Hasil untuk "Human anatomy"

Sergio Gianesini

Abstract withdrawn to prevent dissemination of unpublished results.

Wusa Makena, Monday Nwankwo, Aisha Idris et al.

BackgroundProstate cancer is one of the most common types of cancer morbidity and mortality among Nigerian men, but there are still limited comprehensive epidemiological data. Understanding the hospital-based patterns of prostate cancer burden, risk factor distributions, and treatment modalities is paramount in supporting policy, clinical practice, and resource allocation in the healthcare system of Nigeria.PurposeThe aim of the systematic review and meta-analysis was to summarize information regarding the burden, clinical features, and management patterns of prostate cancer in Nigeria.MethodsA systematic review and meta-analysis conducted in PRISMA 2020 were implemented. PubMed, Scopus, Web of Science, and Google Scholar were searched since January 2000. Research studies that had reported prostate cancer percentage, risk, or treatment patterns among Nigerian men were eligible.Results32 studies (19,050 participants) were included, mainly retrospective (44.1) and cross-sectional (41.2), mostly hospital-based (82.4) and were done in Southwest and south-south Nigeria. In 70.6% of the cases, the diagnosis was confirmed using histology. The combined percentage of prostate cancer was 16.4 in hospital-based researches (95% CI: 8.6%29.2; I 2 = 99.3) and 14.0 in population-based research (95% CI: 4.1 - 40.0%; I2 = 98.0), with broad prediction intervals. Greater percentages were found in tertiary compared to primary care, in single- compared to multi-centre studies and in populations with an average age over 60 years (p<0.05). The regional estimates were between 5.1 and 33.0. Included in the common risk factors were older age (26.8%), family history (25%), and diet (12.5%). The most widespread therapy (36 percent) was hormonal therapy.ConclusionsProstate cancer is a significant problem in healthcare facilities of Nigeria, and radical heterogeneity along with methodological constraints make it impossible to make conclusive estimates regarding prevalence. Existing health disparities and insufficient access to curative therapies are also distinctive to the areas. Cancer control policies in Nigeria are in urgent need of population-based cancer registries, multicentre cohort studies, and implementation research to inform evidence-based cancer control policies.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD420261325315.

Fangfang Zhou, Yi Yang, Jiayou Liu et al.

ABSTRACT Human anatomy is a fundamental core course in medical education, and its teaching effectiveness directly influences students' understanding and application of medical knowledge. However, traditional anatomy instruction often faces challenges such as limited teaching resources and the high cognitive difficulty students experience. With the rapid advancement of artificial intelligence (AI), its application in medical education is receiving increasing attention. Graphics serve as a vivid and intuitive form of communication, and learning anatomy through visual representations proves more effective than relying solely on textual information. This paper explores the integration of AI and drawing in human anatomy education, analyzing its advantages and implementation strategies. Through practical teaching cases, the effectiveness of this approach is validated, providing new perspectives and methods for the reform of anatomy teaching.

Haichang Li, Anjun Zhu, Arpit Narechania

In real-world collaboration, alignment, process structure, and outcome quality do not exhibit a simple linear or one-to-one correspondence: similar alignment may accompany either rapid convergence or extensive multi-branch exploration, and lead to different results. Existing accounts often isolate these dimensions or focus on specific participant types, limiting structural accounts of collaboration. We reconceptualize collaboration through two complementary lenses. The task lens models collaboration as trajectory evolution in a structured task space, revealing patterns such as advancement, branching, and backtracking. The intent lens examines how individual intents are expressed within shared contexts and enter situated decisions. Together, these lenses clarify the structural relationships among alignment, decision-making, and trajectory structure. Rather than reducing collaboration to outcome quality or treating alignment as the sole objective, we propose a unified dynamic view of the relationships among alignment, process, and outcome, and use it to re-examine collaboration structure across Human-Human, AI-AI, and Human-AI settings.

Irenee Niyongombwa, Isaie Sibomana, Lorraine S. Wallace et al.

Abstract Simulation is a valuable technique in training health professionals, but the availability of resources determines which equipment, trainers, and methods to use in each simulation program. Low-income countries face barriers such as a shortage of trained staff and equipment, inadequate infrastructure, and other context-specific challenges in using simulation for training healthcare professionals. This qualitative study, employing semi-structured interviews, aimed to explore medical students’ experiences with learning by simulation in Rwanda, a low-income country. This is among the first studies to explore simulation-based education from student perspectives in Rwanda. Fifteen (n = 15) medical students (male: n = 12; female: n = 3) provided consent to participate in semi-structured interviews. Despite the many challenges faced by medical students as they engaged in simulation-based learning, study participants demonstrated an interest in learning through simulation. They perceived it as essential in acquiring and enhancing procedural skills in a relaxed and no-stakes learning environment. Additionally, learning by simulation methods helps medical students approach patients confidently, especially those with complex and/or less common medical conditions. Study participants identified organizational issues and limited simulation capacity as significant barriers to the effective implementation of simulation-based medical education in Rwanda. They expressed the need to invest more time and resources in simulation for a more impactful simulation-based training program. Integrating standardized simulation into a formal medical curriculum would address most of the organizational issues encountered and lead to a structured and consistent skills training program throughout undergraduate medical training in Rwanda.

Bekalu Getachew, Mekbeb Afework, Girmaye Tamrat

Objectives: Thyroidectomy is a surgical procedure that reduces or removes the thyroid gland the aim of this systematic review and meta-analysis was to assess the pooled prevalence and sub-types of thyroidectomy and characterize its postoperative complications in some low and middle income African countries. Methods: The studies were identified through an exhaustive search of reputable databases Twenty-two studies were selected based on the inclusion and exclusion criteria. Data were extracted using a standardized and pre-tested data extraction checklist, and the analysis was done using STATA version 14 statistical software. Heterogeneity was assessed using I2 statistics. Result: Toxic goiters were the most common indication for thyroidectomy accounting for 46.62 % of cases. Cosmetic reasons (41.07 %) and suspicion of malignancy (11.30 %) were the other common indications. Regarding surgical procedures, sub-total thyroidectomy (39.27 %) was the predominant surgical procedure, followed by lobectomy and isthmusectomy (34.88 %) and near-total thyroidectomy (34.77 %) respectively. The pooled prevalence of postoperative complications following thyroidectomy was 26.6 % [95%CI, 18.3–34.89]. Hypoparathyroidism (8.49 %) was the most common complication, followed by recurrent laryngeal nerve injury (7.96 %) and dysphonia (7.28 %). Conclusion: A toxic goiter was the most common indication for thyroidectomy. The pooled prevalence of postoperative complications was comparably higher than international figures. Hypoparathyroidism was the predominant postoperative complication.

B. R. Chaithra Rao, B. Asha, S. V. Uma

Background: Using traditional PowerPoint (tPPT) as a teaching tool for cross-sections in neuroanatomy can limit interactive engagement and the depth of spatial understanding needed for complex anatomical structures. Utilizing “animated drawings” in PPT (aPPT) can enhance the effectiveness of teaching cross-sectional neuroanatomy. Thus, we compared the effectiveness of both methods in improving the drawing skills of cross-sections in neuroanatomy. Methodology: Institutional Ethical Clearance and informed consent were obtained. Two cross sections of neuroanatomy, namely the Transverse Section of Pons at the Lower level and the Upper level, were taught using tPPT and aPPT methods. A diagram test (5 marks) was conducted and assessed using rubrics for both sections taught by the two techniques to evaluate their short-term (immediately) and long-term memory (after 4 weeks) of the skills learned. The test scores were compared using the Wilcoxon signed-rank test. Students’ perceptions regarding aPPT method were gathered using a peer-validated questionnaire through Google Forms. Results: The section taught with aPPT showed significantly high immediate short-term (P = 0.002) and long-term memory scores (P = 0.018). The majority of the students expressed that the approach was useful in understanding and drawing the cross sections in neuroanatomy and has good overall satisfaction and effectiveness. Conclusions: The study results validate the effectiveness of using “aPPT” which not only enhanced knowledge acquisition but also received high appreciation from students, indicating its potential as a valuable teaching tool.

Kevin L. Wei, Patricia Paskov, Sunishchal Dev et al.

In this position paper, we argue that human baselines in foundation model evaluations must be more rigorous and more transparent to enable meaningful comparisons of human vs. AI performance, and we provide recommendations and a reporting checklist towards this end. Human performance baselines are vital for the machine learning community, downstream users, and policymakers to interpret AI evaluations. Models are often claimed to achieve "super-human" performance, but existing baselining methods are neither sufficiently rigorous nor sufficiently well-documented to robustly measure and assess performance differences. Based on a meta-review of the measurement theory and AI evaluation literatures, we derive a framework with recommendations for designing, executing, and reporting human baselines. We synthesize our recommendations into a checklist that we use to systematically review 115 human baselines (studies) in foundation model evaluations and thus identify shortcomings in existing baselining methods; our checklist can also assist researchers in conducting human baselines and reporting results. We hope our work can advance more rigorous AI evaluation practices that can better serve both the research community and policymakers. Data is available at: https://github.com/kevinlwei/human-baselines

Guilherme Guerino, Luiz Rodrigues, Bruna Capeleti et al.

Heuristic evaluation is a widely used method in Human-Computer Interaction (HCI) to inspect interfaces and identify issues based on heuristics. Recently, Large Language Models (LLMs), such as GPT-4o, have been applied in HCI to assist in persona creation, the ideation process, and the analysis of semi-structured interviews. However, considering the need to understand heuristics and the high degree of abstraction required to evaluate them, LLMs may have difficulty conducting heuristic evaluation. However, prior research has not investigated GPT-4o's performance in heuristic evaluation compared to HCI experts in web-based systems. In this context, this study aims to compare the results of a heuristic evaluation performed by GPT-4o and human experts. To this end, we selected a set of screenshots from a web system and asked GPT-4o to perform a heuristic evaluation based on Nielsen's Heuristics from a literature-grounded prompt. Our results indicate that only 21.2% of the issues identified by human experts were also identified by GPT-4o, despite it found 27 new issues. We also found that GPT-4o performed better for heuristics related to aesthetic and minimalist design and match between system and real world, whereas it has difficulty identifying issues in heuristics related to flexibility, control, and user efficiency. Additionally, we noticed that GPT-4o generated several false positives due to hallucinations and attempts to predict issues. Finally, we highlight five takeaways for the conscious use of GPT-4o in heuristic evaluations.

Nanjie Yao, Gangjian Zhang, Wenhao Shen et al.

Monocular 3D clothed human reconstruction aims to create a complete 3D avatar from a single image. To tackle the human geometry lacking in one RGB image, current methods typically resort to a preceding model for an explicit geometric representation. For the reconstruction itself, focus is on modeling both it and the input image. This routine is constrained by the preceding model, and overlooks the integrity of the reconstruction task. To address this, this paper introduces a novel paradigm that treats human reconstruction as a holistic process, utilizing an end-to-end network for direct prediction from 2D image to 3D avatar, eliminating any explicit intermediate geometry display. Based on this, we further propose a novel reconstruction framework consisting of two core components: the Anatomy Shaping Extraction module, which captures implicit shape features taking into account the specialty of human anatomy, and the Twins Negotiating Reconstruction U-Net, which enhances reconstruction through feature interaction between two U-Nets of different modalities. Moreover, we propose a Comic Data Augmentation strategy and construct 15k+ 3D human scans to bolster model performance in more complex case input. Extensive experiments on two test sets and many in-the-wild cases show the superiority of our method over SOTA methods. Our demos can be found in : https://e2e3dgsrecon.github.io/e2e3dgsrecon/.

Martijn IJtsma, Salvatore Hargis

Studies of human-robot interaction in dynamic and unstructured environments show that as more advanced robotic capabilities are deployed, the need for cooperative competencies to support collaboration with human problem-holders increases. Designing human-robot systems to meet these demands requires an explicit understanding of the work functions and constraints that shape the feasibility of alternative joint work strategies. Yet existing human-robot interaction frameworks either emphasize computational support for real-time execution or rely on static representations for design, offering limited support for reasoning about coordination dynamics during early-stage conceptual design. To address this gap, this article presents a novel computational framework for analyzing joint work strategies in human-robot systems by integrating techniques from functional modeling with graph-theoretic representations. The framework characterizes collective work in terms of the relationships among system functions and the physical and informational structure of the work environment, while explicitly capturing how coordination demands evolve over time. Its use during conceptual design is demonstrated through a case study in disaster robotics, which shows how the framework can be used to support early trade-space exploration of human-robot coordination strategies and to identify cooperative competencies that support flexible management of coordination overhead. These results show how the framework makes coordination demands and their temporal evolution explicit, supporting design-time reasoning about cooperative competency requirements and work demands prior to implementation.

Emmanuel Fashae, Michael Burke, Leimin Tian et al.

Robotic systems for household object rearrangement often rely on latent preference models inferred from human demonstrations. While effective at prediction, these models offer limited insight into the interpretable factors that guide human decisions. We introduce an explicit formulation of object arrangement preferences along four interpretable constructs: spatial practicality (putting items where they naturally fit best in the space), habitual convenience (making frequently used items easy to reach), semantic coherence (placing items together if they are used for the same task or are contextually related), and commonsense appropriateness (putting things where people would usually expect to find them). To capture these constructs, we designed and validated a self-report questionnaire through a 63-participant online study. Results confirm the psychological distinctiveness of these constructs and their explanatory power across two scenarios (kitchen and living room). We demonstrate the utility of these constructs by integrating them into a Monte Carlo Tree Search (MCTS) planner and show that when guided by participant-derived preferences, our planner can generate reasonable arrangements that closely align with those generated by participants. This work contributes a compact, interpretable formulation of object arrangement preferences and a demonstration of how it can be operationalized for robot planning.

Rene Human-Baron, Tania Hanekom

Lipan Niu, Lipan Niu, Pei Yang et al.

Diabetes mellitus-induced erectile dysfunction (DMED) is a common complication in patients with diabetes mellitus. Necroptosis is regarded as a form of cell death that is intimately associated with the inflammatory response, which is not only initiated by inflammatory factors such as TNF-α, but also triggers the inflammatory cascade through the rupture of the dying cell. There is no definitive study on the role of necroptosis in the pathological process of DMED. In light of the pathological features of high inflammation levels in DMED patients, we assessed whether the necroptosis plays an important role in the course of DMED. Our study revealed that penile tissues of DMED rats showed high levels of key necroptosis factors such as receptor-interacting protein kinase 3 (RIP3), mixed-lineage kinase domain-like protein (MLKL), and transient receptor potential melatonin 7 (TRPM7). Furthermore, the inhibition of necroptosis with a receptor-interacting protein kinase 3 (RIP3) inhibitor or Yimusake (a common herbal remedy for ED) effectively rescued damage to corpus cavernosum smooth muscle cells (CCSMC) under high glucose conditions. Our findings suggest that inhibition of the RIP3/MLKL/TRPM7 necroptotic pathway could effectively ameliorate CCSMCs fibrosis and death induced by high glucose and inhibited the inflammatory response.

Gloria M. Alfosea-Cuadrado, Javier Zarzoso-Foj, Albert Adell et al.

(1) Background: Fibromyalgia syndrome (FMS) is a chronic pain condition with widespread pain and multiple comorbidities, for which conventional therapies offer limited benefits. The reserpine-induced myalgia (RIM) model is an efficient animal model of FMS in rodents. This study aimed to develop a pharmacokinetic–pharmacodynamic (PK–PD) model of reserpine in rats, linking to its impact on monoamines (MAs). (2) Methods: Reserpine was administered daily for three consecutive days at dose levels of 0.1, 0.5, and 1 mg/kg. A total of 120 rats were included, and 120 PK and 828 PD observations were collected from 48 to 96 h after the first dose of reserpine. Non-linear mixed-effect data analysis was applied for structural PK–PD model definition, variability characterization, and covariate analysis. (3) Results: A one-compartment model best described reserpine in rats (V = 1.3 mL/kg and CL = 4.5 × 10⁻¹ mL/h/kg). A precursor-pool PK–PD model (k_in = 6.1 × 10⁻³ mg/h, k_p = 8.6 × 10⁻⁴ h⁻¹ and k_out = 2.7 × 10⁻² h⁻¹) with a parallel transit chain (k₀ = 1.9 × 10⁻¹ h⁻¹) characterized the longitudinal levels of MA in the prefrontal cortex, spinal cord, and amygdala in rats. Reserpine stimulates the degradation of MA from the pool compartment (Slope₁ = 1.1 × 10⁻¹ h) and the elimination of MA (Slope₂ = 1.25 h) through the transit chain. Regarding the reference dose (1 mg/kg) of the RIM model, the administration of 4 mg/kg would lead to a mean reduction of 65% (C_max), 80% (C_min), and 70% (AUC) of MA across the brain regions tested. (4) Conclusions: Regional brain variations in neurotransmitter depletion were identified, particularly in the amygdala, offering insights for therapeutic strategies and biomarker identification in FMS research.

Robert M. Malina, António Antunes, Élvio Gouveia et al.

Background Lifestyles of contemporary children are largely organised with relatively little time for free play.Aim To compare the growth, maturity status, motor proficiency and physical fitness of non-participants and participants in organised sports 7–10 years.Subjects and methods Height, weight, skeletal age (SA), physical activity, fundamental motor skills, motor coordination and fitness were assessed in 234 boys and 235 girls. Sex-specific comparisons of the characteristics of sport participants and non-participants 7–8 and 9–10 years were evaluated with Student’s t and Mann–Whitney U tests.Results Boys and girls in each age group active in sport had significantly higher levels of sport-related physical activity. At 7–8 years, boys active in sport were significantly taller and heavier than peers not active in sport, while girls not active in sport performed significantly better in ball rolling and balance. At 9–10 years, boys active in sport were more proficient in catching, while girls active in sport were more proficient in hopping and side-to-side jumping.Conclusion SA and performances among children 7–10 years active and not active in sport were largely non-significant statistically, while those active in sport were physically more active.

Martin Wolfgang Lauer-Schmaltz, Philip Cash, John Paulin Hansen et al.

Human Digital Twins (HDTs) are a fast-emerging technology with significant potential in fields ranging from healthcare to sports. HDTs extend the traditional understanding of Digital Twins by representing humans as the underlying physical entity. This has introduced several significant challenges, including ambiguity in the definition of HDTs and a lack of guidance for their design. This survey brings together the recent advances in the field of HDTs to guide future developers by proposing a first cross-domain definition of HDTs based on their characteristics, as well as eleven key design considerations that emerge from the associated challenges.

Eike Schneiders, Steve Benford, Alan Chamberlain et al.

We reflect on the design of a multispecies world centred around a bespoke enclosure in which three cats and a robot arm coexist for six hours a day during a twelve-day installation as part of an artist-led project. In this paper, we present the project's design process, encompassing various interconnected components, including the cats, the robot and its autonomous systems, the custom end-effectors and robot attachments, the diverse roles of the humans-in-the-loop, and the custom-designed enclosure. Subsequently, we provide a detailed account of key moments during the deployment and discuss the design implications for future multispecies systems. Specifically, we argue that designing the technology and its interactions is not sufficient, but that it is equally important to consider the design of the `world' in which the technology operates. Finally, we highlight the necessity of human involvement in areas such as breakdown recovery, animal welfare, and their role as audience.

Romy Müller

Saliency maps can explain how deep neural networks classify images. But are they actually useful for humans? The present systematic review of 68 user studies found that while saliency maps can enhance human performance, null effects or even costs are quite common. To investigate what modulates these effects, the empirical outcomes were organised along several factors related to the human tasks, AI performance, XAI methods, images to be classified, human participants and comparison conditions. In image-focused tasks, benefits were less common than in AI-focused tasks, but the effects depended on the specific cognitive requirements. Moreover, benefits were usually restricted to incorrect AI predictions in AI-focused tasks but to correct ones in image-focused tasks. XAI-related factors had surprisingly little impact. The evidence was limited for image- and human-related factors and the effects were highly dependent on the comparison conditions. These findings may support the design of future user studies.

Priyadarshi Chinmoy Kumar, Kalachand Sain

A carbonate build-up or reef is a thick carbonate deposit consisting of mainly skeletal remains of organisms that can be large enough to develop a favourable topography. Delineation of such geologic features provides important input in understanding the basin's evolution and petroleum prospects. Here, we introduce a new attribute called the Reef Cube (RC) meta-attribute that has been computed by fusing several other seismic attributes that are characteristics of the reef through a supervised machine-learning algorithm. The neural learning resulted in a minimum nRMS error of 0.28 and 0.30 and a misclassification percentage of 1.13% and 1.06% for the train and test data sets. The Reef Cube meta-attribute has efficiently captured the anatomy of carbonate reef buried at ∼450 m below the seafloor from high-resolution 3D seismic data in the NW shelf of Australia. The novel approach not only picks up the subsurface architecture of the carbonate reef accurately but also accelerates the process of interpretation with a much-reduced intervention of human analysts. This can be efficiently suited for delimiting any subsurface geologic feature from a large volume of surface seismic data.