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Recent pandemic viruses jumped to humans without prior adaptation, study finds

A new University of California San Diego study published in Cell challenges a long-standing assumption about how animal viruses become capable of sparking human epidemics and pandemics. Using a phylogenetic, genome-wide analysis across multiple viral families, researchers report that most zoonotic viruses—infectious pathogens that spread from animals to humans, including the cause of COVID-19—do not show evidence of special evolutionary adaptation before spilling over into humans.

“This work has direct relevance to the ongoing controversy around COVID-19 origins,” said Joel Wertheim, Ph.D., senior author and professor of medicine in the Division of Infectious Diseases and Global Public Health at UC San Diego School of Medicine.

“From an evolutionary perspective, we find no evidence that SARS-CoV-2 was shaped by selection in a laboratory or prolonged evolution in an intermediate host prior to its emergence. That absence of evidence is exactly what we would expect from a natural zoonotic event—and it represents another nail in the coffin for theories invoking laboratory manipulation.”

Present state and future of screening for atrial fibrillation: a state-of-the-art review

AFib AF


Atrial fibrillation (AF) is the most common arrhythmia and is a leading cause of stroke and heart failure yet often remains undiagnosed. Screening has been proposed to identify asymptomatic AF and initiate preventive treatment, but evidence for reduction in hard clinical endpoints such as stroke and heart failure remains inconclusive. In this state-of-the-art review, we critically examine major AF screening trials across opportunistic, systematic and consumer-driven strategies, focusing on design features, population selection, monitoring strategies and outcomes. Variability in trial design, particularly in randomisation timing, participation rates and intensity of monitoring, significantly affects both AF detection and clinical outcomes. Systematic screening shows promise, but many trials were underpowered for hard outcomes.

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Experts featured in this video include Demis Hassabis, Tristan Harris, Aza Raskin, Elon Musk, David Deutsch, Michio Kaku, Brian Greene and Nick Bostrom.

Chapter:
0:00 A dangerous truth?
1:29 AI advancement.
3:46 AI pretending not to know.
7:29 Interactive tutoring.
9:37 That’s it from our sponsor!
10:21 The merging of QC and AI
12:03 IBM 100,000 qubits.
14:34 AI wipes out humanity?
16:05 Google Willow.
17:06 The misuse of AI and QC
18:22 Singularity and Turing test.
22:51 Reverse Turing test.
29:39 Quantum-AI consequences.
32:25 The double slit experiment.
36:15 Quantum multiverse.
41:05 Computing history.
46:49 AGI timeline.
51:45 Philosophical consequence.

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Understanding the Role of Gut Microbial Enzyme in CMD

Studies of the putative functional relationships between the gut microbiota and host cardiometabolic diseases (CMDs), including atherosclerosis, diabetes, and metabolic dysfunction-associated steatohepatitis (MASH), have garnered unprecedented attention in recent years.1,2 Although causality has not yet been unequivocally established, interventions targeting the gut microbiota, such as antibiotics and fecal microbiota transplantation, have been demonstrated to improve health.3 Although such interventions show unique clinical value in specific scenarios such as recurrent Clostridioides difficile infection,4 they typically show interindividual variability in efficacy and raise safety concerns, altogether underscoring the need for safer, more precise, and targeted strategies.5 A deeper understanding of the molecular mechanisms by which gut microbiota exert their functions in health and disease will be crucial to such goals.

Enzymes are intracellular proteins that perform defined biological processes, and enzyme-targeting drugs constitute a significant proportion of current therapeutics.6 In recent years, growing evidence has indicated that gut microbial enzymes are key mediators of microbiota-derived functions.7 Such enzymes contribute to CMDs pathogenesis primarily through 3 mechanisms: generating bioactive metabolites that influence intestinal barrier integrity, inflammation, and other essential physiological processes; regulating the homeostasis of critical host metabolites, such as ceramides and cholesterol; and metabolizing xenobiotics derived from diet and drugs, thereby modulating nutrient absorption and drug efficacy.

Given the complexity of the functions of gut microbiota, it is arguably overly simplistic to categorize them as symbionts that are probiotic or pathogenic. Rather, by identifying and characterizing key microbial enzymes, we will be able to precisely modulate gut microbiota functions in health and disease. When a clear enzymatic cause is identified, therapies targeting microbial enzymes capitalize on a function-driven mechanism. This allows for precision that is independent of taxonomy and avoids off-target consequences stemming from compositional heterogeneity of the functional microbes across individuals. The operational feasibility and druggability of these therapies are further supported by mature enzyme-based therapy development paradigms. Ultimately, enzyme-targeted interventions are expected to work alongside conventional whole-microbiota or strain-level approaches, thereby enriching the toolkit for developing gut microbiome-based therapeutics.

Why lungs age unevenly: Vulnerable cells may guide new therapies

Aging is associated with increased risk for nearly every lung disease, including acute conditions like pneumonia and chronic diseases like chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, and lung cancer. Now, one of the most comprehensive analyses of human lung aging has found that not all cells age equally.

The study, published in Nature Communications, has found that certain types of lung cells are especially vulnerable to aging. The findings could inform treatments that target the defective cells, say the researchers.

“This data allows us to start thinking about lung aging not as a passive state that we have to accept, but as something that we may be able to modify with therapies and interventions,” says senior author Naftali Kaminski, MD, Boehringer Ingelheim Pharmaceuticals, Inc. Professor of Medicine (Pulmonary) at Yale School of Medicine and chief of pulmonary, critical care and sleep medicine at Yale.

Why simulating an entire cell cycle took years, multiple GPUs and six days per run

By simulating the life cycle of a minimal bacterial cell—from DNA replication to protein translation to metabolism and cell division—scientists have opened a new frontier of computer vision into the essential processes of life. The researchers, led by chemistry professor Zan Luthey-Schulten at the University of Illinois Urbana-Champaign, present their findings in the journal Cell.

The team simulated a living cell at nanoscale resolution and recapitulated how every molecule within that cell behaved over the course of a full cell cycle. The work took many years: vast computer resources, large experimental datasets, a suite of experimental and computational techniques and an understanding of the roles, behaviors and physical interactions of thousands of molecular players.

The researchers had to account for every gene, protein, RNA molecule and chemical reaction occurring within the cell to recreate the timing of cellular events. For example, their model had to accurately reflect the processes that allow the cell to double in size prior to cell division.

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