That is just baloney! anything more than 5.6 will result in all kind of complications (Kidney failure,retina failure (blindness) amputations& heart attack etc etc
Spoke with my neighbour. A type 2 diabetic on medications and realized even now there is so much ignorance on this subject. I have done extensive research on this subject (was forced 2 after being diagnosed)
Here sharing it with you all. retweet it, it may save someone's life.
That is just baloney! anything more than 5.6 will result in all kind of complications (Kidney failure,retina failure (blindness) amputations& heart attack etc etc
FACT: If you are NOT on any insulin enhancing drugs. you can fast as much as you want. (metformin is OK)
FACT: if you can change your diet, you can change your sugar and insulin levels and no longer need these drugs. (advise not aplicable to type 1 diabetics, they wud always need insulin injection
FACT: Fuck WHO, and don't eat carbohydrates. infact another name of diabetes is carb intolerance. You can never acheive 5.6 or lower while eating your rice and roti.
Fact: Nobody is asking you to go keto (although even that is fine). you can eat your veggies and it has all the carbs you need in a day. Carbs are sugar, its just an addiction. you don't feel complete without it coz u r addicted.
Fact: I don't know any fat eating person who is fat. Dietary fat has nothing 2 do with cholestrol and trygly. in body
The main culprit is seed oil, make ur food in desi ghee and butter
1 big disclaimer here. If you are NOT disciplined and are unable to leave carbs, please continue with your medications otherwise u will have worse of both worlds result. you cannot have it both ways. https://t.co/gYMAO6uFbS
— Manish Dhawan. (@mysticfuture) January 6, 2022
Also follow this youtube channel.
https://t.co/EyGDtZcArQ
More from Health
1/
Remember woman who tuk multiple @SriSriTattva products 4 range of problems frm diabetes 2 gas 2 liver disease & developed liver failure, listed for liver transplant?
Here is original thread:
https://t.co/PXxI1Slyv2
23 samples, Analysis results
#MedTwitter #livertwitter
2/
Before I go into results, I must say this was overwhelming. There was SO MUCH the lab identified, impossible to put everything here. So I made a summary. At the end of this thread, I have linked a full analysis described in Excel format. Some results were VERY concerning
3/
How did we analyse?
Here R links 2 methods
They R high end, done under strict protocols
Frm Ministry of Forest, Environment, Climate / NABL approvd Lab
ICP-OES https://t.co/O1CLhqVQAu
GC MSMS https://t.co/zRJoXyWQIr
FTIR https://t.co/goAembQ08p
Here is list V analysed 👇
4/
Sample names written on top (each column).
First 5 samples: C what we identified in #Ayurveda #medicines
Antibiotics
Steroids (anabolic/synthetic)
#NARCOTICS - LSD, Morphine
Blood thinners (possible reason Y bleeding tests were off the roof in the patient)
Heavy metals!
5/
Next 5 samples (total 10 now)
Mercury is clear winner. Almost all samples
See controlled substances - Butyrolactones https://t.co/CPz0FwPEOm, methylamine https://t.co/OZnXY7U9UQ
Alcohols, industrial solvents
Rare metals - cobalt, lithium
Again lots of blood thinners
#Ayush
Remember woman who tuk multiple @SriSriTattva products 4 range of problems frm diabetes 2 gas 2 liver disease & developed liver failure, listed for liver transplant?
Here is original thread:
https://t.co/PXxI1Slyv2
23 samples, Analysis results
#MedTwitter #livertwitter
Middle-aged woman wit jaundice (bilirubin 34), liver failure. Liver #Transplant this week.
— (Cyriac) Abby Philips (@drabbyphilips) December 7, 2020
\U0001f633Cause\U0001f447#Ayurveda #medicines total 23\U0001f616 by @SriSriTattva & @SriSri 3-6 mnth 4 sugar, pressure, #COVID19 #ImmuneBoosters, #memory, #liver tonic.
Sent 4 analysis.#livertwitter #MedTwitter pic.twitter.com/uz3FCiVJ3f
2/
Before I go into results, I must say this was overwhelming. There was SO MUCH the lab identified, impossible to put everything here. So I made a summary. At the end of this thread, I have linked a full analysis described in Excel format. Some results were VERY concerning
3/
How did we analyse?
Here R links 2 methods
They R high end, done under strict protocols
Frm Ministry of Forest, Environment, Climate / NABL approvd Lab
ICP-OES https://t.co/O1CLhqVQAu
GC MSMS https://t.co/zRJoXyWQIr
FTIR https://t.co/goAembQ08p
Here is list V analysed 👇
4/
Sample names written on top (each column).
First 5 samples: C what we identified in #Ayurveda #medicines
Antibiotics
Steroids (anabolic/synthetic)
#NARCOTICS - LSD, Morphine
Blood thinners (possible reason Y bleeding tests were off the roof in the patient)
Heavy metals!
5/
Next 5 samples (total 10 now)
Mercury is clear winner. Almost all samples
See controlled substances - Butyrolactones https://t.co/CPz0FwPEOm, methylamine https://t.co/OZnXY7U9UQ
Alcohols, industrial solvents
Rare metals - cobalt, lithium
Again lots of blood thinners
#Ayush
You gotta think about this one carefully!
Imagine you go to the doctor and get tested for a rare disease (only 1 in 10,000 people get it.)
The test is 99% effective in detecting both sick and healthy people.
Your test comes back positive.
Are you really sick? Explain below 👇
The most complete answer from every reply so far is from Dr. Lena. Thanks for taking the time and going through
You can get the answer using Bayes' theorem, but let's try to come up with it in a different —maybe more intuitive— way.
👇
Here is what we know:
- Out of 10,000 people, 1 is sick
- Out of 100 sick people, 99 test positive
- Out of 100 healthy people, 99 test negative
Assuming 1 million people take the test (including you):
- 100 of them are sick
- 999,900 of them are healthy
👇
Let's now test both groups, starting with the 100 people sick:
▫️ 99 of them will be diagnosed (correctly) as sick (99%)
▫️ 1 of them is going to be diagnosed (incorrectly) as healthy (1%)
👇
Imagine you go to the doctor and get tested for a rare disease (only 1 in 10,000 people get it.)
The test is 99% effective in detecting both sick and healthy people.
Your test comes back positive.
Are you really sick? Explain below 👇
The most complete answer from every reply so far is from Dr. Lena. Thanks for taking the time and going through
Really doesn\u2019t fit well in a tweet. pic.twitter.com/xN0pAyniFS
— Dr. Lena Sugar \U0001f3f3\ufe0f\u200d\U0001f308\U0001f1ea\U0001f1fa\U0001f1ef\U0001f1f5 (@_jvs) February 18, 2021
You can get the answer using Bayes' theorem, but let's try to come up with it in a different —maybe more intuitive— way.
👇
Here is what we know:
- Out of 10,000 people, 1 is sick
- Out of 100 sick people, 99 test positive
- Out of 100 healthy people, 99 test negative
Assuming 1 million people take the test (including you):
- 100 of them are sick
- 999,900 of them are healthy
👇
Let's now test both groups, starting with the 100 people sick:
▫️ 99 of them will be diagnosed (correctly) as sick (99%)
▫️ 1 of them is going to be diagnosed (incorrectly) as healthy (1%)
👇