The elucidation of the molecular architecture of cell membranes is a central goal for cell biology, as structure lies at the heart of function. The erythrocyte plasma membrane has long provided a favored testing ground for this inquiry. Human red blood cells are readily available, relatively homogeneous, and relevant to medicine. Their plasma membranes can be easily isolated intact and essentially free of contamination from other cells, organelles, and cytoplasmic contents. This membrane is complex enough to be interesting and, to some degree, representative, yet it is simple enough to be analyzed as a whole. These circumstances make it likely that the human red cell plasma membrane will be the first whose molecular anatomy is known in any degree of satisfying detail. The literature concerning the proteins of erythrocyte membranes and membranes in general has been the subject of repeated review (1 9). This article will focus on the localization and modes of association of individual major polypeptides within the human red cell membrane.
The discovery that experience-driven changes in the human brain can occur from a neural to a cortical level throughout the lifespan has stimulated a proliferation of research into how neural function changes in response to experience, enabled by neuroimaging methods such as positron emission tomography and functional magnetic resonance imaging. Studies attempt to characterize these changes by examining how practice on a task affects the functional anatomy underlying performance. Results are incongruous, including patterns of increases, decreases and functional reorganization of regional activations. Following an extensive review of the practice-effects literature, we distinguish a number of factors affecting the pattern of practice effects observed, including the effects of task domain, changes at the level of behavioural and cognitive processes, the time-window of imaging and practice, and of a number of other influences and miscellaneous confounding factors. We make a novel distinction between patterns of reorganization and redistribution as effects of task practice on brain activation, and emphasize the need for careful attention to practice-related changes occurring on the behavioural, cognitive and neural levels of analysis. Finally, we suggest that functional and effective connectivity analyses may make important contributions to our understanding of changes in functional anatomy occurring as a result of practice on tasks.
D. S. Chutkov, D. V. Tikhvinskii, A. V. Dubovoy
et al.
Diseases of the brachiocephalic arteries are among the most common pathologies of the human cardiovascular system. Stenosis of the carotid bifurcation is treated surgically by performing either carotid endarterectomy or carotid artery stenting. Endarterectomy can be performed using different techniques, including variations in incision shape and the use or non-use of a patch. The advantages of each option are debated within the scientific community and depend on the arterial anatomy and other patient-specific factors.The aim of this study was to conduct a statistical analysis of the anatomical characteristics of the common carotid artery bifurcation in order to identify relationships within and between samples of patients with stenosis and those without significant carotid artery pathology.Results. It was demonstrated that, in the group of patients without pathology, there is a regression relationship (p < 0.007) between the diameters of the common, internal, and external carotid arteries on both the left and right sides. In the pathology group, such relationships are absent. In addition, statistically significant differences were found between the groups in the diameters of the common carotid artery (p = 0.0004) and the external carotid artery (p = 0.0003), length of the carotid sinus (p = 0.05) and common carotid artery (p = 0.01). No significant differences were identified between the groups in the diameter of the internal carotid artery, the carotid sinus, or the branching angles of the daughter arteries.Conclusions. The results obtained may be highly useful for constructing numerical models of carotid arteries under normal conditions and in stenosis, as well as for manufacturing laboratory phantoms or for diagnostic purposes. The obtained results hold promise for application in both fundamental and applied research: in the development of numerical models of carotid arteries in normal and stenotic conditions, as well as in the design of laboratory phantoms and the refinement of diagnostic techniques for this pathology.
Natalia A. Pluta, Manuela Gaviria, Casey M. Sabbag
et al.
Peripheral nerve injuries (PNIs) commonly result from trauma, compression, or iatrogenic causes, leading to functional deficits. Despite the peripheral nervous system’s regenerative capacity, current treatments yield inconsistent outcomes. Basic science and translational research supporting nerve repair remain underdeveloped, partly due to the absence of standardized protocols, limiting reproducibility. Animal models are essential for studying injury mechanisms, repair strategies, and therapeutic development. This review examines commonly used animal models in PNI research, from non-mammalian species to rodents and large mammals. We discuss the relevance of injury types, experimental variables (i.e., age, sex, nerve type), and study design elements (i.e., nerve gap size, injury induction methods). Assessing these models’ strengths and limitations, this review aims to guide researchers in selecting appropriate models that enhance preclinical relevance. It also addresses the need for standardized protocols and future directions for improving PNI research and patient outcomes. Various PNI treatments—including microsurgery, nerve grafts, scaffolds, stem cells, immunomodulators, nerve augmentation strategies, and polyethylene glycol-mediated fusion—have been developed using animal models. These models are essential for driving innovation and translating emerging therapies to improve outcomes across a broad range of peripheral nerve injuries.
Objective This quasi-experimental study aimed to evaluate the impact of a flipped classroom (FC) combined with case-based learning (CBL) on the academic performance of first-year clinical medicine students in a human anatomy course in China, with a specific focus on higher-order cognitive skills and self-efficacy.Study design A quasi-experimental design was implemented, with participants randomly assigned to an intervention group (flipped classroom case learning (FCCL), n=64) or a control group (traditional lecture-based instruction, n=64). Learning outcomes and cognitive levels were compared between the two groups.Setting The study was conducted at a medical school in Meizhou, China, over an 18-week period. The curriculum covered the anatomy of nine major organ systems (excluding the nervous system).Participants A total of 128 first-year clinical medicine students participated. The FCCL group (63.5% male) had a mean age of 19.13±1.351 years, and the traditional group (67.1% male) had a mean age of 19.33±1.481 years. No significant differences were found in gender (p=0.580) or age (p=0.414) between the groups.Interventions The FCCL group engaged in pre-class activities via the ChaoXing platform, which included instructional videos, key concept outlines and clinical cases. In-class sessions were dedicated to group discussions, specimen practice and case analysis. The control group received traditional PowerPoint-based lectures and completed post-class assignments. Both groups were taught by the same instructors, shared identical learning objectives and used the same laboratory materials.Main outcome measures Outcomes included scores on a theoretical examination (TCE) and a laboratory examination (LCE), both designed based on Bloom’s taxonomy; responses on a self-efficacy questionnaire (incorporating Likert-scale and open-ended items); and qualitative analysis of reflective journals from the FCCL group.Results No significant difference was observed in TCE scores between the FCCL and traditional groups (59.52%±15.67% vs 55.5%±14.31%, p=0.136). However, the FCCL group scored significantly higher on the LCE (65.94%±13.71% vs 57.27%±16.95%, p=0.004). Furthermore, the intervention group demonstrated superior performance on higher-order cognitive questions (application-type: +8%, p=0.036; analysis-type: +11%, p=0.009). Questionnaire results indicated that the FCCL approach enhanced students’ learning motivation, critical thinking and collaborative skills (mean Likert scores >4.5).Conclusion The integration of FC with CBL effectively enhanced medical students’ higher-order cognitive abilities in anatomy, particularly in practical application and analytical skills, although its effect on the short-term retention of theoretical knowledge was limited. This approach offers a viable pathway for reforming anatomy education, though future studies with larger samples and longer follow-up are warranted.
Jules Lecomte, Konrad Zinner, Michael Neumeier
et al.
Human machine interaction is a huge source of inspiration in today's media art and digital design, as machines and humans merge together more and more. Its place in art reflects its growing applications in industry, such as robotics. However, those interactions often remains too technical and machine-driven for people to really engage into. On the artistic side, new technologies are often not explored in their full potential and lag a bit behind, so that state-of-the-art research does not make its way up to museums and exhibitions. Machines should support people's imagination and poetry in a seamless interface to their body or soul. We propose an artistic sound installation featuring neuromorphic body sensing to support a direct yet non intrusive interaction with the visitor with the purpose of creating sound scapes together with the machine. We design a neuromorphic multihead human pose estimation neural sensor that shapes sound scapes and visual output with fine body movement control. In particular, the feature extractor is a spiking neural network tailored for a dedicated neuromorphic chip. The visitor, immersed in a sound atmosphere and a neurally processed representation of themselves that they control, experience the dialogue with a machine that thinks neurally, similarly to them.
Debasmita Ghose, Oz Gitelson, Marynel Vazquez
et al.
To collaborate with humans, robots must infer goals that are often ambiguous, difficult to articulate, or not drawn from a fixed set. Prior approaches restrict inference to a predefined goal set, rely only on observed actions, or depend exclusively on explicit instructions, making them brittle in real-world interactions. We present BALI (Bidirectional Action-Language Inference) for goal prediction, a method that integrates natural language preferences with observed human actions in a receding-horizon planning tree. BALI combines language and action cues from the human, asks clarifying questions only when the expected information gain from the answer outweighs the cost of interruption, and selects supportive actions that align with inferred goals. We evaluate the approach in collaborative cooking tasks, where goals may be novel to the robot and unbounded. Compared to baselines, BALI yields more stable goal predictions and significantly fewer mistakes.
Developing human-controllable artificial intelligence (AI) and achieving meaningful human control (MHC) has become a vital principle to address these challenges, ensuring ethical alignment and effective governance in AI. MHC is also a critical focus in human-centered AI (HCAI) research and application. This chapter systematically examines MHC in AI, articulating its foundational principles and future trajectory. MHC is not simply the right to operate, but the unity of human understanding, intervention, and the traceablity of responsibility in AI decision-making, which requires technological design, AI governance, and humans to play a role together. MHC ensures AI autonomy serves humans without constraining technological progress. The mode of human control needs to match the levels of technology, and human supervision should balance the trust and doubt of AI. For future AI systems, MHC mandates human controllability as a prerequisite, requiring: (1) technical architectures with embedded mechanisms for human control; (2) human-AI interactions optimized for better access to human understanding; and (3) the evolution of AI systems harmonizing intelligence and human controllability. Governance must prioritize HCAI strategies: policies balancing innovation and risk mitigation, human-centered participatory frameworks transcending technical elite dominance, and global promotion of MHC as a universal governance paradigm to safeguard HCAI development. Looking ahead, there is a need to strengthen interdisciplinary research on the controllability of AI systems, enhance ethical and legal awareness among stakeholders, moving beyond simplistic technology design perspectives, focus on the knowledge construction, complexity interpretation, and influencing factors surrounding human control. By fostering MHC, the development of human-controllable AI can be further advanced, delivering HCAI systems.
Chengbo Zheng, Tim Miller, Alina Bialkowski
et al.
High stakes decision-making often requires a continuous interplay between evolving evidence and shifting hypotheses, a dynamic that is not well supported by current AI decision support systems. In this paper, we introduce a mixed-initiative framework for AI assisted decision making that is grounded in the data-frame theory of sensemaking and the evaluative AI paradigm. Our approach enables both humans and AI to collaboratively construct, validate, and adapt hypotheses. We demonstrate our framework with an AI-assisted skin cancer diagnosis prototype that leverages a concept bottleneck model to facilitate interpretable interactions and dynamic updates to diagnostic hypotheses.
Dibakar Borthakur, Rajesh Kumar, Kishore Sesham
et al.
Background: Schwannoma and neurofibroma constitute benign peripheral nerve sheath tumors (BPNSTs), which may present as single or multiple lesions. Multiple schwannomas are rare in the peripheral nerves of the upper extremity. Solitary small schwannoma is usually asymptomatic, while larger and multiple schwannomas may have different symptoms due to pressure effects. Median and ulnar nerve communications are often encountered in the upper limb, making these anastomoses clinically important. However, such communications with schwannomas in the axilla are rare.
Case Report: The study protocol was designed per the prevailing guidelines of the Institute on the use of human cadavers for teaching and research. Written informed consent was obtained from the family of the body donor at the time of whole-body donation. The upper extremities, including the brachial plexuses of both sides, were carefully dissected as per the instructions in Cunningham’s Manual of Practical Anatomy15th edition.
Discussion: Unusual anastomoses in the axilla and two encapsulated, highly vascular fusiform masses were observed. Another fairly large mass was noted on the right chest wall. Histological examination confirmed the masses to be schwannomas.
Conclusion: Asymptomatic schwannomas often remain undiagnosed, and those lying in the axilla can be misdiagnosed. Awareness of such variant anatomy will enable clinicians and surgeons to plan more appropriate diagnostic and therapeutic interventions.
Immunologic diseases. Allergy, Diseases of the circulatory (Cardiovascular) system
Toru Higaki, Fuminari Tatsugami, Mickaël Ohana
et al.
Purpose: Super-resolution deep-learning-based reconstruction: SR-DLR is a newly developed and clinically available deep-learning-based image reconstruction method that can improve the spatial resolution of CT images. The image quality of the output from non-linear image reconstructions, such as DLR, is known to vary depending on the structure of the object being scanned, and a simple phantom cannot explicitly evaluate the clinical performance of SR-DLR. This study aims to accurately investigate the quality of the images reconstructed by SR-DLR by utilizing a structured phantom that simulates the human anatomy in coronary CT angiography. Methods: The structural phantom had ribs and vertebrae made of plaster, a left ventricle filled with dilute contrast medium, a coronary artery with simulated stenosis, and an implanted stent graft. By scanning the structured phantom, we evaluated noise and spatial resolution on the images reconstructed with SR-DLR and conventional reconstructions. Results: The spatial resolution of SR-DLR was higher than conventional reconstructions; the 10 % modulation transfer function of hybrid IR (HIR), DLR, and SR-DLR were 0.792-, 0.976-, and 1.379 cycle/mm, respectively. At the same time, image noise was lowest (HIR: 21.1-, DLR: 19.0-, and SR-DLR: 13.1 HU). SR-DLR could accurately assess coronary artery stenosis and the lumen of the implanted stent graft. Conclusions: SR-DLR can obtain CT images with high spatial resolution and lower noise without special CT equipments, and will help diagnose coronary artery disease in CCTA and other CT examinations that require high spatial resolution.
Medical physics. Medical radiology. Nuclear medicine
Statistical Shape Modeling (SSM) effectively analyzes anatomical variations within populations but is limited by the need for manual localization and segmentation, which relies on scarce medical expertise. Recent advances in deep learning have provided a promising approach that automatically generates statistical representations (as point distribution models or PDMs) from unsegmented images. Once trained, these deep learning-based models eliminate the need for manual segmentation for new subjects. Most deep learning methods still require manual pre-alignment of image volumes and bounding box specification around the target anatomy, leading to a partially manual inference process. Recent approaches facilitate anatomy localization but only estimate population-level statistical representations and cannot directly delineate anatomy in images. Additionally, they are limited to modeling a single anatomy. We introduce MASSM, a novel end-to-end deep learning framework that simultaneously localizes multiple anatomies, estimates population-level statistical representations, and delineates shape representations directly in image space. Our results show that MASSM, which delineates anatomy in image space and handles multiple anatomies through a multitask network, provides superior shape information compared to segmentation networks for medical imaging tasks. Estimating Statistical Shape Models (SSM) is a stronger task than segmentation, as it encodes a more robust statistical prior for the objects to be detected and delineated. MASSM allows for more accurate and comprehensive shape representations, surpassing the capabilities of traditional pixel-wise segmentation.
Byunghwee Lee, Rachith Aiyappa, Yong-Yeol Ahn
et al.
Beliefs form the foundation of human cognition and decision-making, guiding our actions and social connections. A model encapsulating beliefs and their interrelationships is crucial for understanding their influence on our actions. However, research on belief interplay has often been limited to beliefs related to specific issues and relied heavily on surveys. We propose a method to study the nuanced interplay between thousands of beliefs by leveraging an online user debate data and mapping beliefs onto a neural embedding space constructed using a fine-tuned large language model (LLM). This belief space captures the interconnectedness and polarization of diverse beliefs across social issues. Our findings show that positions within this belief space predict new beliefs of individuals and estimate cognitive dissonance based on the distance between existing and new beliefs. This study demonstrates how LLMs, combined with collective online records of human beliefs, can offer insights into the fundamental principles that govern human belief formation.