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Using Data Analytics to Improve Cloud Security- a comparative analysis.

Today, technological advancements and digitalization of the business world have taken place at an unanticipated pace. Unfortunately, this technology and digitalization have come with the increased risk of cyber threats. Big data analytics is considered to be the perfect solution to protect organizations and their data from cyber-attacks. Cyber-attack criminals are using sophisticated methods and tools to attack companies. These companies are mostly those that have been in operation for many years. Companies all over the world have applied various strategies to prevent cyber-attacks. However, the main challenges facing this endeavor have been the large volume of data and scalability. As a result, there is a need to come up with more effective strategies to be safe against cyber-attacks. In other words, companies must rethink how they respond to cybersecurity threats. This paper looks at how we can use data analytics to improve cloud security.

Using Data Analytics to Improve Cloud Security

In simple terms, big data may be defined as the large-scale data analysis and management technologies that are beyond the ability of the conventional techniques used to process data. There exists a difference between big data and traditional technologies. This differentiation can be done in three significant ways. First, big data and traditional technologies are different in terms of the amount (or volume) of data. Second, the two are different in terms of the rates of data transmission and generation (velocity). Lastly, big data is different from the other conventional technologies in terms of the types of unstructured and structured data (variety).

Today, people create approximately 2.5 quintillion bytes of data daily. There has been an increase in the rates of data creation, and this has seen the creation of 90% of the data in the past two years alone. As a result of the accelerated production of data and information, there has been a need to create newer technologies to effectively analyze such massive sets of data (Big Data Working Group, 2013).

This big data can be incorporated to change security analytics by offering new opportunities and tools to leverage the large amounts of both unstructured and structured sets of data. In that regard, it is prudent to define the concept of big data analytics. In the simplest terms, it implies the entire process of mining and analyzing big sets of data. Big data analytics may bring forth business and operational knowledge at an unexpected specificity and scale. The urgent need to critically analyze and also leverage the trend data obtained by enterprises is among the major driving forces when analyzing big data. There have been technological developments in the analysis, processing, as well as storage of big sets of data. For example, in recent years, there has been rapidly declining costs of data storage, including CPU power. The cost-effectiveness and flexibility of data centers, as well as cloud computing for elastic storage and computation, have also witnessed immense advancements.

There has also been the introduction and development of modern frameworks, including Hadoop. These new frameworks have enabled the users of data to take full advantage of the shared computing mechanisms storing large amounts of data via parallel and flexible processing. These advancements have resulted in the difference witnessed between big data analytics and traditional analytics.

Today, big data analysis can be used to address the various challenges facing cybersecurity. There has been growth in the complexity of IT networks. As a result, there has also been fast growth in the level of complexity and inventiveness of cybersecurity attacks, as well as threats. For example, between June and November 2016, close to 1 billion malware-related events took place. The estimated total cost of cyber crimes is up to the tune of $1 billion. Also, 99% of all computers in the world are prone to cyber threats and attacks. The image below shows the total costs of cyber-related crimes in selected seven countries:

There have been numerous efforts to combat cybersecurity threats and risks. As the malware threats and attacks continue to rise both in complexity and volume, it has become more challenging for the traditional analytical infrastructure and tools to efficiently keep up. The first challenge to combating cybersecurity threats has to do with data volumes or quantity. For example, on a single day at SophosLabs, about 300,000 new files that are potentially harmful that need to be analyzed are reported. The other challenge revolves around scalability. SQL based infrastructure and tooling do not scale well, and it is also expensive to maintain.

Data analytics is considered to be the perfect path to achieve cybersecurity. A company has to protect itself from all kinds of threats or cyber-attacks. However, a potential attacker only requires a single successful try. With such odds, a company cannot just attempt to prevent cyber-attacks from occurring. It is paramount to detect as well as respond to threats fast effectively. This is known as the PDR paradigm implying to the Prevention, Detection, and Responding to threats. This is where the concept and application of data analytics come in. Organizations and other key analyst companies have recognized that various issues can be easily overcome through the use of data analytics. Analyst companies have continued to write reports as well as advice their various clients about the effect of big data analytics on cybersecurity in various industries. For example, the CDC states that cloud and also big data analytics can keep off the various cyber threats that target health institutions. Companies and businesses are being actively involved and investing heavily in combating data breaches. For example, companies are identifying the anomalies in device behavior, network, including any abnormalities in contractor and employee behavior. Companies are also assessing network vulnerabilities and even risks.

Big data has been significantly changing the general analytics environment. More precisely, data analytics may be carefully leveraged to enhance situational awareness as well as information security. For example, data analytics may be incorporated to analyze the log files, analyze financial transactions, as well as analyze network anomalies to identify any defects or suspicious activities. It is also possible to use data analytics to correlate various data sources into a more coherent view. Big data operationalization has multiple benefits. This implies that just detecting the potential risks is never enough. PDF approach translates to preventing, detecting, and responding to threats. However, the real value of big data emanates from driving actions from the business teams. One needs operationalization capability that can easily sift through the data, identify the existing right signals, and then initiate the most appropriate move (Datameer, 2018).

Big data is significantly improving cybersecurity. Big data and analytics have shown great promise towards the effectiveness of cybersecurity. For example, according to 90 percent of the respondents from MeriTalk’s new US government study, there has been a significant decline in the number and incidence of security breaches. Also, 84 percent of the participants stated that they had applied big data to prevent cyber-attacks successfully. Keeping up with the volume of data has been a vital concern. However, there exist various challenges as the new cybersecurity threats keep popping up daily. Some of these challenges have to do with an overwhelming volume of data, lack of the right systems, as well as stale data by the time it reaches the cybersecurity manager.

If big data is poorly mined for purposes of improving cybersecurity, it can be ineffective for threat analysis. The metadata may be available, buts it may prove difficult to obtain maximum benefits from it. As a result, the problem could be identifying the right people who are well-versed with mining data for trends. Cybersecurity requires actionable intelligence as well as the risk management that is more prevalent in big data analysis. As such, it is advisable to have the necessary tools that can effectively analyze large sets of data. However, the secret lies in automating various tasks. This automation will ensure that any required data is readily available, and also, the required analysis is dispatched to the right individuals early enough. This, in turn, will enable data analysts to conveniently classify the cyber threats and risks without the usual extensive delays that might make the data in question irrelevant to the existing attacks. (SentinelOne, 2016).

The business world has witnessed massive digitalization. However, this digitalization has come with the increased risk of cyber-attacks. The good news is that big data analysis may be used to offer the required protection against a wide range of cyber-attacks. There have been highly complicated attack methods applied by cybercriminals and a growing role of the malicious insiders in some of the recent incidences of a security breach. This is a clear indication that the conventional approaches to ensuring information security are no longer effective and cannot, therefore, keep up. Companies, therefore, must rethink their cybersecurity approaches and concepts. Analytics is considered to be a pivotal element to leverage cyber resilience. This rethinking is necessary based on the increasingly advanced and also persistent attacks. At the core of big data analytics is improved detection. Detection is the starting point to effectively deal with cyber threats and attacks (BiSurvey.com, 2020).

The data-guided information security can be traced back to the detection of bank frauds and the anomaly-based tampering detection systems. Today, the detection of fraud is the most common use of data analytic methods. For many decades, credit card firms have rolled out fraud detection strategies. Unfortunately, the customized mechanisms used to extract big data for purposes of detecting fraud was not adequately economical to perfectly adapt to other fraud detecting applications. Today, off the counter, big data techniques and tools are mainly focusing on analytics for purposes of fraud detection in insurance, healthcare, among other areas.

A few years ago, it was difficult to analyze system events or even the logs for forensics. It was also a challenge to detect intrusion. There are various reasons why conventional approaches fail to deliver the necessary tools to fully support large scale and long-term data analysis. First, the storing or retaining of huge data quantities were not feasible in economic terms. Therefore, a lot of event logs, as well as other recorded computer activities, were easily deleted and lost after a certain fixed duration. Second, the performing of some complex queries or even analytics on huge and structured sets of data was highly inefficient. This was mainly because the traditional tools never leveraged on the big data technologies. Third, the various traditional data analysis tools were not adequately for analyzing and also managing unstructured sets of data. Therefore, the traditional analysis tools presented rigid and defined schemas. The big data tools, such as regular expressions and pig Latin scripts, can be used to query data in some of the most flexible formats. Lastly, big data systems usually incorporate various cluster computing infrastructure. As such, the systems remain more available and reliable. The systems also offer a guarantee that all the queries in the specific system have been processed adequately to full completion.

The analysis and storage of large and heterogeneous data sets are happening at an unexpected speed and also scale. This has been made possible by the new big data analysis technologies, for instance, databases that are related to the Hadoop ecosystem. These different technologies will, in turn, transform the security analytics in various ways. For example, there will be a transformation in the collection of data on a large scale from multiple internal company sources, as well as externally, for instance, the vulnerability database. There will be a transformation in the performing of more in-depth analytics on various data. There will be a more consolidated perspective of security-related information. Lastly, it will be possible to achieve real-time analyzing of streaming sets of data. It is, however, crucial to note that big data analytics still need system architects, as well as analysts. This will make it possible to obtain a more profound understanding of their existing system, to effectively configure the tools of data analysis.

There exist various ways in which we can use big data analytics to enhance security. The first use is network security. Today companies such as Zions Ban corporation are using the Hadoop clusters and other business intelligence mechanisms to quickly analyze more data in contrast to the conventional SIEM tools. In the company’s experience, the amount of data, as well as the frequency analysis of various events, are excessive for the conventional SIEMS to effectively handle alone. For instance, when using the traditional systems, it would take between 20 minutes to one hour to search from a month’s data load. However, by using the new Hadoop system to run queries with Hive, similar results can be obtained in approximately a minute. The security information warehouse that drives the implementation has various benefits for users. The users can extract useful and relevant security-related data from diverse sources such as security devices and also firewalls. The users can also extract information from business processes, website traffic, as well as from other daily transactions. This introduction of many disparate data sets and unstructured data into a single analytical framework is among the major promises of big sets of data.

Big data analytics may also be widely used for enterprise events analytics. Today, an enterprise will routinely collect enormous amounts of security-relevant data, such as people section events, various network events, or even software application events, for multiple reasons, such as the need for post hoc forensics analysis and regulatory compliance. Sadly, such a high data volume can potentially overwhelm the enterprise. An enterprise can hardly store the data, leave alone use it to do anything useful. For instance, it is projected that a large business enterprise such as HP can produce about 1 trillion events each day. This translates to approximately 12 million events every second. Those numbers are expected to grow as the enterprise runs more software, deploys more devices, hires more employees, or even enables event logging in more data sources.

The existing data analytical strategies cannot function effectively at this large scale, and the result will be a lot of false positives that their overall efficacy will be undermined. This issue will worsen as the enterprise moves to cloud architecture and continue collecting much more data. This will have a negative impact because as more data is collected, the data will lead to less actionable information. Recently, there has been researching at HP, whose goal is to move towards a situation n where more data results in better analytics as well as more actionable information. To achieve this, systems, as well as algorithms, have to be structured and also implemented to easily identify any actionable security-related information from the vast data sets. As a result, the false-positive rates will be lowered to levels that are easily manageable. In this situation, collecting more data will translate to more value from such data. However, it will be crucial first to solve multiple challenges and then realize the real capability of big data analytics. These common challenges include privacy, legal, and other technical matters regarding scalable data visualization, analysis, storage, transport, or collection. Despite the various drawbacks, the team at the HP lab has managed to address multiple big data analytics for security issues. Other enterprises can, therefore, borrow from the efforts of HP to use big data analytics for enterprise events analytics. This will, in turn, translate to enhanced security.

Big data analytics can also be used for advanced persistent threat detection. An advanced persistent threat refers to a targeted attack against any physical system or an asset of high value. Compared to the mass spreading risky malware such as trojans, viruses, or worms, the APT cyber-attackers will work in a low and slow mechanism. Low mode maintains a low profile in the network. On the other hand, the slow mode provides for a long execution time. The APT attackers avoid triggering alerts by leveraging stolen user credentials or even zero-day exploits. As a result, this kind of attack can happen over a long period, while the target enterprise is still unaware.

APTs are some of the most severe threats to information security that companies face today. The basic objective of the APT is to steal the IP from a target company. The APT will then gain complete access to confidential and sensitive user data or even access some of the strategic business data that may be later used for illegal insider trading, data poisoning, embarrassment, blackmail, financial gain, or also disrupting the company’s business. APTs are mostly utilized by motivated, well-financed, and highly skilled cyber-attackers who target sensitive data from specific enterprises. Today APTs ere becoming more advanced and sophisticated in both the technologies and methods used. This is particularly their ability to use the employees in the target organization to anonymously penetrate the existing IT mechanisms by using various social engineering strategies. The users will often be tricked to open a spear-phishing message that is customized for each target victim, such as PUSH messages, SMS, and emails. The attackers will then download and install a specially designed malware that might contain zero-day exploits.

The effective detection of threats heavily relies on the knowledge and expertise of the human data analysts to build secure, customized signatures and also conduct manual investigations. The process is not scalable, hard to generalize, as well as labor-intensive. Big data analytics is a practical approach to detecting APTs. However, there exists a problem in the form of the massive amounts of data that must be sifted in the search for any anomalies. This data is usually extracted from diverse ever-rising sources of information that must be audited first. This process makes the detection task more difficult. Based on the large volumes of data, the conventional network perimeter defense system can end up being ineffective in the detection of targeted cyber-attacks. This is because such conventional systems are not easily scalable to the enterprise networks that are ever-increasing in size. As a result, there is a need for a new and more effective approach. Most organizations collect their data relating to user hosts’ and users’ activity within an organization’s existing network, as logged by VPN users, intrusion detection systems, domain controllers, web proxies, and firewalls.

Technology has had immense benefits, such as the digitalization of the business world. Despite the various benefits, companies are still facing a significant risk of cyber-attacks. For example, companies have suffered the immense loss of data at the hands of cyber-attack criminals. To solve this problem, companies have turned to big data analytics. Today, there has been a growing adoption of mobile and cloud services. As a result, there has been the emergence of more sophisticated tools and methods used by modern cybercriminals. For many years, companies have relied on traditional tools, but they have proven ineffective. This calls for an urgent rethinking of the concepts that companies have on cybersecurity. Companies and businesses must move past the pure prevention approach and employ the PRD strategy. The PDR paradigm entails preventing, detecting, and responding.  By using big data analytics, it will be possible to improve cloud security.

References

Big Data Working Group. (2013). Big data analytics for security intelligence. Cloud Security Alliance, 1-22. Retrieved from https://downloads.cloudsecurityalliance.org/initiatives/bdwg/Big_Data_Analytics_for_Security_Intelligence.pdf

BiSurvey.com (2020). Big Data Security Analytics: A Weapon Against Rising Cyber Security Attacks? Retrieved from https://bi-survey.com/big-data-security-analytics

Datameer (2018). Challenges to Cyber Security and how Big Data Analytics Can Help. Retrieved from https://www.datameer.com/blog/challenges-to-cyber-security-and-how-big-data-analytics-can-help/

SentinelOne (2016). How Big Data is Improving Cyber Security. Retrieved from https://www.csoonline.com/article/3139923/how-big-data-is-improving-cyber-security.html

drrmehedi.com

Healthy DIET for Healthy & Fit Pregnancy

iet Advice during pregnancy

According to the American College of Obstetricians and Gynecologists, a woman will need more calcium, folic acid, iron and protein during pregnancy to support the healthy development of the growing baby.

Calcium

 

Developing baby needs calcium to build healthy bones and teeth. Calcium also helps a baby grow a healthy heart. Vitamin D will also be required as it aids the absorption of calcium from the stomach.
Food that is rich in calcium included salmon, broccoli, kale, and yogurt.

Iron

Iron is essential to make hemoglobin in the red blood cell. During pregnancy, the amount of blood in your body increases by about 50% to meet the needs of the healthy development of the growing baby. You will, therefore, need extra iron to make more hemoglobin.
Getting too little iron during pregnancy can lead to anemia, a common problem during pregnancy which can result in fatigue and an increased risk of infections.
Iron is also essential for a healthy immune system.
Red meat is rich in iron.

Folic acid

Folic acid is also known as folate. It is a B vitamin that is crucial in helping to prevent birth defects in the baby’s brain and spine which is also known as neural tube defects.
Most gynecologists recommend that pregnant women take a daily vitamin supplement containing 600 micrograms of folic acid, an amount commonly found in a regular prenatal vitamin.
Food that is rich in folic acid includes leafy green vegetables, fortified or enriched cereals, bread, and pasta.

Protein

More protein is needed during pregnancy. Protein is the building blocks of our body’s cell. It is especially important to have enough protein in second and third trimester as the baby is growing faster during these periods.
Food that is rich in protein includes meat, poultry, fish, dried beans and peas, eggs, nuts, tofu.

Fruits and vegetables
What food should you eat during pregnancy?

Fruits and vegetables are nutrient-dense and are filled with fiber, vitamins, and minerals that you and your baby need. Eating fruits and vegetables also help prevent constipation, a common symptom during pregnancy.
Fruits that are beneficial included: avocado, lemon, banana, apples, and berries.
Vegetables like sweet potato, broccoli, beans and other green, leafy vegetables are all beneficial to the pregnant women.

Lean protein

Pregnant women should include good protein sources at every meal to support the baby’s growth. Source of the lean protein includes meat, poultry, fish, hard-boiled eggs, beans, tofu, cheese, and nuts.

Whole grains

Whole grains are a vital source of energy in the diet, and they also provide fiber, iron, and B-vitamins.

 

What food should you avoid during pregnancy?

Raw fish

 

Do not eat any undercooked or raw fish as they may contain parasites or bacteria.

Fish that has a high level of mercury
Pregnant women should avoid fishes that contain a high level of mercury such as king mackerel, marlin, shark, swordfish, tilefish, and tuna. Mercury consumed during pregnancy has been linked to developmental delays and brain damage in the baby.

Shellfish

It is best to avoid raw shellfish during pregnancy. Fresh shellfish like clam, scallop, and oyster contain Vibrio bacteria.

Smoked seafood

Refrigerated and smoked seafood should be avoided because it could be contaminated with listeria.
Undercooked or raw meat
Raw or undercooked beef or poultry should be avoided during pregnancy because of the risk of contamination with coliform bacteria, toxoplasmosis, and salmonella.

Soft cheese

Soft cheese made from unpasteurized milk may contain the virus E.coli or Listeria.

Unpasteurized milk

Pregnant women should avoid unpasteurized milk as it contains bacteria such as Campylobacter, E. coli, Listeria, or Salmonella.
Pregnant women should be drinking pasteurized milk.

Raw egg
Pregnant women should avoid unpasteurized or undercooked eggs as they may contain Salmonella.

Caffeine
Coffee, tea, chocolate and some soft drinks have a high level of caffeine. Pregnant women should limit their consumption to one or two cups in a day.
According to one study published by the National Institute of Health, high maternal caffeine intake during pregnancy is associated with the risk of low birth weight in babies.

Alcohol
Pregnant women should stay away from alcohol as it increases the risk of miscarriage and stillbirth. Many studies have confirmed that even a small amount can negatively impact the brain development of your baby.
It can also cause fetal alcohol syndrome. This syndrome involves facial deformities, heart defects, and mental retardation.

22584223 - 3d rendered medical illustration - wrong sitting posture

Occupational Overuse Syndrome (OOS)

Occupational Overuse Syndrome

OOS is mainly a collective form of the conditions that occur due to repeated movements of muscles or some particular posture. Thereby, it is quite common in people required to work in prolonged periods of immobility, like using computers at work.

Warning Signs!

when at work, you could be suffering from OOS with these kind of feature:

Pain and stiffness of a joint

Difficulty in moving the joint

Swelling in the forearm, wrist, shoulder

Muscle weakness

Discomfort in the neck or shoulders

Backache

In the beginning, it is not painful, as the symptoms come and go. Also, discontinuing the task tends to improve the symptoms. But, continuous use can cause severe back and neck pain and make simple tasks like holding a glass of water or opening the door look difficult.

Not just a minor pain!

Although all OOS begin with mild pain, what may appear minor discomfort at the beginning could later lead to:

Tiny muscle tears

Nerve damage due to compression

Abnormal posture

Abnormal muscle movement

Premature degenerative changes

Are you at risk?

If your job entails too much of the following activities, you are at an increased risk of OOS:

Typing

Process work such as assembly line and packing

Sewing

Manual work

Computer work

How can you prevent OOS?

Here are some easy ways to help you avoid OOS:

Take regular stretching and walking breaks every half an hour.

Deep breathe or meditate for 5 minutes to relax.

Watch your posture and remain active.

Utilize all your tea, meal breaks, and eat a nutritious and balanced diet.

Try to take a break from the tasks that involve repetitive movements.

A healthy lifestyle includes regular exercise, a balanced diet, and a sound sleep pattern. However, most of us seldom make time to abide by this golden rule. To enjoy the bliss of a healthy life, we need to remind ourselves of the long-term benefits of these corrective measures. The experience will be much more comfortable and happy!
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Kidney Diseases why and how?

Our kidneys are two bean formed organs arranged in the focal point of our back just beneath the rib confine or more the midsection.
Solid kidneys work to evacuate squander side-effects of assimilation and strong movement from our blood; balance water and centralization of minerals in our body; combine the compound renin required to keep up ideal circulatory strain and erythropoietin which thusly animates the creation of erythrocytes.
Kidneys additionally help to keep up ideal bone wellbeing by incorporating a functioning type of nutrient D. Proceeded with loss of renal capacity over some undefined time frame which may run from months to years is alluded to as kidney illness.
Kidney ailment happens because of hereditary issues, some injury, or overcompensation to certain medications. Individuals with diabetes, hypertension, or hereditary inclination are at more serious dangers of creating it. Proceeded with loss of kidney working is additionally achieved by the sickness glomerulonephritis. Kidney stones, prostate infection, pinworm invasion of kidney, vasculitis, polycystic kidney ailment, and hemolytic-uremic disorder are likewise the causative elements of kidney malady. Marathon runners who don’t focus on liquid utilization may create intense renal disappointment that harms their kidneys.
Side effects: 
The accompanying signs and side effects are markers of improvement of a kidney problem: Symptoms of Kidney Disease – Nausea
Changes in pee: Frequency of pee increments or diminishes; pee may have blood; the desperation to pee around evening time builds; pee is frothy and there is torment or trouble in peeing and totally mitigating the weight.
Growing in face, hands or legs.
Weakness because of adjusted creation of the hormone erythropoietin.
Epidermal rashes or tingling because of affidavit of waste material in blood.
Loss of hunger, queasiness and retching and a metallic preference for the mouth because of uremia.
Hypertension, hyperphosphatemia and hypocalcemia are found in the patient.
Sexual brokenness and fast atherosclerosis is seen.
Torment in bones or breaks.
Torment in the chest because of aggravation around the heart.
Gathering of liquid in the lungs causing windedness.
Deadness in feet or hand, a sleeping disorder and anxious leg condition.
 
Conclusion: 
Blood tests are never really check the levels blood urea nitrogen (BUN), creatinine, and glomerular filtration rate (GFR). In the event of a kidney infection the blood levels of BUN and creatinine are seen as high though there is a significant reduction in GFR.
Pee tests are likewise done to check the degree of protein and electrolytes just as nearness of anomalous cells. On the off chance that an individual is experiencing kidney malady minute examination of the pee would uncover undesirable clustering of red and white platelets. A correlation of levels of electrolytes in the blood and pee is utilized to choose whether the kidneys are working typically to screen and channel the blood.
Ultrasound of the mid-region is done to check the size and any block in kidneys.
Kidney biopsy is directed for minuscule investigation of kidney tissues.
GFR under 60 mL/min/1.73m2 for at least 3 months demonstrates interminable kidney ailment.
Phases of kidney illness: 
Stage 1: Kidney work is lessened just a bit. GFR is around 90 or above at this stage. Blood or pee examination shows certain markers of variations from the norm or harm. At this stage patients are required to assume responsibility for their hypertension and diabetes, practice consistently, cut down on pressure and abstain from smoking and liquor.
Stage 2: GFR diminishes and is in the middle of 60 to 89. Aside from the safety measures of stage 1 the specialists may endorse different medications to upgrade the soundness of the veins for dialysis in future.
Stage 3: GFR is between 30 to 59 and indications like paleness and bone issues become evident.
Stage 4: GFR is as low as 15 to 29 and different intricacies identified with incessant kidney illness are seen. Groundwork for hemodialysis or peritoneal dialysis or kidney transplantation starts.
Stage 5: GFR is under 15 and kidney disappointment happens. Dialysis or transplantation is compulsory.
More youthful patients who arrive at the last phases of kidney sickness are regularly at the danger of creating malignant growth.
Treatment: 
Since it is a dynamic illness early distinguishing proof and treatment is a must to diminish inconveniences. In the underlying stages way of life changes and meds for hypertension, elevated cholesterol levels, and diabetes are endorsed to bring down the pace of movement of a kidney ailment. Angiotensin changing over chemical inhibitors (ACEIs) or angiotensin II receptor enemies (ARBs) are utilized generally to hinder movement to organize 5. In the propelled stage substitution of the hormones erythropoietin and calcitriol just as phosphate fasteners are finished. Stage 5 includes hemodialysis threefold per week or peritoneal dialysis that can be directed at home day by day. Kidney transplantation is done to place a sound kidney in an individual who has arrived at stage 5 that does the elements of the two harmed kidneys. Post transplantation dialysis isn’t required.
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Why I am Diagnosed with Fatty Liver ?

The main functions of the liver are removing toxins from the body and processes food nutrients.

Fatty Liver is a condition in which excess fat is stored inside liver cells, making it harder for the liver to metabolize.

One most common cause of fat buildup in the liver is heavy alcohol use,

referred to as an alcoholic fatty liver disease.

This is a common but preventable one.

Alcoholic Fatty Liver

As the name clearly states, alcoholic fatty liver disease (AFLD) is developed by excessive alcohol intake. Unlike the brain, the liver is a very resilient organ. It is capable of immediately regenerating new cells to replace the dead ones.

Every time the liver filters alcohol, some of its cells die. Regardless of how resilient the liver is, excessive and prolonged alcohol intake reduces the liver’s ability to regenerate cells. This causes fat to buildup on the liver, resulting in eventually alcoholic fatty liver disease.

When in its first stages, this condition is easily reversible by merely quitting alcohol for a minimum of 2 weeks.

Non-Alcoholic Fatty Liver

As the name implies, Non-Alcoholic Fatty Liver Disease (NAFLD) is the result of too much fat accumulation on the liver.

There are no exact reasons why some livers accumulate fats, and the same goes for liver inflammation.

So, Non-alcoholic fatty liver disease is a condition in which the liver accumulates excess fat, not due to excessive alcohol consumption.

It’s a widespread condition in clinical practice. It affects about 20% of the world population.

We care about liver disease because it is one of the problems which can cause liver failure, liver cancer, and the consequences may need liver transplantation.

The accumulation or store of extra fat in the liver can cause chronic irritation of the liver cells and subsequently cause further liver damage.

There are 03 common causes of fatty liver disease,

  1. being overweight,
  2. patients with diabetes,
  3. with high cholesterol.

Keeping in mind some people with healthy body weight can also have fatty liver disease.

The increasing epidemics of fatty liver disease has been recently attributed to overweight and the high prevalence of diabetes. The fatty liver disease used to be a rare condition in children under 16.

However, Recently with the increasing problem of obesity in children, Fatty liver disease became affecting about 10% of children Early in the course of the disease, many patients have no symptoms.

When the scaring and the inflammation of the liver progress.

 Many patients complain about problems related to the enlargement of the liver or fatigue.

If complications happened from Fatty liver diseases such as cirrhosis or complete scaring, patients could have problems with bleeding and symptoms of other complications of liver disease such as liver cancer or fulminant hepatitis.

we do a blood test for a patient with fatty liver disease often,

where many patients will have elevated liver function tests. Imaging modalities can be beneficial for diagnoses of fatty liver disease, such as ultrasound, CT scan, MRI, or fiber scan. Occasionally, patients might need a liver biopsy to assess the significance of the damage in the liver.

Fatty liver disease in the early stage could be completely reversible. However, if the patient continues to have excess fat in the liver, this can lead to the liver’s complete scarring or what we call sclerosis.

And subsequently, liver failure and its complications such as liver cancer and the need for liver transplantation.

The fatty liver disease becomes a more common indication for liver transplantation globally.

Right now there is no specific treatment for fatty liver disease.

The most important step is to control the risk factors for liver disease;

such as high cholesterol and control of the blood sugar and lose weight for obese individuals. There are recent studies that confirm the benefit of coffee for patients with liver disease.

Some of the symptoms of the alcoholic and non-alcoholic fatty liver are:

  • Slight fatigue
  • Confusion or drowsiness
  • Weight loss with swelling in the tummy and ankles
  • Overweight and obesity
  • Insulin resistance
  • High levels of fat in the blood
  • Hyperglycemia (High Blood Pressure)

The most crucial food to avoid for patients with fatty liver disease is a diet rich in refined sugars, such as bread, rice, potatoes, and corn. We recommend that patients consume lean meat and increase the number of vegetables and salads in their diet.

They are conducting several clinical trials at Johns Hopkins Hospital and looking for a novel therapy for fatty liver disease. We’re using tablets form for specific drugs that alter the pathophysiology of fatty liver disease. What we are hoping is to prevent the accumulation of fat in the liver. It reduces the amount of scarring in the liver from fatty liver disease and eventually prevents liver cancer and liver failure.

To be healthy and Hessle free liver, stay tune with my blogs, and keep exercise/run every day morning at least 30 minutes a day.

Dr. Mehedi Hassan

For more please visit and read……….

  1. Non-alcoholic Fatty Liver Disease: A Clinical Update
  2. everydayhealth.com-liver-disease
  3. liverfoundation.org
  4. motivatorkerja.com
  5. Thedailycrisp.com