bluuebunny/crossref_metadata_2025_split huggingface dataset:
Participation of children with obstructive sleep apnea in clinical trials: A systematic review of barriers and interventions
Authors: Wu Lan , Li Xin , Wang Xin , Du Zhuo , Zhang Xinli , Liu Zhijian 
Published: July 2024

Abstract

Background
Obstructive sleep apnea (OSA) in children is associated with negative effects on neurocognitive development and metabolic health. Despite this, children with OSA remain underrepresented in research, setting the standards for clinical diagnoses and treatments. Consequently, most evidence on treatment is based on adult data. This review focuses on studies assessing the barriers to clinical trial participation for children with OSA and discusses the intervention studies and efforts to overcome these barriers.
Method
We retrieved articles from Scopus, Embase, PubMed, and the Cochrane Library without date restrictions until May 2024. The language was restricted to English. The Joanna Briggs Institute (JBI) tool was used to assess methodological quality.
Results
From 25 studies included, 14 barrier subcategories were identified, which were classified as an objective factor, provider, and patient/caregiver.
Conclusions
Our findings emphasize the multifaceted barriers to enrolling children with OSA in clinical trials. Furthermore, we provide several underutilized solutions to facilitate children's participation in clinical trials. However, formal targeted strategies are still needed in the future because these recommendations alone might not solve the evidence gap in children.
Registration:
Registered in PROSPERO with ID: CRD42024533326.
Data-Driven City: An Innovative Approach to Urban Area Delineation
Authors: Fang Chenyu , Zhou Lin , Gu Xinyue , Liu Xing , Werner Martin 
Published: July 2024

Abstract

This study introduces a data-driven, bottom-up approach to urban delineation, integrating feature engineering with the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, marking a significant shift from traditional methodologies reliant on simplistic OpenStreetMap (OSM) road node data aggregations. By employing a broad array of OSM categories and refining data selection through feature engineering, our research significantly enhances the precision and relevance of urban clustering. Using Bavaria, Germany, as a case study, we demonstrate that feature engineering effectively reduces noise and mitigates common DBSCAN clustering pitfalls by filtering out irrelevant and autocorrelated data. The method's robustness is validated through a comprehensive assessment involving accuracy metrics, optimal clustering selections based on entropy values, and empirical and theoretical confirmations using nighttime light data and Zipf’s Law, respectively. This study contributes to urban studies by providing a scalable, replicable model that incorporates advanced data processing techniques and multidimensional data sources, supporting improved urban planning and policy-making while effectively delineating urban boundaries in varied settings.
Standardization of Robot-Assisted Radical Nephroureterectomy via Intraperitoneal Approach: Insights from a High-Volume Japanese Center
Authors: Yajima Shugo , Nakanishi Yasukazu , Hirose Kohei , Kataoka Madoka , Masuda Hitoshi 
Published: July 2024

Abstract

We present the trial-and-error process of standardizing robot-assisted radical nephroureterectomy (RANU) at a high-volume center in Japan. Our urology team performed 53 RANU cases using the Da Vinci Xi system, undergoing five major evolutionary stages. We performed RANU via transperitoneal approach in all cases and lymph node dissection in selected cases. During the evolution, we adopted a lithotomy position and significantly modified port placement to facilitate lower ureter management. However, we ultimately arrived at a method that minimizes port and patient repositioning during lower ureter processing. By strategically placing ProGrasp™ forceps in the most caudal port, we effectively retracted the bladder and grasped the opened bladder wall during lower ureter manipulation. This approach also allowed us to perform pelvic, para-aortic, and renal portal lymph node dissection without major changes in patient positioning or port placement. Nevertheless, we acknowledge that some variations in positioning and techniques may be necessary depending on specific case requirements.
Optimizing AI models for predicting esophageal squamous cell carcinoma risk: incorporation of small datasets of soft palate images
Authors: Waki Kotaro , Gushima Ryosuke , Okubo Keishi , Kiyama Masato , Nagaoka Katsuya , Ohno Kento , Honda Munenori , Yamasaki Akira , Matsuno Kenshi , Furuta Yoki , Miyamoto Hideaki , Naoe Hideaki , Amagasaki Motoki , Tanaka Yasuhito 
Published: July 2024

Abstract

There is a currently an unmet need for non-invasive methods to predict the risk of esophageal squamous cell carcinoma (ESCC). Previously, we found that specific soft palate morphologies are strongly associated with increased ESCC risk. However, there is currently no artificial intelligence (AI) system that utilizes oral images for ESCC risk assessment. Here, we evaluated three AI models and three fine-tuning approaches with regard to their ESCC predictive power. Our dataset contained 539 cases, which were subdivided into 221 high-risk cases (2491 images) and 318 non-high-risk cases (2524 images). We used 480 cases (4295 images) for the training dataset, and the rest for validation. The Bilinear convolutional neural network (CNN) model (especially when pre-trained on fractal images) demonstrated diagnostic precision that was comparable to or better than other models for distinguishing between high-risk and non-high-risk groups. In addition, when tested with a small number of images containing soft palate data, the model showed high precision: the best AUC model had 0.91 (sensitivity 0.86, specificity 0.79). This study presents a significant advance in the development of an AI-based non-invasive screening tool for the identification of high-risk ESCC patients. The approach may be particularly suitable for institutes with limited medical imaging resources.
A Multi-Scale Attention-Based Model for Image Enhancement and Classification
Authors: guo mingyu , Takubo Tomohito 
Published: July 2024

Abstract

This paper presents EnhancedDetailNet, an advanced convolutional neural network meticulously crafted to address the intricate challenges of detail preservation and holistic comprehension inherent in the realm of image processing. Through the judicious incorporation of an all-encompassing image attention mechanism and a global feature encoding stratum, the model adeptly captures and amplifies pivotal image attributes, obviating the prerequisite for explicit image partitioning. By seamlessly amalgamating self-attention and multi-scale attention modules, EnhancedDetailNet acquires a nuanced appreciation of inter-scale feature interdependencies, judiciously elevating the salience of critical local regions and attributes. Moreover, the model accrues substantial benefits from the augmentation of its convolutional strata and the introduction of residual connections, thereby augmenting its aptitude for feature articulation and the expeditious propagation of gradients. Experimental assessments conducted on image classification and object detection benchmarks, leveraging the PlantVillage dataset, resoundingly underscore EnhancedDetailNet's supremacy in capturing intricate image nuances while concurrently curtailing parameter dimensions and preserving overarching features and semantic contexts. In comparative terms, EnhancedDetailNet emerges as the preeminent choice, outpacing traditional convolutional counterparts in the realms of detail retention and performance enhancement. Significantly, even when tailored to the same model dimensions as conventional approaches, EnhancedDetailNet persists in manifesting substantial performance strides, thus underscoring its preeminence in plant pathology image recognition endeavors. https://github.com/guo-mingyu/EnhancedDetailNet.git
Research on Agricultural Disease Recognition Methods Based on Very Large Kernel Convolutional Network-RepLKNet
Authors: Pei Guoquan , Wu Wendou , Zhou Bing , Liu Zigao , Li Peiyao , Qian Xueying , Yang Hongxin 
Published: July 2024

Abstract

Agriculture diseases are unavoidable problems in agricultural production. With the extensive adoption of deep learning in agricultural diseases, the impact of diseases on crops has been effectively reduced. Many existing disease recognition models use multi-level small kernel convolutional structures, but small kernel convolution is not conducive to extracting global information and cannot capture the relationship between distant pixels. To solve this problem, this paper adopts the large kernel convolutional network RepLKNet to identify the Plant Diseases Training Dataset, and the large kernel convolutional network can effectively increase the receptive field and extract long-term dependencies. Improving model accuracy and training speed using transfer learning techniques. In this paper, experiments were conducted at Plant Diseases Training Dataset and the overall accuracy of the model was 93.6%, with an average accuracy of 91.9% and a Kappa coefficient of 93.3%, which proves its validity and reliability in the identification of agricultural diseases.
Modeling and Mechanical Performance Analysis of Porous Gradient Bone Scaffolds
Authors: Lin Kaixin , Yu Kaihuan , Li Ning 
Published: July 2024

Abstract

Currently, personalized adaptive technology using 3D printing scaffold is widely employed in bone tissue repair engineering to aid in healing and restoring large segmental bone defects. Although the scaffold matches the external appearance, its internal tissue structure differs significantly from that of natural bone tissue, and issues of mechanical matching and stress shielding persist after transplantation, impacting patient recovery. Drawing on the tissue structure of natural bone, this study develops a bionic bone scaffold model that replicates the microstructure of natural bone using a noise topology approach. Model samples are created with pore sizes and porosity corresponding to the bone density of various ages. The mechanical properties of bone structures with varying pore sizes and porosities are assessed using finite element simulation analysis software. Mechanical simulation results for bionic bone scaffolds across different ages indicate that the modeling method can adjust the pore size and porosity of the bionic bone structure through voxel parameters, aligning the mechanical characteristics with those of natural bone. This alignment effectively mitigates the stress mismatch between the implant structure and natural tissue post-bone grafting. The hierarchical gradient porous structure and mechanical model of bionic bone scaffold offer valuable insights for the structural design and clinical selection of bone implants.
The metabolic profiles of phenolic acids and aromatic amino acids in the Orobanche crenata parasite and its host faba bean at different infestation stages
Authors: El-Mergawi Ragab , El-Dabaa Mahmoud , El-Khawaga Fathia 
Published: July 2024

Abstract

Orobanche crenata is a root holoparasite that depends on its host for nutritional requirements. The shikimate pathway that metabolizes about 30% of the assimilated carbon in photosynthetic plants plays a role in host-parasite relationships. Aromatic amino acids (AAAs) and phenolic acids are derived from the shikimate pathway and serve as precursors of a wide number of primary and secondary metabolites. The effects of the O. crenata parasite on the phenolic acid and AAA profiles of two faba bean varieties and the differences of these compounds between the host organs and attached parasite at infestation stages were studied. Hosts and attached parasites were collected and divided, depending on the stage of parasite development, into four stages. The results revealed that phenolic acids and free AAAs tended to increase in parasitized roots compared to those of healthy roots, and the greatest increase in phenolic contents occurred at the first infestation stage. Syringic acid was observed to be unique to the parasite. Profiles of phenolic acids and AAAs were changed during the developmental stages of the parasite and differed from those of its host. Caffeic in the parasite reached more than 100 times that in Nubaria 4 host roots at the fourth infestation stage. Free phenylalanine in the parasite ranged between 2.2 and 5.5 times its level in host roots at all infestation stages. This study provides much evidence that indicates O. crenata is able to self-regulate its phenolic and AAA metabolites during its developmental stages, which differ from those of its host.
Reference Values for the Adolescent Post Version of the Postconcussion Symptom Inventory from the German General Population
Authors: Zeldovich Marina , Krol Leonie , Timmermann Dagmar , Gioia Gerard , Cunitz Katrin , Buchheim Anna , von Steinbuechel Nicole 
Published: July 2024

Abstract

Purpose
The present study aims at providing reference values from the general pediatric population for the German version of the 21-item self-report post version of the Postconcussion Symptom Inventory for adolescents aged 13–17 years (PCSI-SR13) following pediatric traumatic brain injury (pTBI).
Methods
A total of N = 950 adolescents completed an adapted version of the PCSI-SR13. Prior to establishing reference values using percentiles, psychometric properties (i.e., reliability and factorial validity) and regression analyses were examined to identify factors contributing to PCSI-SR13 scores. In addition, construct assessment in the general population sample was compared to that in the pTBI sample (N = 234) using measurement invariance analyses and direct comparisons at the score levels.
Results
The results indicate good reliability (Cronbach’s α and McDonald’s ω of 0.97 each). The four-factor structure covering physical, emotional, cognitive, and fatigue symptom groups could be replicated with χ2(183) = 995.96, p < 0.001, χ2/df = 5.44, CFI = 0.99, TLI = 0.98, RMSEA[90% CI] = 0.068[0.064, 0.073], SRMR = 0.03. With minor restrictions, assessment of symptoms was comparable between the general population and the TBI samples. Participants in the pTBI sample reported a significantly higher symptom burden than those in the general population sample. Reference values were provided using the total sample without further stratification.
Conclusions
For the German post version of the PCSI-SR13, reference values are now available for direct score comparisons and for drawing conclusions about the clinical relevance of the reported symptoms, while considering the prevalence in a comparable general population without a history of pTBI.
Trial registration
The study is retrospectively registered in German Clinical Trials Register and in International Clinical Trials Registry Platform (ID DRKS00032854).
Accelerated Ensemble Optimization using Momentum Methods
Authors: Nilsen Mathias M. , Stordal Andreas S. , Lorentzen Rolf J. , Raanes Patrick N. , Eikrem Kjersti S. 
Published: July 2024

Abstract

In this paper, we investigate the use of various momentum methods combined with an ensemble approximation of gradients, for accelerated optimization. Although momentum gradient descent methods are popular in machine learning, it is unclear how they perform when applied to time-consuming dynamic problems such as production optimization for petroleum reservoir management. Four different momentum methods are extensively tested on a reservoir test case in one deterministic and one robust setting. The numerical experiments show that momentum strategies yield, on average, a higher net present value with fewer simulations needed.
Molecular characterisation of human rabies in East Africa - a case series report and phylogenetic investigation.
Authors: Jaswant Gurdeep , Campbell Kathryn , Czupryna Anna , Mwatondo Athman , Ogoti Brian , Embregts Carmen W.E. , Kayuki Charles , Kuchaka Davis , Wambura Gati , Oigo James , Changalucha Joel , Oyugi Julius O. , Lushasi Kennedy , Sikana Lwitiko , Zwetselaar Marco van , Dekker Marieke C.J. , Muturi Mathew , Maritim Marybeth , Mutunga Mumbua , Durrant Rowan , Abala Tom , Chuchu Veronicah , Brunker Kirstyn , Thumbi S.M. , Hampson Katie 
Published: July 2024

Abstract

Background: Rabies remains a major public health problem in low- and middle-income countries. However, human rabies deaths are rarely laboratory-confirmed, especially in Africa. Here, we use rabies virus sequence data to enhance investigations for a series of 5 human rabies deaths in East Africa and discuss the implications of these at individual, healthcare, and societal-level.
Case presentation: The epidemiological context and care of these cases is contrasting: three of the bite victims did not receive any post-exposure vaccinations to prevent the fatal onset of disease, despite one attending a health facility on the day of exposure; one received only their first post-exposure vaccination; while the last received timely post-exposure vaccination but following a vaccination timeline that is not recommended by WHO. These differences raise concerns about health-seeking behaviour, competency of healthcare professionals in handling rabies exposures and accessibility and effectiveness of post-exposure prophylaxis as it is administered in these settings.
Result and conclusion: Our investigation confirms dog-mediated rabies as the cause of each of these deaths. The viral genomic data highlight the transboundary circulation of rabies within domestic dog populations across the region. We conclude that urgent action is needed to improve awareness around the need for emergency post-exposure prophylaxis that should be accessible in local communities and administered appropriately, as well as investment in coordinated dog vaccination to control dog-mediated rabies, the underlying cause of these deaths.
Epidemiology of boxwood blight in hotspots of western North Carolina and Virginia and validation of the boxwood blight infection risk model
Authors: Khaliq Ihsanul , Avenot Herve F. , Baudoin Anton , Coop Leonard , Hong Chuanxue 
Published: July 2024

Abstract

Boxwood blight is a highly invasive emerging disease. Since the first US report in North Carolina and Connecticut in 2011, boxwood blight has spread to over 30 US states, risking more than 90% of boxwood production. A boxwood blight infection risk model was developed from limited studies in controlled environments. Our study investigated the disease field epidemiology and validated the model’s prediction, using leaf wetness estimated by leaf wetness sensor or algorithms, by analysing weekly blight monitoring data collected on detector plants exposed to the prevailing environmental conditions from spring through fall of 2014 to 2017. Boxwood blight was recorded in 61 of 86 weeks, with the highest infected leaf count recorded in late summer or early fall. Rainfall, high relative humidity outside rainy periods and optimal temperatures during prolonged leaf wetness had a significant positive effect on boxwood blight development. Classification analyses showed that disease predictions from the model using leaf wetness estimated by leaf wetness sensor were more closely aligned with observations from the field than predictions based on algorithms. This study improved our understanding of disease field epidemiology, provided leads to improve the existing model, and generated essential knowledge for formulating effective strategies for blight mitigation.
Neuroprotective Effects of VGLUT1 Inhibition in HT22 Cells Overexpressing VGLUT1 Under Oxygen Glucose Deprivation Conditions
Authors: Pomierny Bartosz , Krzyżanowska Weronika , Skórkowska Alicja , Budziszewska Bogusława , Pera Joanna 
Published: July 2024

Abstract

Glutamate (Glu) is a major excitatory neurotransmitter in the brain, essential for synaptic plasticity, neuronal activity, and memory formation. However, its dysregulation leads to excitotoxicity, implicated in neurodegenerative diseases and brain ischemia. Vesicular glutamate transporters (VGLUTs) regulate Glu loading into synaptic vesicles, crucial for maintaining optimal extracellular Glu levels. This study investigates the neuroprotective effects of VGLUT1 inhibition in HT22 cells overexpressing VGLUT1 under oxygen glucose deprivation (OGD) conditions. HT22 cells, a hippocampal neuron model, were transduced with lentiviral vectors to overexpress VGLUT1. Cells were subjected to OGD, with pre-incubation of Chicago Sky Blue 6B (CSB6B), an unspecific VGLUT inhibitor. Cell viability, lactate dehydrogenase (LDH) release, mitochondrial membrane potential, and hypoxia-related protein markers (PARP1, AIF, NLRP3) were assessed. Results indicated that VGLUT1 overexpression increased vulnerability to OGD, evidenced by higher LDH release and reduced cell viability. CSB6B treatment improved cell viability and reduced LDH release in OGD conditions, particularly at 0.1 µM and 1.0 µM concentrations. Moreover, CSB6B preserved mitochondrial membrane potential and decreased levels of PARP1, AIF, and NLRP3 proteins, suggesting neuroprotective effects through mitigating excitotoxicity. This study demonstrates that VGLUT1 inhibition could be a promising therapeutic strategy for ischemic brain injury, warranting further investigation into selective VGLUT1 inhibitors.
Object Detection Algorithm Based on Improved YOLOv8 for Drill Pipe on Coal Mines
Authors: Li Xiaojun , Zhao Mingyang , Li Miao 
Published: July 2024

Abstract

Gas extraction is an important measure for coal mine gas disaster control. Its effect is closely correlated to the drilling depth. The existing methods usually determine the drilling depth by manually counting the number of drill pipes, and the number of drill pipes can be automatically counted by object detection and real-time tracking algorithms. An improved object detection model DSD-YOLOv8 was proposed for the problem of the poor performance of the object detection algorithm due to such interference factors as bright light, low illuminance and heavy dust fog in coal mines. In order to solve the problem of leak detection caused by the irregular shape that appears due to the interference of bright light, the deformable convolution DCNv2 module was used in the C2f module to make the sampling points of the convolution kernel diffuse irregularly, so as to fully extract the shape features of the drill pipe and then improve the detection rate of the model. For the problem of too low confidence of the model in detecting drill pipes due to uneven illumination, the attention paid by the model to the features of the drill pipe could be improved by embedding the SimAM non-parametric attention mechanism module in the backbone network, which can further improve the confidence of the drill pipe. For the problem of low average category detection accuracy caused by the changeable environment of the underground drilling site, the dynamic head was used to improve the ability of the model to extract the features of the drill pipe in scale, space, and channel, and improve the average category detection accuracy of the drill pipe. Finally, the improved detection algorithm was verified with the homemade drill pipe verification data set. The experimental results showed that: the improved DSD-YOLOv8 model effectively alleviated the problem of partial leak detection of the original network for scenes such as heavy dust fog and uneven illumination; the recall rate increased by 4.1%; the mean average accuracy was improved by 2.9%. At the same time, it maintains a high real-time performance (the FPS is 125), providing the basis of the drill pipe detection model for the application of real-time tracking of the number of drill pipes.