18 October 2018

Combining Genetic and Sun Exposure Data Improves Skin Cancer Risk Estimates

Thursday, October 18, 2018 0
www.trendsnowdays.com
By combining data on individuals' lifetime sun exposure and their genetics, researchers can generate improved predictions of their risk of skin cancer, according to findings presented at the American Society of Human Genetics (ASHG) 2018 Annual Meeting in San Diego, Calif.

Pierre Fontanillas, PhD, and colleagues at 23andMe, Inc., collected genetic and survey data from over 210,000 consented research participants of European descent. They analyzed the data to identify correlations between previously known and potentially novel skin cancer risk factors and the occurrence of three forms of skin cancer: melanoma, basal cell carcinoma (BCC), and squamous cell carcinoma (SCC). Past studies had found that exposure to ultraviolet (UV) light increases skin cancer risk, as do other environmental factors such as living in a sunnier climate or at a higher altitude, and personal factors such as lighter skin pigmentation, higher numbers of moles on the skin, and family history of skin cancer.

"We aimed to validate previously known skin cancer risk factors in a large cohort, add detail to these and explore potential new ones, and find out whether and how these factors might interact with genetic risk," said Dr. Fontanillas.

They found that while each single factor was not particularly significant on its own, multiple factors could be combined into statistical models that were more informative. The best-performing models incorporated a genetic risk score composed of data on up to 50 genetic variants, along with survey data on family history, skin pigmentation and sensitivity, number of moles, estimated current sun exposure, sunbathing frequency before the age of 30, and body mass index (BMI).

The new models achieved a high predictive accuracy (area under the curve [AUC], between 0.81 and 0.85). Genetic factors alone accounted for 8.3 to 15.2 percent of the variance explained in skin cancer risk. Although the three skin cancers have different physiology, models did not find fundamental differences between the three cancer types, nor did they show strong interaction between genetic and environmental risk factors. While the self-reported nature of the survey data permitted researchers to collect a large dataset, it also presented some challenges, Dr. Fontanillas noted.

"Measuring lifetime exposure is generally challenging. It is particularly hard to capture sun exposure and when in life it happened, and it may be that some of the other correlates we found, like higher BMI, reflect a lack of outdoor activity rather than being directly correlated with risk of skin cancer," he said.

Moving forward, the researchers plan to expand their sample to groups with non-European ancestry and are exploring additional methods of calculating genetic risk score and measuring sun exposure. They hope to eventually obtain risk estimates accurate enough to be used by individuals and clinicians.

Story Source:
Materials provided by American Society of Human Genetics. Note: Content may be edited for style and length.

Cite This Page:
American Society of Human Genetics. "Combining genetic and sun exposure data improves skin cancer risk estimates." ScienceDaily. ScienceDaily, 17 October 2018. <www.sciencedaily.com/releases/2018/10/181017140931.htm>.

Image credit

17 October 2018

Automated System Identifies Dense Tissue, a Risk Factor for Breast Cancer, in Mammograms

Wednesday, October 17, 2018 0
Deep-learning model has been used successfully on patients, may lead to more consistent screening procedures

www.trendsnowdays.com
Researchers from MIT and Massachusetts General Hospital have developed an automated model that assesses dense breast tissue in mammograms -- which is an independent risk factor for breast cancer -- as reliably as expert radiologists.

This marks the first time a deep-learning model of its kind has successfully been used in a clinic on real patients, according to the researchers. With broad implementation, the researchers hope the model can help bring greater reliability to breast density assessments across the nation.

It's estimated that more than 40 percent of U.S. women have dense breast tissue, which alone increases the risk of breast cancer. Moreover, dense tissue can mask cancers on the mammogram, making screening more difficult. As a result, 30 U.S. states mandate that women must be notified if their mammograms indicate they have dense breasts.

But breast density assessments rely on subjective human assessment. Due to many factors, results vary -- sometimes dramatically -- across radiologists. The MIT and MGH researchers trained a deep-learning model on tens of thousands of high-quality digital mammograms to learn to distinguish different types of breast tissue, from fatty to extremely dense, based on expert assessments. Given a new mammogram, the model can then identify a density measurement that closely aligns with expert opinion.

"Breast density is an independent risk factor that drives how we communicate with women about their cancer risk. Our motivation was to create an accurate and consistent tool, that can be shared and used across health care systems," says second author Adam Yala, a PhD student in MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL).

The other co-authors are first author Constance Lehman, professor of radiology at Harvard Medical School and the director of breast imaging at the MGH; and senior author Regina Barzilay, the Delta Electronics Professor at CSAIL and the Department of Electrical Engineering and Computer Science at MIT.

Mapping density

The model is built on a convolutional neural network (CNN), which is also used for computer vision tasks. The researchers trained and tested their model on a dataset of more than 58,000 randomly selected mammograms from more than 39,000 women screened between 2009 and 2011. For training, they used around 41,000 mammograms and, for testing, about 8,600 mammograms.

Each mammogram in the dataset has a standard Breast Imaging Reporting and Data System (BI-RADS) breast density rating in four categories: fatty, scattered (scattered density), heterogeneous (mostly dense), and dense. In both training and testing mammograms, about 40 percent were assessed as heterogeneous and dense.

During the training process, the model is given random mammograms to analyze. It learns to map the mammogram with expert radiologist density ratings. Dense breasts, for instance, contain glandular and fibrous connective tissue, which appear as compact networks of thick white lines and solid white patches. Fatty tissue networks appear much thinner, with gray area throughout. In testing, the model observes new mammograms and predicts the most likely density category.

Matching assessments

The model was implemented at the breast imaging division at MGH. In a traditional workflow, when a mammogram is taken, it's sent to a workstation for a radiologist to assess. The researchers' model is installed in a separate machine that intercepts the scans before it reaches the radiologist, and assigns each mammogram a density rating. When radiologists pull up a scan at their workstations, they'll see the model's assigned rating, which they then accept or reject.

"It takes less than a second per image ... [and it can be] easily and cheaply scaled throughout hospitals." Yala says.

On over 10,000 mammograms at MGH from January to May of this year, the model achieved 94 percent agreement among the hospital's radiologists in a binary test -- determining whether breasts were either heterogeneous and dense, or fatty and scattered. Across all four BI-RADS categories, it matched radiologists' assessments at 90 percent. "MGH is a top breast imaging center with high inter-radiologist agreement, and this high quality dataset enabled us to develop a strong model," Yala says.

In general testing using the original dataset, the model matched the original human expert interpretations at 77 percent across four BI-RADS categories and, in binary tests, matched the interpretations at 87 percent.

In comparison with traditional prediction models, the researchers used a metric called a kappa score, where 1 indicates that predictions agree every time, and anything lower indicates fewer instances of agreements. Kappa scores for commercially available automatic density-assessment models score a maximum of about 0.6. In the clinical application, the researchers' model scored 0.85 kappa score and, in testing, scored a 0.67. This means the model makes better predictions than traditional models.

In an additional experiment, the researchers tested the model's agreement with consensus from five MGH radiologists from 500 random test mammograms. The radiologists assigned breast density to the mammograms without knowledge of the original assessment, or their peers' or the model's assessments. In this experiment, the model achieved a kappa score of 0.78 with the radiologist consensus.

Next, the researchers aim to scale the model into other hospitals. "Building on this translational experience, we will explore how to transition machine-learning algorithms developed at MIT into clinic benefiting millions of patients," Barzilay says. "This is a charter of the new center at MIT -- the Abdul Latif Jameel Clinic for Machine Learning in Health at MIT -- that was recently launched. And we are excited about new opportunities opened up by this center."

Story Source:
Materials provided by Massachusetts Institute of Technology. Original written by Rob Matheson. Note: Content may be edited for style and length.

Journal Reference:
Constance D. Lehman, Adam Yala, Tal Schuster, Brian Dontchos, Manisha Bahl, Kyle Swanson, Regina Barzilay. Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation. Radiology, 2018; 180694 DOI: 10.1148/radiol.2018180694

Cite This Page:
Massachusetts Institute of Technology. "Automated system identifies dense tissue, a risk factor for breast cancer, in mammograms: Deep-learning model has been used successfully on patients, may lead to more consistent screening procedures." ScienceDaily. ScienceDaily, 16 October 2018. <www.sciencedaily.com/releases/2018/10/181016131933.htm>.

Image credit

16 October 2018

Study Points to Possible New Therapy for Hearing Loss

Tuesday, October 16, 2018 0
www.trendsnowdays.com
Researchers have taken an important step toward what may become a new approach to restore the hearing loss. In a new study, out today in the European Journal of Neuroscience, scientists have been able to regrow the sensory hair cells found in the cochlea -- a part of the inner ear -- that converts sound vibrations into electrical signals and can be permanently lost due to age or noise damage.



Hearing impairment has long been accepted as a fact of life for the aging population -- an estimated 30 million Americans suffer from some degree of hearing loss. However, scientists have long observed that other animals -- namely birds, frogs, and fish -- have been shown to have the ability to regenerate lost sensory hair cells.

"It's funny, but mammals are the oddballs in the animal kingdom when it comes to cochlear regeneration," said Jingyuan Zhang, Ph.D., with the University of Rochester Department of Biology and a co-author of the study. "We're the only vertebrates that can't do it."

Research conducted in the lab of Patricia White, Ph.D., in 2012 identified a family of receptors -- called epidermal growth factor (EGF) -- responsible for activating support cells in the auditory organs of birds. When triggered, these cells proliferate and foster the generation of new sensory hair cells. She speculated that this signaling pathway could potentially be manipulated to produce a similar result in mammals. White is a research associate professor in the University of Rochester Medical Center (URMC) Del Monte Institute for Neuroscience and lead author of the current study.

"In mice, the cochlea expresses EGF receptors throughout the animal's life, but they apparently never drive regeneration of hair cells," said White. "Perhaps during mammalian evolution, there have been changes in the expression of intracellular regulators of EGF receptor family signaling. Those regulators could have altered the outcome of signaling, blocking regeneration. Our research is focused on finding a way switch the pathway temporarily, in order to promote both regeneration of hair cells and their integration with nerve cells, both of which are critical for hearing."

In the new study, which involved researchers from URMC and the Massachusetts Ear and Eye Infirmary, which is part of Harvard Medical School, the team tested the theory that signaling from the EGF family of receptors could play a role in cochlear regeneration in mammals. The researchers focused on a specific receptor called ERBB2 which is found in cochlear support cells.

The researchers investigated a number of different methods to activate the EGF signaling pathway. One set of experiments involved using a virus to target ERBB2 receptors. Another, involved mice genetically modified to overexpress an activated ERBB2. A third experiment involved testing two drugs, originally developed to stimulate stem cell activity in the eyes and pancreas, that are known activate ERBB2 signaling.

The researchers found that activating the ERBB2 pathway triggered a cascading series of cellular events by which cochlear support cells began to proliferate and start the process of activating other neighboring stem cells to become new sensory hair cells. Furthermore, it appears that this process not only could impact the regeneration of sensory hair cells, but also support their integration with nerve cells.

"The process of repairing hearing is a complex problem and requires a series of cellular events," said White. "You have to regenerate sensory hair cells and these cells have to function properly and connect with the necessary network of neurons. This research demonstrates a signaling pathway that can be activated by different methods and could represent a new approach to cochlear regeneration and, ultimately, restoration of hearing."

Story Source:
Materials provided by University of Rochester Medical Center. Note: Content may be edited for style and length.

Journal Reference:
Jingyuan Zhang, Quan Wang, Dunia Abdul-Aziz, Jonelle Mattiacio, Albert S.B. Edge, Patricia M. White. ERBB2 signaling drives supporting cell proliferation in vitro and apparent supernumerary hair cell formation in vivo in the neonatal mouse cochlea. European Journal of Neuroscience, 2018; DOI: 10.1111/ejn.14183

Cite This Page:
University of Rochester Medical Center. "Study points to possible new therapy for hearing loss." ScienceDaily. ScienceDaily, 15 October 2018. <www.sciencedaily.com/releases/2018/10/181015132953.htm>.

Image credit: © 9nong / Fotolia

15 October 2018

Memory 'Brainwaves' Look the Same in Sleep and Wakefulness

Monday, October 15, 2018 0
www.trendsnowdays.com
Identical brain mechanisms are responsible for triggering memory in both sleep and wakefulness, new research at the University of Birmingham has shown.

The study sheds new light on the processes used by the brain to 'reactivate' memories during sleep, consolidating them so they can be retrieved later.

Although the importance of sleep in stabilising memories is a well-established concept, the neural mechanisms underlying this are still poorly understood.

In this study, published in Cell Reports, scientists have been able to show for the first time in humans that distinctive neural patterns in the brain which are triggered when remembering specific memories while awake, reappear during subsequent sleep.

The findings provide further evidence of the beneficial effects of sleep on memory formation.

Gaining a more sophisticated understanding of these mechanisms also enhances our understanding of how memories are formed. This could ultimately help scientists unravel the foundations of memory disorders such as Alzheimer's and lead to the development of memory boosting interventions.

Working in partnership with researchers at the Donders Institute, in Holland, the team used a technique called Targeted Memory Reactivation, which is known to enhance memory. In the experiment, previously learned information -- in this case foreign vocabulary -- is played back to a person while asleep.

Using electroencephalography, the brain signals of the study participants were recorded while learning and remembering the foreign vocabulary before sleep.

Subsequently, the researchers recorded the distinct neural pathways activated as the sleeping volunteers' brains reacted to hearing the words they had learned.

Comparing neural signals fired by the brain in each state, the researchers were able to show clear similarities in brain activity.


Dr Thomas Schreiner, of the University of Birmingham's School of Psychology, who led the research, says: "Although sleep and wakefulness might seem to have little in common, this study shows that brain activity in each of these states might be more similar than we previously thought. The neural activity we recorded provides further evidence for how important sleep is to memory and, ultimately, for our well-being."

"If we can better understand how memory really works, this could lead to new approaches for the treatment of memory disorders, such as Alzheimer's disease."

Dr Tobias Staudigl, of the Donders Institute, is co-lead author of the study. He said: "Understanding how memories are reactivated in different states also provides insight into how these memories could be altered -- which might for example be interesting in therapeutic settings."

The team are planning a follow-on study, devising ways to investigate spontaneous memory activation during sleep. Using advanced machine learning techniques, the researchers can record and interpret neural patterns in the brain, identifying where memories are activated without the need for an external prompt.

The study was funded by the Swiss National Science Foundation and the European Research Council.

Story Source:
Materials provided by University of Birmingham. Note: Content may be edited for style and length.

Journal Reference:
Thomas Schreiner, Christian F. Doeller, Ole Jensen, Björn Rasch, Tobias Staudigl. Theta Phase-Coordinated Memory Reactivation Reoccurs in a Slow-Oscillatory Rhythm during NREM Sleep. Cell Reports, 2018; 25 (2): 296 DOI: 10.1016/j.celrep.2018.09.037

Cite This Page:
University of Birmingham. "Memory 'brainwaves' look the same in sleep and wakefulness." ScienceDaily. ScienceDaily, 9 October 2018. <www.sciencedaily.com/releases/2018/10/181009115003.htm>.

Image credit

Molecular Link Between Body Weight, Early Puberty Identified

Monday, October 15, 2018 0
Enzyme that can activate or suppress puberty gene behaves differently in fat, thin rats

www.trendsnowdays.com
Becoming overweight at a young age can trigger a molecular chain reaction that leads some girls to experience puberty early, according to new research published in Nature Communications.

Scientists have discovered a molecular mechanism that leads overweight female rats to have early-onset puberty.

"Knowing how nutrition and specific molecules play a role in starting puberty early could one day help physicians prevent the condition in humans," said one of the study's corresponding authors, Alejandro Lomniczi, Ph.D., a research assistant professor at OHSU's Oregon National Primate Research Center.

Girls have been experiencing puberty earlier in life for the last 150 years or so, with 12.5 years being the average age girls start puberty today. Early-onset puberty can lead girls to experience health problems later, including increased incidence of ovarian, uterine and breast cancers, as well as being at a higher risk for cardiovascular and metabolic diseases.



But the human genome -- the complete set of nucleic acid sequences in human DNA -- hasn't changed substantially in the past 150 years. So Lomniczi and colleagues are now exploring if the difference might be due to epigenetics, or changes caused by gene expression rather than changes in the genetic code itself. Gene expression is when genes make functional products such as proteins.

Lomniczi's previous research identified two gene families that keep puberty in check when rodents and nonhuman primates are young. This new study builds on that earlier work by specifically examining how gene expression and body weight are involved in puberty in rats.

For this study, the team raised three kinds of female rats: overweight, lean and average-sized. While focusing on the hypothalamus, the bottom part of the brain that controls reproductive development, they found that a puberty-activating gene called Kiss1 was expressed differently in each rat type.

Lomniczi and colleagues identified the enzyme SIRT1 in the hypothalamus as being a key player in transmitting body weight information to the brain. In overweight rats, there is less SIRT1 in the hypothalamus, allowing the Kiss1 gene to be expressed earlier, leading the rats to undergo puberty early. In lean rats, SIRT1 is higher for a prolonged period of time, taking longer for Kiss1 gene activation, which delayed puberty in those rats.

Lomniczi continues to explore the causes of early-onset puberty through animal model studies. He's also examining how the circadian clock and endocrine disruptors might play a role.

Story Source:
Materials provided by Oregon Health & Science University. Note: Content may be edited for style and length.

Journal Reference:
M. J. Vazquez, C. A. Toro, J. M. Castellano, F. Ruiz-Pino, J. Roa, D. Beiroa, V. Heras, I. Velasco, C. Dieguez, L. Pinilla, F. Gaytan, R. Nogueiras, M. A. Bosch, O. K. Rønnekleiv, A. Lomniczi, S. R. Ojeda, M. Tena-Sempere. SIRT1 mediates obesity- and nutrient-dependent perturbation of pubertal timing by epigenetically controlling Kiss1 expression. Nature Communications, 2018; 9 (1) DOI: 10.1038/s41467-018-06459-9

Cite This Page:
Oregon Health & Science University. "Molecular link between body weight, early puberty identified: Enzyme that can activate or suppress puberty gene behaves differently in fat, thin rats." ScienceDaily. ScienceDaily, 11 October 2018. <www.sciencedaily.com/releases/2018/10/181011090526.htm>.

Image credit

12 October 2018

Blood Sugar Test – Blood Sugar Level, Normal Range, Glucose Tolerance Test, HbA1c

Friday, October 12, 2018 0
www.trendsnowdays.com

What is a blood sugar test?

A blood sugar test is a procedure that measures the amount of sugar, or glucose, in your blood. Your doctor may order this test to help diagnose diabetes. People with diabetes can also use this test to manage their condition.
Blood sugar tests provide instant results and let you know the following:
  • Whether your diet or exercise routine needs to change
  • How your diabetes medications or treatment is working
  • How your blood sugar levels are, high or low
  • How your overall treatment goals for diabetes are manageable
Your doctor may also order a blood sugar test as part of a routine checkup. They may also be looking to see if you have diabetes or prediabetes, a condition where your blood sugar levels are higher than normal.

Factors of high blood sugar level

Your risk for diabetes increases if any of the following factors;
  • If you are 45 years old or older
  • If you are overweight
  • If you don’t exercise much
  • If you have high blood pressure, high triglycerides, or low good cholesterol levels (HDL)
  • If you have a history of gestational diabetes or giving birth to a baby who weighed over 4kgs.
  • If you have a history if insulin resistance
  • IF you have a history of strokes or hypertension
  • If you have a family history of diabetes

What does a blood sugar test do?

Your doctor may refer a blood sugar test to see if you have diabetes or prediabetes. The test will measure the amount of glucose in your blood.
Your body takes carbohydrates found in foods like grains and fruits and converts them into glucose. Glucose, a sugar, is one of the body’s main sources of energy.
For people with diabetes, a home test (Glucometer) helps monitor blood sugar levels. Taking a blood sugar test can help determine your blood sugar level to see if you need to adjust your diet, exercise, or diabetes medications.


Risk factors;
Low blood sugar (hypoglycemia) can lead to seizures or a coma if left untreated. High blood sugar (hyperglycemia) can lead to ketoacidosis, a life-threatening condition that’s often a concern for those with type 1 diabetes. Ketoacidosis occurs when your body starts using only fat for fuel. Hyperglycemia over a long period can increase your risk for neuropathy (nerve damage), along with heart, kidney, and eye diseases.

Types of blood sugar tests

You can take a blood sugar test two ways.
  1. People who are monitoring or managing their diabetes prick their finger using a glucometer for daily testing.
  2. Blood samples are generally used to screen for diabetes. Your doctor will refer a fasting blood sugar (FBS) test. This test measures your blood sugar levels, or a glycosylated hemoglobin, also called a hemoglobin A1C test or HbA1c. The results of this test reflect your blood sugar levels over the previous 90 days. The results will show if you have prediabetes or diabetes and can monitor how your diabetes is controlled.
  3. Blood sugar testing after meal (2hrs gap after meal). Commonly referred as postprandial or pp
  4. Blood sugar random testing. This can tested anytime during the day
*Self-management of blood sugar level using glucometer is not advisable and risk.

When to test blood sugar

When and how often you should test your blood sugar depends on the type of diabetes you have and your treatment.

Type 1 diabetes

According to the American Diabetes Association (ADA), if you’re managing type 1 diabetes with multiple dose insulin or an insulin pump, you’ll want to monitor your blood sugar before:
  • eating a meal or snack
  • exercising
  • sleeping
  • critical tasks like driving or babysitting

High blood sugar

You’ll want to check your blood sugar levels if you have diabetes and feel increasing thirst and the urge to urinate. These could be symptoms of high blood sugar and you may need to modify your treatment plan.
If your diabetes is well-controlled but you still have symptoms, it may mean you’re getting sick or that you’re under stress.
Exercising and managing your carbohydrate intake may help with lowering your blood sugar levels. If these changes don’t work, you may need to meet with your doctor to decide how to get your blood sugar levels back into target range.

Low blood sugar

Check your blood sugar levels if you feel any of the following symptoms:
  • shaky
  • sweaty or chilly
  • irritated or impatient
  • confused
  • lightheaded or dizzy
  • hungry and nauseous
  • sleepy
  • tingly or numb in the lips or tongue
  • weak
  • angry, stubborn, or sad
Some symptoms like delirium, seizures, or unconsciousness can be symptoms of low blood sugar or insulin shock. If you’re on daily insulin injections, ask your doctor about glucagon, a prescription medicine that can help if you’re having a severe low blood sugar reaction.
You can also have low blood sugar and show no symptoms. This is called hypoglycemia unawareness. If you have a history of hypoglycemia unawareness, you may need to test your blood sugar more often.

Pregnancy

Some women develop gestational diabetes during pregnancy. This is when hormones interfere with the way your body uses insulin. It causes sugar to accumulate in the blood.
Your doctor will recommend testing your blood sugar regularly if you have gestational diabetes. Testing will make sure that your blood glucose level is within a healthy range. Gestational diabetes usually goes away after childbirth.

What is a glucose tolerance test?

A glucose tolerance test measures how well your body’s cells are able to absorb glucose (sugar) after you consume a specific amount of sugar. Doctors primarily use a glucose tolerance test to diagnose diabetes during pregnancy (called gestational diabetes).
Doctors should screen all pregnant women for gestational diabetes. Gestational diabetes can cause pregnancy complications, so early detection and prompt treatment are important.If you’re pregnant, your doctor will usually recommend that you have this test between weeks 24 and 28 of your pregnancy. Your doctor may also recommend that you do this test earlier if you’re experiencing diabetes symptoms or if you were at risk of having diabetes before you were pregnant.
A two-hour, 75-gram oral glucose tolerance test (OGTT) is used to test for diabetes. Laboratory will take draw the blood for fasting glucose level first. They’ll then ask you to drink 8 ounces of a syrupy glucose solution that contains 75 grams of sugar.
You’ll then wait in laboratory for two hours. The laboratory technician will draw blood at the one- and two-hour marks.
Fastinggreater than 95 mg/dL
After 1 hourgreater than 180 mg/dL
After 2 hoursgreater than 155 mg/dL
After 3 hoursgreater than 140 mg/dL


Blood Sugar Normal Ranges

Your doctor will provide a more specific target range for your blood sugar levels depending on the following factors:
  • personal history
  • how long you’ve had diabetes
  • presence of diabetes complications
  • age
  • pregnancy
  • overall health
Tracking your blood sugar levels is one way to take control of your diabetes. You may find it helpful to log your results in a journal or app. Trends like continuously having levels that are too high or too low may mean adjusting your treatment for better results.
NormalPrediabetesDiabetes
under 100 mg/dLbetween 110–125 mg/dLgreater than or equal to 126 mg/dL
HbA1c
under 5.7 percent5.7-6.4 percentgreater than or equal to 6.5 percent

Story Source: healthcarentsickcare.com
Images: credit