PubMed

The mortality effect reported in this study can be explained by time-dependent bias. Out of 1,859 included patients, 416 (25%) of 1,676 treated with NAI died compared to 76 (42%) of 183 of patients untreated. This provides a crude relative risk of 0.60 for NAI (40% reduction in mortality). However this crude analysis fails to take account of the time-dependent nature of NAI exposure. Another naïve way to compare groups is based on rate of events per person day in hospital. The median length of stay for NAI patients was 10 days compared to 6 days for untreated patients. If we assume mean length of stay is similar we have 1,676 x 10 = 16,760 person days in hospital at risk of death for patients treated with NAI, and event rate of 416 / 16,760 = 0.025 deaths per person day. In comparison we have 183 x 6 = 1,098 person days in hospital for untreated patients with event rate 76 / 1,098 = 0.069 thus mortality rate ratio = 0.025 / 0.069 = 0.36, an even larger protective effect of NAI on mortality (64%). However we need to take into account that patients received NAI hours or even days after hospital admission. The delay was a median of 1 day after admission as time from onset of symptoms to admission was a median of 3 days whereas time from onset to treatment was a median of 4 days. If we assume mean delay is also 1 day we need to subtract 16760 days from the person-days estimate of the NAI treatment group and add it to the person-days estimate of the untreated group. This now more accurately reflects the total number of person days at risk of death while untreated as well as total number of person days at risk of death after treatment with NAI. It also makes our estimates of deaths per person day: 416 / 15,084 = 0.028 after NAI treatment compared to 76 / 2,774 = 0.027 while untreated, with rate ratio 1.0, i.e. no difference between groups. Of course this is an approximate analysis and a better analysis would involve including NAI treatment as a time-dependent exposure in a hazards based survival model. It is concerning that the authors did not conduct an appropriate analysis and also that the journal review did not pick up this fatal flaw.

Acknowledgements

We thank Dr. Daniel Seabra and Dr. Eliney Ferreira Faria for their collaboration in collecting the samples and Dr. Ligia Maria Kerr for pathological review. This work was supported by São Paulo State Research Foundation - FAPESP (2010/01661-2) and CNPq.

One point worth making may be that the studies reported here were presumably done under pathogen-free conditions, and in the absence of predators or any other environmental stresses. "Normal" hemaopoiesis in the real world may therefore have to call on blood production from the traditional 'transplantation HSCs' more often than is suggested here.

The results of this study confirm those of an earlier study by Pheby et al. in 2009. That study also noted two onset peaks, one from ages 11-20 and another from 31-40.

Pheby's article can be accessed online here: http://www.iacfsme.org/BULLETINWINTER200910/Winter0910PhebySneddonHeinrichCHROMEReport/tabid/413/Default.aspx

This is a nice paper. The abstract refers to using 24 epitope tags (24mer), much of the paper uses a 10mer. Just doing GFP is boring. When I came up with the "Tattletales" (TALE-FPn ... I came up with the idea before sgRNA:Cas9 became popular), I immediately realized that multimerizing FP biosensors. The current paper is the same as my what I refer to as "Binary Tattletales", as in: 1. TALE-(linker-epitope tag)n 2. "binder"-(linker-FP)m with Tattletales being T-cells -- TALE FPs/Biosensors. Since I moved to MD Anderson Cancer Center, the first T now refers to "T-cells and Tumor cells". Likewise T-bow refers to rainbow T-cells and Tumor cells for promoter bashing and otherwise multicolor dots labeling cells (rainbow in homage of course to Brainbow mice etc, and especially to real rainbows). For more on Tattletales, Binary Tattletales, and T-Bow, see http://works.bepress.com/gmcnamara/63 http://works.bepress.com/gmcnamara/42

Giving credit where credit is due: The authors really should have cited the first mammalian cell paper localizing a lot of FPs in one spot (they came 'close' with a Gordon 1997 Cell paper on GFP:LacO in E.coli, but the Tanenbaum paper is all mammalian cells): Robinett et al 1996 JCB http://www.ncbi.nlm.nih.gov/pubmed/8991083 http://jcb.rupress.org/content/135/6/1685.long See their figure 4A. Straight, Robinett et al also published a yeast paper in 1996, http://www.ncbi.nlm.nih.gov/pubmed/8994824 and it would have been useful to cite that.

The PDF download at http://works.bepress.com/gmcnamara/63 has a table of 130 FP biosensors (if you are Laconic about ATeam and Fire, too bad) and an extensive reference list with ZF-FP, TALE-FP, Cas9-FP (the latter from the Weissman group), and more (PUF's and PPR's are RNA binding protein families with structural similarities to TALEs). My favorite name -- besides Tattletales and T-Bow, of course -- is "TALE-Lights" from Yuan, Shermoen, O'Farrell 2014, http://www.ncbi.nlm.nih.gov/pubmed/24556431

Potentially a very important finding. However, it should be read with caution. Table 2 indicates a range of 6-8 for VAS. This is problematic in four ways: (a) VAS is not listed among the measures, which were a faces scale and a numerical rating scale. (b) Perhaps what is meant by "VAS" is actually a combination of scores on the faces scale and the numerical rating scale. But these different scales cannot be combined as if they were the same thing. They should be reported separately unless a rationale is provided for treating them as the same. (c) The idea that ALL 82 subjects with a chronic illness used only the scores 6, 7 or 8 (out of the 11 scores available on the self-report scale) is not plausible: this would represent an extraordinarily unlikely restriction of range on the 0-10 metric, or else there was some bias in the way the question was asked. (d) If all scores were 6, 7 or 8, then the mean could not have been greater than 8 as reported in the abstract. I'd invite the authors to check this line of Table 2 before the paper is finalized. Thank you.

It is not clear that the FAD assay used here to normalize SQR and SDH activity is applicable for covalently bound FAD, such as that of SDH1p. Unless I badly misunderstand the assay, all of SDH1p flavin will go down in the perchlorate precipitation step, and they are actually measuring noncovalent FAD released by other flavoproteins (and FAD present soluble in the mitochondrial matrix). This could account for the dramatic activation upon incorporation into nanodisks and purifying by IMAC, since a lot of those proteins would not incorporate into discs. This would not invalidate the main conclusions of the paper, which are clear from the fluorescent gels, but the quantitative turnover numbers for Complex II should be taken with a grain of salt until this is cleared up

associated datasets available via PRIDE/ProteomeXchange: http://www.ebi.ac.uk/pride/archive/simpleSearch?q=PXD001042+PXD001390&submit=Search

This is an excellent review. However, it seems to have overlooked the paper "Neonatal mortality, the male disadvantage" by Naeye RL, Burt LS, Wright DL, Blanc WA, Tatter D, in Pediatrics. 1971 Dec;48(6):902-6, PMID: 5129451, that concluded that the male excess in infant mortality "must" be X-linked. Naeye et al.'s conclusion that an X-linkage is involved has been supported by work following this 1983 paper as shown in the following:

Is excess male infant mortality from sudden infant death syndrome and other respiratory diseases X-linked? Mage DT, Donner EM. Acta Paediatr. 2014 Feb;103(2):188-93. doi: 10.1111/apa.12482. Epub 2013 Dec 20. PMID: 24164639

The X-linkage hypotheses for SIDS and the male excess in infant mortality. Mage DT, Donner M. Med Hypotheses. 2004;62(4):564-7. PMID: 15050108

The fifty percent male excess of infant respiratory mortality. Mage DT, Donner EM. Acta Paediatr. 2004 Sep;93(9):1210-5. PMID: 15384886

A genetic basis for the sudden infant death syndrome sex ratio. Mage DT, Donner M. Med Hypotheses. 1997 Feb;48(2):137-42.PMID: 9076695

Female resistance to hypoxia: does it explain the sex difference in mortality rates? Mage DT, Donner M. J Womens Health (Larchmt). 2006 Jul-Aug;15(6):786-94. Review. PMID: 16910910

The authors have done an outstanding and excellent study. However, they seem to have failed to notice here that, in Table 2, their reported male fraction of SIDS (0.612) in 260 cases (excluding matched set)is identical to the male fraction of SIDS reported by Mage and Donner (PMID 9076695) of 41,238 male and 26,140 female in 36 SIDS data sets giving a male fraction of 0.6120. This is important to note because the consistancy of the male fraction of SIDS throughout time and among various countries supports a recessive X-linkage of susceptibility to SIDS that seems to be ignored inspite of the early conclusion of Naeye et al. in PMID 5129451, that male susceptibility to infant death "must" be X-linked.

It is interesting to remind that pioglitazone the drug use in the experiments is associated with an increased risk of bladder cancer (http://onlinelibrary.wiley.com/doi/10.1111/bcp.12306/abstract;jsessionid=0BF19A6B1135280E0C6DAAB5F9A2BF21.f02t03) and was withdrawn in France for this reason. It could be very interesting that the authors discuss this strange fact. Why imagine to use a drug known to cause be associated with an increase risk of cancer to treat cancer?

For clarification of this abstract, it may be worth noting that K-Lysine acetyltransferase 2a (Kat2a) is more widely known by its former name General control of amino-acid synthesis 5 (Gcn5) or General control of amino-acid synthesis 5 like 2 (Gcn5l2). http://www.ncbi.nlm.nih.gov/gene/14534

I am very happy to see more published information about this topic. I have been speaking professionally abut this topic for 10 years and it is becoming increasingly recognized as to it's importance in the management of MS patients. This article confirms the often unrecognized frequency of this issue. When MS patients say they "are tired all the time," we need to think about the details. Physical fatigue, cognitive fatigue, psychological fatigue (depression) and sleepiness may all be part of this complaint.

Your spatial-temporal correlation is especially suggestive for the pathophysiology expected in multiple neuropsychiatric diagnostic categories. Further work in animal models on measures of action potential speed/velocity are expected to reveal abnormalities in such neuropsychiatric categories. Best wishes, Joe Ray Newton

Readers should note that the whole genome-profiling in this study used outbred mice, with a sample size of n=4 per group. In my opinion, this is a very serious limitation, as it will result in the identification of many differences between groups that are simply due to the underlying genetic variation of the mice being tested, and are unrelated to nutrition status.

The authors cite our paper (Buehler E, 2012) as evidence that “…pools of siRNA targeting different mRNAs, such as those used in libraries, results in increased off-target effects”. Unfortunately, that sentence is not supported by our paper. The method demonstrated in our paper was applied to an arrayed screen of individual siRNAs in which there were no pools, and no conclusions about pooling of siRNAs can be reached from that manuscript. Furthermore, the authors state that “The use of this siRNA pool allows avoiding too high concentration of each single siRNA and thus prevents off-target effects” and cite Jackson AL, 2010 to support this. Again, this assertion is not supported by the cited reference. Jackson AL, 2010 states that “Pooling of multiple siRNAs to the same target may help to reduce off-target silencing, due to competition among the siRNAs in the pool” (emphasis added). No evidence exists to support the claim that pools 3 or 4 siRNAs will prevent off-target effects. To the contrary, we have demonstrated previously that pooling siRNAs does not generate more reproducible phenotypes than single siRNAs (see Marine S, 2012, Figure 1, B and C) and that pools of siRNAs can and do generate many off-target phenotypes, which can then be confirmed by deconvolution of those pools (see Marine S, 2012, Figure 2). Unfortunately, the authors base most of their conclusions on experiments using a single reagent (a pool of four different siRNAs targeting CDT2) and a single non-silencing control. This means that many or all of the phenotypes they observed experimentally may be due to siRNA off-target effects (see Chung HY, 2014 for a case study in how off-target effects can result in false positives). This is not to say that their conclusions are incorrect, only that I believe that RNAi experiments require multiple independently tested reagents and/or matched controls to guard against the false positives due to off-target effects that are so frequent in these experiments.

This study strongly endorses the hypothesis that counteracting neurohormonal hyperactivation represents a cornerstone for the treatment of heart failure. Remarkably, the Authors did not provide any data on the smoking status of the patients nor on catecholamine levels, which I believe would contribute compelling pathophysiological insights about the modulation of autonomic nervous system by neprilysin/angiotensin receptor inhibition. Indeed, cigarette smoking has been shown to increase catecholamine release, generating important effects on cardiovascular regulation essentially via sympathetic nervous system activation (1). Supporting this view, when examining the diabetic population (one third of the enrolled patients) no significant difference was found in the specific outcome ‘death from cardiovascular causes’. This finding could be at least in part ascribable to the autonomic neuropathy and neurovascular dysfunction described in diabetes mellitus (2). In this sense, an analysis that takes into account the duration of diabetes and therapy [i.e. insulin vs oral hypoglycemics (3)] in this population would also be interesting to the Reader.

References 1. Grassi G, Seravalle G, Calhoun DA, et al. Mechanisms responsible for sympathetic activation by cigarette smoking in humans. Circulation 1994;90:248-53. 2. Vinik AI, Maser RE, Mitchell BD, Freeman R. Diabetic autonomic neuropathy. Diabetes care 2003;26:1553-79. 3. Sardu C, Marfella R, Santulli G. Impact of diabetes mellitus on the clinical response to cardiac resynchronization therapy in elderly people. J Cardiovasc Transl Res. 2014 Apr;7(3):362-8.

Gaetano Santulli, MD, PhD Columbia University Medical Center, New York, NY, USA

To Whom It May Concern:

We have reviewed the concerns regarding some of the images presented in our study brought up by Paul Brookes on PubMed and Derek Lowe on his blog. In the wake of numerous controversial studies in the stem cell field, we appreciate the scrutiny of our results as skepticism plays a critical role in the progression of scientific research.

We have addressed all of the concerns raised on the internet regarding duplication of images and cropping of western blots and detail these responses separately.

First, regarding the similar radial contrast patterns in Figure 1C and 2D when black images are overexposed, we now provide high magnification versions of these images showing that while the radial contrast is similar between images (as would be expected when taking images from an essentially blank field on the same microscope with the same detector), there are clear differences observable upon higher magnification, demonstrating that these blank images were acquired independently.

Also, we have now provided all of the original western blots to the Journal that can be published in an erratum if the Journal deems this necessary. There were no sign of splicing or cropping of these original western data in Figure 4C, and we cut out the unnecessary part of images for the display on the limited area of paper in Figure 4F.

Finally, with regard to the image duplication of the brightfield micrograph of proliferating fibroblasts in Figure 2b: Yes, these are the same image, because this is the Time 0 timepoint of the experiment, prior to induction of EMF and factor infection. This began as a single culture at Time 0, after which half of the culture was exposed to EMF and the other half (control) was not (all in the presence of the Yamanaka factors). It is unclear to us why this is a concern for Paul Brookes, as he asserts this image was used to ‘represent different conditions’, however at Time 0, prior to the induction of EMF, these are in fact the same condition.

We are more than happy to provide all original images and discussion regarding these points in an erratum to the manuscript if the Journal deems it to be necessary.

On another note, we would like to point out significant errors made by Paul Brookes and Derek Lowe in their respective blogs in their interpretation of our study. Brookes states “in the wake of the STAP stem cell controversy, a paper claiming that iPSCs can be made using magnetic fields deserves special scrutiny”, and Lowe states “Yep, this paper says that stem cells can be produced from ordinary somatic cells by exposure to electromagnetic fields”. To us this suggests that neither Brookes nor Lowe actually read our manuscript, and rather just began altering contrast of images in the paper to find what they perceive as discrepancies.

Nowhere in our manuscript do we claim “iPSCs can be made using magnetic fields”. This would be highly suspect indeed. Rather, we demonstrate that in the context of highly reproducible and well-established reprogramming to pluripotency with the Yamanaka factors (Oct4, Sox2, Klf4, and cMyc/or Oct4 alone), EMF influences the efficiency of this process. Such a result is, to us, not surprising given that EMF has long been noted to have effects on biological system(Adey 1993, Del Vecchio et al. 2009, Juutilainen 2005)(There are a thousand of papers for biological effects of EMF on Pubmed) and given that numerous other environmental parameters are well-known to influence reprogramming by the Yamanaka factors, including Oxygen tension (Yoshida et al. 2009), the presence of Vitamin C (Esteban et al. 2010), among countless other examples.

For individuals such as Brookes and Lowe to immediately discount the validity of the findings without actually attempting to reproduce the central experimental finding is not only non-scientific, but borders on slanderous. We suggest that these individuals take their skepticism to the laboratory bench so that something productive can result from the time they invest prior to their criticizing the work of others.

“Often those that criticize others reveal what he himself lacks.” ― Shannon L. Alder

http://i.imgur.com/XgKBX47.jpg?1

http://i.imgur.com/gvZdZAQ.jpg

http://i.imgur.com/ljqPnxU.jpg

http://i.imgur.com/dEB1aoX.jpg

JP Kim

JONGPIL KIM, Ph.D. Assistant Professor Dept of Biomedical Engineering Dongguk University Seoul, Korea Tel: 82-02-2260-3371 Fax: 82-02-2260-8726 Email: jpkim153@dongguk.edu, jk2316@gmail.com

The abstract uses 2 nearly identical rs#s, one of which is an error. rs1059111 appears to be correct, rs105911 appears to be a typo