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  • Frontiers Media SA  (14)
  • 1
    Online Resource
    Online Resource
    Data_Sheet_1.docx
    Frontiers Media SA ; 2020
    In:  Frontiers in Chemistry Vol. 8 ( 2020-2-26)
    Details
    In: Frontiers in Chemistry, Frontiers Media SA, Vol. 8 ( 2020-2-26)
    Type of Medium: Online Resource
    ISSN: 2296-2646
    URL: Article
    URL: Article
    DOI: 10.3389/fchem.2020.00049
    DOI: 10.3389/fchem.2020.00049.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2020
    detail.hit.zdb_id: 2711776-5
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  • 2
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    Table_1.DOCX
    Frontiers Media SA ; 2020
    In:  Frontiers in Chemistry Vol. 8 ( 2020-5-26)
    Details
    In: Frontiers in Chemistry, Frontiers Media SA, Vol. 8 ( 2020-5-26)
    Type of Medium: Online Resource
    ISSN: 2296-2646
    URL: Article
    URL: Article
    DOI: 10.3389/fchem.2020.00399
    DOI: 10.3389/fchem.2020.00399.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2020
    detail.hit.zdb_id: 2711776-5
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  • 3
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    Sustainable manufacture and application of biochar to improve soil properties and remediate soil contaminated with organic impurities: a systematic review
    Frontiers Media SA ; 2023
    In:  Frontiers in Environmental Science Vol. 11 ( 2023-11-20)
    Details
    In: Frontiers in Environmental Science, Frontiers Media SA, Vol. 11 ( 2023-11-20)
    Abstract: Biochar production and application have become increasingly popular in the past 15 years. Biochar, derived from diverse biomass types, offers a rich carbon source created through thermal combustion. Biochar production primarily depends on pyrolysis conditions and feedstock type. This review focuses on the multifaceted aspects of biochar, encompassing hydrothermal carbonization, gasification, and pyrolysis temperatures in biochar production and its role in bioeconomy and soil remediation. Biochar has yielded valuable insights, notably in decreasing nutrient leaching, curbing greenhouse gas (GHG) emissions, reducing the bioavailability of environmental pollutants, sequestering carbon (C) in soils, and enhancing agricultural productivity. Consequently, it has emerged as a valuable commodity for the bioeconomy, which involves harnessing bioresources through bioengineering to create economically valuable products. As a marketable output, biochar finds application in energy, diverse biochar-based product manufacturing, and the agricultural sector. Thus, biochar production not only enhances soil quality but also unlocks additional revenue streams. This review underscores the critical role of feedstock selection and pyrolysis conditions in optimizing biochar production. Furthermore, it highlights biochar as a sustainable and effective tool for improving various soil types and remediating soil contamination caused by organic impurities, including persistent organic compounds and antibiotics.
    Type of Medium: Online Resource
    ISSN: 2296-665X
    URL: Article
    DOI: 10.3389/fenvs.2023.1277240
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2741535-1
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  • 4
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    Corrigendum: Machine learning prediction model for post- hepatectomy liver failure in hepatocellular carcinoma: a multicenter study
    Frontiers Media SA ; 2023
    In:  Frontiers in Oncology Vol. 13 ( 2023-8-31)
    Details
    In: Frontiers in Oncology, Frontiers Media SA, Vol. 13 ( 2023-8-31)
    Type of Medium: Online Resource
    ISSN: 2234-943X
    URL: Article
    DOI: 10.3389/fonc.2023.1278295
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2649216-7
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  • 5
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    Noninvasively Assessed Portal Hypertension Grade Predicts Post-Hepatectomy Liver Failure in Patients With HepatocellCarcinoma: A Multicenter Study
    Frontiers Media SA ; 2022
    In:  Frontiers in Oncology Vol. 12 ( 2022-7-14)
    Details
    In: Frontiers in Oncology, Frontiers Media SA, Vol. 12 ( 2022-7-14)
    Abstract: To determine the predictive value of portal hypertension (PH) for the development of post-hepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC). Patients and methods This study enrolled a total of 659 patients with HCC that received hepatectomy as a first-line therapy. PH was classified as grade 0, 1, and 2 according to whether the indirect criteria for PH were met: 1) patients had obvious varicose veins and 2) splenomegaly was present and platelet count & lt; 100 × 10 9 /L. The effects of each variable on the occurrence of PHLF were assessed using univariate and multivariate analyses. Results PH grade 2 (odds ratio [OR] = 2.222, p = 0.011), higher age (OR = 1.031, p = 0.003), hepatitis C infection (OR = 3.711, p = 0.012), open surgery (OR = 2.336, p & lt; 0.001), portal flow blockage (OR = 1.626, p = 0.023), major hepatectomy (OR = 2.919, p = 0.001), hyperbilirubinemia (≥ 17.2 μmol/L, OR = 2.113, p = 0.002), and high levels of alpha-fetoprotein ( & gt; 400n g/ml, OR = 1.799, p = 0.008) were significantly associated with PHLF occurrence. We performed a subgroup analysis of liver resection and found that the extent of liver resection and PH grade were good at distinguishing patients at high risk for PHLF, and we developed an easy-to-view roadmap. Conclusion PH is significantly related to the occurrence of PHLF in patients who underwent hepatectomy. Noninvasively assessing PH grade can predict PHLF risk.
    Type of Medium: Online Resource
    ISSN: 2234-943X
    URL: Article
    DOI: 10.3389/fonc.2022.934870
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2649216-7
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  • 6
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    Table_1.docx
    Frontiers Media SA ; 2022
    In:  Frontiers in Oncology Vol. 12 ( 2022-11-2)
    Details
    In: Frontiers in Oncology, Frontiers Media SA, Vol. 12 ( 2022-11-2)
    Abstract: Post-hepatectomy liver failure (PHLF) is one of the most serious complications and causes of death in patients with hepatocellular carcinoma (HCC) after hepatectomy. This study aimed to develop a novel machine learning (ML) model based on the light gradient boosting machines (LightGBM) algorithm for predicting PHLF. Methods A total of 875 patients with HCC who underwent hepatectomy were randomized into a training cohort (n=612), a validation cohort (n=88), and a testing cohort (n=175). Shapley additive explanation (SHAP) was performed to determine the importance of individual variables. By combining these independent risk factors, an ML model for predicting PHLF was established. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, and decision curve analyses (DCA) were used to evaluate the accuracy of the ML model and compare it to that of other noninvasive models. Results The AUCs of the ML model for predicting PHLF in the training cohort, validation cohort, and testing cohort were 0.944, 0.870, and 0.822, respectively. The ML model had a higher AUC for predicting PHLF than did other non-invasive models. The ML model for predicting PHLF was found to be more valuable than other noninvasive models. Conclusion A novel ML model for the prediction of PHLF using common clinical parameters was constructed and validated. The novel ML model performed better than did existing noninvasive models for the prediction of PHLF.
    Type of Medium: Online Resource
    ISSN: 2234-943X
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.3389/fonc.2022.986867
    DOI: 10.3389/fonc.2022.986867.s001
    DOI: 10.3389/fonc.2022.986867.s002
    DOI: 10.3389/fonc.2022.986867.s003
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2649216-7
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  • 7
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    Effect of epidural labor analgesia on maternal and infant outcomes in parturients with gestational diabetes mellitus—A prospective cohort study
    Frontiers Media SA ; 2022
    In:  Frontiers in Pediatrics Vol. 10 ( 2022-12-12)
    Details
    In: Frontiers in Pediatrics, Frontiers Media SA, Vol. 10 ( 2022-12-12)
    Abstract: The occurrence of gestational diabetes mellitus (GDM) is caused by a variety of factors and associated with increased risks of several adverse outcomes for both mothers and infants. However, the effects of epidural labor analgesia in parturients with GDM on maternal and infant outcomes have not been characterized. Methods According to parturients' choice, they were divided into the epidural group ( n  = 133) and no epidural (control) group ( n  = 135). Data for relative variables in the perinatal period were collected, and the potential associations of epidural labor analgesia with infant outcomes were analyzed by univariate analysis and multivariate logistic regression analyses. Results The rate of neonatal admission to the neonatal intensive care unit (NICU) for hypoglycemia was higher in the epidural group (7.52%) than in the control group (1.48%; P   & lt; 0.05). Epidural labor analgesia and drug-based diabetes control were independent predictors of the rate of neonate transfer to the NICU for hypoglycemia. Conclusion Epidural labor analgesia was associated with an increased risk of neonatal transfer to the NICU for hypoglycemia. Thus, monitoring of neonatal blood glucose levels after administration of epidural labor analgesia in parturients with GDM may be beneficial. Trial registration: The study was registered in the China Clinical Registration Center (Registration No. ChiCTR-OOC-17013164, Registered on 30 October 2017).
    Type of Medium: Online Resource
    ISSN: 2296-2360
    URL: Article
    DOI: 10.3389/fped.2022.1022291
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2711999-3
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  • 8
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    Data_Sheet_1.PDF
    Frontiers Media SA ; 2024
    In:  Frontiers in Medicine Vol. 10 ( 2024-1-5)
    Details
    In: Frontiers in Medicine, Frontiers Media SA, Vol. 10 ( 2024-1-5)
    Abstract: Parkinson’s disease (PD) is the second most common neurodegenerative disease. An objective diagnosis method is urgently needed in clinical practice. In this study, deep learning and radiomics techniques were studied to automatically diagnose PD from healthy controls (HCs). Methods 155 PD patients and 154 HCs were randomly divided into a training set (246 patients) and a testing set (63 patients). The brain subregions identification and segmentation were automatically performed with a VB-net, and radiomics features of billateral thalamus, caudatum, putamen and pallidum were extracted. Five independent machine learning classifiers [Support Vector Machine (SVM), Stochastic gradient descent (SGD), random forest (RF), quadratic discriminant analysis (QDA) and decision tree (DT)] were trained on the training set, and validated on the testing. Delong test was used to compare the performance of different models. Results Our VB-net could automatically identify and segment the brain into 109 regions. 2,264 radiomics features were automatically extracted from the billateral thalamus, caudatum, putamen or pallidum of each patient. After four step of features dimensionality reduction, Delong tests showed that the SVM model based on combined features had the best performance, with AUCs of 0.988 (95% CI: 0.979 ~ 0.998, specificity = 91.1%, sensitivity =100%, accuracy = 89.4% and precision = 88.2%) and 0.976 (95% CI: 0.942 ~ 1.000, specificity = 100%, sensitivity = 87.1%, accuracy = 93.5% and precision = 88.6%) in the training set and testing set, respectively. Decision curve analysis showed that the clinical benefit of the line graph model was high. Conclusion The SVM model based on combined features could be used to diagnose PD with high accuracy. Our fully automatic model could rapidly process the MRI data and distinguish PD and HCs in one minute. It greatly improved the diagnostic efficiency and has a great potential value in clinical practice to help the early diagnosis of PD.
    Type of Medium: Online Resource
    ISSN: 2296-858X
    URL: Article
    URL: Article
    DOI: 10.3389/fmed.2023.1303501
    DOI: 10.3389/fmed.2023.1303501.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2024
    detail.hit.zdb_id: 2775999-4
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  • 9
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    Propidium Monoazide Combined With RT-qPCR Detects Infectivity of Porcine Epidemic Diarrhea Virus
    Frontiers Media SA ; 2022
    In:  Frontiers in Veterinary Science Vol. 9 ( 2022-7-15)
    Details
    In: Frontiers in Veterinary Science, Frontiers Media SA, Vol. 9 ( 2022-7-15)
    Abstract: Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) allows sensitive detection of viral particles and viruses in epidemic samples but it cannot discriminate noninfectious viruses from infectious ones. Propidium monoazide (PMA) coupled with quantitative polymerase chain reaction (qPCR) was assessed to detect infectious viruses. Currently, there is no established test method to detect the infection of the porcine epidemic diarrhea virus (PEDV). In this study, propidium monoazide coupled with qPCR detects infectivity of PEDV. We optimized the method from the selection of primers, the working concentration of PMA, and the inactivation method using heat or ultraviolet (UV). The viruses which were treated with PMA before qPCR were inactivated using heat or UV. However, the addition of PMA alone did not affect the detection of live viruses, which indicates that a viral capsid break may be essential for PMA to bind to the genome. A repetition of the method on naked PEDV RNA suggests that it can be used to detect potentially infectious PEDV. The results indicated that an optimal plan of PMA could be extremely useful for evaluating infectious and noninfectious viruses.
    Type of Medium: Online Resource
    ISSN: 2297-1769
    URL: Article
    DOI: 10.3389/fvets.2022.931392
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2834243-4
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  • 10
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    Data_Sheet_1.docx
    Frontiers Media SA ; 2022
    In:  Frontiers in Medicine Vol. 9 ( 2022-5-16)
    Details
    In: Frontiers in Medicine, Frontiers Media SA, Vol. 9 ( 2022-5-16)
    Abstract: To investigate the association between myopia and risk of metabolic syndrome (MetS) in a prospective cohort from the UK Biobank Study. Methods Volunteers (aged 40 years and above) free of baseline MetS and cataract included from the UK Biobank Study, a prospective follow-up cohort. Myopia was defined using uncycloplegic autorefraction, self-report-myopia, and medical records for refractive error at baseline. MetS as well as components of MetS were diagnosed based on health records, blood biochemistry, and questionnaires. Questionnaires determined the status of smoking, drinking, physical activity and dietary supplements, as well as ethnicity and education. Results A total of 91,591 participants were available in the analysis, with a mean age of 55.37 ± 8.07 years at baseline and a median follow-up years of 11.16 years. The proportion of myopia was 49.7%, and a total of 937 (1.0%) participants were identified as having incident MetS (0.09/100 person years). Subjects with myopia were more likely to have MetS compared with non-myopic subjects (0.82 vs. 0.21%, Log-rank test P & lt; 0.001). Mopes had greater risk of incident MetS (Hazard ratio [HR] = 4.19, 95% confidence interval [CI] 3.57–4.93, P & lt; 0.001) adjusting for baseline age, gender, education and ethnicity. After further controlling for lifestyle factors (smoking, drinking, physical activity, and fish oil supplement) or baseline metabolic disorders, the risk of incident MetS were 3.88- and 4.06-fold greater in myopic subjects than those without myopia, respectively ( P & lt; 0.001 for both models). The severity of myopia was not significantly correlated to incident MetS in multivariate-adjusted models. Conclusions An increased risk of incident MetS among the elderly is associated with myopia, but not the degree of myopia. These findings highlighted the need of prevention of MetS among older adults with myopia.
    Type of Medium: Online Resource
    ISSN: 2296-858X
    URL: Article
    URL: Article
    DOI: 10.3389/fmed.2022.872013
    DOI: 10.3389/fmed.2022.872013.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2775999-4
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