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      A meta-analysis of Th1 and Th2 cytokine profiles differentiating tuberculous from malignant pleural effusion

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      1 , 2 , 2 , , 2 ,
      Scientific Reports
      Nature Publishing Group UK
      Lung cancer, Tuberculosis

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          Abstract

          To clarify the predominance of Th1 or Th2 immune responses in malignant and tuberculous pleural effusion (MPE and TPE, respectively), we performed a meta-analysis of previously published results of the levels of Th1/Th2 cytokines associated with these two types of pleural effusion to evaluate the use of Th1/Th2 cytokine profiles in distinguishing TPE from MPE. We searched the PubMed and EMBASE databases for studies indexed from 2000 to March 2021. We included studies that (a) diagnosed TPE and MPE based on culture or pleural tissue biopsy and that (b) compared levels of Th1/Th2 cytokines between TPE and MPE. Pooled data based on a random-effects model or fixed-effects model and standardized mean differences (SMDs) across studies were used to compare TPE and MPE. We also performed Egger’s test to assess publication bias. Of 917 identified studies, a total of 42 studies were selected for the meta-analysis. Compared with MPE subjects, TPE subjects had a significantly higher level of TNF-α [2.22, (1.60–2.84)], an elevated level of IFN-γ [3.30, (2.57–4.40)] in pleural effusion, a situation where the Th1 immune response dominated. Conversely, the levels of interleukin-4 (IL-4) and IL-10 (Th2 cytokines) were higher in the MPE subjects than in the TPE subjects, showing statistically nonsignificant tiny effects [−0.15, (−0.94 to 0.63) and −0.04, (−0.21 to 0.12), respectively]. We confirmed that TPE, a situation in which the Th1 cytokines are predominant. The slight preponderance of Th2 cytokines in MPE, which is not convincing enough to prove.

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          Most cited references63

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          The PRISMA 2020 statement: an updated guideline for reporting systematic reviews

          The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.
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            Estimating the mean and variance from the median, range, and the size of a sample

            Background Usually the researchers performing meta-analysis of continuous outcomes from clinical trials need their mean value and the variance (or standard deviation) in order to pool data. However, sometimes the published reports of clinical trials only report the median, range and the size of the trial. Methods In this article we use simple and elementary inequalities and approximations in order to estimate the mean and the variance for such trials. Our estimation is distribution-free, i.e., it makes no assumption on the distribution of the underlying data. Results We found two simple formulas that estimate the mean using the values of the median (m), low and high end of the range (a and b, respectively), and n (the sample size). Using simulations, we show that median can be used to estimate mean when the sample size is larger than 25. For smaller samples our new formula, devised in this paper, should be used. We also estimated the variance of an unknown sample using the median, low and high end of the range, and the sample size. Our estimate is performing as the best estimate in our simulations for very small samples (n ≤ 15). For moderately sized samples (15 70), the formula range/6 gives the best estimator for the standard deviation (variance). We also include an illustrative example of the potential value of our method using reports from the Cochrane review on the role of erythropoietin in anemia due to malignancy. Conclusion Using these formulas, we hope to help meta-analysts use clinical trials in their analysis even when not all of the information is available and/or reported.
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              CD4(+) T cells regulate pulmonary metastasis of mammary carcinomas by enhancing protumor properties of macrophages.

              During breast cancer development, increased presence of leukocytes in neoplastic stroma parallels disease progression; however, the functional significance of leukocytes in regulating protumor versus antitumor immunity in the breast remains poorly understood. Utilizing the MMTV-PyMT model of mammary carcinogenesis, we demonstrate that IL-4-expressing CD4(+) T lymphocytes indirectly promote invasion and subsequent metastasis of mammary adenocarcinomas by directly regulating the phenotype and effector function of tumor-associated CD11b(+)Gr1(-)F4/80(+) macrophages that in turn enhance metastasis through activation of epidermal growth factor receptor signaling in malignant mammary epithelial cells. Together, these data indicate that antitumor acquired immune programs can be usurped in protumor microenvironments and instead promote malignancy by engaging cellular components of the innate immune system functionally involved in regulating epithelial cell behavior.
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                Author and article information

                Contributors
                zhouhai339339@163.com
                qiyu1987xiaobao@163.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                17 February 2022
                17 February 2022
                2022
                : 12
                : 2743
                Affiliations
                [1 ]GRID grid.265073.5, ISNI 0000 0001 1014 9130, Department of Anesthesiology, Graduate School of Medical and Dental Sciences, , Tokyo Medical and Dental University, ; Tokyo, Japan
                [2 ]GRID grid.452207.6, ISNI 0000 0004 1758 0558, Department of Anesthesiology, , Xuzhou Central Hospital, ; 199 Jiefang South Road, Xuzhou, 221009 China
                Article
                6685
                10.1038/s41598-022-06685-8
                8854582
                35177742
                ec3f46c8-d49b-43dc-ae59-0c788c21a6d8
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 25 October 2021
                : 3 February 2022
                Funding
                Funded by: The Science and Technology Planning Project of Xuzhou
                Award ID: No. KC21251
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                lung cancer,tuberculosis
                Uncategorized
                lung cancer, tuberculosis

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