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.

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.

DOCTORS SUCCESSFULLY TREAT TWO BABIES WITH LEUKEMIA USING GENE-EDITED IMMUNE CELLS

Scientists are using gene-editing techniques to fight cancer.

IT’S A PROMISING APPROACH, BUT STILL NEEDS A LOT MORE RESEARCH

In a study out this week in the journal Science Translational Medicine, a group of British doctors reported that they had successfully “cured” two infants of the blood cancer leukemia using a treatment that involves genetically modified immune cells from a donor.

The study was incredibly small—just two babies—and the infants have only been free of leukemia for 16 and 18 months. Technically, that’s not long enough to say they are cured. Declaring someone who previously had cancer as “cured” usually doesn’t happen until that person has been free of the disease for a few years, at least. But what’s significant about this study is that it combines a promising, novel approach—CAR T cell therapy—with a relatively new gene-editing technique called TALENS, which enables the direct manipulation of genes within a person’s DNA.

In the cancer community, CAR T cell therapy is already touted as a promising immunotherapy treatment (which involves harnessing a person’s immune system to fight cancer on its own), but in preliminary trials, it’s had its limitations. Before it can become a universal cancer treatment, these kinks and logistics need to be worked out. And researchers in the field think that many of them can be solved using gene-editing techniques such as TALENS, the one used in this study, as well as CRISPR, supposedly the easiest such technique to date.


First, what is CAR T-cell treatment?

CAR T, which stands for chimeric antigen receptor T cell, is a new type of cancer treatment which is not yet publicly available, but is in active clinical trials in the United States as well as many other countries such as the United Kingdom and China. The therapy involves removing some T cells (specialized immune cells) from a patient's blood. Then those cells are genetically altered in a lab, giving them special receptors on their surface called CARs. Once the cells are ready, they are infused back into the patient’s blood, where the new (CAR) receptors seek out tumor cells, attach to them, and kill them.
CAR T-cell trials are currently in phase II clinical trials in the United States. A few drug companies, including Novartis, have plans to make the therapy available as early as this year.


How does gene-editing help?

This new treatment has worked really well for blood cancers like leukemia, especially in young children. The problem, as the researchers point out in their study, is that each set of T cells have to be custom made for each patient. That takes a lot of time, and a lot of money. Further, it’s not always feasible, or even possible, to harvest T cells from leukemia patients who simply don’t have enough healthy ones to begin with.
And that’s where gene-editing comes in. The researchers took T cells from donor recipients and made a total of four genetic changes. The two they made with TALENS enabled the T cells to become universal—allowing them to be used in any person without the risk of rejection (a phenomenon called graft-versus-host disease, where the recipient’s immune system creates such an overwhelming response to the foreign cells that the patient can die as a result). The other genetic alterations added that signature receptor to seek out and attack cancer.


What are the limitations of this study?

The two infants in the study—aged 11 and 18 months—both had an aggressive form of leukemia, and had already been subjected to other treatments like chemotherapy and stem cell transplants. And the fact that they have remained cancer free is extremely promising. But again, the study was small. Further, according to a report in MIT Technology Review, many CAR T experts argue that because the children also received other treatments simultaneously (one had a stem cell transplant soon after receiving the CAR T cells) it’s impossible to know for sure whether the CAR T cells were the sole reason the cancer cells stayed away. “There is a hint of efficacy but no proof,” Stephan Grupp, director of cancer immunotherapy at the Children’s Hospital of Philadelphia, told MIT Tech Review. “It would be great if it works, but that just hasn’t been shown yet.


What’s next?

The combination of CAR T cell immunotherapy with gene-editing remains an incredibly promising area of research. Not only to create a “universal donor” CAR T cell, but also to make the treatment more effective. Researchers at the University of Pennsylvania are currently researching using the the gene-editing technique CRISPR to edit out two genes—called checkpoint inhibitors—that prevent CAR T from working as well as it should. The trial, which could take place this year, would be the first case of a CRISPR-altered cell being used in a human patient in the United States. In November, a Chinese group tested their first CRISPR gene-edited T cells in a patient with lung cancer.
However, it’s important to remember that CAR T cell therapy is in its early stages, and CRISPR/TALEN gene edited CAR T is even newer. There’s still a lot more work to be done, including many, many more studies like this one, with a lot more patients, before it’s available for everyone.

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

Ranking of countries with the healthiest people 2018

In a study published in the medical journal The Lance was revealed the top countries with the healthiest population.

The research, funded by the Bill & Melinda Gates Foundation, was driven largely by the United Nations.

,, The study started after a massive collaboration that lasted 10 years, relying on distribution of diseases globally, '' said Bloomberg. The first place was ranked Iceland and Britain was ranked fifth.

In last place, the 188 th, Central African Reblica is followed by Somalia, South Sudan, Nigeria and Cian.

,, Our analysis highlighted the importance source of income, education and fertility as indicators of improving health, '' said study author.

In the study, Romania was ranked 74. Among the most common problems facing Romanians include tuberculosis, alcoholism and smoking.

Full rankings can be viewed here. 

Source: IFL Science