Hasil untuk "Human anatomy"

Interuniversity Institute of Myology

Over the past decades, the role of patient organizations in neuromuscular research has evolved from passive involvement to strategic partnership. Since its founding in the mid-1990s, Parent Project aps has become a key player in advancing care and research for Duchenne and Becker muscular dystrophy (DMD/BMD), promoting a patient-centered approach to research, aiming to ensure that the priorities of patients and families are fully reflected in clinical and scientific strategies. A cornerstone of this effort is the Italian DMD/BMD patient registry, which has supported clinical trial recruitment, feasibility assessments, and the identification of unmet needs across different regions. Data from the registry have helped shaping advocacy efforts and targeted projects - including "Parent Project on the Road" (home-based respiratory monitoring), telecardiology consultations initiated during the pandemic, and the launch of a series of international clinical workshops focused on key topics in patient management, particularly relevant for adult patients in the evolving therapeutic landscape. Soon, we will integrate Patient Reported Outcome Measures (PROMs) and Patient Preferences into the registry, offering deeper insight into disease progression, treatment responses, and quality of life. This will further strengthen the registry's role in shaping research priorities and care strategies. This approach underscores the strategic value of involving patient organizations in the design and implementation of research and data collection efforts. It proves especially effective in large-scale, multi-partner research projects, where close collaboration between patient organizations and scientific partners is key to ensuring that research goals remain aligned with real-world patient needs and lead to more effective and equitable outcomes.

Uldis Berzins, Uldis Berzins, Uldis Berzins et al.

Younger, replicative cells with longer telomeres can enhance regenerative therapies, however, there is a lack of a standard method to assess telomere length in live cells. The present study investigated whether the relative size of human adipose tissue-derived mesenchymal stromal cells (AD–MSCs) can influence their telomere length. During early culture, a smaller-sized AD–MSC subpopulation was identified based on characteristic colony emergence. Telomere lengths in total and smaller-sized cell populations were measured. Polymerase chain reaction revealed expression of Nanog and OCT3/4 in small-sized AD–MSCs. Their safety was evaluated in immunodeficient BALB/c nude mice. Smaller AD–MSCs revealed distinct growth properties, with the cell monolayer rolling up into a large aggregate. These cells had longer telomeres (18,121.43 base pairs [bp]) than the total population (15,870.44 bp) and formed teratoma-like structures with skin-like morphology (including hair). In conclusion, AD–MSC size reliably isolates cells with longer telomeres and potential.

Junxiang Wang, Cindy Wang, Rana Soltani Zarrin et al.

Effective physical human-robot interaction requires systems that are not only adaptable to user preferences but also transparent about their actions. This paper introduces BRIDGE, a system for bidirectional human-robot communication in physical assistance. Our method allows users to modify a robot's planned trajectory -- position, velocity, and force -- in real time using natural language. We utilize a large language model (LLM) to interpret any trajectory modifications implied by user commands in the context of the planned motion and conversation history. Importantly, our system provides verbal feedback in response to the user, either assuring any resulting changes or posing a clarifying question. We evaluated our method in a user study with 18 older adults across three assistive tasks, comparing BRIDGE to an ablation without verbal feedback and a baseline. Results show that participants successfully used the system to modify trajectories in real time. Moreover, the bidirectional feedback led to significantly higher ratings of interactivity and transparency, demonstrating that the robot's verbal response is critical for a more intuitive user experience. Videos and code can be found on our project website: https://bidir-comm.github.io/

Greg Nyilasy, Brock Bastian, Jennifer Overbeck et al.

As organizations increasingly deploy AI as a teammate rather than a standalone tool, morally consequential mistakes often arise from joint human-AI workflows in which causality is ambiguous. We ask how people allocate responsibility in these hybrid-agent settings. Across four experiments (N = 1,801) in an AI-assisted lending context (e.g., discriminatory rejection, irresponsible lending, and low-harm filing errors), participants consistently attributed more responsibility to the human decision maker when the human was paired with AI than when paired with another human (by an average of 10 points on a 0-100 scale across studies). This AI-Induced Human Responsibility (AIHR) effect held across high and low harm scenarios and persisted even where self-serving blame-shifting (when the human in question was the self) would be expected. Process evidence indicates that AIHR is explained by inferences of agent autonomy: AI is seen as a constrained implementer, which makes the human the default locus of discretionary responsibility. Alternative mechanisms (mind perception; self-threat) did not account for the effect. These findings extend research on algorithm aversion, hybrid AI-human organizational behavior and responsibility gaps in technology by showing that AI-human teaming can increase (rather than dilute) human responsibility, with implications for accountability design in AI-enabled organizations.

Taufiq Daryanto, Xiaohan Ding, Kaike Ping et al.

As AI assistance becomes embedded in programming practice, researchers have increasingly examined how these systems help learners generate code and work more efficiently. However, these studies often position AI as a replacement for human collaboration and overlook the social and learning-oriented aspects that emerge in collaborative programming. Our work introduces human-human-AI (HHAI) triadic programming, where an AI agent serves as an additional collaborator rather than a substitute for a human partner. Through a within-subjects study with 20 participants, we show that triadic collaboration enhances collaborative learning and social presence compared to the dyadic human-AI (HAI) baseline. In the triadic HHAI conditions, participants relied significantly less on AI-generated code in their work. This effect was strongest in the HHAI-shared condition, where participants had an increased sense of responsibility to understand AI suggestions before applying them. These findings demonstrate how triadic settings activate socially shared regulation of learning by making AI use visible and accountable to a human peer, suggesting that AI systems that augment rather than automate peer collaboration can better preserve the learning processes that collaborative programming relies on.

Wanying Mou, Ning Yu, Fengrun Sun et al.

Summary: Chronic post-thoracotomy pain (CPTP) is a debilitating postoperative complication associated with persistent hypersensitivity and neuroinflammatory changes. Here, we identify an astrocyte-neuron signaling cascade mediated by NF-κB/C3/C3aR that drives excitatory synaptic remodeling in the spinal dorsal horn during CPTP. Using a rat model, we show that the activation of astroglial NF-κB promotes C3 synthesis, which interacts with neuronal C3aR to enhance GluR1 expression and synaptic localization, thereby facilitating pain hypersensitivity. The pharmacological inhibition of NF-κB or knockdown of astroglial C3 or neuronal C3aR markedly attenuated mechanical and cold allodynia, accompanied by reduced GluR1 expression. These findings define a mechanistic link between glial NF-κB activation, complement signaling, and neuronal excitatory transmission, highlighting the NF-κB/C3/C3aR pathway as a potential therapeutic target for chronic postoperative pain.

Concussion diagnosis still depends largely on self-reported symptoms and clinical history, underscoring the need for objective biomarkers.1 This study introduces BioVRSea, an innovative framework combining virtual reality with a moving platform to probe postural control through multimodal measurements, including electroencephalography (EEG), electromyography (EMG), heart rate, and center of pressure (CoP). Fifty-four professional athletes, classified by self-reported concussion history, underwent both a standardized symptom questionnaire (SCAT5) and BioVRSea testing. Neurophysiological and biomechanical responses were compared before and after platform perturbations. Distinct patterns emerged: concussed individuals showed altered EEG spectral activity (notably in delta and theta bands),2,3 a discriminative Soleus median frequency in EMG among those with balance issues,4 and frequency-based CoP differences in the anterior–posterior axis.5 Integrating these multimodal features with SCAT5 via machine learning yielded classification accuracies up to 95.5%. These findings highlight the potential of BioVRSea as a quantitative tool for identifying concussion-related alterations and move toward the development of objective, data-driven biomarkers for concussion assessment. Ethics statement: All participants received detailed written information about the study and provided their signed informed consent. The research was conducted in accordance with the principles embodied in the Declaration of Helsinki and Icelandic statutory requirements. The study protocol was approved by the Icelandic National Bioethics Committee (no: VSN-20–101).

Carmelo Pirri, Nina Pirri, Diego Guidolin et al.

This study investigates the influence of probe angulation on echogenicity and thickness measurements of the deep fascia, addressing methodological challenges in musculoskeletal ultrasound examination. The anisotropic nature of connective tissues can lead to distortions, affecting US imaging accuracy and diagnostic reliability. Echogenicity and thickness variations were analyzed across different probe inclinations in both transverse and longitudinal orientations. Measurements at 0° were compared with −5° and +5° angles to assess their impact on imaging consistency due to 3D-printed support. Echogenicity differed significantly with probe angulation, in particular in transverse scan at 0°, which showed substantial variation at −5° (mean diff. = 55.14, <i>p</i> < 0.0001) and +5° (mean diff. = 43.75, <i>p</i> = 0.0024). Thickness measurements also varied, reinforcing that non-perpendicular probe angulation introduces distortions. The same results were reported for longitudinal scans. These findings highlight the need for the use of standardized scanning protocols to improve reliability. The protean nature of deep fascia anisotropy, highly sensitive to minimal changes in probe orientation, necessitates precise and consistent imaging to accurately reveal its structural organization. Optimizing probe orientation is essential for advancing fascial US diagnostics.

Yaoyu Chen, Jens Hausmann, Benjamin Zimmermann et al.

Abstract Retinal degeneration protein 3 (RD3) plays a crucial role in controlling guanylate cyclase activity in photoreceptor rod and cone cells, and mediates trafficking processes within photoreceptor cells. Loss of RD3 function correlates with severe forms of retinal dystrophy and the development of aggressive neuroblastoma cancer. In the present study, we analyzed RD3 expression in glioblastoma in comparison to non-tumor tissue using public databases and qRT-PCR. We found that RD3 is downregulated in glioblastoma compared to non-tumor tissues. To better understand the cellular function of RD3 in the context of tumor development, we performed first functional cell culture studies to clarify a possible involvement of RD3 in cell survival and the cell cycle. Interestingly, RD3 overexpression significantly decreased cell viability, which subsequently led to cell-cycle arrest at the G2/M phase and induced cell apoptosis. Conversely, single-point mutations in RD3 at the exposed protein surface involved in RD3-target interaction diminished the impact of RD3. Therefore, a controlled RD3 expression level seems to be important for a balance of cell death and cell survival rate. These new functional mechanisms of RD3 expression could help in understanding tumor development and growth

Jason Wen Yau Lee, Li Xiang Tessa Low, Dennis Wenhui Ong et al.

Gabriel Skantze, Bahar Irfan

Turn-taking is a fundamental aspect of conversation, but current Human-Robot Interaction (HRI) systems often rely on simplistic, silence-based models, leading to unnatural pauses and interruptions. This paper investigates, for the first time, the application of general turn-taking models, specifically TurnGPT and Voice Activity Projection (VAP), to improve conversational dynamics in HRI. These models are trained on human-human dialogue data using self-supervised learning objectives, without requiring domain-specific fine-tuning. We propose methods for using these models in tandem to predict when a robot should begin preparing responses, take turns, and handle potential interruptions. We evaluated the proposed system in a within-subject study against a traditional baseline system, using the Furhat robot with 39 adults in a conversational setting, in combination with a large language model for autonomous response generation. The results show that participants significantly prefer the proposed system, and it significantly reduces response delays and interruptions.

Shuangge Wang, Anjiabei Wang, Sofiya Goncharova et al.

As robot deployments become more commonplace, people are likely to take on the role of supervising robots (i.e., correcting their mistakes) rather than directly teaching them. Prior works on Learning from Corrections (LfC) have relied on three key assumptions to interpret human feedback: (1) people correct the robot only when there is significant task objective divergence; (2) people can accurately predict if a correction is necessary; and (3) people trade off precision and physical effort when giving corrections. In this work, we study how two key factors (robot competency and motion legibility) affect how people provide correction feedback and their implications on these existing assumptions. We conduct a user study ($N=60$) under an LfC setting where participants supervise and correct a robot performing pick-and-place tasks. We find that people are more sensitive to suboptimal behavior by a highly competent robot compared to an incompetent robot when the motions are legible ($p=0.0015$) and predictable ($p=0.0055$). In addition, people also tend to withhold necessary corrections ($p < 0.0001$) when supervising an incompetent robot and are more prone to offering unnecessary ones ($p = 0.0171$) when supervising a highly competent robot. We also find that physical effort positively correlates with correction precision, providing empirical evidence to support this common assumption. We also find that this correlation is significantly weaker for an incompetent robot with legible motions than an incompetent robot with predictable motions ($p = 0.0075$). Our findings offer insights for accounting for competency and legibility when designing robot interaction behaviors and learning task objectives from corrections.

Kazuki Kawamura, Jun Rekimoto

While current AI illustration tools can generate high-quality images from text prompts, they rarely reveal the step-by-step procedure that human artists follow. We present SakugaFlow, a four-stage pipeline that pairs diffusion-based image generation with a large-language-model tutor. At each stage, novices receive real-time feedback on anatomy, perspective, and composition, revise any step non-linearly, and branch alternative versions. By exposing intermediate outputs and embedding pedagogical dialogue, SakugaFlow turns a black-box generator into a scaffolded learning environment that supports both creative exploration and skills acquisition.

Luis Chauca-Bajaña, Alba Pérez-Jardón, Fábio França Vieira E Silva et al.

Introduction: Gingival recession, characterized by the apical displacement of the gingival margin, presents challenges to oral health. This study compares the effectiveness of the coronally advanced flap (CAF) and the tunnel technique (TT) for treating gingival recessions. Methods: Bibliographical searches included PubMed, Embase, Web of Science, Cochrane, Scopus, and the grey literature, with keywords “root coverage” “coronary advanced flap”, and “tunnel”. A systematic coreview was performed that included 26 studies evaluating root coverage, and 14 articles were included for the meta-analysis. Three groups were analyzed: Group 1 compared TT with connective tissue graft (CTG) versus CAF with CTG; Group 2 examined TT with CTG and/or other biomaterials versus TT with CTG alone; Group 3 compared TT with CAF, regardless of complementary biomaterials. Meta-analysis assessed mean root coverage (MRC), complete root coverage (CRC), and keratinized tissue gain (KTG). Results: In Group 1, TT with CTG demonstrated superior MRC compared with CAF with CTG (−8.68 CI95% −17.19 to −0.17; <i>p</i> = 0.0457). In Group 2, TT with CTG and/or other biomaterials showed similar MRC (4.17 CI95% −17.91 to 26.26; <i>p</i> = 0.7110) and CRC (0.37 CI95% −1.14 to 1.89; <i>p</i> = 0.6269) to TT with CTG alone, with variations in keratinized tissue gain. Group 3 indicated higher potential MRC for TT compared with CAF (5.73 CI95% −8.90 to 13.55; <i>p</i> = 0.685) but without statistically significant differences. Conclusions: This study suggests that TT with CTG might offer better root coverage than CAF with CTG; however, biomaterial selection requires consideration.

Kenichi Shibata, Masataka Kameshima, Takuji Adachi et al.

Abstract Background The importance of preoperative physical function assessment for post‐operative intervention has been reported in older patients undergoing cardiovascular surgery. Phase angle (PhA), measured using bioelectrical impedance analysis, is an indicator of cellular health and integrity and is reported as a prognostic factor in several chronic diseases; however, its association with the long‐term prognosis of cardiovascular surgery remains unclear. This study aimed to investigate the prognostic value of PhA for long‐term mortality in patients undergoing cardiovascular surgery. Methods This retrospective cohort study included consecutive patients who underwent elective cardiovascular surgery between October 2016 and March 2021 at Nagoya Heart Center, Japan. PhA was assessed using bioelectrical impedance analysis before surgery, and physical function measures (gait speed, grip strength and short physical performance battery [SPPB]) were measured synchronously. The association between PhA and all‐cause mortality after discharge was assessed using Kaplan–Meier and multivariate Cox regression analyses. The incremental prognostic value of PhA was compared with other physical function measures using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Results A total of 858 patients were included in the present analysis (mean age = 68.4 ± 11.9 years, 67.6% male). PhA positively correlated with body mass index (ρ = 0.38, P < 0.001), skeletal muscle mass index (ρ = 0.58, P < 0.001), usual gait speed (ρ = 0.44, P < 0.001), grip strength (ρ = 0.73, P < 0.001) and SPPB (ρ = 0.51, P < 0.001). The mean follow‐up period, within which 44 (4.7%) died, was 908.9 ± 499.9 days for the entire cohort. Kaplan–Meier survival curves based on the PhA tertiles showed that higher PhA was associated with better survival (log‐rank test, P < 0.001). The Cox regression analysis showed the independent association of PhA with mortality risk (hazard ratio: 0.91 per 0.1° increment; 95% confidence interval [CI]: 0.87–0.95; P < 0.001). The NRI and IDI showed significant improvements in predicting mortality after adding PhA to the clinical model consisting of age, sex and cardiac and renal function (NRI: 0.426, 95% CI: 0.124–0.729, P = 0.006; IDI: 0.037, 95% CI: 0.012–0.062, P = 0.003). The predictive model consisting of the clinical model and PhA was superior to the model consisting of the clinical model and each of the other physical function indicators (P < 0.05). Conclusions PhA correlated with physical function and independently predicted long‐term mortality after cardiovascular surgery. The additive prognostic value of PhA compared with the other physical function measures suggests the clinical usefulness of preoperative PhA for risk stratification in planning post‐operative treatment and rehabilitation.

Daniel S. Katz, Jeffrey C. Carver

This is a virtual dialog between Jeffrey C. Carver and Daniel S. Katz on how people learn programming languages. It's based on a talk Jeff gave at the first US-RSE Conference (US-RSE'23), which led Dan to think about human languages versus computer languages. Dan discussed this with Jeff at the conference, and this discussion continued asynchronous, with this column being a record of the discussion.

Basma Ahmed, Mai H. Ragab, Rania A. Galhom et al.

Abstract Background To study the odontogenic potential of dental pulp stem cells (DPSCs) after induction with three different bioactive materials: activa bioactive (base/liner) (AB), TheraCal LC (TC), and mineral trioxide aggregate (MTA), when combined with two different types of scaffolds. Methods DPSCs were isolated from freshly extracted premolars of young orthodontic patients, cultured, expanded to passage 4 (P), and characterized by flow cytometric analysis. DPSCs were seeded onto two scaffolds in contact with different materials (AB, TC, and MTA). The first scaffold contained polycaprolactone-nano-chitosan and synthetic hydroxyapatite (PCL-NC-HA), whereas the second scaffold contained polycaprolactone-nano-chitosan and synthetic Mg-substituted hydroxyapatite (PCL-NC-Mg-HA). DPSC viability and proliferation were evaluated at various time points. To assess odontoblastic differentiation, gene expression analysis of dentin sialophosphoprotein (DSPP) by quantitative real-time polymerase chain reaction (qRT-PCR) and morphological changes in cells were performed using inverted microscope phase contrast images and scanning electron microscopy. The fold-change in DSPP between subgroups was compared using a one-way ANOVA. Tukey's test was used to compare the fold-change in DSPP between the two subgroups in multiple comparisons, and P was set at p < 0.05. Results DSPP expression was significantly higher in the PCL-NC-Mg-HA group than in the PCL-NC-HA group, and scanning electron microscopy revealed a strong attachment of odontoblast-like cells to the scaffold that had a stronger odontogenic differentiation effect on DPSCs than the scaffold that did not contain magnesium. MTA has a significantly higher odontogenic differentiation effect on cultured DPSCs than AB or TC does. The combination of scaffolds and bioactive materials improves DPSCs induction in odontoblast-like cells. Conclusions The PCL-NC-Mg-HA scaffold showed better odontogenic differentiation effects on cultured DPSCs. Compared to AB and TC, MTA is the most effective bioactive material for inducing the odontogenic differentiation of cultured DPSCs.

Meseret Derbew Molla, Haileab Fekadu Wolde, Ephrem Tafesse Teferi et al.

PurposeObesity, especially the hidden type of obesity (central obesity), has been believed to be the major risk factor for developing and progressing non-communicable diseases, including cancers. However, there are limited studies regarding the issue in Ethiopia and the study area. Therefore, this study aimed to evaluate the magnitude of central obesity and its associated factors among cancer patients visited the oncology unit of the University of Gondar Comprehensive Specialized Hospital.MethodsAn institutional-based cross-sectional study was conducted from January 10 to March 10, 2021. A total of 384 study participants were enrolled using a systematic sampling technique. The data were collected using a semi-structured interviewer-administered questionnaire and were pretested to address the quality of assurance. The weight of the participants was assessed using body mass index (BMI) and central obesity. Both bivariate and multivariate logistic regressions were conducted to identify the factors associated with central obesity, and p-values less than 0.05 with multivariate were considered statistically significant associations.ResultMost respondents (60.16%) were stage I cancer patients. The study found that about 19.27% of the participants were prevalent central obesity, and none of them were obese by body mass index (BMI) categorization criteria. However, about 12.24% and 7.03% of the participants were found to be underweight and overweight, respectively. The variables associated with central obesity were sex (AOR=14.40; 95% CI: 5.26 - 39.50), occupation (AOR=4.32; 95%CI: 1.10 - 17.01), and residency (AOR=0.30; 95% CI: 0.13 - 0.70).ConclusionA significant number of the respondents (19.27%) were centrally obese. Being female, urban residency and having an occupation other than a farmer, merchant, and governmental were the factors associated with central obesity. Hence, cancer patients may be centrally obese with average body weight.

Daniel Schroter, Daryna Dementieva, Georg Groh

This paper presents the best-performing approach alias "Adam Smith" for the SemEval-2023 Task 4: "Identification of Human Values behind Arguments". The goal of the task was to create systems that automatically identify the values within textual arguments. We train transformer-based models until they reach their loss minimum or f1-score maximum. Ensembling the models by selecting one global decision threshold that maximizes the f1-score leads to the best-performing system in the competition. Ensembling based on stacking with logistic regressions shows the best performance on an additional dataset provided to evaluate the robustness ("Nahj al-Balagha"). Apart from outlining the submitted system, we demonstrate that the use of the large ensemble model is not necessary and that the system size can be significantly reduced.

Andrew Fuchs, Andrea Passarella, Marco Conti

In the context of humans operating with artificial or autonomous agents in a hybrid team, it is essential to accurately identify when to authorize those team members to perform actions. Given past examples where humans and autonomous systems can either succeed or fail at tasks, we seek to train a delegating manager agent to make delegation decisions with respect to these potential performance deficiencies. Additionally, we cannot always expect the various agents to operate within the same underlying model of the environment. It is possible to encounter cases where the actions and transitions would vary between agents. Therefore, our framework provides a manager model which learns through observations of team performance without restricting agents to matching dynamics. Our results show our manager learns to perform delegation decisions with teams of agents operating under differing representations of the environment, significantly outperforming alternative methods to manage the team.