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IMAGE: A mouse brain section highlighting the hippocampus is overlaid with the molecular structures of the anesthetics isoflurane (purple), medetomidine/midazolam/fentanyl (orange), and ketamine/xylazine (red). The four panels in the lower part. view more
Credit: Simon Wiegert, CC-BY
Memory loss is common after general anesthesia, particularly for events occurring immediately before surgery a phenomenon called retrograde amnesia. But a new study publishing on April 1st 2021 in the open access journal
PLOS Biology, led by Simon Wiegert at the University Medical Center Hamburg-Eppendorf in Germany, shows that changes in the hippocampus the part of the brain used to make new memories differ depending on which general anesthetic is used. Consequently, their effects on memory formation also differ.
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Machine learning can be used to comb through online reviews of substance use treatment facilities to home in on qualities that are important to patients but remain hard to capture via formal means, such as surveys, researchers from the Perelman School of Medicine at the University of Pennsylvania show. The researchers found that professionalism and staff dedication to patients were two of the top qualities that could be attributed to either a negative or positive review of the facility. Findings from this study were published today in the
Journal of General Internal Medicine. Searching for - and connecting with - therapy can be very difficult and confusing. Many individuals start their search online, where they are likely to see an online review accompanying other information about a treatment facility, said the study s lead author, Anish Agarwal, MD, a clinical innovation manager in the Penn Medicine Center for Digital Health and an assistant professor of Emergency M
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Credit: Stefan Zimmerman
More than 1,200 people with rare diseases have received a diagnosis thanks to the integration of large-scale genomics into the Stockholm region s healthcare system. This is according to a study from Karolinska Institutet in Sweden that analysed the result of the first five years of collaboration on whole genome sequencing between Karolinska University Hospital and SciLifeLab. The work, published in
Genome Medicine, constitutes a major leap forward in the emerging field of precision medicine. We ve established a way of working where hospital and university collaborate on sequencing each patients entire genome in order to find genetic explanations for different diseases, says the paper s first author Henrik Stranneheim, researcher at the Department of Molecular Medicine and Surgery, Karolinska Institutet. This is an example of how precision medicine can be used to make diagnoses and tailor treatments to individual patients.
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IMAGE: More than one in 10 patients with lung cancer do not know what type of tumor they have, according to data from a 17-country study carried out by the Global. view more
Credit: ESMO credit
- The increasing complexity of treatments for lung cancer and language differences can make it difficult for patients to communicate with their medical teams
- Risks of jeopardising the treatment and care journey as well as recent progress in patient empowerment.
Lugano, Switzerland; Denver, CO, USA, 17 March 2021 - More than one in 10 patients with lung cancer do not know what type of tumour they have, according to data from a 17-country study carried out by the Global Lung Cancer Coalition (GLCC) to be presented at the European Lung Cancer Conference (ELCC) (1). Nearly one in five patients surveyed did not feel involved in decisions about their treatment and care, and a similar proportion felt that they had never or only sometimes been treated with dignity and re
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BOSTON - There are several effective interventions to reduce the risk of suicide, the tenth-leading cause of death in the United States, but difficulties in identifying people at risk for suicide and concerns about the potentially high costs of suicide-prevention strategies have hampered their wider use.
But as researchers at Massachusetts General Hospital (MGH) demonstrate, statistical suicide risk prevention models could be implemented cost-effectively in U.S. health care systems and might help save many lives each year.
By evaluating data on the incidence of suicide and suicide attempts, the costs to society and the health care system of suicide, and the cost and effectiveness of suicide risk-reduction interventions, Eric L. Ross, MD, a resident in the Department of Psychiatry at MGH and colleagues found that several existing suicide risk prediction models are sufficiently accurate at identifying at-risk individuals to allow cost-effective implementation in clinical p