Black seed oil guide (the oil is pretty safe and sometimes works)

Black seed oil comes from the Nigella Sativa plant’s seeds. Long haulers should definitely consider trying this because it is pretty safe (sold in some supermarkets), works for some people, and doesn’t cost much.

A similar intervention, Nigella Sativa seeds (not the oil but the seed), is currently a first-line therapy in the FLCCC protocol for vaccine injury.

Mechanism of action

How it works is unclear at the moment.

  1. Thymoquinone in black seed oil binds to the S1 spike protein, perhaps causing the body to react less to it.
  2. Thymoquinone, carvacrol, and other chemicals in the oil have antimicrobial properties. Carvacrol is effective against microbial biofilms and works better against them than many prescription antibiotics. See the wiki page on multiple persistent infections for links to scientific studies (e.g. papers from Zhang’s lab).


39% saw an improvement.

7.0% reported negative effects. 1 person vomited and answered “symptoms stayed the same” so their response did not show up as slight worsening.


The second React19 survey (on persistent symptoms) asked participants which drugs helped them. 2.4% said that black seed oil (either seed or oil) was one of the drugs that helped them.


The Maju brand’s packaging recommends 4 teaspoons a day, which is roughly 20mL a day. Another brand (Amazing Herbs) suggests starting with a half-teaspoon a day until the taste improves.

The FLCCC protocol calls for 0.2-0.5 grams of seed twice a day, which could add up to 1g/day. One teaspoon/day of the oil (which is likely more potent than the seed) is roughly 5g/day.

There isn’t good information right now regarding dosing. In theory, higher doses may be needed to fight biofilm infections from bacteria and fungi.

Where to buy

It is sometimes sold in ethnic supermarkets for Iranians, Pakistanis, and Indians.

Some online retailers such as iHerb sell a larger supermarket-sized bottle. If your plan is to consume 20mL/day, then a 250mL bottle will last 12 and a half days. Pure Indian Foods, Organic Cold Pressed Virgin Black Seed Oil, 250 ml


It should be noted that thymoquinone (the active ingredient of Nigella Sativa) decreases the absorption of cyclosporine and phenytoin. Patients taking these drugs should, therefore, avoid taking Nigella Sativa. Furthermore, two cases of serotonin syndrome have been reported in patients taking Nigella Sativa who underwent general anaesthesia (probable interaction with opiates).

You can skim through the 1-star reviews on Amazon, iHerb, etc. to see what can go wrong. (The 5-star reviews often say that black seed oil cures everything.)

The supplement Quadramune with ingredients Pterostilbene, Green Tea Extract, Sulforaphane and Thymoquinone (extracted from black seed oil) will likely be even more effective than Black Seed oil supplements, per the mechanism explained in this reddit comment. In addition to blocking the central profibrotic mediators of endothelial injury TGF-β and PDGF, Quadramune also blocks serotonin, the other central profibrotic mediator, via suppressing indolamine 2,3 deoxygenase (IDO), increased levels of which in SLE patients were found linked to increased levels of bioactive serotonin in plasma, per this study.

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On the basis of the wonderful work of React19 and @GlennChan, I have tried black seed oil.

Whilst I can’t say it definitely worked, because I’ve started taking it alongside a few other changes, I have definitely improved at this point.

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It may have synergistic effect with other drugs. Research from Zhang’s lab (as well as Anna Goc’s lab) shows that most antimicrobials have synergistic effects with other antimicrobials.

Megan Weitner’s 2016 thesis provides good evidence of this:’s_2016_thesis

So there could be something to the combination that you’re taking. It may take a lot more time to figure all this stuff out though!

Thanks for that - I’m the guy who did the data analysis on the latest React19 survey, so you’re talking to somebody involved in React19 research.

I’m skeptical about the research coming out from Pretorius et al. and Patterson et al.

  • Patterson et al.: The latest paper suggests that the cytokine panel can differentiate between post vax, long COVID, Lyme, etc. One potential issue with the paper is that there are FEWER long COVID patients than the machine learning paper. There should be more because more people have done the test. Also, I’d be willing to bet that neither Patterson or Yogendra understand the paper. I certainly don’t because I am not familiar with Gini trees.
  • Pretorius, Kell, Laubscher et al. - see Etiology - Long Haul Wiki
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Thanks for your response. I read Patterson et al’s latest paper titled Cytokine Hub Classication of PASC, ME-CFS and other PASC-like Conditions which describes their decision tree to differentiate between PASC, post-vax, Lyme, ME-CSF conditions based on cytokines measured via IncellDx’s 14-plex bead based flow cytometric assay on fresh plasma (as previously disclosed in the study titled “Immune-Based Prediction of COVID-19 Severity and Chronicity Decoded Using Machine Learning”, doi: 10.3389/fimmu.2021.700782) and specifically looked at the decision gini tree shown in Supplementary Figure 2. The reddit post that I cited in my previous comment cited Patterson et al’s other recent paper titled “Targeting the Monocytic-Endothelial-Platelet Axis with Maraviroc and Pravastatin as a Therapeutic Option to Treat Long COVID/ Post-Acute Sequelae of COVID (PASC)”, doi: 10.21203/ The decision gini tree shows that the decision between the 2 separate branches from the root where classification as PASC or post-vax is located is made based on the levels of the GM-CSF cytokine, as measured via the above assay. However, the study titled Generation of a novel CD30+ B cell subset producing GM‐CSF and its possible link to the pathogenesis of systemic sclerosis showed that in autoimmune diseases like SSc GM-CSF is expressed by B cells and can be measured by Quantikine ELISA tests on isolated B cells from PBMC, which is not same as the IncellDx cytokine panel that is done on fresh plasma. So, GM-CSF level measurement via IncellDx test may not be the appropriate test to determine SSc like autoimmune disorder pathology. Instead, measuring plasma levels of IL-4 which induces GM-CSF production in B cells may be a better indicative marker and those levels are high in post-vax long haulers per the decision gini tree. That would align with the recent REACT19 patient-led survey of people experiencing persistent neurological symptoms after COVID vaccination, which found the symptoms consistent with those seen in autoimmune disorders.

As to Etheresia Pretorius et al’s paper the main concerns raised were about the anti-clotting treatment protocol, but the measurements in those study that showed platelet hyperactivation have not been disputed. That platelet activation aligns with autoimmune disorder etiology and that would also explain why apheresis helps. That is why targeting the central profibrotic mediators TGF-β, serotonin and PDGF, which cause endothelial injury that leads to platelet activation will also help and explains why Black Seed oil is helpful per the mechanism that I explained in my previous comment. That is why Quadramune whose other 3 ingredients, in addition to black seed oil are also potent inhibitors of TGF-β, serotonin and PDGF will likely be even more effective.

I believe additional tests should be done for TGF-β, serotonin and PDGF levels in plasma and if found in high levels it will confirm the SSc like autoimmune disorder etiology in post-vax long haulers and long COVID patients.

Actually, I meant that the decision to reach the branch of the tree from the root, where classification as PASC or post-vax is located is made based on low levels of the GM-CSF cytokine being detected in plasma, as measured via the above assay.

@GlennChan I see that the below snippet from page 5 of Patterson et al’s latest paper clarifies that they indeed observed elevated GM-CSF levels in the plasma of post-vax injured, PASC and Lyme patients as they follow a common path on the decision tree with respect to GM-CSF levels:

The PASC classication highlights the importance of the proinammatory cytokines IL-2 and IFN-γ as we have previously reported2, while in PTLD disease, two classication profiles were identied. Interestingly, both profiles follow a common path including the proinflammatory cytokines GM-CSF and IL-2 in concordance with IL-2 mediated GM-CSF production previously reported9.

The decision is likely based on comparison ratio between mean GM-CSF values observed in controls and that in patients, like those observed in ME/CFS patients shown in table 3 from the study titled “Cytokine signature associated with disease severity in chronic fatigue syndrome patients” (doi: 10.1073/pnas.1710519114), which essentially implies that the GM-CSF levels observed in post-vax injured, PASC and Lyme patients is higher than that observed in ME/CFS patients, both being elevated compared to controls (hence cytokine level ratio in controls compared to that in patients is less than 1). It would make sense that all the cytokine level checks in the decision tree are for mean cytokine level ratios in controls compared to patients, since lower the ratio value and more the number of cytokines with lower ratio values more severe is the classification of the disease per the decision tree. This interpretation would imply that the cytokine levels measured in post-vax injured patients match those observed in patients with autoimmune disorders like SSc. I wish Patterson et al had clearly specified in their paper what the units of the values being compared against in the decision tree were, to remove any ambiguity in interpretation.

I’m going to be honest with you… I don’t understand the paper (because I’m not familiar with Gini trees and their statistical analysis method). And I’m pretty sure that Patterson and Yogendra don’t understand their own paper.

The writing style is a little weird… like they want to put in spurious details (e.g. use of Pandas library) without explaining what it is that they did. I don’t think the writer wants the audience to understand what they did. Especially when they fail to explain why there are fewer long COVID patients despite IncellDX having access to more long COVID data.

Unfortunately I don’t have the time to read up on Gini trees.