concentration-dependent uptake kinetics of HO-PEG8-OH polymers in MCF-7 cells, showing 3.2-fold accumulation between 0.5 and 48 h exposure. These findings offer important insights for PEG-based drug delivery system optimization and establish a new standard for environmentally conscious bioanalytical method development.","PeriodicalId":462,"journal":{"name":"Analytical and Bioanalytical Chemistry","volume":" ","pages":"4383-4394"},"PeriodicalIF":3.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144482739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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