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Medical Application of Hydrogen in Hematological Diseases.

ABSTRACT: Hydrogen gas has been reported to have medical efficacy since the 1880s. Still, medical researchers did not pay much attention to hydrogen gas until the 20th century. Recent research, both basic and clinical, has proven that hydrogen is an important physiological regulatory factor with antioxidative, anti-inflammatory, and antiapoptotic effects. In the past two decades, more than 1000 papers have been published on the topic, including organ ischemia-reperfusion injury, radiation injury, diabetes, atherosclerosis, hypertension, or cancer. We have previously hypothesized and proven the therapeutic effects of hydrogen gas in graft-versus-host disease following stem cell transplantation. In the current manuscript, we present the clinical advances of hydrogen gas in hematological disorders.

SUBMITTER: Qian L

PROVIDER: S-EPMC6906850 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Medical Application of Hydrogen in Hematological Diseases.

Qian Liren L Wu Zhengcheng Z Cen Jian J Pasca Sergiu S Tomuleasa Ciprian C

Oxidative medicine and cellular longevity 20191128

Hydrogen gas has been reported to have medical efficacy since the 1880s. Still, medical researchers did not pay much attention to hydrogen gas until the 20th century. Recent research, both basic and clinical, has proven that hydrogen is an important physiological regulatory factor with antioxidative, anti-inflammatory, and antiapoptotic effects. In the past two decades, more than 1000 papers have been published on the topic, including organ ischemia-reperfusion injury, radiation injury, diabetes ...[more]

PMID: 31871547

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Project description:Background Owing to the linguistic situation, Japanese natural language processing (NLP) requires morphological analyses for word segmentation using dictionary techniques.Objective We aimed to clarify whether it can be substituted with an open-end discovery-based NLP (OD-NLP), which does not use any dictionary techniques.Methods Clinical texts at the first medical visit were collected for comparison of OD-NLP with word dictionary-based-NLP (WD-NLP). Topics were generated in each document using a topic model, which later corresponded to the respective diseases determined in International Statistical Classification of Diseases and Related Health Problems 10 revision. The prediction accuracy and expressivity of each disease were examined in equivalent number of entities/words after filtration with either term frequency and inverse document frequency (TF-IDF) or dominance value (DMV).Results In documents from 10,520 observed patients, 169,913 entities and 44,758 words were segmented using OD-NLP and WD-NLP, simultaneously. Without filtering, accuracy and recall levels were low, and there was no difference in the harmonic mean of the F-measure between NLPs. However, physicians reported OD-NLP contained more meaningful words than WD-NLP. When datasets were created in an equivalent number of entities/words with TF-IDF, F-measure in OD-NLP was higher than WD-NLP at lower thresholds. When the threshold increased, the number of datasets created decreased, resulting in increased values of F-measure, although the differences disappeared. Two datasets near the maximum threshold showing differences in F-measure were examined whether their topics were associated with diseases. The results showed that more diseases were found in OD-NLP at lower thresholds, indicating that the topics described characteristics of diseases. The superiority remained as much as that of TF-IDF when filtration was changed to DMV.Conclusion The current findings prefer the use of OD-NLP to express characteristics of diseases from Japanese clinical texts and may help in the construction of document summaries and retrieval in clinical settings.

Dataset Information

Medical Application of Hydrogen in Hematological Diseases.

Publications

Medical Application of Hydrogen in Hematological Diseases.

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