Hasil untuk "Human anatomy"

Lama R AlZamil, Erythrocytes RBCs

3. Compare the relative diameters of the afferent and efferent arterioles and explain the significance in this size differential. The afferent arteriole is wider than the efferent arteriole which means that blood enters the glomerulus through a wider opening than the blood exiting the glomerulus, thus creating an increased “back pressure” (=hydrostatic filtration pressure). They hydrostatic pressure is higher in the glomerulus than in other capillaries. By varying the size of the afferent and/or arterioles, the glomerular filtration rate (GFR) may be increased or decreased.

Chantale Lauer, Peter Pfeiffer, Nijat Mehdiyev

Integrating Large Language Models (LLMs) into business process management tools promises to democratize Business Process Model and Notation (BPMN) modeling for non-experts. While automated frameworks assess syntactic and semantic quality, they miss human factors like trust, usability, and professional alignment. We conducted a mixed-methods evaluation of our proposed solution, an LLM-powered BPMN copilot, with five process modeling experts using focus groups and standardized questionnaires. Our findings reveal a critical tension between acceptable perceived usability (mean CUQ score: 67.2/100) and notably lower trust (mean score: 48.8\%), with reliability rated as the most critical concern (M=1.8/5). Furthermore, we identified output-quality issues, prompting difficulties, and a need for the LLM to ask more in-depth clarifying questions about the process. We envision five use cases ranging from domain-expert support to enterprise quality assurance. We demonstrate the necessity of human-centered evaluation complementing automated benchmarking for LLM modeling agents.

Xuechen Li, Shuai Zhang, Nan Cao et al.

While the proliferation of foundation models has significantly boosted individual productivity, it also introduces a potential challenge: the homogenization of creative content. In response, we revisit Design-by-Analogy (DbA), a cognitively grounded approach that fosters novel solutions by mapping inspiration across domains. However, prevailing perspectives often restrict DbA to early ideation or specific data modalities, while reducing AI-driven design to simplified input-output pipelines. Such conceptual limitations inadvertently foster widespread design fixation. To address this, we expand the understanding of DbA by embedding it into the entire creative process, thereby demonstrating its capacity to mitigate such fixation. Through a systematic review of 85 studies, we identify six forms of representation and classify techniques across seven stages of the creative process. We further discuss three major application domains: creative industries, intelligent manufacturing, and education and services, demonstrating DbA's practical relevance. Building on this synthesis, we frame DbA as a mediating technology for human-AI collaboration and outline the potential opportunities and inherent risks for advancing creativity support in HCI and design research.

Jiachen Chen, Jiachen Chen, Jiachen Chen et al.

IntroductionThere are notable sex differences in the gray matter of Alzheimer’s disease(AD) patients’ brains, but current evidence is insufficient to prove these differences aid diagnosis effectively.MethodsMultivariate analysis of variance was performed on the preprocessed gray matter of healthy female and healthy male groups to identify the gray matter clusters with significant intergroup differences. Subsequently, multiple machine learning models were employed to develop sex-specific diagnostic models for AD.ResultsWe identified 11 brain regions showing sex differences, of which 8 were sex-specific in both female and male AD patients, exhibiting significant atrophy. Graph theory analysis demonstrated that the sex-specific gray matter structural brain networks in female and male AD patients exhibited distinct network alterations. We subsequently employed five advanced machine learning algorithms to develop diagnostic models for AD based on these sex-specific gray matter clusters, resulting in a notable improvement in performance.DiscussionSex-specific gray matter characteristics can facilitate more accurate diagnosis of AD.

Katelyn Xiaoying Mei, Nic Weber

The recent rapid advancement of LLM-based AI systems has accelerated our search and production of information. While the advantages brought by these systems seemingly improve the performance or efficiency of human activities, they do not necessarily enhance human capabilities. Recent research has started to examine the impact of generative AI on individuals' cognitive abilities, especially critical thinking. Based on definitions of critical thinking across psychology and education, this position paper proposes the distinction between demonstrated and performed critical thinking in the era of generative AI and discusses the implication of this distinction in research and development of AI systems that aim to augment human critical thinking.

Chaeyeon Lim

While generative artificial intelligence (Gen AI) increasingly transforms academic environments, a critical gap exists in understanding and mitigating human biases in AI interactions, such as anchoring and confirmation bias. This position paper advocates for metacognitive AI literacy interventions to help university students critically engage with AI and address biases across the Human-AI interaction workflows. The paper presents the importance of considering (1) metacognitive support with deliberate friction focusing on human bias; (2) bi-directional Human-AI interaction intervention addressing both input formulation and output interpretation; and (3) adaptive scaffolding that responds to diverse user engagement patterns. These frameworks are illustrated through ongoing work on "DeBiasMe," AIED (AI in Education) interventions designed to enhance awareness of cognitive biases while empowering user agency in AI interactions. The paper invites multiple stakeholders to engage in discussions on design and evaluation methods for scaffolding mechanisms, bias visualization, and analysis frameworks. This position contributes to the emerging field of AI-augmented learning by emphasizing the critical role of metacognition in helping students navigate the complex interaction between human, statistical, and systemic biases in AI use while highlighting how cognitive adaptation to AI systems must be explicitly integrated into comprehensive AI literacy frameworks.

Jacinto Colan, Ana Davila, Yutaro Yamada et al.

Human-robot collaboration in surgery represents a significant area of research, driven by the increasing capability of autonomous robotic systems to assist surgeons in complex procedures. This systematic review examines the advancements and persistent challenges in the development of autonomous surgical robotic assistants (ASARs), focusing specifically on scenarios where robots provide meaningful and active support to human surgeons. Adhering to the PRISMA guidelines, a comprehensive literature search was conducted across the IEEE Xplore, Scopus, and Web of Science databases, resulting in the selection of 32 studies for detailed analysis. Two primary collaborative setups were identified: teleoperation-based assistance and direct hands-on interaction. The findings reveal a growing research emphasis on ASARs, with predominant applications currently in endoscope guidance, alongside emerging progress in autonomous tool manipulation. Several key challenges hinder wider adoption, including the alignment of robotic actions with human surgeon preferences, the necessity for procedural awareness within autonomous systems, the establishment of seamless human-robot information exchange, and the complexities of skill acquisition in shared workspaces. This review synthesizes current trends, identifies critical limitations, and outlines future research directions essential to improve the reliability, safety, and effectiveness of human-robot collaboration in surgical environments.

Chao Xu, Suyu Zhang, Yang Liu et al.

Vision-Language-Action (VLA) models are driving a revolution in robotics, enabling machines to understand instructions and interact with the physical world. This field is exploding with new models and datasets, making it both exciting and challenging to keep pace with. This survey offers a clear and structured guide to the VLA landscape. We design it to follow the natural learning path of a researcher: we start with the basic Modules of any VLA model, trace the history through key Milestones, and then dive deep into the core Challenges that define recent research frontier. Our main contribution is a detailed breakdown of the five biggest challenges in: (1) Representation, (2) Execution, (3) Generalization, (4) Safety, and (5) Dataset and Evaluation. This structure mirrors the developmental roadmap of a generalist agent: establishing the fundamental perception-action loop, scaling capabilities across diverse embodiments and environments, and finally ensuring trustworthy deployment-all supported by the essential data infrastructure. For each of them, we review existing approaches and highlight future opportunities. We position this paper as both a foundational guide for newcomers and a strategic roadmap for experienced researchers, with the dual aim of accelerating learning and inspiring new ideas in embodied intelligence. A live version of this survey, with continuous updates, is maintained on our \href{https://suyuz1.github.io/VLA-Survey-Anatomy/}{project page}.

Jeremy C. -H. Wang, Ming Hou, David Dunwoody et al.

This paper examines how trust is formed, maintained, or diminished over time in the context of human-autonomy teaming with an optionally piloted aircraft. Whereas traditional factor-based trust models offer a static representation of human confidence in technology, here we discuss how variations in the underlying factors lead to variations in trust, trust thresholds, and human behaviours. Over 200 hours of flight test data collected over a multi-year test campaign from 2021 to 2023 were reviewed. The dispositional-situational-learned, process-performance-purpose, and IMPACTS homeostasis trust models are applied to illuminate trust trends during nominal autonomous flight operations. The results offer promising directions for future studies on trust dynamics and design-for-trust in human-autonomy teaming.

Kristine Godziuk, Mary Forhan, Flavio T. Vieira et al.

ABSTRACT Background Treatments aimed at improving physical function and body composition, including reducing fat mass (FM) and increasing muscle mass, may benefit individuals with advanced knee osteoarthritis (OA) and obesity. We investigated the feasibility and efficacy of a multimodal behavioural intervention compared to usual care to enhance physical function and muscle mass in this population. Methods The POMELO (Prevention Of MusclE Loss in Osteoarthritis) study is a two‐arm pilot randomized controlled trial; NCT05026385. Participants aged 40–75 years, with a BMI ≥ 35 kg/m2 and knee OA were randomized 1:1 to either the intervention group (POMELO) or usual care (UC). The 3‐month POMELO intervention incorporated progressive resistance exercise (3 sessions/week), individualized nutrition counselling targeted for OA, and 12 group education sessions on nutrition and arthritis self‐management. The UC group received standard clinical care. After the 3‐month supervised intervention, both groups were followed for 6 months without support. Assessments at baseline, 3 months and 9 months (primary endpoint) included body composition (DXA, measuring FM and appendicular lean soft tissue [ALST]), physical function (chair‐sit‐to‐stands [CSTS], 6‐min walk [6MWT], maximal handgrip strength [HGS]), and health‐related quality of life (Euroqol visual analog scale [EQ‐5D VAS]). Co‐primary outcomes were feasibility (intervention completion ≥ 80% and per‐protocol adherence ≥ 60% [i.e., attendance at 12 education sessions and exercise 3 ×/week]) and acceptability (4‐item Likert‐scale satisfaction survey, and open‐ended questions). Secondary outcomes included changes in physical function and ALST. Results Fifty participants were randomized (POMELO = 25, UC = 25), with 32 completing the study (69% female, mean age 64.9 ± 1.2 years, BMI 42.1 ± 1.0 kg/m2). The POMELO intervention group had 80% completion and 74% adherence, confirming feasibility. Higher satisfaction rates were observed in POMELO compared to UC (3.5 vs. 2.2, p < 0.001) indicating greater acceptability. The POMELO group had improvements in CSTS (mean difference [MD] 3.96, ES 1.2, p < 0.001), 6MWT (MD 31.6 m, ES 0.4, p = 0.039) and EQ‐5D VAS (MD 7.9 points, ES = 0.4, p = 0.01) compared to UC. Both groups experienced FM loss, but only the UC group lost ALST and HGS. Conclusion The POMELO intervention, combining personalized nutrition, resistance exercise and self‐management support, was feasible, acceptable and showed greater efficacy than usual care to improve physical function in patients with knee OA and obesity. Our pilot study of this intervention showed potential benefits on body composition and quality of life without focusing on weight reduction. A larger study is needed to confirm these results, as this approach may offer advantages over usual care, potentially leading to better mobility and health outcomes.

Zhen Ye, Aishe Dun, Hanming Jiang et al.

Background Three-dimensional (3D) printing is an emerging technology widely used in medical education. However, its role in the teaching of human anatomy needs further evaluation. Methods PubMed, Embase, EBSCO, SpringerLink, and Nature databases were searched systematically for studies published from January 2011 to April 2020 in the English language. GRADEprofiler software was used to evaluate the quality of literature. In this study, a meta-analysis of continuous and binary data was conducted. Both descriptive and statistical analyses were used. Results Comparing the post-training tests in neuroanatomy, cardiac anatomy, and abdominal anatomy, the standardized mean difference (SMD) of the 3D group and the conventional group were 1.27, 0.37, and 2.01, respectively ( p  < 0.05). For 3D vs. cadaver and 3D vs. 2D, the SMD were 0.69 and 1.05, respectively ( p <  0.05). For answering time, the SMD of the 3D group vs. conventional group was – 0.61 ( P  < 0.05). For 3D print usefulness, RR = 2.29( P <  0.05). Five of the six studies showed that satisfaction of the 3D group was higher than that of the conventional group. Two studies showed that accuracy of answering questions in the 3D group was higher than that in the conventional group. Conclusions Compared with students in the conventional group, those in the 3D printing group had advantages in accuracy and answering time. In the test of anatomical knowledge, the test results of students in the 3D group were not inferior (higher or equal) to those in the conventional group. The post-training test results of the 3D group were higher than those in the cadaver or 2D group. More students in the 3D printing group were satisfied with their learning compared with the conventional group. The results could be influenced by the quality of the randomized controlled trials. In a framework of ethical rigor, the application of the 3D printing model in human anatomy teaching is expected to grow further.

É. Brumpt, E. Bertin, Laurent Tatu et al.

Background Three-dimensional-printed anatomical models (3DPAMs) appear to be a relevant tool due to their educational value and their feasibility. The objectives of this review were to describe and analyse the methods utilised for creating 3DPAMs used in teaching human anatomy and for evaluating its pedagogical contribution. Methods An electronic search was conducted on PubMed using the following terms: education, school, learning, teaching, learn, teach, educational, three-dimensional, 3D, 3-dimensional, printing, printed, print, anatomy, anatomical, anatomically, and anatomic. Data retrieved included study characteristics, model design, morphological evaluation, educational performance, advantages, and disadvantages. Results Of the 68 articles selected, the cephalic region was the most studied (33 articles); 51 articles mentioned bone printing. In 47 articles, the 3DPAM was designed from CT scans. Five printing processes were listed. Plastic and its derivatives were used in 48 studies. The cost per design ranged from 1.25 USD to 2800 USD. Thirty-seven studies compared 3DPAM to a reference model. Thirty-three articles investigated educational performance. The main advantages were visual and haptic qualities, effectiveness for teaching, reproducibility, customizability and manipulability, time savings, integration of functional anatomy, better mental rotation ability, knowledge retention, and educator/student satisfaction. The main disadvantages were related to the design: consistency, lack of detail or transparency, overly bright colours, long printing time, and high cost. Conclusion This systematic review demonstrates that 3DPAMs are feasible at a low cost and effective for teaching anatomy. More realistic models require access to more expensive 3D printing technologies and substantially longer design time, which would greatly increase the overall cost. Choosing an appropriate image acquisition modality is key. From a pedagogical viewpoint, 3DPAMs are effective tools for teaching anatomy, positively impacting the learning outcomes and satisfaction level. The pedagogical effectiveness of 3DPAMs seems to be best when they reproduce complex anatomical areas, and they are used by students early in their medical studies.

M. Castellano, Ignacio Contreras-McKay, Andrés Neyem et al.

Gamification has appeared as an alternative educational methodology to traditional tools. Specifically, in anatomy teaching, multiple technological applications have emerged in response to the difficulties of accessing cadaveric material; however, there is insufficient information about the effects of these applications on the performance achieved by students, or about to the best way to adapt learning to meet their educational needs. In this study, we investigated how teaching human anatomy through a mobile gamified technological tool containing recommendation systems can be combined with a virtual assistant to improve the learning and academic performance of medical students in the Anatomy Department at the Universidad de La Frontera in Temuco, Chile and the Anatomy Department at the Pontificia Universidad Católica de Chile. In total, 131 students participated in the experiment, which was divided into two case studies. The main findings led to the conclusion that gamified components support students in learning anatomy. In addition, the predictions and recommendations provided by the virtual assistant enabled the academic aspects that the students needed to improve to be extracted adequately. Future work is expected to support adaptive learning by incorporating new artificial intelligence in education elements that can generate personalized scenarios for studying anatomy based on the application.

Saskia Laura Schröer, Giovanni Apruzzese, Soheil Human et al.

Our society increasingly benefits from Artificial Intelligence (AI). Unfortunately, more and more evidence shows that AI is also used for offensive purposes. Prior works have revealed various examples of use cases in which the deployment of AI can lead to violation of security and privacy objectives. No extant work, however, has been able to draw a holistic picture of the offensive potential of AI. In this SoK paper we seek to lay the ground for a systematic analysis of the heterogeneous capabilities of offensive AI. In particular we (i) account for AI risks to both humans and systems while (ii) consolidating and distilling knowledge from academic literature, expert opinions, industrial venues, as well as laypeople -- all of which being valuable sources of information on offensive AI. To enable alignment of such diverse sources of knowledge, we devise a common set of criteria reflecting essential technological factors related to offensive AI. With the help of such criteria, we systematically analyze: 95 research papers; 38 InfoSec briefings (from, e.g., BlackHat); the responses of a user study (N=549) entailing individuals with diverse backgrounds and expertise; and the opinion of 12 experts. Our contributions not only reveal concerning ways (some of which overlooked by prior work) in which AI can be offensively used today, but also represent a foothold to address this threat in the years to come.

Jinlong Zhang, Jie Yan, Shuyue Li et al.

Methamphetamine is a potent and highly addictive neurotoxic psychostimulant that triggers a spectrum of adverse emotional responses during withdrawal. G-protein coupled receptor 55 (GPR55), a novel endocannabinoid receptor, is closely associated with mood regulation. Herein, we developed a murine model of methamphetamine-induced anxiety- and depressive-like behavior during abstinence which showed a decreased GPR55 expression in the hippocampus. Activation of GPR55 mitigated these behavioral symptoms, concomitantly ameliorating impairments in hippocampal neurogenesis and reducing neuroinflammation. These findings underscore the pivotal role of GPR55 in mediating the neuropsychological consequences of methamphetamine withdrawal, potentially via mechanisms involving the modulation of hippocampal neurogenesis and inflammation.

Matthias Jung, Hanna Rieder, Marco Reisert et al.

Abstract Background There is increasing evidence that myosteatosis, which is currently not assessed in clinical routine, plays an important role in risk estimation in individuals with impaired glucose metabolism, as it is associated with the progression of insulin resistance. With advances in artificial intelligence, automated and accurate algorithms have become feasible to fill this gap. Methods In this retrospective study, we developed and tested a fully automated deep learning model using data from two prospective cohort studies (German National Cohort [NAKO] and Cooperative Health Research in the Region of Augsburg [KORA]) to quantify myosteatosis on whole‐body T1‐weighted Dixon magnetic resonance imaging as (1) intramuscular adipose tissue (IMAT; the current standard) and (2) quantitative skeletal muscle (SM) fat fraction (SMFF). Subsequently, we investigated the two measures for their discrimination of and association with impaired glucose metabolism beyond baseline demographics (age, sex and body mass index [BMI]) and cardiometabolic risk factors (lipid panel, systolic blood pressure, smoking status and alcohol consumption) in asymptomatic individuals from the KORA study. Impaired glucose metabolism was defined as impaired fasting glucose or impaired glucose tolerance (140–200 mg/dL) or prevalent diabetes mellitus. Results Model performance was high, with Dice coefficients of ≥0.81 for IMAT and ≥0.91 for SM in the internal (NAKO) and external (KORA) testing sets. In the target population (380 KORA participants: mean age of 53.6 ± 9.2 years, BMI of 28.2 ± 4.9 kg/m2, 57.4% male), individuals with impaired glucose metabolism (n = 146; 38.4%) were older and more likely men and showed a higher cardiometabolic risk profile, higher IMAT (4.5 ± 2.2% vs. 3.9 ± 1.7%) and higher SMFF (22.0 ± 4.7% vs. 18.9 ± 3.9%) compared to normoglycaemic controls (all P ≤ 0.005). SMFF showed better discrimination for impaired glucose metabolism than IMAT (area under the receiver operating characteristic curve [AUC] 0.693 vs. 0.582, 95% confidence interval [CI] [0.06–0.16]; P < 0.001) but was not significantly different from BMI (AUC 0.733 vs. 0.693, 95% CI [−0.09 to 0.01]; P = 0.15). In univariable logistic regression, IMAT (odds ratio [OR] = 1.18, 95% CI [1.06–1.32]; P = 0.004) and SMFF (OR = 1.19, 95% CI [1.13–1.26]; P < 0.001) were associated with a higher risk of impaired glucose metabolism. This signal remained robust after multivariable adjustment for baseline demographics and cardiometabolic risk factors for SMFF (OR = 1.10, 95% CI [1.01–1.19]; P = 0.028) but not for IMAT (OR = 1.14, 95% CI [0.97–1.33]; P = 0.11). Conclusions Quantitative SMFF, but not IMAT, is an independent predictor of impaired glucose metabolism, and discrimination is not significantly different from BMI, making it a promising alternative for the currently established approach. Automated methods such as the proposed model may provide a feasible option for opportunistic screening of myosteatosis and, thus, a low‐cost personalized risk assessment solution.

Yanyan Yang, Qianqian Li, Xinrui Yang et al.

Background At present, there are no available genetic data on the AGCU EX22 Kit from the Wuhu Han population.Aim This study investigates the applicability of the AGCU EX22 kit, designed for the Chinese population for forensic analysis and population genetics of the Wuhu Han population.Subjects and methods Bloodstains from 1565 unrelated healthy individuals in Wuhu city, Anhui Province, were collected for analysis. The AGCU EX22 kit was used for amplification, and capillary electrophoresis was used to separate the amplification products. Allele frequencies and forensic parameters were determined. The Wuhu Han population was compared to 10 reference populations through genetic distance, a phylogenetic neighbor-joining tree and principal component analysis.Results In total, 281 alleles and 1187 genotypes were observed. No significant deviations from Hardy-Weinberg equilibrium at any locus were found after Bonferroni’s correction. The 21 autosomal short tandem repeat (STR) genetic markers exhibited high informativeness and polymorphism. The cumulative power of discrimination and power of exclusion were 0.999999999999999999999999913380 and 0.999999996752339, respectively. Population comparisons revealed a genetic affinity between Wuhu Han and southern Han populations, except for the Guangdong Han population, which aligned with the traditional geographical division in China.Conclusion The AGCU EX22 Kit, containing 21 STR loci, is suitable for forensic application and population genetics studies in the Wuhu Han population.

Katelyn E. Brown, Natascha Heise, C. Eitel et al.

The arrival of COVID-19 restrictions and the increasing demand of online instruction options posed challenges to education communities worldwide, especially in human anatomy. In response, Colorado State University developed and deployed an 8-week-long large-scale virtual reality (VR) course to supplement online human anatomy instruction. Students ( n  = 75) received a VR-capable laptop and head-mounted display and participated in weekly synchronous group laboratory sessions with instructors. The software enabled students to remotely collaborate in a common virtual space to work with human anatomy using an artist-rendered cadaver. Qualitative data were collected on student engagement, confidence, and reactions to the new technology. Quantitative data assessed student knowledge acquisition and retention of anatomical spatial relationships. Results indicated that students performed better in the online course (mean = 82.27%) when compared to previous in-person laboratories (mean = 80.08%). The utilization of VR promoted student engagement and increased opportunities for student interaction with teaching assistants, peers, and course content. Notably, students reported benefits that focused on unique aspects of their virtual learning environment, including the ability to infinitely scale the cadaver and walk inside and around anatomical structures. Results suggested that using VR was equivalent to 2D methods in student learning and retention of anatomical relationships. Overall, the virtual classroom maintained the rigor of traditional gross anatomy laboratories without negatively impacting student examination scores and provided a high level of accessibility, without compromising learner engagement.

Wieslaw Bartkowski, Andrzej Nowak, Filip Ignacy Czajkowski et al.

When we go for a walk with friends, we can observe an interesting effect: From step lengths to arm movements - our movements unconsciously align; they synchronize. Prior research found that this synchronization is a crucial aspect of human relations that strengthens social cohesion and trust. Generalizing from these findings in synchronization theory, we propose a dynamical approach that can be applied in the design of non-humanoid robots to increase trust. We contribute the results of a controlled experiment with 51 participants exploring our concept in a between-subjects design. For this, we built a prototype of a simple non-humanoid robot that can bend to follow human movements and vary the movement synchronization patterns. We found that synchronized movements lead to significantly higher ratings in an established questionnaire on trust between people and automation but did not influence the willingness to spend money in a trust game.

Jurriaan van Diggelen, Karel van den Bosch, Mark Neerincx et al.

We propose methods for analysis, design, and evaluation of Meaningful Human Control (MHC) for defense technologies from the perspective of military human-machine teaming (HMT). Our approach is based on three principles. Firstly, MHC should be regarded as a core objective that guides all phases of analysis, design and evaluation. Secondly, MHC affects all parts of the socio-technical system, including humans, machines, AI, interactions, and context. Lastly, MHC should be viewed as a property that spans longer periods of time, encompassing both prior and realtime control by multiple actors. To describe macrolevel design options for achieving MHC, we propose various Team Design Patterns. Furthermore, we present a case study, where we applied some of these methods to envision HMT, involving robots and soldiers in a search and rescue task in a military context.