MethodsX最新文献_第6页

BRAIN-META: A reproducible CNN–vision transformer meta-ensemble pipeline for explainable brain tumor classification brain - meta：一个可重复的cnn -视觉转换器元集成管道，用于解释脑肿瘤分类

IF 1.9

MethodsX Pub Date : 2026-06-01 Epub Date: 2025-12-17 DOI: 10.1016/j.mex.2025.103769

Komal Kumar Napa , Sangeetha Murugan , J.Senthil Murugan , A. Jayanthi

{"title":"BRAIN-META: A reproducible CNN–vision transformer meta-ensemble pipeline for explainable brain tumor classification","authors":"Komal Kumar Napa , Sangeetha Murugan , J.Senthil Murugan , A. Jayanthi","doi":"10.1016/j.mex.2025.103769","DOIUrl":"10.1016/j.mex.2025.103769","url":null,"abstract":"<div><div>This study presents BRAIN-META, a reproducible deep learning methodology designed for multi-class brain tumor classification using structural MRI. The proposed approach combines ten hybrid CNN–Vision Transformer (ViT) models with a meta-learning ensemble framework. The dataset includes 2D MRI images representing four tumor categories: glioma, meningioma, pituitary, and notumor. A standardized preprocessing pipeline involving image resizing, normalization, and CLAHE (Contrast Limited Adaptive Histogram Equalization) is applied to improve image quality and feature visibility. Ten pre-trained CNN architectures—DenseNet121, DenseNet169, DenseNet201, MobileNet, MobileNetV2, EfficientNetB0, EfficientNetB1, EfficientNetB4, InceptionV3, and Xception—are fused with Vision Transformer blocks to extract both local and global features. Each CNN-ViT model is trained independently, and the softmax outputs from validation data are used to generate stacked feature vectors. These vectors are input to two meta-learners, Logistic Regression and XGBoost, which are trained to produce final predictions. Evaluation metrics include accuracy, precision, recall, F1-score, and confusion matrix. XGBoost meta-learner achieved the highest accuracy of 97.10%, followed by Logistic Regression meta-learner at 97.03%, outperforming all individual base models. To enhance interpretability, Grad-CAM was employed, visually highlighting regions influencing classification. The proposed method is accurate, explainable, and modular, making it a strong candidate for clinical decision support in neuro-oncology.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"Article 103769"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SatTCR: a pipeline for performing saturation analysis of the T cell receptor repertoire and a case study of a healthy canine SatTCR：一个管道执行饱和分析的T细胞受体库和一个健康犬的案例研究

IF 1.9

MethodsX Pub Date : 2026-06-01 Epub Date: 2025-11-27 DOI: 10.1016/j.mex.2025.103733

Rene Welch Schwartz , Cindy L. Zuleger , Michael A. Newton , David M. Vail , Mark R. Albertini , Irene M. Ong

{"title":"SatTCR: a pipeline for performing saturation analysis of the T cell receptor repertoire and a case study of a healthy canine","authors":"Rene Welch Schwartz , Cindy L. Zuleger , Michael A. Newton , David M. Vail , Mark R. Albertini , Irene M. Ong","doi":"10.1016/j.mex.2025.103733","DOIUrl":"10.1016/j.mex.2025.103733","url":null,"abstract":"<div><h3>Motivation</h3><div>Profiling the T cell receptor (TCR) repertoire using next-generation sequencing (NGS) to quantify adaptive immune responses has become common in human and animal research. Companion dogs with spontaneous tumors have similarities with humans who have cancer. T cells undergo clonal expansion when they recognize specific antigens via surface TCRs. TCR counts from NGS data provide a way to quantify T cell response to vaccines, cancer, or infectious diseases for preclinical and clinical health studies. One complication is that the power and accuracy of TCR experiments depend substantially on the TCR sequencing depth, therefore it is important to determine the optimal read depth of an experiment to verify whether a subject’s repertoire is correctly represented.</div></div><div><h3>Results</h3><div>The optimal TCR sequencing depth for future experiments can be determined by randomly sampling lower TCR sequencing depths from a sequencing experiment, assembling the TCR clonotypes, and determining where the saturation of power and accuracy occurs. Moreover, one can determine whether an existing experiment has sufficient sequencing depth to justify its conclusions. We provide guidelines to determine whether the sequencing depth is adequate and a computational pipeline that:</div><div>Samples pairs of sequences and assembles clonotypes</div><div>Summarizes the results in a parametrized report</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"Article 103733"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145749536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Innovative parallel grasshopper optimization algorithm for reliability optimization 创新的并行蚱蜢优化算法用于可靠性优化

IF 1.9

MethodsX Pub Date : 2026-06-01 Epub Date: 2025-12-14 DOI: 10.1016/j.mex.2025.103759

Dipti Singh, Neha Chand

{"title":"Innovative parallel grasshopper optimization algorithm for reliability optimization","authors":"Dipti Singh, Neha Chand","doi":"10.1016/j.mex.2025.103759","DOIUrl":"10.1016/j.mex.2025.103759","url":null,"abstract":"<div><div>This study introduces a novel Parallel Grasshopper Optimization Algorithm (p-GOA), specifically designed to address reliability optimization problems. Although several hybrid algorithms exist in this field, the proposed p-GOA distinctly differs through its parallel cooperative strategy. Unlike sequential methods that apply techniques one after another, p-GOA simultaneously divides the population into two groups operating in parallel: one group employs a migration strategy (SOMA) for broad global exploration of the search space, while the other utilizes a mutation operator (NUMO) for focused local refinement of solutions. This dual-strategy parallel operation creates achieving a stronger balance between global exploration and local refinement, while a smart penalty-free method naturally steers the search toward workable solutions. When tested on four well-known reliability problems, the results demonstrate that our method consistently finds more reliable systems and converges faster than existing approaches, demonstrating its effectiveness in handling real-world engineering constraints.</div><div>● This study introduces a Parallel Grasshopper Optimization Algorithm (p-GOA) that integrates GOA, SOMA, and a Non-Uniform Mutation Operator (NUMO). It employs mutation, migration, and a parallel approach to efficiently explore both feasible and near-feasible regions without relying on penalty functions.</div><div>● The p-GOA dividing the population into two parallel groups—one updated using SOMA-based migration and the other using NUMO-based mutation. This dual-strategy, simultaneous processing not only accelerates convergence but also strengthens the balance between global search and local optimization.</div><div>● Specifically targets reliability optimization problems, particularly redundancy allocation issues where components must meet specific reliability and resource consumption (cost, weight, volume) constraints.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"Article 103759"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145798160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A multivariate correlated poisson generalized inverse gaussian regression model for dependent count data: Estimation and testing procedures 依赖计数数据的多元相关泊松广义逆高斯回归模型：估计和检验程序

IF 1.9

MethodsX Pub Date : 2026-06-01 Epub Date: 2025-12-17 DOI: 10.1016/j.mex.2025.103772

Yusrianti Hanike , Purhadi , Achmad Choiruddin

{"title":"A multivariate correlated poisson generalized inverse gaussian regression model for dependent count data: Estimation and testing procedures","authors":"Yusrianti Hanike , Purhadi , Achmad Choiruddin","doi":"10.1016/j.mex.2025.103772","DOIUrl":"10.1016/j.mex.2025.103772","url":null,"abstract":"<div><div>Regression modeling for multivariate count data often struggles with assumption of overdispersion and correlation among response variables. To address these issues, this study proposes a new model called Multivariate Correlated Poisson Generalized Inverse Gaussian Regression (MCPGIGR), which integrates random effects through common shock variables and allows for flexible mean structures via a log-link function. This research develops a Maximum Likelihood Estimation (MLE) and Maximum Likelihood Ratio Tests (MLRT) to evaluate both simultaneous and partial significance of predictors. We conduct simulation studies to assess the consistency and performance of the proposed estimators. Furthermore, in an application to maternal and neonatal mortality across 38 districts/cities in East Java (Indonesia), MCPGIGR substantially improves model fit relative to a Multivariate Poisson Regression (MPR) baseline (AICc decreases from 2378.63 to 1924.60 for <span><math><mrow><mi>γ</mi><mo>=</mo><mo>−</mo><mrow><mn>1</mn><mspace></mspace><mo>/</mo><mspace></mspace><mn>2</mn></mrow></mrow></math></span>). The proposed framework provides a practical and flexible tool for analyzing correlated, overdispersed multivariate counts in public health and related domains. The highlights of this research are:</div><div>• The MCPGIGR model introduces a correlated multivariate count regression framework with exposure adjustment.</div><div>• It provides robust parameter estimation and hypothesis testing via MLE and MLRT.</div><div>• MCPGIGR demonstrates improved model fit and practical interpretability in public health applications.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"Article 103772"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Resume data extract and job recruitment Chatbot features for AI-based resume screening & analytics 基于人工智能的简历筛选和分析的聊天机器人功能

IF 1.9

MethodsX Pub Date : 2026-06-01 Epub Date: 2025-12-17 DOI: 10.1016/j.mex.2025.103775

Kar Weng Chong, Kok Why Ng, Yong Hong Fu

{"title":"Resume data extract and job recruitment Chatbot features for AI-based resume screening & analytics","authors":"Kar Weng Chong, Kok Why Ng, Yong Hong Fu","doi":"10.1016/j.mex.2025.103775","DOIUrl":"10.1016/j.mex.2025.103775","url":null,"abstract":"<div><div>AI technologies are changing the field of manpower recruitment since they make it much more efficient, accurate, and scalable than conventional approaches. The project builds an AI recruitment system that is combined to have two main elements of an integrated AI recruitment system including a Resume Screening AI and a Job Recruitment Chatbot, which have the goal of improving the process in hiring as well as making the experience of the candidates better in the process.</div><div>The Resume Screening AI uses a mixed approach incorporating both classical document processing, and capabilities of advanced language models. The unstructured data of raw resumes are mined and normalized into standard forms to allow evaluation and ranking of the candidates in an organized and systematic manner according to the position’s requirements. The Job Recruitment Chatbot entails a programmed chat system of interactive communication during the job recruitment procedure comprising the component of FAQ, conversation-based direction, and voice-to-text dynamic to make the system more accessible to a diverse group of users.<ul><li><span>•</span><span><div><strong>Document Processing Pipeline</strong>: Parsed all-format resumes by the means of PyPDF2 and python-docx libraries and programmed the data in a structured manner, via Google Gemini 1.5 Flash API and engineered special prompts to validate the set of JSON-Schema.</div></span></li><li><span>•</span><span><div><strong>Intelligent Screening System</strong>: Created automated candidate screening based on (large language model) inference process to compare resume text with job requirements, producing relevance scores and classified evaluations.</div></span></li><li><span>•</span><span><div><strong>Interactive Chatbot Development</strong>: Developed natural language processing AI interface with chat capabilities and with speech-to-text and FAQ automation that could be used to answer candidate questions and optimize the recruitment process.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"Article 103775"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analyzing the mahakam river water quality using the geographically weighted panel regression model 利用地理加权面板回归模型分析玛哈干河水质

IF 1.9

MethodsX Pub Date : 2026-06-01 Epub Date: 2025-12-19 DOI: 10.1016/j.mex.2025.103773

Zabrina Nathania Fauziyah, Suyitno Suyitno, Darnah, Memi Nor Hayati, Meirinda Fauziyah

{"title":"Analyzing the mahakam river water quality using the geographically weighted panel regression model","authors":"Zabrina Nathania Fauziyah, Suyitno Suyitno, Darnah, Memi Nor Hayati, Meirinda Fauziyah","doi":"10.1016/j.mex.2025.103773","DOIUrl":"10.1016/j.mex.2025.103773","url":null,"abstract":"<div><div>This study discusses the geographically weighted panel regression (GWPR) model. GWPR is an extension of geographically weighted regression model, designed for spatially heterogeneous panel data. In this study, GWPR model is applied to panel data on biochemical oxygen demand (BOD) in Mahakam River water 2022–2024. The model is estimated at each spatial location using a fixed effects model (FEM) as the global model, with temporal effects addressed through a demeaning transformation. All statistical analyses and spatial processing are conducted using R software, GNU Octave, QGIS, and Google Earth. This study aims to map factors influencing Mahakam River water BOD using GWPR model. The results indicate that GWPR outperforms FEM, with AIC = -60.6419, <span><math><mrow><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup><mo>=</mo><mn>80.321</mn><mo>%</mo></mrow></math></span>, and root mean square error of 0.7122. The factors influencing BOD include temperature, water pH, color degree, nitrate, ammonia, total suspended solids, and sulfate.<ul><li><span>•</span><span><div><em>We present a GWPR model using FEM as global model, applied to the spatially heterogeneous panel data, namely demeaned Mahakam River water BOD data 2022–2024.</em></div></span></li><li><span>•</span><span><div><em>The mapping of factors influencing BOD is analyzed locally using GWPR model.</em></div></span></li><li><span>•</span><span><div><em>The optimal adaptive bandwidth is determined using Akaike Information Criterion, and model goodness-of-fit is evaluated using the coefficient of determination and root mean square error.</em></div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"Article 103773"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Real-time vehicle control via edge cloud sensor fusion and CNN based perceptron 基于边缘云传感器融合和CNN感知器的实时车辆控制

IF 1.9

MethodsX Pub Date : 2026-06-01 Epub Date: 2025-12-24 DOI: 10.1016/j.mex.2025.103779

Sumukh Chaurasia , Parambrata Sanyal , Gagandeep Kaur , Satvik Barhanpure , Kshitij Bhele , Amol D. Wable , Suhashini Awadhesh Chaurasia , Rutuja Rajendra Patil , Devika Verma

{"title":"Real-time vehicle control via edge cloud sensor fusion and CNN based perceptron","authors":"Sumukh Chaurasia , Parambrata Sanyal , Gagandeep Kaur , Satvik Barhanpure , Kshitij Bhele , Amol D. Wable , Suhashini Awadhesh Chaurasia , Rutuja Rajendra Patil , Devika Verma","doi":"10.1016/j.mex.2025.103779","DOIUrl":"10.1016/j.mex.2025.103779","url":null,"abstract":"<div><div>Reliable real-time vehicle control is essential for intelligent transport systems where accurate perception and decision-making depend on fast sensor data processing. This study developed a hybrid edge–cloud method integrating deep learning with Internet of Things (IoT) sensor fusion for adaptive vehicle control. Ultrasonic range data were combined with convolutional neural networks (CNNs) to enable object detection, stopping-time prediction, and braking control under varying environmental conditions. The CNN-based model was trained and evaluated under normal and simulated adverse driving scenarios. Results indicated strong performance with R² = 0.99 under normal and 0.98 under adverse conditions, and a mean squared error (MSE) of 0.0085. Average inference latency is 110–116 ms on Jetson Nano and 210–230 ms on Raspberry Pi, confirming suitability for real-time deployment on edge hardware.</div><div>The hybrid edge–cloud method enables adaptive, real-time vehicle control through IoT sensor fusion.</div><div>CNN-based perception enhances prediction accuracy and operational safety under variable driving conditions.</div><div>Demonstrates feasibility of deep learning deployment on low-cost edge devices for intelligent transport applications.</div><div>Thus, integrating deep learning with IoT-enabled sensors on an edge–cloud platform provides a reliable and scalable pathway toward safe, adaptive, and efficient vehicle control in intelligent transportation systems.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"Article 103779"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An accessible HPLC-DAD method for the direct detection of acrolein-trapping compounds in complex plant matrices 直接检测复杂植物基质中丙烯醛捕获化合物的高效液相色谱- dad方法

IF 1.9

MethodsX Pub Date : 2026-06-01 Epub Date: 2025-12-03 DOI: 10.1016/j.mex.2025.103747

Andy Zedet , Alison Aebischer , Marc Pudlo , Luca Marchisio , Samba Fama Ndoye , Corine Girard , François Senejoux

{"title":"An accessible HPLC-DAD method for the direct detection of acrolein-trapping compounds in complex plant matrices","authors":"Andy Zedet , Alison Aebischer , Marc Pudlo , Luca Marchisio , Samba Fama Ndoye , Corine Girard , François Senejoux","doi":"10.1016/j.mex.2025.103747","DOIUrl":"10.1016/j.mex.2025.103747","url":null,"abstract":"<div><div>Acrolein is a highly reactive α,β-unsaturated aldehyde implicated in numerous diseases and pathological conditions. Developing strategies to alleviate its harmful effects is thus of key importance, with scavengers that trap acrolein emerging as a promising approach. Recent efforts have focused on identifying effective phytoconstituents, but detecting active components in complex plant matrices remains a challenging and time-consuming task. This study introduces a new application of HPLC-DAD for the instantaneous detection of acrolein scavengers in such complex extracts. To mimic this chemical diversity and test the method’s efficiency, a multicomponent mixture of ten phytochemical standards was employed. The procedure involved pre-column incubation of the mixture with varying concentrations of acrolein, allowing for the selective identification of active components through signal reduction. The results were further validated through conventional evaluation of individual constituents, confirming the method's reliability.<ul><li><span>•</span><span><div>Development of a novel application of HPLC-DAD for the instantaneous detection of acrolein-trapping constituents in complex plant matrices</div></span></li><li><span>•</span><span><div>Application of the method to a ten-phytoconstituent mixture designed to simulate the chemical complexity of a plant extract</div></span></li><li><span>•</span><span><div>Validation of method efficiency through comparison with conventional scavenging evaluations of individual compounds</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"Article 103747"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145749535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A statistical inference framework for FSNBLR: Modeling underdeveloped regional status in Eastern Indonesia FSNBLR的统计推断框架：模拟印尼东部欠发达地区状况

IF 1.9

MethodsX Pub Date : 2026-06-01 Epub Date: 2025-12-01 DOI: 10.1016/j.mex.2025.103746

Muhammad Zulfadhli, I Nyoman Budiantara, Vita Ratnasari, Afiqah Saffa Suriaslan, Risdiana Chandra Dhewy

{"title":"A statistical inference framework for FSNBLR: Modeling underdeveloped regional status in Eastern Indonesia","authors":"Muhammad Zulfadhli, I Nyoman Budiantara, Vita Ratnasari, Afiqah Saffa Suriaslan, Risdiana Chandra Dhewy","doi":"10.1016/j.mex.2025.103746","DOIUrl":"10.1016/j.mex.2025.103746","url":null,"abstract":"<div><div>Persistent regional disparities in Indonesia, particularly in Eastern provinces, necessitate advanced modeling to understand underdevelopment determinants. This study enhances the Fourier Series Nonparametric Binary Logistic Regression (FSNBLR) model by introducing a statistical inference framework comprising simultaneous and partial hypothesis testing using the Likelihood Ratio Test (LRT). Applying the model to data from 232 regencies in Eastern Indonesia (2021) identifies infrastructure quality and local fiscal capacity as significant predictors of underdevelopment. Compared with the conventional Binary Logistic Regression (BLR), the FSNBLR with significant parameters demonstrates superior classification accuracy and lower AIC values, effectively capturing nonlinear relationships among predictors. The proposed framework strengthens the inferential foundation of FSNBLR and broadens its applicability to complex binary response analyses in socioeconomic studies. The highlights of this study are:</div><div>Developed inferential hypothesis testing for the FSNBLR model.</div><div>Implemented LRT for simultaneous and partial inference.</div><div>The FSNBLR model outperforms BLR model in capturing nonlinearities.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"Article 103746"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145749542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Synthesis of hydrazine-functionalized Polyrotaxane and formation of adaptable sliding hydrogels 肼功能化聚轮烷的合成及适应性滑动水凝胶的形成。

IF 1.9

MethodsX Pub Date : 2026-06-01 Epub Date: 2026-05-23 DOI: 10.1016/j.mex.2026.103970

Sarah J. Loveland , Xinming Tong , Hung-Pang Lee , Christine L. Zhou , Fan Yang

{"title":"Synthesis of hydrazine-functionalized Polyrotaxane and formation of adaptable sliding hydrogels","authors":"Sarah J. Loveland , Xinming Tong , Hung-Pang Lee , Christine L. Zhou , Fan Yang","doi":"10.1016/j.mex.2026.103970","DOIUrl":"10.1016/j.mex.2026.103970","url":null,"abstract":"<div><div>This work details the synthesis and characterization of Polyrotaxane-Hydrazine (PR-Hyd), a polyethylene glycol-based polymer utilized in the formation of Adaptable Sliding Hydrogels (ASG). ASG is a viscoelastic 3D cell culture system with reversible hydrazone bonds that can be leveraged to present cells with tunable stress relaxation and mechanical plasticity. ASG builds upon our lab’s previously reported Sliding Hydrogel (SG) platform which is formed via irreversible covalent crosslinking between norbornene functionalized Polyrotaxane (PR-NB) and a dithiol crosslinker. To generate PR-Hyd, we adapted and optimized the synthesis protocol for PR-NB and used proton NMR spectroscopy to quantify polymer functionality and reduce batch-to-batch variability. In addition, this work outlines our protocol for facile ASG scaffold formation using aldehyde functionalized polyethylene glycol macromers for crosslinking. Taken together, these methods enable reproducible synthesis of PR-Hyd and the fabrication of reversibly crosslinked ASG scaffolds that can be leveraged as a 3D cell niche for applications including regenerative medicine and in vitro disease modeling.<ul><li><span>•</span><span><div>Synthesis method for Polyrotaxane-Hydrazine, a polyethylene glycol-based polymer precursor designed for forming Adaptable Sliding Hydrogels (ASG).</div></span></li><li><span>•</span><span><div>Characterization of polymer functionality via proton NMR spectroscopy.</div></span></li><li><span>•</span><span><div>Reproducible fabrication of ASG hydrogels.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"Article 103970"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148150597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0