Hydrogen Sulfide Mitigates Myocardial Infarction via Promotion of Mitochondrial Biogenesis-Dependent M2 Polarization of Macrophages
Authors: Xiaoming Xin
Yi-Zhun Zhu
Matthew Whiteman
Hong Xin
Yaqi Shen
Lei Miao
Philip K Moore
Xiaoyan Shen
Journal: Antioxidants & Redox Signaling (2016)
Discipline: BIOLOGY & BIOCHEMISTRY
Keywords: Macrophages; tissue repair; myocardial infarction; hydrogen sulfide; cardioprotective effects
Affiliations: 1 Department of Pharmacology, School of Pharmacy and Institutes of Biomedical Sciences, Fudan University , Shanghai, China ., 2 University of Exeter Medical School , Exeter, United Kingdom ., 3 Department of Pharmacology, Yoo Loo Lin School of Medicine, National University of Singapore , Singapore, Singapore .

Abstract:  Aims: Macrophages are of key importance for tissue repair after myocardial infarction (MI). Hydrogen sulfide (H2S) has been shown to exert cardioprotective effects in MI. However, the mechanisms by which H2S modulates cardiac remodeling and repair post-MI remain to be clarified. Results: In our current study, we showed that H2S supplementation ameliorated pathological remodeling and dysfunction post-MI in wild-type (WT) and CSE KO mice, resulting in decreased infarct size and mortality, accompanied by an increase in the number of M2-polarized macrophages at the early stage of MI. Strikingly, adoptive transfer of NaHS-treated bone marrow-derived macrophages into WT and CSE KO mice with depleted macrophages also ameliorated MI-induced cardiac functional deterioration. Further mechanistic studies demonstrated that NaHS-induced M2 polarization was achieved by enhanced mitochondrial biogenesis and fatty acid oxidation. Innovation and conclusion: Our study shows (for the first time) that H2S may have the potential as a therapeutic agent for MI via promotion of M2 macrophage polarization. Rebound Track: This work was rejected during standard peer review and rescued by Rebound Peer Review (Antioxid Redox Signal 16:293-296, 2012) with the following serving as open reviewers: Hideo Kimura, Chaoshu Tang, Xiaoli Tian, and Kenneth Olson. Antioxid. Redox Signal. 25, 268-281. 

Retracted: No

PubPeer comments:
 In 2014, Alan Welch presented his work along with Emma Knight to the AIS where they looked into a method of statistics called Magnitude Based Inference (MBI). MBI has never been published in a statistical or mathematical journal but is heavily used by the sports science field. You can view the presentation here https: maths.anu.edu.au files AIS-slides.pdf . After presenting MBI was supposedly discontinued at the AIS after Alan and Emma showed the method to be fundamentally flawed. In 2018 a 538 article was written about MBI uptake within the sports science field. https: fivethirtyeight.com features how-shoddy-statistics-found-a-home-in-sports-research This article notes to further work was done by Kristin Sainani, showing that MBI has numerous issues. She gives reproducible mathematics to show this. https: www.ncbi.nlm.nih.gov pubmed 29683920 https: twitter.com biomechstu status 1229403524695576579 , 
 Magnitude-based inference, or as it is now known, magnitude-based decisions (MBD), does not give  false results . It turns out to be equivalent to several hypothesis tests with well-defined and acceptable Type-2 error rates (failed discovery of substantial effects, 5% for the non-clinical version used in this study), and the Type-1 rates (false discovery of substantial effects) for the non-clinical version are only ever slightly more than 5% and usually a lot less. An article substantiating these assertions first-authored by a mathematics professor and intended for a statistical journal is in preparation. I am also publishing something at Sportscience shortly that will be more accessible to researchers without substantial training in statistics. Meantime, for some recent background, see my posting and other postings each side of it in the datamethods forum: https: discourse.datamethods.org t what-are-credible-priors-and-what-are-skeptical-priors 580 53 It is worth noting one of statistician Sander Greenland s postings to that conversation in the datamethods forum:  Earlier applications of MBI did not necessarily reach incorrect conclusions. Instead when a report that used MBI is seen as supplying data relevant to a topic, that report needs to be scrutinized to see if its conclusions hold up or fail in light of critical insights about what MBI was actually producing (as opposed to the original MBI descriptions). Note that this advice involves study-specific examinations, and thus is a far cry from simply discarding everything that was published using MBI. Being able to follow it in a particular case hinges on how well the report described its design, execution and data, and if the needed summary statistics were given or computable from the report; if not, the report authors would need to supply those items. You will find that we supply compatibility limits and identify smallest important effects for the main outcomes in all our papers using MBI MBD, so it is a simple matter for readers to evaluate for themselves whether precision of the estimates is adequate for a probabilistic conclusion, in the manner that epidemiologist Ken Rothman and psychologist Geoff Cumming evaluate outcomes. Rothman uses 90% confidence compatibility limits in his introductory text on epidemiology three times more frequently than 95% CIs. Cumming s default is 95%. MBI MBD uses 90% CIs for non-clinical effects, and 99% on the harm side and 50% on the benefit side for clinical effects, all for very good reasons. , 
 How long has MBI been out for, as yet there has not been any mathematical formulation in a peer reviewed statistics or mathematics journal. I believe Sander Greenlands comments mean well, however it doesn t represent what is underlying the spreadsheets which Kristin, Alan and Emma have all shown to be misleading at best, serious academic misconduct at worst. All critiques of MBI are done with reproducible simulations and show the underlying formula. No response has. , 
 How long has MBI been out for, as yet there has not been any mathematical formulation in a peer reviewed statistics or mathematics journal. I believe Sander Greenlands comments mean well, however it doesn t represent what is underlying the spreadsheets which Kristin, Alan and Emma have all shown to be misleading at best. All critiques of MBI are done with reproducible simulations and show the underlying formula. No response has. As it stands now, we shouldn t accept any findings until they can be reproduced mathematically. There exists no version control with MBI (spreadsheet). So who knows if the new version to be published is any different to this version and all its issues with again Kristin, Alan and Emma have shown via simulation and mathematics that MBI is fundamentally flawed and misleading ,