IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2024-10-22 DOI: 10.1016/j.ymeth.2024.10.008

Sonia Arca-Lafuente , Cristina Yépez-Notario , Pablo Cea-Callejo , Violeta Lara-Aguilar , Celia Crespo-Bermejo , Luz Martín-Carbonero , Ignacio de los Santos , Verónica Briz , Ricardo Madrid

Purpose

Globally, it is estimated that 1.0 million individuals are newly infected by Hepatitis C virus (HCV) every year, and nearly 50 million people live with a chronic infection, according to World Health Organization. To overcome underdiagnosis of HCV infection among hard-to-reach populations, it is essential to develop new rapid and easy-to-use molecular diagnostic systems. In this work, we have developed a pangenotypic diagnostic tool based on Loop-Mediated Isothermal Amplification (LAMP), coupled to a direct sample lysis procedure for molecular detection of HCV at point-of-care (POC).

Methods

Procedure validation was performed using 129 different samples from HCV infected patients (116 serum samples, and 13 fresh blood samples), 27 individuals who tested negative for HCV but positive for HIV, and 11 healthy donors. Serum was collected, lysed for 10 min at room temperature, and assayed by RT-LAMP. To achieve this, a set of 9 LAMP-primers was used for the first time. Parallel RT-qPCR assays were conducted for HCV to both validate the procedure and quantify viral loads.

Results

HCV was detected by RT-LAMP in 109/116 HCV positive serum samples, and in 11/13 positive blood samples in less than 40 min. Compared to RT-qPCR results, our RT-LAMP procedure showed a sensitivity of 94 %, 100 % specificity, and a limit of detection of 3.26 log₁₀ IU/mL (10–20 copies per reaction).

Conclusions

We have developed an accurate system, more affordable than the current available rapid tests for HCV. Since no prior RNA purification step from capillary blood is required, we strongly recommend our RT-LAMP system as a valuable and rapid tool for the molecular detection of HCV at POC.

目的：据世界卫生组织估计，全球每年有 100 万人新感染丙型肝炎病毒（HCV），近 5000 万人患有慢性感染。为了解决丙型肝炎病毒感染在难以接触人群中诊断不足的问题，必须开发新的快速、易用的分子诊断系统。在这项工作中，我们开发了一种基于环路介导等温扩增（LAMP）的泛基因型诊断工具，并将其与直接样本裂解程序相结合，用于在护理点（POC）对 HCV 进行分子检测：方法：使用 129 份不同的样本（116 份血清样本和 13 份新鲜血液样本）进行了程序验证，这些样本分别来自 HCV 感染者、27 名 HCV 检测阴性但 HIV 检测阳性者和 11 名健康捐献者。采集血清后，在室温下裂解 10 分钟，然后用 RT-LAMP 进行检测。为此，首次使用了一套 9 个 LAMP-引物。同时还对 HCV 进行了 RT-qPCR 检测，以验证该程序并量化病毒载量：结果：在不到 40 分钟的时间内，RT-LAMP 法检测了 109/116 份 HCV 阳性血清样本和 11/13 份阳性血液样本中的 HCV。与 RT-qPCR 结果相比，我们的 RT-LAMP 程序的灵敏度为 94%，特异性为 100%，检测限为 3.26 log10 IU/mL（每次反应 10-20 个拷贝）：结论：我们开发了一种准确的系统，比目前可用的 HCV 快速检测方法更经济实惠。由于无需事先从毛细管血液中纯化 RNA，我们强烈推荐我们的 RT-LAMP 系统，它是在 POC 上进行 HCV 分子检测的重要而快速的工具。

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引用次数: 0

HLA-DR4Pred2: An improved method for predicting HLA-DRB1*04:01 binders HLA-DR4Pred2：预测 HLA-DRB1*04:01 结合者的改进方法。

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2024-10-19 DOI: 10.1016/j.ymeth.2024.10.007

Sumeet Patiyal , Anjali Dhall , Nishant Kumar , Gajendra P.S. Raghava

HLA-DRB1*04:01 is associated with numerous diseases, including sclerosis, arthritis, diabetes, and COVID-19, emphasizing the need to scan for binders in the antigens to develop immunotherapies and vaccines. Current prediction methods are often limited by their reliance on the small datasets. This study presents HLA-DR4Pred2, developed on a large dataset containing 12,676 binders and an equal number of non-binders. It’s an improved version of HLA-DR4Pred, which was trained on a small dataset, containing 576 binders and an equal number of non-binders. All models were trained, optimized, and tested on 80 % of the data using five-fold cross-validation and evaluated on the remaining 20 %. A range of machine learning techniques was employed, achieving maximum AUROC of 0.90 and 0.87, using composition and binary profile features, respectively. The performance of the composition-based model increased to 0.93, when combined with BLAST search. Additionally, models developed on the realistic dataset containing 12,676 binders and 86,300 non-binders, achieved a maximum AUROC of 0.99. Our proposed method outperformed existing methods when we compared the performance of our best model to that of existing methods on the independent dataset. Finally, we developed a standalone tool and a webserver for HLADR4Pred2, enabling the prediction, design, and virtual scanning of HLA-DRB1*04:01 binding peptides, and we also released a Python package available on the Python Package Index (https://webs.iiitd.edu.in/raghava/hladr4pred2/; https://github.com/raghavagps/hladr4pred2; https://pypi.org/project/hladr4pred2/).

HLA-DRB1*04:01 与多种疾病相关，包括硬化症、关节炎、糖尿病和 COVID-19，这强调了扫描抗原中的结合剂以开发免疫疗法和疫苗的必要性。目前的预测方法往往受限于对小型数据集的依赖。本研究提出的 HLA-DR4Pred2 是在包含 12,676 个结合者和同等数量的非结合者的大型数据集上开发的。它是 HLA-DR4Pred 的改进版，HLA-DR4Pred 是在包含 576 个绑定者和同等数量的非绑定者的小型数据集上训练出来的。所有模型都在 80% 的数据上使用五倍交叉验证进行了训练、优化和测试，并在剩余的 20% 数据上进行了评估。采用了一系列机器学习技术，利用成分特征和二元剖面特征分别获得了 0.90 和 0.87 的最大 AUROC。当与 BLAST 搜索相结合时，基于成分的模型的性能提高到了 0.93。此外，在包含 12,676 个粘合剂和 86,300 个非粘合剂的现实数据集上开发的模型，最大 AUROC 为 0.99。在独立数据集上比较最佳模型和现有方法的性能时，我们提出的方法优于现有方法。最后，我们为 HLADR4Pred2 开发了一个独立工具和一个网络服务器，实现了 HLA-DRB1*04:01 结合肽的预测、设计和虚拟扫描，我们还发布了一个 Python 软件包，可在 Python 软件包索引 (https://webs.iiitd.edu.in/raghava/hladr4pred2/; https://github.com/raghavagps/hladr4pred2; https://pypi.org/project/hladr4pred2/) 上查阅。

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引用次数: 0

A heterogeneous graph transformer framework for accurate cancer driver gene prediction and downstream analysis 用于准确预测癌症驱动基因和下游分析的异构图转换器框架

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2024-10-18 DOI: 10.1016/j.ymeth.2024.09.018

Shuwen Xiong , Junming Zhang , Hong Luo , Yongqing Zhang , Qinyin Xiao

Accurately predicting cancer driver genes remains a formidable challenge amidst the burgeoning volume and intricacy of cancer genomic data. In this investigation, we propose HGTDG, an innovative heterogeneous graph transformer framework tailored for precisely predicting cancer driver genes and exploring downstream tasks. A heterogeneous graph construction module is central to the framework, which assembles a gene-protein heterogeneous network leveraging the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and protein-protein interactions sourced from the STRING (search tool for recurring instances of neighboring genes) database. Moreover, our framework introduces a pioneering heterogeneous graph transformer module, harnessing multi-head attention mechanisms for nuanced node embedding. This transformative module proficiently captures distinct representations for both nodes and edges, thereby enriching the model's predictive capacity. Subsequently, the generated node embeddings are seamlessly integrated into a classification module, facilitating the discrimination between driver and non-driver genes. Our experimental findings evince the superiority of HGTDG over existing methodologies, as evidenced by the enhanced performance metrics, including the area under the receiver operating characteristic curves (AUROC) and the area under the precision-recall curves (AUPRC). Furthermore, the downstream analysis utilizing the newly identified cancer driver genes underscores the efficacy and versatility of our proposed framework.

随着癌症基因组数据量的激增和复杂性的增加，准确预测癌症驱动基因仍然是一项艰巨的挑战。在这项研究中，我们提出了 HGTDG，这是一个创新的异构图转换器框架，专为精确预测癌症驱动基因和探索下游任务而量身定制。异构图构建模块是该框架的核心，它利用《京都基因与基因组百科全书》（KEGG）中的通路和 STRING（相邻基因重复实例搜索工具）数据库中的蛋白质-蛋白质相互作用，构建基因-蛋白质异构网络。此外，我们的框架还引入了一个开创性的异构图转换器模块，利用多头关注机制进行细微的节点嵌入。这一转换模块能熟练捕捉节点和边的不同表征，从而丰富模型的预测能力。随后，生成的节点嵌入被无缝集成到分类模块中，从而有助于区分驱动基因和非驱动基因。我们的实验结果表明，与现有方法相比，HGTDG 具有更优越的性能指标，包括接收者操作特征曲线下面积（AUROC）和精度-召回曲线下面积（AUPRC）。此外，利用新发现的癌症驱动基因进行的下游分析也凸显了我们提出的框架的有效性和多功能性。

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引用次数: 0

Multi-view contrastive clustering for cancer subtyping using fully and weakly paired multi-omics data 利用完全配对和弱配对多组学数据进行癌症亚型的多视角对比聚类。

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2024-10-17 DOI: 10.1016/j.ymeth.2024.09.016

Yabin Kuang , Minzhu Xie , Zhanhong Zhao , Dongze Deng , Ergude Bao

The identification of cancer subtypes is crucial for advancing precision medicine, as it facilitates the development of more effective and personalized treatment and prevention strategies. With the development of high-throughput sequencing technologies, researchers now have access to a wealth of multi-omics data from cancer patients, making computational cancer subtyping increasingly feasible. One of the main challenges in integrating multi-omics data is handling missing data, since not all biomolecules are consistently measured across all samples. Current computational models based on multi-omics data for cancer subtyping often struggle with the challenge of weakly paired omics data. To address this challenge, we propose a novel unsupervised cancer subtyping model named Subtype-MVCC. This model leverages graph convolutional networks to extract and represent low-dimensional features from each omics data type, using intra-view and inter-view contrastive learning approaches. By incorporating a weighted average fusion strategy to unify the dimension of each sample, Subtype-MVCC effectively handles weakly paired multi-omics datasets. Comprehensive evaluations on established benchmark datasets demonstrate that Subtype-MVCC outperforms nine leading models in this domain. Additionally, simulations with varying levels of missing data highlight the model's robust performance in handling weakly paired omics data. The clinical relevance and survival outcomes associated with the identified subtypes further validate the interpretability and reliability of the clustering results produced by Subtype-MVCC.

癌症亚型的确定对于推进精准医疗至关重要，因为这有助于开发更有效、更个性化的治疗和预防策略。随着高通量测序技术的发展，研究人员现在可以从癌症患者那里获得大量的多组学数据，这使得计算癌症亚型变得越来越可行。整合多组学数据的主要挑战之一是处理缺失数据，因为并非所有生物分子都能在所有样本中得到一致的测量。目前基于多组学数据进行癌症亚型分析的计算模型往往难以应对弱配对 omics 数据的挑战。为了应对这一挑战，我们提出了一种名为 Subtype-MVCC 的新型无监督癌症亚型分析模型。该模型利用图卷积网络，采用视图内和视图间对比学习方法，从每种 omics 数据类型中提取并表示低维特征。通过采用加权平均融合策略来统一每个样本的维度，Subtype-MVCC 能有效处理弱配对的多组学数据集。在已建立的基准数据集上进行的综合评估表明，Subtype-MVCC 优于该领域的九种领先模型。此外，不同程度的数据缺失模拟也凸显了该模型在处理弱配对组学数据时的强大性能。与已识别亚型相关的临床相关性和生存结果进一步验证了 Subtype-MVCC 生成的聚类结果的可解释性和可靠性。

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引用次数: 0

DGSIST: Clustering spatial transcriptome data based on deep graph structure Infomax DGSIST：基于深度图结构的空间转录组数据聚类 Infomax.

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2024-10-15 DOI: 10.1016/j.ymeth.2024.10.002

Yu-Han Xiu , Si-Lin Sun , Bing-Wei Zhou , Ying Wan , Hua Tang , Hai-Xia Long

Although spatial transcriptomics data provide valuable insights into gene expression profiles and the spatial structure of tissues, most studies rely solely on gene expression information, underutilizing the spatial data. To fully leverage the potential of spatial transcriptomics and graph neural networks, the DGSI (Deep Graph Structure Infomax) model is proposed. This innovative graph data processing model uses graph convolutional neural networks and employs an unsupervised learning approach. It maximizes the mutual information between graph-level and node-level representations, emphasizing flexible sampling and aggregation of nodes and their neighbors. This effectively captures and incorporates local information from nodes into the overall graph structure. Additionally, this paper developed the DGSIST framework, an unsupervised cell clustering method that integrates the DGSI model, SVD dimensionality reduction algorithm, and k-means++ clustering algorithm. This aims to identify cell types accurately. DGSIST fully uses spatial transcriptomics data and outperforms existing methods in accuracy. Demonstrations of DGSIST’s capability across various tissue types and technological platforms have shown its effectiveness in accurately identifying spatial domains in multiple tissue sections. Compared to other spatial clustering methods, DGSIST excels in cell clustering and effectively eliminates batch effects without needing batch correction. DGSIST excels in spatial clustering analysis, spatial variation identification, and differential gene expression detection and directly applies to graph analysis tasks, such as node classification, link prediction, or graph clustering. Anticipation lies in the contribution of the DGSIST framework to a deeper understanding of the spatial organizational structures of diseases such as cancer.

虽然空间转录组学数据为了解基因表达谱和组织的空间结构提供了宝贵的视角，但大多数研究仅依赖于基因表达信息，对空间数据利用不足。为了充分利用空间转录组学和图神经网络的潜力，我们提出了 DGSI（深度图结构 Infomax）模型。这种创新的图数据处理模型使用图卷积神经网络，并采用无监督学习方法。它最大化了图层和节点层表征之间的互信息，强调节点及其邻居的灵活采样和聚合。这能有效捕捉节点的局部信息并将其纳入整体图结构中。此外，本文还开发了 DGSIST 框架，这是一种无监督细胞聚类方法，集成了 DGSI 模型、SVD 降维算法和 k-means++ 聚类算法。其目的是准确识别细胞类型。DGSIST 充分利用了空间转录组学数据，其准确性优于现有方法。DGSIST 在各种组织类型和技术平台上的能力展示表明，它能有效准确地识别多个组织切片中的空间域。与其他空间聚类方法相比，DGSIST 在细胞聚类方面表现出色，能有效消除批次效应，无需批次校正。DGSIST 在空间聚类分析、空间变异识别和差异基因表达检测方面表现出色，并可直接应用于节点分类、链接预测或图聚类等图分析任务。我们期待 DGSIST 框架能为深入了解癌症等疾病的空间组织结构做出贡献。

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引用次数: 0

Determination of adipogenesis stages of human umbilical cord-derived mesenchymal stem cells using three-dimensional label-free holotomography 利用三维无标记全息图确定人脐带间充质干细胞的脂肪生成阶段

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2024-10-11 DOI: 10.1016/j.ymeth.2024.10.005

Mahesh Prakash Bhatta , Gun-Woo Won , Seung Hoon Lee , Seung-Hyeon Choi , Cheong-Hae Oh , Ji Hyun Moon , Hong-Hoa Hoang , Jaehyeok Lee , Sang Do Lee , Jong-Il Park

Adipogenesis involves complex changes in gene expression, morphology, and cytoskeletal organization. However, the quantitative analysis of live cell images to identify their stages through morphological markers is limited. Distinct adipogenesis markers on human umbilical cord-derived mesenchymal stem cells (UC-MSCs) were identified through holotomography, a label-free live cell imaging technique. In the MSC-to-preadipocyte transition, the nucleus-to-cytoplasm ratio (0.080 vs. 0.052) and lipid droplet (LD) refractive index variation decreased (0.149 % vs. 0.061 %), whereas the LD number (20 vs. 65) increased. This event was also accompanied by the downregulation and upregulation of THY1 and Preadipocyte Factor-1 (PREF-1), respectively. In the preadipocyte to immature adipocyte shift, cell sphericity (0.20 vs. 0.43) and LD number (65 vs. 200) surged, large LDs (>10 μm³) appeared, and the major axis of the cell was reduced (143.7 μm vs. 83.12 μm). These findings indicate features of preadipocyte and immature adipocyte stages, alongside the downregulation of PREF-1 and upregulation of Peroxisome Proliferator-Activated Receptor gamma (PPARγ). In adipocyte maturation, along with PPARγ and Fatty Acid-Binding Protein 4 upregulation, cell compactness (0.15 vs. 0.29) and sphericity (0.43 vs. 0.59) increased, and larger LDs (>30 μm³) formed, marking immature and mature adipocyte stages. The study highlights the distinct adipogenic morphological biomarkers of adipogenesis stages in UC-MSCs, providing potential applications in biomedical and clinical settings, such as fostering innovative medical strategies for treating metabolic disease.

脂肪生成涉及基因表达、形态和细胞骨架组织的复杂变化。然而，通过形态标记对活细胞图像进行定量分析以确定其阶段的方法还很有限。通过无标记活细胞成像技术--全息图像技术，确定了人脐带间充质干细胞（UC-MSCs）上不同的脂肪生成标记。在间充质干细胞向脂肪细胞转变的过程中，细胞核与细胞质的比率（0.080 对 0.052）和脂滴（LD）折射率变化降低（0.149 % 对 0.061 %），而脂滴数量增加（20 对 65）。与此同时，THY1 和前脂肪细胞因子-1（PREF-1）也分别出现了下调和上调。在前脂肪细胞向未成熟脂肪细胞转变的过程中，细胞球形度（0.20 对 0.43）和 LD 数量（65 对 200）激增，出现大的 LD（10 μm3），细胞主轴缩小（143.7 μm 对 83.12 μm）。这些发现显示了前脂肪细胞和未成熟脂肪细胞阶段的特征，以及 PREF-1 的下调和过氧化物酶体激活受体γ（PPARγ）的上调。在脂肪细胞成熟过程中，随着 PPARγ 和脂肪酸结合蛋白 4 的上调，细胞紧密度（0.15 vs. 0.29）和球形度（0.43 vs. 0.59）增加，形成更大的 LD（30 μm3），标志着脂肪细胞的未成熟和成熟阶段。该研究强调了 UC 间充质干细胞中不同脂肪生成阶段的脂肪生成形态生物标志物，为生物医学和临床提供了潜在的应用前景，如促进治疗代谢性疾病的创新医疗策略。

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引用次数: 0

Multi-kernel clustering with tensor fusion on Grassmann manifold for high-dimensional genomic data 格拉斯曼流形上的多核聚类与张量融合，用于高维基因组数据

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2024-10-11 DOI: 10.1016/j.ymeth.2024.09.015

Fei Qi , Jin Guo , Junyu Li , Yi Liao , Wenxiong Liao , Hongmin Cai , Jiazhou Chen

The high dimensionality and noise challenges in genomic data make it difficult for traditional clustering methods. Existing multi-kernel clustering methods aim to improve the quality of the affinity matrix by learning a set of base kernels, thereby enhancing clustering performance. However, directly learning from the original base kernels presents challenges in handling errors and redundancies when dealing with high-dimensional data, and there is still a lack of feasible multi-kernel fusion strategies. To address these issues, we propose a Multi-Kernel Clustering method with Tensor fusion on Grassmann manifolds, called MKCTM. Specifically, we maximize the clustering consensus among base kernels by imposing tensor low-rank constraints to eliminate noise and redundancy. Unlike traditional kernel fusion approaches, our method fuses learned base kernels on the Grassmann manifold, resulting in a final consensus matrix for clustering. We integrate tensor learning and fusion processes into a unified optimization model and propose an effective iterative optimization algorithm for solving it. Experimental results on ten datasets, comparing against 12 popular baseline clustering methods, confirm the superiority of our approach. Our code is available at https://github.com/foureverfei/MKCTM.git.

基因组数据的高维度和噪声挑战给传统聚类方法带来了困难。现有的多核聚类方法旨在通过学习一组基核来改善亲和矩阵的质量，从而提高聚类性能。然而，在处理高维数据时，直接从原始基核学习会在处理错误和冗余方面带来挑战，而且仍然缺乏可行的多核融合策略。为了解决这些问题，我们提出了一种在格拉斯曼流形上进行张量融合的多核聚类法，称为 MKCTM。具体来说，我们通过施加张量低阶约束来消除噪声和冗余，从而最大化基础内核之间的聚类共识。与传统的内核融合方法不同，我们的方法是在格拉斯曼流形上融合学习到的基础内核，从而形成最终的聚类共识矩阵。我们将张量学习和融合过程整合到一个统一的优化模型中，并提出了一种有效的迭代优化算法。在十个数据集上的实验结果与 12 种流行的基线聚类方法相比，证实了我们的方法的优越性。我们的代码见 https://github.com/foureverfei/MKCTM.git。

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引用次数: 0

Protocol for a pilot study: Feasibility of a web-based platform to improve nutrition, mindfulness, and physical function in people living with Post COVID-19 condition (BLEND) 试点研究协议：基于网络的平台改善 COVID-19 后遗症患者营养、正念和身体功能的可行性（BLEND）。

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2024-10-09 DOI: 10.1016/j.ymeth.2024.10.004

Montserrat Montes-Ibarra , Kristine Godziuk , Richard B Thompson , Catherine B. Chan , Edith Pituskin , Douglas P. Gross , Grace Lam , Mathias Schlögl , João Felipe Mota , D. Ian Paterson , Carla M. Prado

Individuals with Post COVID-19 condition (PCC), or long COVID, experience symptoms such as fatigue, muscle weakness, and psychological distress, including anxiety, depression, or sleep disorders that persist after recovery from COVID-19. These ongoing symptoms significantly compromise quality of life and diminish functional capacity and independence. Multimodal digital interventions targeting behavioural factors such as nutrition and mindfulness have shown promise in improving health outcomes of people with chronic health conditions and may be beneficial for those with PCC. The BLEND study (weB-based pLatform to improve nutrition, mindfulnEss, and physical function, in patients with loNg COVID) study is an 8-week pilot randomized controlled trial evaluating the feasibility of a digital wellness platform compared to usual care among individuals with PCC. The web-based wellness platform employed in this study, My Viva Plan (MVP)®, integrates a holistic, multicomponent approach to promote wellness. The intervention group receives access to the digital health platform for 8 weeks with encouragement for frequent interactions to improve dietary intake and mindfulness. The control group receives general content focusing on improvements in dietary intake and mindfulness. Assessments are conducted at baseline and week 8. The primary outcome is the feasibility of platform use. Secondary and exploratory outcomes include a between-group comparison of changes in body composition, nutritional status, quality of life, mindfulness, physical activity, and physical performance after 8 weeks. Findings of this study will inform the development of effective web-based wellness programs tailored for individuals with PCC to promote sustainable behavioural changes and improved health outcomes.

COVID-19 后遗症（PCC）或长期 COVID 患者在 COVID-19 康复后会持续出现疲劳、肌肉无力和心理困扰等症状，包括焦虑、抑郁或睡眠障碍。这些持续存在的症状严重影响了生活质量，削弱了功能能力和独立性。针对营养和正念等行为因素的多模式数字化干预措施在改善慢性病患者的健康状况方面取得了良好的效果，可能对 PCC 患者有益。BLEND 研究（基于微信平台的改善慢性阻塞性肺病患者营养、注意力和身体功能的研究）是一项为期 8 周的试点随机对照试验，旨在评估数字健康平台与常规护理相比在 PCC 患者中的可行性。本研究采用的网络健康平台 My Viva Plan (MVP)® 整合了一种促进健康的整体、多成分方法。干预组可在 8 周内访问数字健康平台，并鼓励他们经常互动，以改善饮食摄入和心态。对照组则接受侧重于改善饮食摄入和正念的一般内容。评估在基线和第 8 周进行。主要结果是平台使用的可行性。次要和探索性结果包括对 8 周后身体成分、营养状况、生活质量、正念、体育活动和体能表现的变化进行组间比较。本研究的结果将为开发有效的基于网络的健康计划提供参考，该计划专为 PCC 患者量身定制，旨在促进可持续的行为改变并改善健康状况。

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引用次数: 0

Study protocol for a single-arm pilot trial investigating the feasibility of a multimodal digital technology for managing metabolic syndrome in patients with chronic obstructive pulmonary disease 慢性阻塞性肺病患者代谢综合征管理多模式数字技术可行性单臂试点试验研究方案。

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2024-10-09 DOI: 10.1016/j.ymeth.2024.10.003

Bruna R. da Silva , Amanda I. Radil , Liam Collins , Nathanial Maeda , Carla M. Prado , Martin Ferguson-Pell , Doug Klein

Individuals diagnosed with Chronic Obstructive Pulmonary Disease (COPD) are exposed to an increased risk of metabolic syndrome (MetS), which negatively affects their health outcomes and quality of life. Lifestyle interventions have shown promise in managing MetS. This study outlines the protocol for a web-based multimodal self-care program, Digital Metabolic Rehabilitation, for managing MetS in patients with COPD. The Digital Metabolic Rehabilitation is a single-arm pilot trial that integrates the Canadian Health Advanced by Nutrition and Graded Exercise (CHANGE) Program and a web-based wellness platform. The web-based wellness platform employed in this study is My Viva Plan (MVP)®, which integrates a holistic, multicomponent approach to promote wellness. The intervention will primarily focus on lifestyle changes for patients with COPD. Over 6 months, participants will use the web-based wellness platform and engage in weekly online support group sessions. Fifty patients diagnosed with stage I-II COPD and MetS will participate. Blood tests, anthropometrics, body composition, physical function, muscle strength, physical activity, energy metabolism, quality of life and mental health will be assessed at baseline, 3, and 6 months. The Digital Metabolic Rehabilitation program aims to explore whether a multimodal integrative intervention delivered through a web-based wellness platform can be implemented by patients with COPD with MetS. By combining the expertise of the CHANGE Program with the digital delivery format, the intervention seeks to enhance self-monitoring and foster better self-management practices. The protocol outlines a novel and potentially impactful intervention for managing MetS in patients with COPD.

被诊断为慢性阻塞性肺病（COPD）的患者患代谢综合征（MetS）的风险增加，这对他们的健康状况和生活质量产生了负面影响。生活方式干预对控制代谢综合征很有帮助。本研究概述了基于网络的多模式自我保健计划 "数字代谢康复 "的方案，用于控制慢性阻塞性肺病患者的代谢综合征。数字代谢康复是一项单臂可行性试验，它整合了加拿大营养和分级运动健康高级计划（CHANGE）和基于网络的健康平台。本研究采用的网络健康平台是 "我的万岁计划"（MVP）®，它整合了一种促进健康的整体、多成分方法。干预措施将主要侧重于慢性阻塞性肺病患者生活方式的改变。在 6 个月的时间里，参与者将使用基于网络的健康平台，并参加每周一次的在线支持小组会议。50 名被诊断为 I-II 期慢性阻塞性肺病和 MetS 的患者将参与其中。将在基线、3 个月和 6 个月时对血液检测、人体测量、身体成分、身体功能、肌肉力量、体力活动、能量代谢、生活质量和心理健康进行评估。数字代谢康复计划旨在探索慢性阻塞性肺病合并代谢综合征患者能否通过网络健康平台实施多模式综合干预。通过将 "改变 "计划的专业知识与数字交付形式相结合，该干预措施旨在加强自我监测，促进更好的自我管理实践。该方案概述了一种管理慢性阻塞性肺病患者 MetS 的新颖且具有潜在影响力的干预措施。

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Development and validation of a stability-indicating HPLC method for assay of tonabersat in pharmaceutical formulations 开发和验证用于检测药物制剂中托那酯的稳定性指示高效液相色谱法。

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

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

Santosh Bhujbal, Ilva D. Rupenthal, Priyanka Agarwal

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 (R² = 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.

本研究开发了一种稳定性指示反相高效液相色谱法（RP-HPLC），用于检测托那酯并评估其在药物制剂中的稳定性。色谱分离采用 Kinetex® C18 色谱柱（2.6 µm，150 x 3 mm，100 Å），温度为 50 °C，进样量为 20 µL。乙腈水溶液（5 - 33.5%）的线性梯度为 1 分钟，然后在 26 分钟内逐渐升至 100%，流速为 0.5 mL/min。在 275 纳米和 210 纳米波长下分别检测托那伯沙特及其降解产物。采用优化后的方法评估了托那伯沙特在脂质药物制剂中 5 ± 3 °C、25 ± 2 °C/60 ± 5 % 相对湿度和 40 ± 2 °C/75 ± 5 % 相对湿度条件下 3 个月的稳定性。该方法按照 ICH 指南进行了验证，在 5 - 200 µg/mL 范围内线性关系良好（R2 = 0.99994），准确度（回收率 98.25 - 101.58 %）和精密度（RSD < 2.5 %）良好。检测和定量限分别为 0.8 µg/mL 和 5 µg/mL。强制降解研究表明，在 70 °C的碱性（90.33 ± 0.80 %）、酸性（70.60 ± 1.57 %）和氧化应激（33.95 ± 0.69 %）条件下会出现明显降解，但在热应激或光解应激条件下未观察到降解。两种制剂在储存过程中均未出现化学降解。因此，该方法灵敏、特异，适用于药物制剂中托那酯稳定性的检测。

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