Israeli MedTech companies have experienced quite the acceleration process following the COVID-19 outbreak, with digital health companies garnering the bulk of the focus. The AI medical train has left the station with companies like Ibex Medical, Aidoc, Scopio, and more all scoring funding and coveted FDA approvals this year. However, in the land of medical-AI FDA clearances, one horse stands alone, or should I say Zebra. Israeli startup Zebra Medical Vision, the deep-learning medical imaging analytics company, announces today its seventh FDA clearance. The newly approved solution utilizes the power of AI to extract bone measurements from X-ray scans, similar in quality to CT scans, for the purpose of planning orthopedic surgical procedures.

Today, AI is used with increasing regularity across nearly every industry, with AI-based systems and technologies introducing new efficiencies, unlocking extraordinary opportunities and delivering powerful new insights and capabilities that were previously unattainable -- perhaps even unthinkable. Not only are health care and pharmaceuticals no exception to that rule, but life sciences actually represents one of the most innovative and exciting new frontiers for AI technology and machine learning. In recent years, AI usage has exploded in pharma, health care and biotech. Life sciences companies and institutions have used AI to develop and test new drugs, advance new therapeutics and treatment protocols, and, in some cases, completely transform the drug development and distribution process. The power and potential of AI-based technology in life sciences has arguably never been more important.

Today, AI is used with increasing regularity across nearly every industry, with AI-based systems and technologies introducing new efficiencies, unlocking extraordinary opportunities and delivering powerful new insights and capabilities that were previously unattainable -- perhaps even unthinkable. Not only are health care and pharmaceuticals no exception to that rule, but life sciences actually represents one of the most innovative and exciting new frontiers for AI technology and machine learning. In recent years, AI usage has exploded in pharma, health care and biotech. Life sciences companies and institutions have used AI to develop and test new drugs, advance new therapeutics and treatment protocols, and, in some cases, completely transform the drug development and distribution process. The power and potential of AI-based technology in life sciences has arguably never been more important.

I frequently emphasize the importance of data in the U.S. Food and Drug Administration's work as a science-based regulatory agency, and the need to "unleash the power of data" through sophisticated mechanisms for collection, review and analysis so that it may become preventive, action-oriented information. As one example of this commitment, I would like to tell you about cross-cutting work the agency is undertaking to leverage our use of artificial intelligence (AI) as part of the FDA's New Era of Smarter Food Safety initiative. This work promises to equip the FDA with important new ways to apply available data sources to strengthen our public health mission. The ultimate goal is to see if AI can improve our ability to quickly and efficiently identify products that may pose a threat to public health. One area in which the FDA is assessing the use of AI is in the screening of imported foods.

While a hot topic of late, it is easy to forget that the concept of artificial intelligence (AI) is not new. Early philosophers and mathematicians theorized that mechanical reasoning could one day be taught to robots, automatons, and smart machines. However, AI advancements slowed over the next few decades due to competing funding priorities, moral/ethical concerns, and the limitations of computing technology and data storage. It was not until the late 1990s/early 2000s that most of these challenges and concerns were alleviated and computer and data technologies advanced, becoming more affordable. Today, significant investment can be seen in health care-related AI with well-known companies like Microsoft, Google, and IBM heavily involved in promoting AI solutions in eye care,and smaller startups even attaining FDA-approval as standalone diagnostic technology.2-6

More than 70 years ago, electrical stimulation of the human visual cortex was shown to elicit the perception of a brief flash of light, or phosphene ([ 1 ][1]). Subsequently, there were numerous attempts to develop cortical visual prostheses (CVPs) that electrically stimulate the visual cortex to restore vision to people with acquired blindness ([ 2 ][2]–[ 4 ][3]). The basic design of a CVP is simple: A head-mounted camera captures the visual scene, and a computer translates it into patterned brain stimulation. However, CVP implementation foundered on technological limitations, especially the size and complexity of the stimulation hardware. Advances in miniaturization and the efficiency of digital circuits suggest that it is time to try again ([ 5 ][4], [ 6 ][5]). On page 1191 of this issue, Chen et al. ([ 7 ][6]) describe the implantation of more than 1000 electrodes in the visual cortex of nonhuman primates (NHPs) to create artificial vision. This technical tour de force relied on features of early visual cortex shared by humans and NHPs. The visual cortex takes up a substantial fraction of the cerebral tissue, ∼20% in humans. This creates a surface area of many square centimeters that can accommodate the implantation of electrodes. The visual cortex is retinotopic, meaning that there is an orderly mapping between each location in the visual scene and each location in the brain. A CVP with an array of electrodes can provide an array of phosphenes, similar to individual lights comprising a stadium scoreboard, that can be activated to produce visual sensations (percepts). In natural vision, information from the visual scene moves through a hierarchical network of processing stages, from the retina to the thalamus to primary visual cortex (V1) and higher visual areas, such as the fourth visual area (V4). Chen et al. implanted electrode arrays in both V1 and V4 of NHPs. For CVPs to function effectively, the current level for each electrode must be individually adjusted so that the current is sufficient to produce a detectable phosphene but not so high that the phosphene expands to cover an extended region of space. This requires time-consuming calibration in which the participant reports their percept at multiple different current levels for every electrode. Chen et al. address this problem by stimulating electrodes in V1 while recording from electrodes in V4. They show that it is possible to estimate the appropriate V1 stimulation current from the recorded neuronal responses in V4, a process that could be conducted automatically and rapidly for multiple electrodes. For millions of patients with damaged or diseased eyes leading to blindness, there are few or no treatment options. Recently, six patients were implanted with a CVP in a U.S. Food and Drug Administration (FDA)–approved clinical trial. However, the device has only 60 electrodes, limiting patients to simple tasks such as detecting the light or dark areas in a visual scene. By contrast, the device developed by Chen et al. comprises 16 arrays of 64 electrodes each, for a total of 1024 electrodes. The high electrode count meant that Chen et al. could arrange phosphenes in the shape of different letters, which the NHPs were trained to discriminate (see the figure). In addition, the NHPs were able to accurately perform simpler tasks, such as making eye movements to the location of a phosphene, determining whether two phosphenes were in a horizontal or vertical configuration, and deciding whether two phosphenes were stimulated in one order or another, creating the impression of apparent motion. ![Figure][7] Brain stimulation to create artificial vision Electrical stimulation of the visual cortex is used to create the perception of letters. Letter shapes were decomposed into dot patterns and shown on a computer display to train nonhuman primates (NHPs). The NHPs learned 16 different letters. (An example training pattern for the letter “A” is shown.) Then, between 8 and 15 visual cortex electrodes were stimulated to create artificial percepts. Simulated percepts for the letters “A” (left) and “L” (right) are shown. GRAPHIC: N. CARY/ SCIENCE FROM CHEN ET AL. ([ 7 ][6]) The electrodes used by Chen et al. penetrated into the cortex. Because intracortical electrodes are near the stimulated neurons, the stimulation currents are 10- to 100-fold less than that required for electrodes that sit further away atop the cortex, as in the FDA-approved CVP. When hundreds of electrodes are stimulated at once, low currents are essential to minimize both the power consumption of the device and the amount of current injected into the brain. A number of technological and biological issues remain. On the technological front, the electrode arrays used by Chen et al. require a wired connection between the brain and the rest of the CVP. A wireless device will be necessary for long-term implantation of a clinical device in humans. Fortunately, considerable advances in neural stimulation with biocompatible wireless devices mean that solutions are close at hand ([ 8 ][8]). Phosphenes are experienced as bright flashes, not the rich colors and forms that characterize natural vision. The reason for this difference is likely that neurons in V1 respond to simple visual features, such as oriented lines. Stimulating these neurons produces a correspondingly simple percept ([ 9 ][9], [ 10 ][10]). Neurons in higher-level visual areas respond to more complex features, and electrical stimulation of these areas can produce the experience of seeing colors ([ 11 ][11]) or faces ([ 12 ][12]). It is intriguing to speculate whether the NHPs in the Chen et al. study could be induced to see more naturalistic patterns if V4 and V1 were stimulated at the same time. Even with 1024 electrodes, the letter shapes that can be generated are crude (see the figure). New array technologies with orders-of-magnitude more electrodes will facilitate the generation of more refined shapes. Advanced stimulation algorithms, akin to software that accompanies the CVP hardware, can also be applied. With current steering, electricity is delivered to adjacent electrodes to stimulate tissue between the implanted electrodes, creating more phosphene locations that fill in the retinotopic map ([ 13 ][13]). Another technique, called dynamic stimulation, uses the sequence of stimulated electrodes to convey information. For instance, the letter T could be conveyed as a horizontal stroke followed by a vertical stroke. Human patients implanted with small numbers of visual cortex electrodes were able to identify letter shapes delivered using a combination of current steering and dynamic stimulation ([ 14 ][14]). Future studies should also investigate the full realm of possible transformations between the visual scene and patterned brain stimulation. Advanced machine vision can extract relevant information from the visual scene, which could change based on circumstance. For example, in a navigation task, arrow shapes could by delivered to signal the correct heading direction ([ 15 ][15]). After decades of false starts, there is a bright future for CVPs. Chen et al. set a new benchmark for the next generation of CVPs by demonstrating that 1000 electrodes are sufficient to create percepts of letters, orientation, and motion. Advances in wireless stimulation, high-density electrode fabrication, and stimulation algorithms offer hope that new devices will provide useful visual function for people living with blindness. 1. [↵][16]1. W. Penfield, 2. T. Rasmussen , The Cerebral Cortex of Man (The Macmillan Company, 1950). 2. [↵][17]1. G. S. Brindley, 2. W. S. Lewin , J. Physiol. 196, 479 (1968). [OpenUrl][18][CrossRef][19][PubMed][20][Web of Science][21] 3. 1. W. H. Dobelle et al ., Nature 259, 111 (1976). [OpenUrl][22][CrossRef][23][PubMed][24] 4. [↵][25]1. E. M. Schmidt et al ., Brain 119, 507 (1996). [OpenUrl][26][CrossRef][27][PubMed][28][Web of Science][29] 5. [↵][30]1. V. P. Gabel 1. P. R. Troyk , in Artificial Vision: A Clinical Guide, V. P. Gabel, Ed. (Springer, 2017). 6. [↵][31]1. W. H. Bosking et al ., Annu. Rev. Vis. Sci. 3, 141 (2017). [OpenUrl][32] 7. [↵][33]1. X. Chen et al ., Science 370, 1191 (2020). [OpenUrl][34][Abstract/FREE Full Text][35] 8. [↵][36]1. A. Singer et al ., Neuron 107, 631 (2020). [OpenUrl][37] 9. [↵][38]1. J. Winawer, 2. J. Parvizi , Neuron 92, 1213 (2016). [OpenUrl][39] 10. [↵][40]1. D. K. Murphey et al ., Proc. Natl. Acad. Sci. U.S.A. 106, 5389 (2009). [OpenUrl][41][Abstract/FREE Full Text][42] 11. [↵][43]1. D. K. Murphey et al ., Curr. Biol. 18, 216 (2008). [OpenUrl][44][CrossRef][45][PubMed][46][Web of Science][47] 12. [↵][48]1. V. Rangarajan et al ., J. Neurosci. 34, 12828 (2014). [OpenUrl][49][Abstract/FREE Full Text][50] 13. [↵][51]1. J. B. Firszt et al ., Otol. Neurotol. 28, 629 (2007). [OpenUrl][52][CrossRef][53][PubMed][54][Web of Science][55] 14. [↵][56]1. M. S. Beauchamp et al ., Cell 181, 774 (2020). [OpenUrl][57][CrossRef][58][PubMed][59] 15. [↵][60]1. Y. Liu et al ., eLife 7, e37841 (2018). [OpenUrl][61] Acknowledgments: D.Y. is a principal investigator for a clinical trial of the Second Sight Orion CVP. [1]: #ref-1 [2]: #ref-2 [3]: #ref-4 [4]: #ref-5 [5]: #ref-6 [6]: #ref-7 [7]: pending:yes [8]: #ref-8 [9]: #ref-9 [10]: #ref-10 [11]: #ref-11 [12]: #ref-12 [13]: #ref-13 [14]: #ref-14 [15]: #ref-15 [16]: #xref-ref-1-1 "View reference 1 in text" [17]: #xref-ref-2-1 "View reference 2 in text" [18]: {openurl}?query=rft.jtitle%253DThe%2BJournal%2Bof%2BPhysiology%26rft.stitle%253DJ.%2BPhysiol.%26rft.aulast%253DBrindley%26rft.auinit1%253DG.%2BS.%26rft.volume%253D196%26rft.issue%253D2%26rft.spage%253D479%26rft.epage%253D493%26rft.atitle%253DThe%2Bsensations%2Bproduced%2Bby%2Belectrical%2Bstimulation%2Bof%2Bthe%2Bvisual%2Bcortex%26rft_id%253Dinfo%253Adoi%252F10.1113%252Fjphysiol.1968.sp008519%26rft_id%253Dinfo%253Apmid%252F4871047%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [19]: /lookup/external-ref?access_num=10.1113/jphysiol.1968.sp008519&link_type=DOI [20]: /lookup/external-ref?access_num=4871047&link_type=MED&atom=%2Fsci%2F370%2F6521%2F1168.atom [21]: /lookup/external-ref?access_num=A1968B164600015&link_type=ISI [22]: {openurl}?query=rft.jtitle%253DNature%26rft.stitle%253DNature%26rft.aulast%253DDobelle%26rft.auinit1%253DW.%2BH.%26rft.volume%253D259%26rft.issue%253D5539%26rft.spage%253D111%26rft.epage%253D112%26rft.atitle%253D%2526quot%253BBraille%2526quot%253B%2Breading%2Bby%2Ba%2Bblind%2Bvolunteer%2Bby%2Bvisual%2Bcortex%2Bstimulation.%26rft_id%253Dinfo%253Adoi%252F10.1038%252F259111a0%26rft_id%253Dinfo%253Apmid%252F1246346%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [23]: /lookup/external-ref?access_num=10.1038/259111a0&link_type=DOI [24]: /lookup/external-ref?access_num=1246346&link_type=MED&atom=%2Fsci%2F370%2F6521%2F1168.atom [25]: #xref-ref-4-1 "View reference 4 in text" [26]: {openurl}?query=rft.jtitle%253DBrain%26rft.stitle%253DBrain%26rft.aulast%253DSchmidt%26rft.auinit1%253DE.%2BM.%26rft.volume%253D119%26rft.issue%253D2%26rft.spage%253D507%26rft.epage%253D522%26rft.atitle%253DFeasibility%2Bof%2Ba%2Bvisual%2Bprosthesis%2Bfor%2Bthe%2Bblind%2Bbased%2Bon%2Bintracortical%2Bmicro%2Bstimulation%2Bof%2Bthe%2Bvisual%2Bcortex%26rft_id%253Dinfo%253Adoi%252F10.1093%252Fbrain%252F119.2.507%26rft_id%253Dinfo%253Apmid%252F8800945%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [27]: /lookup/external-ref?access_num=10.1093/brain/119.2.507&link_type=DOI [28]: /lookup/external-ref?access_num=8800945&link_type=MED&atom=%2Fsci%2F370%2F6521%2F1168.atom [29]: /lookup/external-ref?access_num=A1996UN42300015&link_type=ISI [30]: #xref-ref-5-1 "View reference 5 in text" [31]: #xref-ref-6-1 "View reference 6 in text" [32]: {openurl}?query=rft.jtitle%253DAnnu.%2BRev.%2BVis.%2BSci.%26rft.volume%253D3%26rft.spage%253D141%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [33]: #xref-ref-7-1 "View reference 7 in text" [34]: 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/lookup/external-ref?access_num=17667771&link_type=MED&atom=%2Fsci%2F370%2F6521%2F1168.atom [55]: /lookup/external-ref?access_num=000248558300007&link_type=ISI [56]: #xref-ref-14-1 "View reference 14 in text" [57]: {openurl}?query=rft.jtitle%253DCell%26rft.volume%253D181%26rft.spage%253D774%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.cell.2020.04.033%26rft_id%253Dinfo%253Apmid%252F32413298%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [58]: /lookup/external-ref?access_num=10.1016/j.cell.2020.04.033&link_type=DOI [59]: /lookup/external-ref?access_num=32413298&link_type=MED&atom=%2Fsci%2F370%2F6521%2F1168.atom [60]: #xref-ref-15-1 "View reference 15 in text" [61]: 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Manufacturer Nvidia recently welcomed the 1000th health care AI startup company to its Nvidia Inception program, a fast-track support for companies seeking to use machine learning for their ventures. Today the firm officially declared The Nvidia Inception Alliance for Healthcare, which gives members access to resources, particularly for the healthcare business, the GE Healthcare Edison Developer Program. Nvidia Inception is thought of as personalized service for startup companies working with machine learning, providing training and technical assistance with artificial intelligence, in addition to early access to the manufacturer's cutting edge hardware. The Alliance for Healthcare specifically introduces a host of benefits for FDA-approved Premier members, namely access to Nuance AI Marketplace for Diagnostic Imaging, a database of confirmed medical models for coaching algorithms. Nvidia will probably be hosting a particular address open to the public tomorrow at 5 pm CT (11 am Monday GMT) to discuss how the program is working with healthcare professionals in fields ranging from radiology and information science, which has the capacity to automate mundane, repetitive jobs in medical labs and much more rapidly interpret complex information, to the growth of medical devices, including prosthetic limbs that accurately forecast the user's moves.

The accurate detection of disease outcomes still remains a challenging obstacle for physicians. As a result, machine learning (ML) has emerged as a popular tool for researchers. It can aid in discovering and identifying patterns and relationships from complex datasets, while predicting future outcomes. Now, researchers at Aalto University, the University of Helsinki, and the University of Turku in Finland report they have developed a machine learning model that can predict how combinations of different cancer drugs kill various types of cancer cells. The new AI model was trained with a large set of data obtained from previous studies, which had investigated the association between drugs and cancer cells.

It is the first and remains the only DEEP LEARNING system to be FDA cleared for use on both 2D and 3D mammograms and now is first of its kind to …

SCI COMMUN### Infectious diseases The 11th Ebola outbreak in the Democratic Republic of the Congo (DRC) is officially over, giving the country respite from the disease for the first time in more than 2 years. On 18 November, the World Health Organization (WHO) announced that no new cases had been identified for 42 days, twice the incubation period for the deadly virus. The outbreak, in the western Équateur province, started in late May, just as a bigger one in the eastern DRC was coming to an end. (That outbreak had killed 2200 people.) The Équateur outbreak sickened 130 and killed 55; a campaign that vaccinated more than 40,000 people is credited with helping end it. Special portable coolers that keep the vaccine at −80°C for up to 1 week allowed health workers to administer the shots in communities deep in the rainforest, accessible only by boat or helicopter. The same technology will be useful in efforts to distribute COVID-19 vaccines in Africa, says Matshidiso Moeti, WHO's regional director. The coronavirus pandemic complicated the fight against Ebola, WHO says, but the expertise gained by local health workers in earlier outbreaks in the region was a major advantage. They will remain on the lookout for potential flare-ups. $1,000,000 —Gift from entertainer Dolly Parton in April to support development of Moderna's coronavirus vaccine, which the company last week said showed an efficacy of 94.5%. “I felt so proud to have been part of that little seed money,” Parton told BBC. ### Marine ecology The Allen Coral Atlas, a project to map the world's shallow coral reefs with high-resolution satellites, last week launched a monitoring system to detect coral bleaching events as they occur. When corals face extreme heat, they expel their algal symbionts, leaving them bone white and vulnerable to stress; repeated bleaching episodes, growing more common with global warming, can cause massive die-offs. The system detects the whitening using imagery from the privately owned Planet satellite constellation, processed with machine learning. A pilot has begun in Hawaii to use the data as an early warning system for researchers, to help them identify and study species both vulnerable and resistant to warming extremes. The monitoring of bleaching is expected to expand next year to shallow reefs globally. ### Diagnostics The U.S. Food and Drug Administration (FDA) issued its first emergency use authorization last week for an at-home diagnostic test that can detect the pandemic coronavirus in just minutes. However, the test might not be widely available until spring 2021. Produced by Lucira Health, a biotech company, it is expected to cost less than $50 and require a doctor's prescription. The company says it will soon distribute tests in parts of California and Florida; it says it needs time to scale up manufacturing for national distribution. Lucira's test amplifies viral genetic material, making it nearly as accurate as laboratory tests that use the polymerase chain reaction, the current gold standard. FDA previously approved at-home tests that must be mailed to a laboratory for analysis. Several other companies are working on rapid antigen tests, which detect viral particles, for home use. But concerns remain about antigen tests' reliability. Still, some public health specialists consider widely available, low-cost, at-home testing vital for controlling the pandemic. ### Funding A new U.S. National Institutes of Health (NIH) award will allow early-career investigators who want to shift research directions when applying for their first independent award to submit a proposal without first generating preliminary data to support their idea. Reviewers will instead assess the soundness of the project's approach. The Katz award is named for Stephen Katz, a longtime champion of young researchers who was director of the National Institute of Arthritis and Musculoskeletal and Skin Diseases when he died in 2018. The grant will build on an NIH policy that prioritizes proposals from early-stage investigators—those no more than 10 years from completing their training who are applying for their first research grant. The policy has been credited with raising their numbers from fewer than 600 supported in 2013 to more than 1300 last year. Applications for the first Katz awards are due on 26 January 2021. ### Leadership Democrats in Congress say a political appointee given a senior post at the U.S. National Institute of Standards and Technology (NIST) is unfit for the job because he lacks technical skills and holds pseudoscientific views about racial differences on IQ tests. On 9 November, Jason Richwine, an independent public policy analyst, took up the new position of deputy undersecretary of commerce for standards and technology, and Commerce Secretary Wilbur Ross subsequently issued an order that would put Richwine in charge of the $1 billion research agency if NIST Director Walter Copan leaves or is fired. On 17 November, Representative Eddie Bernice Johnson (D–TX), who leads the science committee in the U.S. House of Representatives, asked Ross to justify the moves. Richwine has advocated for more restrictive immigration policies, and his 2009 doctoral thesis argued that lower IQ scores by Mexican and Hispanic immigrants suggest a genetic component to intelligence that is “likely to persist over several generations.” ### Diversity The editors of Nature Communications say they are reviewing a paper that drew scalding criticism after it suggested that encouraging female junior scientists to work with female mentors could “hinder the careers of women.” The 17 November study, led by data scientist Bedoor AlShebli of New York University, Abu Dhabi, examined 3 million mentor-protégé pairs and how gender influenced the impact of papers later published by the protégés. Female protégés, it concluded, did better if they worked with male mentors. Critics pounced, noting the authors ignored reviewer complaints about the study's methods and arguing the journal was promoting a harmful and unfounded message. The article's authors said they welcome the review. ### Animal diseases European authorities reported on 19 November they have detected highly pathogenic avian influenza in 302 birds in eight countries. Only 18 cases were in poultry; most of the rest were in wild birds, the European Food Safety Authority and its partners said. The number of infected birds is expected to rise with winter migrations. Several flu strains were identified, but no people were reported to be infected, and the risk of that occurring is considered low; researchers studying the viruses found no genetic markers indicating they had adapted to infect mammals. But the threat to poultry is high, and the report's authors recommended bird producers increase precautions against infections. VACCINE APPLICATION Days after making public the final analysis of their 40,000-person COVID-19 vaccine trial, which found 95% efficacy, Pfizer and its German partner BioNTech filed for emergency authorization of the messenger RNA vaccine from the U.S. Food and Drug Administration—the first such request for a vaccine during the pandemic. They plan to seek additional approvals in other countries soon. Pfizer hopes to supply up to 50 million doses this year. REMDESIVIR PANNED A World Health Organization panel recommended against using the antiviral drug remdesivir to treat most hospitalized COVID-19 patients. Its review of four studies of 7000 people found that the drug, which the U.S. Food and Drug Administration approved last month for hospitalized patients, did not reduce mortality or speed recovery. But the panel encouraged further study of it. AMMO BAN Denmark has become the first nation to ban all lead-based hunting ammunition, including bullets and shotgun pellets, to protect wildlife. Hunters annually release about 2 tons of lead into Denmark's environment; waterbirds and other species eat the toxic material and die. European regulators are considering a ban like Denmark's.