bluuebunny/crossref_metadata_2025_split huggingface dataset:
The correlation between visceral fat area to skeletal muscle mass ratio and multi-organ insulin resistance in Chinese population with obesity: a cross-sectional study
Authors: Zhang Yanju , Du Meiyang , Li Zhouhuiling , Wang Xincheng , Leng Mingxin , Huang Yaping , Li Libin , Zhang Shi , Li Chunjun 
Published: September 2023

Abstract
Background: Insulin resistance (IR) is an important risk factor for obesity and cardiometabolic diseases, and our previous findings have demonstrated that visceral fat area to skeletal muscle mass ratio (VSR) is significantly and positively associated with the risk of cardiometabolic diseases. Hence, this study aimed to the relationship between VSR and multi-organ IR, and provide a new approach to improve body composition, reduce the risk of cardiometabolic diseases in patients with obesity, and also set the basis for VSR to increase the incidence of cardiometabolic diseases.
Methods: The present study included 398 patients who underwent anthropometric measurements, body composition assessment and biochemical measurements. Body composition was assessed using a bioelectrical impedance analysis method (Inbody770). Spearman correlation analysis was used to investigate the correlation between VSR and homeostatic model assessment for insulin resistance (HOMA-IR) as well as multi-organ IR, including homeostasis model assessment adiponectin (HOMA-AD), adipose tissue insulin resistance (ADIPO-IR), 1/hepatic insulin sensitivity (HISI). We established a new predictive model that included indicator of visceral obesity for IR in previous study, and incorporated the New Model into the present study. Logistic regression was used to analyze the odds ratio (OR) of VSR on the risk of multi-organ IR. The predictive value of VSR for HOMA-IR and New Model were evaluated using the receiver operating characteristic (ROC) curve and the optimal cut-off point was also calculated.
Results: VSR was significantly associated with HOMA-IR, HOMA-AD, ADIPO-IR, 1/HISI, and New Model (p<0.001). With the increase of VSR, the OR increased significantly for HOMA-IR and New Model, this association remained even after adjustment of other possible confounding variables(p<0.05). Then all multi-organ IR indicators were quantified, compared to the lowest quartile group, increased VSR was more likely to exacerbate the risk of IR in the highest quartile (p trend<0.001). The area under the curve for predicting IR using VSR for HOMA-IR and New Model was 0.88 for men and 0.85 for women and 0.73 for men and 0.76 for women respectively.
Conclusions: There was a significant correlation between VSR and multi-organ IR, and the risk of multi-organ IR increased with increasing VSR.
Registration number and date: ChiCTR2100044305, March 16, 2021.
α2-antiplasmin is a key regulator of macrophage activation syndrome progression through modulation of IFN-γ-induced responses and fibrin deposition
Authors: Kanno Yosuke , Toyama Kinomi , Shibata Haruna , Matsuo Osamu , Ozaki Kei-ichi 
Published: September 2023

Abstract
Background:
Macrophage activation syndrome (MAS) is a life-threatening condition, characterized by cytopenia, multi-organ dysfunction, and coagulopathy associated with excessive activation of macrophages. The aim of this study was to clarify the role of a2-antiplasmin (α2AP) in the pathogenesis of MAS.
Methods:
We investigated the role of α2AP in the progression of MAS using fulminant MAS mouse model induced by Toll-like receptor-9 (TLR-9) agonist (CpG) and d-galactosamine (DG).
Results:
α2AP deficiency attenuated macrophage accumulation, hypocellular bone marrow (BM), liver damage, and fibrin deposition in the MAS model mice. Interferon-g (IFN-γ) is associated with macrophage activation, including phagocytosis, invasion, and migration, and plays a pivotal role in MAS progression. α2AP enhanced the IFN-γ-induced phagocytosis, invasion, migration, and tissue factor (TF) production. Additionally, we showed that fibrin induced macrophage activation and tumor necrosis factor-a (TNF-α) production. Moreover, the blockade of α2AP by neutralizing antibodies attenuated macrophage accumulation, hypocellular BM, liver damage, and fibrin deposition in the MAS model mice.
Conclusion:
α2AP regulates macrophage activation through modulation of IFN-γ-induced responses and fibrin deposition and is associated with MAS progression.
Multi-Omics and Single-Cell Sequencing Reveal the Genomic Features and Metabolic Heterogeneity of Low Ki-67 Triple-Negative Breast Cancer
Authors: Han Bo-yue , Han Xiang-chen , Luo Hong , Nasir Javaria , Chen Chao , Shao Zhi-Ming , Ling Hong , Hu Xin 
Published: September 2023

Abstract
Background: TNBC displays high heterogeneity and the majority of TNBC cases were characterized by a high Ki-67 expression. In contrast, TNBC with low Ki-67 expression accounts for only a small fraction, and this subset of TNBC has been relatively less extensively studied.
Methods: Using the study institution's largest single-center multi-omics TNBC dataset combined with a single-cell dataset, the authors analyzed the clinical, genomic, and metabolic characteristics of patients with low Ki-67 TNBC.
Results: This study included a total of 2217 low Ki-67 TNBC patients for the analysis of clinical and pathological characteristics. The results revealed that patients with low Ki-67 TNBC had a higher age at diagnosis, a lower proportion of invasive ductal carcinoma (IDC), increased alterations in the PI3K-AKT-mTOR pathway, upregulated lipid metabolism pathways, and enhanced infiltration of M2 macrophages. In contrast, high Ki-67 TNBC patients exhibited a higher prevalence of TP53 mutations, elevated nucleotide metabolism, and increased infiltration of M1 macrophages.
Conclusions: We identified specific genomic and metabolic characteristics unique to low Ki-67 TNBC, which have implications for the development of precision therapies and patient stratification strategies.
Validation Study on a Prediction Formula to Estimate the Weight of Children &amp; Adolescents with Special Needs Aged 2-18 Years Old
Authors: I Nurul Huda , K Norasimah , O Salimah , O Azahadi , S Norsuhaila , A Anis Aslah , AAP Norafiza , MN Nabilah , Z Maizatul Naqiah , AS Nadzrul Anuar , S Muhamad Farid , AR Noorfadzlina , M Siti Farhana 
Published: September 2023

Abstract
Nutrition disorders and compromised nutritional status are common among children with special needs. It may be challenging to evaluate and obtain an accurate measurement of these children due to their spasticity or uncooperative behaviours. Weight estimations in children are required when actual weight cannot be obtained with standard measurement. This study aims to validate two predictive formulas of weight estimating strategies in children with special needs, namely, the Cattermole formula and the Mercy formula. A cross-sectional study with a universal sampling of children with special needs (cerebral palsy, Down syndrome, autism and attention-deficit/hyperactivity disorder) was conducted at Community-Based Rehabilitation in Selangor, Negeri Sembilan, and Wilayah Persekutuan Kuala Lumpur and Putrajaya. Anthropometric measurements, including height, weight, humeral length, and mid-upper arm circumference, were collected using standard procedures. A total of 502 children with a median age of 7 (6) years were enrolled in this study. The results showed that the Mercy formula demonstrated a smaller degree of bias than the Cattermole formula (PE = 1.97 ± 15.99% and 21.13 ± 27.76%, respectively). The Mercy formula showed the highest intraclass correlation coefficient (0.936 vs 0.858) and predicted weight within 20% of the actual value in the largest proportion of participants (84% vs 48%). The Mercy formula also demonstrated lower RE (0.3 vs 3.6) and RMSE (3.84 vs 6.56) compared to the Cattermole formula. Mercy offered the best option for weight estimation in children with special needs in our study population.
VO2 Nanowires Based Composite Films with Broadband and High Tunable Electromagnetic Interference Shielding Performance
Authors: Liang Shuhui , Guan Huan , Zhang Hainan , Han Xiangge , Hao Sue , Dou Shuliang , Zhou Haoxin , Geng Chenchen , Zhao Tao , Gu Jinxin , Wei Hang , Li Yao 
Published: September 2023

Abstract
Traditional electromagnetic interference (EMI) shielding materials typically have constant shielding efficiencies, which cannot respond to the real-time changing shielding requirements for future electromagnetic (EM) devices. For the first time, the VO2 NWs/poly(vinylidene fluoride-co-hexafluoropropylene) (VO2/PVDF-HFP) composite films have been developed with off/on switchable EMI shielding performance, which exhibit excellent tunability in X-band and Ku-band with maximum values of 44.8 dB and 59.4 dB, respectively. The unique insulator-to-metal transition (IMT) and perfect conductive network of VO2 NWs result in three orders of magnitude in relative conductivity change, which leads to excellent off/on switchable EMI shielding performance. Furthermore, the off/on switchable EMI shielding performance is also confirmed with good cycling stability and the EMI shielding performance of the composite film can be precisely modulated using temperature. This study provides a unique approach for dynamic switching of EM response with the potential to construct multifunctional smart EM response systems for various applications such as of aerospace, artificial intelligence, advanced electronics, and stealth technology.
Research on the Impact of Digital Economy on Low Carbon Development of Manufacturing Industry
Authors: Li Shanhong , Lv Yanqin , Ping Yang 
Published: September 2023

Abstract
To explore the impact of digital economy on the low-carbon development of manufacturing industry, this paper constructs theoretical and empirical models, and studies from both theoretical and empirical perspectives. The results show that there is a U-shaped relationship between digital economy and low-carbon development of manufacturing industry. In terms of theoretical model analysis, we adopt the Green Solow model as the analytical framework, and improve and expand the CES production function to introduce digital economy into it. By deducing the theoretical model, we draw the conclusion that there is a possibility of an inverted U-shaped curve between the development of digital economy and carbon emissions. In terms of empirical verification, this paper applies fixed-effect and intermediate-effect empirical models, and relies on panel data of 30 provinces and cities in China from 2011 to 2020 to conduct an empirical study on the relationship between digital economy and low-carbon development of manufacturing industry. The results show that the impact of digital economy on the low-carbon development of manufacturing industry is not linear, but exhibits a U-shaped relationship. In regions with high and medium levels of marketization, digital economy has a significant impact on the low-carbon development of manufacturing industry, while it has no obvious impact in regions with low levels of marketization. Furthermore, this study finds that digital economy can influence the low-carbon development of manufacturing industry through various ways, such as innovative green technology, reducing capital mismatch, and promoting industrial upgrading of manufacturing industry, based on the analysis of influencing mechanism.
Cyber Threat Assessment and Management for Securing Healthcare Ecosystems using Natural Language Processing
Authors: Silvestri Stefano , Islam Shareful , Amelin Dmitry , Weiler Gabriele , Papastergiou Spyridon , Ciampi Mario 
Published: September 2023

Abstract
The healthcare sectors have constantly faced significant challenge due to the rapid rise of cyber threats. These threats can pose any potential risk within the system context and disrupt the critical healthcare service delivery. It is therefore necessary for the healthcare organisations to understand and tackle the threats to ensure overall security and resilience. However, threats are continuously evolved and there is large amount of unstructured security related textual information is available. This makes the threat assessment and management task very challenging. There are a number of existing works that consider Machine Learning models for detection and prediction of cyber attack but they lack of focus on the Natural Language Processing (NLP) to extract the threat information from unstructured security related text. To this end, this work proposes a novel method to assess and manage threats by adopting natural language processing. The proposed method has been tailored for the healthcare ecosystem and allows to identify and assess the possible threats within healthcare information infrastructure so that appropriate control and mitigation actions can be taken into consideration to tackle the threat. In detail, NLP techniques are used to extract the useful threat information related to specific assets of the healthcare ecosystems from the largely available security related information on Internet (e.g., cyber security news), to evaluate the level of the identified threats and to select the required mitigation actions. We have performed experiments on real healthcare ecosystems in Fraunhofer Institute for Biomedical Engineering (IBMT), considering in particular three different healthcare scenarios, namely implantable medical devices, wearables, and biobank, with the purpose of demonstrating the feasibility of our approach, which is able to provide a realistic manner to identify and assess the threats, evaluate the threat level and suggest the required mitigation actions.
Multiple Classification of Brain MRI Autism Spectrum Disorder by Age and Gender Using Deep Learning
Authors: Nogay Hidir Selcuk , Adeli Hojjat 
Published: September 2023

Abstract
The fact that the rapid and definitive diagnosis of autism cannot be made today and that autism cannot be treated provides an impetus to look into novel technological solutions. To contribute to the resolution of this problem through multiple classifications by considering age and gender factors, in this study, two quadruple and one octal classifications were performed using a deep learning (DL) approach. Gender in one of the four classifications and age groups in the other were considered. In the octal classification, classes were created considering gender and age groups. In addition to the diagnosis of ASD (Autism Spectrum Disorders), another goal of this study is to find out the contribution of gender and age factors to the diagnosis of ASD by making multiple classifications based on age and gender for the first time. Brain structural MRI (sMRI) scans of participators with ASD and TD (Typical Development) were pre-processed in the system originally designed for this purpose. Using the Canny Edge Detection (CED) algorithm, the sMRI image data was cropped in the data pre-processing stage, and the data set was enlarged five times with the data augmentation (DA) techniques. The most optimal convolutional neural network (CNN) models were developed using the grid search optimization (GSO) algorism. The proposed DL prediction system was tested with the five-fold cross-validation technique. The accuracy rates acquired for all three CNN models designed to be utilized within the system were compared with successfull pre-trained CNN models through the transfer learning (TL) practice. As a result, it was revealed that age and gender factors were effective in the diagnosis of ASD with the system developed for ASD multiple classifications, and it was seen that higher accuracy rates were achieved than pre-trained models.
Assessing consistency in the co-composting of faecal sludge and food waste in Ghana
Authors: Nartey Eric Gbenatey , Sakrabani Ruben , Tyrrel Sean , Cofie Olufunke 
Published: September 2023

Abstract
Background
A faecal sludge (FS) co-composting study assessed the extent of consistency in compost characteristics between and within batches. The study focused on the consistency of the co-composting process by measuring the variability of key parameters.
Method
The set up consisted of 12 FS and FW co-composting piles in three successive batches (1, 2 and 3). Between batch consistency was assessed in the three successive batches of co-composted FS and food waste (FW). Within batch consistency was assessed in each of the three batches by dividing the batch into four separate replicate piles. Characteristics of interest were E. coli, as well as selected physico-chemical (pH, EC, Mg, Ca, N, NH4, NO3, P, avail. P, and K) and heavy metals (Se, Fe, Cd, Cu, Hg, Ni, Pb and Cr). Data were subjected to analysis of variance (ANOVA) using SPSS.
Result
Results show that, E. coli levels were not consistent between the successive batches during the entire co-composting process. While variations between batches were only observed for EC and nutrient parameters, variations were evident for several measured characteristics within batches. The measured coefficient of variations (CVs) within batches ranged between 0 – 125% and 3 – 111% for heavy metals and nutrients, respectively.
Conclusion
It is recommended that a threshold value be created for determining what is an acceptable level of variation in FS co-composting.
Microsatellite marker Based Genetic Monitoring of Exotic Common Carp Strains (Cyprinus carpio L. and Cyprinus carpio communis) in the Chenab River, Punjab, Pakistan
Authors: Tabassum Fouzia , parveen shakeela , Tayyab Muhammad , Hussain Muhammad , Batool Uzma , Sultana Mehwish 
Published: September 2023

Abstract
Conservation and management of Native fauna require understanding of invasive species genetic structure and diversity. Genotyping with 10 microsatellite markers was used to evaluate 300 samples of common carp and scale carp (Cyprinus carpio L. and Cyprinus carpio communis) from five natural populations on River Chenab in Pakistan. These strains exhibit low to moderate level of heterozygosities. The average FIS for C. carpio was 0.507 to 0.5914 while in C. c. communis, ranged from 0.5310 to 0.6166. The FST assessments showed there was a modest degree of genetic difference between the strains. Genetic distance was largest among strains and lowest among strain populations. AMOVA showed that 90.38% of C. carpio’s variants were from intra-population variability and 9.62% from inter-population diversity. In C. c. communis, intra-population diversity fluctuated 12.92% and inter-population diversity 87.08%. STRUCTURE Bayesian clustering analysis grouped both strains populations into 10 groups. No genetic evidence of mixing was found for pristine, original strains. A high heterozygosity relative to expected heterozygosity in common carp strain populations suggests a bottleneck. The common carp strains' directional relative migratory network showed that HT (C.c) was the core population with the most genetic exchange with the other 5 peripheral populations. Nevertheless, no C.c. communis population showed migration event except HK C.c.c. Unweighted pair group method with averages (UPGMA) dendrogram shown two clusters: common carp and scale carp. We believe our findings may strengthen exotic species management practices.
Unveiling the role of the cyanosphere: interactions between cyanobacteria and bacterial associates during blooms
Authors: Fuster Maxime , Ruiz Thomas , Lamarque Amélie , Coulon Marianne , Legrand Benjamin , Sabart Marion , Latour Delphine , Mallet Clarisse 
Published: September 2023

Abstract
Global change severely increases the recurrence of freshwater cyanobacterial proliferations, with known deleterious consequences for ecosystem structure and functioning. A few studies highlighted the existence of bacterial communities associated to cyanobacteria (i.e., Cyanosphere), comparable to the well-known phycosphere. While phycosphere in general is expected to exchange nutrients with their associated phytoplankton communities, specific functions and dynamic of the cyanosphere remain unknown. Here, based on metabarcoding approaches, we showed that during bloom episodes, cyanosphere accounts for up to 30% of total bacterial abundance. More importantly, we demonstrated for the first-time the dynamics aspect of cyanosphere community composition at crucial bloom phases. In our study, these community changes imply variations in cyanosphere ecological functions over time. Our results highlight the ability of cyanosphere to regulate nutrient and energy exchange with cyanobacteria during the entire bloom period, modulating bloom intensity and dynamics. In the latter bloom phases, cyanosphere additionally tends to create microhabitats favoring cyanobacterial persistence in deleterious environmental conditions. At wider scale, as blooms constitute major turnovers of matter and energy in aquatic ecosystems, we argue that cyanosphere can have an important, yet underestimated, role in mediating C and nutrient cycles in environments.
Predicting 24-hour Intraocular Pressure Fluctuations using Machine Learning: A Retrospective Analysis
Authors: Chen Ranran , Lei Jinming , Liao Yujie , Jin Yiping , Bi Yanlong , Zhu Haohao , Wang Xue , Li Hong 
Published: September 2023

Abstract
24-hour intraocular pressure fluctuation is at a risk of glaucomatous progression. We thus aimed to develop a machine learning-based predictive model for 24-hour intraocular pressure (IOP) fluctuation. It was a retrospective analysis of electronic medical records from 978 eyes, focusing on 24-hour IOP monitoring data collected at Shanghai Fifth People's Hospital between January 2014 and July 2023. The dataset included various parameters such as blood pressure and corneal thickness. We divided the data into training (80%) and validation (20%) sets. Employing algorithms such as Support Vector Machine and Random Forest, our model predicted whether fluctuations in 24-hour intraocular pressure exceeded 8 mmHg. The Random Forest algorithm demonstrated the best performance in validation. The model achieved accuracy, specificity, precision, sensitivity, f1-score, and AUC area of 0.899, 0.965, 0.884, 0.718, 0.793, and 0.89. We eventually established a predictive model, demonstrating the potential to rectify intraocular pressure measurements using the Random Forest algorithm.
Analysis of perioperative and long-term outcomes among presentations of anomalous left coronary artery from the pulmonary artery diagnosed beyond infancy versus during infancy
Authors: Straka Nadine , Gauvreau Kimberlee , Huang Yisong , DeWitt Elizabeth , Allan Catherine , Thiagarajan Ravi , Nathan Meena 
Published: September 2023

Abstract
Background
Anomalous Left Coronary Artery from the Pulmonary Artery (ALCAPA) typically presents in infancy; however, there are cases of patients who survive the infant period and present later in life. We aimed to characterize patients with late ALCAPA diagnoses and to assess perioperative and functional outcomes.
Methods
A retrospective chart review of patients who underwent ALCAPA repair between 1996 to 2020 at Boston Children’s Hospital was performed. This cohort was divided into early ALCAPA (<1 year) and late ALCAPA (≥ 1 year) groups. Perioperative data were collected. Longitudinal functional assessments were made by echocardiography, exercise stress test, and cardiac magnetic resonance imaging.
Results
The median age of the late ALCAPA group was 7.6 years with 25% (6/24) of patients over 18 years. The late ALCAPA group was more likely to present as an incidental finding (63%) and required less preoperative intervention compared to the early group. On preoperative echocardiogram, the late ALCAPA group had less moderate or severe mitral regurgitation (16.7% vs 62%, p<0.001) or left ventricular dysfunction (16.7% vs 89%, p <0.001) compared to the early group. Reoperation was uncommon and both groups demonstrated almost complete resolution of mitral regurgitation and left ventricular dysfunction over time.
Conclusion
There are important differences between late and early ALCAPA subtypes. Revascularization results in excellent outcomes in both early and late groups but long-term surveillance of ALCAPA patients is warranted as they may have functional deficits after repair.