bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessing the oral and suprahyoid muscles in healthy adults using muscle ultrasound to inform the swallowing process: a proof-of-concept study
Authors: Kelly Eileen , Nazeer Saira , Fazzini Brigitta , Sutt Anna-Liisa , Olusanya Segun , Campion Thomas , Puthucheary Zudin 
Published: June 2024

AbstractThe oral and suprahyoid muscles are responsible for movements of swallowing. Our study aimed to determine the reproducibility of static and dynamic measurements of these muscles using bedside ultrasound equipment. Forty healthy participants were recruited prospectively. Primary outcomes were evaluation of mass measurements of the anterior bellies of the digastric, mylohyoid, geniohyoid and tongue in B-mode ultrasound. Secondary outcomes were evaluation of geniohyoid muscle layer thickness and function using M-mode. Muscle mass measurements demonstrated little within-participant variability. Coefficient of Variance (CoV) across muscles were: anterior belly digastric (5.0%), mylohyoid (8.7%), geniohyoid (5.0%) and tongue (3.2%). A relationship between sex (r2 = 0.131 p = 0.022) was demonstrated for the geniohyoid muscle, with males having higher transverse Cross Sectional Area (CSA) (14.3 ± 3.6 mm vs. 11.9 ± 2.5 mm, p = 0.002). Tongue size was correlated with weight (r2 = 0.356, p = 0.001), height (r2 = 0.156, p = 0.012) and sex (r2 = 0.196, p = 0.004). Resting thickness of the geniohyoid muscle layer changed with increasing bolus sizes (f = 3.898, p = 0.026). Velocity increased with bolus size (p =  < 0.001, F = 8.974). However swallow time and slope distance did not, potentially influenced by higher coefficients of variation. Oral and suprahyoid muscle mass are easily assessed using bedside ultrasound. Ultrasound may provide new information about muscle mass and function during swallowing.
Towards improving aspect-oriented software reusability estimation
Authors: Magableh Aws A. , Ata Hana’a Bani , Saifan Ahmad A. , Rawashdeh Adnan 
Published: June 2024

AbstractNowadays, large numbers of organizations may opt for Aspect-Oriented Programming (AOP), which is an enhancement to Object-Oriented Programming (OOP). This is due to the addition of a number of concepts that have assisted in the production of more flexible and reusable components. One of the most important elements added by AOP is software reuse, which is based on reusability attributes. These attributes indicate the possibility of reusing one or more components in the development of a new system. It is one of the most essential attributes to evaluate the quality of a system’s components. Thus far, little attention has been paid to the process of measuring AOP reusability, and it has not yet been standardized. The objective of the current study is to come up with a reasonable measurement for AOP software reuse, which is simultaneously a significant topic for researchers while offering several advantages for organizations. Although numerous models have been built to estimate the reusability of software, most of them are not dedicated to Aspect-Oriented Software (AOS). In this study, a model has been designed for AOS reusability estimation and measurement based on a new equation depending on five attributes that have a range of positive and negative impacts on AOS reusability. Three of those attributes, namely coupling, cohesion, and design size, have been included in previous studies. This study proposes complexity and generality as two new attributes to be considered. Each of these attributes was measured based on the metrics also proposed in this study. A new equation to calculate AOS reusability was constructed based on the most important reusability attributes and metrics. Seven aspect projects were employed as a case study to apply the proposed equation. After the proposed equation was applied to the selected projects, we obtained new values of reusability to compare with the values that resulted from applying the previous equation. The fact that new values emerged indicates that the proposed reusability metrics and attributes had a significant effect.
Diet-related inflammation is positively associated with atherogenic indices
Authors: Heidarzadeh-Esfahani Neda , Hajahmadi Salimeh , Pasdar Yahya , Darbandi Mitra , Najafi Farid , Moradinazar Mehdi , Bonyani Mitra , Feyz-BashiPoor Roxana , Soltani Shahin 
Published: June 2024

AbstractCurrent evidence suggests that non-traditional serum lipid ratios are more effective than traditional serum lipid parameters in predicting vascular diseases, and both of them are associated with dietary patterns. Therefore, this study aimed to investigate the relationship between the dietary inflammatory index (DII) and atherogenic indices using traditional serum lipid parameters (triglyceride (TG), total cholesterol (TC), LDL cholesterol (LDL–c), high-density lipoprotein cholesterol (HDL–c)) and non-traditional serum lipid ratios (atherogenic index of plasma (AIP), Castelli's index-I (CRI_I), Castelli's index-II (CRI_II), the lipoprotein combination index (LCI), and the atherogenic coefficient (AC)). Basic information from the Ravansar Non-Communicable Diseases cohort study was utilized in the present cross-sectional observational study. The study included 8870 adults aged 35–65 years. A validated food frequency questionnaire (FFQ) was used to measure DII. We compared the distributions of outcomes by DII score groups using multivariable linear regression. The difference between DII score groups was evaluated by the Bonferroni test. The mean ± SD DII was − 2.5 ± 1.43, and the prevalence of dyslipidemia was 44%. After adjusting for age, sex, smoking status, alcohol consumption status, physical activity, systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood sugar (FBS), body mass index (BMI) and socioeconomic status (SES), participants in the highest quartile of DII had a greater risk for CRI_I (β = 0.11, CI 0.05, 0.18), CRI_II (β = 0.06, CI 0.01, 0.11), LCI (β = 0.11, CI 288.12, 8373.11), AC (β = 0.11, CI 0.05, 0.17) and AIP (β = 0.06, CI 0.02, 0.10). Moreover, according to the adjusted logistic regression model, the risk of dyslipidemia significantly increased by 24% (OR: 1.24, 95% CI 1.08–1.41), 7% (OR: 1.07, 95% CI 0.94, 1.21) and 3% (OR: 1.03, 95% CI 0.91, 1.16) in Q4, Q3 and Q2 of the DII, respectively. Finally, diet-related inflammation, as estimated by the DII, is associated with a higher risk of CRI-I, CRI-II, LCI, AC, and AIP and increased odds of dyslipidemia.
Investigating the potential of 6-substituted 3-formyl chromone derivatives as anti-diabetic agents using in silico methods
Authors: Saif Minhaz Zabin , Esha Nusrat Jahan Ikbal , Quayum Syeda Tasnim , Rahman Shofiur , Al-Gawati Mahmoud A. , Alsowygh Ghadah , Albrithen Hamad , Alodhayb Abdullah N. , Poirier Raymond A. , Uddin Kabir M. 
Published: June 2024

AbstractIn exploring nature's potential in addressing diabetes-related conditions, this study investigates the therapeutic capabilities of 3-formyl chromone derivatives. Utilizing in silico methodologies, we focus on 6-substituted 3-formyl chromone derivatives (1–16) to assess their therapeutic potential in treating diabetes. The research examined the formyl group at the chromone’s C-3 position. ADMET, biological activities, were conducted along with B3LYP calculations using 3 different basis sets. The analogues were analyzed based on their parent structure obtained from PubChem. The HOMO–LUMO gap confirmed the bioactive nature of the derivatives, NBO analysis was performed to understand the charge transfer. PASS prediction revealed that 3-formyl chromone derivatives are potent aldehyde oxidase inhibitors, insulin inhibitors, HIF1A expression inhibitors, and histidine kinase. Molecular docking studies indicated that the compounds had a strong binding affinity with proteins, including CAD, BHK, IDE, HIF-α, p53, COX, and Mpro of SARS-CoV2. 6-isopropyl-3-formyl chromone (4) displayed the highest affinity for IDE, with a binding energy of − 8.5 kcal mol−1. This result outperformed the affinity of the reference standard dapagliflozin (− 7.9 kcal mol−1) as well as two other compounds that target human IDE, namely vitexin (− 8.3 kcal mol−1) and myricetin (− 8.4 kcal mol−1). MD simulations were revealed RMSD value between 0.2 and 0.5 nm, indicating the strength of the protein–ligand complex at the active site.
Neural underpinnings of ethical decisions in life and death dilemmas in naïve and expert firefighters
Authors: Duarte Isabel C. , Dionísio Ana , Oliveira Joana , Simões Marco , Correia Rita , Dias Joana A. , Caldeira Salomé , Redondo João , Castelo-Branco Miguel 
Published: June 2024

AbstractWhen a single choice impacts on life outcomes, faculties to make ethical judgments come into play. Here we studied decisions in a real-life setting involving life-and-death outcomes that affect others and the decision-maker as well. We chose a genuine situation where prior training and expertise play a role: firefighting in life-threatening situations. By studying the neural correlates of dilemmas involving life-saving decisions, using realistic firefighting situations, allowed us to go beyond previously used hypothetical dilemmas, while addressing the role of expertise and the use of coping strategies (n = 47). We asked the question whether the neural underpinnings of deontologically based decisions are affected by expertise. These realistic life-saving dilemmas activate the same core reward and affective processing network, in particular the ventromedial prefrontal cortex, nucleus accumbens and amygdala, irrespective of prior expertise, thereby supporting general domain theories of ethical decision-making. We found that brain activity in the hippocampus and insula parametrically increased as the risk increased. Connectivity analysis showed a larger directed influence of the insula on circuits related to action selection in non-experts, which were slower than experts in non rescuing decisions. Relative neural activity related to the decision to rescue or not, in the caudate nucleus, insula and anterior cingulate cortex was negatively associated with coping strategies, in experts (firefighters) suggesting practice-based learning. This shows an association between activity and expert-related usage of coping strategies. Expertise enables salience network activation as a function of behavioural coping dimensions, with a distinct connectivity profile when facing life-rescuing dilemmas.
Multimodal fusion for anticipating human decision performance
Authors: Tran Xuan-The , Do Thomas , Pal Nikhil R. , Jung Tzyy-Ping , Lin Chin-Teng 
Published: June 2024

AbstractAnticipating human decisions while performing complex tasks remains a formidable challenge. This study proposes a multimodal machine-learning approach that leverages image features and electroencephalography (EEG) data to predict human response correctness in a demanding visual searching task. Notably, we extract a novel set of image features pertaining to object relationships using the Segment Anything Model (SAM), which enhances prediction accuracy compared to traditional features. Additionally, our approach effectively utilizes a combination of EEG signals and image features to streamline the feature set required for the Random Forest Classifier (RFC) while maintaining high accuracy. The findings of this research hold substantial potential for developing advanced fault alert systems, particularly in critical decision-making environments such as the medical and defence sectors.
Disparate nonlinear neural dynamics measured with different techniques in macaque and human V1
Authors: Zhou Jingyang , Whitmire Matt , Chen Yuzhi , Seidemann Eyal 
Published: June 2024

AbstractDiverse neuro-imaging techniques measure different aspects of neural responses with distinct spatial and temporal resolutions. Relating measured neural responses across different methods has been challenging. Here, we take a step towards overcoming this challenge, by comparing the nonlinearity of neural dynamics measured across methods. We used widefield voltage-sensitive dye imaging (VSDI) to measure neural population responses in macaque V1 to visual stimuli with a wide range of temporal waveforms. We found that stimulus-evoked VSDI responses are surprisingly near-additive in time. These results are qualitatively different from the strong sub-additive dynamics previously measured using fMRI and electrocorticography (ECoG) in human visual cortex with a similar set of stimuli. To test whether this discrepancy is specific to VSDI—a signal dominated by subthreshold neural activity, we repeated our measurements using widefield imaging of a genetically encoded calcium indicator (GcaMP6f)—a signal dominated by spiking activity, and found that GCaMP signals in macaque V1 are also near-additive. Therefore, the discrepancies in the extent of sub-additivity between the macaque and the human measurements are unlikely due to differences between sub- and supra-threshold neural responses. Finally, we use a simple yet flexible delayed normalization model to capture these different dynamics across measurements (with different model parameters). The model can potentially generalize to a broader set of stimuli, which aligns with previous suggestion that dynamic gain-control is a canonical computation contributing to neural processing in the brain.
Selective and sensitive CQD-based sensing platform for Cu2+ detection in Wilson’s disease
Authors: Zarei Armin , Rezaei Aram , Shahlaei Mohsen , Asani Zhaleh , Ramazani Ali , Wang Chuanyi 
Published: June 2024

AbstractExcessive Cu2+ intake can cause neurological disorders (e.g. Wilson’s disease) and adversely affect the gastrointestinal, liver, and kidney organs. The presence of Cu2+ is strongly linked to the emergence and progression of Wilson's disease (WD), and accurately measuring the amount of copper is a crucial step in diagnosing WD at an early stage in a clinical setting. In this work, CQDs were fabricated through a facile technique as a novel fluorescence-based sensing platform for detecting Cu(II) in aqueous solutions, and in the serum samples of healthy and affected individuals by WD. The CQDs interact with Cu(II) ions to produce Turn-on and Turn-off states at nano-molar and micro-molar levels, respectively, with LODs of 0.001 µM and 1 µM. In fact, the Cu2+ ions can act like a bridge between two CQDs by which the charge and electron transfer between the CQDs may increase, possibly can have significant effects on the spectroscopic features of the CQDs. To the best of our knowledge, this is the first reported research that can detect Cu(II) at low levels using two different complexation states, with promising results in testing serum. The potential of the sensor to detect Cu(II) was tested on serum samples from healthy and affected individuals by WD, and compared to results obtained by ICP-OES. Astonishingly, the results showed an excellent correlation between the measured Cu(II) levels using the proposed technique and ICP-OES, indicating the high potential of the fluorimetric CQD-based probe for Cu(II) detection. The accuracy, sensitivity, selectivity, high precision, accuracy, and applicability of the probe toward Cu(II) ions make it a potential diagnostic tool for Wilson's disease in a clinical setting.
N-phenyl pyrazoline derivative inhibits cell aggressiveness and enhances paclitaxel sensitivity of triple negative breast cancer cells
Authors: Satriyo Pamungkas Bagus , Mustofa Mustofa , Wahyuningsih Tutik Dwi , Damayanti Ema , Wiraswati Hesti Lina , Satria Denny , Bashari M. Hasan , Sholikhah Eti Nurwening 
Published: June 2024

AbstractProtein kinase dysregulation induces cancer cell aggressiveness leading to rapid tumor progression and poor prognosis in TNBC patients. Many small-molecule kinase inhibitors have been tested in clinical trials to treat TNBC patients. In the previous study, we found that N-phenylpyrazoline small molecule acts as a protein kinase inhibitor in cervical cancer cells. However, there remains unknown about N-phenyl pyrazoline potency as a kinase inhibitor and its anti-cancer activity in TNBC cells. In this study, we investigated the activity of N-phenyl pyrazoline against TNBC cells via tyrosine kinase inhibition. Based on the MTT assay, the IC50 values for the N-phenyl pyrazoline 2, 5, A, B, C, and D against Hs578T were 12.63 µM, 3.95 µM, not available, 18.62 µM, 30.13 µM, and 26.79 µM, respectively. While only P5 exhibited the IC50 against MDA MB 231 (21.55 µM). Further, N-phenyl pyrazoline 5 treatment significantly inhibited the cell proliferation rate of Hs578T and MDA MB 231 cells. The migration assay showed that treatment with the compound N-phenyl pyrazoline 5 with 4 µM concentration significantly reduced cell migration of Hs578T cells. N-phenyl pyrazoline 5 treatment at 1 µM and 2 µM was able to reduce the tumorsphere size of Hs578t cells. A combination treatment of P5 and paclitaxel showed a synergistic effect with a combination index score > 1 in both TNBC cells. Further, the P5 predictively targeted the protein kinases that significantly correlated to breast cancer prognosis. The GSEA analysis result shows that receptor tyrosine kinase, Notch3, Notch4, and Ephrin signaling pathways were targeted by P5. The P5 treatment reduced the EGFR expression level and activation in TNBC cells.
Patient-centered radiology reports with generative artificial intelligence: adding value to radiology reporting
Authors: Park Jiwoo , Oh Kangrok , Han Kyunghwa , Lee Young Han 
Published: June 2024

AbstractThe purposes were to assess the efficacy of AI-generated radiology reports in terms of report summary, patient-friendliness, and recommendations and to evaluate the consistent performance of report quality and accuracy, contributing to the advancement of radiology workflow. Total 685 spine MRI reports were retrieved from our hospital database. AI-generated radiology reports were generated in three formats: (1) summary reports, (2) patient-friendly reports, and (3) recommendations. The occurrence of artificial hallucinations was evaluated in the AI-generated reports. Two radiologists conducted qualitative and quantitative assessments considering the original report as a standard reference. Two non-physician raters assessed their understanding of the content of original and patient-friendly reports using a 5-point Likert scale. The scoring of the AI-generated radiology reports were overall high average scores across all three formats. The average comprehension score for the original report was 2.71 ± 0.73, while the score for the patient-friendly reports significantly increased to 4.69 ± 0.48 (p < 0.001). There were 1.12% artificial hallucinations and 7.40% potentially harmful translations. In conclusion, the potential benefits of using generative AI assistants to generate these reports include improved report quality, greater efficiency in radiology workflow for producing summaries, patient-centered reports, and recommendations, and a move toward patient-centered radiology.
Integrated proteomics and metabolomics analysis of sclerosis-related proteins and femoral head necrosis following internal fixation of femoral neck fractures
Authors: Liu Yang , Ma Yongsheng , Yang Wenming , Lin Qitai , Xing Yugang , Shao Huifeng , Li Pengcui , He Yong , Duan Wangping , Wei Xiaochun 
Published: June 2024

AbstractFemoral head necrosis (FHN) is a serious complication after femoral neck fractures (FNF), often linked to sclerosis around screw paths. Our study aimed to uncover the proteomic and metabolomic underpinnings of FHN and sclerosis using integrated proteomics and metabolomics analyses. We identified differentially expressed proteins (DEPs) and metabolites (DEMs) among three groups: patients with FNF (Group A), sclerosis (Group B), and FHN (Group C). Using the Kyoto Encyclopedia of Genes and Genomes and Gene Ontology enrichment analyses, we examined the roles of these proteins and metabolites. Our findings highlight the significant differences across the groups, with 218 DEPs and 44 DEMs identified between the sclerosis and FNF groups, 247 DEPs and 31 DEMs between the FHN and sclerosis groups, and a stark 682 DEPs and 94 DEMs between the FHN and FNF groups. Activities related to carbonate dehydratase and hydrolase were similar in the FHN and sclerosis groups, whereas extracellular region and lysosome were prevalent in the FHN and FNF groups. Our study also emphasized the involvement of the PI3K-Akt pathway in sclerosis and FHN. Moreover, the key metabolic pathways were implicated in glycerophospholipid metabolism and retrograde endocannabinoid signaling. Using western blotting, we confirmed the pivotal role of specific genes/proteins such as ITGB5, TNXB, CA II, and CA III in sclerosis and acid phosphatase 5 and cathepsin K in FHN. This comprehensive analyses elucidates the molecular mechanisms behind sclerosis and FHN and suggests potential biomarkers and therapeutic targets, paving the way for improved treatment strategies. Further validation of the findings is necessary to strengthen the robustness and reliability of the results.
Biologically meaningful genome interpretation models to address data underdetermination for the leaf and seed ionome prediction in Arabidopsis thaliana
Authors: Raimondi Daniele , Passemiers Antoine , Verplaetse Nora , Corso Massimiliano , Ferrero-Serrano Ángel , Nazzicari Nelson , Biscarini Filippo , Fariselli Piero , Moreau Yves 
Published: June 2024

AbstractGenome interpretation (GI) encompasses the computational attempts to model the relationship between genotype and phenotype with the goal of understanding how the first leads to the second. While traditional approaches have focused on sub-problems such as predicting the effect of single nucleotide variants or finding genetic associations, recent advances in neural networks (NNs) have made it possible to develop end-to-end GI models that take genomic data as input and predict phenotypes as output. However, technical and modeling issues still need to be fixed for these models to be effective, including the widespread underdetermination of genomic datasets, making them unsuitable for training large, overfitting-prone, NNs. Here we propose novel GI models to address this issue, exploring the use of two types of transfer learning approaches and proposing a novel Biologically Meaningful Sparse NN layer specifically designed for end-to-end GI. Our models predict the leaf and seed ionome in A.thaliana, obtaining comparable results to our previous over-parameterized model while reducing the number of parameters by 8.8 folds. We also investigate how the effect of population stratification influences the evaluation of the performances, highlighting how it leads to (1) an instance of the Simpson’s Paradox, and (2) model generalization limitations.