Hasil untuk "Human anatomy"

Yuying Tang, Jiayi Zhou, Haotian Li et al.

Generative AI has greatly transformed creative work in various domains, such as screenwriting. To understand this transformation, prior research often focused on capturing a snapshot of human-AI co-creation practice at a specific moment, with less attention to how humans mobilize, regulate, and reflect to form the practice gradually. Motivated by Bandura's theory of human agency, we conducted a two-week study with 19 professional screenwriters to investigate how they embraced AI in their creation process. Our findings revealed that screenwriters not only mindfully planned, foresaw, and responded to AI usage, but, more importantly, through reflections on practice, they developed themselves and human-AI co-creation paradigms, such as cognition, strategies, and workflows. They also expressed various expectations for how future AI should better support their agency. Based on our findings, we conclude this paper with extensive discussion and actionable suggestions to screenwriters, tool developers, and researchers for sustainable human-AI co-creation.

Bingsheng Yao, Chaoran Chen, April Yi Wang et al.

The emergence of Large Language Model (LLM) agents enables us to build agent-based intelligent systems that move beyond the role of a "tool" to become genuine collaborators with humans, thereby realizing a novel human-agent collaboration paradigm. Our vision is that LLM agents should resemble remote human collaborators, which allows HCI researchers to ground the future exploration in decades of research on trust, awareness, and common ground in remote human collaboration, while also revealing the unique opportunities and challenges that emerge when one or more partners are AI agents. This workshop establishes a foundational research agenda for the new era by posing the question: How can the rich understanding of remote human collaboration inspire and inform the design and study of human-agent collaboration? We will bring together an interdisciplinary group from HCI, CSCW, and AI to explore this critical transition. The 180-minute workshop will be highly interactive, featuring a keynote speaker, a series of invited lightning talks, and an exploratory group design session where participants will storyboard novel paradigms of human-agent partnership. Our goal is to enlighten the research community by cultivating a shared vocabulary and producing a research agenda that charts the future of collaborative agents.

Yujie Zhang, Jingran Hao, Yueyao Feng et al.

ABSTRACT Background Glucocorticoids (GCs) are the most important and frequently used class of anti‐inflammatory drugs. However, the mechanisms underlying excessive glucocorticoid‐mediated induction of muscle atrophy remain incompletely understood. Methods We generated skeletal muscle‐specific Klf9 transgenic mice (mKlf9TG) and skeletal muscle‐specific Klf9 knockout mice (Klf9mlc−/−). The body weight, tissue weight, body composition, grip strength, running distance and muscle fibre cross section of mKlf9TG, Klf9mlc−/− mice and their littermate controls were examined. Expression of genes related to muscle protein synthesis and degradation pathways were also tested in the mKlf9TG mice, Klf9mlc−/− mice and their littermate controls. We performed Klf9 gain‐ or loss‐of‐function studies in differentiated C2C12 myotubes using lentiviruses encoding Klf9 or the shRNA specific to Klf9 in vitro. Luciferase reporter gene assay and ChIP assay were performed to explore the molecular mechanism of Klf9 action. Klf9mlc−/− and Klf9fl/fl mice were treated with dexamethasone (Dex). Multiple genetic and pharmacological approaches were also used to investigate the intracellular signalling cascades underlying the Dex/Klf9‐ediated skeletal muscle wasting. Results Skeletal muscle Klf9 gene expression was significantly upregulated by Dex (p < 0.05 or p < 0.01 vs. vehicle group). Compared with littermate control mice (R‐loxP), mKlf9TG mice exhibited decreased skeletal muscle mass (TA 0.101 ± 0.018 vs. 0.040 ± 0.007 g, p < 0.001) and impaired grip strength (forelimb 157.4 ± 3.7 vs. 93.45 ± 9.8 and four limbs 255.3 ± 23.1 vs. 170.1 ± 36.2, p < 0.001). Conversely, compared with Klf9fl/fl, Klf9mlc−/− mice exhibited increased skeletal muscle mass (TA 0.103 ± 0.012 vs. 0.123 ± 0.005 g, p < 0.001) and enhanced grip strength (forelimb 110.3 ± 5.8 vs. 156.8 ± 10.0 and four limbs 155.5 ± 6.3 vs. 226.5 ± 19.7, p < 0.001). Skeletal muscle Klf9 deficiency alleviated muscle atrophy induced by acute high‐dose Dex treatment (p < 0.001). Mechanistically, Klf9 induces the expression of myostatin (Mstn) and muscle atrophy F‐box (MAFbx) by directly binding to and activating the transcription of their promoters. Treatment of AAV‐MSTN reduced the increased grip strength of Klf9mlc−/− mice (forelimb 143.5 ± 22.3 vs. 118.8 ± 3.1 and four limbs 249.8 ± 24.7 vs. 208.7 ± 9.0, p < 0.001). Conclusions In summary, our study provides novel insights into the mechanisms underlying GC‐induced muscular atrophy and reveals that skeletal muscle induction of Klf9 expression is a mechanism underlying GC therapy‐induced muscle loss. Thus, targeting Klf9 may offer novel approaches to the treatment of skeletal muscle wasting diseases.

Hannes Petersen, Paolo Gargiulo, John F Golding et al.

Motion sickness, in all its forms, remains a universal human challenge. Whether it is seasickness that plagued the Vikings as they crossed the North Atlantic, carsickness affecting millions of daily commuters, or cybersickness emerging with the rapid growth of immersive digital technologies, motion-induced discomfort continues to shape how humans travel, work, and interact with their environments. It is estimated that up to one-third of people experience significant motion sickness under certain conditions, while nearly everyone is susceptible given sufficient stimulus intensity. Such prevalence makes motion sickness not a niche concern, but a global issue at the intersection of health, safety, and performance.[...]

Harang Ju, Sinan Aral

Here we examine how AI agent "personalities" interact with human personalities to shape human-AI collaboration and performance. In a large-scale, preregistered randomized experiment, we paired 1,258 participants with AI agents prompted to exhibit varying levels of the Big Five personality traits. These human-AI teams produced 7,266 display ads for a real think tank, which we evaluated using 1,995 independent human raters and a field experiment on X that generated nearly 5 million impressions. We found that human and AI personalities individually shaped ad quality and teamwork. When examined together, human-AI personality pairings directly effected ad quality outcomes. For example, extraverted humans paired with conscientious AI produced the lowest-quality ads, followed by conscientious humans paired with agreeable AI and neurotic humans paired with conscientious AI. In the field experiment, ad quality significantly influenced ad performance, measured by click-through rates and cost-per-click, and neurotic humans paired with neurotic AI achieved higher click-through rates, even after controlling for ad quality. Together, these results provide the first large-scale causal experimental evidence that specific personality pairings can improve human-AI collaboration and motivate future research on the implications of AI personalization for performance and teamwork dynamics in human-AI teams.

Jingxiao Chen, Xinyao Li, Jiahang Cao et al.

Humanoid robots have shown success in locomotion and manipulation. Despite these basic abilities, humanoids are still required to quickly understand human instructions and react based on human interaction signals to become valuable assistants in human daily life. Unfortunately, most existing works only focus on multi-stage interactions, treating each task separately, and neglecting real-time feedback. In this work, we aim to empower humanoid robots with real-time reaction abilities to achieve various tasks, allowing human to interrupt robots at any time, and making robots respond to humans immediately. To support such abilities, we propose a general humanoid-human-object interaction framework, named RHINO, i.e., Real-time Humanoid-human Interaction and Object manipulation. RHINO provides a unified view of reactive motion, instruction-based manipulation, and safety concerns, over multiple human signal modalities, such as languages, images, and motions. RHINO is a hierarchical learning framework, enabling humanoids to learn reaction skills from human-human-object demonstrations and teleoperation data. In particular, it decouples the interaction process into two levels: 1) a high-level planner inferring human intentions from real-time human behaviors; and 2) a low-level controller achieving reactive motion behaviors and object manipulation skills based on the predicted intentions. We evaluate the proposed framework on a real humanoid robot and demonstrate its effectiveness, flexibility, and safety in various scenarios.

Hemanth Vikram P․R, Gunjan Kumar, Rajashree Deka et al.

Nitrosamine impurities identified in several pharmaceuticals during recent times has raised concerns leading to product recalls worldwide and necessitating sensitive liquid and gas chromatographic methods for trace level detection of nitrosamine impurities. This study developed and validated a ultra-fast liquid chromatography-tandem mass spectrometry (UFLC-MS/MS) method for the quantification of NDMA in Levosulpuride drug substance and tablet formulation. Current method utilizes a triple quadrupole analyzer, atmospheric pressure chemical ionization (APCI) ionization source and multiple reaction monitoring (MRM) scan mode for the analysis. Chromatographic separation was achieved on a Gemini NX-C18 column (150 × 4.6 mm, 3 µm) maintained at 40 °C. The mobile phase consisted of a binary gradient of solvent A (0.1 % formic acid in water) and solvent B (methanol), with a total run time of 18 minutes. Current method achieved excellent linearity, recovery, precision, and sensitivity. Greenness of the developed method was evaluated using the GAPI, AGREE, and AES metrics. Current method is sensitive and selective for NDMA in levosulpuride drug substance and tablet formulations and can be employed for routine quality control analysis in pharmaceutical industry.

Arturo Cruz Cidoncha, Jaime Ruíz-Tovar, Pablo Tutor de Ureta et al.

Introduction: Innovation in medical teaching in recent years has undergone major changes. Traditionally, the teacher used to rely exclusively on words, the blackboard, and anatomical dissection. Nowadays, classes are supported by numerous tools with the help of the computer. Methods: In the Department of Human Anatomy of the Faculty of Medicine of our University, during the first 4-month period of the course, we introduced in the classes the projection of 13 short videos with an average duration of 2.33 min, from laparoscopic and open surgical interventions, endoscopic and radiological studies. We surveyed at the end of the first 4 months period to evaluate the student´s opinions of the videos shown. Results: Regarding the usefulness of the video projection, 82.8% considered the projection very useful and 17.2% considered them somewhat useful. None of the students considered the projection of the videos to be useless for their learning. As for the duration of the videos, 97.3% considered the duration to be adequate. In the survey, the students freely expressed diverse opinions. Among others, they stated that they help to understand the real anatomy of the structures studied in 3 dimensions and that they help to review and consolidate theoretical knowledge of what has already been explained. They also said that they help with motivation in preclinical subjects. Others felt that the videos help to raise awareness of the practical usefulness of the subject in the context of the practice of medicine. Conclusion: We can assure that the projection of short prepared videos, taking sequences of open or laparoscopic surgical interventions and endoscopic or radiological studies, is very useful for improving the understanding of the subject of human anatomy, helping to clarify concepts and consolidating knowledge and increasing the student's motivation, as well as the performance in the study. Resumen: Introduccion: La innovación en la docencia médica en los últimos años ha sufrido grandes cambios. Tradicionalmente, el docente disponía exclusivamente de su palabra, de la pizarra y de la disección anatómica. Hoy en día las clases se apoyan numerosas herramientas con el apoyo del ordenador. Metodos: En el departamento de Anatomía humana de la Facultad de Medicina de nuestra Universidad, durante el primer cuatrimestre del curso introdujimos en las clases la proyección de 13 videos cortos con una duración media de 2,33 minutos, procedentes de intervenciones quirúrgicas laparoscópicas y abiertas, estudios endoscópicos y radiológicos. Realizamos una encuesta al final del cuatrimestre evaluando la opinión de los alumnos sobre los videos proyectados. Resultados: Respecto a la utilidad de la proyección de los videos, el 82,8% consideró muy útil la proyección y el 17,2% las consideró algo útiles. Ninguno de los alumnos consideró que la proyección de los videos fuera inútil para su aprendizaje. En cuanto a la duración de los videos al 97,3% les pareció que la duración era adecuada. En la encuesta los alumnos manifestaron libremente diversas opiniones. Entre otras, manifestaron que ayudan a comprender la anatomía real de las estructuras estudiadas en tres dimensiones y que ayudan a repasar y a asentar conocimientos teóricos de lo ya explicado. También que ayudan a la motivación con las asignaturas preclínicas. Otros opinaron que los videos ayudan a tomar conciencia de la utilidad práctica de la asignatura en el contexto del ejercicio de la medicina. Conclusion: Podemos asegurar que la proyección de videos cortos preparados, tomando secuencias de intervenciones quirúrgicas abiertas o laparoscópicas y de estudios endoscópicos o radiológicos, tiene una gran utilidad para la mejora de la comprensión de la asignatura de la anatomía humana, ayudando a aclarar conceptos y afianzar conocimientos e incrementando la motivación del alumno, así como el rendimiento en el estudio.

Jing Luo, Jing Zhang, Bomiao Ju et al.

Abstract Objective To investigate the distribution and activation of B-cell subpopulations in rheumatoid arthritis (RA) patients treated with Janus kinase inhibitors (JAKis) and to analyze their correlation with disease remission. Methods Peripheral blood samples were collected from 23 adult healthy controls and 58 RA patients, 31 of whom were treated with JAKis and assessed during a 24-month follow-up. The number of peripheral B-cell subpopulations (including naive B cells, nonswitched memory B (NSMB) cells, switched memory B cells, and double-negative B cells), their activation, and phosphorylation of SYK and AKT upon B-cell receptor (BCR) stimulation in each population were analyzed by flow cytometry. Results Compared with that in healthy controls, the frequency of NSMB cells was significantly lower in new-onset untreated RA patients. However, expression of CD40, CD80, CD95, CD21low and pAKT significantly increased in these NSMB cells. Additionally, the number of NSMB cells correlated negatively with DAS28-ESR and IgG and IgA levels in these patients; expression of CD80, CD95 and CD21low on NSMB cells correlated positively with DAS28-ESR and IgG and IgA levels. After treatment with JAKis, the serum IgG concentration significantly decreased in RA patients in remission, but CD40, CD95 and pAKT levels in NSMB cells significantly decreased. Conclusion RA patients present different B-cell subpopulations, in which the frequency of NSMB cells is negatively associated with disease activity. However, treatment with JAKis can inhibit activation of NSMB cells, restore the balance of kinase phosphorylation, and facilitate disease remission in RA patients.

Rohan Alur, Manish Raghavan, Devavrat Shah

We introduce a novel framework for incorporating human expertise into algorithmic predictions. Our approach leverages human judgment to distinguish inputs which are algorithmically indistinguishable, or "look the same" to predictive algorithms. We argue that this framing clarifies the problem of human-AI collaboration in prediction tasks, as experts often form judgments by drawing on information which is not encoded in an algorithm's training data. Algorithmic indistinguishability yields a natural test for assessing whether experts incorporate this kind of "side information", and further provides a simple but principled method for selectively incorporating human feedback into algorithmic predictions. We show that this method provably improves the performance of any feasible algorithmic predictor and precisely quantify this improvement. We find empirically that although algorithms often outperform their human counterparts on average, human judgment can improve algorithmic predictions on specific instances (which can be identified ex-ante). In an X-ray classification task, we find that this subset constitutes nearly $30\%$ of the patient population. Our approach provides a natural way of uncovering this heterogeneity and thus enabling effective human-AI collaboration.

Bryce McLaughlin, Jann Spiess

Algorithms frequently assist, rather than replace, human decision-makers. However, the design and analysis of algorithms often focus on predicting outcomes and do not explicitly model their effect on human decisions. This discrepancy between the design and role of algorithmic assistants becomes particularly concerning in light of empirical evidence that suggests that algorithmic assistants again and again fail to improve human decisions. In this article, we formalize the design of recommendation algorithms that assist human decision-makers without making restrictive ex-ante assumptions about how recommendations affect decisions. We formulate an algorithmic-design problem that leverages the potential-outcomes framework from causal inference to model the effect of recommendations on a human decision-maker's binary treatment choice. Within this model, we introduce a monotonicity assumption that leads to an intuitive classification of human responses to the algorithm. Under this assumption, we can express the human's response to algorithmic recommendations in terms of their compliance with the algorithm and the active decision they would take if the algorithm sends no recommendation. We showcase the utility of our framework using an online experiment that simulates a hiring task. We argue that our approach can make sense of the relative performance of different recommendation algorithms in the experiment and can help design solutions that realize human-AI complementarity. Finally, we leverage our approach to derive minimax optimal recommendation algorithms that can be implemented with machine learning using limited training data.

Xinzhe Luo, Xin Wang, Linda Shapiro et al.

This article presents a general Bayesian learning framework for multi-modal groupwise image registration. The method builds on probabilistic modelling of the image generative process, where the underlying common anatomy and geometric variations of the observed images are explicitly disentangled as latent variables. Therefore, groupwise image registration is achieved via hierarchical Bayesian inference. We propose a novel hierarchical variational auto-encoding architecture to realise the inference procedure of the latent variables, where the registration parameters can be explicitly estimated in a mathematically interpretable fashion. Remarkably, this new paradigm learns groupwise image registration in an unsupervised closed-loop self-reconstruction process, sparing the burden of designing complex image-based similarity measures. The computationally efficient disentangled network architecture is also inherently scalable and flexible, allowing for groupwise registration on large-scale image groups with variable sizes. Furthermore, the inferred structural representations from multi-modal images via disentanglement learning are capable of capturing the latent anatomy of the observations with visual semantics. Extensive experiments were conducted to validate the proposed framework, including four different datasets from cardiac, brain, and abdominal medical images. The results have demonstrated the superiority of our method over conventional similarity-based approaches in terms of accuracy, efficiency, scalability, and interpretability.

Tiffany Zhu, Iain Weissburg, Kexun Zhang et al.

As AI advances in text generation, human trust in AI generated content remains constrained by biases that go beyond concerns of accuracy. This study explores how bias shapes the perception of AI versus human generated content. Through three experiments involving text rephrasing, news article summarization, and persuasive writing, we investigated how human raters respond to labeled and unlabeled content. While the raters could not differentiate the two types of texts in the blind test, they overwhelmingly favored content labeled as "Human Generated," over those labeled "AI Generated," by a preference score of over 30%. We observed the same pattern even when the labels were deliberately swapped. This human bias against AI has broader societal and cognitive implications, as it undervalues AI performance. This study highlights the limitations of human judgment in interacting with AI and offers a foundation for improving human-AI collaboration, especially in creative fields.

Zilin Huang, Zihao Sheng, Sikai Chen

In the field of autonomous driving, developing safe and trustworthy autonomous driving policies remains a significant challenge. Recently, Reinforcement Learning with Human Feedback (RLHF) has attracted substantial attention due to its potential to enhance training safety and sampling efficiency. Nevertheless, existing RLHF-enabled methods often falter when faced with imperfect human demonstrations, potentially leading to training oscillations or even worse performance than rule-based approaches. Inspired by the human learning process, we propose Physics-enhanced Reinforcement Learning with Human Feedback (PE-RLHF). This novel framework synergistically integrates human feedback (e.g., human intervention and demonstration) and physics knowledge (e.g., traffic flow model) into the training loop of reinforcement learning. The key advantage of PE-RLHF is its guarantee that the learned policy will perform at least as well as the given physics-based policy, even when human feedback quality deteriorates, thus ensuring trustworthy safety improvements. PE-RLHF introduces a Physics-enhanced Human-AI (PE-HAI) collaborative paradigm for dynamic action selection between human and physics-based actions, employs a reward-free approach with a proxy value function to capture human preferences, and incorporates a minimal intervention mechanism to reduce the cognitive load on human mentors. Extensive experiments across diverse driving scenarios demonstrate that PE-RLHF significantly outperforms traditional methods, achieving state-of-the-art (SOTA) performance in safety, efficiency, and generalizability, even with varying quality of human feedback. The philosophy behind PE-RLHF not only advances autonomous driving technology but can also offer valuable insights for other safety-critical domains. Demo video and code are available at: \https://zilin-huang.github.io/PE-RLHF-website/

M. Pennesi, M. Neuringer, R. J. Courtney

Katherine M. Collins, Matthew Barker, Mateo Espinosa Zarlenga et al.

Placing a human in the loop may abate the risks of deploying AI systems in safety-critical settings (e.g., a clinician working with a medical AI system). However, mitigating risks arising from human error and uncertainty within such human-AI interactions is an important and understudied issue. In this work, we study human uncertainty in the context of concept-based models, a family of AI systems that enable human feedback via concept interventions where an expert intervenes on human-interpretable concepts relevant to the task. Prior work in this space often assumes that humans are oracles who are always certain and correct. Yet, real-world decision-making by humans is prone to occasional mistakes and uncertainty. We study how existing concept-based models deal with uncertain interventions from humans using two novel datasets: UMNIST, a visual dataset with controlled simulated uncertainty based on the MNIST dataset, and CUB-S, a relabeling of the popular CUB concept dataset with rich, densely-annotated soft labels from humans. We show that training with uncertain concept labels may help mitigate weaknesses of concept-based systems when handling uncertain interventions. These results allow us to identify several open challenges, which we argue can be tackled through future multidisciplinary research on building interactive uncertainty-aware systems. To facilitate further research, we release a new elicitation platform, UElic, to collect uncertain feedback from humans in collaborative prediction tasks.

Mariann Eilertsen, Wayne Iwan Lee Davies, Wayne Iwan Lee Davies et al.

Salmonids are ideal models as many species follow a distinct developmental program from demersal eggs and a large yolk sac to hatching at an advanced developmental stage. Further, these economically important teleosts inhabit both marine- and freshwaters and experience diverse light environments during their life histories. At a genome level, salmonids have undergone a salmonid-specific fourth whole genome duplication event (Ss4R) compared to other teleosts that are already more genetically diverse compared to many non-teleost vertebrates. Thus, salmonids display phenotypically plastic visual systems that appear to be closely related to their anadromous migration patterns. This is most likely due to a complex interplay between their larger, more gene-rich genomes and broad spectrally enriched habitats; however, the molecular basis and functional consequences for such diversity is not fully understood. This study used advances in genome sequencing to identify the repertoire and genome organization of visual opsin genes (those primarily expressed in retinal photoreceptors) from six different salmonids [Atlantic salmon (Salmo salar), brown trout (Salmo trutta), Chinook salmon (Oncorhynchus tshawytcha), coho salmon (Oncorhynchus kisutch), rainbow trout (Oncorhynchus mykiss), and sockeye salmon (Oncorhynchus nerka)] compared to the northern pike (Esox lucius), a closely related non-salmonid species. Results identified multiple orthologues for all five visual opsin classes, except for presence of a single short-wavelength-sensitive-2 opsin gene. Several visual opsin genes were not retained after the Ss4R duplication event, which is consistent with the concept of salmonid rediploidization. Developmentally, transcriptomic analyzes of Atlantic salmon revealed differential expression within each opsin class, with two of the long-wavelength-sensitive opsins not being expressed before first feeding. Also, early opsin expression in the retina was located centrally, expanding dorsally and ventrally as eye development progressed, with rod opsin being the dominant visual opsin post-hatching. Modeling by spectral tuning analysis and atomistic molecular simulation, predicted the greatest variation in the spectral peak of absorbance to be within the Rh2 class, with a ∼40 nm difference in λmax values between the four medium-wavelength-sensitive photopigments. Overall, it appears that opsin duplication and expression, and their respective spectral tuning profiles, evolved to maximize specialist color vision throughout an anadromous lifecycle, with some visual opsin genes being lost to tailor marine-based vision.

Kyu-Ho Yi, Hyung-Jin Lee, Kyle K. Seo et al.

Postoperative pain after breast reconstruction surgery with the latissimus dorsi flap is a common occurrence. Botulinum neurotoxin (BoNT) injection during surgery is effective in reducing postoperative pain. This study aimed to determine the most appropriate locations for BoNT injection. A modified Sihler’s method was performed on the latissimus dorsi muscles in 16 specimens. Intramuscular nerve arborization was noted under the landmark of the medial side surgical neck of the humerus to the line crossing the spinous process of T5 and the middle of the iliac crest. The latissimus dorsi muscles were divided into medial, middle, and lateral segments with 10 transverse divisions to give 10 sections (each 10%). Intramuscular nerve arborization of the latissimus dorsi muscle was the largest from the medial and lateral part of the muscle ranging from 40 to 60%, middle part from 30 to 60% and medial, middle and lateral part from 70 to 90%. The nerve entry points were at the medial and lateral part with 20–40% regarding the medial side of surgical neck of the humerus to the line crossing spinous process of T5 to the middle of iliac crest. These outcomes propose that an injection of BoNT into the latissimus dorsi muscles should be administered into specific zones.

Dunja Bijelić, Marija Adžić, Mina Perić et al.

Understanding processes that occur after injuries to the central nervous system is essential in order to gain insight into how the restoration of function can be improved. Extracellular glycoprotein tenascin-C (TnC) has numerous functions in wound healing process depending on the expression time, location, isoform and binding partners which makes it interesting to study in this context. We used an in vitro injury model, the mixed culture of cortical astrocytes and microglia, and observed that without TnC microglial cells tend to populate gap area in greater numbers and proliferate more, whereas astrocytes build up in the border region to promote faster gap closure. Alternatively spliced domain of TnC, fibronectin type III-like repeat D (FnD) strongly affected physiological properties and morphology of both astrocytes and microglia in this injury model. The rate of microglial proliferation in the injury region decreased significantly with the addition of FnD. Additionally, density of microglia also decreased, in part due to reduced proliferation, and possibly due to reduced migration and increased contact inhibition between enlarged FnD-treated cells. Overall morphology of FnD-treated microglia resembled the activated pro-inflammatory cells, and elevated expression of iNOS was in accordance with this phenotype. The effect of FnD on astrocytes was different, as it did not affect their proliferation, but stimulated migration of reactivated astrocytes into the scratched area 48 h after the lesion. Elevated expression and secretion of TNF-α and IL-1β upon FnD treatment indicated the onset of inflammation. Furthermore, on Western blots we observed increased intensity of precursor bands of β1 integrin and appearance of monomeric bands of P2Y12R after FnD treatment which substantiates and clarifies its role in cellular shape and motility changes. Our results show versatile functions of TnC and in particular FnD after injury, mostly contributing to ongoing inflammation in the injury region. Based on our findings, FnD might be instrumental in limiting immune cell infiltration, and promoting astrocyte migration within the injury region, thus influencing spaciotemporal organization of the wound and surrounding area.

Nuria Sarroca, María José Luesma, José Valero et al.

Abstract People with lower limb amputation present greater displacements of their centre of gravity in a static situation than able-bodied individuals, as they depend on visual information to a greater extent, which implies an altered stability pattern. The efficacy of different hardness of plantar support to help maintain stability has not yet been determined. The aim of the present study is to assess stability in people with unilateral transtibial amputation with prosthesis in a static situation with insoles of different degrees of hardness and visual conditions with respect to the able-bodied population. For this purpose, 25 patients with amputation and 25 able-bodied individuals were included in both groups, postural stability was assessed by stabilometry. This assessment was carried out under normal conditions (on the floor of the dynamometric platform with eyes open), and under altered conditions (with the interposition of different materials such as plantar support: rigid and soft insoles and, eyes shut). Three variables were considered to assess stability: length of movement of the barycenter (mm), lateral velocity (mm/sg) and anterior velocity (mm/sg). All of them were analysed with the patient in static on the dynamometric platform. The results showed statistically significant differences between the two groups, (amputees and controls) with less stability in the amputee group (p < 0.05) when analysing the variables of length of movement of the barycenter, lateral velocity and anterior velocity. Amputee patients with open eyes exhibited greater stability than those with closed eyes. The hard insoles improved the stability data in amputees (length of movement of the barycenter and anterior velocity) with respect to the barefoot condition, and the soft insoles showed less stability than the patients with hard insoles, or than the barefoot patients. From the results obtained in this study, we can conclude that the PP-DWST 4 mm rigid insoles improve static stability in people with amputation. However, soft insoles impair stability and are therefore discouraged.