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During evolution, living species have adapted to environmental constraints according to the mechanism of natural selection; when a mutation that aids the survival (and reproduction) of an individual appears in the genome, it then spreads throughout the rest of the species until, after several hundreds or even thousands of generations, it is carried by all individuals. But does this selection, which occurs on a specific gene in the genome of a species, also occur on the same gene in neighboring species? On which set of genes has natural selection acted specifically in each species? Researchers in the Dynamique et Organisation des Génomes team at the Institut de Biologie of the Ecole Normale Supérieure (CNRS/ENS/INSERM) have studied the genome of humans and three other primate species (chimpanzee, orangutan and macaque) using bioinformatics tools. Their work consisted in comparing the entire genomes of each species in order to identify the genes having undergone selection during the past 200,000 years. The result was that a few hundred genes have recently undergone selection in each of these species. These include around 100 genes detected in man that are shared by two or three other species, which is twice as many as might be anticipated as a random phenomenon. Thus a not inconsiderable proportion of the genes involved in human adaptation are also present in the chimpanzee, orangutan or macaque, and sometimes in several species at the same time. Natural selection acts not only by distancing different species from each other when new traits appear. But by acting on the same gene, it can also give rise to the same trait in species that have already diverged, but still have a relatively similar genome. This study thus provides a clearer understanding of the group of genes that are specifically implicated in human evolution (during the past 200,000 years), as it allows the identification of those genes which did not undergo selection in another primate line. An example that has been confirmed by this study is the well-known case of the lactase gene that can metabolize lactose during adulthood (a clear advantage with the development of agriculture and animal husbandry). The researchers have also identified a group of genes involved in some neurological functions and in the development of muscles and skeleton.

 »We’ll just have to wait for the results of the laboratory tests.« These words are familiar to many patients. It then usually takes several days for specimens to be sent to the laboratory and analyzed and for the doctor to receive the results. For many illnesses, however, a speedy diagnosis is crucial if the treatment is to be successful. In future, the patient might only have to sit in the waiting room for a few minutes until the results are ready. In a joint project, researchers from seven Fraunhofer institutes have developed a modular platform for in vitro diagnosis which enables various types of bioanalysis – of blood and saliva for example – to be conducted in the doctor’s surgery. »Thanks to its modular design our IVD platform is so flexible that it can be used for all possible bioanalytical tasks,« states Dr. Eva Ehrentreich-Förster from the Fraunhofer Institute for Biomedical Engineering (IBMT) in Potsdam-Golm.The core element of the mini-laboratory is a disposable cartridge made of plastic which can be fitted with various types of sensor. For an analysis the doctor fills the cartridge with reagents – binding agents which indicate the presence of certain substances such as antigens in the specimen material. Various tests or assays are available for different types of analysis. To perform an assay, the doctor only has to place the relevant substances in the cartridge and the test then takes place automatically. »We have optimized the assays so that up to 500 assay reactions can be conducted in parallel in a single analysis step,« explains Dr. Ehrentreich-Förster. Even in the case of complex analyses the doctor obtains a result within about 30 minutes. A new module on the reverse side of the cartridge also makes it possible to analyze the specimen material at DNA level.Once the cartridge has been prepared, the doctor places it in the measurement system. The results can be read out with either optical or electrochemical biosensors. The researchers have installed a readout window for both methods in the measurement system, which features a bypass through which the specimen is pumped.

This is the largest nationally representative study to date of the toll taken by sepsis and pneumonia, two conditions often caused by deadly microbes, including the antibiotic-resistant bacteria MRSA. Such infections can lead to longer hospital stays, serious complications and even death. "In many cases, these conditions could have been avoided with better infection control in hospitals," said Ramanan Laxminarayan, Ph.D., principal investigator for Extending the Cure, a project examining antibiotic resistance based at the Washington, D.C. think-tank Resources for the Future. "Infections that are acquired during the course of a hospital stay cost the United States a staggering amount in terms of lives lost and health care costs," he said. "Hospitals and other health care providers must act now to protect patients from this growing menace." Laxminarayan and his colleagues analyzed 69 million discharge records from hospitals in 40 states and identified two conditions caused by health care-associated infections: sepsis, a potentially lethal systemic response to infection and pneumonia, an infection of the lungs and respiratory tract. The researchers looked at infections that developed after hospitalization. They zeroed in on infections that are often preventable, like a serious bloodstream infection that occurs because of a lapse in sterile technique during surgery, and discovered that the cost of such infections can be quite high: For example, people who developed sepsis after surgery stayed in the hospital 11 days longer and the infections cost an extra $33,000 to treat per person. Even worse, the team found that nearly 20 percent of people who developed sepsis after surgery died as a result of the infection. "That's the tragedy of such cases," said Anup Malani, a study co-author, investigator at Extending the Cure, and professor at the University of Chicago. "In some cases, relatively healthy people check into the hospital for routine surgery. They develop sepsis because of a lapse in infection control—and they can die." The team also looked at pneumonia, an infection that can set in if a disease-causing microbe gets into the lungs—in some cases when a dirty ventilator tube is used. They found that people who developed pneumonia after surgery, which is also thought to be preventable, stayed in the hospital an extra 14 days. Such cases cost an extra $46,000 per person to treat. In 11 percent of the cases, the patient died as a result of the pneumonia infection.

Statistical validation of results, as Shaffer described it, simply involves testing the null hypothesis: that the pattern you detect in your data occurs at random. If you can reject the null hypothesis—and science and medicine have settled on rejecting it when there's only a five percent or less chance that it occurred at random—then you accept that your actual finding is significant. The problem now is that we're rapidly expanding our ability to do tests. Various speakers pointed to data sources as diverse as gene expression chips and the Sloan Digital Sky Survey, which provide tens of thousands of individual data points to analyze. At the same time, the growth of computing power has meant that we can ask many questions of these large data sets at once, and each one of these tests increases the prospects than an error will occur in a study; as Shaffer put it, "every decision increases your error prospects." She pointed out that dividing data into subgroups, which can often identify susceptible subpopulations, is also a decision, and increases the chances of a spurious error. Smaller populations are also more prone to random associations. In the end, Young noted, by the time you reach 61 tests, there's a 95 percent chance that you'll get a significant result at random. And, let's face it—researchers want to see a significant result, so there's a strong, unintentional bias towards trying different tests until something pops out. Young went on to describe a study, published in JAMA, that was a multiple testing train wreck: exposures to 275 chemicals were considered, 32 health outcomes were tracked, and 10 demographic variables were used as controls. That was about 8,800 different tests, and as many as 9 million ways of looking at the data once the demographics were considered.