Methods最新文献

{"title":"A novel methodology for mapping interstitial fluid dynamics in murine brain tumors using DCE-MRI","authors":"Cora Carman-Esparza ,&nbsp;Kathryn Kingsmore ,&nbsp;Andrea Vaccari ,&nbsp;Skylar Davis ,&nbsp;Jessica Cunningham ,&nbsp;Maosen Wang ,&nbsp;Jennifer Munson","doi":"10.1016/j.ymeth.2024.09.008","DOIUrl":"10.1016/j.ymeth.2024.09.008","url":null,"abstract":"<div><div>We present a comprehensive methodology for measuring heterogeneous interstitial fluid flow in murine brain tumors using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) coupled with the computational tool, <em>Lymph4D</em>. This four-part protocol encompasses glioma cell preparation, tumor inoculation, MRI imaging protocol, and histological verification using Evans Blue. While conventional DCE-MRI analysis primarily focuses on vascular perfusion, our methods reveal untapped potential to extract crucial information about interstitial fluid dynamics, including directions, velocities, and diffusion coefficients. This methodology extends beyond glioma research, with applicability to conditions routinely imaged with DCE-MRI, thereby offering a versatile tool for investigating interstitial fluid dynamics across a wide range of diseases and conditions. Our methodology holds promise for accelerating discoveries and advancements in biomedical research, ultimately enhancing diagnostic and therapeutic strategies for a wide range of diseases and conditions.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"231 ","pages":"Pages 78-93"},"PeriodicalIF":4.2,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142278228","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":"Digital Intervention for behaviouR changE and Chronic disease prevenTION (DIRECTION): Study protocol for a randomized controlled trial of a web-based platform integrating nutrition, physical activity, and mindfulness for individuals with obesity","authors":"Camila E. Orsso ,&nbsp;Teresita Gormaz ,&nbsp;Sabina Valentine ,&nbsp;Claire F. Trottier ,&nbsp;Iasmin Matias de Sousa ,&nbsp;Martin Ferguson-Pell ,&nbsp;Steven T. Johnson ,&nbsp;Amy A. Kirkham ,&nbsp;Douglas Klein ,&nbsp;Nathanial Maeda ,&nbsp;João F. Mota ,&nbsp;Sarah E. Neil-Sztramko ,&nbsp;Maira Quintanilha ,&nbsp;Bukola Oladunni Salami ,&nbsp;Carla M. Prado","doi":"10.1016/j.ymeth.2024.09.009","DOIUrl":"10.1016/j.ymeth.2024.09.009","url":null,"abstract":"<div><p>Excess body weight, suboptimal diet, physical inactivity, alcohol consumption, sleep disruption, and elevated stress are modifiable risk factors associated with the development of chronic diseases. Digital behavioural interventions targeting these factors have shown promise in improving health and reducing chronic disease risk. The <em>Digital Intervention for behaviouR changE and Chronic disease prevenTION</em> (<em>DIRECTION</em>) study is a parallel group, two-arm, randomized controlled trial evaluating the effects of adding healthcare professional guidance and peer support via group-based sessions to a web-based wellness platform (experimental group, n = 90) compared to a self-guided use of the platform (active control group, n = 90) among individuals with a body mass index (BMI) of 30 to &lt;35 kg/m² and aged 40–65 years. Obesity is defined by a high BMI. The web-based wellness platform employed in this study is My Viva Plan (MVP)®, which holistically integrates nutrition, physical activity, and mindfulness programs. Over 16 weeks, the experimental group uses the web-based wellness platform daily and engages in weekly online support group sessions. The active control group exclusively uses the web-based wellness platform daily. Assessments are conducted at baseline and weeks 8 and 16. The primary outcome is between-group difference in weight loss (kg) at week 16, and secondary outcomes are BMI, percent weight change, proportion of participants achieving 5% or more weight loss, dietary intake, physical activity, alcohol consumption, sleep, and stress across the study. A web-based wellness platform may be a scalable approach to promote behavioural changes that positively impact health. This study will inform the development and implementation of interventions using web-based wellness platforms and personalized digital interventions to improve health outcomes and reduce chronic disease risk among individuals with obesity.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"231 ","pages":"Pages 45-54"},"PeriodicalIF":4.2,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S104620232400207X/pdfft?md5=787b2ff333611543fa5dd8dde7ef9999&pid=1-s2.0-S104620232400207X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241847","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":"Gluconeogenesis unraveled: A proteomic Odyssey with machine learning","authors":"Seher Ansar Khawaja ,&nbsp;Fahad Alturise ,&nbsp;Tamim Alkhalifah ,&nbsp;Sher Afzal Khan ,&nbsp;Yaser Daanial Khan","doi":"10.1016/j.ymeth.2024.09.002","DOIUrl":"10.1016/j.ymeth.2024.09.002","url":null,"abstract":"<div><div>The metabolic pathway known as gluconeogenesis, which produces glucose from non-carbohydrate substrates, is essential for maintaining balanced blood sugar levels while fasting. It's extremely important to anticipate gluconeogenesis rates accurately to recognize metabolic disorders and create efficient treatment strategies. The implementation of deep learning and machine learning methods to forecast complex biological processes has been gaining popularity in recent years. The recognition of both the regulation of the pathway and possible therapeutic applications of proteins depends on accurate identification associated with their gluconeogenesis patterns. This article analyzes the uses of machine learning and deep learning models, to predict gluconeogenesis efficiency. The study also discusses the challenges that come with restricted data availability and model interpretability, as well as possible applications in personalized healthcare, metabolic disease treatment, and the discovery of drugs. The predictor utilizes statistics moments on the structures of gluconeogenesis and their enzymes, while Random Forest is utilized as a classifier to ensure the accuracy of this model in identifying the best outcomes. The method was validated utilizing the independent test, self-consistency, 10k fold cross-validations, and jackknife test which achieved 92.33 %, 91.87%, 87.88%, and 87.02%. An accurate prediction of gluconeogenesis has significant implications for understanding metabolic disorders and developing targeted therapies. This study contributes to the rising field of predictive biology by mixing algorithms for deep learning, and machine learning, with metabolic pathways.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"232 ","pages":"Pages 29-42"},"PeriodicalIF":4.2,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142278229","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":"DeepDBS: Identification of DNA-binding sites in protein sequences by using deep representations and random forest","authors":"Yaser Daanial Khan ,&nbsp;Tamim Alkhalifah ,&nbsp;Fahad Alturise ,&nbsp;Ahmad Hassan Butt","doi":"10.1016/j.ymeth.2024.09.004","DOIUrl":"10.1016/j.ymeth.2024.09.004","url":null,"abstract":"<div><p>Interactions of biological molecules in organisms are considered to be primary factors for the lifecycle of that organism. Various important biological functions are dependent on such interactions and among different kinds of interactions, the protein DNA interactions are very important for the processes of transcription, regulation of gene expression, DNA repairing and packaging. Thus, keeping the knowledge of such interactions and the sites of those interactions is necessary to study the mechanism of various biological processes. As experimental identification through biological assays is quite resource-demanding, costly and error-prone, scientists opt for the computational methods for efficient and accurate identification of such DNA-protein interaction sites. Thus, herein, we propose a novel and accurate method namely DeepDBS for the identification of DNA-binding sites in proteins, using primary amino acid sequences of proteins under study. From protein sequences, deep representations were computed through a one-dimensional convolution neural network (1D-CNN), recurrent neural network (RNN) and long short-term memory (LSTM) network and were further used to train a Random Forest classifier. Random Forest with LSTM-based features outperformed the other models, as well as the existing state-of-the-art methods with an accuracy score of 0.99 for self-consistency test, 10-fold cross-validation, 5-fold cross-validation, and jackknife validation while 0.92 for independent dataset testing. It is concluded based on results that the DeepDBS can help accurate and efficient identification of DNA binding sites (DBS) in proteins.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"231 ","pages":"Pages 26-36"},"PeriodicalIF":4.2,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241846","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":"New methods in biomolecular nuclear magnetic resonance spectroscopy II","authors":"T. Michael Sabo","doi":"10.1016/j.ymeth.2024.09.006","DOIUrl":"10.1016/j.ymeth.2024.09.006","url":null,"abstract":"","PeriodicalId":390,"journal":{"name":"Methods","volume":"231 ","pages":"Pages 57-60"},"PeriodicalIF":4.2,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142253897","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":"Artificial intelligence and computer-aided drug discovery: Methods development and application","authors":"Haiping Zhang,&nbsp;Yanjie Wei,&nbsp;Konda Mani Saravanan","doi":"10.1016/j.ymeth.2024.09.005","DOIUrl":"10.1016/j.ymeth.2024.09.005","url":null,"abstract":"","PeriodicalId":390,"journal":{"name":"Methods","volume":"231 ","pages":"Pages 55-56"},"PeriodicalIF":4.2,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241767","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 the potential of epigenetic clocks in aging research","authors":"Yuduo Hao ,&nbsp;Kaiyuan Han ,&nbsp;Ting Wang ,&nbsp;Junwen Yu ,&nbsp;Hui Ding ,&nbsp;Fuying Dao","doi":"10.1016/j.ymeth.2024.09.001","DOIUrl":"10.1016/j.ymeth.2024.09.001","url":null,"abstract":"<div><p>The process of aging is a notable risk factor for numerous age-related illnesses. Hence, a reliable technique for evaluating biological age or the pace of aging is crucial for understanding the aging process and its influence on the progression of disease. Epigenetic alterations are recognized as a prominent biomarker of aging, and epigenetic clocks formulated on this basis have been shown to provide precise estimations of chronological age. Extensive research has validated the effectiveness of epigenetic clocks in determining aging rates, identifying risk factors for aging, evaluating the impact of anti-aging interventions, and predicting the emergence of age-related diseases. This review provides a detailed overview of the theoretical principles underlying the development of epigenetic clocks and their utility in aging research. Furthermore, it explores the existing obstacles and possibilities linked to epigenetic clocks and proposes potential avenues for future studies in this field.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"231 ","pages":"Pages 37-44"},"PeriodicalIF":4.2,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196501","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":"Biomedical event causal relation extraction with deep knowledge fusion and Roberta-based data augmentation","authors":"Lishuang Li,&nbsp;Yi Xiang,&nbsp;Jing Hao","doi":"10.1016/j.ymeth.2024.08.007","DOIUrl":"10.1016/j.ymeth.2024.08.007","url":null,"abstract":"<div><p>Biomedical event causal relation extraction (BECRE), as a subtask of biomedical information extraction, aims to extract event causal relation facts from unstructured biomedical texts and plays an essential role in many downstream tasks. The existing works have two main problems: i) Only shallow features are limited in helping the model establish potential relationships between biomedical events. ii) Using the traditional oversampling method to solve the data imbalance problem of the BECRE tasks ignores the requirements for data diversifying. This paper proposes a novel biomedical event causal relation extraction method to solve the above problems using deep knowledge fusion and Roberta-based data augmentation. To address the first problem, we fuse deep knowledge, including structural event representation and entity relation path, for establishing potential semantic connections between biomedical events. We use the Graph Convolutional Neural network (GCN) and the predicated tensor model to acquire structural event representation, and entity relation paths are encoded based on the external knowledge bases (GTD, CDR, CHR, GDA and UMLS). We introduce the triplet attention mechanism to fuse structural event representation and entity relation path information. Besides, this paper proposes the Roberta-based data augmentation method to address the second problem, some words of biomedical text, except biomedical events, are masked proportionally and randomly, and then pre-trained Roberta generates data instances for the imbalance BECRE dataset. Extensive experimental results on Hahn-Powell's and BioCause datasets confirm that the proposed method achieves state-of-the-art performance compared to current advances.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"231 ","pages":"Pages 8-14"},"PeriodicalIF":4.2,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142144767","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":"Godanti bhasma (anhydrous CaSO4) induces massive cytoplasmic vacuolation in mammalian cells: A model for phagocytosis assay","authors":"Subrata K. Das ,&nbsp;Alpana Joshi ,&nbsp;Laxmi Bisht ,&nbsp;Vishakha Goswami ,&nbsp;Abul Faiz ,&nbsp;Gaurav Dutt ,&nbsp;Shiva Sharma","doi":"10.1016/j.ymeth.2024.08.006","DOIUrl":"10.1016/j.ymeth.2024.08.006","url":null,"abstract":"<div><p>Phagocytosis is an essential physiological mechanism; its impairment is associated with many diseases. A highly smart particle is required for understanding detailed sequential cellular events in phagocytosis. Recently, we identified an Indian traditional medicine named Godanti Bhasma (GB), a bioactive calcium sulfate particle prepared by thermo-transformation of<!--> <!-->gypsum. Thermal processing of the gypsum transforms its native physicochemical properties by removing water molecules into the anhydrous GB, which was confirmed by Raman and FT-IR spectroscopy. GB particle showed a 0.5–5 µm size range and a neutral surface charge. Exposure of mammalian cells to GB particles showed a rapid cellular uptake through phagocytosis and induced massive cytoplasmic vacuolation in cells. Interestingly, no cellular uptake and cytoplasmic vacuolation were observed with the parent gypsum particle. The presence of the GB particles in intra-vacuolar space was confirmed using FESEM coupled with EDX. Flow cytometry analysis and live tracking of GB-treated cells showed particle internalization, vacuole formation, particle dissolution, and later vacuolar turnover. Quantification of GB-induced vacuolation was done using neutral red uptake assay in cells. Treatment of lysosomal inhibitors (BFA1 or CQ) with GB could not induce vacuolation, suggesting the requirement of an acidic environment for the vacuolation. In the mimicking experiment, GB particle dissolution in acidic cell-free solution suggested that degradation of GB occurs by acidic pH inside the cell vacuole. Vacuole formation generally accompanies with cell death, whereas GB-induced massive vacuolation does not cause cell death. Moreover, the cell divides and proliferates with the vacuolar process, intra-vacuolar cargo degradation, and eventually vacuolar turnover. Taken together, the sequential cellular events in this study suggest that GB can be used as a smart particle for phagocytosis assay development in animal cells.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"230 ","pages":"Pages 158-168"},"PeriodicalIF":4.2,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142096960","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":"MFF-DTA: Multi-scale feature fusion for drug-target affinity prediction","authors":"Xiwei Tang ,&nbsp;Wanjun Ma ,&nbsp;Mengyun Yang ,&nbsp;Wenjun Li","doi":"10.1016/j.ymeth.2024.08.008","DOIUrl":"10.1016/j.ymeth.2024.08.008","url":null,"abstract":"<div><p>Accurately predicting drug-target affinity is crucial in expediting the discovery and development of new drugs, which is a complex and risky process. Identifying these interactions not only aids in screening potential compounds but also guides further optimization. To address this, we propose a multi-perspective feature fusion model, MFF-DTA, which integrates chemical structure, biological sequence, and other data to comprehensively capture drug-target affinity features. The MFF-DTA model incorporates multiple feature learning components, each of which is capable of extracting drug molecular features and protein target information, respectively. These components are able to obtain key information from both global and local perspectives. Then, these features from different perspectives are efficiently combined using specific splicing strategies to create a comprehensive representation. Finally, the model uses the fused features to predict drug-target affinity. Comparative experiments show that MFF-DTA performs optimally on the Davis and KIBA data sets. Ablation experiments demonstrate that removing specific components results in the loss of unique information, thus confirming the effectiveness of the MFF-DTA design. Improvements in DTA prediction methods will decrease costs and time in drug development, enhancing industry efficiency and ultimately benefiting patients.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"231 ","pages":"Pages 1-7"},"PeriodicalIF":4.2,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1046202324001890/pdfft?md5=a691264b50021b10f091a9d3d57ce863&pid=1-s2.0-S1046202324001890-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142103007","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}