Each year, snake bites kill upwards of 100,000 people and permanently disable hundreds of thousands more, according to estimates from the World Health Organization. Promising new science, enabled by state-of-the-art technology, could help quell the threat. Researchers have successfully designed two proteins to neutralize some of the most lethal venom toxins, using a suite of artificial intelligence tools, per a study published January 15 in the journal Nature. These "de novo" proteins–molecules not found anywhere in nature–protected 100% of mice from certain death when mixed with the deadly snake compounds and administered in lab experiments. "I think we could revolutionize the treatment [of snake bites]," says Susana Vázquez Torres, lead study author and a biochemist who completed this research as part of her doctoral thesis in David Baker's lab at the University of Washington.

Computers powered by human brain cells may sound like science fiction, but a team of researchers in the United States believes such machines, part of a new field called "organoid intelligence," could shape the future -- and now they have a plan to get there. Organoids are lab-grown tissues that resemble organs. These three-dimensional structures, usually derived from stem cells, have been used in labs for nearly two decades, where scientists have been able to avoid harmful human or animal testing by experimenting on the stand-ins for kidneys, lungs and other organs. Brain organoids don't actually resemble tiny versions of the human brain, but the pen dot-size cell cultures contain neurons that are capable of brainlike functions, forming a multitude of connections. Scientists call the phenomenon "intelligence in a dish."

Noting that overlapping imaging features on contrast-enhanced computed tomography (CT) can make it challenging to differentiate between acute diverticulitis and colon cancer, researchers say an emerging deep learning model may provide enhanced sensitivity and specificity for these conditions. In a retrospective study recently published in JAMA Network Open, researchers developed and tested a three-dimensional (3D) convolutional neural network (CNN) for 585 patients (mean age of 63.2) who underwent surgery for colon cancer or acute diverticulitis between July 1, 2005 and October 1, 2020, had venous phase CT imaging within 60 days prior to surgery and had segmental wall thickening in the colon that was independent of disease stage. In comparison to mean sensitivity and specificity rates of 77.6 percent and 81.6 percent, respectively, for radiologist readers, the study authors noted an 83.3 percent sensitivity rate and an 86.6 percent specificity rate for the 3D CNN model. The combination of the deep learning model and radiologist assessment resulted in an eight percent increase in sensitivity (85.6 percent) and a 9.7 percent increase in specificity (91.3 percent) over radiologist assessments, according to the study findings. The study authors also noted the reduction of false-negative rates with the 3D CNN model.

But in the remaining 32% of cases, the subjects were tricked. And that's despite just 8% of the falsified abstracts meeting the specific formatting and style requirement for the listed journal. Plus, the reviewers falsely identified 14% of the real article abstracts as having been AI-generated. "Reviewers indicated that it was surprisingly difficult to differentiate between the two," wrote the study researchers in the pre-print. While they were sorting the abstracts, the reviewers noted that they thought the generated samples were vaguer and more formulaic.

A miracle of the modern age, countless works of science fiction have predicted an inevitable confrontation in the not-so-distant future: man versus machine. Now, according to researchers at Rutgers University, it appears machines have already bested humanity when it comes to at least one scientific subject. Professor Vikas Nanda of Rutgers University has spent over two decades meticulously studying the intricate nature of proteins, the highly complex substances present in all living organisms. He has dedicated his professional life to contemplating and understanding the unique patterns of amino acids that make up proteins and determine if they become hemoglobin, collagen, etc. Additionally, Prof. Nanda is an expert on the mysterious step of self-assembly, in which certain proteins clump together to form even more complex substances.

Noting that nearly 40 percent of pancreatic cancer tumors smaller than 2 cm are missed on computed tomography (CT) assessment, the authors of a new study suggest that an emerging deep learning tool could have an impact in improving detection. In the study, conducted in Taiwan and published earlier today in Radiology, researchers examined the effectiveness of a deep learning tool for detecting malignant pancreatic tumors on contrast-enhanced CT in a nationwide validation test set consisting of 669 patients with pancreatic cancer and 804 participants in the control group.1 The deep learning tool was trained with contrast-enhanced CT scans from 546 patients with pancreatic cancer and 733 healthy control patients, according to the study. The researchers found that the deep learning tool had an 89.7 sensitivity rate and a 92.8 percent specificity rate for detecting pancreatic cancer in the nationwide validation test set. In local test set data drawn from 109 patients with pancreatic cancer at a tertiary referral center and 147 control participants, the study authors noted no significant differences in sensitivity between assessment by attending radiologists (96.1 percent) and the deep learning tool (90.2 percent).1 "This study developed an end-to-end, deep learning-based, computer-aided detection (CAD) tool that could accurately and robustly detect pancreatic cancers on contrast-enhanced CT scans. The CAD tool may be a useful supplement for radiologists to enhance detection of (prostate cancer)."

An emerging artificial intelligence (AI) algorithm may be beneficial in facilitating earlier detection of large vessel occlusions on non-contrast computed tomography (CT) scans and subsequent identification of stroke patients who are good candidates for a minimally invasive thrombectomy. In a study abstract presentation at the Society of Neurointerventional Surgery's (SNIS) 19th Annual Meeting in Toronto, researchers noted that the AI algorithm detects clinical symptoms of ipsiversive gaze deviation on non-contrast CT and was trained with 200 CT scans. In a subsequent study of 116 patients who received endovascular therapy for large vessel occlusions, the study authors found an ipsiversive gaze deviation in 71.1 percent of patients (59 out of 83 patients) with proximal occlusions and the AI algorithm had a 79 percent accuracy rate (47 out of 59 patients) in identifying ipsiversive gaze deviation. The study authors said the AI algorithm could result in more expeditious treatment decisions for patients with acute ischemic stroke. "Simply put, the faster we act, the better our stroke patients' outcomes will be. Our results represent an advance that has the potential to speed up the identification of (large vessel occlusion) stroke during the triage process at the hospital," emphasized lead study author Jason Tarpley, M.D., Ph.D, the stroke medical director at the Pacific Stroke and Aneurysm Center in Santa Monica, Ca.

That was still an improvement over earlier sepsis-warning systems, which had accuracy rates of about 12%, the study says. "This is a breakthrough in many ways," Albert Wu, the study author, said in a news release. "Up to this point, most of these types of systems have guessed wrong much more often than they get it right. Those false alarms undermine confidence." That was still an improvement over earlier sepsis-warning systems, which had accuracy rates of about 12%, the study says.

'The Five' discusses Apple's new software update allowing users the opportunity to edit and unsend unwanted messages. Facial recognition and fingerprint verification are becoming common security features on our phones and now your breath may be a potential option for biometric security, according to a report published in Chemical Communications. Researchers from Kyushu University's Institute for Materials Chemistry and Engineering worked with the University of Tokyo and have developed an olfactory (smell) sensor that can identify a person by analyzing their breath, the report said. "Recently, human scent has been emerging as a new class of biometric authentication, essentially using your unique chemical composition to confirm who you are," first author of the study, Chaiyanut Jirayupat, said in a release. Bangkok, Thailand - December 12, 2015: Apple iPhone5s held in one hand showing its screen for entering the passcode. Researchers from Kyushu University's Institute for Materials Chemistry and Engineering who worked with the University of Tokyo developed an olfactory (smell) sensor that can identify a person by analyzing their breath, the report said.

Could artificial intelligence (AI) assessment have comparable diagnostic accuracy to clinician assessment for fracture detection? In a recently published meta-analysis of 42 studies, the study authors noted 92 percent sensitivity and 91 percent specificity for AI in comparison to 91 percent sensitivity and 92 percent specificity for clinicians based on internal validation test sets. For the external validation test sets, clinicians had 94 percent specificity and sensitivity in comparison to 91 percent specificity and sensitivity for AI, according to the study. In essence, the study authors found no statistically significant differences between AI and clinician diagnosis of fractures. "The results from this meta-analysis cautiously suggest that AI is noninferior to clinicians in terms of diagnostic performance in fracture detection, showing promise as a useful diagnostic tool," wrote Dominic Furniss, DM, MA, MBBCh, FRCS(Plast), a professor of plastic and reconstructive surgery in the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences at the Botnar Research Centre in Oxford, United Kingdom., and colleagues.