APL Bioengineering

This study explores the mechanisms of glucocorticoid-induced osteoporosis (OP) and Rheumatoid arthritis (RA), focusing on apoptosis and its role in the progression from RA to OP. Using microarray data from the GEO database, differential gene expression analysis was conducted with the limma package, identifying significant genes in RA and OP. Weighted Gene Co-expression Network Analysis (WGCNA) further examined gene relationships with the disease status, identifying co-expression patterns. Key genes were pinpointed by intersecting differentially expressed genes from RA and OP datasets with WGCNA module genes. Functional enrichment analysis using the “clusterProfiler” package focused on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. Machine learning methods, including Lasso and Random Forest, refined the selection of key genes related to apoptosis. Immune infiltration analysis using CIBERSORT assessed immune cell differences between disease and normal samples. The study highlighted two crucial genes: ATXN2L and MMP14. These genes were identified through various analyses and found to be significantly associated with the progression of RA and OP. Gene Set Enrichment Analysis of ATXN2L and MMP14 revealed their involvement in specific biological processes and pathways. Correlation analysis between these key genes and immune cell infiltration showed significant associations. The ROC analysis evaluated the diagnostic performance of ATXN2L and MMP14, with miRNA regulatory networks related to these genes also predicted. In summary, this research provides valuable insights into the molecular mechanisms of RA and OP, emphasizing the importance of apoptosis and immune processes.

Hao-Ju Lo

Chun-Hao Tsai

Tsan-Wen Huang

Apoptosis-associated genetic mechanisms in the transition from rheumatoid arthritis to osteoporosis: A bioinformatics and functional analysis approach

类风湿性关节炎向骨质疏松转变过程中与凋亡相关的遗传机制：生物信息学与功能分析方法

Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for analyzing complex medical data and extracting meaningful relationships in datasets, for several clinical aims. Specifically, in the brain care domain, several innovative approaches have achieved remarkable results and open new perspectives in terms of diagnosis, planning, and outcome prediction. In this work, we present an overview of different artificial intelligent techniques used in the brain care domain, along with a review of important clinical applications. A systematic and careful literature search in major databases such as Pubmed, Scopus, and Web of Science was carried out using “artificial intelligence” and “brain” as main keywords. Further references were integrated by cross-referencing from key articles. 155 studies out of 2696 were identified, which actually made use of AI algorithms for different purposes (diagnosis, surgical treatment, intra-operative assistance, and postoperative assessment). Artificial neural networks have risen to prominent positions among the most widely used analytical tools. Classic machine learning approaches such as support vector machine and random forest are still widely used. Task-specific algorithms are designed for solving specific problems. Brain images are one of the most used data types. AI has the possibility to improve clinicians' decision-making ability in neuroscience applications. However, major issues still need to be addressed for a better practical use of AI in the brain. To this aim, it is important to both gather comprehensive data and build explainable AI algorithms.

Alice Segato

Aldo Marzullo

Francesco Calimeri

Elena De Momi

Artificial intelligence for brain diseases: A systematic review

脑部疾病的人工智能：系统综述

Electrical stimulation (ES) shows promise as a therapy to promote recovery and regeneration after spinal cord injury. ES therapy establishes beneficial electric fields (EFs) and has been investigated in numerous studies, which date back nearly a century. In this review, we discuss the various engineering approaches available to generate regenerative EFs through direct current electrical stimulation and very low frequency electrical stimulation. We highlight the electrode-tissue interface, which is important for the appropriate choice of electrode material and stimulator circuitry. We discuss how to best estimate and control the generated field, which is an important measure for comparability of studies. Finally, we assess the methods used in these studies to measure functional recovery after the injury and treatment. This work reviews studies in the field of ES therapy with the goal of supporting decisions regarding best stimulation strategy and recovery assessment for future work.

Lukas Matter

Bruce Harland

Brad Raos

Darren Svirskis

Maria Asplund

Generation of direct current electrical fields as regenerative therapy for spinal cord injury: A review

直流电场作为脊髓损伤再生疗法的产生：综述

Understanding the detailed mechanisms driving fibroblast migration within native tissue settings during pathophysiological events presents a critical research challenge. In this study, we elucidate how stromal cells migrate and contribute to the development of three-dimensional (3D) cellular aggregates within confined microcavities. Integrin α5β1 and β-catenin (β-cat) are central in guiding this collective migration and achieving optimal filling of the microcavity. When β-cat is suppressed, cells tend to migrate more sporadically, leading to less efficient cellular organization. Furthermore, we also detail the pivotal roles of Cx43 and N-cadherin (N-cad) in orchestrating collective migration and in shaping efficient cellular stacking. Suppressing gap junctions, especially Cx43, significantly impacts the extracellular matrix expression, integrin α5 and β1, and other elements in the 3D construct, emphasizing the importance of physicochemical cell-cell interactions. The distribution patterns of N-cad and focal adhesion kinase (FAK) further corroborate the essential roles in forming cell-cell junctions and FAK in establishing the foundational layer that underpins the cell stacking within the microcavity. Interestingly, neither Rho-associated protein kinase (ROCK) nor RhoA significantly alter the cell migration pattern toward microcavity. These findings provide fresh perspectives on fibroblast activities in 3D space, enriching our understanding and offering implications for advancements in wound healing and tissue engineering.

Avelino Dos Santos Da Costa

Hyuntae Jeong

Ramesh Subbiah

Kwideok Park

In-Suk Choi

Jennifer H. Shin

Intercellular junction-driven stromal cell stacking in a confined 3D microcavity

细胞间连接驱动的受限三维微腔中基质细胞堆叠

RNA translation to protein is paramount to creating life, yet RNA and protein correlations vary widely across tissues, cells, and species. To investigate these perplexing results, we utilize a time-series fixation method that combines static stimulation and a programmable formaldehyde perfusion to map pseudo-Signaling with Omics signatures (pSigOmics) of single-cell data from hundreds of thousands of cells. Using the widely studied nuclear factor kappa B (NFκB) mammalian signaling pathway in mouse fibroblasts, we discovered a novel asynchronous pseudotime regulation (APR) between RNA and protein levels in the quintessential NFκB p65 protein using single molecule spatial imaging. Prototypical NFκB dynamics are successfully confirmed by the rise and fall of NFκB response as well as A20 negative inhibitor activity by 90 min. The observed p65 translational APR is evident in both statically sampled timepoints and dynamic response gradients from programmable formaldehyde fixation, which successfully creates continuous response measurements. Finally, we implement a graph neural network model capable of predicting APR cell subpopulations from GAPDH RNA spatial expression, which is strongly correlated with p65 RNA signatures. Successful decision tree classifiers on Potential of Heat-diffusion for Affinity-based Trajectory Embedding embeddings of our data, which illustrate partitions of APR cell subpopulations in latent space, further confirm the APR patterns. Together, our data suggest an RNA-protein regulatory framework in which translation adapts to signaling events and illuminates how immune signaling is timed across various cell subpopulations.

Nicholas Zhang

Mingshuang Wang

Dhruv Nambiar

Samyukta Iyer

Priyam Kadakia

Qianqi Luo

Sicheng Pang

Aaron Qu

Nivik Sanjay Bharadwaj

Peng Qiu

Ahmet F. Coskun

High cell throughput, programmable fixation reveals the RNA and protein co-regulation with spatially resolved NFκB pseudo-signaling

高通量细胞处理与可编程固定揭示空间解析的NFκB伪信号的RNA与蛋白质共调控

Gas vesicles (GVs) based on acoustic reporter genes have emerged as potent contrast agents for cellular and molecular ultrasound imaging. These air-filled, genetically encoded protein nanostructures can be expressed in a variety of cell types in vivo to visualize cell location and activity or injected systemically to label and monitor tissue function. Distinguishing GV signal from tissue deep inside intact organisms requires imaging approaches such as amplitude modulation (AM) or collapse-based pulse sequences. However, these approaches have limitations either in sensitivity or require the destruction of GVs, restricting the imaging of dynamic cellular processes. To address these limitations, we developed harmonic imaging to enhance the sensitivity of nondestructive GV imaging. We hypothesized that harmonic imaging, integrated with AM, could significantly elevate GV detection sensitivity by leveraging the nonlinear acoustic response of GVs. We tested this hypothesis by imaging tissue-mimicking phantoms embedded with purified GVs, mammalian cells genetically modified to express GVs, and mice liver in vivo post-systemic infusion of GVs. Our findings reveal that harmonic cross-propagating wave AM (HxAM) imaging markedly surpasses traditional xAM in isolating GVs' nonlinear acoustic signature, demonstrating significant (p &amp;lt; 0.05) enhancements in imaging performance. HxAM imaging improves detection of GV producing cells up to three folds in vitro, enhances in vivo imaging performance by over 10 dB, while extending imaging depth by up to 20%. Investigation into the backscattered spectra further elucidates the advantages of harmonic imaging. These advancements bolster ultrasound's capability in molecular and cellular imaging, underscoring the potential of harmonic signals to improve GV detection.

Rohit Nayak

Mengtong Duan

Bill Ling

Zhiyang Jin

Dina Malounda

Mikhail G. Shapiro

Harmonic imaging for nonlinear detection of acoustic biomolecules

非线性检测声学生物分子的谐波成像

The advent of advanced robotic platforms and workflow automation tools has revolutionized the landscape of biological research, offering unprecedented levels of precision, reproducibility, and versatility in experimental design. In this work, we present an automated and modular workflow for exploring cell behavior in two-dimensional culture systems. By integrating the BioAssemblyBot® (BAB) robotic platform and the BioApps™ workflow automater with live-cell fluorescence microscopy, our workflow facilitates execution and analysis of in vitro migration and proliferation assays. Robotic assistance and automation allow for the precise and reproducible creation of highly customizable cell-free zones (CFZs), or wounds, in cell monolayers and “hands-free,” schedulable integration with real-time monitoring systems for cellular dynamics. CFZs are designed as computer-aided design models and recreated in confluent cell layers by the BAB 3D-Bioprinting tool. The dynamics of migration and proliferation are evaluated in individual cells using live-cell fluorescence microscopy and an in-house pipeline for image processing and single-cell tracking. Our robotics-assisted approach outperforms manual scratch assays with enhanced reproducibility, adaptability, and precision. The incorporation of automation further facilitates increased flexibility in wound geometry and allows for many experimental conditions to be analyzed in parallel. Unlike traditional cell migration assays, our workflow offers an adjustable platform that can be tailored to a wide range of applications with high-throughput capability. The key features of this system, including its scalability, versatility, and the ability to maintain a high degree of experimental control, position it as a valuable tool for researchers across various disciplines.

短名	APL Bioeng.
Journal Impact	6.47
国际分区	ENGINEERING, BIOMEDICAL(Q1)
期刊索引	SCI Q1中科院 3 区

ISSN	2473-2877
h-index	34
国内分区	医学(3区)医学工程生物医学(3区)

涉及主题	生物医学化学遗传学生物化学内科学材料科学工程类物理细胞生物学心理学计算机科学纳米技术有机化学神经科学病理生物医学工程细胞基因
出版信息	出版商: American Institute of Physics，出版周期: ，期刊类型: journal，开源期刊: 是
基本数据	创刊年份: 2017，原创研究文献占比: 60.53%，自引率:1.50%， Gold OA占比: 93.26%
期刊费用	$2200详情
平均审稿周期	网友分享经验：19 Weeks

APL Bioengineering

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Apoptosis-associated genetic mechanisms in the transition from rheumatoid arthritis to osteoporosis: A bioinformatics and functional analysis approach

Artificial intelligence for brain diseases: A systematic review

Generation of direct current electrical fields as regenerative therapy for spinal cord injury: A review

Intercellular junction-driven stromal cell stacking in a confined 3D microcavity

High cell throughput, programmable fixation reveals the RNA and protein co-regulation with spatially resolved NFκB pseudo-signaling

Harmonic imaging for nonlinear detection of acoustic biomolecules

Intercellular junction-driven stromal cell stacking in a confined 3D microcavity

High cell throughput, programmable fixation reveals the RNA and protein co-regulation with spatially resolved NFκB pseudo-signaling

Apoptosis-associated genetic mechanisms in the transition from rheumatoid arthritis to osteoporosis: A bioinformatics and functional analysis approach

A programmable platform for probing cell migration and proliferation