Methods最新文献

{"title":"Low friction hydrogel with diclofenac eluting ability for dry eye therapeutic contact lenses","authors":"Diana C. Silva ,&nbsp;Margarida Oliveira ,&nbsp;Carolina Marto-Costa ,&nbsp;João Teixeira ,&nbsp;Madalena Salema Oom ,&nbsp;Carlos A. Pinto ,&nbsp;Jorge A. Saraiva ,&nbsp;Ana Clara Marques ,&nbsp;Laurence Fitzhenry ,&nbsp;Ana Paula Serro","doi":"10.1016/j.ymeth.2024.11.015","DOIUrl":"10.1016/j.ymeth.2024.11.015","url":null,"abstract":"<div><div>When placed in the eye, contact lenses (CLs) disturb the tear fluid and affect the natural tribological behaviour of the eye. The disruption in the contact mechanics between the ocular tissues can increase frictional shear stress and ocular dryness, causing discomfort. Ultimately, continuous CLs wear can trigger inflammation which is particularly critical for people suffering from dry eye. In this work, a double strategy was followed to obtain therapeutic daily disposable CLs for dry eye: a hydroxyethyl methacrylate (HEMA) based hydrogel was coated with two natural polysaccharides, chitosan (CHI) and hyaluronic acid (HA) and posteriorly loaded with an anti-inflammatory drug (diclofenac, DCF). Material sterilisation was carried out by high hydrostatic pressure (HHP) combined with moderate temperature. The friction coefficient (μ) was determined in the presence of different tear biomolecules (cholesterol, lysozyme and albumin) using a nanotribometer. Drug release experiments were performed in static and in hydrodynamic conditions. The material was extensively characterised, regarding surface morphology/topography, optical properties, water content and swelling behaviour, wettability, ionic and oxygen permeability and mechanical properties. It was found that the coating did not impair the physico-chemical properties relevant for the material’s application in CLs. Besides, it also ensured a sustained release of DCF for 24 h in tests performed in hydrodynamic conditions that simulate those found in the eye, increasing significantly the amount of drug released. It reduced friction, improving the lubrication ability of the hydrogel, and presented antibacterial properties against <em>S. aureus</em>, <em>P. aeruginosa</em> and <em>B. Cereus</em>. The coated samples did not reveal any signs of cytotoxicity or potential eye irritation. Overall, the coating of the hydrogel may be useful to produce daily CLs able to alleviate dry eye symptoms and the discomfort of CLs wearers.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"234 ","pages":"Pages 67-84"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142765111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advances in machine learning for epigenetics and biomedical applications","authors":"Hao Lin,&nbsp;Hao Lv,&nbsp;Fuying Dao","doi":"10.1016/j.ymeth.2025.01.018","DOIUrl":"10.1016/j.ymeth.2025.01.018","url":null,"abstract":"","PeriodicalId":390,"journal":{"name":"Methods","volume":"235 ","pages":"Pages 53-54"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A roadmap to cysteine specific labeling of membrane proteins for single-molecule photobleaching studies","authors":"Melanie Ernst,&nbsp;Robyn Mahoney-Kruszka,&nbsp;Nathan B. Zelt,&nbsp;Janice L. Robertson","doi":"10.1016/j.ymeth.2024.10.013","DOIUrl":"10.1016/j.ymeth.2024.10.013","url":null,"abstract":"<div><div>Single-molecule photobleaching analysis is a useful approach for quantifying reactive membrane protein oligomerization in membranes. It provides a binary readout of a fluorophore attached to a protein subunit at dilute conditions. However, quantification of protein stoichiometry from this data requires information about the subunit labeling yields and whether there is non-specific background labeling. Any increases in subunit-specific labeling improves the ability to determine oligomeric states with confidence. A common strategy for site-specific labeling is by conjugation of a fluorophore bearing a thiol-reactive maleimide group to a substituted cysteine. Yet, cysteine reactivity can be difficult to predict as it depends on many factors such as solvent accessibility and electrostatics from the surrounding protein structure. Here we report a general methodology for screening potential cysteine labeling sites on purified membrane proteins. We present the results of two example systems for which the dimerization reactions in membranes have been characterized: (1) the CLC-ec1 Cl^-/H⁺ antiporter, an <em>Escherichia coli</em> homologue of voltage-gated chloride ion channels in humans and (2) a mutant form of a member of the family of fluoride channels Fluc from <em>Bordetella pertussis</em> (Fluc-Bpe-N43S). To demonstrate how we identify such sites, we first discuss considerations of residue positions hypothesized to be suitable and then describe the specific steps to rigorously assess site-specific labeling while maintaining functional activity and robust single-molecule fluorescence signals. We find that our initial, well rationalized choices are not strong predictors of success, as rigorous testing of the labeling sites shows that only ≈ 30 % of sites end up being useful for single-molecule photobleaching studies.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"234 ","pages":"Pages 21-35"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142714807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and characterization of hollow microneedles for localized intrascleral drug delivery of ocular formulations","authors":"Shilpkala Gade,&nbsp;Lalitkumar K. Vora,&nbsp;Raghu Raj Singh Thakur","doi":"10.1016/j.ymeth.2024.12.004","DOIUrl":"10.1016/j.ymeth.2024.12.004","url":null,"abstract":"<div><div>Effective drug delivery to the posterior segment of the eye remains a challenge owing to the limitations of conventional methods such as intravitreal injections, which are associated with significant side effects. This study explored the use of hollow microneedles (HMNs) for localized intrascleral drug delivery as a minimally invasive alternative. Stainless steel HMNs with bevel angles of 30°, 45°, 60°, and 75° were fabricated using wire electron discharge machining. The penetration force of these HMNs in ex vivo porcine sclera was assessed using a texture analyser, revealing that the 60° bevel angle required the lowest force (&lt;2N), making it optimal for scleral penetration. To ensure precision in drug delivery, 3D-printed adapters were developed to control the injection angles and volumes. The distribution of a model dye, rhodamine B, was studied via digital imaging, multiphoton microscopy, and confocal microscopy. The results showed that HMNs with a 60° bevel angle could penetrate the sclera to a depth of approximately 450 µm at a 45° injection angle, providing enhanced distribution within the scleral layers. This study confirmed that the use of HMNs enables effective and controlled intrascleral drug delivery, resulting in the formation of localized depots with minimal tissue damage. This research demonstrates the potential of HMNs as a promising alternative to traditional ocular drug delivery methods, offering improved bioavailability and the potential to reduce patient discomfort.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"234 ","pages":"Pages 196-210"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142880855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AntiT2DMP-Pred: Leveraging feature fusion and optimization for superior machine learning prediction of type 2 diabetes mellitus","authors":"Shaherin Basith ,&nbsp;Balachandran Manavalan ,&nbsp;Gwang Lee","doi":"10.1016/j.ymeth.2025.01.003","DOIUrl":"10.1016/j.ymeth.2025.01.003","url":null,"abstract":"<div><div>Pancreatic α-amylase breaks down starch into isomaltose and maltose, which are further hydrolyzed by α-glucosidase in the intestine into monosaccharides, rapidly raising blood sugar levels and contributing to type 2 diabetes mellitus (T2DM). Synthetic inhibitors of carbohydrate-digesting enzymes are used to manage T2DM but may harm organ function over time. Bioactive peptides offer a safer alternative, avoiding such adverse effects. Computational methods for predicting antidiabetic peptides (ADPs) can significantly reduce the time and cost of experimental testing. While machine learning (ML) has been applied to identify ADPs, advancements in data analysis and algorithms continue to drive progress in the field. To address this, we developed AntiT2DMP-Pred, the first ML-based tool specifically designed for predicting type 2 antidiabetic peptides (T2ADPs). This tool employs a feature fusion strategy, combining ten highly discriminative feature descriptors chosen from a pool of 32 descriptors and eight ML algorithms, tested across a range of baseline models. AntiT2DMP-Pred demonstrated excellent performance, surpassing both baseline and feature-optimized models, with an accuracy (ACC) and Matthews’ correlation coefficient (MCC) of 0.976 and 0.953 on the training dataset, and an ACC and MCC of 0.957 and 0.851 on the independent dataset. The web server (<span><span>https://balalab-skku.org/AntiT2DMP-Pred</span><svg><path></path></svg></span>) is freely accessible, enabling researchers worldwide to utilize it in their experimental workflows and contribute to the discovery and understanding of T2ADPs, ultimately supporting peptide-based therapeutic development for diabetes management.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"234 ","pages":"Pages 264-274"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated analyses of prognostic and immunotherapeutic significance of EZH2 in uveal melanoma","authors":"Junfang Li ,&nbsp;Yifei Zhang ,&nbsp;Qiu Yang ,&nbsp;Yi Qu","doi":"10.1016/j.ymeth.2025.01.004","DOIUrl":"10.1016/j.ymeth.2025.01.004","url":null,"abstract":"<div><div>The EZH2 expression shows significantly associated with immunotherapeutic resistance in several tumors. A comprehensive analysis of the predictive values of EZH2 for immune checkpoint blockade (ICB) effectiveness in uveal melanoma (UM) remains unclear. We analyzed UM data from The Cancer Genome Atlas (TCGA) database, identified 888 differentially expressed genes (DEGs) associated with EZH2 expression, then conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses to elucidate biological features of EZH2 in UM assays. The correlation of the expression of EZH2 with tumor immunity related factors such as immune-related pathways, infiltration of various immune cells, immune score and immune checkpoints were explored. The evaluation of EZH2′s capability to predict immune therapy outcomes in UM was assessed by incorporating the Tumor Immune Dysfunction and Exclusion (TIDE) score. Lastly, programmed death-ligand 1 (PD-L1) expression was detected in an independent UM patient cohort by immunohistochemical analyses, the correlation of EZH2 with PD-L1 was evaluated. Results highlighted that the EZH2 expression was correlated with immune-related pathways, infiltration of various immune cells, immune score, the expression of immune checkpoints and immunotherapy sensitivity. Collectively, we suggested that EZH2 might be considered as predictor on the therapeutic effects of ICBs on UM patients, and a potential target for combined immunotherapy.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"234 ","pages":"Pages 242-252"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142942260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting cyclins based on key features and machine learning methods","authors":"Cheng-Yan Wu ,&nbsp;Zhi-Xue Xu ,&nbsp;Nan Li ,&nbsp;Dan-Yang Qi ,&nbsp;Hong-Ye Wu ,&nbsp;Hui Ding ,&nbsp;Yan-Ting Jin","doi":"10.1016/j.ymeth.2024.12.009","DOIUrl":"10.1016/j.ymeth.2024.12.009","url":null,"abstract":"<div><div>Cyclins are a group of proteins that regulate the cell cycle process by modulating various stages of cell division to ensure correct cell proliferation, differentiation, and apoptosis. Research on cyclins is crucial for understanding the biological functions and pathological states of cells. However, current research on cyclin identification based on machine learning only focuses on accuracy ignoring the interpretability of features. Therefore, in this study, we pay more attention to the interpretation and analysis of key features associated with cyclins. Firstly, we developed an SVM-based model for identifying cyclins with an accuracy of 92.8% through 5-fold. Then we analyzed the physicochemical properties of the 14 key features used in the model construction and identified the G and charged C1 features that are critical for distinguishing cyclins from non-cyclins. Furthermore, we constructed an SVM-based model using only these two features with an accuracy of 81.3% through the leave-one-out cross-validation. Our study shows that cyclins differ from non-cyclins in their physicochemical properties and that using only two features can achieve good prediction accuracy.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"234 ","pages":"Pages 112-119"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142851969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deepstack-ACE: A deep stacking-based ensemble learning framework for the accelerated discovery of ACE inhibitory peptides","authors":"Phasit Charoenkwan ,&nbsp;Pramote Chumnanpuen ,&nbsp;Nalini Schaduangrat ,&nbsp;Watshara Shoombuatong","doi":"10.1016/j.ymeth.2024.12.005","DOIUrl":"10.1016/j.ymeth.2024.12.005","url":null,"abstract":"<div><div>Identifying angiotensin-I-converting enzyme (ACE) inhibitory peptides accurately is crucial for understanding the primary factor that regulates the renin-angiotensin system and for providing guidance in developing new potential drugs. Given the inherent experimental complexities, using computational methods for <em>in silico</em> peptide identification could be indispensable for facilitating the high-throughput characterization of ACE inhibitory peptides. In this paper, we propose a novel deep stacking-based ensemble learning framework, termed Deepstack-ACE, to precisely identify ACE inhibitory peptides. In Deepstack-ACE, the input peptide sequences are fed into the word2vec embedding technique to generate sequence representations. Then, these representations were employed to train five powerful deep learning methods, including long short-term memory, convolutional neural network, multi-layer perceptron, gated recurrent unit network, and recurrent neural network, for the construction of base-classifiers. Finally, the optimized stacked model was constructed based on the best combination of selected base-classifiers. Benchmarking experiments showed that Deepstack-ACE attained a more accurate and robust identification of ACE inhibitory peptides compared to its base-classifiers and several conventional machine learning classifiers. Remarkably, in the independent test, our proposed model significantly outperformed the current state-of-the-art methods, with a balanced accuracy of 0.916, sensitivity of 0.911, and Matthews correlation coefficient scores of 0.826. Moreover, we developed a user-friendly web server for Deepstack-ACE, which is freely available at <span><span>https://pmlabqsar.pythonanywhere.com/Deepstack-ACE</span><svg><path></path></svg></span>. We anticipate that our proposed Deepstack-ACE model can provide a faster and reasonably accurate identification of ACE inhibitory peptides.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"234 ","pages":"Pages 131-140"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142870920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ins-ATP: Deep estimation of ATP for organoid based on high throughput microscope images","authors":"Xuesheng Bian ,&nbsp;Shuting Chen ,&nbsp;Weiquan Liu","doi":"10.1016/j.ymeth.2025.01.012","DOIUrl":"10.1016/j.ymeth.2025.01.012","url":null,"abstract":"<div><div>Adenosine triphosphate (ATP) is a high-energy phosphate compound, the most direct energy source in organisms. ATP is an important biomarker for evaluating cell viability in biology. Researchers often use ATP bioluminescence to measure the ATP of organoid after drug to evaluate the drug efficacy. However, ATP bioluminescence has limitations, leading to unreliable drug screening results. ATP bioluminescence measurement requires the lysis of organoid cells, making it impossible to continuously monitor the long-term viability changes of organoids after drug administration. To overcome the disadvantages of ATP bioluminescence, we propose Ins-ATP, a non-invasive strategy, the first organoid ATP estimation model based on the high-throughput microscope image. Ins-ATP directly estimates the ATP of organoids from high-throughput microscope images so that it does not influence the drug reactions of organoids. Therefore, the ATP change of organoids can be observed for a long time to obtain more stable results. Experimental results show that the ATP estimation by Ins-ATP is in good agreement with those determined by ATP bioluminescence. Specifically, the predictions of Ins-ATP are consistent with the results measured by ATP bioluminescence in the efficacy evaluation experiments of different drugs.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"235 ","pages":"Pages 34-44"},"PeriodicalIF":4.2,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143073381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ZeRPI: A graph neural network model for zero-shot prediction of RNA-protein interactions","authors":"Yifei Gao ,&nbsp;Runhan Shi ,&nbsp;Gufeng Yu ,&nbsp;Yuyang Huang ,&nbsp;Yang Yang","doi":"10.1016/j.ymeth.2025.01.014","DOIUrl":"10.1016/j.ymeth.2025.01.014","url":null,"abstract":"<div><div>RNA-protein interactions are crucial for biological functions across multiple levels. RNA binding proteins (RBPs) intricately engage in diverse biological processes through specific RNA molecule interactions. Previous studies have revealed the indispensable role of RBPs in both health and disease development. With the increase of experimental data, machine-learning methods have been widely used to predict RNA-protein interactions. However, most current methods either train models for individual RBPs or develop multi-task models for a fixed set of multiple RBPs. These approaches are incapable of predicting interactions with previously unseen RBPs. In this study, we present ZeRPI, a zero-shot method for predicting RNA-protein interactions. Based on a graph neural network model, ZeRPI integrates RNA and protein information to generate detailed representations, using a novel loss function based on contrastive learning principles to augment the alignment between interacting pairs in feature space. ZeRPI demonstrates competitive performance in predicting RNA-protein interactions across a wide array of RBPs. Notably, our model exhibits remarkable versatility in accurately predicting interactions for unseen RBPs, demonstrating its capacity to transfer knowledge learned from known RBPs.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"235 ","pages":"Pages 45-52"},"PeriodicalIF":4.2,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143073378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}