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I came across two different papers claiming the opposite thing.

Although there was a dose dependent increase in serum TNF-α levels in the CSHE treated groups as compared to control, the synovial expression of macrophage derived pro-inflammatory cytokines/cytokine receptor was found to be lower in the CSHE treated groups as compared to control.

And this

The TNF α levels were statistically reduced by CSEO group compared to Indomethacin group.

So does Corriander Seed increase or decrease tnf alpha and il-6?

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  • @ManuelMilla This question has absolutely nothing to do with covid-19, not sure why you edited to add that tag. Dec 8 '20 at 18:28
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Both of these studies involve a rodent inflammation model where complete freund's adjuvant is injected to incite inflammation, and then rats are given an alcohol-based coriander seed extract or other injections.

These are pretty low-quality papers in low-quality journals. I'll start with the one you link second:


Deepa, B., Acharya, S., & Holla, R. (2020). Evaluation of antiarthritic activity of Coriander seed essential oil in Wistar albino rats. Research Journal of Pharmacy and Technology, 13(2), 761-766.

This study, which declares "The TNF α levels were statistically reduced by CSEO group compared to Indomethacin group." makes this declaration based on "p<0.05" where the p-value they actually report is 0.055. 0.055 is not less than 0.05, and based on their statistical threshold they should not have rejected the null hypothesis of no effect.

Importantly, they also completely failed their Stats 101 class and instead of comparing the interaction between Time (i.e., baseline vs 21 days) and Treatment, they just compare Baseline to Day 21 in individual groups. This is wrong, and not meaningful. At worst, they should have compared Day 21 among treatment groups, but they did not do this. Their statistical tests do not measure what they say they do.

You can throw this study in the bin, it's worthless as presented, and it's embarrassing to the journal, the researchers, and their institution that it was published.


Nair, V., Singh, S., & Gupta, Y. K. (2012). Evaluation of disease modifying activity of Coriandrum sativum in experimental models. The Indian journal of medical research, 135(2), 240.

The other paper is not quite as bad, but still lacking in statistical methodology. They don't really provide enough information to critique their analysis, which to me is sufficient to consider it junk. You don't make your case better by leaving out details that can be used to critique, we have to assume that those details are hiding something when they are missing. They present standard errors that are miniscule compared to what one would expect in these sorts of experiments, so I have to assume that they may have included pseudoreplication by testing multiple samples from the same animals and not accounting for these multiple comparisons in the statistical analysis.

They've also used a vehicle (control) that is sufficiently different from their treatment that it is just as likely that the non-active ingredients cause the effects they see.

Their IL-6 results are purely qualitative, and even the qualitative approach they use is seriously lacking. Basically "look at these example pictures and trust us".


In summary, these studies do not actually conflict because they do not actually show anything robust - on this they are actually quite in agreement.

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  • Hey thanks for the amazing and detailed answer man, sorry I'm a noob and had to make you go through this. Can you just clarofy one point here you said, "instead of comparing the interaction between Time (i.e., baseline vs 21 days) and Treatment, they just compare Baseline to Day 21 in individual groups" I couldn't understand the difference bw these two. Aren't they comparing baseline levels with day 21 levels. What am I missing? can u please clarify. Dec 8 '20 at 5:18
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    @VARUN.NRAO If we compare A and B and find B is significantly larger than A, and compare C and D and find D is not significantly larger than C, we cannot say any of these things: 1) C and D are the same. "Not significantly different" does not mean "equal", it means the difference we observe between them isn't far larger than expected by chance. 2) B is larger than D. We didn't compare B and D, we only compared them to A and C. Dec 8 '20 at 17:27
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    3) The difference between A and B is larger than the difference between C and D because one is significant and the other is not. This is wrong. You need to actually directly compare the difference between A and B to the difference between C and D. This is equivalent to testing an "interaction" in a statistical model. The reasoning is similar to the reasoning for (1). There are lots of situations where you find a smaller difference to be significant when a larger difference in another comparison is not, such as if you have more variability or fewer samples. Dec 8 '20 at 17:28

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