IF 4.2 3区生物学

Methods Pub Date : 2024-10-03 DOI: 10.1016/j.ymeth.2024.10.001

Santosh Bhujbal, Ilva D. Rupenthal, Priyanka Agarwal

{"title":"Development and validation of a stability-indicating HPLC method for assay of tonabersat in pharmaceutical formulations","authors":"Santosh Bhujbal, Ilva D. Rupenthal, Priyanka Agarwal","doi":"10.1016/j.ymeth.2024.10.001","DOIUrl":"10.1016/j.ymeth.2024.10.001","url":null,"abstract":"<div><div>A stability-indicating reversed-phase high-performance liquid chromatography (RP-HPLC) method was developed to assay tonabersat and assess its stability in pharmaceutical formulations. Chromatographic separation was achieved using a Kinetex® C18 column (2.6 µm, 150 x 3 mm, 100 Å) at 50 °C, with a 20 µL injection volume. A linear gradient of acetonitrile in water (5 – 33.5 %) was applied for 1 min, followed by a gradual increase to 100 % over 26 min at a flow rate of 0.5 mL/min. Tonabersat and its degradation products were detected at 275 nm and 210 nm, respectively. The optimized method was used to evaluate the stability of tonabersat in lipid-based pharmaceutical formulations at 5 ± 3 °C, 25 ± 2°C/60 ± 5 % RH, and 40 ± 2 °C/75 ± 5 % RH over 3 months. The method was validated as per ICH guidelines and demonstrated linearity in the range of 5 – 200 µg/mL (R<sup>2</sup> = 0.99994) with good accuracy (98.25 – 101.58 % recovery) and precision (% RSD < 2.5 %). The limits of detection and quantitation were 0.8 µg/mL and 5 µg/mL, respectively. Forced degradation studies showed significant degradation on exposure to alkaline (90.33 ± 0.80 %), acidic (70.60 ± 1.57 %), and oxidative stress (33.95 ± 0.69 %) at 70 °C, but no degradation was observed on exposure to thermal or photolytic stress. No chemical degradation was observed in either formulation on storage. Thus, the method was sensitive, specific, and suitable for stability testing of tonabersat in pharmaceutical formulations.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"231 ","pages":"Pages 178-185"},"PeriodicalIF":4.2,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142378942","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}

引用次数: 0

Addressing heterogeneous sensitivity in biomarker screening with application in NanoString nCounter data 应用 NanoString nCounter 数据解决生物标记物筛选中的异质性灵敏度问题。

IF 4.2 3区生物学

Methods Pub Date : 2024-10-01 DOI: 10.1016/j.ymeth.2024.09.007

Chang Yu, Zhijin Wu

引用次数: 0

Temperature-jump microscopy and interaction of Hsp70 heat shock protein with a client protein in vivo 温度跃迁显微镜和 Hsp70 热休克蛋白与体内客户蛋白的相互作用。

IF 4.2 3区生物学

Methods Pub Date : 2024-10-01 DOI: 10.1016/j.ymeth.2024.09.019

Aniket Ravan , Samuel Procopio , Yann R. Chemla , Martin Gruebele

引用次数: 0

Identification of target genes co-regulated by four key histone modifications of five key regions in hepatocellular carcinoma 确定肝细胞癌中五个关键区域的四个关键组蛋白修饰共同调控的靶基因。

IF 4.2 3区生物学

Methods Pub Date : 2024-09-28 DOI: 10.1016/j.ymeth.2024.09.017

Yu-Xian Liu , Jia-Le Song , Xiao-Ming Li , Hao Lin , Yan-Ni Cao

{"title":"Identification of target genes co-regulated by four key histone modifications of five key regions in hepatocellular carcinoma","authors":"Yu-Xian Liu , Jia-Le Song , Xiao-Ming Li , Hao Lin , Yan-Ni Cao","doi":"10.1016/j.ymeth.2024.09.017","DOIUrl":"10.1016/j.ymeth.2024.09.017","url":null,"abstract":"<div><div>Hepatocellular carcinoma (HCC) is a cancer with high morbidity and mortality. Studies have shown that histone modification plays an important regulatory role in the occurrence and development of HCC. However, the specific regulatory effects of histone modifications on gene expression in HCC are still unclear. This study focuses on HepG2 cell lines and hepatocyte cell lines. First, the distribution of histone modification signals in the two cell lines was calculated and analyzed. Then, using the random forest algorithm, we analyzed the effects of different histone modifications and their modified regions on gene expression in the two cell lines, four key histone modifications (H3K36me3, H3K4me3, H3K79me2, and H3K9ac) and five key regions that co-regulate gene expression were obtained. Subsequently, target genes regulated by key histone modifications in key regions were screened. Combined with clinical data, Cox regression analysis and Kaplan-Meier survival analysis were performed on the target genes, and four key target genes (<em>CBX2</em>, <em>CEBPZOS</em>, <em>LDHA</em>, and <em>UMPS</em>) related to prognosis were identified. Finally, through immune infiltration analysis and drug sensitivity analysis of key target genes, the potential role of key target genes in HCC was confirmed. Our results provide a theoretical basis for exploring the occurrence of HCC and propose potential biomarkers associated with histone modifications, which may be potential drug targets for the clinical treatment of HCC.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"231 ","pages":"Pages 165-177"},"PeriodicalIF":4.2,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142338564","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}

引用次数: 0

The application of advanced deep learning in biomedical graph analysis 高级深度学习在生物医学图谱分析中的应用。

IF 4.2 3区生物学

Methods Pub Date : 2024-09-27 DOI: 10.1016/j.ymeth.2024.09.013

Wen Zhang , Shikui Tu , Xiaopeng Zhu , Shichao Liu

引用次数: 0

Model-agnostic confidence measurement for aggregating multimodal ensemble models in automatic diagnostic systems 在自动诊断系统中聚合多模态集合模型的模型诊断可信度测量。

IF 4.2 3区生物学

Methods Pub Date : 2024-09-26 DOI: 10.1016/j.ymeth.2024.09.012

Chan-Yang Ju, Dong-Ho Lee

{"title":"Model-agnostic confidence measurement for aggregating multimodal ensemble models in automatic diagnostic systems","authors":"Chan-Yang Ju, Dong-Ho Lee","doi":"10.1016/j.ymeth.2024.09.012","DOIUrl":"10.1016/j.ymeth.2024.09.012","url":null,"abstract":"<div><div>Automatic diagnostic systems (ADSs) have been garnering increased attention because they can alleviate the workload of clinicians by assisting in diagnosis and offering low-cost access to healthcare for people in medically underserved areas. ADS can suggest potential diseases by analyzing a patient's self-report. Previous research on ADS has leveraged diagnostic case data from various patients and medical knowledge to diagnose diseases, with multimodal ensemble methods proving particularly effective. However, the existing multimodal ensemble method combines the probabilities of different models in the aggregating process, which can not properly combine the probabilities that are produced by different criteria. To address these issues, we propose an effective aggregation framework for multimodal ensembles that can properly aggregate model-agnostic confidence scores and predictions from each model. Our framework transforms probability scores from different criteria into unified aggregation rule-based scores and reflects the gap between the probabilities that may be blurred in the aggregation process through the confidence score. In particular, The proposed confidence measurement method employs a post-analysis approach with the developed model or algorithm, making it adaptable in a model-agnostic manner and suitable for multimodal ensemble learning that utilizes heterogeneous prediction results. Our experimental results demonstrate that our framework outperforms existing approaches by more effectively leveraging the strengths of each ensemble member.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"231 ","pages":"Pages 103-114"},"PeriodicalIF":4.2,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142338565","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}

引用次数: 0

Cancer subtype identification by multi-omics clustering based on interpretable feature and latent subspace learning 基于可解释特征和潜在子空间学习的多组学聚类癌症亚型识别。

IF 4.2 3区生物学

Methods Pub Date : 2024-09-24 DOI: 10.1016/j.ymeth.2024.09.014

Tianyi Shi, Xiucai Ye, Dong Huang, Tetsuya Sakurai

{"title":"Cancer subtype identification by multi-omics clustering based on interpretable feature and latent subspace learning","authors":"Tianyi Shi, Xiucai Ye, Dong Huang, Tetsuya Sakurai","doi":"10.1016/j.ymeth.2024.09.014","DOIUrl":"10.1016/j.ymeth.2024.09.014","url":null,"abstract":"<div><div>In recent years, multi-omics clustering has become a powerful tool in cancer research, offering a comprehensive perspective on the diverse molecular characteristics inherent to various cancer subtypes. However, most existing multi-omics clustering methods directly integrate heterogeneous features from different omics, which may struggle to deal with the noise or redundancy of multi-omics data and lead to poor clustering results. Therefore, we propose a novel multi-omics clustering method to extract interpretable and discriminative features from various omics before data integration. The clinical information is used to supervise the process of feature extraction based on SHAP (SHapley Additive exPlanation) values. Singular value decomposition (SVD) is then applied to integrate the extracted features of different omics by constructing a latent subspace. Finally, we utilize shared nearest neighbor-based spectral clustering on the latent representation to obtain the clustering result. The proposed method is evaluated on several cancer datasets across three levels of omics, in comparison to several state-of-the-art multi-omics clustering methods. The comparison results demonstrate the superior performance of the proposed method in multi-omics data analysis for cancer subtyping. Additionally, experiments reveal the efficacy of utilizing clinical information based on SHAP values for feature extraction, enhancing the performance of clustering analyses. Moreover, enrichment analysis of the identified gene signatures in different subtypes is also performed to further demonstrate the effectiveness of the proposed method.</div><div><strong>Availability:</strong> The proposed method can be freely accessible at <span><span>https://github.com/Tianyi-Shi-Tsukuba/Multi-omics-clustering-based-on-SHAP</span><svg><path></path></svg></span>. Data will be made available on request.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"231 ","pages":"Pages 144-153"},"PeriodicalIF":4.2,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142338563","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}

引用次数: 0

Production, analysis, and safety assessment of a soil and plant-based natural material with microbiome- and immune-modulatory effects 一种以土壤和植物为基础、具有微生物和免疫调节作用的天然材料的生产、分析和安全评估。

IF 4.2 3区生物学

Methods Pub Date : 2024-09-19 DOI: 10.1016/j.ymeth.2024.09.011

Anirudra Parajuli , Iida Mäkelä , Marja I. Roslund , Emma Ringqvist , Juulia Manninen , Yan Sun , Noora Nurminen , Sami Oikarinen , Olli H. Laitinen , Heikki Hyöty , Malin Flodström-Tullberg , Aki Sinkkonen

{"title":"Production, analysis, and safety assessment of a soil and plant-based natural material with microbiome- and immune-modulatory effects","authors":"Anirudra Parajuli , Iida Mäkelä , Marja I. Roslund , Emma Ringqvist , Juulia Manninen , Yan Sun , Noora Nurminen , Sami Oikarinen , Olli H. Laitinen , Heikki Hyöty , Malin Flodström-Tullberg , Aki Sinkkonen","doi":"10.1016/j.ymeth.2024.09.011","DOIUrl":"10.1016/j.ymeth.2024.09.011","url":null,"abstract":"<div><div>It has been suggested that reduced contact with microbiota from the natural environment contributes to the rising incidence of immune-mediated inflammatory disorders (IMIDs) in western, highly urbanized societies. In line with this, we have previously shown that exposure to environmental microbiota in the form of a blend comprising of soil and plant-based material (biodiversity blend; BDB) enhances the diversity of human commensal microbiota and promotes immunoregulation that may be associated with a reduced risk for IMIDs. To provide a framework for future preclinical studies and clinical trials, this study describes how the preparation of BDB was standardized, its microbial content analysed and safety assessments performed. Multiple batches of BDB were manufactured and microbial composition analysed using 16S rRNA gene sequencing. We observed a consistently high alpha diversity and relative abundance of bacteria normally found in soil and vegetation. We also found that inactivation of BDB by autoclaving effectively inactivates human and murine bacteria, viruses and parasites. Finally, we demonstrate that experimental mice prone to develop IMIDs (non-obese diabetic, NOD, mouse model) can be exposed to BDB without causing adverse effects on animal health and welfare. Our study provides insights into a potentially safe, sustainable, and cost-effective approach for simulating exposure to natural microbiota, which could have substantial impacts on health and socio-economic factors.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"231 ","pages":"Pages 94-102"},"PeriodicalIF":4.2,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1046202324002093/pdfft?md5=bd4f9846358c697b0dd4799224c609ba&pid=1-s2.0-S1046202324002093-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142278230","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}

引用次数: 0

GATDE: A graph attention network with diffusion-enhanced protein-protein interaction for cancer classification GATDE：用于癌症分类的具有扩散增强蛋白质-蛋白质相互作用的图注意网络

IF 4.2 3区生物学

Methods Pub Date : 2024-09-18 DOI: 10.1016/j.ymeth.2024.09.003

Ruike Song , Xiaofeng Wang , Jiahao Zhang , Shengquan Chen , Jianyu Zhou

引用次数: 0

AtML: An Arabidopsis thaliana root cell identity recognition tool for medicinal ingredient accumulation AtML：拟南芥根细胞身份识别工具，用于药用成分的积累。

IF 4.2 3区生物学

Methods Pub Date : 2024-09-16 DOI: 10.1016/j.ymeth.2024.09.010

Shicong Yu , Lijia Liu , Hao Wang , Shen Yan , Shuqin Zheng , Jing Ning , Ruxian Luo , Xiangzheng Fu , Xiaoshu Deng

{"title":"AtML: An Arabidopsis thaliana root cell identity recognition tool for medicinal ingredient accumulation","authors":"Shicong Yu , Lijia Liu , Hao Wang , Shen Yan , Shuqin Zheng , Jing Ning , Ruxian Luo , Xiangzheng Fu , Xiaoshu Deng","doi":"10.1016/j.ymeth.2024.09.010","DOIUrl":"10.1016/j.ymeth.2024.09.010","url":null,"abstract":"<div><p><em>Arabidopsis thaliana</em> synthesizes various medicinal compounds, and serves as a model plant for medicinal plant research. Single-cell transcriptomics technologies are essential for understanding the developmental trajectory of plant roots, facilitating the analysis of synthesis and accumulation patterns of medicinal compounds in different cell subpopulations. Although methods for interpreting single-cell transcriptomics data are rapidly advancing in Arabidopsis, challenges remain in precisely annotating cell identity due to the lack of marker genes for certain cell types. In this work, we trained a machine learning system, AtML, using sequencing datasets from six cell subpopulations, comprising a total of 6000 cells, to predict Arabidopsis root cell stages and identify biomarkers through complete model interpretability. Performance testing using an external dataset revealed that AtML achieved 96.50% accuracy and 96.51% recall. Through the interpretability provided by AtML, our model identified 160 important marker genes, contributing to the understanding of cell type annotations. In conclusion, we trained AtML to efficiently identify Arabidopsis root cell stages, providing a new tool for elucidating the mechanisms of medicinal compound accumulation in Arabidopsis roots.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"231 ","pages":"Pages 61-69"},"PeriodicalIF":4.2,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1046202324002081/pdfft?md5=3b64d8bc9dc85039798d03e6014db597&pid=1-s2.0-S1046202324002081-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142253896","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}

引用次数: 0

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