bluuebunny/crossref_metadata_2025_split huggingface dataset:
Patient-specific placental vessel segmentation with limited data
Authors: Sarwin Gary , Lussi Jonas , Gervasoni Simone , Moehrlen Ueli , Ochsenbein Nicole , Nelson Bradley J. 
Published: June 2024

AbstractA major obstacle in applying machine learning for medical fields is the disparity between the data distribution of the training images and the data encountered in clinics. This phenomenon can be explained by inconsistent acquisition techniques and large variations across the patient spectrum. The result is poor translation of the trained models to the clinic, which limits their implementation in medical practice. Patient-specific trained networks could provide a potential solution. Although patient-specific approaches are usually infeasible because of the expenses associated with on-the-fly labeling, the use of generative adversarial networks enables this approach. This study proposes a patient-specific approach based on generative adversarial networks. In the presented training pipeline, the user trains a patient-specific segmentation network with extremely limited data which is supplemented with artificial samples generated by generative adversarial models. This approach is demonstrated in endoscopic video data captured during fetoscopic laser coagulation, a procedure used for treating twin-to-twin transfusion syndrome by ablating the placental blood vessels. Compared to a standard deep learning segmentation approach, the pipeline was able to achieve an intersection over union score of 0.60 using only 20 annotated images compared to 100 images using a standard approach. Furthermore, training with 20 annotated images without the use of the pipeline achieves an intersection over union score of 0.30, which, therefore, corresponds to a 100% increase in performance when incorporating the pipeline. A pipeline using GANs was used to generate artificial data which supplements the real data, this allows patient-specific training of a segmentation network. We show that artificial images generated using GANs significantly improve performance in vessel segmentation and that training patient-specific models can be a viable solution to bring automated vessel segmentation to the clinic.
Insights from telesurgery expert conference on recent clinical experience and current status of remote surgery
Authors: Rocco Bernardo , Moschovas Marcio Covas , Saikali Shady , Gaia Giorgia , Patel Vipul , Sighinolfi Maria Chiara 
Published: June 2024

AbstractRemote surgery provides opportunity for enhanced surgical capabilities, wider healthcare reach, and potentially improved patient outcomes. The network reliability is the foundation of successful implementation of telesurgery. It relies on a robust, high-speed communication network, with ultra-low latency. Significant lag has been shown to endanger precision and safety. Furthermore, the full-fledged adoption of telerobotics demands careful consideration of ethical challenges too. A deep insight into these issues has been investigated during the first Telesurgery Consensus Conference that took place in Orlando, Florida, USA, on the 3rd and 4th of February, 2024. During the Conference, the state of the art of remote surgery has been reported from robotic systems displaying telesurgery potential. The Hinotori, a robotic-assisted surgery platform developed by Medicaroid, experienced remote surgery as pre-clinical testing only; the Edge Medical Company, Shenzen, China, reported more than one hundred animal and 30 live human surgeries; the KanGuo reported human telesurgical cases performed with distances more than 3000 km; the Microport, China, collected more than 100 human operations at a distance up to 5000 km. Though, several issues—cybersecurity, data privacy, technical malfunctions — are yet to be addressed before a successful telesurgery implementation. Expanding the discussion to encompass ethical, financial, regulatory, and legal considerations is essential too. The Telesurgery collaborative community is working together to address and establish the best practices in the field.
Relationship between aging and excess body fat with markers of inflammation, skeletal muscle mass and strength in Mexican community-dwelling people
Authors: Mendoza-Núñez Víctor Manuel , Aguilar-Curiel Jimena Valeria , Castillo-Martínez Lilia , Rodríguez-García Wendy Daniella , Vaquero-Barbosa Nayeli , Rosado-Pérez Juana , Arista-Ugalde Taide Laurita 
Published: June 2024

Abstract
Introduction
Aging is accompanied by changes in body composition, such as an increase in fat mass (FM), a decrease in skeletal muscle mass index (SMMI) and muscle strength, combined with a chronic inflammatory process (CI).

Objective
Determine the relationship between age and excess body fat with markers of chronic inflammation, skeletal muscle mass and strength.

Methods
A cross-sectional alitical study was carried out in a convenience sample of adults 45 to 59 years old (n = 100) and older adults 60 to 74 years old (n = 133). All participants had their body composition measured with an impedance meter. They were subsequently divided into two groups: (i) with excess fat (WEF), (ii) without excess fat (NEF), in order to relate excess fat and age with inflammation, muscle mass and strength.

Results
NEF adults and older adults had similar values of SMMI (9.1 ± 1.5 vs. 8.8 ± 1.3, p > 0.05) and strength (28 ± 8 vs. 27 ± 8.6, p > 0.05). Likewise, WEF adults showed significantly lower values than NEF adults in the SMMI (7.9 ± 0.8 vs. 9.1 ± 1.5, p < 0.05) and strength (28 ± 8 vs. 22 ± 5, p < 0.001). Also, WEF older adults presented significantly lower values in the SMMI (15.9 ± 1.8 vs. 22.8 ± 5.1, p < 0.05) and strength (17.9 ± 4.8 vs. 27 ± 8.6, p < 0.001).

Conclusions
Our findings suggest that excess fat mass is a risk factor that has a significantly greater influence than aging per se on the index of skeletal muscle mass and strength.
AI Through Ethical Lenses: A Discourse Analysis of Guidelines for AI in Healthcare
Authors: Arbelaez Ossa Laura , Milford Stephen R. , Rost Michael , Leist Anja K. , Shaw David M. , Elger Bernice S. 
Published: June 2024

AbstractWhile the technologies that enable Artificial Intelligence (AI) continue to advance rapidly, there are increasing promises regarding AI’s beneficial outputs and concerns about the challenges of human–computer interaction in healthcare. To address these concerns, institutions have increasingly resorted to publishing AI guidelines for healthcare, aiming to align AI with ethical practices. However, guidelines as a form of written language can be analyzed to recognize the reciprocal links between its textual communication and underlying societal ideas. From this perspective, we conducted a discourse analysis to understand how these guidelines construct, articulate, and frame ethics for AI in healthcare. We included eight guidelines and identified three prevalent and interwoven discourses: (1) AI is unavoidable and desirable; (2) AI needs to be guided with (some forms of) principles (3) trust in AI is instrumental and primary. These discourses signal an over-spillage of technical ideals to AI ethics, such as over-optimism and resulting hyper-criticism. This research provides insights into the underlying ideas present in AI guidelines and how guidelines influence the practice and alignment of AI with ethical, legal, and societal values expected to shape AI in healthcare.
Empirical comparison of assembly complexity assessment approaches in manufacturing
Authors: Capponi Matteo , Mastrogiacomo Luca , Franceschini Fiorenzo 
Published: June 2024

AbstractInvestigating assembly complexity is a continuing concern within the manufacturing field, as it may impact the quality and costs of products. A growing body of literature is aimed at developing novel and reliable methods to assess assembly complexity. The first aim of this paper is to provide an extensive literature review, identifying the main approaches to this problem. Three main approaches emerged: product-centred approach, information-centred approach and system-centred approach. A second goal is to conduct a comparative analysis of these approaches, highlighting their advantages and limitations and providing guidance for process and product designers seeking to control assembly complexity. For each approach, a representative method was chosen, analysed in detail, and applied to five different real products. As concluding findings it was observed that product-centred approaches provide a more objective analysis, focusing strictly on product features. On the other hand, information-centred approaches focus on quantity and variety of information handled by assembly operators, thus proposing a valuable link between complexity and effort required. Finally, system-centred approaches provide a holistic assessment of assembly complexity which is typically only qualitative.
The Clinicopathological Significance of Tumor Cell Subtyping in Appendiceal Neuroendocrine Tumors: A Series of 135 Tumors
Authors: Mete Ozgur , Dodington David W. , Shen Daniel L. , Asa Sylvia L. 
Published: June 2024

AbstractAppendiceal neuroendocrine tumors (NETs) are common and often are identified as incidental lesions at the time of appendectomy. The guidelines for management are based on tumor size, degree of invasion, and the Ki67 proliferation index. Most small bowel NETs are composed of serotonin-producing EC-cells, but there are multiple other neuroendocrine cell types. In the rectum, there are L-cell tumors that express peptide YY (PYY), glucagon-like peptides (GLPs), and pancreatic polypeptide (PP); they are thought to have a better prognosis than serotonin-producing tumors. We investigated whether the appendix has distinct neuroendocrine tumor types based on cell type and whether that distinction has clinical significance. We collected 135 appendiceal NETs from the pathology archives of UHN Toronto and UHCMC (Cleveland). We analyzed the expression of biomarkers including CDX2, SATB2, PSAP, serotonin, glucagon (that detects GLPs), PYY, and pancreatic polypeptide (PP) and correlated the results with clinicopathologic parameters. Immunohistochemistry identified three types of appendiceal NETs. There were 75 (56%) classified as EC-cell tumors and 37 (27%) classified as L-cell tumors; the remaining 23 (17%) expressed serotonin and one of the L-cell biomarkers and were classified as mixed. EC-cell tumors were significantly larger with more extensive invasion involving the muscularis propria, subserosa, and mesoappendix compared with L-cell tumors. Mixed tumors were intermediate in all of these parameters. Both EC-cell and mixed tumors had lymphatic and/or vascular invasion while L-cell tumors had none. Unlike EC-cell NETs, L-cell tumors were not associated with lymph node metastasis. Tumor type correlated with pT stage and the only patient with distant metastatic disease in this series had an EC-cell tumor. Our study confirms that appendiceal NETs are not a homogeneous tumor population. There are at least three types of appendiceal NET, including EC-cell, L-cell, and mixed tumors. This information is important for surveillance of patients, as monitoring urinary 5HIAA levels is only appropriate for patients with serotonin-producing tumors, whereas measurement of GLPs and/or PP is more appropriate for patients with L-cell tumors. Our data also show that tumor type is of significance with EC-cell tumors exhibiting the most aggressive behavior.
Hepatic-associated vascular morphological assessment to predict overt hepatic encephalopathy before TIPS: a multicenter study
Authors: Chen Xiaoqiong , Huang Mingsheng , Yu Xiangrong , Chen Jinqiang , Xu Chunchun , Jiang Yunzheng , Li Yiting , Zhao Yujie , Duan Chongyang , Luo Yixin , Zhang Jiawei , Lv Weifu , Li Qiyang , Luo Junyang , Dong Dandan , An Taixue , Lu Ligong , Fu Sirui 
Published: June 2024

Abstract
Background
To provide patients the chance of accepting curative transjugular intrahepatic portosystemic shunt (TIPS) rather than palliative treatments for portal hypertension-related variceal bleeding and ascites, we aimed to assess hepatic-associated vascular morphological change to improve the predictive accuracy of overt hepatic encephalopathy (HE) risks.

Methods
In this multicenter study, 621 patients undergoing TIPS were subdivided into training (413 cases from 3 hospitals) and external validation datasets (208 cases from another 3 hospitals). In addition to traditional clinical factors, we assessed hepatic-associated vascular morphological changes using maximum diameter (including absolute and ratio values). Three predictive models (clinical, hepatic-associated vascular, and combined) were constructed using logistic regression. Their discrimination and calibration were compared to test the necessity of hepatic-associated vascular assessment and identify the optimal model. Furthermore, to verify the improved performance of ModelC−V, we compared it with four previous models, both in discrimination and calibration.

Results
The combined model outperformed the clinical and hepatic-associated vascular models (training: 0.814, 0.754, 0.727; validation: 0.781, 0.679, 0.776; p < 0.050) and had the best calibration. Compared to previous models, ModelC−V showed superior performance in discrimination. The high-, middle-, and low-risk populations displayed significantly different overt HE incidence (p < 0.001). Despite the limited ability of pre-TIPS ammonia to predict overt HE risks, the combined model displayed a satisfactory ability to predict overt HE risks, both in the low- and high-ammonia subgroups.

Conclusion
Hepatic-associated vascular assessment improved the predictive accuracy of overt HE, ensuring curative chances by TIPS for suitable patients and providing insights for cirrhosis-related studies.

Graphical Abstract
N6-methyladenosine modification and post-translational modification of epithelial–mesenchymal transition in colorectal cancer
Authors: Wang Yingnan , Chen Yufan , Zhao Miaomiao 
Published: June 2024

AbstractColorectal cancer is a leading cause of cancer-related mortality worldwide. Traditionally, colorectal cancer has been recognized as a disease caused by genetic mutations. However, recent studies have revealed the significant role of epigenetic alterations in the progression of colorectal cancer. Epithelial–mesenchymal transition, a critical step in cancer cell metastasis, has been found to be closely associated with the tumor microenvironment and immune factors, thereby playing a crucial role in many kinds of biological behaviors of cancers. In this review, we explored the impact of N6-methyladenosine and post-translational modifications (like methylation, acetylation, ubiquitination, SUMOylation, glycosylation, etc.) on the process of epithelial–mesenchymal transition in colorectal cancer and the epigenetic regulation for the transcription factors and pathways correlated to epithelial-mesenchymal transition. Furthermore, we emphasized that the complex regulation of epithelial-mesenchymal transition by epigenetics can provide new strategies for overcoming drug resistance and improving treatment outcomes. This review aims to provide important scientific evidence for the prevention and treatment of colorectal cancer based on epigenetic modifications.
On quantum computing for artificial superintelligence
Authors: Grabowska Anna , Gunia Artur 
Published: June 2024

AbstractArtificial intelligence algorithms, fueled by continuous technological development and increased computing power, have proven effective across a variety of tasks. Concurrently, quantum computers have shown promise in solving problems beyond the reach of classical computers. These advancements have contributed to a misconception that quantum computers enable hypercomputation, sparking speculation about quantum supremacy leading to an intelligence explosion and the creation of superintelligent agents. We challenge this notion, arguing that current evidence does not support the idea that quantum technologies enable hypercomputation. Fundamental limitations on information storage within finite spaces and the accessibility of information from quantum states constrain quantum computers from surpassing the Turing computing barrier. While quantum technologies may offer exponential speed-ups in specific computing cases, there is insufficient evidence to suggest that focusing solely on quantum-related problems will lead to technological singularity and the emergence of superintelligence. Subsequently, there is no premise suggesting that general intelligence depends on quantum effects or that accelerating existing algorithms through quantum means will replicate true intelligence. We propose that if superintelligence is to be achieved, it will not be solely through quantum technologies. Instead, the attainment of superintelligence remains a conceptual challenge that humanity has yet to overcome, with quantum technologies showing no clear path toward its resolution.
Ethnopharmacological study on Adenosma buchneroides Bonati inhibiting inflammation via the regulation of TLR4/MyD88/NF-κB signaling pathway
Authors: Shi Yuru , Zhang Xiaoqian , Pei Shengji , Wang Yuhua 
Published: June 2024

AbstractAdenosma buchneroides Bonati, also known as fleagrass, is an important medicinal plant used by the Akha (Hani) people of China for treating inflammation-related skin swelling, acne, and diarrhoea, among other conditions. In this study, we aimed to evaluate the anti-inflammatory activities and explore the molecular mechanisms of fleagrass on treating skin swelling and acne. The results demonstrated that fleagrass inhibited the enzymatic activities of 5-LOX and COX-2 in vitro, and decreased the release of NO, IL-6, TNF-α, and IL-10 in the LPS-induced RAW264.7 macrophages. The levels of proteins associated with the nuclear factor-kappa B (NF-κB) pathway were examined by western blotting and immunofluorescence, demonstrating that fleagrass downregulated the expression of TLR4, MyD88, NF-κB/p65, and iNOS and blocked the nuclear translocation of NF-κB/p65. Furthermore, fleagrass exhibited acute anti-inflammatory activity in paw oedema models. The results confirm that fleagrass exhibits remarkable anti-inflammatory activity and can be used in alleviating inflammation, suggesting that fleagrass has the potential to be a novel anti-inflammatory agent.
Graphical Abstract
Prioritizing complex health levels beyond autism triage using fuzzy multi-criteria decision-making
Authors: Albahri A. S. , Hamid Rula A. , Alzubaidi Laith , Homod Raad Z. , Zidan Khamis A. , Mubark Hassan , Shayea Ghadeer Ghazi , Albahri O. S. , Alamoodi A. H. 
Published: June 2024

AbstractThis study delves into the complex prioritization process for Autism Spectrum Disorder (ASD), focusing on triaged patients at three urgency levels. Establishing a dynamic prioritization solution is challenging for resolving conflicts or trade-offs among ASD criteria. This research employs fuzzy multi-criteria decision making (MCDM) theory across four methodological phases. In the first phase, the study identifies a triaged ASD dataset, considering 19 critical medical and sociodemographic criteria for the three ASD levels. The second phase introduces a new Decision Matrix (DM) designed to manage the prioritization process effectively. The third phase focuses on the new extension of Fuzzy-Weighted Zero-Inconsistency (FWZIC) to construct the criteria weights using Single-Valued Neutrosophic 2-tuple Linguistic (SVN2TL). The fourth phase formulates the Multi-Attributive Border Approximation Area Comparison (MABAC) method to rank patients within each urgency level. Results from the SVN2TL-FWZIC weights offer significant insights, including the higher criteria values "C12 = Laughing for no reason" and "C16 = Notice the sound of the bell" with 0.097358 and 0.083832, indicating their significance in identifying potential ASD symptoms. The SVN2TL-FWZIC weights offer the base for prioritizing the three triage levels using MABAC, encompassing medical and behavioral dimensions. The methodology undergoes rigorous evaluation through sensitivity analysis scenarios, confirming the consistency of the prioritization results with critical analysis points. The methodology compares with three benchmark studies, using four distinct points, and achieves a remarkable 100% congruence with these prior investigations. The implications of this study are far-reaching, offering a valuable guide for clinical psychologists in prioritizing complex cases of ASD patients.
A multi-scale spatial–temporal capsule network based on sequence encoding for bearing fault diagnosis
Authors: Wang Youming , Chen Lisha 
Published: June 2024

AbstractThe Capsule Network (CapsNet) has been shown to have significant advantages in improving the accuracy of bearing fault identification. Nevertheless, the CapsNet faces challenges in identifying the type of bearing fault under nonstationary and noisy conditions. These challenges arise from the distinctive nature of its dynamic routing algorithm and the use of fixed single-scale kernels. To address these challenges, a multi-scale spatial–temporal capsule network (MSCN) based on sequence encoding is proposed for bearing fault identification under nonstationary and noisy environments. A spatial–temporal sequence encoding module focuses on feature correlations at various times and positions. Dilated convolution-based multiscale capsule layer (MCaps) is designed to capture spatial–temporal features at different scales. MCaps establishes connections between various layers, enhancing the comprehension and interpretation of spatial–temporal features. Furthermore, the Bhattacharyya coefficient is introduced into the dynamic routing to compare the similarity between capsules. The validity of the model is verified through comparative experiments, and the results show that MSCN has significant advantages over traditional methods.
Bayesian network structure learning with a new ensemble weights and edge constraints setting mechanism
Authors: Liu Kaiyue , Zhou Yun , Huang Hongbin 
Published: June 2024

AbstractBayesian networks (BNs) are highly effective in handling uncertain problems, which can assist in decision-making by reasoning with limited and incomplete information. Learning a faithful directed acyclic graph (DAG) from a large number of complex samples of a joint distribution is currently a challenging combinatorial problem. Due to the growing volume and complexity of data, some Bayesian structure learning algorithms are ineffective and lack the necessary precision to meet the required needs. In this paper, we propose a new PCCL-CC algorithm. To ensure the accuracy of the network structure, we introduce the new ensemble weights and edge constraints setting mechanism. In this mechanism, we employ a method that estimates the interaction between network nodes from multiple perspectives and divides the learning process into multiple stages. We utilize an asymmetric weighted ensemble method and adaptively adjust the network structure. Additionally, we propose a causal discovery method that effectively utilizes the causal relationships among data samples to correct the network structure and mitigate the influence of Markov equivalence classes (MEC). Experimental results on real datasets demonstrate that our approach outperforms state-of-the-art methods.