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Five Bad Habits That Are Actually Good For You: Health Benefits



bad habits good for you

Most probably you would have a bad habit you don’t like and can’t get rid of that. Maybe your parents in childhood pointed you every time to quit these bad manners, but either you ignored them or failed to give up and now become user-to.

Don’t worry, all bad habits are not harmful. As per the research and studies, many of them are beneficial for your health. So, in the context of the same, we have brought the list of such shameful habits and how they are good for your body.

5 Bad Habits That Are Good For Your Body

bad habits that are good for you

#1 Biting Your Nails

Biting your nails is one of the very common bad habits you can notice among many people around you. Though, while biting nails, dirt goes directly into your stomach can be harmful, as it may contain germs due to exposure of your fingers.

nail biting benefits

But as per the research and studies, biting your nails can actually build up immunities, which is actually a good thing for your health. Actually, your body also registers the bacteria in its memory bank, so if such bacteria are encountered the second time, you’ll already have the lymphocytes that are able to beat it.

#2 Spitting Frequently

It is also one of the most disgusting practices you accumulate your saliva and spit publicly at various places or may sometimes where you shouldn’t do. People chewing tobacco products usually have such habits can’t resist without that.

spitting benefits

Actually, when we breathe through our nose it warms up the air and makes it more humid, allowing the body to absorb oxygen more efficiently.

And while running or doing athlete tasks, we tend to breathe through our mouth and this causes it to produce more saliva that interferes with our breathing patterns. Hence, it is necessary to spit and get rid of the excess mucus from the mouth.

#3 Farting Very Often

This awful habit you would have most probably noticed in your adults especially old age people who never hesitate to fart loudly amid many people. If you have this habit and can’t control this, don’t worry, find out why farting is necessary.

frequent farting benefits

Do you know in a day your body releases gas about 14 times and about 3-5 times when you sleep? Actually, after six hours of taking meals, our digestive tract starts producing the carbon dioxide and methane that should be released. If you try to hold this you can face abdominal pain or bloating and acidity related problems.

Also Read: Five Natural Remedies for Acid Reflux and Heartburn

#4 Eating Your Boogers

This is one of the most disgusting habits especially found in kids. Yes, how does it feels when you pick out a piece of dried nasal mucus and put it into your mouth. But according to science eating, boogers also have some benefits.

benefit of eating boogers

Actually, it helps boost your immune system as the snot you eat enriched with salivary mucins that can fight cavity-causing bacteria. And every time when you eat boogers you trigger your immune system to release the white blood cells into your body that helps your body to defend itself against the harmful bacteria.

#5 Peeing While Bathing

It is very awkward speaking about such things, but it is also one of the unknown habits people do while taking the bath. Though, people do not do it regularly, but as per the researchers, almost 75% of people have done it at least once in their lifetime.

benefits of peeing on your feet

If you do this, don’t get ashamed, as your urine contains uric acid and ammonia and when you pee, it passes through your thighs, legs and toes preventing the skin from fungal infections. Another advantage of peeing while bathing is that you could save water bill and toilet paper used every time while such excretion.

Maybe such bad habits benefit your body but it sounds disgusting especially when others notice you and point outs what you are doing. So, to stay healthy it’s better to do some workouts or regular exercise to maintain your fitness level.

Also Read: How To Check Your Fitness Level And Health At Home

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New Coronavirus Myths And Facts: 15 Myth Buster Graphics by WHO



coronavirus myths and facts

The new coronavirus (2019-nCoV), originated from China, now spread all over the world infecting many people globally mainly Chinese. And after killing more than 2000 and infecting over tens of thousands of people globally it is not stopping.

This highly contagious disease spread so fast that it has also created lots of fear and myths among the people globally. The situation is now that, people believe anything about this infection and take extraordinary prevention to avoid getting infected. So, right here we brought the list of top coronavirus myths buster released by WHO.

Coronavirus Myths and Facts busted by WHO in Graphics

Myth1: Are hand dryers effective in killing the new coronavirus?

Myth2: Can an ultraviolet disinfection lamp kill the new coronavirus?

Myth3: How effective are thermal scanners in detecting people infected with the new coronavirus?

Myth4: Can spraying alcohol or chlorine all over your body kill the new coronavirus?

Myth5: Is it safe to receive a letter or a package from China?

Myth6: Can pets at home spread the new coronavirus (2019-nCoV)?

Myth7: Do vaccines against pneumonia protect you against the new coronavirus?

Myth8: Can regularly rinsing your nose with saline help prevent infection with the new coronavirus?

Myth9: Can gargling mouthwash protect you from infection with the new coronavirus?

Myth10: Can eating garlic help prevent infection with the new coronavirus?

Myth11: Does putting on sesame oil block the new coronavirus from entering the body?

Myth12: Does the new coronavirus affect older people, or are younger people also susceptible?

Myth13: Are antibiotics effective in preventing and treating the new coronavirus?

Myth14: Are there any specific medicines to prevent or treat the new coronavirus?

Myth15: Who is most at risk?

Similarly, in the 2002-2003 SARS outbreak, 8,422 people were infected and there were 916 deaths worldwide. The overall death rate for infected people was 11%. But for infected people 24 and younger, the death rate was just 1%, while for those aged 65+ it was 55%.

In brief , anyone can catch a virus. But the effect it will have on you, and how seriously ill you might become, can be dependent on several other factors.

Older people and anyone with pre-existing medical conditions, like asthma, diabetes or heart disease, appear to be more vulnerable to becoming severely ill with the new coronavirus, according to the WHO.

Though, scientist has not yet found any medicine and vaccine to control the coronavirus but globally, medical experts and veteran researchers are using the new ways to develop the effective treatment to cure the coronavirus infection.

Also Read: How AI Can Predict Coronavirus like Epidemic Before it Outbreaks?

Sources: WHO & World Economic Forum

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Coronavirus Microscope Pictures Released: See how these Deadly Pathogen look



coronavirus microscope pictures

The microscopic images of coronavirus (2019-nCoV) has been released by U.S. researchers that has infected tens of thousands of people in China and other nations killed more than 1500 people worldwide became a global health emergency.

The images were released by National Institute of Allergy and Infectious Diseases, US. The science team made with scanning and transmission electron microscopes to see these viruses at microscopic level. But with normal microscope that can be too small to see, so Electron microscopes use a beam of energy to take detailed pictures of objects.

Coronavirus Microscopic Images Gallery:

LAB CULTURE: A scanning electron microscope image of the virus, grown from a lab culture. The images were released Thursday by the U.S. National Institute of Allergy and Infectious Diseases. They were made with scanning and transmission electron microscopes. Image Credit
COVID-19 IMAGE: A scanning electron microscope image of the Covid-19 grown from a lab culture. Electron microscopes use a beam of energy to take detailed pictures of objects that can be too small to see with normal microscope. Image Credit
MAGNIFIED: A transmission electron microscope image of the Covid-19. The images of the virus, known as 2019-nCoV, have been colorised to make them easier to view. Researchers have been growing samples of the virus in labs in order to study it, and to begin testing experimental and existing drugs against the disease. Coronaviruses are named for the crown-like shapes on their surface. Image Credit
Researchers at the Peter Doherty Institute for Infection and Immunity in Melbourne, Australia, grew the novel coronavirus from a patient sample. (Images Credit)

Video: NovelCoronavirus (2019-nCoV) in Culture

The images of coronavirus have been colorized to make them easier to view for further studies and drug developments. Researchers have been growing samples of the virus in labs in order to study it, and to begin testing experimental and existing drugs against the disease. And scientists are discovering the drugs and vaccines to control this disease with medical researching team and using most advance technologies like artificial intelligence.  

Also Read: How AI Can Predict Coronavirus like Epidemic Before it Outbreaks?

Sources: Gulfnews, Bloomberg & Indiatoday

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Artificial Intelligence

How AI Can Predict Coronavirus like Epidemic Before it Outbreaks?



AI coronavirus prediction

AI in healthcare is already developed enough to diagnosis various types of critical diseases, but in case of the epidemic it failed and not able to predict timely, that took the life of many people across the world and still spreading further became a health emergency.   

I’m talking about “Coronavirus Infection” – that started in mid-December in China and now transmitted to all major countries worldwide. This high contagious infection took lives of more than 800 people and infected over 37000 people globally.

Also Read: Coronavirus Infection, Symptoms, Transmission & Treatment: Everything You Need to Know About This Deadly Disease

The question arises here, why artificial intelligence has not been used to detect the risk associated with this kind of disease or AI is unable to detect such epidemic with right predictions, so that medical experts can envisage the situation timely.

Artificial Intelligence Coronavirus Prediction

A Canadian based global health monitoring platform – BlueDot, reportedly notified its clients of the outbreak of coronavirus on Dec. 31.  But nobody has taken AI prediction seriously and now the situation became out of control in China.

BlueDot is the mastermind of Kamran Khan, who is an infectious disease physician and professor of Medicine and Public Health at the University of Toronto. Keep in mind that he was a frontline healthcare worker during the SARS outbreak.

How BluDot’s works in Epidemic Prediction?

BlueDot’s algorithm uses machine learning (ML) and natural language processing (NLP) technology to detect signs of potential disease outbreaks from the collected information that becomes a training data while developing such AI models.

Video: How BluseDot AI Predicted Coronavirus?

And such AI’s findings are reviewed and verified by human epidemiologists before sending a report to the company’s clients in government, industry and public health, as well as other public health officials, airlines and hospitals in the affected regions.

Data Used in AI Coronavirus Prediction

In the case of the coronavirus outbreak, the algorithm reportedly used airline ticketing information and pick news of such outbreaks like murmuring or forums or blogs or indications of some kind of unusual events going on to accurately predict the virus’ rapid spread from Wuhan, China, to Bangkok, Seoul, Taipei, Tokyo and other nations.  

Here, in coronavirus prediction, BlueDot uses an AI-driven algorithm that scours foreign-language news reports, animal and plant disease networks, and official proclamations to give its clients advance warning to avoid danger zones like Wuhan.

Another AI PredictsCoronavirus Could Kill 53 million and infect 2.5 billion

Yes, as per an article published on Forbes, AI predicts coronavirus could infect 2.5 billionand kill 53 million. But doctorssaid that it is not credible.

Actually, since the coronavirus infection transmission started more than 30,000 people infected and died around 600. But conditions of infection are changing, which in turn changes incredibly important factors that the AI isn’t aware of.

To predict this epidemic along with infection and death data, AI neural net using a recurrent neural network (RNN) model and ran the simulation ten million times. That output dictated the forecast for the following day. Once the following day’s output was published, added it to the training data, and re-ran ten million times, the results are shocking. 

ai coronavirus prediction
AI coronavirus unreliable prediction

From 50,000 infections and 1,000 deaths after a week to 208,000 infections and almost 4,400 deaths after two weeks, the numbers keep growing as each infected person infects others in turn. And in 30 days, the AI model says, two million could die and in just 15 more days, the death toll skyrockets enough to eliminate humans in millions. 

Artificial Intelligence in Medical Epidemiology Prediction

As per the report AI in medical epidemiology predicted dengue with more than 80 percent accuracy in Malaysia. AI in medical epidemiology predicted the outbreaks of dengue in Penang, Malaysia for 37 locations while the actual outbreak was 30, accounting more than 80% accuracy in prediction, making AI reliable in such epidemic prediction.

Artificial Intelligence in Medical Epidemiology

Though, scientists are developing ways to use AI to predict the spread of such contagious diseases before they happen. Though, the process is extremely complicated, successful implementation of predictive modeling could represent a major leap forward in the fight to rid the world of some of the most insidious infectious diseases.

How AI Can be Improved to Predict Coronavirus Like Epidemic?

However, as per multiple doctors and medical professionals, there is good news, the model doesn’t know every factor, as conditions and data fed into the neural network are changing and these conditions change, the results will also change.

However, in coronavirus like epidemic AI could predict the number of potential new cases by area and which types of populations will be at risk the most. This type of technology could be used to warn travelers so that vulnerable populations can wear proper medical masks while traveling or take other precautions to prevent such infections.  

Video: How AI Can Help to Control Coronavirus or Other Deadly Diseases?

Earlier researchers have built AI-based models that can predict outbreaks of the Zika virus in real time, which can inform how doctors respond to potential crises. AI could also be used to guide how public health officials distribute resources during a crisis. That will effectively work like AI stands to be a new first line of defense against such diseases.

AI in healthcare is already playing a vital role in assisting with researching new drugs, tackling rare diseases, and detecting breast cancer. AI was even used to identify insects that spread Chagas, an incurable and potentially deadly disease that has infected an estimated 8 million people in Mexico and Central and South America.

And now AI increasing interest in using non-health data like social media posts helping health policymakers and drug companies understand the breadth of a health crisis. For instance, AI that can mine social media posts to track illegal opioid sales, and keep public health officials informed about these controlled substances spread.

The Uncertainty Factor While Relying on AI

One of the core strengths of AI is while identifying and limiting the effects of virus outbreaks is its incredibly insistent nature. The machine never tire, can sift through enormous amounts of healthcare data, and identify possible correlations and causations that humans can’t in a fast manner and if the amount of data is huge or very complex to analyze.  

While on the other hand, there are limitations of AI – the ability to both identify virus outbreaks and predict how they will spread. Let’s take the best-known example comes from the neighboring field of big data analytics.

At its launch, Google Flu Trends was heralded as a great leap forward in relation to identifying and estimating the spread of the flu — until it underestimated the 2013 flu season by a whopping 140 percent and was quietly put to rest.

Data Quality is Important for AI-based Predictions

Poor data quality was identified as one of the main reasons Google Flu Trends failed. Unreliable or faulty data can create confusion on AI-based prediction. In our increasingly interconnected world, tracking the movements of potentially infected individuals (by car, trains, buses, or planes) is just one vector surrounded by a lot of uncertainty.

But, BlueDot was able to correctly identify the coronavirus, in part dueto its AI technology, illustrates that smart computer systems canbe incredibly useful in helping us navigate these uncertainties.

And most importantly, it is not the same as AI being at a point where it precisely does so on its own – and that is the reason BlueDot employs only human experts to validate the AI’s findings.

Nevertheless, to ensure the accuracy of AI-based predictions for such an epidemic, a quality and reliable source of training data is necessary for supervised machine learning.

Also Read: Reasons Why AI and ML Projects Fail Due to Training Data Issues

Hospitals, medical centers and healthcare organizations need to share the labeled medical images of such infected people to AI developers, so that they utilize the same and help the medical science and AI engineers develop a reliable AI model.  

 CT Scan of a coronavirus infected patient in China
CT Scan of a coronavirus infected patient in China showing ground glass lesions in the lungs. Images Credit: Radiological Society of North America.

So that symptoms could be identified by the doctors and annotated to make it recognizable to computer vision through machine learning algorithms. In this case, the radiologist described Novel Coronavirus (2019-nCoV) Pneumonia through CT Imaging.

And when a huge amount of such CT scan images are manually annotated by experienced radiologists, it is used as a training data for machine learning AI models, that can in future detect such infections if similar symptoms are visible among the people. And as much as data used in the algorithm training, the accuracy of prediction by the model would be high.        

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