bluuebunny/crossref_metadata_2025_split huggingface dataset:
Identifying myoglobin as a mediator of diabetic kidney disease: a machine learning-based cross-sectional study
Authors: Wu Ruoru , Shu Zhihao , Zou Fei , Zhao Shaoli , Chan Saolai , Hu Yaxian , Xiang Hong , Chen Shuhua , Fu Li , Cao Dongsheng , Lu Hongwei 
Published: December 2022

AbstractIn view of the alarming increase in the burden of diabetes mellitus (DM) today, a rising number of patients with diabetic kidney disease (DKD) is forecasted. Current DKD predictive models often lack reliable biomarkers and perform poorly. In this regard, serum myoglobin (Mb) identified by machine learning (ML) may become a potential DKD indicator. We aimed to elucidate the significance of serum Mb in the pathogenesis of DKD. Electronic health record data from a total of 728 hospitalized patients with DM (286 DKD vs. 442 non-DKD) were used. We developed DKD ML models incorporating serum Mb and metabolic syndrome (MetS) components (insulin resistance and β-cell function, glucose, lipid) while using SHapley Additive exPlanation (SHAP) to interpret features. Restricted cubic spline (RCS) models were applied to evaluate the relationship between serum Mb and DKD. Serum Mb-mediated renal function impairment induced by MetS components was verified by causal mediation effect analysis. The area under the receiver operating characteristic curve of the DKD machine learning models incorporating serum Mb and MetS components reached 0.85. Feature importance analysis and SHAP showed that serum Mb and MetS components were important features. Further RCS models of DKD showed that the odds ratio was greater than 1 when serum Mb was > 80. Serum Mb showed a significant indirect effect in renal function impairment when using MetS components such as HOMA-IR, HGI and HDL-C/TC as a reason. Moderately elevated serum Mb is associated with the risk of DKD. Serum Mb may mediate MetS component-caused renal function impairment.
The association of problematic gaming characteristics with dietary habits among Finnish vocational school students
Authors: Vaarala Susanna , Ruotsalainen Heidi , Hylkilä Krista , Kääriäinen Maria , Konttila Jenni , Männistö Merja , Männikkö Niko 
Published: December 2022

AbstractDigital gaming is a popular pastime among young people, but its links to dietary habits have been little studied. The purpose of the study was to describe dietary habits and to examine their associations to problematic gaming behavior with regard to the degree of daily digital gaming time and the overall levels of disordered-like gaming behavior among students in vocational education in the Oulu region of Finland. This cross-sectional study consisted of a total of 773 first-year vocational school students who had played digital games regularly. Data was collected by using an online survey measuring sociodemographic information, dietary habits, amount of digital gaming time, and symptoms of problematic gaming behavior. Most prevalent weekly consumed food types were chicken (90.1%), chips (87.7%), and sausages/cold cuts (85.4%). Around one-fourth of students skipped breakfast on weekdays and at weekends. A higher amount of digital gaming time was associated with skipping breakfast on weekdays. More elevated levels of disordered gaming behavior were particularly associated with the use of a group of food types encompassing carbohydrate-dense and fast food. Current research provides indications that digital gaming may have an impact on youths’ dietary habits, while at the same time, however, emphasizing that the issue can be affected by several interrelated and complex factors.
Artificial neural network applied to fragile X-associated tremor/ataxia syndrome stage diagnosis based on peripheral mitochondrial bioenergetics and brain imaging outcomes
Authors: Giulivi Cecilia , Wang Jun Yi , Hagerman Randi J. 
Published: December 2022

AbstractNo proven prognosis is available for the neurodegenerative disorder fragile X-associated tremor/ataxia syndrome (FXTAS). Artificial neural network analyses (ANN) were used to predict FXTAS progression using data from 127 adults (noncarriers and FMR1 premutation carriers with and without FXTAS) with five outcomes from brain MRI imaging and 22 peripheral bioenergetic outcomes from two cell types. Diagnosis accuracy by ANN predictions ranged from 41.7 to 86.3% (depending on the algorithm used), and those misclassified usually presented a higher FXTAS stage. ANN prediction of FXTAS stages was based on a combination of two imaging findings (white matter hyperintensity and whole-brain volumes adjusted for intracranial volume) and four bioenergetic outcomes. Those at Stage 3 vs. 0–2 showed lower mitochondrial mass, higher oxidative stress, and an altered electron transfer consistent with mitochondrial unfolded protein response activation. Those at Stages 4–5 vs. 3 had higher oxidative stress and glycerol-3-phosphate-linked ATP production, suggesting that targeting mGPDH activity may prevent a worse prognosis. This was confirmed by the bioenergetic improvement of inhibiting mGPDH with metformin in affected fibroblasts. ANN supports the prospect of an unbiased molecular definition in diagnosing FXTAS stages while identifying potential targets for personalized medicine.
Visual imagery skills and risk attitude
Authors: Zeng Cathy , Fielding David , Peeters Ronald , Wesselbaum Dennis 
Published: December 2022

AbstractSeveral of Kahneman and Tversky’s seminal works in the 1970s found evidence of the importance of framing in decision making under risk. They hypothesized that imaginability (visual imagery ability) may play an important role in the evaluation of subjective probabilities. However, the impact of visual imagery ability on choice under risk has not yet been explored. This is the main purpose of our study. In an online experiment, we collected participants’ visual imagery ability using the Vividness of Visual Imagery Questionnaire and their risk attitude using two choice-based risk elicitation tasks. Participants made their risk decisions either in an environment where risk was visualized (visual frame) or not (non-visual frame), and were randomly assigned to one of the two decision frames. Our results suggest that neither visual imagery ability nor decision frame has a substantial impact on risk attitude.
A novel fast method for identifying the origin of Maojian using NIR spectroscopy with deep learning algorithms
Authors: Chang Chenjie , Li Zongyuan , Li Hongyi , Hou Zhuoya , Zuo Enguang , Zhao Deyi , Lv Xiaoyi , Zhong Furu , Chen Cheng , Tian Feng 
Published: December 2022

AbstractMaojian is one of China’s traditional famous teas. There are many Maojian-producing areas in China. Because of different producing areas and production processes, different Maojian have different market prices. Many merchants will mix Maojian in different regions for profit, seriously disrupting the healthy tea market. Due to the similar appearance of Maojian produced in different regions, it is impossible to make a quick and objective distinction. It often requires experienced experts to identify them through multiple steps. Therefore, it is of great significance to develop a rapid and accurate method to identify different regions of Maojian to promote the standardization of the Maojian market and the development of detection technology. In this study, we propose a new method based on Near infra-red (NIR) with deep learning algorithms to distinguish different origins of Maojian. In this experiment, the NIR spectral data of Maojian from different origins are combined with the back propagation neural network (BPNN), improved AlexNet, and improved RepSet models for classification. Among them, improved RepSet has the highest accuracy of 99.30%, which is 8.67% and 0.70% higher than BPNN and improved AlexNet, respectively. The overall results show that it is feasible to use NIR and deep learning methods to quickly and accurately identify Maojian from different origins and prove an effective alternative method to discriminate different origins of Maojian.
Machine learning-based risk factor analysis of necrotizing enterocolitis in very low birth weight infants
Authors: Cho Hannah , Lee Eun Hee , Lee Kwang-Sig , Heo Ju Sun 
Published: December 2022

AbstractThis study used machine learning and a national prospective cohort registry database to analyze the major risk factors of necrotizing enterocolitis (NEC) in very low birth weight (VLBW) infants, including environmental factors. The data consisted of 10,353 VLBW infants from the Korean Neonatal Network database from January 2013 to December 2017. The dependent variable was NEC. Seventy-four predictors, including ambient temperature and particulate matter, were included. An artificial neural network, decision tree, logistic regression, naïve Bayes, random forest, and support vector machine were used to evaluate the major predictors of NEC. Among the six prediction models, logistic regression and random forest had the best performance (accuracy: 0.93 and 0.93, area under the receiver-operating-characteristic curve: 0.73 and 0.72, respectively). According to random forest variable importance, major predictors of NEC were birth weight, birth weight Z-score, maternal age, gestational age, average birth year temperature, birth year, minimum birth year temperature, maximum birth year temperature, sepsis, and male sex. To the best of our knowledge, the performance of random forest in this study was among the highest in this line of research. NEC is strongly associated with ambient birth year temperature, as well as maternal and neonatal predictors.
Rapid and label-free Listeria monocytogenes detection based on stimuli-responsive alginate-platinum thiomer nanobrushes
Authors: Oliveira Daniela A. , McLamore Eric S. , Gomes Carmen L. 
Published: December 2022

AbstractIn this work, we demonstrate the development of a rapid and label-free electrochemical biosensor to detect Listeria monocytogenes using a novel stimulus–response thiomer nanobrush material. Nanobrushes were developed via one-step simultaneous co-deposition of nanoplatinum (Pt) and alginate thiomers (ALG-thiomer). ALG-thiomer/Pt nanobrush platform significantly increased the average electroactive surface area of electrodes by 7 folds and maintained the actuation properties (pH-stimulated osmotic swelling) of the alginate. Dielectric behavior during brush actuation was characterized with positively, neutral, and negatively charged redox probes above and below the isoelectric point of alginate, indicating ALG-thiomer surface charge plays an important role in signal acquisition. The ALG-thiomer platform was biofunctionalized with an aptamer selective for the internalin A protein on Listeria for biosensing applications. Aptamer loading was optimized and various cell capture strategies were investigated (brush extended versus collapsed). Maximum cell capture occurs when the ALG-thiomer/aptamer is in the extended conformation (pH > 3.5), followed by impedance measurement in the collapsed conformation (pH < 3.5). Low concentrations of bacteria (5 CFU mL−1) were sensed from a complex food matrix (chicken broth) and selectivity testing against other Gram-positive bacteria (Staphylococcus aureus) indicate the aptamer affinity is maintained, even at these pH values. The new hybrid soft material is among the most efficient and fastest (17 min) for L. monocytogenes biosensing to date, and does not require sample pretreatment, constituting a promising new material platform for sensing small molecules or cells.
Predicting an unstable tear film through artificial intelligence
Authors: Fineide Fredrik , Storås Andrea Marheim , Chen Xiangjun , Magnø Morten S. , Yazidi Anis , Riegler Michael A. , Utheim Tor Paaske 
Published: December 2022

AbstractDry eye disease is one of the most common ophthalmological complaints and is defined by a loss of tear film homeostasis. Establishing a diagnosis can be time-consuming, resource demanding and unpleasant for the patient. In this pilot study, we retrospectively included clinical data from 431 patients with dry eye disease examined in the Norwegian Dry Eye Clinic to evaluate how artificial intelligence algorithms perform on clinical data related to dry eye disease. The data was processed and subjected to numerous machine learning classification algorithms with the aim to predict decreased tear film break-up time. Moreover, feature selection techniques (information gain and information gain ratio) were applied to determine which clinical factors contribute most to an unstable tear film. The applied machine learning algorithms outperformed baseline classifications performed with ZeroR according to included evaluation metrics. Clinical features such as ocular surface staining, meibomian gland expressibility and dropout, blink frequency, osmolarity, meibum quality and symptom score were recognized as important predictors for tear film instability. We identify and discuss potential limitations and pitfalls.
Direct encapsulation of biomolecules in semi-permeable microcapsules produced with double-emulsions
Authors: Michielin Grégoire , Maerkl Sebastian J. 
Published: December 2022

AbstractCompartmentalization can serve different purposes such as the protection of biological active substances from the environment, or the creation of a unique combination of biomolecules for diagnostic, therapeutic, or other bioengineering applications. We present a method for direct encapsulation of molecules in biocompatible and semi-permeable microcapsules made from low-molecular weight poly(ethylene glycol) diacrylate (PEG-DA 258). Microcapsules are produced using a non-planar PDMS microfluidic chip allowing for one-step production of water-in-PEG-DA 258-in-water double-emulsions, which are polymerized with UV light into a poly-PEG-DA 258 shell. Semi-permeable microcapsules are obtained by adding an inert solvent to the PEG-DA 258. Due to the favorable hydrophilicity of poly-PEG-DA 258, proteins do not adsorb to the capsule shell, and we demonstrate the direct encapsulation of enzymes, which can also be dried in the capsules to preserve activity. Finally, we leverage capsule permeability for the implementation of a two-layer communication cascade using compartmentalized DNA strand displacement reactions. This work presents the direct encapsulation of active biomolecules in semi-permeable microcapsules, and we expect our platform to facilitate the development of artificial cells and generating encapsulated diagnostics or therapeutics.
Potential hazard characteristics of trees with hollows, cavities and fruiting bodies growing along pedestrian routes
Authors: Suchocka Marzena , Wojnowska-Heciak Magdalena , Jankowski Paweł , Mojski Jacek , Milanowska Agata , Kubus Marcin , Kalaji Hazem M. 
Published: December 2022

AbstractThis article is a study of risk assessment of trees with hollows, cavities and fruiting bodies for the improvement of the management and protection of urban trees growing along pedestrian routes. 317 trees were examined using TRAQ risk classes, VTA and ISA BMP methodology, Roloff's vitality classification, and sonic tomography (SoT) during the spring and summer of 2021. The collected data was analysed using the Kruskal–Wallis H-test, the Dunn multiple comparison test, the pairwise comparison of proportions with Holm correction, the U-Manna-Whitney test, and the Fisher exact test. The analysed trees grow alongside public footpaths and footways in central Zakopane, Poland. The study results indicate that tree trunk hollows are judged to have no adverse effects on a tree’s vitality when assessed using visual methods and are deemed to have a limited effect on vitality estimated with SoT. Though most high and moderate-risk trees, according to SoT (88% and 80%, respectively), had hollows, such trees were a small fraction of all 171 trees with hollows, cavities and/or fruiting bodies, 2.3% and 8.8%, respectively. Therefore, the decision to remove a tree should be based on advice from a professional arborist, supported by sonic tomography (SoT) or similar objective methods.
Stage-specific and cell type-specific requirements of ikzf1 during haematopoietic differentiation in zebrafish
Authors: Hess Isabell , Sagar  , O´Meara Connor , Grün Dominic , Schorpp Michael , Boehm Thomas 
Published: December 2022

AbstractThe zinc finger transcription factor Ikaros1 (Ikzf1) is required for lymphoid development in mammals. Four zinc fingers constitute its DNA binding domain and two zinc fingers are present in the C-terminal protein interaction module. We describe the phenotypes of zebrafish homozygous for two distinct mutantikzf1alleles. The IT325 variant lacks the C-terminal two zinc fingers, whereas the fr105 variant retains only the first zinc finger of the DNA binding domain. An intactikzf1gene is required for larval T cell development, whereas low levels of adult lymphoid development recover in the mutants. By contrast, the mutants exhibit a signature of increased myelopoiesis at larval and adult stages. Both mutations stimulate erythroid differentiation in larvae, indicating that the C-terminal zinc fingers negatively regulate the extent of red blood cell production. An unexpected differential effect of the two mutants on adult erythropoiesis suggests a direct requirement of an intact DNA binding domain for entry of progenitors into the red blood cell lineage. Collectively, our results reinforce the biological differences between larval and adult haematopoiesis, indicate a stage-specific function ofikzf1in regulating the hierarchical bifurcations of differentiation, and assign distinct functions to the DNA binding domain and the C-terminal zinc fingers.
Identification of cuproptosis-related long noncoding RNA signature for predicting prognosis and immunotherapy response in bladder cancer
Authors: Huang Gaomin , Huang Yawei , Zhang Chiyu , Jiang Yi , Ye Zhenfeng , He Chen , Yu Fanfan , Chen Zitong , Xi Xiaoqing 
Published: December 2022

AbstractBladder cancer (BC) is the most common malignant tumour of the urinary system and one of the leading causes of cancer-related death. Cuproptosis is a novel form of programmed cell death, and its mechanism in tumours remains unclear. This study aimed to establish the prognostic signatures of cuproptosis-related lncRNAs and determine their clinical prognostic value. RNA sequencing data from The Cancer Genome Atlas were used to detect the expression levels of cuproptosis-related genes in BC. Cuproptosis-related lncRNAs linked to survival were identified using co-expression and univariate Cox regression. Furthermore, consensus cluster analysis divided the lncRNAs into two subtypes. Subsequently, we established a signature model consisting of seven cuproptosis-related lncRNAs (AC073534.2, AC021321.1, HYI-AS1, PPP1R26-AS1, AC010328.1, AC012568.1 and MIR4435-2Hg) using least absolute shrinkage and selection operator regression. Survival analysis based on risk score showed that the overall survival and progression-free survival of patients in the high-risk group were worse than those in the low-risk group. Multivariate Cox analysis demonstrated the independent prognostic potential of this signature model for patients with BC. Moreover, age and clinical stage were also significantly correlated with prognosis. The constructed nomogram plots revealed good predictive power for the prognosis of patients with BC and were validated using calibration plots. Additionally, enrichment analysis, Single sample gene set enrichment analysis and immune infiltration abundance analysis revealed significant differences in immune infiltration between the two risk groups, with high levels of immune cell subset infiltrations observed in the high-risk group accompanied by various immune pathway activation. Moreover, almost all the immune checkpoint genes showed high expression levels in the high-risk group. Moreover, TIDE analysis suggested that the high-risk group was more responsive to immunotherapy. Finally, eight drugs with low IC50 values were screened, which may prove to be beneficial for patients in the high-risk group.
Developing an integrated approach based on geographic object-based image analysis and convolutional neural network for volcanic and glacial landforms mapping
Authors: Kazemi Garajeh Mohammad , Li Zhenlong , Hasanlu Saber , Zare Naghadehi Saeid , Hossein Haghi Vahid 
Published: December 2022

AbstractRapid detection and mapping of landforms are crucially important to improve our understanding of past and presently active processes across the earth, especially, in complex and dynamic volcanoes. Traditional landform modeling approaches are labor-intensive and time-consuming. In recent years, landform mapping has increasingly been digitized. This study conducted an in-depth analysis of convolutional neural networks (CNN) in combination with geographic object-based image analysis (GEOBIA), for mapping volcanic and glacial landforms. Sentinel-2 image, as well as predisposing variables (DEM and its derivatives, e.g., slope, aspect, curvature and flow accumulation), were segmented using a multi-resolution segmentation algorithm, and relevant features were selected to define segmentation scales for each landform category. A set of object-based features was developed based on spectral (e.g., brightness), geometrical (e.g., shape index), and textural (grey level co-occurrence matrix) information. The landform modelling networks were then trained and tested based on labelled objects generated using GEOBIA and ground control points. Our results show that an integrated approach of GEOBIA and CNN achieved an ACC of 0.9685, 0.9780, 0.9614, 0.9767, 0.9675, 0.9718, 0.9600, and 0.9778 for dacite lava, caldera, andesite lava, volcanic cone, volcanic tuff, glacial circus, glacial valley, and suspended valley, respectively. The quantitative evaluation shows the highest performance (Accuracy > 0.9600 and cross-validation accuracy > 0.9400) for volcanic and glacial landforms and; therefore, is recommended for regional and large-scale landform mapping. Our results and the provided automatic workflow emphasize the potential of integrated GEOBIA and CNN for fast and efficient landform mapping as a first step in the earth’s surface management.
Inner limiting membrane bridges within Bruch’s membrane defects in pathological myopia
Authors: Panda-Jonas Songhomitra , Jonas Jost B. , Jonas Rahul A. 
Published: December 2022

AbstractThe purpose of the study was to examine peculiarities of the inner limiting membrane (ILM) in axially elongated eyes. The histomorphometric study included human globes enucleated due to reasons such as painful secondary angle-closure glaucoma or malignant uveal melanomas. Using light microscopy, we searched for regions with ILM-specific features in association with a marked axial elongation. Out of 279 eyes (279 patients) (mean age: 61.8 ± 13.9 years; axial length: 25.5 ± 3.1 mm (range: 20.0–37.0 mm)), two eyes (axial length: 30 mm and 34 mm, respectively) showed one region and two regions, respectively, characterized by ILM presence and absence of all other retinal layers, retinal pigment epithelium, Bruch´s membrane (BM) and choroid. The length of these regions, called ILM-bridges, was 1.06 mm, 0.73 mm, and 0.62 mm, respectively. All ILM-bridges were spatially associated with a larger, underlying BM defect and with localized scleral thinning without a staphylomatous scleral configuration. The distance between the ILM-bridges and the optic disc ranged between 1.92 mm and 4.21 mm. In univariable analysis, ILM-bridge number increased with longer axial length (beta: 0.19; P = 0.002) and higher BM defect prevalence (beta: 0.21; P = 0.001), while in multivariable analysis, the ILM-bridges number remained to be significantly correlated only with a higher prevalence of BM defect (beta: 0.15; P = 0.048). ILM-bridges occur in eyes with pathologic myopia in spatial association with underlying, larger BM defects. They may be due to an axial elongation-associated local stretching and rupture of all other retinal layers, caused by the BM defect-related enlargement of the retinal undersurface. Future studies may explore whether these histologic observations support the notion of the ILM having a relatively high biomechanical strength against myopic stretching-associated forces.