bluuebunny/crossref_metadata_2025_split huggingface dataset:
Docetaxel vs. Novel Hormonal Agent for Upfront Management of De Novo Metastatic Hormone-Sensitive Prostate Cancer: A Comprehensive Report of a Single-Center Experience
Authors: Guin Sunny , Liaw Bobby K. , Jun Tomi , Ayers Kristin , Patel Bonny , O’Connell Timmy , Deitz Matthew , Klein Michael , Mullaney Tommy , Prentice Tony , Newman Scott , Fink Marc , Zhou Xiang , Schadt Eric E . , Chen Rong , Oh William 
Published: July 2021

Abstract
Background: Upfront docetaxel or novel hormonal agents (NHA) such as abiraterone and enzalutamide have become the standard of care for metastatic hormone sensitive prostate cancer (mHSPC). However, data comparing the efficacy of docetaxel and NHAs in this setting are limited.Patients and Methods: This was a retrospective cohort study of patients with de novo mHSPC treated with upfront docetaxel or an NHA between January 1, 2014 and April 30, 2019 within the Mount Sinai Health System. Clinical data were extracted from the medical record. The primary outcome was failure-free survival (FFS), defined as the time to next treatment. The primary predictor was treatment with docetaxel or NHA. FFS was compared between the two groups using the Kaplan Meier method and multivariable Cox proportional hazards models. We additionally assessed the prognostic value of post-treatment PSA.Results: We identified 94 de novo mHSPC patients; 52 and 42 treated with upfront docetaxel and NHAs, respectively. NHAs were associated with significantly longer FFS compared to docetaxel (20.7 vs. 10.1 months, p=0.023). In a multivariable model adjusting for demographics and clinical factors, docetaxel was independently associated with worse FFS compared to NHAs (HR 1.96, 95% CI 1.12−3.45, p=0.019). High metastasis burden patients had a significantly longer FFS with NHAs than docetaxel (25.12 vs. 9.63 months, p=0.014), while there was no significant difference in FFS among low metastasis burden patients (NHA 20.71 vs. Docetaxel 26.5 months, p=0.9). Regardless of treatment, lower post-treatment PSA levels were associated with improved FFS (58.95 vs. 11.57 vs. 9.4 months for PSA ≤0.2, 0.2-0.4, >0.4ng/ml, respectively; p<0.001) Conclusion: Comparative analysis of real-world data demonstrated longer FFS in de novo mHSPC treated with NHA compared to docetaxel. In addition, the depth of PSA response following combination treatment may hold prognostic value for mHSPC outcomes.
Density Exponent Analysis -- A new vision towards gravitational collapse of molecular clouds
Authors: Li Guang-Xing , Zhou Ji-Xuan 
Published: July 2021

Abstract
The evolution of molecular interstellar clouds, during which stars form, is a complex, multi-scale process. The power-law density exponent describes the steepness of density profiles in the log-log space, and it has been used to characterize the density structures of the clouds. Its effectiveness results from the widespread emergence of power-law-like density structures in complex systems that have reached intermediate asymptotic states. However, its usage is usually limited to spherically symmetric systems. Importing the Level-Set Method, we develop a new formalism that generates robust maps of a generalized density exponent kp at every location for complex density distributions. By applying it to a high fidelity, high dynamical range map of the Perseus molecular cloud constructed using data from the Herschel and Planck satellites, we find that the density exponent exhibits a surprisingly wide range of variation (-3.5 < kp < -0.5) Regions at later stages of gravitational collapse are associated with steeper density profiles. Inside a region, gas located in the vicinities of dense structures has very steep density profiles with kp ~ -3, which form because of depletion. This density exponent analysis reveals diverse density structures in a molecular cloud, forming a coherent picture that gravitational collapse and accretion contribute to a continued steepening of the density profile. We expect our method to be effective in studying other power-law-like density structures, including the density structure of granular materials and the Large-Scale Structure of the Universe.
Surface biofunctionalization with liquid-phase plasma treatment of collagen molecules
Authors: Tanaka Shiori , Kanemura Shingo , Okumura Masaki , Iwaikawa Kazuyuki , Funamoto Kenichi , Miyaji Godai , Muraoka Takahiro , Misawa Kazuhiko , Yoshino Daisuke 
Published: July 2021

Abstract
Surface functionalization is a key process in rendering various materials biocompatible. Whereas a number of techniques and technologies have been developed for the purpose of biofunctionalization, plasma treatment enables highly efficient surface modification. Extending plasma treatment to biomolecules in the liquid phase will control biofunctionalization via a simple process. However, interactions between plasma discharge and biomolecules or solvents are poorly understood, potentially leading to the technical limitation as to the utility of plasma treatment. In this study, we developed a technology for substrate biofunctionalization that does not require surface modification but involves direct treatment of a collagen molecules with liquid-phase plasma discharge. Biofunctionalization of collagen by plasma treatment comprises three processes that increase its reactivity with hydrophobic substrates: (1) charge-dependent changes in surface and interfacial properties of the collagen solution; (2) local conformational changes of the collagen molecules without their global structural alterations; and (3) induction of a micelle-like association formed by collagen molecules. We anticipate such plasma-induced functionalization of protein molecules to provide a versatile technique in the applications of biomaterials, including those related to pharmaceuticals and cosmetics.
Implementation of Supervised PCA for Global Sensitivity Analysis of Models with Correlated Inputs
Authors: Sharbaf M. A. M. J. , Abedini Mohammad Javad 
Published: July 2021

Abstract
Global Sensitivity Analysis (GSA) plays a significant role in quantifying the tangible impact of model inputs on the uncertainty of response variable. As GSA results are strongly affected by correlated inputs, several studies considered this issue, but most of them are quite time-consuming, labor-intensive, and difficult to implement. Accordingly, this paper puts forward a novel strategy based on the Supervised Principal Component analysis (Supervised PCA), benefiting from the Reproducing Kernel Hilbert Space (RKHS). Indeed, by conducting one kind of variance-based sensitivity analysis (SA), a renowned method exclusively customized for models with orthogonal inputs, on Supervised PCA (SPCA) regression, the impact of correlation structure of input variables is effectively taken into account. The ability of the suggested technique is evaluated with five test cases as well as two hydrologic and hydraulic models, and the results are compared and contrasted with those obtained from the correlation ratio method taken as a robust benchmark solution. It is found that the proposed method satisfactorily identifies the sensitivity ordering of model inputs. Furthermore, it is proved in this study that the performance of the proposed approach is also supported by the total contribution index in the derived covariance decomposition equation. Moreover, the proposed method compared to the correlation ratio method, is found to be time efficient and easy to implement. Overall, the proposed scheme is appropriate for high dimensional, relatively nonlinear or expensive models with correlated inputs whose coefficient of determination is larger than 0.5.
Identification Hub Genes of Consensus Molecular Subtype Correlation With Immune Infiltration and Predict Prognosis in Gastric Cancer
Authors: Yu Bin , Yu Xin , Xiong Jianping , Ma Mei 
Published: July 2021

Abstract
Gastric Cancer (GC) has a great fatality rate, meanwhile, there is still a lack of available biomarkers for prognosis. The goal of the research was to discover key and novel potential biomarkers for GC. We screened for the expression of significantly altered genes based on survival rates from two consensus molecular subtypes (CMS) of GC. Subsequently, functional enrichment analysis showed these genes involved in many cancers. And we picked 6 hub genes that could both secreted in the tumor microenvironment and expression enhanced in immune cells. Then, Kaplan Meier survival and expression detected in the tumor pathological stage were utilized to clarify the prognostic of these 6 hub genes. The results indicated that OGN, CHRDL2, C2orf40, THBS4, CHRDL1, and ANGPTL1, respectively, were significantly associated with poor OS in GC patients. And their expression increased with cancer advanced. Moreover, immune infiltration analysis displayed that those hub genes expression positively with M2 macrophage, CD8+ T Cell, most immune inhibitors, and majority immunostimulators. In summary, our results suggested that OGN, CHRDL2, C2orf40, THBS4, CHRDL1, and ANGPTL1 were all potential biomarkers for GC prognosis and might also be potential therapeutic targets for GC.
Environmental and human health at risk – scenarios to achieve the EU’s 50% pesticide reduction goals.
Authors: Silva Vera , Yang Xiaomei , Fleskens Luuk , Ritsema Coen , Geissen Violette 
Published: July 2021

Abstract
The recently released Farm to Fork Strategy sets, for the first time ever, pesticide reduction goals at the EU level: 50% reduction in overall use and risk of chemical pesticides and 50% use reduction of more hazardous pesticides. However, little guidance is provided to achieve these targets. In this study, we compiled the characteristics and recommended application rates of 230 EU approved, synthetic, open-field use active substances and explored the potential of eight pesticide reduction scenarios (defined based on application rates, pesticide type, persistence, and hazard) to achieve the reduction goals. Our approach revealed that all 230 substances are potentially harmful to humans or ecosystems, and that only severe pesticide use restrictions such as full conversion to organic farming or allowing only low hazard substances will result in 50% reductions. Our results emphasis the need of an EC action plan on how to achieve and maintain the aimed reduction levels.
Identification of Appropriate Housekeeping Genes for Gene Expression Studies in Human Renal Cell Carcinoma Under Hypoxic Conditions
Authors: Teixeira Luiz Felipe S. , Gigliotti Rodrigo , Ferreira Luana da Silva , Bellini MARIA Helena 
Published: July 2021

Abstract
Background: Due to the loss of von Hippel-Lindau tumor suppressor function, clear renal cell carcinoma (ccRCC) deregulates hypoxia pathways. Quantitative PCR is a powerful tool for quantifying differential expression between normal and cancer cells. Reliable gene expression analysis requires the use of genes encoding housekeeping genes. Therefore, in this study, eight reference candidate genes were evaluated to determine their stability in 786-0 cells under normoxic and hypoxic conditions. Methods and Results: Four different tools were used to rank the most stable genes: GeNorm, NormFinder, BestKeeper, and Comparative Ct (ΔCt), and a general ranking was performed using the RankAggreg. According to the four algorithms, the TFRC reference gene was identified as the most stable, and therefore, no agreement was observed for the 2nd and 3rd positions. A general classification was then established using the RankAggreg tool. Finally, the three most suitable reference genes to be used in 786-0 cells under normoxic and hypoxic conditions were TFRC, RPLP0, and SDHA. Conclusions: To our knowledge, this is the first study to evaluate reliable genes that can be used in gene expression analysis in ccRcc under a hypoxic environment.
Examining the Effects of Climate Change and Political Instability on Maize Production in Somalia
Authors: Warsame Abdimalik Ali , Sheik-Ali Ibrahim Abdukadir , Barre Galad Mohamed , Ahmed Abdulnasir 
Published: July 2021

Abstract
Agricultural production is sensirive to climate variabilities, so climate change-agriculture sector nexus is topical in developing countries. Given that, this study models the impact of climate change variables – rainfall and temperature – on maize production in Somalia. The study also incorporates political instability as a main determinant variable of maize production. Several econometric methodologies – Johansen and juselius cointegration, Fully modified ordinary least square(FMOLS), Canonical cointegration regression(CCR), variance decomposition(VD) and impulse response function(IRF) – were espoused with a time series data spanning between 1980 and 2018. The empirical results of Johansen and juselius cointegration show that the explanatory variables are cointegrated to the maize output in the long-run. The study also revealed that average temperature and political instability significantly inhibit maize production in the long-run. Whereas rainfall has a favourable effect on maize production in the long-run. Furthermore, land degradation and land area under maize cultivation – incorporated in the model as control variables – are observed to have positive effects on maize production in the long-run. Based on these findings, the study emphesizes the importane of the de-escalation of conflicts and the implementation of irrigation facilities which will enhance the prdocutiivity of maize crop production.
Factors Influencing Breast Milk Donation To a Human Milk Bank in Iran: Implications for Policymakers and Planners
Authors: Doshmangir Leila , Gholipour Kamal , Nagshi Maryam , Ahmadi Ayat , Gordeev Vladimir Sergeevich 
Published: July 2021

Abstract
Introduction:

World Health Organization actively promotes breastfeeding as the best nourishment source for infants and young children. However, access to human milk for all newborns is not always possible, leading to nutritional benefits deprivation or financial burden related to acquiring alternatives. Establishing human milk banks (HMB) could facilitate equitable access to donated human milk. However, various factors may impede human milk donation. This study identifies factors influencing milk donation to HMB in Iran.
Methods

We conducted a case-control study among mothers who had given birth at least one year before the study, with 51 cases (mothers who donated their milk to HMB) and 153 controls. Data were collected by a questionnaire designed to obtain retrospective information about individual health, social network, and other factors. Logistic regressions were used to examine relationships between breast milk donation and these factors. Qualitative data were collected during face-to-face interviews and analysed using a content analysis approach to elicit viewpoints of HMB senior staff and mothers on the factors influencing milk donation.
Results

Family factors, social factors, individual social welfare score, and breast milk adequacy were associated with increased milk donation. Family support for human milk donation was among the strongest predictors.
Conclusion

To establish HMB and promote human milk donation, health policy planners and makers should use strategies and policies that motivate mothers' donation to HMBs.
The Lived Experience of HIV-Infected Patients in the Face of a Positive Diagnosis of the Disease: A Phenomenological Study
Authors: Zandi Shirdel , Imani Behzad , khazaei Salman , Mirzaei Mohamad 
Published: July 2021

Abstract
Background: AIDS as a human crisis may lead to devastating psychological trauma and stress for patients. Therefore, it is necessary to study different aspects of their lives for better support and care. Accordingly, this study aimed to explain the lived experience of HIV-infected patients in the face of a positive diagnosis of the disease.Methods: This qualitative study is a descriptive phenomenological study. Sampling was done purposefully and participants were selected based on the inclusion and exclusion criteria. Data collection was conducted, using semi-structured interviews. Data analysis was performed using Colaizzi’s method.Results: 12 AIDS patients participated in this study. As a result of data analysis, 5 main themes and 12 sub-themes were identified, which include: emotional shock (loathing, motivation of social isolation), the fear of the consequences (fear of the death, fear of loneliness, fear of disgrace), the feeling of the guilt (feeling of regret, feeling guilty, feeling of conscience-stricken), the discouragement (suicidal ideation, disappointment), and the escape from reality (denial, trying to hide).Conclusions: The results of this study showed that patients will experience unpleasant phenomenon in the face of the positive diagnosis of the disease and will be subjected to severe psychological pressures that require attention and support of medical and laboratory centers.
Neuroinflammatory and neurometabolic consequences from inhaled 2020 California wildfire smoke-derived particulate matter at a remote location
Authors: Scieszka David , Hunter Russell , Begay Jessica , Bitsui Marsha , Lin Yan , Galewsky Joseph , Morishita Masako , Klaver Zachary , Wagner James , Harkema Jack , Herbert Guy , Lucas Selita , McVeigh Charlotte , Bolt Alicia , Bleske Barry , Canal Christopher , Mostovenko Ekaterina , Ottens Andrew , Gu Haiwei , Campen Matthew , Noor Shahani 
Published: July 2021

Abstract
Utilizing a mobile laboratory located >300 km away from wildfire smoke (WFS) sources, this study examined the systemic immune response profile, with a focus on neuroinflammatory and neurometabolic consequences, resulting from inhalation exposure to naturally occurring wildfires in California and Arizona in 2020. After a 20-day exposure period, WFS-derived particulate matter inhalation resulted in significant neuroinflammation while immune activity in the peripheral (lung, bone marrow) appeared to be resolved in C57BL/6 mice. Importantly, WFS exposure increased cerebrovascular endothelial cell activation and expression of adhesion molecules (VCAM-1 and ICAM-1) in addition to increased glial activation and peripheral immune cell infiltration into the brain. Flow cytometry analysis revealed proinflammatory phenotypes of microglia and peripheral immune subsets in the brain of WFS-exposed mice. Interestingly, endothelial cell neuroimmune activity was differentially associated with levels of PECAM-1 expression, suggesting that subsets of cerebrovascular endothelial cells were transitioning to resolution of inflammation following the 20-day exposure. Neurometabolites related to protection against aging, such as NAD+ and taurine, were decreased by WFS exposure. Additionally, increased pathological amyloid-beta protein accumulation, a hallmark of neurodegeneration, was observed. Neuroinflammation, together with decreased levels of key neurometabolites, have important implications in priming inflammaging and aging-related neurodegenerative phenotypes.
Bioactive nano-selenium antagonizes cobalt nanoparticle- mediated oxidative stress via the Keap1-Nrf2-ARE signaling pathway
Authors: Wang Siqi , Wang Chen , Zhang Weinan , Fan Wentao , Liu Fan , Liu yake 
Published: July 2021

Abstract
BackgroundAt present, no effective treatment exists for the clinical toxicity of cobalt nanoparticles (CoNPs) after metal-on-metal (MOM) artificial joint replacement. As such, a better understanding of the CoNPs-toxicity mechanism is necessary and urgent for the development of effective and safe detoxification drugs. Our purpose was to explore the role of bioactive nano-selenium (BNS) in antagonizing the toxicity of CoNPs and its mechanism through the Keap1-Nrf2-ARE signaling pathway. MethodsTo examine BNS detoxification, we exposed HUVEC cells to CoNPs (400μmol/l) and BNS (50μg/ml) for 24h, before measuring cell activity, reactive oxygen species (ROS), inflammatory factors, and KNA signaling pathway related transcript and protein expression. ResultsCoNPs stimulate intracellular inflammation and ROS production to bring about significant downregulation of cellular activity. Conversely, BNS reduces ROS generation and suppresses inflammatory factors within cells to reduce CoNPs-mediated cytotoxicity, possibly via the KNA signaling pathway. ConclusionsBased on our results, BNS antagonizes CoNPs toxic effects by suppressing ROS production through the KNA pathway. Our research provides new insight into the clinical treatment of CoNPs toxicity and explores the potential of BNS in detoxification therapy.
Mechanism of Electric Field Assisted Screening for Candida Tropicalis
Authors: Zhang Chaozheng , Zhang Tianshuang , Tan Peng , Zhang Xueqing , Zhao Hua 
Published: July 2021

Abstract
Electric field assisted technology has been widely used in many fields. Candida tropicalis degrading thiophene has been successfully screened by electric field assisted screening (EFAS). However, the mechanism of EFAS is yet unclear. In this study, the cell concentration of C. tropicalis at different locations in the electric field was studied. In the electric field, C. tropicalis cells migrate towards the cathode plate. When the loading electric field intensity was 0.6 V /cm, the concentration of cells near the cathode plate reached the maximum and it was nearly stable at 15 minutes of loading time. Analysis of the cell morphology at different locations showed that only the budding cells could migrate in the electric field. The charged characteristic of the budding cells in the electric field explained the screening mechanism of thiophene-degrading C. tropicalis by EFAS. The results can provide a theoretical foundation for EFAS in screening other biodegradable microorganisms.
Predicting the Side Effects of Drugs using Matrix Factorization on Spontaneous Reports Database
Authors: Fukuto Kohei , Takagi Tatsuya , Tian Yu-Shi 
Published: July 2021

Abstract
Background Drugs with severe side effects can be threatening to patients and compromise pharmaceutical companies financially. Various computational techniques have been proposed to predict the side effects of drugs, including methods that utilize chemical, biological, and phenotypic features. Among them, matrix factorization (MF), which harnesses the known side effects of different drugs, has shown promising results. However, methods encapsulating all characteristics of side-effect prediction have not been investigated thus far. To this effect, we employed the logistic matrix factorization (Logistic MF) algorithm, i.e., MF modified for implicit feedback data, on a spontaneous reports database to improve the accuracy of side-effect prediction.Results A weighting strategy was applied to account for differences in the importance of the drug-side effect pairs. The impact of the cold-start problem and means to tackle it using the attribute-to-feature mapping were also explored. The experimental results demonstrate that the proposed model improved the prediction accuracy by 2.3% and efficiently handled the cold-start problem.Conclusion The proposed methodology is envisaged to benefit applications such as warning systems in clinical settings.