As per the definition found in Britannica by Copeland, AI is commonly referred to as a computer system with human intellectual features, e.g., reasoning, discovering, generalizing, and learning from prior exposure [1,2]. U.S Food and Drug Administration (US-FDA) has also stated in 2019 that AI has the potential to transform the healthcare industry by its ability to derive new information from the vast dataset that feeds into it [2,3]. Machine learning (ML) can be simply understood as a subset of an application of AI in which machines analyze and use a large dataset to produce unique algorithms capable of "statistical learning" as described by Gutierrez [2]. The use of ML has surged in critical care in the field of the discovery of drugs, diagnostic tools, medical imaging, and therapeutics amongst others. It can potentially help us better understand the vast set of data available to us in an intensive care unit (ICU) and apply it to tackle a multitude of medical conditions [2,4]. ML can be divided into two main models based on learning tasks, which are supervised and unsupervised learning algorithms.

He's 11-years-old and, until he can receive his shots, Gronvall's been using at-home COVID-19 test kits in order to determine if his sniffles are more than allergies or a slight cold. The test swabs are longer than a Q-tip, but easier on the nasal cavity than a flu diagnostic or the original "brain swab" used to test for COVID since early in the pandemic. "There's often a lot of stuff coming out of their nose," Gronvall said of her kids, with a slight chuckle, when we talked recently. As an associate professor at the Johns Hopkins Bloomberg School of Public Health, Gronvall knows the importance of testing. "We can't all rely on everybody being extra scrupulous and paying attention to all of the COVID restrictions," she said.

Scientists at Case Western Reserve University have used artificial intelligence (AI) to identify new biomarkers for breast cancer that can predict whether the cancer will return after treatment -- and which can be identified from routinely acquired tissue biopsy samples of early-stage breast cancer. The key to that initial determination is collagen, a common protein found throughout the body, including in breast tissue. Previous research had suggested that the collagen network, or arrangement of the fibers, relates strongly to breast cancer aggressiveness. But this work by Case Western Reserve researchers definitively demonstrated collagen's critical role -- using only standard tissue biopsy slides and AI. The researchers, using machine-learning technology to analyze a dataset of digitized tissue samples from breast cancer patients, were able to prove that a well-ordered arrangement of collagen is a key prognostic biomarker for an aggressive tumor and a likely recurrence.

Writing in the July 12, 2021 online issue of Nature Communications, researchers at University of California San Diego School of Medicine describe a new approach that uses machine learning to hunt for disease targets and then predicts whether a drug is likely to receive FDA approval. The study findings could measurably change how researchers sift through big data to find meaningful information with significant benefit to patients, the pharmaceutical industry and the nation's health care systems. "Academic labs and pharmaceutical and biotech companies have access to unlimited amounts of'big data' and better tools than ever to analyze such data. However, despite these incredible advances in technology, the success rates in drug discovery are lower today than in the 1970s," said Pradipta Ghosh, MD, senior author of the study and professor in the departments of Medicine and Cellular and Molecular Medicine at UC San Diego School of Medicine. "This is mostly because drugs that work perfectly in preclinical inbred models, such as laboratory mice, that are genetically or otherwise identical to each other, don't translate to patients in the clinic, where each individual and their disease is unique. It is this variability in the clinic that is believed to be the Achilles heel for any drug discovery program."

Paige is a software company helping pathologists and clinicians make faster, more informed diagnostic and treatment decisions by mining decades of data from the world's experts in cancer care. We are leading a digital transformation in pathology by leveraging advanced Artificial Intelligence (AI) technology to create value for the oncology clinical team. We are the first company to develop clinical grade AI tools for the pathologist, which resulted in our receiving FDA breakthrough designation for our first product. Paige has also received FDA-clearance for our digital viewer, FullFocus . We have also established multiple relationships with biopharma, laboratory, and equipment manufacturers that enables Paige to develop an ecosystem ready to help patients receive better diagnoses and treatment.

Medical imaging AI, which can help diagnose health problems doctors don't alway see, is only getting more sophisticated--and more lucrative. Just last month, Tel-Aviv-based Aidoc raised $65 million for it's AI-powered medical imaging platform and other local companies are attracting investors at a rapid clip. The software can find, and in some cases, diagnose polyps, tumors or anomalies that may otherwise go undetected by the human eye – a feat that has the potential to save lives. Beyond its most promising attributes, AI-driven technology could also dramatically decrease wait times at hospitals and doctors' offices by automating some of the most tedious work, allowing doctors to see and treat more patients. But critics of the unregulated technology say results can be inconsistent.

Health care organizations are using artificial intelligence (AI)--which the U.S. Food and Drug Administration defines as "the science and engineering of making intelligent machines"--for a growing range of clinical, administrative, and research purposes. This AI software can, for example, help health care providers diagnose diseases, monitor patients' health, or assist with rote functions such as scheduling patients. Although AI offers unique opportunities to improve health care and patient outcomes, it also comes with potential challenges. AI-enabled products, for example, have sometimes resulted in inaccurate, even potentially harmful, recommendations for treatment.1 These errors can be caused by unanticipated sources of bias in the information used to build or train the AI, inappropriate weight given to certain data points analyzed by the tool, and other flaws. The regulatory framework governing these tools is complex. FDA regulates some--but not all--AI-enabled products used in health care, and the agency plays an important role in ensuring the safety and effectiveness of those products under its jurisdiction. The agency is currently considering how to adapt its review process for AI-enabled medical devices that have the ability to evolve rapidly in response to new data, sometimes in ways that are difficult to foresee.2 This brief describes current and potential uses of AI in health care settings and the challenges these technologies pose, outlines how and under what circumstances they are regulated by FDA, and highlights key questions that will need to be addressed to ensure that the benefits of these devices outweigh their risks.

Feeding your dog the remains of your dinner can seem like a harmless thing to do, but a new study warns peas could increase their risk of getting heart disease. Scientists in Massachusetts have found a link between canine consumption of peas and the development of canine dilated cardiomyopathy (DCM) – an often fatal condition that causes a dog's heart muscle to enlarge. As the heart dilates and becomes larger, it becomes harder to pump, which can lead to heart valve leaks or a build-up of fluids in the chest. Worrying, peas and other legumes including lentils and chickpeas have been ingredients in some'grain-free' dog foods for years – and could be responsible for hundreds of dog deaths. 'Grain-free' dog foods containing legumes instead of grain have already been investigated by the US Food and Drug Administration (FDA).

Few of life's experiences evoke greater apprehension than a diagnosis of Alzheimer's disease (AD). Virtually unknown to the public until the 1980s, it is alone among the 10 most common fatal diseases of developed nations in lacking a disease-modifying treatment. AD affects people of all ethnicities; in the United States, African Americans have twice the prevalence of European Americans ([ 1 ][1]). The cumulative financial cost to society of late-life dementias (of which AD comprises ∼60%) is estimated to exceed those of heart disease and cancer ([ 2 ][2]). This dismal reality may now be changing. The properties of the key proteins comprising the amyloid plaques [amyloid-β (Aβ)] and neurofibrillary tangles (tau) that define the neuropathology of AD have been identified. Coupled with extensive genetic studies, a sequence of lesion formation in brain networks serving memory and cognition is suggested. Antibodies that target these proteins are in advanced trials, and aducamumab, which clears Aβ, was recently approved, though not without controversy. Through longitudinal analyses of humans with rare, causative mutations in APP (the Aβ precursor protein) and presenilin (the catalytic subunit of γ-secretase, which cleaves APP to generate Aβ), it has become clear that biochemical alterations in the brain begin at least two decades before cognitive symptoms develop. During this long presymptomatic interval, extracellular accumulation of the self-aggregating Aβ42 peptide into initially soluble oligomers and then increasingly large polymers and insoluble fibrils is accompanied by binding of the oligomers to the plasma membranes of microglia, astrocytes, and myriad neurites and synapses (see the figure). Although this amyloid hypothesis of AD is often drawn linearly for simplicity ([ 3 ][3]), many of the changes likely arise in temporal proximity ([ 4 ][4]). Genome-wide association studies in typical late-onset AD (i.e., after age 65) have converged on risk alleles in diverse genes mediating cholesterol and lipid regulation, synaptic network functions, and especially microgliosis (altered microglia) and neuroinflammation. The most potent genetic risk factor is the apolipoprotein E ( APOE ) ϵ4 variant: Heterozygosity raises AD risk 2- to 5-fold, and homozygosity increases it >5- to 10-fold. Its pathogenic mechanism appears to involve decreased glial-mediated clearance of Aβ from the brain's extracellular space, leading to more amyloid in cerebral plaques and microvessels ([ 5 ][5]). In mice, the APOE4 protein can also promote tau-mediated neurodegeneration and glial activation, both in the presence and absence of amyloid ([ 6 ][6]). Some other AD genetic risk factors have likewise been linked to enhanced Aβ deposition and/or the macrophage and microglial reaction to it. Two decades ago, theories about AD pathogenesis seemed divided over the primacy of amyloid versus tau deposition. This false dichotomy has been supplanted by a growing consensus that Aβ aggregation in the brain [indicated by declines in soluble Aβ monomers in cerebrospinal fluid (CSF) and accrual of insoluble plaques seen on amyloid-PET (positron emission tomography) scans] begins early in people destined to develop AD and is followed by glia-mediated inflammation and the accumulation and spread of tau tangles in brain regions that serve cognition ([ 7 ][7], [ 8 ][8]). Rising amounts of extracellular Aβ lead to aggregates, including soluble oligomers, that appear to enhance the accrual of tau tangles and altered neurites beyond the medial temporal lobe, where these lesions are often present in older people without AD. Such tau accumulation and spread in the brain, perhaps via neuron-to-neuron connections, seems necessary for the development of cognitive symptoms in AD ([ 9 ][9]). In APP transgenic mice, deletion of the gene that encodes tau does not alter amyloid plaques but significantly lessens their behavioral consequences. Thus, Aβ oligomerization appears to initiate AD neuropathology, leading to altered tau in neurites and cell bodies as well as microgliosis and blood monocyte infiltration into the brain. The failure to reach primary and secondary outcomes in numerous trials of potentially AD-modifying agents may be explained in one or more ways: failure of the agent to achieve robust and selective target engagement in the brain; initiating treatment at a clinical stage that is too advanced to be effective; underpowered trials; adverse side effects on cognition; and faulty trial execution. The precise reasons differ among the unsuccessful trials to date. But a few recent trials appear to have met their primary endpoints or come close to them and have also achieved some secondary endpoints. ![Figure][10] Drug targets for Alzheimer's disease Alzheimer's disease neuropathology includes extracellular amyloid plaques containing myriad amyloid-β (Aβ) oligomers and intraneuronal tangles containing phosphorylated tau. Microglia and astrocytes become activated, leading to neuroinflammation and the spread of neuropathology. Antibodies to Aβ, administered intravascularly, can clear amyloid plaques. GRAPHIC: KELLIE HOLOSKI/ SCIENCE The clearest evidence of disease modification so far has come from secondary biomarker endpoints, principally a substantial decrease in amyloid plaques over 18 months, as measured by amyloid-PET. For example, this occurred in the two phase 3 trials of aducanumab, an Aβ monoclonal antibody that was approved by the US Food and Drug Administration (FDA) on 7 June 2021. Additional biomarker changes included a decrease in the elevated CSF concentration of phosphorylated tau protein and a reduction of brain tau-PET signal, but these outcomes were only measured in a small minority of aducanumab recipients. Although the marked decrease in amyloid deposits can be viewed as biological evidence of disease modification, this was accompanied by a decidedly mixed outcome on cognitive testing, with one aducanumab trial (EMERGE, NCT02484547) meeting its prespecified primary and secondary endpoints at the highest dose, whereas the other (ENGAGE, NCT02477800) did not achieve them. Although differences in cumulative dosing and uneven trial execution have been offered as explanations for this discrepancy, an FDA advisory committee was unconvinced and voted against approval. Nonetheless, the FDA granted an “accelerated approval,” citing robust amyloid lowering across both trials and an expectation that this should lead to less cognitive decline. It also required that a confirmatory trial be performed while marketing commences. The controversy over aducanumab should be considered in the context of other recent AD immunotherapy trials. A large phase 2 trial of the monoclonal antibody lecanemab, designed to bind and clear Aβ protofibrils and oligomers, achieved its primary and secondary endpoints, including substantial amyloid plaque lowering and significantly less cognitive decline ([ 10 ][11]), and has advanced to phase 3 (NCT03887455). Another Aβ monoclonal antibody, gantenerumab, produced amyloid plaque reductions in phase 2 with less cognitive decline ([ 11 ][12]) and is in phase 3 (NCT03443973). Moreover, the antibody donanemab, which targets a low-abundance but aggregation-prone variant of Aβ with a modified amino terminus containing pyroglutamate-3, was recently shown in a moderate-sized phase 2 trial to markedly lower amyloid burden, accompanied by significant slowing of decline in psychometric tests and daily activities ([ 12 ][13]). Notably, donanemab conferred its cognitive effects in patients with relatively low tau burdens at trial entry (as judged by tau-PET), not in those with higher tau concentrations. Stratifying patients by tau burden was wise and could be used in future anti-Aβ trials. Unfortunately, however, both tau-PET and amyloid-PET only quantify fibrillar deposits, not soluble oligomers that appear to be responsible for neurotoxicity. These four antibodies against Aβ unambiguously clear amyloid deposits from brain regions that are important for cognition, and this effect is accompanied by a variable 20 to 40% slowing of cognitive decline in 18-month trials. Collectively, these data represent the closest the AD field has come to a disease-modifying approach. So far, cognitive benefits are modest, and the challenge of assessing their clinical meaningfulness for patients and caregivers remains. But this challenge has been experienced in other chronic diseases, e.g., the controversy over the initial limited benefits of the antiretroviral drug zidovudine for HIV and AIDS when it was first approved ([ 13 ][14]). Disease-modifying agents for AD are expected to slow cognitive decline more effectively the longer—and earlier—they are given. Indeed, treating amyloid-positive individuals in the presymptomatic period is more likely to be efficacious. In mouse models of AD, early treatment with aducanumab reduced Aβ deposition and downstream neuropathology later in life. Gaining real-world experience with a first, albeit modest, treatment should encourage development of more potent second-generation agents. Overnight, managing an untreatable, ultimately fatal disease has been converted into the complex challenge of offering treatment plans to myriad AD patients. Surprisingly, the indication on the aducanumab label initially read “Alzheimer's disease,” but after facing criticism, the FDA soon changed that to mild cognitive impairment and mild AD, mirroring the entry criteria for the phase 3 trials. AD clinicians will likely also require evidence of amyloid pathology. The latter can be established through amyloid-PET imaging, but this is not widely accessible, so CSF profiling will be relied upon to document the characteristic decrease in Aβ42 monomers and increase in phospho-tau that has long been used to confirm AD. A special challenge to clinicians will be considering amyloid-positive patients who are more impaired than those in the trials for treatment. AD practices offering aducanumab should establish transparent guidelines for patient eligibility, hopefully with limited variation among sites. The drug label specifies dose and infusion intervals, but criteria for how long to treat patients will evolve as any slowing of cognitive decline becomes apparent. The practical challenges of an infusible therapeutic will lead to subcutaneous formulations that can be administered at home. Parenthetically, the slower-release subcutaneous route may lessen the occurrence of the key adverse effect of antibodies against Aβ: focal cerebral edema (ARIA-E), which is self-limited and asymptomatic in three-quarters of those who develop it and may be a sign of amyloid clearance or an inflammatory response at local vessels. Occasional microhemorrhages (ARIA-H) developed in a minority of those aducanumab recipients who had ARIA-E, and these appeared to be asymptomatic. The initial price of aducanumab (∼$56,000/year) is very high and will need to be covered by insurance or national health care providers. These and other challenges in the march to implement the first approved AD therapeutic require thoughtful planning and resourcefulness, but this is just the process that patients and caregivers have long awaited. A key advance has been the emergence of blood tests that can detect AD neuropathology. Plasma assays for certain fragments ([ 14 ][15]) and phospho-epitopes ([ 15 ][16]) of tau appear particularly promising, because tau alteration follows Aβ accumulation in those who develop AD symptoms. Comparison of various tau and Aβ plasma assays for their sensitivity in diagnosing AD and monitoring progression is needed. Accelerating the development of plasma biomarkers is critical to meet the challenge of screening innumerable patients globally for eligibility for AD-modifying agents. Additional therapeutic approaches are crucial. Among small-molecule approaches, β-secretase inhibitors have been thwarted by mechanism-based side effects, although lower doses are being considered. An understudied class is the γ-secretase modulators that allosterically alter the conformation of presenilin and thereby shift APP processing from longer, amyloidogenic forms (Aβ42, Aβ43) to shorter, anti-amyloidogenic forms (Aβ37, Aβ38). Beyond Aβ, effort is focused on slowing tau accumulation, e.g., by immunotherapy or antisense oligonucleotides. Modulating the pathological responses of macrophages and microglia is of great interest, given the strong genetic evidence for their involvement in AD. Nonpharmacological approaches toward preventing AD must also be pursued, including exercise, sleep hygiene, a Mediterranean diet, and intellectual and social enrichment. For many chronic diseases, the initial therapeutic compounds have limited efficacy and are often steadily replaced by more effective drugs. The emerging immunotherapeutics slow the AD biological process but confer modest clinical benefit. The approval of aducanumab may provide a proof of concept that can be rapidly improved upon. It may also enable combination treatments, as is typical in chronic diseases. In therapeutics, as in life, one must walk before one can run. 1. [↵][17]1. K. B. Rajan, 2. J. Weuve, 3. L. L. Barnes, 4. R. S. Wilson, 5. D. A. Evans , Alzheimers Dement. 15, 1 (2019). [OpenUrl][18][CrossRef][19] 2. [↵][20]1. M. D. Hurd, 2. P. Martorell, 3. A. Delavande, 4. K. J. Mullen, 5. K. M. Langa , N. Engl. J. Med. 368, 1326 (2013). [OpenUrl][21][CrossRef][22][PubMed][23][Web of Science][24] 3. [↵][25]1. D. J. Selkoe, 2. J. Hardy , EMBO Mol. Med. 8, 595 (2016). [OpenUrl][26][Abstract/FREE Full Text][27] 4. [↵][28]1. B. De Strooper, 2. E. Karran , Cell 164, 603 (2016). [OpenUrl][29][CrossRef][30][PubMed][31] 5. [↵][32]1. J. M. Castellano et al ., Sci. Transl. Med. 3, 89ra57 (2011). [OpenUrl][33][Abstract/FREE Full Text][34] 6. [↵][35]1. Y. Shi et al ., Nature 549, 523 (2017). [OpenUrl][36][CrossRef][37][PubMed][38] 7. [↵][39]1. R. J. Bateman et al ., N. Engl. J. Med. 367, 795 (2012). [OpenUrl][40][CrossRef][41][PubMed][42][Web of Science][43] 8. [↵][44]1. C. R. Jack Jr. et al ., Lancet Neurol. 12, 207 (2013). [OpenUrl][45][CrossRef][46][PubMed][47][Web of Science][48] 9. [↵][49]1. J. S. Sanchez et al ., Sci. Transl. Med. 13, eabc0655 (2021). [OpenUrl][50][Abstract/FREE Full Text][51] 10. [↵][52]1. C. J. Swanson et al ., Alzheimers Res. Ther. 13, 80 (2021). [OpenUrl][53] 11. [↵][54]1. R. Doody , J. Prev. Alzheimers Dis. 4, 264 (2017). [OpenUrl][55] 12. [↵][56]1. M. Mintun et al ., N. Engl. J. Med. 384, 1691 (2021). [OpenUrl][57] 13. [↵][58]1. M. A. Fischl et al ., N. Engl. J. Med. 317, 185 (1987). [OpenUrl][59][CrossRef][60][PubMed][61][Web of Science][62] 14. [↵][63]1. J. P. Chhatwal et al ., Nat. Commun. 11, 6024 (2020). [OpenUrl][64] 15. [↵][65]1. S. Janelidze et al ., Nat. Med. 26, 379 (2020). [OpenUrl][66][CrossRef][67][PubMed][68] Acknowledgments: D.J.S. is a director of and consultant for Prothena Biosciences. [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5 [6]: #ref-6 [7]: #ref-7 [8]: #ref-8 [9]: #ref-9 [10]: pending:yes [11]: #ref-10 [12]: #ref-11 [13]: #ref-12 [14]: #ref-13 [15]: #ref-14 [16]: #ref-15 [17]: #xref-ref-1-1 "View reference 1 in text" [18]: {openurl}?query=rft.jtitle%253DAlzheimers%2BDement.%26rft.volume%253D15%26rft.spage%253D1%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.jalz.2018.07.216%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=10.1016/j.jalz.2018.07.216&link_type=DOI [20]: #xref-ref-2-1 "View reference 2 in text" [21]: {openurl}?query=rft.jtitle%253DN.%2BEngl.%2BJ.%2BMed.%26rft.volume%253D368%26rft.spage%253D1326%26rft_id%253Dinfo%253Adoi%252F10.1056%252FNEJMsa1204629%26rft_id%253Dinfo%253Apmid%252F23550670%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 [22]: /lookup/external-ref?access_num=10.1056/NEJMsa1204629&link_type=DOI [23]: /lookup/external-ref?access_num=23550670&link_type=MED&atom=%2Fsci%2F373%2F6555%2F624.atom [24]: /lookup/external-ref?access_num=000316989900009&link_type=ISI [25]: #xref-ref-3-1 "View reference 3 in text" [26]: {openurl}?query=rft.jtitle%253DEMBO%2BMol.%2BMed.%26rft_id%253Dinfo%253Adoi%252F10.15252%252Femmm.201606210%26rft_id%253Dinfo%253Apmid%252F27025652%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/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6NjoiZW1ib21tIjtzOjU6InJlc2lkIjtzOjc6IjgvNi81OTUiO3M6NDoiYXRvbSI7czoyMjoiL3NjaS8zNzMvNjU1NS82MjQuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9 [28]: #xref-ref-4-1 "View reference 4 in text" [29]: {openurl}?query=rft.jtitle%253DCell%26rft.volume%253D164%26rft.spage%253D603%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.cell.2015.12.056%26rft_id%253Dinfo%253Apmid%252F26871627%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 [30]: /lookup/external-ref?access_num=10.1016/j.cell.2015.12.056&link_type=DOI [31]: /lookup/external-ref?access_num=26871627&link_type=MED&atom=%2Fsci%2F373%2F6555%2F624.atom [32]: #xref-ref-5-1 "View reference 5 in text" [33]: {openurl}?query=rft.jtitle%253DScience%2BTranslational%2BMedicine%26rft.stitle%253DSci%2BTransl%2BMed%26rft.aulast%253DCastellano%26rft.auinit1%253DJ.%2BM.%26rft.volume%253D3%26rft.issue%253D89%26rft.spage%253D89ra57%26rft.epage%253D89ra57%26rft.atitle%253DHuman%2BapoE%2BIsoforms%2BDifferentially%2BRegulate%2BBrain%2BAmyloid-%257Bbeta%257D%2BPeptide%2BClearance%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscitranslmed.3002156%26rft_id%253Dinfo%253Apmid%252F21715678%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 [34]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6MTE6InNjaXRyYW5zbWVkIjtzOjU6InJlc2lkIjtzOjExOiIzLzg5Lzg5cmE1NyI7czo0OiJhdG9tIjtzOjIyOiIvc2NpLzM3My82NTU1LzYyNC5hdG9tIjt9czo4OiJmcmFnbWVudCI7czowOiIiO30= [35]: #xref-ref-6-1 "View reference 6 in text" [36]: {openurl}?query=rft.jtitle%253DNature%26rft.volume%253D549%26rft.spage%253D523%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fnature24016%26rft_id%253Dinfo%253Apmid%252F28959956%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 [37]: /lookup/external-ref?access_num=10.1038/nature24016&link_type=DOI [38]: /lookup/external-ref?access_num=28959956&link_type=MED&atom=%2Fsci%2F373%2F6555%2F624.atom [39]: #xref-ref-7-1 "View reference 7 in text" [40]: {openurl}?query=rft.jtitle%253DNew%2BEngland%2BJournal%2Bof%2BMedicine%26rft.stitle%253DNEJM%26rft.aulast%253DBateman%26rft.auinit1%253DR.%2BJ.%26rft.volume%253D367%26rft.issue%253D9%26rft.spage%253D795%26rft.epage%253D804%26rft.atitle%253DClinical%2Band%2Bbiomarker%2Bchanges%2Bin%2Bdominantly%2Binherited%2BAlzheimer%2527s%2Bdisease.%26rft_id%253Dinfo%253Adoi%252F10.1056%252FNEJMoa1202753%26rft_id%253Dinfo%253Apmid%252F22784036%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 [41]: /lookup/external-ref?access_num=10.1056/NEJMoa1202753&link_type=DOI [42]: /lookup/external-ref?access_num=22784036&link_type=MED&atom=%2Fsci%2F373%2F6555%2F624.atom [43]: /lookup/external-ref?access_num=000308067400001&link_type=ISI [44]: #xref-ref-8-1 "View reference 8 in text" [45]: {openurl}?query=rft.jtitle%253DLancet%2BNeurol.%26rft.volume%253D12%26rft.spage%253D207%26rft_id%253Dinfo%253Adoi%252F10.1016%252FS1474-4422%252812%252970291-0%26rft_id%253Dinfo%253Apmid%252F23332364%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 [46]: /lookup/external-ref?access_num=10.1016/S1474-4422(12)70291-0&link_type=DOI [47]: /lookup/external-ref?access_num=23332364&link_type=MED&atom=%2Fsci%2F373%2F6555%2F624.atom [48]: /lookup/external-ref?access_num=000314330200021&link_type=ISI [49]: #xref-ref-9-1 "View reference 9 in text" [50]: {openurl}?query=rft.jtitle%253DScience%2BTranslational%2BMedicine%26rft.stitle%253DSci%2BTransl%2BMed%26rft.aulast%253DSanchez%26rft.auinit1%253DJ.%2BS.%26rft.volume%253D13%26rft.issue%253D577%26rft.spage%253Deabc0655%26rft.epage%253Deabc0655%26rft.atitle%253DThe%2Bcortical%2Borigin%2Band%2Binitial%2Bspread%2Bof%2Bmedial%2Btemporal%2Btauopathy%2Bin%2BAlzheimers%2Bdisease%2Bassessed%2Bwith%2Bpositron%2Bemission%2Btomography%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscitranslmed.abc0655%26rft_id%253Dinfo%253Apmid%252F33472953%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 [51]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6MTE6InNjaXRyYW5zbWVkIjtzOjU6InJlc2lkIjtzOjE1OiIxMy81NzcvZWFiYzA2NTUiO3M6NDoiYXRvbSI7czoyMjoiL3NjaS8zNzMvNjU1NS82MjQuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9 [52]: #xref-ref-10-1 "View reference 10 in text" [53]: {openurl}?query=rft.jtitle%253DAlzheimers%2BRes.%2BTher.%26rft.volume%253D13%26rft.spage%253D80%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 [54]: #xref-ref-11-1 "View reference 11 in text" [55]: {openurl}?query=rft.jtitle%253DJ.%2BPrev.%2BAlzheimers%2BDis.%26rft.volume%253D4%26rft.spage%253D264%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 [56]: #xref-ref-12-1 "View reference 12 in text" [57]: {openurl}?query=rft.jtitle%253DN.%2BEngl.%2BJ.%2BMed.%26rft.volume%253D384%26rft.spage%253D1691%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]: #xref-ref-13-1 "View reference 13 in text" [59]: {openurl}?query=rft.jtitle%253DNew%2BEngland%2BJournal%2Bof%2BMedicine%26rft.stitle%253DNEJM%26rft.aulast%253DFischl%26rft.auinit1%253DM.%2BA.%26rft.volume%253D317%26rft.issue%253D4%26rft.spage%253D185%26rft.epage%253D191%26rft.atitle%253DThe%2Befficacy%2Bof%2Bazidothymidine%2B%2528AZT%2529%2Bin%2Bthe%2Btreatment%2Bof%2Bpatients%2Bwith%2BAIDS%2Band%2BAIDS-related%2Bcomplex.%2BA%2Bdouble-blind%252C%2Bplacebo-controlled%2Btrial%26rft_id%253Dinfo%253Adoi%252F10.1056%252FNEJM198707233170401%26rft_id%253Dinfo%253Apmid%252F3299089%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 [60]: /lookup/external-ref?access_num=10.1056/NEJM198707233170401&link_type=DOI [61]: /lookup/external-ref?access_num=3299089&link_type=MED&atom=%2Fsci%2F373%2F6555%2F624.atom [62]: /lookup/external-ref?access_num=A1987J229500001&link_type=ISI [63]: #xref-ref-14-1 "View reference 14 in text" [64]: {openurl}?query=rft.jtitle%253DNat.%2BCommun.%26rft.volume%253D11%26rft.spage%253D6024%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 [65]: #xref-ref-15-1 "View reference 15 in text" [66]: {openurl}?query=rft.jtitle%253DNat.%2BMed.%26rft.volume%253D26%26rft.spage%253D379%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fs41591-020-0755-1%26rft_id%253Dinfo%253Apmid%252Fhttp%253A%252F%252Fwww.n%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 [67]: /lookup/external-ref?access_num=10.1038/s41591-020-0755-1&link_type=DOI [68]: /lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fsci%2F373%2F6555%2F624.atom

A small company developing an implantable brain computer interface to help treat conditions like paralysis has received the go-ahead from the Food and Drug Administration (FDA) to kick off clinical trials of its flagship device later this year. New York-based Synchron announced Wednesday it has received FDA approval to begin an early feasibility study of its Stentrode implant later this year at Mount Sinai Hospital with six human subjects. The study will examine the safety and efficacy of its motor neuroprosthesis in patients with severe paralysis, with the hopes the device will allow them to use brain data to "control digital devices and achieve improvements in functional independence." "Patients begin using the device at home soon after implantation and may wirelessly control external devices by thinking about moving their limbs. The system is designed to facilitate better communication and functional independence for patients by enabling daily tasks like texting, emailing, online commerce and accessing telemedicine," the company said in a release.