Springer最新文献

{"title":"Coulometric titration of sulfanilamide as a standard for diazotization titration and nitrite determination.","authors":"Toshiaki Asakai","doi":"10.1007/s00216-025-05971-4","DOIUrl":"10.1007/s00216-025-05971-4","url":null,"abstract":"<p><p>Sulfanilamide and nitrite have been standardized to each other by diazotization titration; however, no literature was found on determination methods for either material on an absolute basis. In the present study, coulometric titration with electrogenerated bromine was first used to determine sulfanilamide. Coulometric titration provides absolute assays of the amount of substances, based on Faraday's laws of electrolysis, and also has a high degree of accuracy compared to other instrumental methods. Nitrite was assayed by diazotization titration based on the coulometrically determined sulfanilamide. This idea provides a new methodology for the absolute determination of the purity of basic chemicals whose purity could previously only be assessed in relative terms.</p>","PeriodicalId":462,"journal":{"name":"Analytical and Bioanalytical Chemistry","volume":" ","pages":"4291-4298"},"PeriodicalIF":3.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144332151","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}

{"title":"The effect of ambient temperature on the running dynamics of professional soccer players during a Brasileirão Série A season.","authors":"Diêgo Augusto, Paweł Chmura, Fabrício Vasconcellos","doi":"10.1007/s00484-025-02950-3","DOIUrl":"10.1007/s00484-025-02950-3","url":null,"abstract":"<p><p>In recent years, heat stress has represented an increasing risk to players. Thus, studies involving outdoor sports and high temperatures have been conducted on athletes' performances. The purpose of this study was to investigate the effect of ambient temperature on the running dynamics of professional soccer players during a Brasileirão Série A season. Thirty matches were analyzed from 20 professional soccer players during the 2019 Brazilian Elite Championship. To quantify running dynamics during soccer matches, players used GPS device units. Ambient temperatures were classified by the k-means Cluster method into three groups: low (14.1-20.9 C), moderate (21-28.4 C) and high (29.5-35.9 C). Our main finding was that the 1-minute peak of acceleration (p = 0.02) and deceleration distance (p = 0.03) was greater in high temperature situations. In matches with low temperatures, distance in accelerations, decelerations, high-intensity running, and sprinting were greater (p = 0.01-0.02). Significant correlations, negative and low, were found for sprinting (r = - 0.23; p = 0.01), acceleration (r= -0.29; p = 0.001) and deceleration (r= -0.24; r = 0.007) with ambient temperature. These results may be of genuine practical interest to different sectors of soccer, and changes can be made to maintain the intensity and the players' health.</p>","PeriodicalId":588,"journal":{"name":"International Journal of Biometeorology","volume":" ","pages":"2045-2051"},"PeriodicalIF":3.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144482777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"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":"<p><p>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.</p>","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}

{"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":"<p><p>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.</p>","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}

{"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":"<p><p>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.</p>","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}

{"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":"<p><p>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.</p>","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}

{"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":"<p><p>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.</p>","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}

{"title":"Effect of focused ultrasound on shearwave production in a hyperelastic media.","authors":"Aniket Sabale, Mohd Suhail Rizvi, Viswanath Chinthapenta, Avinash Eranki","doi":"10.1007/s10237-025-01967-2","DOIUrl":"10.1007/s10237-025-01967-2","url":null,"abstract":"<p><p>Focused ultrasound (FUS) is an emerging noninvasive modality for treating various medical conditions. It encompasses both therapeutic and diagnostic applications, utilizing ultrasound waves at different intensities. In diagnostic modalities, ultrasound energy is deposited at the focus to generate acoustic radiation force (ARF), resulting in the generation of shear stress and waves, which are utilized in elastography to evaluate the mechanical properties of tissue. However, therapeutic modalities utilizing higher intensities may lead to elevated shear stress levels. The shear stress induced in the focal region during FUS procedures can potentially affect biological processes, such as cell membrane permeability and gene regulation. To better understand the mechanical stress generated during FUS procedures, we developed a finite element model (FEM) to simulate sonication using a single-element FUS transducer. We modeled soft tissue using a neo-Hookean hyperelastic constitutive behavior, offering a more realistic representation of tissue behavior compared to the linear elasticity assumptions commonly employed in ultrasound-based elastography techniques. Operational parameters were varied to simulate different acoustic powers of the transducer by applying mechanical surface pressure at various operating frequencies. The model depicted FUS wave propagation with amplified surface pressure at the focus, generating relevant focal pressures consistent with clinical setups. The focal beam size within the soft tissue material was characterized and exhibited dependency on the operating frequency of the transducer. As the FUS wave converged at the focus, an ARF was exerted, resulting in displacement and induced shear stress around the focal region, which were quantified. The displacement and shear stress that were analyzed were dependent on the applied transducer surface pressure. These findings deepen the understanding of the mechanics of low-intensity FUS and provide valuable insights into its shear-related effects due to displacement and deformation of the media.</p>","PeriodicalId":489,"journal":{"name":"Biomechanics and Modeling in Mechanobiology","volume":" ","pages":"1279-1294"},"PeriodicalIF":3.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144092443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"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":"<p><p>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.</p>","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}

{"title":"Societal value of seagrass from historical to contemporary perspectives.","authors":"Nicole R Foster, Eugenia T Apostolaki, Katelyn DiBenedetto, Carlos M Duarte, David Gregory, Karina Inostroza, Dorte Krause-Jensen, Benjamin L H Jones, Eduard Serrano, Rym Zakhama-Sraieb, Oscar Serrano","doi":"10.1007/s13280-025-02167-z","DOIUrl":"10.1007/s13280-025-02167-z","url":null,"abstract":"<p><p>Seagrasses have been entwined with human culture for millennia, constituting a natural resource that has supported humanity throughout this history. Understanding the societal value of seagrass fosters appreciation of these ecosystems, encouraging conservation and restoration actions to counteract historic and predicted losses. This study overviews the plethora of seagrass use in human history, ranging from spiritual and ceremonial roles, direct and indirect food resources, medicines and raw materials, dating back more than 180 000 years. While many past uses have been abandoned in modern societies, others have persisted or are being rediscovered, and new applications are emerging. As these uses of seagrasses depend on harvesting, we also underscore the need for sustainable practices to (re)generate positive interactions between seagrasses and society. Our review contributes to revalue seagrass societal ecosystem services, highlighting ancient and more recent human and seagrass relationships to incentivize conservation and restoration actions.</p>","PeriodicalId":461,"journal":{"name":"Ambio","volume":" ","pages":"1289-1305"},"PeriodicalIF":5.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12214141/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143699334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}