bluuebunny/crossref_metadata_2025_split huggingface dataset:
Heat shock factor 1 inhibition enhances the effects of modulated electro hyperthermia in a triple negative breast cancer mouse model
Authors: Viana Pedro H. L. , Schvarcz Csaba A. , Danics Lea O. , Besztercei Balázs , Aloss Kenan , Bokhari Syeda M. Z. , Giunashvili Nino , Bócsi Dániel , Koós Zoltán , Benyó Zoltán , Hamar Péter 
Published: April 2024

AbstractFemale breast cancer is the most diagnosed cancer worldwide. Triple negative breast cancer (TNBC) is the most aggressive type and there is no existing endocrine or targeted therapy. Modulated electro-hyperthermia (mEHT) is a non-invasive complementary cancer therapy using an electromagnetic field generated by amplitude modulated 13.56 MHz frequency that induces tumor cell destruction. However, we have demonstrated a strong induction of the heat shock response (HSR) by mEHT, which can result in thermotolerance. We hypothesized that inhibition of the heat shock factor 1 (HSF1) can synergize with mEHT and enhance tumor cell-killing. Thus, we either knocked down the HSF1 gene with a CRISPR/Cas9 lentiviral construct or inhibited HSF1 with a specific small molecule inhibitor: KRIBB11 in vivo. Wild type or HSF1-knockdown 4T1 TNBC cells were inoculated into the mammary gland’s fat pad of BALB/c mice. Four mEHT treatments were performed every second day and the tumor growth was followed by ultrasound and caliper. KRIBB11 was administrated intraperitoneally at 50 mg/kg daily for 8 days. HSF1 and Hsp70 expression were assessed. HSF1 knockdown sensitized transduced cancer cells to mEHT and reduced tumor growth. HSF1 mRNA expression was significantly reduced in the KO group when compared to the empty vector group, and consequently mEHT-induced Hsp70 mRNA upregulation diminished in the KO group. Immunohistochemistry (IHC) confirmed the inhibition of Hsp70 upregulation in mEHT HSF1-KO group. Demonstrating the translational potential of HSF1 inhibition, combined therapy of mEHT with KRIBB11 significantly reduced tumor mass compared to either monotherapy. Inhibition of Hsp70 upregulation by mEHT was also supported by qPCR and IHC. In conclusion, we suggest that mEHT-therapy combined with HSF1 inhibition can be a possible new strategy of TNBC treatment with great translational potential.
Concept-drifts adaptation for machine learning EEG epilepsy seizure prediction
Authors: Pontes Edson David , Pinto Mauro , Lopes Fábio , Teixeira César 
Published: April 2024

AbstractSeizure prediction remains a challenge, with approximately 30% of patients unresponsive to conventional treatments. Addressing this issue is crucial for improving patients’ quality of life, as timely intervention can mitigate the impact of seizures. In this research field, it is critical to identify the preictal interval, the transition from regular brain activity to a seizure. While previous studies have explored various Electroencephalogram (EEG) based methodologies for prediction, few have been clinically applicable. Recent studies have underlined the dynamic nature of EEG data, characterised by data changes with time, known as concept drifts, highlighting the need for automated methods to detect and adapt to these changes. In this study, we investigate the effectiveness of automatic concept drift adaptation methods in seizure prediction. Three patient-specific seizure prediction approaches with a 10-minute prediction horizon are compared: a seizure prediction algorithm incorporating a window adjustment method by optimising performance with Support Vector Machines (Backwards-Landmark Window), a seizure prediction algorithm incorporating a data-batch (seizures) selection method using a logistic regression (Seizure-batch Regression), and a seizure prediction algorithm with a dynamic integration of classifiers (Dynamic Weighted Ensemble). These methods incorporate a retraining process after each seizure and use a combination of univariate linear features and SVM classifiers. The Firing Power was used as a post-processing technique to generate alarms before seizures. These methodologies were compared with a control approach based on the typical machine learning pipeline, considering a group of 37 patients with Temporal Lobe Epilepsy from the EPILEPSIAE database. The best-performing approach (Backwards-Landmark Window) achieved results of 0.75 ± 0.33 for sensitivity and 1.03 ± 1.00 for false positive rate per hour. This new strategy performed above chance for 89% of patients with the surrogate predictor, whereas the control approach only validated 46%.
Mechanism of elevated LH/FSH ratio in lean PCOS revisited: a path analysis
Authors: Pratama Gita , Wiweko Budi , Asmarinah  , Widyahening Indah S. , Andraini Trinovita , Bayuaji Hartanto , Hestiantoro Andon 
Published: April 2024

AbstractPolycystic ovary syndrome (PCOS) is the most common endocrine disorder affecting 5–20% of reproductive-age women. However, the treatment of PCOS is mainly based on symptoms and not on its pathophysiology. Neuroendocrine disturbance, as shown by an elevated LH/FSH ratio in PCOS patients, was thought to be the central mechanism of the syndrome, especially in lean PCOS. LH and FSH secretion are influenced by GnRH pulsatility of GnRH neurons in the hypothalamus. Kisspeptin is the main regulator of GnRH secretion, whereas neurokinin B (NKB) and dynorphin regulate kisspeptin secretion in KNDy neurons. This study aims to deepen the understanding of the neuroendocrine disorder in lean PCOS patients and its potential pathophysiology-based therapy. A cross-sectional study was performed at Dr. Cipto Mangunkusumo Kencana Hospital and the IMERI UI HRIFP cluster with 110 lean PCOS patients as subjects. LH, FSH, LH/FSH ratio, kisspeptin, NKB, dynorphin, leptin, adiponectin, AMH, fasting blood glucose, fasting insulin, HOMA-IR, testosterone, and SHBG were measured. Bivariate and path analyses were performed to determine the relationship between variables. There was a negative association between dynorphin and kisspeptin, while NKB levels were not associated with kisspeptin. There was no direct association between kisspeptin and the LH/FSH ratio; interestingly, dynorphin was positively associated with the LH/FSH ratio in both bivariate and pathway analyses. AMH was positively correlated with the LH/FSH ratio in both analyses. Path analysis showed an association between dynorphin and kisspeptin levels in lean PCOS, while NKB was not correlated with kisspeptin. Furthermore, there was a correlation between AMH and the LH/FSH ratio, but kisspeptin levels did not show a direct significant relationship with the LH/FSH ratio. HOMA-IR was negatively associated with adiponectin levels and positively associated with leptin and FAI levels. In conclusion, AMH positively correlates with FAI levels and is directly associated with the LH/FSH ratio, showing its important role in neuroendocrinology in lean PCOS. From the path analysis, AMH was also an intermediary variable between HOMA-IR and FAI with the LH/FSH ratio. Interestingly, this study found a direct positive correlation between dynorphin and the LH/FSH ratio, while no association between kisspeptin and the LH/FSH ratio was found. Further research is needed to investigate AMH and dynorphin as potential therapeutic targets in the management of lean PCOS patients.
MORITS: An improved method to predict peptides from heterologous proteins that are recognized by the same T-cell receptor
Authors: Bruhn Matthias , Spatz Moritz , Kalinke Ulrich 
Published: April 2024

AbstractAntigen-specific priming of T cells results in the activation of T cells that exert effector functions by interaction of their T-cell receptor (TCR) with the corresponding self-MHC molecule presenting a peptide on the surface of a target cell. Such antigen-specific T cells potentially can also interact with peptide-MHC complexes that contain peptides from unrelated antigens, a phenomenon that often is referred to as heterologous immunity. For example, some individuals that were pre-immunized against an allergen, could subsequently mount better anti-viral T-cell responses than non-allergic individuals. So far only few peptide pairs that experimentally have been shown to provoke heterologous immunity were identified, and available prediction tools that can identify potential candidates are imprecise. We developed the MORITS algorithm to rapidly screen large lists of peptides for sequence similarities, while giving enhanced consideration to peptide residues presented by MHC that are particularly relevant for TCR interactions. In combination with established peptide-MHC binding prediction tools, the MORITS algorithm revealed peptide similarities between the SARS-CoV-2 proteome and certain allergens. The method outperformed previously published workflows and may help to identify novel pairs of peptides that mediate heterologous immune responses.
Identification of 6-methyladenosine sites using novel feature encoding methods and ensemble models
Authors: Alromema Nashwan , Suleman Muhammad Taseer , Malebary Sharaf J. , Ahmed Amir , Ali Mohammed Al-Rami Al-Ghamdi Bandar , Khan Yaser Daanial 
Published: April 2024

AbstractN6-methyladenosine (6 mA) is the most common internal modification in eukaryotic mRNA. Mass spectrometry and site-directed mutagenesis, two of the most common conventional approaches, have been shown to be laborious and challenging. In recent years, there has been a rising interest in analyzing RNA sequences to systematically investigate mutated locations. Using novel methods for feature development, the current work aimed to identify 6 mA locations in RNA sequences. Following the generation of these novel features, they were used to train an ensemble of models using methods such as stacking, boosting, and bagging. The trained ensemble models were assessed using an independent test set and k-fold cross validation. When compared to baseline predictors, the suggested model performed better and showed improved ratings across the board for key measures of accuracy.
Cosine similarity knowledge distillation for surface anomaly detection
Authors: Sheng Siyu , Jing Junfeng , Wang Zhen , Zhang Huanhuan 
Published: April 2024

AbstractThe current state-of-the-art anomaly detection methods based on knowledge distillation (KD) typically depend on smaller student networks or reverse distillation to address vanishing representations discrepancy on anomalies. These methods often struggle to achieve precise detection when dealing with complex texture backgrounds containing anomalies due to the similarity between anomalous and non-anomalous regions. Therefore, we propose a new paradigm—Cosine Similarity Knowledge Distillation (CSKD), for surface anomaly detection and localization. We focus on the superior performance of the same deeper teacher and student encoders by the distillation loss in traditional knowledge distillation-based methods. Essentially, we introduce the Attention One-Class Embedding (AOCE) in the student network to enhance learning capabilities and reduce the effect of the teacher–student (T–S) model on response similarity in anomalous regions. Furthermore, we find the optimal models by different classes’ hard-coded epochs, and an adaptive optimal model selection method is designed. Extensive experiments on the MVTec dataset with 99.2% image-level AUROC and 98.2%/94.7% pixel-level AUROC/PRO demonstrate that our method outperforms existing unsupervised anomaly detection algorithms. Additional experiments on DAGM dataset, and one-class anomaly detection benchmarks further show the superiority of the proposed method.
DP2 receptor activity sensor suited for antagonist screening and measurement of receptor dynamics in real-time
Authors: Kurz Michael , Ulrich Michaela , Bittner Alwina , Bünemann Moritz 
Published: April 2024

AbstractThe DP2 receptor is a G-protein coupled receptor involved in allergic inflammation and is the target of recently developed antagonists already being tested in clinics. To get insights into DP2 receptor dynamics and to study its pharmacology on the level of the receptor, we constructed a fluorescence resonance energy transfer-based conformation sensor. The sensor reflects the selectivity profile of the DP2 receptor-wt and is suited for screening of agonists and antagonists due to its robust response. Furthermore, the sensor enables the direct measurement of DP2 receptor dynamics in real-time and revealed markedly distinct on- and off-rates of prostaglandin D2 between DP2 and DP1 receptors, suggesting a different mechanism of ligand receptor interaction.
Automated Lunar Crater Identification with Chandrayaan-2 TMC-2 Images using Deep Convolutional Neural Networks
Authors: Sinha Mimansa , Paul Sanchita , Ghosh Mili , Mohanty Sachi Nandan , Pattanayak Radha Mohan 
Published: April 2024

AbstractTerrestrial planets and their moons have impact craters, contributing significantly to the complex geomorphology of planetary bodies in our Solar System. Traditional crater identification methods struggle with accuracy because of the diverse forms, locations, and sizes of the craters. Our main aim is to locate lunar craters using images from Terrain Mapping Camera-2 (TMC-2) onboard the Chandrayaan-II satellite. The crater-based U-Net model, a convolutional neural network frequently used in image segmentation tasks, is a deep learning method presented in this study. The task of crater detection was accomplished with the proposed model in two steps: initially, it was trained using Resnet18 as the backbone and U-Net based on Image Net as weights. Secondly, TMC-2 images from Chandrayaan-2 were used to detect craters based on the trained model. The model proposed in this study comprises a neural network, feature extractor, and optimization technique for lunar crater detection. The model achieves 80.95% accuracy using unannotated data and precision and recall are much better with annotated data with an accuracy of 86.91% in object detection with TMC-2 ortho images. 2000 images have been considered for the present work as manual annotation is a time-consuming process and the inclusion of more images can enhance the performance score of the model proposed.
Outcomes of epiretinal proliferation embedding technique in the surgery for full-thickness macular hole
Authors: Choi Jaehwan , Kim Sang Jin , Kang Se Woong , Hwang Sungsoon , Son Ki Young 
Published: April 2024

AbstractTo compare visual and anatomical outcomes between peeling and embedding of epiretinal proliferation in patients with full-thickness macular holes (FTMH) with epiretinal proliferation (EP), this retrospective cohort study classified patients into two groups based on whether EP was completely peeled (peeling group, n = 25 eyes), or embedded into the hole (embedding group, n = 31 eyes) during surgery. Preoperative characteristics and postoperative outcomes, including best-corrected visual acuity and the length of the disrupted external limiting membrane and ellipsoid zone, were compared. Preoperative features including visual acuity and hole size did not differ between the two groups. All studied eyes achieved closure of the macular hole postoperatively. Visual acuity significantly improved at 3, 6, and 12 months postoperatively in both groups. The visual acuity 1-month after surgery was better in the embedding group than that in the peeling group (0.28 ± 0.29 vs. 0.50 ± 0.42 logarithm of the minimum angle of resolution, P = 0.016), although the difference was not noted after 3 months postoperatively. The embedding group showed shorter disruption of the external limiting membrane than the peeling group postoperatively (62.6 ± 40.2 μm vs. 326.2 ± 463.9 μm at postoperative 12 months, P = 0.045). In conclusion, the embedding technique during surgical repair of a FTMH with EP facilitates recovery of the outer foveal layers and promotes earlier restoration of visual function.
Long-span fiber composite truss made by coreless filament winding for large-scale satellite structural systems demonstrated on a planetary sunshade concept
Authors: Mindermann Pascal , Acker Denis , Wegner Robert , Fasoulas Stefanos , Gresser Götz T. 
Published: April 2024

AbstractClimate change necessitates exploring innovative geoengineering solutions to mitigate its effects—one such solution is deploying planetary sunshade satellites at Sun–Earth Lagrange point 1 to regulate solar radiation on Earth directly. However, such long-span space structures present unique technical challenges, particularly structural scalability, on-orbit manufacturing, and in-situ resource utilization. This paper proposes a structural concept for the sunshade’s foil support system and derives from that a component-level modular system for long-span fiber composite lightweight trusses using coreless filament winding. Within a laboratory-scale case study, the component scalability, as well as the manufacturing and material impacts, were experimentally investigated by bending deflection testing. Based on these experimental results, FE models of the proposed structural concept were calibrated to estimate the maximum displacement and mass of the foil support structure, while comparing the influences of foil edge length, orbital load case, and material selection.
Fe3O4@SiO2@CSH+VO3− as a novel recyclable heterogeneous catalyst with core–shell structure for oxidation of sulfides
Authors: Al-Zubaidi Ula Zuhair Ismael , Bahrami Kiumars , Khodamorady Minoo 
Published: April 2024

AbstractIron nanoparticles, with low toxicity and many active sites, are among the materials that not only reduce waste along with green chemistry but also increase the separation power and recover the catalyst from the reaction environment. In this study, first, the surface of iron nanoparticles was silanized, and in the next step, the complex of chitosan HCl.VO3 was placed on the surface of Fe3O4 (Fe3O4@SiO2@CSH+VO3−). This nanocatalyst is a novel, recoverable, and potent nanocatalyst with high selectivity for the oxidation of sulfides to sulfoxides. Various physicochemical techniques such as IR, XRD, TGA, SEM, EDX, mapping, TEM, and VSM were used to affirm the well synthesis of the catalyst. Oxidation of sulfides in the presence of hydrogen peroxide as a green oxidant and in ethanol was catalyzed by the Fe3O4@SiO2@CSH+VO3−. All sulfoxides were achieved with high efficiency and in a short time. The notable privileges of this method include facile and economic catalyst synthesis, proper catalyst durability, great performance, simple catalyst isolation, good recovery capability, at least up to 5 times without an index drop in catalytic power.
RSAD2 is abundant in atherosclerotic plaques and promotes interferon-induced CXCR3-chemokines in human smooth muscle cells
Authors: Hayderi Assim , Kumawat Ashok K. , Shavva Vladimir S. , Dreifaldt Mats , Sigvant Birgitta , Petri Marcelo H. , Kragsterman Björn , Olofsson Peder S. , Sirsjö Allan , Ljungberg Liza U. 
Published: April 2024

AbstractIn atherosclerotic lesions, monocyte-derived macrophages are major source of interferon gamma (IFN-γ), a pleotropic cytokine known to regulate the expression of numerous genes, including the antiviral gene RSAD2. While RSAD2 was reported to be expressed in endothelial cells of human carotid lesions, its significance for the development of atherosclerosis remains utterly unknown. Here, we harnessed publicly available human carotid atherosclerotic data to explore RSAD2 in lesions and employed siRNA-mediated gene-knockdown to investigate its function in IFN-γ-stimulated human aortic smooth muscle cells (hAoSMCs). Silencing RSAD2 in IFN-γ-stimulated hAoSMCs resulted in reduced expression and secretion of key CXCR3-chemokines, CXCL9, CXCL10, and CXCL11. Conditioned medium from RSAD2-deficient hAoSMCs exhibited diminished monocyte attraction in vitro compared to conditioned medium from control cells. Furthermore, RSAD2 transcript was elevated in carotid lesions where it was expressed by several different cell types, including endothelial cells, macrophages and smooth muscle cells. Interestingly, RSAD2 displayed significant correlations with CXCL10 (r =  0.45, p = 0.010) and CXCL11 (r = 0.53, p = 0.002) in human carotid lesions. Combining our findings, we uncover a novel role for RSAD2 in hAoSMCs, which could potentially contribute to monocyte recruitment in the context of atherosclerosis.
Estimating the prevalence of Non-Verbal Learning Disability (NVLD) from the ABCD sample
Authors: Coccaro Ambra , Banich Marie , Mammarella Irene C. , Liotti Mario 
Published: April 2024

AbstractNon-Verbal Learning Disability (NVLD) is a neurodevelopmental disorder characterized by deficits in processing visuospatial information but with age-appropriate verbal skills. This cognitive profile has been hypothesized to be associated with atypical white matter, but at the present there is a lack of evidence for this hypothesis. Currently, the condition is not characterized within the main diagnostic systems, in part because no clear set of criteria for characterizing the disorder exists. This report is the first attempt to estimate NVLD prevalence, using two sets of diagnostic criteria, in a large sample of over 11,000 children who were selected without regards to problems of specific nature, either psychological, neurological, physical and/or social. Furthermore, it examined the association between the profile of cognitive abilities and aspects of whole-brain white matter measures in children with and without symptoms associated with NVLD. Participants were drawn from the Adolescent Brain Cognitive Development (ABCD) study, a 10-year longitudinal study of 11,876 children in the U.S. The data used in the present study were drawn from the initial testing point at which the children were 9–10 years old. Prevalence of NVLD based on two distinct sets of criteria, correlations between the measures used to create the criteria, correlations between criteria measures and measures of white matter integrity. The cognitive criteria included measures of visuospatial processing, reading, intelligence and social skills. By varying the cut-offs applied to social skills in conjunction with visuo-spatial difficulties, spared reading skills and intelligence scores, we calculated prevalence for two NVLD groups. White matter characteristics were measures of volume, fractional anisotropy and mean diffusivity. Based on the criteria used, the estimated prevalence of NVLD varied from 1 to 8%. Furthermore, children with NVLD showed a dissociation between measures of visuo-spatial processing not observed in non-NVLD children. At the neurological level, findings provide preliminary evidence of associations between the cognitive profile of NVLD and abnormalities in white matters tracts. The present study documents that exists, within this large non-selected sample, a proportion of youth who show evidence of NVLD. Given those results, it appears essential to establish the best diagnostic criteria, to improve the treatment options and quality of life for children with this disorder.