- Analysis of Women’s Health Online News Articles Using Topic Modeling
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Kyoung Won Cho, Shine Young Kim, Young Woon Woo
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Osong Public Health Res Perspect. 2019;10(3):158-169. Published online June 30, 2019
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DOI: https://doi.org/10.24171/j.phrp.2019.10.3.07
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Abstract
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Objectives
This research aimed to understand the popularity of topics in the field of women’s health through analysis of online news articles which were chronologically classified and examined to determine how women’s health and diseases had changed over time.
Methods
Women’s health and disease news articles were collated from a popular news website between 1993 to 2015 and preprocessed using gynecological medical terminology, Korean words and nouns (excluding general nouns not related to women’s healthcare topics). The resultant articles (N = 7,710) were analyzed using the Latent Dirichlet Allocation algorithm and major topics were extracted. Topic trends were analyzed by year and period for women’s health.
Results
It was observed that most of the women’s health articles were focused on “Healthcare”, and 9 other topics were identified that represented a relatively small proportion in 1993–2000. In 2001–2005, most of the articles were focused on “Medical Services” and “Dietary Supplements” with some specific topics that peaked people’s interest, as compared to those focused on “Healthcare” in the 1990s. It was also observed that differences in the proportion of each topic was small after 2011.
Conclusion
Changes in topics related to women’s disease were not clearly distinguished in the 1990s but this changed from 2001where articles related to “women disease” appeared as articles on the topics of various diseases.
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- Review on News About Midwifery and Fertility Covered on Newspapers in Turkey
Sümeyye ALTIPARMAK, Emel GÜÇLÜ CİHAN, Hatice Gül ÖZTAŞ, Hülya KAMALAK Medical Records.2021; 3(2): 118. CrossRef
- Instability of Plasma and Serum Progastrin-Releasing Peptide During Repeated Freezing and Thawing
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Jae-Eun Lee, Jin-Hyun Lee, Maria Hong, Seul-Ki Park, Ji-In Yu, So-Youn Shin, Shine Young Kim
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Osong Public Health Res Perspect. 2016;7(6):351-355. Published online December 31, 2016
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DOI: https://doi.org/10.1016/j.phrp.2016.11.004
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2,882
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Progastrin-releasing peptide (proGRP) is a promising biomarker for small cell lung cancer. However, not much is known about how sample processing and storage conditions affect the stability of proGRP. Here, we examined the effects of repeated freeze–thaw cycles on the stability of proGRP in plasma and serum. Methods
Concentrations of proGRP were measured in plasma and serum samples exposed to two, three, or four freeze–thaw cycles and these were compared with values of corresponding samples exposed to one cycle (baseline). We also performed the area under the receiver-operating-characteristic curve (AUC) analysis to determine whether the differences of proGRP concentrations between each paired plasma and serum sample (ΔproGRP) can be used for identifying the samples that have been exposed to multiple freeze–thaw cycles. Results
Concentrations of proGRP gradually decreased in both plasma and serum samples with increasing numbers of freeze–thaw cycles. Reduction rates of proGRP concentrations were greater in serum than in plasma samples and serum proGRP levels declined with statistical significance (p < 0.001) up to 10.1% after four freeze–thaw cycles. The ΔproGRP measurement showed fair accuracy (AUC = 0.741) for identifying samples that had been through four freeze–thaw cycles. The sensitivity was 82.8% and specificity was 62.1% at an optimal cut-off point of > 4.9. Conclusion
Our study shows that the stability of circulating proGRP is affected in both plasma and serum samples by repeated freezing and thawing. We also show that ΔproGRP could be used for identifying paired plasma and serum samples subjected to multiple freeze–thaw cycles.
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- Effect of Repeated Freeze–Thaw Cycles on Influenza Virus Antibodies
Alessandro Torelli, Elena Gianchecchi, Martina Monti, Pietro Piu, Irene Barneschi, Carolina Bonifazi, Rosa Coluccio, Luisa Ganfini, Luciano Michele La Magra, Silvia Marconi, Ginevra Marzucchi, Ramona Pace, Laura Palladino, Bernardo Biagi, Emanuele Montomo Vaccines.2021; 9(3): 267. CrossRef - The influence of different blood samples treatment methods on pro-gastrin-releasing peptide
Huiqin Jiang, Ling Luo, Kang Xiong, Chengwen He, Huaizhou Wang, Yanghua Qin Medicine.2019; 98(26): e16130. CrossRef
- Effect of Repeated Freezing and Thawing on Biomarker Stability in Plasma and Serum Samples
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Jae-Eun Lee, Shine Young Kim, So-Youn Shin
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Osong Public Health Res Perspect. 2015;6(6):357-362. Published online December 31, 2015
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DOI: https://doi.org/10.1016/j.phrp.2015.11.005
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2,761
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The stability of circulating proteins can be affected by repeated freezing and thawing. The aim of our study was to identify the effect of repeated freezing and thawing on the plasma and serum concentrations of eight proteins [interferon-γ, interleukin (IL)-8, IL-15, IL-17A, matrix metalloproteinase (MMP)-7, tumor necrosis factor-α, vascular endothelial growth factor (VEGF), and VEGF receptor 2 (VEGF-R2)]. Methods
We assessed the concentration changes of these proteins in 30 plasma and serum samples subjected to three, four, or five freeze–thaw cycles, and compared these with the concentration changes in the samples that were subjected to two freeze–thaw cycles before analysis. Results
Repeated freezing and thawing by up to five cycles did not modify the plasma and serum concentrations of interferon-γ, IL-8, and VEGF-R2, while levels of MMP-7, tumor necrosis factor-α, and VEGF were significantly changed in both plasma and serum samples. Moreover, MMP-7 and VEGF concentrations tended to increase with freeze–thaw cycles. They were more elevated in plasma samples (up to about 15%) than in serum samples (up to about 7%), suggesting that serum is the preferred sample type for the analysis of circulating proteins. Conclusion
This is the first report on the effect of repeated freezing and thawing on plasma concentrations of MMP-7 and VEGF-R2. Our findings propose that researchers should consider the number of freeze–thaw cycles to select plasma or serum samples, depending on the type of analyte.
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