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Bok's equi-volume principle: Translation, historical context, and a modern perspective

Brain Multiphysics 2022年 3卷

作者： Consolini, Jack Demirci, Nagehan Fulwider, Andrew Hutsler, Jeffrey J. Holland, Maria A. Department of Aerospace and Mechanical Engineering University of Notre Dame Notre Dame 46556 IN United States Bioengineering Graduate Program University of Notre Dame Notre Dame 46556 IN United States Department of Chemical and Biomolecular Engineering University of Notre Dame Notre Dame 46556 IN United States Department of German and Russian Languages and Literatures University of Notre Dame Notre Dame 46556 IN United States Program in Neuroscience University of Nevada Reno 89557 NV United States

The human brain has a complex and unique structure, characterized by intricate three-dimensional folds. These folds, and the mechanisms for their formation, have been studied for over a hundred years. Here we offer a full translation of the pivotal (1929) work by Siegfried Bok, “Der Einflußder in den Furchen und Windungen auftretenden Krümmungen der Großhirnrinde auf die Rindenarchitektur” (“The Influence of the Curvature Occurring in the Folds and Turns of the Cerebral Cortex on Cortical Architecture”). This paper established the influential equi-volume principle, which stated that cortical and laminar thicknesses, along with neuronal shape and fiber orientation, change in order to preserve relative volume throughout the folds of the cortex. We also offer a commentary on the main points of the work, looking at Bok's observations and predictions regarding the structure of neurons, cortical laminae, and the cortex itself, throughout the folds and curves of the brain. His equi-volume principle has held up to decades of experimentation and, even today, has important implications for the analysis of brain structure and function. Statement of Significance: This manuscript presents, for the first time, a full English translation of the foundational neuroanatomy article, “Der Einflußder in den Furchen und Windungen auftretenden Krümmungen der Großhirnrinde auf die Rindenarchitektur” (“The Influence of the Curvature Occurring in the Folds and Turns of the Cerebral Cortex on Cortical Architecture”), written over 90 years ago by Siegfried T. Bok and heavily cited since then. In addition, we provide an assessment of Bok's main points, in light of his contemporaries in research at the time, as well as more recent work during the intervening decades. © 2022 The Author(s)

关键词： Cortical folding Cortical laminae Cortical thickness Neuroanatomy

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Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples

arXiv

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arXiv 2023年

作者： Song, Andrew H. Williams, Mane Williamson, Drew F.K. Jaume, Guillaume Zhang, Andrew Chen, Bowen Serafin, Robert Liu, Jonathan T.C. Baras, Alex Parwani, Anil V. Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Department of Mechanical Engineering Bioengineering Laboratory Medicine & Pathology University of Washington SeattleWA United States Department of Pathology Johns Hopkins Hospital BaltimoreMD United States Department of Biomedical Engineering Johns Hopkins Hospital BaltimoreMD United States Department of Pathology The Ohio State University ColumbusOH United States

Human tissue consists of complex structures that display a diversity of morphologies, forming a tissue microenvironment that is, by nature, three-dimensional (3D). However, the current standard-of-care involves slicing 3D tissue specimens into two-dimensional (2D) sections and selecting a few for microscopic evaluation1,2, with concomitant risks of sampling bias and misdiagnosis3–6. To this end, there have been intense efforts to capture 3D tissue morphology and transition to 3D pathology, with the development of multiple high-resolution 3D imaging modalities7–18. However, these tools have had little translation to clinical practice as manual evaluation of such large data by pathologists is impractical and there is a lack of computational platforms that can efficiently process the 3D images and provide patient-level clinical insights. Here we present Modality-Agnostic Multiple instance learning for volumetric Block Analysis (MAMBA), a deep-learning-based platform for processing 3D tissue images from diverse imaging modalities and predicting patient outcomes. Archived prostate cancer specimens were imaged with open-top light-sheet microscopy12–14 or microcomputed tomography15,16 and the resulting 3D datasets were used to train risk-stratification networks based on 5-year biochemical recurrence outcomes via MAMBA. With the 3D block-based approach, MAMBA achieves an area under the receiver operating characteristic curve (AUC) of 0.86 and 0.74, superior to 2D traditional single-slice-based prognostication (AUC of 0.79 and 0.57), suggesting superior prognostication with 3D morphological features. Further analyses reveal that the incorporation of greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, suggesting that there is value in capturing larger extents of spatially heterogeneous 3D morphology. With the rapid growth and adoption of 3D spatial biology and pathology techniques by researchers and clinicians, M

关键词： Pathology

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Prediction of cardiac differentiation in human induced pluripotent stem cell-derived cardiomyocyte supernatant using surface-enhanced Raman spectroscopy and machine learning

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Biosensors and Bioelectronics 2025年 283卷 117528页

作者： Echeverría-Altamar, Karla Barreto-Gamarra, Carlos Domenech-García, Maribella Resto-Irizarry, Pedro Bioengineering Graduate Program University of Puerto Rico at Mayagüez Mayagüez 00680 Puerto Rico Chemical Engineering Department University of Puerto Rico at Mayagüez Mayagüez 00680 Puerto Rico Mechanical Engineering Department University of Puerto Rico at Mayagüez Mayagüez 00680 Puerto Rico

The efficient manufacturing of cardiomyocytes from human-induced pluripotent stem cells (hiPSCs) is essential for advancing regenerative therapies for myocardial injuries. However, ensuring cell quality during production is challenging since traditional methods are invasive, destructive, and time-consuming. In this study, we monitored cardiomyocyte differentiation of WTC11 hiPSCs by analyzing conditioned media collected at various stages using Raman spectroscopy, multivariate analysis, and machine learning. Differentiation efficiency was confirmed via flow cytometry and immunostaining. Raman spectra were processed using standard normal variate and second derivative transformations before performing a principal component analysis (PCA) and machine learning (Random Forest, K-Nearest Neighbors, and Deep Neural Networks [DNN]). Results show that PCA was unable to distinguish cells based on differentiation stages, while machine learning could reliably predict cell differentiation early in the cardiac cell manufacturing process. DNN models achieved accuracies exceeding 82 % in predicting differentiation, highlighting their potential as quality control tools. These findings underscore the potential of Raman spectroscopy coupled with machine learning as a tool for real-time monitoring of cardiomyocyte production. © 2025 The Authors

关键词： Deep neural networks

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Reconstruction of 3d human lymphatic vessel within tumor immune microenvironment using high-throughput platform 24

Reconstruction of 3d human lymphatic vessel within tumor imm...

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24th International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2020

作者： Kang, Habin Lee, Somin Park, Dohyun Yu, James Koh, Seung Kwon Cho, Duck Kim, Da-Hyun Kang, Kyung-Sun Jeon, Noo Li Interdisciplinary Program for Bioengineering Seoul National University Korea Republic of Department of Mechanical Engineering Seoul National University Korea Republic of Department of Health Sciences and Technology Sungkyunkwan University Korea Republic of Adult Stem Cell Research Center Research Institute for Veterinary Science Seoul National University Korea Republic of

ISBN: (纸本)9781733419017

In this study, we established a 3D in vitro model of human lymphatic vessel (LV) within tumor immune microenvironment (TIME) using an injection-molded plastic array culture platform. Our platform enabled robust and reproducible formation of self-organized LV within the TIME in vitro. Using this co-culture model, we could (1) recapitulate cancer cell type-dependent morphogenesis of LV in vitro, (2) assess the effect of anti-VEGFR3 drug depending on the existence of blood vessels or different types of cancer cells through high-content analysis, and (3) design a trans-endothelial migration assay of cytotoxic primary lymphocytes within reconstructed melanoma TIME. © 2020 CBMS-0001

关键词： Cancer cells

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Self-learning how to swim at low Reynolds number

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Physical Review Fluids 2020年第7期5卷 074101-074101页

作者： Alan Cheng Hou Tsang Pun Wai Tong Shreyes Nallan On Shun Pak Department of Mechanical Engineering Santa Clara University Santa Clara California 95053 USA Department of Bioengineering Stanford University Stanford California 94305 USA Clinical Genomics Program Stanford Health Care Stanford California 94305 USA

Designing locomotory gaits for synthetic microswimmers has been a challenge due to stringent constraints on self-propulsion at low Reynolds numbers (Re). Here, we introduce a new theoretical approach of designing a class of self-learning, adaptive (or “smart”) microswimmers via reinforcement learning. Diverging from the traditional paradigm of specifying locomotory gaits a priori, here a self-learning swimmer can develop and adapt its propulsion strategy based on its interactions with the surrounding medium. We illustrate this new approach using a minimal but representative model swimmer consisting of an assembly of spheres connected by extensible rods. Without requiring any prior knowledge of low Re locomotion, we demonstrate that this self-learning swimmer can recover a previously known propulsion strategy, identify more effective locomotory gaits, and adapt its locomotory gaits in different media. This approach opens an alternative avenue to designing the next generation of smart microrobots with robust locomotive capabilities.

关键词： Biomechanics Locomotion Swimming Microswimmers

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Bioinspired electronics for intelligent soft robots

Nature Reviews Electrical Engineering

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Nature Reviews Electrical engineering 2024年 597-613页

作者： Junhyuk Bang Seok Hwan Choi Kyung Rok Pyun Yeongju Jung Sangwoo Hong Dohyung Kim Youngseok Lee Daeyeon Won Seongmin Jeong Wooseop Shin Seung Hwan Ko Department of Mechanical Engineering Seoul National University Seoul Republic of Korea Institute of Engineering Research/Institute of Advanced Machinery and Design (SNU-IAMD) Seoul National University Seoul Republic of Korea Interdisciplinary Program in Bioengineering Seoul National University Gwanak-ro Gwanak-gu Seoul Republic of Korea

Soft robots, capable of safe interaction with delicate objects through their flexibility and compliance, are attracting attention in various real-world applications as manipulators, biomedical devices and wearable tools. As these technologies advance, the ability to perform complex tasks in a robust and reliable way becomes essential. Thus, the incorporation of embedded intelligence in soft robots, which enables them to perceive external environments and generate appropriate actions, is increasingly important. Inspiration from sophisticated biological systems, which exhibit optimized behaviours through the acquisition of external information, promotes the development of intelligent soft robots. Here, we introduce biomimicry strategies for intelligent soft robotics and highlight progress in how soft robots interact with their environment and perform tasks. First, we discuss sensors inspired by the sensory nervous systems and soft actuators inspired by the musculoskeletal systems. Furthermore, we investigate various applications such as manipulation, exploration, wearable devices, biomedical devices and imperceptible devices. We conclude discussing the challenges and offering a perspective on the future direction of this field.

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A review of fluid-structure interaction: blood flow in arteries

Biomedical Engineering Advances

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Biomedical engineering Advances 2025年 9卷

作者： Zubeir Allum Saib Farid Abed Mergen H. Ghayesh Marco Amabili Biomedical Engineering Graduate Program American University of Sharjah Sharjah United Arab Emirates Department of Bioengineering New York University Abu Dhabi Abu Dhabi United Arab Emirates Department of Civil Engineering American University of Sharjah Sharjah United Arab Emirates School of Electrical and Mechanical Engineering University of Adelaide South Australia Australia School of Engineering Westlake University Hangzhou Zhejiang province China

Over the past decade, Fluid-Structure Interaction studies related to blood vessels have been an active area of research, as they adequately capture the multiphysics of blood flow within the circulatory system. Despite the growing interest, only few state-of-the-art reviews have been published in the literature, each focusing individually on the coronary artery, carotid artery, aorta, heart valves and peripheral arteries. This systematic review assesses the current research and implications of Fluid-Structure Interaction implementation strategies in relation to human arteries. It is meant to comprehensively amalgamate research studies on an array of arteries coupled with cardiovascular complications such as atherosclerosis, plaque calcification, aneurysms, aortic dissections and valve dysfunction. It additionally covers computational finite element and finite volume solver demands, coupling schemes, inlet and outlet boundary conditions specifications, Newtonian and non-Newtonian blood rheological properties, laminar and turbulent flow types, as well as the modelling of the vessel wall’s hyperelastic and viscoelastic mechanical behavior. The research information is retrieved from the last ten years and summarized in a tabulated format, to help researchers in easily extracting useful information for future investigations and reviews.

关键词： Fluid-structure interaction Computational fluid dynamics Finite element analysis Blood flow Hemodynamics Biomechanics Atherosclerosis Aneurysm Calcification Artery Plaque Valve

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Erratum: Bok's equi-volume principle: Translation, historical context, and a modern perspective

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Brain Multiphysics 2024年 7卷

作者： Jack Consolini Nagehan Demirci Andrew Fulwider Jeffrey J. Hutsler Maria A. Holland Department of Aerospace and Mechanical Engineering University of Notre Dame Notre Dame IN 46556 USA Bioengineering Graduate Program University of Notre Dame Notre Dame IN 46556 USA Department of Chemical and Biomolecular Engineering University of Notre Dame Notre Dame IN 46556 USA Department of German and Russian Languages and Literatures University of Notre Dame Notre Dame IN 46556 USA Program in Neuroscience University of Nevada Reno NV 89557 USA

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AI-driven 3D Spatial Transcriptomics

arXiv

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arXiv 2025年

作者： Almagro-Pérez, Cristina Song, Andrew H. Weishaupt, Luca Kim, Ahrong Jaume, Guillaume Williamson, Drew F.K. Hemker, Konstantin Lu, Ming Y. Singh, Kritika Chen, Bowen Le, Long Phi Baras, Alexander S. Jiang, Sizun Bashashati, Ali Liu, Jonathan T.C. Mahmood, Faisal Department of Pathology Mass General Brigham Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Department of Pathology Pusan National University Busan Korea Republic of Department of Pathology and Laboratory Medicine Emory University School of Medicine AtlantaGA United States Department of Computer Science & Technology University of Cambridge Cambridge United Kingdom Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Rutgers Robert Wood Johnson Medical School New BrunswickNJ United States Department of Pathology Johns Hopkins Hospital BaltimoreMD United States Department of Biomedical Engineering Johns Hopkins Hospital BaltimoreMD United States Broad Institute of Harvard and MIT CambridgeMA United States Center for Virology and Vaccine Research Beth Israel Deaconess Medical Center Harvard Medical School BostonMA United States Department of Pathology Dana Farber Cancer Institute BostonMA United States Department of Pathology and Laboratory Medicine University of British Columbia VancouverBC Canada School of Biomedical Engineering University of British Columbia VancouverBC Canada Department of Mechanical Engineering Bioengineering Laboratory Medicine & Pathology University of Washington SeattleWA United States Harvard Data Science Initiative Harvard University CambridgeMA United States

A comprehensive three-dimensional (3D) map of tissue architecture and gene expression is crucial for illuminating the complexity and heterogeneity of tissues across diverse biomedical applications1. However, most spatial transcriptomics (ST) approaches remain limited to two-dimensional (2D) sections of tissue2–4. Although current 3D ST methods hold promise, they typically require extensive tissue sectioning, are complex, are not compatible with non-destructive 3D tissue imaging technologies, and often lack scalability5–8. Here, we present VOlumetrically Resolved Transcriptomics EXpression (VORTEX), an AI framework that leverages 3D tissue morphology and minimal 2D ST to predict volumetric 3D ST. By pretraining on diverse 3D morphology–transcriptomic pairs from heterogeneous tissue samples and then fine-tuning on minimal 2D ST data from a specific volume of interest, VORTEX learns both generic tissue-related and sample-specific morphological correlates of gene expression. This approach enables dense, high-throughput, and fast 3D ST, scaling seamlessly to large tissue volumes far beyond the reach of existing 3D ST techniques. By offering a cost-effective and minimally destructive route to obtaining volumetric molecular insights, we anticipate that VORTEX will accelerate biomarker discovery and our understanding of morphomolecular associations and cell states in complex tissues. Interactive 3D ST volumes can be viewed at https://***/. © 2025, CC BY-NC-ND.

关键词： Vortex flow

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Spontaneous and Induced Oscillations in Confined Epithelia

PRX Life

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PRX Life 2025年第1期3卷 013002-013002页

作者： Toshi Parmar Liam P. Dow Beth L. Pruitt M. Cristina Marchetti Department of Physics University of California Santa Barbara Santa Barbara California 93106 USA Interdisciplinary Program in Quantitative Biosciences University of California Santa Barbara Santa Barbara California 93106 USA Departments of Bioengineering and of Mechanical Engineering University of California Santa Barbara Santa Barbara California 93106 USA

The feedback between mechanical and chemical signals plays a key role in controlling many biological processes and collective cell behavior. Here we focus on the emergence of spatiotemporal density waves in a one-dimensional “cell train.” Combining a minimal theoretical model with in vitro experiments on MDCK epithelial cells confined to a linear pattern, we examine the spontaneous oscillations driven by feedback between myosin activation and mechanical deformations, as well as their effect on the response of the tissue to externally applied deformations. We show that the nature and frequency of spontaneous oscillations is controlled by the size of the cell train, with a transition from size-dependent standing waves to intrinsic spontaneous waves at the natural frequency of the tissue. The response to external boundary perturbations exhibits a resonance at this natural frequency, providing a possible venue for inferring the mechanochemical couplings that control the tissue behavior from rheological experiments.

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