Slashdot Mirror

An anonymous reader quotes a report from Ars Technica: Extremely hot peppers don't just blister your mouth and bum -- they can also spark fiery havoc in your brain, according to a report published Monday in BMJ Case Reports. An otherwise healthy 34-year-old man developed a blood-flow disorder in his brain and suffered several debilitating "thunderclap" headaches after entering a hot pepper eating contest, U.S. doctors reported. The man had managed to get down a Carolina Reaper pepper, which in 2013 earned the title of the world's hottest chili by Guinness World Records.

The searing pepper didn't sit well in the chili-eating contestant. Immediately after slaying a Reaper, the man began dry heaving and developed pain in his neck and the back of his skull. That morphed into a diffuse, painful headache. Over the next few days, he experienced thunderclap headaches at least twice -- but likely more, he just couldn't recall exactly. Thunderclap headaches are severe, sudden, with quick pains that strike like a clap of thunder rumbling through your skull. They tend to peak within 60 seconds and can be accompanied by nausea, vomiting, altered mental state, seizures, and fever. Their stormy aches can be a sign of serious problems, like bleeding in the brain, a brain infection, or a cerebrospinal fluid leak. The pain was excruciating enough that the man went to the emergency room. But doctors didn't find any immediate problems with him to explain the episodes. He didn't have any slurred speech, loss of vision, neurological deficits, muscle weakness, or tingling. His blood pressure was a little high, but not extremely so, at 134/69 mmHg. Initial CT scans found no problems in his neck and head.

First, "A new study finds that nearly 9 in 10 people who go for a second opinion after seeing a doctor are likely to leave with a refined or new diagnosis from what they were first told," according to an article shared by Slashdot reader schwit1: Researchers at the Mayo Clinic examined 286 patient records of individuals who had decided to consult a second opinion, hoping to determine whether being referred to a second specialist impacted one's likelihood of receiving an accurate diagnosis. The study, conducted using records of patients referred to the Mayo Clinic's General Internal Medicine Division over a two-year period, ultimately found that when consulting a second opinion, the physician only confirmed the original diagnosis 12 percent of the time. Among those with updated diagnoses, 66% received a refined or redefined diagnosis, while 21% were diagnosed with something completely different than what their first physician concluded.
But in a related story, Slashdot reader sciencehabit writes that four machine-learning algorithms all performed better than currently-used algorithm of the American College of Cardiology, according to newly-published research, which concludes that "machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others."

"I can't stress enough how important it is," one Stanford vascular surgeon told Science magazine, "and how much I really hope that doctors start to embrace the use of artificial intelligence to assist us in care of patients."

New submitter drunkdrone quotes a report from International Business Times: Scientists say they have developed an artificial intelligence (AI) program that is capable of predicting when patients with a serious heart disorder will die with an 80% accuracy rate. Researchers from the MRC London Institute of Medical Sciences (LMS) believe the software will allow doctors to better treat patients with pulmonary hypertension by determining how aggressive their treatment needs to be. The researchers' program assessed the outlook of 250 patients based on blood test results and MRI scans of their hearts. It then used the data to create a virtual 3D heart of each patient which, combined with the health records of "hundreds" of previous patients, allowed it to learn which characteristics indicated fatal heart failure within five years. The LMS scientists claim that the software was able to accurately predict patients who would still be alive after a year around 80% of the time. The computer was able to analyze patients "in seconds," promising to dramatically reduce the time it takes doctors to identify the most at-risk individuals and ensure they "give the right treatment to the right patients, at the right time." Dr Declan O'Regan, one the lead researchers from LMS, said: "This is the first time computers have interpreted heart scans to accurately predict how long patients will live. It could transform the way doctors treat heart patients. The researchers now hope to field-test the technology in hospitals in London in order to verify the data obtained from their trials, which have been published in the medical journal Radiology.

iONiUM sends news that Mayo Clinic cancer researchers have developed a technique to reprogram cancer cells in a lab, essentially "turning off" their excessive cell growth. That code was unraveled by the discovery that adhesion proteins — the glue that keeps cells together — interact with the microprocessor, a key player in the production of molecules called microRNAs (miRNAs). The miRNAs orchestrate whole cellular programs by simultaneously regulating expression of a group of genes (abstract). The investigators found that when normal cells come in contact with each other, a specific subset of miRNAs suppresses genes that promote cell growth. However, when adhesion is disrupted in cancer cells, these miRNAs are misregulated and cells grow out of control. The investigators showed, in laboratory experiments, that restoring the normal miRNA levels in cancer cells can reverse that aberrant cell growth.

J.P. Duke asks: "There is an excellent research article published by the Mayo Clinic in the J Ortho Sci that compares two common software-based approaches in analyzing scanned protein gels. Among other conclusions, they found that the two most popular applications for this research had different tendencies in quantifying proteins -- and that differences in AI algorithms show clearly different results for proteins that are less-separated on gels. This implies that much major scientific research that depends on these tools might be suspect to flaws very early in analysis. Being a cancer researcher at a large research institution, much of my work depends on software being able to accurately analyze scanned images of protein gels in which proteins are simply displayed as spots on the gel. Among other things, the software needs to be able to precisely calculate the density of protein in a spot as well as the number of actual proteins contained in a spot. What we choose to investigate further as potential biomarkers for cancer depends heavily on the ability of the AI built into these applications." Exactly how far has image-based AI improved in the last several years? Might some of those improvements help someone in J.P.'s situation? "My questions for Slashdot are as follows:

• Overall, how good has research image software AI become in recent years? Have there been any key software or mathematical breakthroughs that have substantially increased the 'intelligence' of software? How far along is this technology?
  
  
• Based on your knowledge of software, what are some things researchers can do to help the software better do its job? For example, using a high quality scanner at higher resolutions generally helps results. What other things can be done to promote better results?
  
  
• Finally, all applications that I know of in this area are expensive commercial solutions. As the companies that produce the applications are for-profit, the algorithms and technology used are completely closed and proprietary. Thus it is hard to understand what the software is really doing. Does anybody know of any open source (or at least 'open algorithm') solutions? Even if they are inferior at this point in time, being able to clearly understand what the AI is doing makes us better off in several ways.

Thank you all for your help, it is greatly appreciated."