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Staphylococcus aureus bacteremia have their antibiotic treatment switched early from IV to oral?


Switching to oral antibiotic therapy can be as effective as prolonged intravenous (IV) therapy for several infections, including bone and joint infections and endocarditis (NEJM JW Infect Dis Jan 30 2019 and N Engl J Med 2019; 380:425; NEJM JW Gen Med Apr 14 2020 and JAMA Intern Med 2020; 180:769). European investigators now report results of an open-label, controlled noninferiority trial comparing a switch to oral antibiotics or continued IV treatment after 5 to 7 days of IV therapy in individuals with low-risk Staphylococcus aureus bacteremia (e.g., clearance of bacteremia within 72 hours, no evidence of deep-seated focus). Total duration of therapy was 14 days; oral options were trimethoprim-sulfamethoxazole, clindamycin, or linezolid; IV options were flucloxacillin, cefazolin, vancomycin, or daptomycin).

The trial was terminated after 213 of 5,063 screened individuals had been enrolled over a 6-year period. Oral therapy was found to be noninferior to IV therapy (in both intention-to-treat and clinically evaluable analyses) for the primary composite outcome of complications from S. aureus infection within 90 days. Two deaths due to S. aureus bacteremia occurred (both in the oral switch group), and 34% of the oral switch group versus 26% of the IV group had a serious adverse event (P=0.29) that was most commonly infectious.

As editorialists note, these results should be viewed cautiously: 5% of the screened patient population was actually enrolled, so secondary endpoints such as all-cause mortality could not be analyzed. However, the results were clearly influenced by the selection of oral agents, and the use of more-potent oral antibiotic regimens could have yielded better outcomes. At least for now, I remain reluctant to recommend anything less than a 14-day IV regimen for S. aureus bacteremia.

The creation of an artificial intelligence (AI) system that can analyze retinal fundus images to detect chronic kidney disease (CKD) and type 2 diabetes mellitus (T2DM) represents a groundbreaking advancement in medical technology. This AI model, developed using a substantial dataset of retinal images and advanced convolutional neural networks, has demonstrated exceptional accuracy in identifying these conditions. Its capability extends beyond mere detection, as it also shows promise in predicting the progression of these diseases based on retinal imaging and clinical metadata.

A notable innovation of this AI system is its ability to analyze smartphone images. This feature significantly enhances the accessibility of sophisticated diagnostic tools, especially in regions with limited healthcare resources. The AI model paves the way for more widespread and convenient health screenings by enabling ubiquitous smartphone technology for medical imaging. This development is particularly impactful in enhancing healthcare delivery and access, as it brings critical diagnostic capabilities into the hands of more people, even in remote or underserved areas.

The AI’s proficiency in predicting the future development of CKD and T2DM is another aspect of its novelty. This predictive ability is crucial for timely intervention, potentially altering the trajectory of these chronic illnesses. Early detection and management are vital in battling CKD and T2DM, and this AI model’s predictive power could significantly improve patient outcomes.

https://youtu.be/tLtbWNi-Cgc?si=3i8BqTCAodSKnpkc

Micro Electro Mechanical Systems (MEMS) are miniature devices that integrate mechanical elements, sensors, actuators, and electronics on a single silicon chip. These systems serve diverse applications, such as accelerometers in smartphones, gyroscopes in navigation systems, and pressure sensors in medical devices. MEMS devices can detect and respond to environmental changes, enabling the creation of smart, responsive technologies. Their small size, low power consumption, and ability to perform various functions make MEMS crucial in fields like telecommunications, healthcare, automotive, and consumer electronics. Learn more about this tiny machines with this video!

#science #technology #microscopic #nanotechnology #robotics #engineering

Californian students from the Art Center College had to imagine four concepts of cars of the future for Lincoln, by 2040. One of them, a four-seater sedan, was entitled to the realization of its 1:1 scale model, presented during Monterey Car Week, completed this weekend. Connected, autonomous, shared (“shared”), and electric, as suggested by the acronym “CASE” used by Ford’s luxury brand, which is found in this sedan called “Anniversary”

Drugs known as antidepressants target the serotonin transporter in nerve cells and are among the most commonly prescribed medicines worldwide, but are sometimes associated with significant side effects. As part of a study, a research group led by Thomas Stockner from MedUni Vienna identified the basic principles of serotonin transport and thus created a possible basis for the development of novel drugs with improved selectivity and with fewer undesirable effects. The results were recently published in the renowned scientific journal “Nature Communications”

While the desired effects of drugs unfold through the interaction with the relevant target structures, the undesirable side effects are often due to a lack of selectivity and therefore due to interactions with other target structures. Accordingly, developing drugs that can differentiate between the various physiologically relevant targets (e.g. transporters and receptors) is one of the challenges for research. A team led by Ralph Gradisch under the supervision of Thomas Stockner from MedUni Vienna’s Center for Physiology and Pharmacology set out to find a way to increase selectivity for the serotonin transporter while reducing interaction with other targets at nerve cells in the brain.