Genetic code expansion, click chemistry, and light-activated PI3K reveal details of membrane protein trafficking downstream of receptor tyrosine kinases.

Ligands such as insulin, epidermal growth factor, platelet-derived growth factor, and nerve growth factor (NGF) initiate signals at the cell membrane by binding to receptor tyrosine kinases (RTKs). Along with G-protein-coupled receptors, RTKs are the main platforms for transducing extracellular signals into intracellular signals. Studying RTK signaling has been a challenge, however, due to the multiple signaling pathways to which RTKs typically are coupled, including MAP/ERK, PLCγ, and Class 1A phosphoinositide 3-kinases (PI3K). The multi-pronged RTK signaling has been a barrier to isolating the effects of any one downstream pathway. Here, we used optogenetic activation of PI3K to decouple its activation from other RTK signaling pathways. In this context, we used genetic code expansion to introduce a click chemistry noncanonical amino acid into the extracellular side of membrane proteins. Applying a cell-impermeant click chemistry fluorophore allowed us to visualize delivery of membrane proteins to the plasma membrane in real time. Using these approaches, we demonstrate that activation of PI3K, without activating other pathways downstream of RTK signaling, is sufficient to traffic the TRPV1 ion channels and insulin receptors to the plasma membrane.

Natural history and biomarkers of KCNV2-associated retinopathy.

BACKGROUND: KCNV2-associated retinopathy is an autosomal recessive inherited retinal disease classically named cone dystrophy with supernormal rod response (CDSRR). This study aims to identify the best biomarker for evaluating the condition. METHODS: A retrospective review of eight patients from seven families with genetically confirmed KCNV2-associated retinopathy was performed. The best corrected visual acuity (BCVA), full-field electroretinogram (ffERG), pattern ERG (pERG), fundus imaging: retinal photograph and fundus autofluorescence (FAF), and optical coherence tomography (OCT) were analysed. RESULTS: There was a disproportionate increase in b-wave amplitude with a relatively small light intensity increase, especially between the two dimmest stimuli of DA 0.002 and 0.01 (-2.7 and -2.0 log cd.s/m2). The a-wave amplitude was normal. The a-wave peak time was delayed in all stimuli. The b-wave peak time was delayed compared to normal, but the gap tightened as intensity increased. The b:a wave ratio was above or at the upper limit for the reference values. FAF bull's eye maculopathy pattern was prominent and variable foveal disruption on OCT was apparent in all patients. Legal blindness was reached before the age of 25. CONCLUSIONS: We identified three potential electrophysiology biomarkers to assist in evaluating future therapies: the disproportionate b-wave amplitude jump, delayed a-wave and b-wave peak time, and the higher than normal b:a wave ratio. Any of these biomarkers found with photoreceptor ellipsoid zone foveal-perifoveal disruption should prompt consideration for KCNV2 retinopathy. The BCVA natural history data suggests the probable optimum therapeutic window in the first three decades of life.

OptoPI3K, genetic code expansion, and click chemistry reveal mechanisms underlying reciprocal regulation between TRPV1 and PI3K.

Receptor tyrosine kinase signaling is characterized by complex webs of interconnected pathways that regulate diverse cellular functions. The complexity of signaling is a barrier to understanding the pathways that control any particular function. In this work, we use a novel combination of approaches and a new click chemistry probe to determine the role of one pathway in regulating cell surface expression of an ion channel and a receptor tyrosine kinase. We applied an optogenetic approach to uncouple activation of the PI3K pathway from other pathways downstream of RTK activation. In this context, we used genetic code expansion to introduce a click chemistry noncanonical amino acid into the extracellular side of membrane proteins. Applying a cell-impermeant click chemistry fluorophore allowed us to visualize delivery of membrane proteins to the PM in real time. Using these approaches, we demonstrate that activation of PI3K, without activating other pathways downstream of RTK signaling, is sufficient to traffic the TRPV1 ion channels and insulin receptors to the plasma membrane.

Disparities in cannabis use and documentation in electronic health records among children and young adults.

The legalizations of medical and recreational cannabis have generated a great deal of interest in studying the health impacts of cannabis products. Despite increases in cannabis use, its documentation during clinical visits is not yet mainstream. This lack of information hampers efforts to study cannabis's effects on health outcomes. A clear and in-depth understanding of current trends in cannabis use documentation is necessary to develop proper guidelines to screen and document cannabis use. Here we have developed and used a natural language processing pipeline to evaluate the trends and disparities in cannabis documentation. The pipeline includes a screening step to identify clinical notes with cannabis use documentation which is then fed into a BERT-based classifier to confirm positive use. This pipeline is applied to more than 23 million notes from a large cohort of 370,087 patients seen in a high-volume multi-site pediatric and young adult clinic over a period of 21 years. Our findings show a very low but growing rate of cannabis use documentation (<2%) in electronic health records with significant demographic and socioeconomic disparities in both documentation and positive use, which requires further attention.

The digital transformation of surgery.

Rapid advances in digital technology and artificial intelligence in recent years have already begun to transform many industries, and are beginning to make headway into healthcare. There is tremendous potential for new digital technologies to improve the care of surgical patients. In this piece, we highlight work being done to advance surgical care using machine learning, computer vision, wearable devices, remote patient monitoring, and virtual and augmented reality. We describe ways these technologies can be used to improve the practice of surgery, and discuss opportunities and challenges to their widespread adoption and use in operating rooms and at the bedside.

Has increased telehealth access during COVID-19 led to over-utilization of primary care?

Telehealth use for primary care has skyrocketed since the onset of the COVID-19 pandemic. Enthusiasts have praised this new medium of delivery as a way to increase access to care while potentially reducing spending. Over two years into the pandemic, the question of whether telehealth will lead to an increase in primary care utilization and spending has been met with contradictory answers. Some evidence suggests that telehealth may be used as an addition to in-person visits. Others like Dixit et al. have found that telehealth can actually substitute for in-person care rather than contribute to overutilization. As telehealth continues to evolve, outcomes, utilization, and quality of care should be closely monitored.

Defining digital surgery for the future.

Innovations in robotics, virtual and augmented reality, and artificial intelligence are being rapidly adopted as tools of "digital surgery". Despite its quickly emerging role, digital surgery is not well understood. A recent study defines the term itself, and then specifies ethical issues specific to the field. These include privacy and public trust, consent, and litigation.

Intelligent risk prediction in public health using wearable device data.

The importance of infection risk prediction as a key public health measure has only been underscored by the COVID-19 pandemic. In a recent study, researchers use machine learning to develop an algorithm that predicts the risk of COVID-19 infection, by combining biometric data from wearable devices like Fitbit, with electronic symptom surveys. In doing so, they aim to increase the efficiency of test allocation when tracking disease spread in resource-limited settings. But the implications of technology that applies data from wearables stretch far beyond infection monitoring into healthcare delivery and research. The adoption and implementation of this type of technology will depend on regulation, impact on patient outcomes, and cost savings.

Health digital twins as tools for precision medicine: Considerations for computation, implementation, and regulation.

Health digital twins are defined as virtual representations ("digital twin") of patients ("physical twin") that are generated from multimodal patient data, population data, and real-time updates on patient and environmental variables. With appropriate use, HDTs can model random perturbations on the digital twin to gain insight into the expected behavior of the physical twin-offering groundbreaking applications in precision medicine, clinical trials, and public health. Main considerations for translating HDT research into clinical practice include computational requirements, clinical implementation, as well as data governance, and product oversight.

Leveraging reimbursement strategies to guide value-based adoption and utilization of medical AI.

With the increasing number of FDA-approved artificial intelligence (AI) systems, the financing of these technologies has become a primary gatekeeper to mass clinical adoption. Reimbursement models adapted for current payment schemes, including per-use rates, are feasible for early AI products. Alternative and complementary models may offer future payment options for value-based AI. A successful reimbursement strategy will align interests across stakeholders to guide value-based and cost-effective improvements to care.

Watching Parkinson's disease with wrist-based sensors.

Parkinson's disease (PD) lacks sensitive, objective, and reliable measures for disease progression and response. This presents a challenge for clinical trials given the multifaceted and fluctuating nature of PD symptoms. Innovations in digital health and wearable sensors promise to more precisely measure aspects of patient function and well-being. Beyond research trials, digital biomarkers and clinical outcome assessments may someday support clinician-initiated or closed-loop treatment adjustments. A recent study from Verily Life Sciences presents results for a smartwatch-based motor exam intended to accelerate the development and evaluation of therapies for PD.