Synaptic gradients transform object location to action.

To survive, animals must convert sensory information into appropriate behaviours1,2. Vision is a common sense for locating ethologically relevant stimuli and guiding motor responses3-5. How circuitry converts object location in retinal coordinates to movement direction in body coordinates remains largely unknown. Here we show through behaviour, physiology, anatomy and connectomics in Drosophila that visuomotor transformation occurs by conversion of topographic maps formed by the dendrites of feature-detecting visual projection neurons (VPNs)6,7 into synaptic weight gradients of VPN outputs onto central brain neurons. We demonstrate how this gradient motif transforms the anteroposterior location of a visual looming stimulus into the fly's directional escape. Specifically, we discover that two neurons postsynaptic to a looming-responsive VPN type promote opposite takeoff directions. Opposite synaptic weight gradients onto these neurons from looming VPNs in different visual field regions convert localized looming threats into correctly oriented escapes. For a second looming-responsive VPN type, we demonstrate graded responses along the dorsoventral axis. We show that this synaptic gradient motif generalizes across all 20 primary VPN cell types and most often arises without VPN axon topography. Synaptic gradients may thus be a general mechanism for conveying spatial features of sensory information into directed motor outputs.

Contextualizing protein representations using deep learning on protein networks and single-cell data.

Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across diverse biological contexts, such as tissues and cell types, remains a significant challenge for existing algorithms. We introduce Pinnacle, a flexible geometric deep learning approach that is trained on contextualized protein interaction networks to generate context-aware protein representations. Leveraging a human multi-organ single-cell transcriptomic atlas, Pinnacle provides 394,760 protein representations split across 156 cell type contexts from 24 tissues and organs. Pinnacle's contextualized representations of proteins reflect cellular and tissue organization and Pinnacle's tissue representations enable zero-shot retrieval of the tissue hierarchy. Pretrained Pinnacle's protein representations can be adapted for downstream tasks: to enhance 3D structure-based protein representations for important protein interactions in immuno-oncology (PD-1/PD-L1 and B7-1/CTLA-4) and to study the effects of drugs across cell type contexts. Pinnacle outperforms state-of-the-art, yet context-free, models in nominating therapeutic targets for rheumatoid arthritis and inflammatory bowel diseases, and can pinpoint cell type contexts that predict therapeutic targets better than context-free models (29 out of 156 cell types in rheumatoid arthritis; 13 out of 152 cell types in inflammatory bowel diseases). Pinnacle is a graph-based contextual AI model that dynamically adjusts its outputs based on biological contexts in which it operates.

Patient Experiences of Telemedicine in Spine Care: A Mixed Methods Study.

STUDY DESIGN: Survey-based study. OBJECTIVE: We performed a mixed methods study involving patients using telemedicine for spine care. We sought to understand factors influencing the utilization and evaluation of this modality. SUMMARY OF BACKGROUND DATA: Telemedicine has been integrated into routine spine care; its long-term viability will depend not only on optimizing its safety, efficiency, and cost-effectiveness, but also on understanding patient valuation of its benefits and limitations. METHODS: We used a clinical registry to identify spine patients seen virtually by providers at our tertiary academic medical center between March and September of 2020. We distributed an online survey that queried patients' experiences with telemedicine. We performed statistical analyses of Likert-scale questions and a thematic analysis of free-form responses. Sociodemographic data were abstracted and analyzed. RESULTS: Overall, we evaluated 139 patient surveys. High levels of patient-rated care and patient-rated experience were observed for both in-person and telemedicine visits; however, in-person visits were rated significantly higher in both respects (9.3/10 vs. 8.7/10 for patient-rated care, P < 0.001; 9.0/10 vs. 8.4/10 for patient-rated experience, P = 0.006). A preference for in-person first-time visits was observed which was not maintained for follow up appointments. Both patient and clinical factors influenced perceptions of telemedicine. Thematic analysis of free-form responses provided by 113 patients (81%) generated favorable, unfavorable, and reflective themes, each further contextualized by subthemes. Responders were not significantly different from nonresponders across sociodemographic characteristics. CONCLUSION: Our quantitative and qualitative findings yield insight into the patient experience of telemedicine in spine care. A preference for in-person visits was notable, particularly for new patient evaluations. This preference was not maintained for follow-up care. Patients acknowledged the benefits of telemedicine and reflected on its effective integration with in-person care. These results may guide best practices to improve access and patient satisfaction in the future.Level of Evidence: 4.

Population-scale identification of differential adverse events before and during a pandemic.

Adverse patient safety events, unintended injuries resulting from medical therapy, were associated with 110,000 deaths in the United States in 2019. A nationwide pandemic (such as COVID-19) further challenges the ability of healthcare systems to ensure safe medication use and the pandemic's effects on safety events remain poorly understood. Here, we investigate drug safety events across demographic groups before and during a pandemic using a dataset of 1,425,371 reports involving 2,821 drugs and 7,761 adverse events. Among 64 adverse events identified by our analyses, we find 54 increased in frequency during the pandemic, despite a 4.4% decrease in the total number of reports. Out of 53 adverse events with a pre-pandemic gender gap, 33 have seen their gap increase with the pandemic onset. We find that the number of adverse events with an increased reporting ratio is higher in adults (by 16.8%) than in older patients. Our findings have implications for safe medication use and preventable healthcare inequality in public health emergencies.

Predicting miRNA-based disease-disease relationships through network diffusion on multi-omics biological data.

With critical roles in regulating gene expression, miRNAs are strongly implicated in the pathophysiology of many complex diseases. Experimental methods to determine disease related miRNAs are time consuming and costly. Computationally predicting miRNA-disease associations has potential applications in finding miRNA therapeutic pathways and in understanding the role of miRNAs in disease-disease relationships. In this study, we propose the MiRNA-disease Association Prediction (MAP) method, an in-silico method to predict and prioritize miRNA-disease associations. The MAP method applies a network diffusion approach, starting from the known disease genes in a heterogenous network constructed from miRNA-gene associations, protein-protein interactions, and gene-disease associations. Validation using experimental data on miRNA-disease associations demonstrated superior performance to two current state-of-the-art methods, with areas under the ROC curve all over 0.8 for four types of cancer. MAP is successfully applied to predict differential miRNA expression in four cancer types. Most strikingly, disease-disease relationships in terms of shared miRNAs revealed hidden disease subtyping comparable to that of previous work on shared genes between diseases, with applications for multi-omics characterization of disease relationships.

Telemedicine in Orthopaedic Surgery: Challenges and Opportunities.

Improvements in technology and a push toward value-based health care have poised the telemedicine industry for growth; however, despite the benefits of virtual care, widespread implementation had not occurred until the coronavirus 2019 (COVID-19) pandemic. Powerful barriers have hindered the widespread adoption of telemedicine, including lack of awareness, implementation costs, inefficiencies introduced, difficulty performing physical examinations, overall lack of perceived benefit of virtual care, negative financial implications, concern for medicolegal liability, and regulatory restrictions. Some of these challenges have been addressed with temporary state and federal mandates in response to the COVID-19 pandemic; however, continued investment in systems and technology as well as refinement of regulations around telemedicine are needed to sustain widespread adoption by patients and providers.

Network Diffusion Approach to Predict LncRNA Disease Associations Using Multi-Type Biological Networks: LION.

Recently, long-non-coding RNAs (lncRNAs) have attracted attention because of their emerging role in many important biological mechanisms. The accumulating evidence indicates that the dysregulation of lncRNAs is associated with complex diseases. However, only a few lncRNA-disease associations have been experimentally validated and therefore, predicting potential lncRNAs that are associated with diseases become an important task. Current computational approaches often use known lncRNA-disease associations to predict potential lncRNA-disease links. In this work, we exploited the topology of multi-level networks to propose the LncRNA rankIng by NetwOrk DiffusioN (LION) approach to identify lncRNA-disease associations. The multi-level complex network consisted of lncRNA-protein, protein-protein interactions, and protein-disease associations. We applied the network diffusion algorithm of LION to predict the lncRNA-disease associations within the multi-level network. LION achieved an AUC value of 96.8% for cardiovascular diseases, 91.9% for cancer, and 90.2% for neurological diseases by using experimentally verified lncRNAs associated with diseases. Furthermore, compared to a similar approach (TPGLDA), LION performed better for cardiovascular diseases and cancer. Given the versatile role played by lncRNAs in different biological mechanisms that are perturbed in diseases, LION's accurate prediction of lncRNA-disease associations helps in ranking lncRNAs that could function as potential biomarkers and potential drug targets.