Methods最新文献

{"title":"Robust temporal knowledge inference via pathway snapshots with liquid neural network","authors":"Peifu Han ,&nbsp;Jianmin Wang ,&nbsp;Dayan Liu ,&nbsp;Lin Liu ,&nbsp;Tao Song","doi":"10.1016/j.ymeth.2025.05.003","DOIUrl":"10.1016/j.ymeth.2025.05.003","url":null,"abstract":"<div><div>Static graphs play a pivotal role in modeling and analyzing biological and biomedical data. However, many real-world scenarios—such as disease progression and drug pharmacokinetic processes—exhibit dynamic behaviors. Consequently, static graph methods often struggle to robustly address new environments characterized by complex and previously unseen relationship changes. Here, we propose a method for constructing temporal knowledge inference agents tailored to disease pathways, enabling effective relation reasoning beyond their training environment under complex shifts. To achieve this, we developed an imitation learning framework using liquid neural networks, a class of continuous-time neural models inspired by the brain function that are causal and adaptable to changing conditions. Our findings indicate that liquid agents can distill the essential tasks from knowledge graph inputs while accounting temporal evolution, thereby enabling the transfer of temporal skills to novel time nodes. Compared to state-of-the-art deep reinforcement learning agents, experiments demonstrate that temporal robustness in decision-making emerges uniquely in liquid networks.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"241 ","pages":"Pages 24-32"},"PeriodicalIF":4.2,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942678","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":"Electrochemical assay for the quantification of anticancer drugs and their inhibition mechanism","authors":"Ricardo Jose Branco Leote ,&nbsp;Caroline G. Sanz ,&nbsp;Victor C. Diculescu ,&nbsp;Madalina Maria Barsan","doi":"10.1016/j.ymeth.2025.05.002","DOIUrl":"10.1016/j.ymeth.2025.05.002","url":null,"abstract":"<div><div>Overexpression of pyruvate kinase (PyK) is linked to many kinds of malignant tumors, representing therefore one of the most promising therapeutic targets for cancer treatment. Inhibition of PyK slows down tumor growth or causes tumor cell death, minimizing cancer cell proliferation, and understanding inhibitor mechanism of action can significantly improve cancer therapy. The present work describes the use of an amperometric bienzymatic biosensor, based on PyK and pyruvate oxidase (PyOx), in enzyme inhibition studies of four kinase inhibitors, CPG77675, Nilotinib, Ruxolitinib, Cerdulatinib. Their inhibition mechanism is studied and discussed in detail, with a thorough evaluation of their enzyme-inhibitor complex binding constants (<em>K_i</em>) and the inhibitor concentration required for 50% inhibition <em>(IC₅₀</em>), employing standard inhibition procedure graphical methods. The biosensor is successfully applied for the quantification of the inhibitors by fixed potential amperometry, with excellent detection limit values in the pM range. It is the first detection method reported for the anticancer drugs CPG77675 and Cerdulatinib. The electrochemical assay based on the biosensor brings several advantages over the available assay kits for high-throughput screening (HTS) of kinase inhibitors, namely: low cost, easy operability and robustness demonstrated by biosensor high reproducibility and both operational and storage stability, offering an opportunity to discover new inhibitors and optimize their therapeutic index.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"241 ","pages":"Pages 13-23"},"PeriodicalIF":4.2,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942677","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":"Optical pooled screening for the discovery of regulators of the alternative lengthening of telomeres pathway","authors":"Isabel Quintanilla ,&nbsp;Benura Azeroglu ,&nbsp;Md Abdul Kader Sagar ,&nbsp;Travis H. Stracker ,&nbsp;Eros Lazzerini Denchi ,&nbsp;Gianluca Pegoraro","doi":"10.1016/j.ymeth.2025.05.001","DOIUrl":"10.1016/j.ymeth.2025.05.001","url":null,"abstract":"<div><div>Telomere elongation is essential for the proliferation of cancer cells. Telomere length control is achieved either by the activation of the telomerase enzyme, or by the recombination-based Alternative Lengthening of Telomeres (ALT) pathway. ALT is active in about 10–15% of human cancers, but its molecular underpinnings remain poorly understood, preventing the discovery of potential novel therapeutic targets. Pooled CRISPR-based functional genomic screens enable the unbiased discovery of molecular factors involved in cancer biology. Recently, Optical Pooled Screens (OPS) have significantly extended the capabilities of pooled functional genomics screens to enable sensitive imaging-based readouts at the single cell level and large scale. To gain a better understanding of the ALT pathway, we developed a novel OPS assay that employs telomeric native DNA FISH (nFISH) as an optical quantitative readout to measure ALT activity. The assay uses standard OPS protocols for library preparation and sequencing. As a critical element, an optimized nFISH protocol is performed before in situ sequencing to maximize the assay performance. We show that the modified nFISH protocol faithfully detects changes in ALT activity upon CRISPR knock-out (KO) of the <em>FANCM</em> and <em>BLM</em> genes, which were previously implicated in ALT. Overall, the OPS-nFISH assay is a reliable method that can provide deep insights into the ALT pathway in a high-throughput format.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"241 ","pages":"Pages 1-12"},"PeriodicalIF":4.2,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143923043","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":"CRISPR revolution: Unleashing precision pathogen detection to safeguard public health and food safety","authors":"Jacob Tizhe Liberty ,&nbsp;Sabri Bromage ,&nbsp;Endurance Peter ,&nbsp;Olivia C. Ihedioha ,&nbsp;Fatemah B. Alsalman ,&nbsp;Tochukwu Samuel Odogwu","doi":"10.1016/j.ymeth.2025.04.018","DOIUrl":"10.1016/j.ymeth.2025.04.018","url":null,"abstract":"<div><div>Foodborne pathogens represent a significant challenge to global food safety, causing widespread illnesses and economic losses. The growing complexity of food supply chains and the emergence of antimicrobial resistance necessitate rapid, sensitive, and portable diagnostic tools. CRISPR technology has emerged as a transformative solution, offering unparalleled precision and adaptability in pathogen detection. This review explores CRISPR’s role in addressing critical gaps in traditional and modern diagnostic methods, emphasizing its advantages in sensitivity, specificity, and scalability. CRISPR-based diagnostics, such as Cas12 and Cas13 systems, enable rapid detection of bacterial and viral pathogens, as well as toxins and chemical hazards, directly in food matrices. Their integration with isothermal amplification techniques and portable biosensors enhances field applicability, making them ideal for decentralized and real-time testing. Additionally, CRISPR’s potential extends beyond food safety, contributing to public health efforts by monitoring antimicrobial resistance and supporting One Health frameworks. Despite these advancements, challenges remain, including issues with performance in complex food matrices, scalability, and regulatory barriers. This review highlights future directions, including AI integration for assay optimization, the development of universal CRISPR platforms, and the adoption of sustainable diagnostic solutions. By tackling these challenges, CRISPR has the potential to redefine global food safety standards and create a more resilient food system. Collaborative research and innovation will be critical to fully unlocking its transformative potential in food safety and public health.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"240 ","pages":"Pages 180-194"},"PeriodicalIF":4.2,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143907880","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":"Challenges, optimization strategies, and future horizons of advanced deep learning approaches for brain lesion segmentation","authors":"Asim Zaman ,&nbsp;Mazen M. Yassin ,&nbsp;Irfan Mehmud ,&nbsp;Anbo Cao ,&nbsp;Jiaxi Lu ,&nbsp;Haseeb Hassan ,&nbsp;Yan Kang","doi":"10.1016/j.ymeth.2025.04.016","DOIUrl":"10.1016/j.ymeth.2025.04.016","url":null,"abstract":"<div><div>Brain lesion segmentation is challenging in medical image analysis, aiming to delineate lesion regions precisely. Deep learning (DL) techniques have recently demonstrated promising results across various computer vision tasks, including semantic segmentation, object detection, and image classification. This paper offers an overview of recent DL algorithms for brain tumor and stroke segmentation, drawing on literature from 2021 to 2024. It highlights the strengths, limitations, current research challenges, and unexplored areas in imaging-based brain lesion classification based on insights from over 250 recent review papers. Techniques addressing difficulties like class imbalance and multi-modalities are presented. Optimization methods for improving performance regarding computational and structural complexity and processing speed are discussed. These include lightweight neural networks, multilayer architectures, and computationally efficient, highly accurate network designs. The paper also reviews generic and latest frameworks of different brain lesion detection techniques and highlights publicly available benchmark datasets and their issues. Furthermore, open research areas, application prospects, and future directions for DL-based brain lesion classification are discussed. Future directions include integrating neural architecture search methods with domain knowledge, predicting patient survival levels, and learning to separate brain lesions using patient statistics. To ensure patient privacy, future research is anticipated to explore privacy-preserving learning frameworks. Overall, the presented suggestions serve as a guideline for researchers and system designers involved in brain lesion detection and stroke segmentation tasks.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"239 ","pages":"Pages 140-168"},"PeriodicalIF":4.2,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900110","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":"Breaking barrier of binding buffer in colorimetric aptasensing of tetracycline in food fish using peroxidase mimic gold NanoZyme","authors":"Dhruba Jyoti Sarkar ,&nbsp;Ramij Raja ,&nbsp;V. Santhana Kumar ,&nbsp;Soumyadeb Bhattacharyya ,&nbsp;Souvik Pal ,&nbsp;Subhankar Mukherjee ,&nbsp;Basanta Kumar Das","doi":"10.1016/j.ymeth.2025.04.017","DOIUrl":"10.1016/j.ymeth.2025.04.017","url":null,"abstract":"<div><div>Tetracycline is extensively used in aquaculture as a therapeutic agent that needs to be monitored due to food safety concerns. Aptasensing has been revealed as a suitable diagnostic platform for tetracycline sensing in food matrix due to its quick, low cost and robust nature. But, the colorimetric aptasensing of tetracycline employing the peroxidase activity of gold nanoparticles (AuNPs) to 3,3,5,5-tetramethylbenzidine (TMB) was unsuitable until now owing to the aptamer-specific alkaline binding buffer. The present study developed a method with an optimized reaction protocol diminishing the inhibitory effect of binding buffer on the sensor probe (AuNPs-aptamer + TMB + H₂O₂). The overall peroxidase activity of the sensor probe was only inhibited by tetracycline through selective adsorption on the AuNPs-aptamer complex. The peroxidase inhibition percentage in the test range of 0.01 to 0.5 mg L^-1 tetracycline gave a logarithmic response (R², 0.99) with a detection limit of 0.017 mg L^-1 which is less than the prescribed limit (0.1 mg L^-1) set by EU and FSSAI. The developed sensing system in fish muscle showed high recovery (111–115 %) with great potential for rapid detection of tetracycline in fish muscle.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"240 ","pages":"Pages 145-153"},"PeriodicalIF":4.2,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891850","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":"Drug-target interaction prediction based on metapaths and simplified neighbor aggregation","authors":"Di Yu ,&nbsp;Xinyu Yang ,&nbsp;Yifan Shang ,&nbsp;Sisi Yuan ,&nbsp;Yuansheng Liu ,&nbsp;Yiping Liu","doi":"10.1016/j.ymeth.2025.04.012","DOIUrl":"10.1016/j.ymeth.2025.04.012","url":null,"abstract":"<div><div>Drug-target interaction (DTI) prediction is critical in drug repositioning and discovery. In current metapath-based prediction methods, attention mechanisms are often used to differentiate the importance of various neighbors, enhancing the model's expressiveness. However, in biological networks with small-scale imbalanced data, attention mechanisms are prone to interference from noise and missing data, leading to instability in weight learning, reduced efficiency, and an increased risk of overfitting. To address these issues, we propose the use of average aggregation to mitigate noise, simplify model complexity, and improve stability. Specifically, we introduce a simplified mean aggregation method for DTI prediction. This approach uses average aggregation, effectively reducing noise interference, lowering model complexity, and preventing overfitting, making it especially suitable for current biological networks. Extensive testing on three heterogeneous biological datasets shows that SNADTI outperforms 12 leading methods across two evaluation metrics, significantly reducing training time and validating its effectiveness in DTI prediction. Complexity analysis reveals that our method offers a substantial computational speed advantage over other methods on the same dataset, highlighting its enhanced efficiency. Experimental results demonstrate that SNADTI excels in prediction accuracy, stability, and reproducibility, confirming its practicality and effectiveness in DTI prediction.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"240 ","pages":"Pages 154-164"},"PeriodicalIF":4.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899247","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":"KRN-DTI: Towards accurate drug-target interaction prediction with Kolmogorov-Arnold and residual networks","authors":"Zhen Li ,&nbsp;Juyuan Huang ,&nbsp;Xinxin Liu ,&nbsp;Peng Xu ,&nbsp;Xinwen Shen ,&nbsp;Chu Pan ,&nbsp;Wei Zhang ,&nbsp;Wenbin Liu ,&nbsp;Henry Han","doi":"10.1016/j.ymeth.2025.04.009","DOIUrl":"10.1016/j.ymeth.2025.04.009","url":null,"abstract":"<div><div>Predicting drug-target interactions (DTIs) accurately is essential in the field of drug discovery. Recently, artificial intelligence (AI) technologies, especially graph convolutional networks (GCNs), have been developed to tackle this challenge. However, as the number of GCN layers increases, models may lose critical information due to excessive smoothing. Moreover, these methods often lack interpretability and are dependent on specific datasets, which limits their generalizability. Consequently, this study introduces a novel method, KRN-DTI, which employs interpretable GCN technology to predict DTIs based on a drug-target heterogeneous network. The method uses GCN technology to identify potential DTIs by leveraging known interactions and dynamically adjusting the weights, thereby enhancing the model's interpretability. Additionally, residual connection technology is employed to integrate GNN outputs, mitigating the over-smoothing issue. Furthermore, the model's interpretability is enhanced by adaptively adjusting weights using Kolmogorov–Arnold Networks (KAN) and attention mechanisms. Experimental results show that KRN-DTI outperforms several advanced computational methods on the benchmark dataset. Case studies further highlight the effectiveness of KRN-DTI in predicting potential DTIs, showcasing its potential for real-world applications in drug discovery. Our code and data are publicly accessible at: <span><span>https://github.com/lizhen5000/KRN-DTI.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"240 ","pages":"Pages 137-144"},"PeriodicalIF":4.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887765","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":"Enabling high-resolution diagnostic oral confocal laser endomicroscopy in mice","authors":"Antonio Celentano ,&nbsp;James A. Rickard ,&nbsp;Jun Low ,&nbsp;Natasha Silke ,&nbsp;Ali Ibrahim Mohammed ,&nbsp;Elham Moslemi ,&nbsp;Rishi S. Ramani ,&nbsp;Paula Demetrio De Souza Franca ,&nbsp;Thomas Reiner ,&nbsp;Michael J. McCullough ,&nbsp;Tami Yap ,&nbsp;John Silke ,&nbsp;Lorraine A. O’Reilly","doi":"10.1016/j.ymeth.2025.04.015","DOIUrl":"10.1016/j.ymeth.2025.04.015","url":null,"abstract":"<div><div>Therapeutic prevention of oral squamous cell carcinoma (OSCC) will avoid significant morbidity and mortality. To observe and measure the <em>in vivo</em> efficacy of therapeutic challenges, microscopic-level diagnosis without animal sacrifice is required. This study introduces a refined diagnostic methodology for non-invasive cellular-level imaging for diagnosis of micro-lesions by utilizing high-resolution scanning-fibre confocal laser endomicroscopy (ViewnVivo) with topical fluorescence imaging agents. We detail the development and standardization of imaging protocols using a fluorescent, cell-permeable cancer-targeting agent (PARPi-FL) as a cancer-targeting agent and a pan-cytoarchitectural (acriflavine) agent in a pre-clinical murine 4-NQO induced OSCC model. We provide comprehensive methodology for the <em>in vivo</em> identification of the progressive stages of oral carcinogenesis from microscopic lesions, supported by an annotated signature guide correlating with conventional histopathology. Our findings demonstrate that <em>in vivo</em> CLE imaging with both PARPi-FL and acriflavine clearly distinguishes between histologically normal and pathological oral tissue. Tissues with histologic dysplasia and carcinoma demonstrated PARPi-FL positivity and an aberrant nuclear staining pattern with acriflavine, compared to the regularly spaced nuclear staining of normal nuclei. Crucially, this methodology detects microscopic changes not visible to the naked eye, but histologically abnormal. Our observation model of progressive oral carcinogenesis has the potential to accelerate standardised interrogation of early molecular diagnostic applications and novel therapeutic efficacy, whilst reducing the need for animal sacrifice. This will result in faster validated translation to human applications, advancing effective early oral cancer detection and prevention.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"239 ","pages":"Pages 169-181"},"PeriodicalIF":4.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943552","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":"Exploring species taxonomic kingdom using information entropy and nucleotide compositional features of coding sequences based on machine learning methods","authors":"Sebu Aboma Temesgen ,&nbsp;Basharat Ahmad ,&nbsp;Bakanina Kissanga Grace-Mercure ,&nbsp;Minghao Liu ,&nbsp;Li Liu ,&nbsp;Hao Lin ,&nbsp;Kejun Deng","doi":"10.1016/j.ymeth.2025.03.023","DOIUrl":"10.1016/j.ymeth.2025.03.023","url":null,"abstract":"<div><div>The flow of genetic information from DNA to protein is governed by the central dogma of molecular biology. Genetic drift and mutations usually lead to changes in DNA composition, thereby affecting the coding sequences (CDS) that encode functional proteins. Analyzing the nucleotide distribution in the coding regions of species is crucial for understanding their evolution. In this study, we applied Markov processes to analyze codon formation in 37,031,061 CDSs across 3,735 species genomes, spanning viruses, archaea, bacteria, and eukaryotes, to explore compositional changes. Our results revealed species preferences for different nucleotides. Information entropies and Markov information densities show that eukaryotes exhibit higher redundancy, followed by viruses, suggesting more gene duplication in eukaryotes and high mutation rates in viruses. Evolutionary trends showed an increase in information entropy and a decrease in Markov entropy, with negative correlations between first- and second-order Markov information densities. Furthermore, uniform manifold approximation and projection (UMAP) was used to reduce information redundancy for revealing unique evolutionary patterns in species classification. The machine learning methods demonstrated excellent performance in species classification accuracy, providing profound insights into CDS evolution and protein synthesis.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"240 ","pages":"Pages 165-179"},"PeriodicalIF":4.2,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899248","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}