Showing posts with label disease. Show all posts
Showing posts with label disease. Show all posts

Wednesday, March 29, 2017

Experts have discovered the first mutation in the development of human evolution.

Pixabay
For the first time, scientists have caught a glimpse of the earliest genetic mutations in human development.

Using whole genome sequencing, they wound back time on cell samples from adults and revealed what took place in the genome when they were still microscopic embryos. It turns out, our first two cells contribute to our development in very different ways.

Biology Reference

Mutations come in two forms: the hereditary ones we get from our parents, which can be found in virtually every cell of the body; and the acquired (or somatic) mutations that can occur at any stage of a person's life, including those very first days when the embryo is just starting to split into multiple cells.

Somatic mutations don't necessarily cause problems, but they can sometimes lead to cancer and other diseases. They also don't necessarily live in every cell (that's called mosaicism). 

We have a fairly murky understanding of the somatic mutations that happen during the earliest life stages, because we can't just watch that stuff happening in real time.

But now researchers have discovered a way to trace these mutations back to their first appearance.

Medical Xpress

"This is the first time that anyone has seen where mutations arise in the very early human development. It is like finding a needle in a haystack," says geneticist Young Seok Ju from the Wellcome Trust Sanger Institute in the UK and the Korea Advanced Institute of Science and Technology.


"There are just a handful of these mutations, compared with millions of inherited genetic variations, and finding them allowed us to track what happened during embryogenesis."

To find these mutations, the team analysed blood and tissue samples from 279 people with breast cancer. Using samples from cancer patients allowed them to test whether mutations were present in both normal blood and tissue, and in surgically removed tumour samples.

Since breast cancer tumours develop from a single cell, a somatic mutation would either be present in every tumour cell, or not at all, which gives a clue to its possible origins.

By tracking and comparing the spread of different mutations in these various tissue samples, the scientists verified a whopping 163 mutations that must have happened within the first few cell divisions of the persons' embryonic development.

University of South Florida

This gave them a unique insight into how early embryonic cells interact.

And that's not all - a statistical analysis revealed that when a fertilised egg divides for the first time, those two cells actually contribute building material for the rest of the body at different proportions.

It appears that one of the first two cells that make us up gives rise to 70 percent of the body tissue, while the other one chips in for the rest.

"We determined the relative contribution of the first embryonic cells to the adult blood cell pool and found one dominant cell - that led to 70 percent of the blood cells - and one minor cell," says molecular biologist Inigo Martincorena from the Sanger Institute.

indiatoday.intoday.in

"This opens an unprecedented window into the earliest stages of human development."

That's exciting, because having that window will let us discover even more about how humans develop and acquire various mutations from the get-go.

Even though the vast majority of mutations are random and harmless, occasionally they can affect an important gene, causing a developmental disorder or a disease.

"Essentially, the mutations are archaeological traces of embryonic development left in our adult tissues, so if we can find and interpret them, we can understand human embryology better," says lead researcher Mike Stratton, director of the Wellcome Trust Sanger Institute.

The researchers hope their discovery is just the first of many steps that will help us gain a better understanding of what happens to humans in the earliest days, when we're all nothing more than just a clump of cells.

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The above post is reprinted from materials provided by Sciencealert . Note: Materials may be edited for content and length.

Friday, March 3, 2017

The World Health Organization has made a list of the most dangerous antibiotic-resistant bacteria

National Institute of Allergy and Infectious Diseases (NIAID)
For the first time ever, the World Health Organization has drawn up a list of the highest priority needs for new antibiotics — marching orders, it hopes, for the pharmaceutical industry.

The list, which was released Monday, enumerates 12 bacterial threats, grouping them into three categories: critical, high, and medium.

"Antibiotic resistance is growing and we are running out of treatment options. If we leave it to market forces alone, the new antibiotics we most urgently need are not going to be developed in time," said Dr. Marie-Paule Kieny, the WHO's assistant director-general for health systems and innovation.

"The pipeline is practically dry."

Three bacteria were listed as critical:

  • Acinetobacter baumannii bacteria that are resistant to important antibiotics called carbapenems. These are highly drug resistant bacteria that can cause a range of infections for hospitalized patients, including pneumonia, wound, or blood infections.
U.S. Centers for Disease Control and Prevention

  • Pseudomonas aeruginosa, which are resistant to carbapenems. These bacteria can cause skin rashes and ear infectious in healthy people but also severe blood infections and pneumonia when contracted by sick people in the hospital.
  • Enterobacteriaceae that are resistant to both carbepenems and another class of antibiotics, cephalosporins. This family of bacteria live in the human gut and includes bugs such as E. coli and Salmonella.

Notably missing from the list is the bacterium that causes tuberculosis. That was not included, Kieny said, because the need for new antibiotics to treat it has already been designated the highest priority.

Although mounting concerns about the worsening problem of antibiotic resistance have reinvigorated research efforts, producing new antibiotics is an expensive and challenging task.

The international team of experts who drew up the new list urged researchers and pharmaceutical companies to focus their efforts on a type of bacteria known as Gram negatives. (The terminology relates to how the bacteria respond to a stain — developed by Hans Christian Gram — used to make them easier to see under a microscope.)

Dr. Nicola Magrini, a scientist with the WHO's department of innovation, access and use of essential medicines, said pharmaceutical companies have recently spent more efforts trying to find antibiotics for Gram positive bacteria, perhaps because they are easier and less costly to develop.

Microscopic image of gram-negative Pseudomonas aeruginosa bacteria (pink-red rods) Credit: wikipedia

Gram negative bacteria typically live in the human gut, which means when they cause illness it can be serious bloodstream infections or urinary tract infections. Gram positive bacteria are generally found outside the body, on the skin or in the nostrils.

Kieny said the 12 bacteria featured on the priority list were chosen based on the level of drug resistance that already exists for each, the numbers of deaths they cause, the frequency with which people become infected with them outside of hospitals, and the burden these infections place on health care systems.

Paradoxically, though, she and colleagues from the WHO could not provide an estimate of the annual number of deaths attributable to antibiotic-resistant infections. The international disease code system does not currently include a code for antibiotic-resistant infections; it is being amended to include one.

The critical pathogens are ones that cause severe infections and high mortality in hospital patients, Kieny said. While they are not as common as other drug-resistant infections, they are costly in terms of health care resources needed to treat infected patients and in lives lost.

Six others were listed as high priority for new antibiotics. That grouping represents bacteria that cause a large number of infections in otherwise healthy people. Included there is the bacteria that causes gonorrhea, for which there are almost no remaining effective treatments.

Three other bacteria were listed as being of medium priority, because they are becoming increasingly resistant to available drugs. This group includes Streptococcus pneumoniae that is not susceptible to penicillin. This bacterium causes pneumonia, ear and sinus infections, as well as meningitis and blood infections.

The creation of the list was applauded by others working to combat the rise of antibiotic resistance.

"This priority pathogens list, developed with input from across our community, is important to steer research in the race against drug resistant infection — one of the greatest threats to modern health," said Tim Jinks, head of drug-resistant infections for the British medical charity Wellcome Trust.

"Within a generation, without new antibiotics, deaths from drug-resistant infection could reach 10 million a year. Without new medicines to treat deadly infection, lifesaving treatments like chemotherapy and organ transplant, and routine operations like caesareans and hip replacements, will be potentially fatal."

The full list is:

Priority 1: Critical
1. Acinetobacter baumannii, carbapenem-resistant

2. Pseudomonas aeruginosa, carbapenem-resistant

3. Enterobacteriaceae, carbapenem-resistant, ESBL-producing


Priority 2: High
4. Enterococcus faecium, vancomycin-resistant

5. Staphylococcus aureus, methicillin-resistant, vancomycin-intermediate and resistant

6. Helicobacter pylori, clarithromycin-resistant

7. Campylobacter spp., fluoroquinolone-resistant

8. Salmonellae, fluoroquinolone-resistant
9. Neisseria gonorrhoeae, cephalosporin-resistant, fluoroquinolone-resistant

Priority 3: Medium
10. Streptococcus pneumoniae, penicillin-non-susceptible

11. Haemophilus influenzae, ampicillin-resistant

12. Shigella spp., fluoroquinolone-resistant


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The above post is reprinted from materials provided by Businessinsider . Note: Materials may be edited for content and length.

Sunday, January 29, 2017

Artificial Intelligence Used to ID Skin Cancer. Deep learning algorithm does as well as dermatologists in identifying skin cancer

A dermatologist using a dermatoscope, a type of handheld microscope, to look at skin. Computer scientists at Stanford have created an artificially intelligent diagnosis algorithm for skin cancer that matched the performance of board-certified dermatologists. Credit: Matt Young
It's scary enough making a doctor's appointment to see if a strange mole could be cancerous. Imagine, then, that you were in that situation while also living far away from the nearest doctor, unable to take time off work and unsure you had the money to cover the cost of the visit. In a scenario like this, an option to receive a diagnosis through your smartphone could be lifesaving.

Universal access to health care was on the minds of computer scientists at Stanford when they set out to create an artificially intelligent diagnosis algorithm for skin cancer. They made a database of nearly 130,000 skin disease images and trained their algorithm to visually diagnose potential cancer. From the very first test, it performed with inspiring accuracy.

"We realized it was feasible, not just to do something well, but as well as a human dermatologist," said Sebastian Thrun, an adjunct professor in the Stanford Artificial Intelligence Laboratory. "That's when our thinking changed. That's when we said, 'Look, this is not just a class project for students, this is an opportunity to do something great for humanity.'"

The final product, the subject of a paper in the Jan. 25 issue of Nature, was tested against 21 board-certified dermatologists. In its diagnoses of skin lesions, which represented the most common and deadliest skin cancers, the algorithm matched the performance of dermatologists.

Why skin cancer

Every year there are about 5.4 million new cases of skin cancer in the United States, and while the five-year survival rate for melanoma detected in its earliest states is around 97 percent, that drops to approximately 14 percent if it's detected in its latest stages. Early detection could likely have an enormous impact on skin cancer outcomes.

Diagnosing skin cancer begins with a visual examination. A dermatologist usually looks at the suspicious lesion with the naked eye and with the aid of a dermatoscope, which is a handheld microscope that provides low-level magnification of the skin. If these methods are inconclusive or lead the dermatologist to believe the lesion is cancerous, a biopsy is the next step.

Bringing this algorithm into the examination process follows a trend in computing that combines visual processing with deep learning, a type of artificial intelligence modeled after neural networks in the brain. Deep learning has a decades-long history in computer science but it only recently has been applied to visual processing tasks, with great success. The essence of machine learning, including deep learning, is that a computer is trained to figure out a problem rather than having the answers programmed into it.

"We made a very powerful machine learning algorithm that learns from data," said Andre Esteva, co-lead author of the paper and a graduate student in the Thrun lab. "Instead of writing into computer code exactly what to look for, you let the algorithm figure it out."

The algorithm was fed each image as raw pixels with an associated disease label. Compared to other methods for training algorithms, this one requires very little processing or sorting of the images prior to classification, allowing the algorithm to work off a wider variety of data.

From cats and dogs to melanomas and carcinomas

Rather than building an algorithm from scratch, the researchers began with an algorithm developed by Google that was already trained to identify 1.28 million images from 1,000 object categories. While it was primed to be able to differentiate cats from dogs, the researchers needed it to know a malignant carcinoma from a benign seborrheic keratosis.

"There's no huge dataset of skin cancer that we can just train our algorithms on, so we had to make our own," said Brett Kuprel, co-lead author of the paper and a graduate student in the Thrun lab. "We gathered images from the internet and worked with the medical school to create a nice taxonomy out of data that was very messy -- the labels alone were in several languages, including German, Arabic and Latin."

After going through the necessary translations, the researchers collaborated with dermatologists at Stanford Medicine, as well as Helen M. Blau, professor of microbiology and immunology at Stanford and co-author of the paper. Together, this interdisciplinary team worked to classify the hodgepodge of internet images. Many of these, unlike those taken by medical professionals, were varied in terms of angle, zoom and lighting. In the end, they amassed about 130,000 images of skin lesions representing over 2,000 different diseases.

During testing, the researchers used only high-quality, biopsy-confirmed images provided by the University of Edinburgh and the International Skin Imaging Collaboration Project that represented the most common and deadliest skin cancers -- malignant carcinomas and malignant melanomas. The 21 dermatologists were asked whether, based on each image, they would proceed with biopsy or treatment, or reassure the patient. The researchers evaluated success by how well the dermatologists were able to correctly diagnose both cancerous and non-cancerous lesions in over 370 images.

The algorithm's performance was measured through the creation of a sensitivity-specificity curve, where sensitivity represented its ability to correctly identify malignant lesions and specificity represented its ability to correctly identify benign lesions. It was assessed through three key diagnostic tasks: keratinocyte carcinoma classification, melanoma classification, and melanoma classification when viewed using dermoscopy. In all three tasks, the algorithm matched the performance of the dermatologists with the area under the sensitivity-specificity curve amounting to at least 91 percent of the total area of the graph.

An added advantage of the algorithm is that, unlike a person, the algorithm can be made more or less sensitive, allowing the researchers to tune its response depending on what they want it to assess. This ability to alter the sensitivity hints at the depth and complexity of this algorithm. The underlying architecture of seemingly irrelevant photos -- including cats and dogs -- helps it better evaluate the skin lesion images.

Health care by smartphone

Although this algorithm currently exists on a computer, the team would like to make it smartphone compatible in the near future, bringing reliable skin cancer diagnoses to our fingertips.

"My main eureka moment was when I realized just how ubiquitous smartphones will be," said Esteva. "Everyone will have a supercomputer in their pockets with a number of sensors in it, including a camera. What if we could use it to visually screen for skin cancer? Or other ailments?"

The team believes it will be relatively easy to transition the algorithm to mobile devices but there still needs to be further testing in a real-world clinical setting.

"Advances in computer-aided classification of benign versus malignant skin lesions could greatly assist dermatologists in improved diagnosis for challenging lesions and provide better management options for patients," said Susan Swetter, professor of dermatology and director of the Pigmented Lesion and Melanoma Program at the Stanford Cancer Institute, and co-author of the paper. "However, rigorous prospective validation of the algorithm is necessary before it can be implemented in clinical practice, by practitioners and patients alike."

Even in light of the challenges ahead, the researchers are hopeful that deep learning could someday contribute to visual diagnosis in many medical fields.

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The above post is reprinted from materials provided by Sciencedaily . Note: Materials may be edited for content and length.

Thursday, November 17, 2016

Anton Pavlovich Chekhov, great writer killed by a terrible disease

On January 29, 1860 was born Anton Pavlovich Chekhov Russian writer (d. July 15, 1904).

Chekhov was born in Taganrog, Azov Sea city.

Between 1867 and 1879 made primary and secondary education in his hometown. Attend theater and leading a students magazine.

After his father flee from Moscow is forced to pay meditations. In 1879, begin medical studies in Moscow and helps his family financially by publishing humorous magazines.

Young Chekhov (left) with brother Nikolai in 1882 photo: wikipedia.org

After graduation in 1884, professes around Moscow. In 1886, the magazine Novoye begin working time (New Times) headed by Alexei Suvorin, who will be editor. During this period, public prose, working and his plays.


Young Chekhov in 1882 photo: wikipedia.org

In 1890, performs a voyage to Sakhalin Island, where recenzează population.


During his voyage to Italy, from 1894, his health worsens. In 1896, Constantin Stanislavski knows who will direct the plays.


In 1897, a hospital, touched the pulmonary tuberculosis. Between 1897 and 1901 his plays (Uncle Vanya, Three Sisters) are published and staged. Olga Knipper married actiţa. In 1903, completes the play The Cherry Orchard. In 1904, the disease is getting worse and on July 2, dies in nursing home in Germany to Badenweiler.


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The above post is reprinted from materials provided by Descopera . Note: Materials may be edited for content and length.

Saturday, November 12, 2016

A medicine that promises to treat Alzheimer has been tested on humans

Alzheimer's has remained the most common form of mental illness and is the sixth cause of death in the United States, but a viable and safe drug could not be achieved until now. According to the Alzheimer's Association di US, about 5.4 million Americans suffer from this condition.

But a new drug has shown that it can provide favorable solutions in terms of treating this disease, evidence that came to light after a few small studies. Moreover, it had no adverse effect on the participants in this study.

The drug, whose name is Verubecestat, target protein plaques in the brain in patients diagnosed with Alzheimer possibly face neurodegeneration. This has the effect that it is an inhibitor of the BACE1, an enzyme essential for the production of the toxic amyloid beta protein. By inhibiting the enzyme, this drug stops the formation of proteins or decreases the possibility that they group together. Drugs had earlier sought the same procedure side effects, such as liver toxicity or neurodegeneration. But it has not made any long-term adverse effect of mice and monkeys, and humans.


The final results of these studies are expected in 2017 and 2019, but this is not the only drug that promises to treat this disease, another called Aducanumab promises direct removal of the plates. Some researchers believe that they could be administered together.


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Source: Futurism