bluuebunny/crossref_metadata_2025_split huggingface dataset:
Droplet digital PCR analysis of CDH13 methylation status in Slovak women with invasive ductal breast cancer
Authors: Baranová Ivana , Samec Marek , Dvorská Dana , Šťastný Igor , Janíková Katarína , Kašubová Ivana , Hornáková Andrea , Lukáčová Eva , Kapinová Andrea , Biringer Kamil , Halašová Erika , Danková Zuzana 
Published: June 2024

AbstractIdentifying novel epigenetic biomarkers is a promising way to improve the clinical management of patients with breast cancer. Our study aimed to determine the methylation pattern of 25 tumor suppressor genes (TSG) and select the best methylation biomarker associated with clinicopathological features in the cohort of Slovak patients diagnosed with invasive ductal carcinoma (IDC). Overall, 166 formalin-fixed, paraffin-embedded (FFPE) tissues obtained from patients with IDC were included in the study. The methylation status of the promoter regions of 25 TSG was analyzed using semiquantitative methylation-specific MLPA (MS-MLPA). We identified CDH13 as the most frequently methylated gene in our cohort of patients. Further analysis by ddPCR confirmed an increased level of methylation in the promoter region of CDH13. A significant difference in CDH13 methylation levels was observed between IDC molecular subtypes LUM A versus HER2 (P = 0.0116) and HER2 versus TNBC (P = 0.0234). In addition, significantly higher methylation was detected in HER2+ versus HER2- tumors (P = 0.0004) and PR− versus PR+ tumors (P = 0.0421). Our results provide evidence that alteration in CDH13 methylation is associated with clinicopathological features in the cohort of Slovak patients with IDC. In addition, using ddPCR as a methylation-sensitive method represents a promising approach characterized by higher precision and technical simplicity to measure the methylation of target CpGs in CDH13 compared to other conventional methods such as MS-MLPA.
Identifying significant structural factors associated with knee pain severity in patients with osteoarthritis using machine learning
Authors: Zhao Zhengkuan , Zhao Mingkuan , Yang Tao , Li Jie , Qin Chao , Wang Ben , Wang Li , Li Bing , Liu Jun 
Published: June 2024

AbstractOur main objective was to use machine learning methods to identify significant structural factors associated with pain severity in knee osteoarthritis patients. Additionally, we assessed the potential of various classes of imaging data using machine learning techniques to gauge knee pain severity. The data of semi-quantitative assessments of knee radiographs, semi-quantitative assessments of knee magnetic resonance imaging (MRI), and MRI images from 567 individuals in the Osteoarthritis Initiative (OAI) were utilized to train a series of machine learning models. Models were constructed using five machine learning methods: random forests (RF), support vector machines (SVM), logistic regression (LR), decision tree (DT), and Bayesian (Bayes). Employing tenfold cross-validation, we selected the best-performing models based on the area under the curve (AUC). The study results indicate no significant difference in performance among models using different imaging data. Subsequently, we employed a convolutional neural network (CNN) to extract features from magnetic resonance imaging (MRI), and class activation mapping (CAM) was utilized to generate saliency maps, highlighting regions associated with knee pain severity. A radiologist reviewed the images, identifying specific lesions colocalized with the CAM. The review of 421 knees revealed that effusion/synovitis (30.9%) and cartilage loss (30.6%) were the most frequent abnormalities associated with pain severity. Our study suggests cartilage loss and synovitis/effusion lesions as significant structural factors affecting pain severity in patients with knee osteoarthritis. Furthermore, our study highlights the potential of machine learning for assessing knee pain severity using radiographs.
Participation and engagement in online cognitive testing
Authors: Sokołowski Daniel Radosław , Pani Jasmine , Hansen Tor Ivar , Håberg Asta Kristine 
Published: June 2024

AbstractWeb-based testing of cognitive abilities allows for large-scale assessments without geographical constraints. Yet, the extent to which it can reach populations beyond the typical demographic groups recruited for cognitive studies is unclear. This study focused on comparing the characteristics of individuals from a general population study (HUNT4) who chose to participate in a cognitive study (HUNT4-Hjernetrim) with those who did not. Additionally, we investigated participants' engagement and user experience. We obtained data on socio-demographics, health conditions (both physical and mental), self-reported cognitive or learning difficulties, and lifestyle factors of Hjernetrim participants and non-participants from the HUNT4 database. Hjernetrim involved 13 cognitive tests, administered through the online platform Memoro. We used logistic regressions to assess participation biases and linear regressions to assess participants’ engagement and user experience. Of 65,851 HUNT4 participants invited via regular mail to Hjernetrim, 5634 (9.4%, aged 13–97, 54% women) participated. The best represented in the sample were 50–79-year-olds, women, tertiary educated, living alone, from urban areas, not occupationally active, and reporting memory complaints. Individuals who were aged 80+, had motor or vision impairments, and teenagers with learning disabilities, were underrepresented. Participants were more likely to have mental health problems, have or survived cancer and less likely to have cardiovascular disease. Participants logged on mainly during weekdays, the preferred time of day varied by age. On average, participants used 42 min and completed 78% of the tasks. Using PCs provided the most complete data. In terms of user experiences, 65% were positive while 14% were negative or reported technical difficulties. Overall, the study demonstrated that web-based methodology allowed for a relatively well-represented sample that included groups typically difficult to reach. The presence of somatic and mental diseases had a variable influence on participation. Participants finished most tests and reported positive experiences overall.
Data imbalance in cardiac health diagnostics using CECG-GAN
Authors: Yang Yang , Lan Tianyu , Wang Yang , Li Fengtian , Liu Liyan , Huang Xupeng , Gao Fei , Jiang Shuhua , Zhang Zhijun , Chen Xing 
Published: June 2024

AbstractHeart disease is the world’s leading cause of death. Diagnostic models based on electrocardiograms (ECGs) are often limited by the scarcity of high-quality data and issues of data imbalance. To address these challenges, we propose a conditional generative adversarial network (CECG-GAN). This strategy enables the generation of samples that closely approximate the distribution of ECG data. Additionally, CECG-GAN addresses waveform jitter, slow processing speeds, and dataset imbalance issues through the integration of a transformer architecture. We evaluated this approach using two datasets: MIT-BIH and CSPC2020. The experimental results demonstrate that CECG-GAN achieves outstanding performance metrics. Notably, the percentage root mean square difference (PRD) reached 55.048, indicating a high degree of similarity between generated and actual ECG waveforms. Additionally, the Fréchet distance (FD) was approximately 1.139, the root mean square error (RMSE) registered at 0.232, and the mean absolute error (MAE) was recorded at 0.166.
A novel radiomics approach for predicting TACE outcomes in hepatocellular carcinoma patients using deep learning for multi-organ segmentation
Authors: Bartnik Krzysztof , Krzyziński Mateusz , Bartczak Tomasz , Korzeniowski Krzysztof , Lamparski Krzysztof , Wróblewski Tadeusz , Grąt Michał , Hołówko Wacław , Mech Katarzyna , Lisowska Joanna , Januszewicz Magdalena , Biecek Przemysław 
Published: June 2024

AbstractTransarterial chemoembolization (TACE) represent the standard of therapy for non-operative hepatocellular carcinoma (HCC), while prediction of long term treatment outcomes is a complex and multifactorial task. In this study, we present a novel machine learning approach utilizing radiomics features from multiple organ volumes of interest (VOIs) to predict TACE outcomes for 252 HCC patients. Unlike conventional radiomics models requiring laborious manual segmentation limited to tumoral regions, our approach captures information comprehensively across various VOIs using a fully automated, pretrained deep learning model applied to pre-TACE CT images. Evaluation of radiomics random survival forest models against clinical ones using Cox proportional hazard demonstrated comparable performance in predicting overall survival. However, radiomics outperformed clinical models in predicting progression-free survival. Explainable analysis highlighted the significance of non-tumoral VOI features, with their cumulative importance superior to features from the largest liver tumor. The proposed approach overcomes the limitations of manual VOI segmentation, requires no radiologist input and highlight the clinical relevance of features beyond tumor regions. Our findings suggest the potential of this radiomics models in predicting TACE outcomes, with possible implications for other clinical scenarios.
Inhibiting spinal cord-specific hsp90 isoforms reveals a novel strategy to improve the therapeutic index of opioid treatment
Authors: Duron David I. , Tanguturi Parthasaradhireddy , Campbell Christopher S. , Chou Kerry , Bejarano Paul , Gabriel Katherin A. , Bowden Jessica L. , Mishra Sanket , Brackett Christopher , Barlow Deborah , Houseknecht Karen L. , Blagg Brian S. J. , Streicher John M. 
Published: June 2024

AbstractOpioids are the gold standard for the treatment of chronic pain but are limited by adverse side effects. In our earlier work, we showed that Heat shock protein 90 (Hsp90) has a crucial role in regulating opioid signaling in spinal cord; Hsp90 inhibition in spinal cord enhances opioid anti-nociception. Building on these findings, we injected the non-selective Hsp90 inhibitor KU-32 by the intrathecal route into male and female CD-1 mice, showing that morphine anti-nociceptive potency was boosted by 1.9–3.5-fold in acute and chronic pain models. At the same time, tolerance was reduced from 21-fold to 2.9 fold and established tolerance was rescued, while the potency of constipation and reward was unchanged. These results demonstrate that spinal Hsp90 inhibition can improve the therapeutic index of morphine. However, we also found that systemic non-selective Hsp90 inhibition blocked opioid pain relief. To avoid this effect, we used selective small molecule inhibitors and CRISPR gene editing to identify 3 Hsp90 isoforms active in spinal cord (Hsp90α, Hsp90β, and Grp94) while only Hsp90α was active in brain. We thus hypothesized that a systemically delivered selective inhibitor to Hsp90β or Grp94 could selectively inhibit spinal cord Hsp90 activity, resulting in enhanced opioid therapy. We tested this hypothesis using intravenous delivery of KUNB106 (Hsp90β) and KUNG65 (Grp94), showing that both drugs enhanced morphine anti-nociceptive potency while rescuing tolerance. Together, these results suggest that selective inhibition of spinal cord Hsp90 isoforms is a novel, translationally feasible strategy to improve the therapeutic index of opioids.
The HANTS-fitted RSEI constructed in the vegetation growing season reveals the spatiotemporal patterns of ecological quality
Authors: Miao Wenna , Chen Yue , Kou Weili , Lai Hongyan , Sazal Ahmed , Wang Jie , Li Youliang , Hu Jiangjie , Wu Yong , Zhao Tianfu 
Published: June 2024

AbstractYuxi, located in China’s central plateau of Yunnan, is grappling with ecological and environmental challenges as it continues to develop its economy. While ecological quality assessment serves as the foundation for ecological protection, it is pivotal to have reliable and long-term methods for assessing the ecological status to support informed decision-making in ecological protection. Reliable and long-term methods for assessing ecological status in order to facilitate informed decision-making in ecological protection are applied. This study utilized Landsat data to reconstruct four indices (greenness, wetness, dryness, and heat) during the vegetation growth in Yuxi from 2000 to 2020 that employs Harmonic Analysis of Time Series (HANTS) method. Subsequently, the annual Remote Sensing Ecological Index (RSEI) was computed by using the reconstructed indices to evaluate ecological quality in Yuxi. Additionally, spatiotemporal patterns and determinants of Yuxi’s ecological quality are unveiled through Sen’s slope estimator and Mann–Kendall test (Sen + MK) trend analysis, spatial auto-correlation analysis, and geographical detectors applied to year-by-year RSEI data. The findings in the paper indicate that the accuracy of the RSEI is significantly influenced by the vegetation season, suggesting that constructing the RSEI model with data from the vegetation growth season is crucial. Moreover, the HANTS optimization method effectively enhances the ecological indices used in the RSEI model, leading to smoother and more continuous filling of missing data. The difference between the reconstructed RSEI and the original RSEI falls within the range of − 0.15 to 0.15. Yuxi has an average RSEI of 0.54 to emphasis a moderate level of comprehensive ecological quality. Compared with river valley plains, the ecological quality of mountainous areas is higher, and the ecological quality of Yuxi presents a distinct center-edge pattern. From 2000 to 2020, Yuxi’s ecological quality exhibited fluctuations, with a slight overall improvement. Land use patterns, particularly in forestry land and impervious surfaces, are identified as the main drivers of these changes. The research offers valuable insights for scientific decision-making related to sustainable development and ecological protection.
Allelopathic effects of Thuidium kanedae on four urban spontaneous plants
Authors: Xie Muyan , Wang Xiurong 
Published: June 2024

AbstractThe spontaneous plant landscape is a key focus in the development of urban environments. While many spontaneous plants can coexist with bryophytes to create appealing wilderness landscapes, the potential allelopathic effects of bryophytes on the growth of neighboring spontaneous plants remain uncertain. This study evaluated the allelopathic impact of Thuidium kanedae aqueous extracts on the germination and seedling growth of prevalent urban spontaneous plants by analyzing seed germination, seedling growth morphology, and associated indices. We also investigated the allelopathic potential of the predominant compounds in the extract on seed germination. Our findings reveal that the aqueous extract significantly impeded the seed germination of Ophiopogon japonicus, Taraxacum mongolicum, and Viola philippica, with the level of inhibition correlating positively with concentration. In contrast, Senecio scandens seed germination showed a concentration-dependent reaction, with low concentrations promoting and high concentrations hindering germination. The extract consistently reduced root length in all four species, yet it appeared to increase root vigor. The chlorophyll content in O. japonicus and V. philippica seedlings reached a maximum at a concentration of 5 g/L and decreased with higher extract concentrations. The treatment resulted in elevated catalase and soluble protein levels in the seedlings, indicating that the extract induced stress and enhanced the stress resistance index. L-phenylalanine and 2-phenylethanol, substances present in the extract, were notably inhibitory to seed germination across all species, except for O. japonicus. Notably, 2-phenylethanol exhibited a stronger allelopathic effect than L-phenylalanine. Allelopathy synthetical effect evaluation showed that high concentration of aqueous extract allelopathic inhibition effect on seed germination of four plant species, but allelopathic promotion effect on physiological and biochemical growth of Taraxacum mongolicum, Senecio scandens and Viola philippica. In summary, the study demonstrates that bryophytes exert allelopathic effects on neighboring spontaneous plants, with the degree of influence varying among species. This suggests that the germination and growth of spontaneous plant seeds may be selective in bryophyte-dominated habitats and that the density of bryophytes could shape the evolution of these landscapes.
Impacts of multiple environmental factors on soil bacterial community assembly in heavy metal polluted paddy fields
Authors: Zou Mengmeng , Zhang Qi , Li Fengchun , Chen Long , Qiu Yifei , Yin Qiqi , Zhou Shenglu 
Published: June 2024

AbstractSoil microorganisms play pivotal roles in driving essential biogeochemical processes in terrestrial ecosystems, and they are sensitive to heavy metal pollution. However, our understanding of multiple environmental factors interaction in heavy metal polluted paddy fields to shape microbial community assembly remain limited. In the current study, we used 16S rRNA amplicon sequencing to characterize the microbial community composition in paddy soils collected from a typical industry town in Taihu region, eastern China. The results revealed that Cd and Pb were the major pollutant, and Proteobacteria, Acidobacteria and Chloroflexi were the dominate indigenous bacterial phyla. Linear regression and random forest analysis demonstrated that soil pH was the most important predictor of bacterial diversity. Mantel analysis showed that bacterial community structure was mainly driven by pH, CEC, silt, sand, AK, total Cd and DTPA-Cd. The constructed bacterial co-occurrence network, utilizing a random matrix theory-based approach, exhibited non-random with scale-free and modularity features. The major modules within the networks also showed significant correlations with soil pH. Overall, our study indicated that soil physiochemical properties made predominant contribution to bacterial community diversity, structure and their association in Cd/Pb polluted paddy fields. These findings expand our knowledge of the key environmental drivers and co-occurrence patterns of bacterial community in polluted paddy fields.
Analysis of physical and mechanical behaviors and microscopic mineral characteristics of thermally damaged granite
Authors: Peng Lei , Li Xianglong , Peng Xin , Gan Yunchuan , Wang Jianguo 
Published: June 2024

AbstractTemperature’s influence on the physical and mechanical properties of rocks is a crucial concern for the rational design of deep rock engineering structures and the assurance of their long-term stability. To systematically comprehend the impact of the evolution of mineral composition and micro characteristics on the physical and mechanical behavior of thermally damaged granite, we observed the microscopic structural defects inside the rocks with a polarizing microscope and revealed the thermal damage mechanism of granite from a microscopic perspective by combining ultrasound detection and XRD phase characteristic analysis. The results show that the physical properties of the specimens changed significantly at three characteristic temperature points: 400 °C, 800 °C, and 1000 °C. Under high temperature conditions, the diffraction intensity of all minerals in granite, except for quartz, generally decreased, and stable minerals decomposed. Albite and potash feldspar decomposed to form anorthoclase, thereby reducing the structural stability of the rock material. In addition, the peak width of various minerals decreased to varying degrees with increasing temperature. The increase in mineral volume further damaged the internal structure of the rock material while promoting the transformation from grain boundary to intergranular cracks and from intragranular cracks to transgranular cracks, ultimately forming a interconnected crack network. Thermal damage significantly reduced the longitudinal wave velocity, uniaxial compressive strength, and elastic modulus of the specimens, while the stress–strain curve relationship indicated that the specimens underwent two opposite processes of transformation from brittleness to ductility and then from ductility to brittleness. The thermal damage threshold of granite in this study was 600 °C.
Calcineurin/NFATc1 pathway represses cellular cytotoxicity by modulating histone H3 expression
Authors: Sato Yuki , Habara Makoto , Hanaki Shunsuke , Sharif Jafar , Tomiyasu Haruki , Miki Yosei , Shimada Midori 
Published: June 2024

AbstractExcess amounts of histones in the cell induce mitotic chromosome loss and genomic instability, and are therefore detrimental to cell survival. In yeast, excess histones are degraded by the proteasome mediated via the DNA damage response factor Rad53. Histone expression, therefore, is tightly regulated at the protein level. Our understanding of the transcriptional regulation of histone genes is far from complete. In this study, we found that calcineurin inhibitor treatment increased histone protein levels, and that the transcription factor NFATc1 (nuclear factor of activated T cells 1) repressed histone transcription and acts downstream of the calcineurin. We further revealed that NFATc1 binds to the promoter regions of many histone genes and that histone transcription is downregulated in a manner dependent on intracellular calcium levels. Indeed, overexpression of histone H3 markedly inhibited cell proliferation. Taken together, these findings suggest that NFATc1 prevents the detrimental effects of histone H3 accumulation by inhibiting expression of histone at the transcriptional level.
Interacting effects of phytohormones and fruit pruning on the morpho-physiological and biochemical attributes of bell pepper
Authors: Moosavi Sayedeh Fatemeh , Haghighi Maryam , Mirmazloum Iman 
Published: June 2024

AbstractSeveral factors, such as pruning and phytohormones, have demonstrated an influence on both the quantity and quality in the bell pepper. A factorial experiment using a completely randomized design was conducted on the Lumos yellow bell in a greenhouse. Treatments were the fruit pruning (0, 10, and 30%) and foliar application of phytohormones auxin (AUX) and gibberellic acid (GA3) at concentrations of 10 µM AUX, 10 µM GA3, 10 µM AUX + 10 µM GA3+, and 20 µM AUX + 10 µM GA3 along with controls. The plants were sprayed with phytohormones in four growth stages (1: flowering stage when 50% of the flowers were on the plant, 2: fruiting stage when 50% of the fruits were the size of peas, 3: fruit growth stage when 50% of the fruits had reached 50% of their growth, and 4: ripening stage when 50% of the fruits were at color break). The results of the present investigation showed that pruning rate of 30% yielded the highest flesh thickness and vitamin C content, decreased seed count and hastened fruit ripening. The use of GA3 along with AUX has been observed to augment diverse fruit quality characteristics. According to the results, the application of 10% pruning in combination with 20 µM AUX and 10 µM GA3 demonstrated the most significant levels of carotenoids, chlorophyll, and fruit length. The experimental group subjected to the combined treatment of 30% pruning and 10 µM AUX + 10 µM GA3 showed the most noteworthy levels of vitamin C, fruit weight, and fruit thickness. The groups that received the 10 µM GA3 and 20 µM AUX + 10 µM GA3 treatments exhibited the most favorable fruit flavor. According to the research results, the implementation of hormonal treatments 10 µM AUX and 10 µM AUX + 10 µM GA3 in combination with a 30% pruning strategy resulted in the most advantageous yield of bell peppers.