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CHICAGO, Feb 4 (Reuters) — A study in monkeys pitting the current Moderna Inc (MRNA.O) COVID-19 booster against an Omicron-specific booster showed no significant differences in protection, suggesting an Omicron-specific booster may not be needed, U.S. government researchers reported on Friday.
The study involved monkeys vaccinated with two doses of Moderna’s vaccine who were dosed nine months later with either the conventional Moderna booster or one specifically targeting the Omicron variant.
The researchers tested various aspects of the animals’ immune responses and exposed them to the virus. They found both boosters produced “comparable and significant increases in neutralizing antibody responses” against all of the variants of concern, including Omicron, according to the study, posted on bioRxiv ahead of peer review.
Pioneering global generic medicine access to improve and extend people’s lives — keren haruvi snir-president, sandoz US, head of north america.
Keren Haruvi is President of Sandoz US and Head of their North America business (https://www.novartis.us/about-us/our-leadership/us-country-l…n-haruvi).
Sandoz is a division of the Novartis Group and a global leader in generic pharmaceuticals and biosimilars and was established in 2003, when Novartis united all of its generics businesses under the name Sandoz – a single global brand with a long history. Since then, Sandoz has grown into a leading global generics business with annual sales of approximately US$10 billion.
In her current role, Keren leads Sandoz’ largest commercial and country organization – the United States – which is responsible for over 35% of Sandoz global revenue. She also oversees Sandoz commercial operations in Canada. In addition to serving on the Novartis Country Leadership Team, Keren is a member of the Global Sandoz Executive Committee.
Prior to joining Sandoz, Keren served as Global Head of M&A at Novartis International AG. Her early career began at Teva Pharmaceutical Industries where she steadily advanced in leadership roles to Senior VP, Global Business Development and Alliance Management.
Delivering what has been so challenging to produce, researchers present an engineered analog of tooth enamel – an ideal model for designing biomimetic materials – designed to closely mimic the composition and structure of biological teeth’s hard mineralized outer layer. It demonstrates exceptional mechanical properties, they say.
Natural tooth enamel – the thin outer layer of our teeth – is the hardest biological material in the human body. It is renowned for its high stiffness, hardness, viscoelasticity, strength, and toughness and exhibits exceptional damage resistance, despite being only several millimeters thick.
Tooth enamel’s unusual combination of properties is a product of its hierarchical architecture – a complex structure made up of mostly hydroxyapatite nanowires interconnected by an amorphous intergranular phase (AIP) consisting of magnesium-substituted amorphous calcium phosphate. However, accurately replicating this type of hierarchical organization in a scalable abiotic composite has remained a challenge.
Overall, the results indicate that failure-oriented people are more likely to make poor health choices, presumably due to a lack of proper motivation. When looking at the cause of death for each author, results also showed a strong link between failure motivation and whether or not the death could be considered preventable.
As we can see from this study and many other similar studies, people who expect failure are generally less effective in maintaining good health habits. On the other hand, a positive outlook can play a powerful role in our health decisions. Healthy behaviors include regular exercise, good nutrition, an active lifestyle, and full compliance with medical advice., all of which require strong motivation to ensure we stay the course. A positive mindset also makes us avoid unhealthy activities such as drinking, smoking, and prolonged inactivity. People who develop a failure mindset, whether due to health setbacks, hopelessness, or a general sense of fatalism, often make lifestyle decisions that can undermine their overall health and, as a result, their longevity.
Full Story:
We often look at our parents and grandparents to predict how long we are going to live and how fast we are going to age. But technology is not standing still and you may benefit from adjusting your longevity expectations and developing a longevity mindset.
This is the keynote presentation delivered to the 39th annual convention of Doctors for Disaster Preparedness in Tucson, Arizona on July 31, 2021. It is a detailed analysis of how Scientism is at the root of both Technocracy and Transhumanism, the historical case and the modern dilemma.
See also on Rumble: https://rumble.com/vkowl9-the-evil-twins-of-technocracy-and-transhumanism.html
For children suffering from rare diseases, it usually takes years to receive a diagnosis. This “diagnostic odyssey” is filled with multiple referrals and a barrage of tests, seeking to uncover the root cause behind mysterious and debilitating symptoms.
A new speed record in DNA sequencing may soon help families more quickly find answers to difficult and life-altering questions.
In just 7 hours, 18 minutes, a team of researchers at Stanford Medicine went from collecting a blood sample to offering a disease diagnosis. This unprecedented turnaround time is the result of ultra-rapid DNA sequencing technology paired with massive cloud storage and computing. This improved method of diagnosing diseases allows researchers to discover previously undocumented sources of genetic diseases, shining new light on the 6 billion letters in the human genome.
Machine learning, a form of artificial intelligence, vastly speeds up computational tasks and enables new technology in areas as broad as speech and image recognition, self-driving cars, stock market trading and medical diagnosis.
Before going to work on a given task, machine learning algorithms typically need to be trained on pre-existing data so they can learn to make fast and accurate predictions about future scenarios on their own. But what if the job is a completely new one, with no data available for training?
Now, researchers at the Department of Energy’s SLAC National Accelerator Laboratory have demonstrated that they can use machine learning to optimize the performance of particle accelerators by teaching the algorithms the basic physics principles behind accelerator operations—no prior data needed.