bluuebunny/crossref_metadata_2025_split huggingface dataset:
Drug screen identifies verteporfin as a regulator of lipid metabolism in macrophage foam cells
Authors: Hoeffner Nicholas , Paul Antoni , Goo Young-Hwa 
Published: November 2023

AbstractArterial macrophage foam cells are filled with cholesterol ester (CE) stored in cytosolic lipid droplets (LDs). Foam cells are central players in progression of atherosclerosis as regulators of lipid metabolism and inflammation, two major driving forces of atherosclerosis development. Thus, foam cells are considered plausible targets for intervention in atherosclerosis. However, a compound that directly regulates the lipid metabolism of LDs in the arterial foam cells has not yet been identified. In this study, we screened compounds that inhibit macrophage foam cell formation using a library of 2697 FDA-approved drugs. From the foam cells generated via loading of human oxidized low-density lipoprotein (oxLDL), we found 21 and 6 compounds that reduced and enhanced accumulations of lipids respectively. Among them, verteporfin most significantly reduced oxLDL-induced foam cell formation whereas it did not display a significant impact on foam cell formation induced by fatty acid. Mechanistically our data demonstrate that verteporfin acts via inhibition of oxLDL association with macrophages, reducing accumulation of CE. Interestingly, while other drugs that reduced foam cell formation did not have impact on pre-existing foam cells, verteporfin treatment significantly reduced their total lipids, CE, and pro-inflammatory gene expression. Together, our study identifies verteporfin as a novel regulator of foam cell lipid metabolism and inflammation and a potential compound for intervention in atherosclerosis.
Vision-related quality of life in patients with childhood-onset craniopharyngioma
Authors: Sowithayasakul Panjarat , Beckhaus Julia , Boekhoff Svenja , Friedrich Carsten , Calaminus Gabriele , Müller Hermann L. 
Published: November 2023

AbstractQuality of life (QoL) is a critical component of aftercare in survivors of childhood-onset craniopharyngioma (CP). Visual impairment adversely affects QoL after CP. This study assessed the frequency of visual impairment in patients with CP and its association with QoL. This study analyzed vision-related QoL in patients recruited 2000–2019 in the prospective cohort studies KRANIOPHARYNGEOM 2000/2007. Ophthalmologic examinations were performed at diagnosis, three, 12, and 36 months, respectively after the diagnosis. The QoL (PEDQOL) scores, were also evaluated at three, 12, and 36 months, respectively after the CP diagnosis. Multivariable logistic regression was used to analyze factors associated with visual impairment during follow-up. One-hundred twenty patients were included in this study. On ophthalmological examination, visual impairment was observed in the majority of the patients (n = 84, 70%) at CP diagnosis. After surgery, vision was restored in 27 patients (32%) with visual impairment at diagnosis. In the first (p = 0.017) and third (p = 0.011) year after diagnosis, parents of patients with visual impairment reported lower social functioning (family). Reduced autonomy was found three years after diagnosis in self- (p = 0.029) and parental (p = 0.048) assessments. Next to visual impairment at diagnosis, no additional risk factors for visual impairment during follow-up could be identified. Visual impairment has a clinically relevant impact on QoL after CP. The visual status at CP diagnosis determines the visual outcome during follow-up. Early detection of visual impairment, regular QoL assessments, and risk-appropriate aftercare are recommended.Clinical Trial Registration KRANIOPHARYNGEOM 2000 (Clinical trial registration number: NCT00258453) and KRANIOPHARYNGEOM 2007 (Clinical trial registration number: NCT01272622).
Local Bayesian Dirichlet mixing of imperfect models
Authors: Kejzlar Vojtech , Neufcourt Léo , Nazarewicz Witold 
Published: November 2023

AbstractTo improve the predictability of complex computational models in the experimentally-unknown domains, we propose a Bayesian statistical machine learning framework utilizing the Dirichlet distribution that combines results of several imperfect models. This framework can be viewed as an extension of Bayesian stacking. To illustrate the method, we study the ability of Bayesian model averaging and mixing techniques to mine nuclear masses. We show that the global and local mixtures of models reach excellent performance on both prediction accuracy and uncertainty quantification and are preferable to classical Bayesian model averaging. Additionally, our statistical analysis indicates that improving model predictions through mixing rather than mixing of corrected models leads to more robust extrapolations.
Proactive cues facilitate faster action reprogramming, but not stopping, in a response-selective stop signal task
Authors: Salomoni Sauro E. , Gronau Quentin F. , Heathcote Andrew , Matzke Dora , Hinder Mark R. 
Published: November 2023

AbstractThe ability to stop simple ongoing actions has been extensively studied using the stop signal task, but less is known about inhibition in more complex scenarios. Here we used a task requiring bimanual responses to go stimuli, but selective inhibition of only one of those responses following a stop signal. We assessed how proactive cues affect the nature of both the responding and stopping processes, and the well-documented stopping delay (interference effect) in the continuing action following successful stopping. In this task, estimates of the speed of inhibition based on a simple-stopping model are inappropriate, and have produced inconsistent findings about the effects of proactive control on motor inhibition. We instead used a multi-modal approach, based on improved methods of detecting and interpreting partial electromyographical responses and the recently proposed SIS (simultaneously inhibit and start) model of selective stopping behaviour. Our results provide clear and converging evidence that proactive cues reduce the stopping delay effect by slowing bimanual responses and speeding unimanual responses, with a negligible effect on the speed of the stopping process.
Histology-informed multiscale modeling of human brain white matter
Authors: Saeidi Saeideh , Kainz Manuel P. , Dalbosco Misael , Terzano Michele , Holzapfel Gerhard A. 
Published: November 2023

AbstractIn this study, we propose a novel micromechanical model for the brain white matter, which is described as a heterogeneous material with a complex network of axon fibers embedded in a soft ground matrix. We developed this model in the framework of RVE-based multiscale theories in combination with the finite element method and the embedded element technique for embedding the fibers. Microstructural features such as axon diameter, orientation and tortuosity are incorporated into the model through distributions derived from histological data. The constitutive law of both the fibers and the matrix is described by isotropic one-term Ogden functions. The hyperelastic response of the tissue is derived by homogenizing the microscopic stress fields with multiscale boundary conditions to ensure kinematic compatibility. The macroscale homogenized stress is employed in an inverse parameter identification procedure to determine the hyperelastic constants of axons and ground matrix, based on experiments on human corpus callosum. Our results demonstrate the fundamental effect of axon tortuosity on the mechanical behavior of the brain’s white matter. By combining histological information with the multiscale theory, the proposed framework can substantially contribute to the understanding of mechanotransduction phenomena, shed light on the biomechanics of a healthy brain, and potentially provide insights into neurodegenerative processes.
Identification of DNA damage response-related genes as biomarkers for castration-resistant prostate cancer
Authors: Oshima Masashi , Takayama Ken-ichi , Yamada Yuta , Kimura Naoki , Kume Haruki , Fujimura Tetsuya , Inoue Satoshi 
Published: November 2023

AbstractAlthough hormone therapy is effective for the treatment of prostate cancer (Pca), many patients develop a lethal type of Pca called castration-resistant prostate cancer (CRPC). Dysregulation of DNA damage response (DDR)-related genes leads to Pca progression. Here, we explored DDR-related signals upregulated in CRPC tissues. We analyzed the gene expression profiles in our RNA-sequence (RNA-seq) dataset containing benign prostate, primary Pca, and CRPC samples. We identified six DDR-related genes (Ribonuclease H2 Subunit A (RNASEH2A), replication factor C subunit 2 (RFC2), RFC4, DNA Ligase 1 (LIG1), DNA polymerase D1 (POLD1), and DNA polymerase E4 (POLE4)) that were upregulated in CRPC compared with Pca tissues. By analyzing public databases and validation studies, we focused on RFC2 as a new biomarker. Functional analysis demonstrated that silencing of RFC2 expression inhibited cell proliferation and induced the expression of DNA damage and apoptosis markers in CRPC model cells. Furthermore, immunohistochemical (IHC) analysis revealed that high expression of RFC2 protein correlated with poor prognosis in patients with Pca and increased expression in CRPC tissues compared with localized Pca. Thus, our study suggests that six DDR-related genes would be important for Pca progression. RFC2 could be a useful biomarker associated with poor outcomes of patients with Pca.
Cell-free DNA as diagnostic and prognostic biomarkers for adult sepsis: a systematic review and meta-analysis
Authors: Charoensappakit Awirut , Sae-khow Kritsanawan , Rattanaliam Pongpera , Vutthikraivit Nuntanuj , Pecheenbuvan Monvasi , Udomkarnjananun Suwasin , leelahavanichkul Asada 
Published: November 2023

AbstractAlthough cell-free DNA (cfDNA) is an emerging sepsis biomarker, the use of cfDNA, especially as diagnostic and prognostic indicators, has surprisingly not been systemically analyzed. Data of adult patients with sepsis that conducted cfDNA measurement within 24 h of the admission was collected from PubMed, ScienceDirect, Scopus, and Cochrane Library until October 2022. The Quality in Prognosis Studies (QUIPS) and Quality Assessment in Diagnostic Studies-2 (QUADAS-2) tools were used to reduce the risk of biased assessment. The mean difference (MD) of cfDNA concentration and the standardized mean difference (SMD) between populations was calculated using Review Manager (RevMan) version 5.4.1 package software. Pooled analysis from 18 included studies demonstrated increased serum cfDNA levels in sepsis when compared with healthy control (SMD = 1.02; 95% confidence interval (CI) 0.46–1.57) or non-sepsis patients in the intensive care unit (ICU) (SMD = 1.03; 95% CI 0.65–1.40), respectively. Meanwhile, a slight decrease in the statistical value was observed when compared with non-sepsis ICU patients with SIRS (SMD = 0.74; 95% 0.41–1.06). The lower cfDNA levels were also observed in sepsis survivors compared to the non-survivors (SMD at 1.43; 95%CI 0.69–2.17) with the pooled area under the receiver operating characteristic curve (AUC) of 0.76 (95% CI 0.64–0.87) for the mortality prediction. Levels of cfDNA showed a pooled sensitivity of 0.81 (95% CI 0.75–0.86) and specificity of 0.72 (95% CI 0.65–0.78) with pooled diagnostic odd ratio (DOR) at 25.03 (95% CI 5.48–114.43) for the identification of sepsis in critically ill conditions. The cfDNA levels were significantly higher in patients with sepsis and being a helpful indicator for the critically ill conditions of sepsis. Nevertheless, results of the test must be interpreted carefully with the context of all clinical situations.
Novel machine-learning analysis of SARS-CoV-2 infection in a subclinical nonhuman primate model using radiomics and blood biomarkers
Authors: Chu Winston T. , Castro Marcelo A. , Reza Syed , Cooper Timothy K. , Bartlinski Sean , Bradley Dara , Anthony Scott M. , Worwa Gabriella , Finch Courtney L. , Kuhn Jens H. , Crozier Ian , Solomon Jeffrey 
Published: November 2023

AbstractDetection of the physiological response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is challenging in the absence of overt clinical signs but remains necessary to understand a full subclinical disease spectrum. In this study, our objective was to use radiomics (from computed tomography images) and blood biomarkers to predict SARS-CoV-2 infection in a nonhuman primate model (NHP) with inapparent clinical disease. To accomplish this aim, we built machine-learning models to predict SARS-CoV-2 infection in a NHP model of subclinical disease using baseline-normalized radiomic and blood sample analyses data from SARS-CoV-2-exposed and control (mock-exposed) crab-eating macaques. We applied a novel adaptation of the minimum redundancy maximum relevance (mRMR) feature-selection technique, called mRMR-permute, for statistically-thresholded and unbiased feature selection. Through performance comparison of eight machine-learning models trained on 14 feature sets, we demonstrated that a logistic regression model trained on the mRMR-permute feature set can predict SARS-CoV-2 infection with very high accuracy. Eighty-nine percent of mRMR-permute selected features had strong and significant class effects. Through this work, we identified a key set of radiomic and blood biomarkers that can be used to predict infection status even in the absence of clinical signs. Furthermore, we proposed and demonstrated the utility of a novel feature-selection technique called mRMR-permute. This work lays the foundation for the prediction and classification of SARS-CoV-2 disease severity.
Erosion behaviour of human, bovine and equine dental hard tissues
Authors: Hertel S. , Basche S. , Schmidt V. , Staszyk C. , Hannig C. , Sterzenbach T. , Hannig M. 
Published: November 2023

AbstractDental hard tissues from different species are used in dental research, but little is known about their comparability. The aim of this study was to compare the erosive behaviour of dental hard tissues (enamel, dentin) obtained from human, bovine and equine teeth. In addition, the protective effect of the pellicle on each hard tissue under erosive conditions was determined. In situ pellicle formation was performed for 30 min on enamel and dentin samples from all species in four subjects. Calcium and phosphate release was assessed during 120 s of HCl incubation on both native and pellicle-covered enamel and dentin samples. SEM and TEM were used to examine surface changes in native enamel and dentin samples after acid incubation and the ultrastructure of the pellicle before and after erosive exposure. In general, bovine enamel and dentin showed the highest degree of erosion after acid exposure compared to human and equine samples. Erosion of human primary enamel tended to be higher than that of permanent teeth, whereas dentin showed the opposite behaviour. SEM showed that eroded equine dentin appeared more irregular than human or bovine dentin. TEM studies showed that primary enamel appeared to be most susceptible to erosion.
24-Epibrassinolide alleviates diazinon oxidative damage by escalating activities of antioxidant defense systems in maize plants
Authors: Mehrian Saeed Karami , Karimi Nasser , Rahmani Fatemeh 
Published: November 2023

AbstractExcessive use of pesticides against pests has contaminated agricultural crops and raised global concerns about food safety. This research investigates the alleviation effects of 24-epibrassinolide (EBL) seed priming on diazinon (DZ) pesticide toxicity. The experiment was conducted with eight groups including control, DZ, EBL (10 µM), EBL (0.1 µM), EBL (0.01 µM), EBL (10 µM) + DZ, EBL (0.1 µM) + DZ, and EBL (0.01 µM) + DZ. Plants grown with the lowest concentration of EBL (0.01 µM) exhibited an upward increase in the activity of SOD, CAT, POD, APX, GR, and GST enzymes under DZ toxicity stress. In contrast, higher concentrations of EBL showed some inhibitory effects on the activity of antioxidant enzymes. In addition, low concentrations of EBL elevated the free radical scavenging capacity (DPPH), iron-reducing antioxidant power (FRAP), photosynthesis rate (Pn), stomatal conductance (Gs) and proline, and protein contents. EBL also reduced lipid peroxidation (MDA levels) in the DZ-exposed plants, leading to membrane integrity. The favorable effects of EBL were more evident when plants were exposed to pesticides than normal growth conditions. The results indicated that EBL seed priming intensifies the antioxidant enzymes system activity, and helps maize plants against toxic effects of DZ under proper concentration.
Assessing causality between osteoarthritis and gastrointestinal disorders: a Mendelian randomization study
Authors: Xu Huiqing , Wei Jiahe , Chen Dingwan , Li Yingjun , Shen Qing 
Published: November 2023

AbstractThe association between osteoarthritis (OA) and gastrointestinal disorders was found in observational studies. However, the causality is still elusive. A bidirectional Mendelian randomization (MR) analysis using genome wide association studies data was conducted to assess the causal association between OA and gastrointestinal diseases [including peptic ulcer disease (PUD), gastroesophageal reflux disease (GORD), and inflammatory bowel disease (IBD)]. A two-step MR (TSMR) was conducted between OA, gastrointestinal diseases and drugs to explore the mediating effects of non-steroidal anti-inflammatory drugs (NSAIDs) and opioids use. We used multivariable MR (MVMR) analysis to further validate the impact of prescription history on diseases. Results had statistical significance at a Bonferroni corrected P-value below 0.008. We observed that genetically predicted OA had a significant positive association with GORD [odds ratio (OR) = 1.26, P = 5e−05], but not with PUD or IBD. Regarding the other direction, gastrointestinal disorders as exposure had a null association with OA. Using TSMR, OA patients tended to increase the use of NSAIDs (OR = 1.45, P = 0.001) and opioids (OR = 1.77, P = 2e−05), but only the use of opioids increased the risk of GORD (OR = 1.43, P = 5e−09). Further MVMR analysis showed that the adverse effect of OA on GORD was significantly reduced after adjusting for opioids use (OR = 1.20, P = 0.038). This study provides evidence for the causal association between OA and increased risk of GORD, which is partly attributed to opioids use in OA patients but not NSAIDs.
High pretransplant FGF23 level is associated with persistent vitamin D insufficiency and poor graft survival in kidney transplant patients
Authors: Ryu Jung-Hwa , Jeon Hee Jung , Han Ro , Jung Hee-Yeon , Kim Myung-Gyu , Huh Kyu Ha , Park Jae Berm , Kang Kyung Pyo , Han Seungyeup , Yang Jaeseok 
Published: November 2023

AbstractVitamin D3 (25[OH]D3) insufficiency and fibroblast growth factor 23 (FGF23) elevation are usually attenuated after kidney transplantation (KT). However, elevated FGF23 may be associated with poor graft outcomes and vitamin D insufficiency after KT. This study investigated the effect of pretransplant FGF23 levels on post-KT 25(OH)D3 status and graft outcomes. Serum FGF23 levels from 400 participants of the KoreaN Cohort Study for Outcome in Patients With Kidney Transplantation were measured. Annual serum 25(OH)D3 levels, all-cause mortality, cardiovascular event, and graft survival were assessed according to baseline FGF23 levels. Serum 25(OH)D3 levels were initially increased 1 year after KT (12.6 ± 7.4 vs. 22.6 ± 6.4 ng/mL). However, the prevalence of post-KT vitamin D deficiency increased again after post-KT 3 years (79.1% at baseline, 30.8% and 37.8% at 3 and 6 years, respectively). Serum FGF23 level was decreased 3 years post-KT. When participants were categorized into tertiles according to baseline FGF23 level (low, middle, high), 25(OH)D3 level in the low FGF23 group was persistently low at a median follow-up of 8.3 years. Furthermore, high baseline FGF23 level was a risk factor for poor graft survival (HR 5.882, 95% C.I.; 1.443–23.976, P = 0.013). Elevated FGF23 levels are associated with persistently low post-transplant vitamin D levels and poor graft survival.
The influence of insulin-related genetic variants on fetal growth, fetal blood flow, and placental weight in a prospective pregnancy cohort
Authors: Reim Pauline K. , Engelbrechtsen Line , Gybel-Brask Dorte , Schnurr Theresia M. , Kelstrup Louise , Høgdall Estrid V. , Hansen Torben 
Published: November 2023

AbstractThe fetal insulin hypothesis proposes that low birthweight and type 2 diabetes (T2D) in adulthood may be two phenotypes of the same genotype. In this study we aimed to explore this theory further by testing the effects of GWAS-identified genetic variants related to insulin release and sensitivity on fetal growth and blood flow from week 20 of gestation to birth and on placental weight at birth. We calculated genetic risk scores (GRS) of first phase insulin release (FPIR), fasting insulin (FI), combined insulin resistance and dyslipidaemia (IR + DLD) and insulin sensitivity (IS) in a study population of 665 genotyped newborns. Two-dimensional ultrasound measurements with estimation of fetal weight and blood flow were carried out at week 20, 25, and 32 of gestation in all 665 pregnancies. Birthweight and placental weight were registered at birth. Associations between the GRSs and fetal growth, blood flow and placental weight were investigated using linear mixed models. The FPIR GRS was directly associated with fetal growth from week 20 to birth, and both the FI GRS, IR + DLD GRS, and IS GRS were associated with placental weight at birth. Our findings indicate that insulin-related genetic variants might primarily affect fetal growth via the placenta.
Calculation of exact Shapley values for explaining support vector machine models using the radial basis function kernel
Authors: Mastropietro Andrea , Feldmann Christian , Bajorath Jürgen 
Published: November 2023

AbstractMachine learning (ML) algorithms are extensively used in pharmaceutical research. Most ML models have black-box character, thus preventing the interpretation of predictions. However, rationalizing model decisions is of critical importance if predictions should aid in experimental design. Accordingly, in interdisciplinary research, there is growing interest in explaining ML models. Methods devised for this purpose are a part of the explainable artificial intelligence (XAI) spectrum of approaches. In XAI, the Shapley value concept originating from cooperative game theory has become popular for identifying features determining predictions. The Shapley value concept has been adapted as a model-agnostic approach for explaining predictions. Since the computational time required for Shapley value calculations scales exponentially with the number of features used, local approximations such as Shapley additive explanations (SHAP) are usually required in ML. The support vector machine (SVM) algorithm is one of the most popular ML methods in pharmaceutical research and beyond. SVM models are often explained using SHAP. However, there is only limited correlation between SHAP and exact Shapley values, as previously demonstrated for SVM calculations using the Tanimoto kernel, which limits SVM model explanation. Since the Tanimoto kernel is a special kernel function mostly applied for assessing chemical similarity, we have developed the Shapley value-expressed radial basis function (SVERAD), a computationally efficient approach for the calculation of exact Shapley values for SVM models based upon radial basis function kernels that are widely applied in different areas. SVERAD is shown to produce meaningful explanations of SVM predictions.