Molecular mechanism of GPCR-mediated arrestin activation.

Despite intense interest in discovering drugs that cause G-protein-coupled receptors (GPCRs) to selectively stimulate or block arrestin signalling, the structural mechanism of receptor-mediated arrestin activation remains unclear1,2. Here we reveal this mechanism through extensive atomic-level simulations of arrestin. We find that the receptor's transmembrane core and cytoplasmic tail-which bind distinct surfaces on arrestin-can each independently stimulate arrestin activation. We confirm this unanticipated role of the receptor core, and the allosteric coupling between these distant surfaces of arrestin, using site-directed fluorescence spectroscopy. The effect of the receptor core on arrestin conformation is mediated primarily by interactions of the intracellular loops of the receptor with the arrestin body, rather than the marked finger-loop rearrangement that is observed upon receptor binding. In the absence of a receptor, arrestin frequently adopts active conformations when its own C-terminal tail is disengaged, which may explain why certain arrestins remain active long after receptor dissociation. Our results, which suggest that diverse receptor binding modes can activate arrestin, provide a structural foundation for the design of functionally selective ('biased') GPCR-targeted ligands with desired effects on arrestin signalling.

National Projections for Clinical Implications of Race-Free Creatinine-Based GFR Estimating Equations.

BACKGROUND: The National Kidney Foundation and American Society of Nephrology Task Force on Reassessing the Inclusion of Race in Diagnosing Kidney Disease recently recommended a new race-free creatinine-based equation for eGFR. The effect on recommended clinical care across race and ethnicity groups is unknown. METHODS: We analyzed nationally representative cross-sectional questionnaires and medical examinations from 44,360 participants collected between 2001 and 2018 by the National Health and Nutrition Examination Survey. We quantified the number and proportion of Black, White, Hispanic, and Asian/Other adults with guideline-recommended changes in care. RESULTS: The new equation, if applied nationally, could assign new CKD diagnoses to 434,000 (95% confidence interval [CI], 350,000 to 517,000) Black adults, reclassify 584,000 (95% CI, 508,000 to 667,000) to more advanced stages of CKD, restrict kidney donation eligibility for 246,000 (95% CI, 189,000 to 303,000), expand nephrologist referrals for 41,800 (95% CI, 19,800 to 63,800), and reduce medication dosing for 222,000 (95% CI, 169,000 to 275,000). Among non-Black adults, these changes may undo CKD diagnoses for 5.51 million (95% CI, 4.86 million to 6.16 million), reclassify 4.59 million (95% CI, 4.28 million to 4.92 million) to less advanced stages of CKD, expand kidney donation eligibility for 3.96 million (95% CI, 3.46 million to 4.46 million), reverse nephrologist referral for 75,800 (95% CI, 35,400 to 116,000), and reverse medication dose reductions for 1.47 million (95% CI, 1.22 million to 1.73 million). The racial and ethnic mix of the populations used to develop eGFR equations has a substantial effect on potential care changes. CONCLUSION: The newly recommended 2021 CKD-EPI creatinine-based eGFR equation may result in substantial changes to recommended care for US patients of all racial and ethnic groups.

Gut Microbiome Dysbiosis and Depression: a Comprehensive Review.

PURPOSE OF REVIEW: The human gut microbiome is involved in a bi-directional communication pathway with the central nervous system (CNS), termed the microbiota-gut-brain axis. The microbiota-gut-brain axis is believed to mediate or modulate various central processes through the vagus nerve. The microbiota-gut-brain axis is involved with the production of microbial metabolites and immune mediators which trigger changes in neurotransmission, neuroinflammation, and behavior. Little is understood about the utilization of microbiome manipulation to treat disease. RECENT FINDINGS: Though studies exploring the role of the microbiome in various disease processes have shown promise, mechanisms remain unclear and evidence-based treatments for most illnesses have not yet been developed. The animal studies reviewed in the present investigation include an array of basic science studies that clarify mechanisms by which the microbiome may affect mental health. More evidence is needed, particularly as it relates to translating this work to humans. The studies presented in this review demonstrate encouraging results in the treatment of depression. Limitations include small sample sizes and heterogeneous methodology. The exact mechanism by which the gut microbiota causes or alters neuropsychiatric disease states is not fully understood. In this review, we focus on recent studies investigating the relationship between gut microbiome dysbiosis and the pathogenesis of depression.

An evaluation of clinical order patterns machine-learned from clinician cohorts stratified by patient mortality outcomes.

OBJECTIVE: Evaluate the quality of clinical order practice patterns machine-learned from clinician cohorts stratified by patient mortality outcomes. MATERIALS AND METHODS: Inpatient electronic health records from 2010 to 2013 were extracted from a tertiary academic hospital. Clinicians (n = 1822) were stratified into low-mortality (21.8%, n = 397) and high-mortality (6.0%, n = 110) extremes using a two-sided P-value score quantifying deviation of observed vs. expected 30-day patient mortality rates. Three patient cohorts were assembled: patients seen by low-mortality clinicians, high-mortality clinicians, and an unfiltered crowd of all clinicians (n = 1046, 1046, and 5230 post-propensity score matching, respectively). Predicted order lists were automatically generated from recommender system algorithms trained on each patient cohort and evaluated against (i) real-world practice patterns reflected in patient cases with better-than-expected mortality outcomes and (ii) reference standards derived from clinical practice guidelines. RESULTS: Across six common admission diagnoses, order lists learned from the crowd demonstrated the greatest alignment with guideline references (AUROC range = 0.86-0.91), performing on par or better than those learned from low-mortality clinicians (0.79-0.84, P < 10-5) or manually-authored hospital order sets (0.65-0.77, P < 10-3). The same trend was observed in evaluating model predictions against better-than-expected patient cases, with the crowd model (AUROC mean = 0.91) outperforming the low-mortality model (0.87, P < 10-16) and order set benchmarks (0.78, P < 10-35). DISCUSSION: Whether machine-learning models are trained on all clinicians or a subset of experts illustrates a bias-variance tradeoff in data usage. Defining robust metrics to assess quality based on internal (e.g. practice patterns from better-than-expected patient cases) or external reference standards (e.g. clinical practice guidelines) is critical to assess decision support content. CONCLUSION: Learning relevant decision support content from all clinicians is as, if not more, robust than learning from a select subgroup of clinicians favored by patient outcomes.

Institutionalizing healthcare hackathons to promote diversity in collaboration in medicine.

BACKGROUND: Medical students and healthcare professionals can benefit from exposure to cross-disciplinary teamwork and core concepts of medical innovation. Indeed, to address complex challenges in patient care, diversity in collaboration across medicine, engineering, business, and design is critical. However, a limited number of academic institutions have established cross-disciplinary opportunities for students and young professionals within these domains to work collaboratively towards diverse healthcare needs. METHODS: Drawing upon best practices from computer science and engineering, healthcare hackathons bring together interdisciplinary teams of students and professionals to collaborate, brainstorm, and build solutions to unmet clinical needs. Over the course of six months, a committee of 20 undergraduates, medical students, and physician advisors organized Stanford University's first healthcare hackathon (November 2016). Demographic data from initial applications were supplemented with responses from a post-hackathon survey gauging themes of diversity in collaboration, professional development, interest in medical innovation, and educational value. In designing and evaluating the event, the committee focused on measurable outcomes of diversity across participants (skillset, age, gender, academic degree), ideas (clinical needs), and innovations (projects). RESULTS: Demographic data (n = 587 applicants, n = 257 participants) reveal participants across diverse academic backgrounds, age groups, and domains of expertise were in attendance. From 50 clinical needs presented representing 19 academic fields, 40 teams ultimately formed and submitted projects spanning web (n = 13) and mobile applications (n = 13), artificial intelligence-based tools (n = 6), and medical devices (n = 3), among others. In post-hackathon survey responses (n = 111), medical students and healthcare professionals alike noted a positive impact on their ability to work in multidisciplinary teams, learn from individuals of different backgrounds, and address complex healthcare challenges. CONCLUSIONS: Healthcare hackathons can encourage diversity across individuals, ideas, and projects to address clinical challenges. By providing an outline of Stanford's inaugural event, we hope more universities can adopt the healthcare hackathon model to promote diversity in collaboration in medicine.

An Extended Hackathon Model for Collaborative Education in Medical Innovation.

To support the next generation of healthcare innovators - whether they be engineers, designers, clinicians, or business experts by training - education in the emerging field of medical innovation should be made easily and widely accessible to undergraduate students, graduate students, and young professionals, early in their careers. Currently, medical innovation curricula are taught through semester-long courses or year-long fellowships at a handful of universities, reaching only a limited demographic of participants. This study describes the structure and preliminary outcomes of a 1-2 week "extended hackathon" course that seeks to make medical innovation education and training more accessible and easily adoptable for academic medical centers. Eight extended hackathons were hosted in five international locations reaching 245 participants: Beijing (June 2015 and August 2016), Hong Kong (June 2016, 2017, and 2018), Curitiba (July 2016), Stanford (October 2017), and São Paulo (May 2018). Pre- and post-hackathon surveys asking respondents to self-assess their knowledge in ten categories of medical innovation were administered to quantify the perceived degree of learning. Participants hailed from a diverse range of educational backgrounds, domains of expertise, and academic institutions. On average, respondents (n = 161) saw a greater than twofold increase (114.1%, P < 0.001) from their pre- to post-hackathon scores. In this study, the extended hackathon is presented as a novel educational model to teach undergraduate and graduate students a foundational skillset for medical innovation. Participants reported gaining significant knowledge across all ten categories assessed. To more robustly assess the educational value of extended hackathons, a standardized assessment for medical innovation knowledge needs to be developed, and a larger sample size of participants surveyed.

Correction to: An Extended Hackathon Model for Collaborative Education in Medical Innovation.

We append two additional funders to our acknowledgments.

Molecular pathology of prostate cancer revealed by next-generation sequencing: opportunities for genome-based personalized therapy.

PURPOSE OF REVIEW: This article reviews recently identified genomic mutations in prostate cancer. RECENT FINDINGS: Advanced sequencing technologies have made it possible to obtain large amounts of data on genomes and transcriptomes of cancers. Such technologies have been used to sequence prostate cancer of different stages, from treatment-naive cancers, to advanced, castration-resistant cancers to the aggressive small cell neuroendocrine carcinomas. For each category of prostate cancer, distinct and overlapping DNA sequence alterations were discovered, including point mutations, small insertions or deletions, copy number changes and chromosomal rearrangements. There appears to be a stepwise increase in genomic alterations from low risk to high risk to advanced cancers. SUMMARY: These novel findings have significantly increased our knowledge of the genetic basis of human prostate cancer and the molecular mechanisms responsible for disease progression and treatment resistance. Some of the lesions are potential therapeutic targets. Studies along this direction will eventually make it possible to design personalized management plans for individual patients.

Topical Sevoflurane: A Novel Treatment for Chronic Pain Caused by Venous Stasis Ulcers.

In the US, an estimated 1 - 2% of chronic venous insufficiency (CVI) patients (of 6 - 7 million nationwide) develop at least one venous stasis ulcer (VSU) during their illness. Of these, approximately 40% develop subsequent ulcers, making VSU prognostically poor. Current management of VSU is costly, with poor prognosis, high recurrence rate, inadequate pain management, and significantly reduced quality of life (QoL). Topical volatile anesthetic agents, such as sevoflurane, offer improved pain relief and symptom control in patients suffering from chronic VSU. The immediate impact of topical sevoflurane in reducing pain associated with ulcer bed debridement has several implications in improving the quality of life in patients with CVI induced ulcers and in the prognosis and healing of the ulcers. This review summarizes a topical formulation of a volatile anesthetic and its implications for the management of VSUs.

Sex-Specific Cross Tissue Meta-Analysis Identifies Immune Dysregulation in Women With Alzheimer's Disease.

Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia in the United States. In spite of evidence of females having a greater lifetime risk of developing Alzheimer's Disease (AD) and greater apolipoprotein E4-related (APOE ε4) AD risk compared to males, molecular signatures underlying these differences remain elusive. Methods: We took a meta-analysis approach to study gene expression in the brains of 1,084 AD patients and age-matched controls and whole blood from 645 AD patients and age-matched controls in seven independent datasets. Sex-specific gene expression patterns were investigated through use of gene-based, pathway-based and network-based approaches. The ability of a sex-specific AD gene expression signature to distinguish Alzheimer's disease from healthy controls was assessed using a linear support vector machine model. Cell type deconvolution from whole blood gene expression data was performed to identify differentially regulated cells in males and females with AD. Results: Strikingly gene-expression, network-based analysis and cell type deconvolution approaches revealed a consistent immune signature in the brain and blood of female AD patients that was absent in males. In females, network-based analysis revealed a coordinated program of gene expression involving several zinc finger nuclease genes related to Herpes simplex viral infection whose expression was modulated by the presence of the APOE ε4 allele. Interestingly, this gene expression program was missing in the brains of male AD patients. Cell type deconvolution identified an increase in neutrophils and naïve B cells and a decrease in M2 macrophages, memory B cells, and CD8+ T cells in AD samples compared to controls in females. Interestingly, among males with AD, no significant differences in immune cell proportions compared to controls were observed. Machine learning-based classification of AD using gene expression from whole blood in addition to clinical features produced an improvement in classification accuracy upon stratifying by sex, achieving an AUROC of 0.91 for females and 0.80 for males. Conclusion: These results help identify sex and APOE ε4 genotype-specific transcriptomic signatures of AD and underscore the importance of considering sex in the development of biomarkers and therapeutic strategies for AD.

Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes.

Computational methods have made substantial progress in improving the accuracy and throughput of pathology workflows for diagnostic, prognostic, and genomic prediction. Still, lack of interpretability remains a significant barrier to clinical integration. We present an approach for predicting clinically-relevant molecular phenotypes from whole-slide histopathology images using human-interpretable image features (HIFs). Our method leverages >1.6 million annotations from board-certified pathologists across >5700 samples to train deep learning models for cell and tissue classification that can exhaustively map whole-slide images at two and four micron-resolution. Cell- and tissue-type model outputs are combined into 607 HIFs that quantify specific and biologically-relevant characteristics across five cancer types. We demonstrate that these HIFs correlate with well-known markers of the tumor microenvironment and can predict diverse molecular signatures (AUROC 0.601-0.864), including expression of four immune checkpoint proteins and homologous recombination deficiency, with performance comparable to 'black-box' methods. Our HIF-based approach provides a comprehensive, quantitative, and interpretable window into the composition and spatial architecture of the tumor microenvironment.

Current Perspectives on Gut Microbiome Dysbiosis and Depression.

The human gut microbiome partakes in a bidirectional communication pathway with the central nervous system (CNS), named the microbiota-gut-brain axis. The microbiota-gut-brain axis is believed to modulate various central processes through the vagus nerve as well as production of microbial metabolites and immune mediators which trigger changes in neurotransmission, neuroinflammation, and behavior. Little is understood about the utilization of microbiome manipulation to treat disease. Though studies exploring the role of the microbiome in various disease processes have shown promise, mechanisms remain unclear and evidence-based treatments for most illnesses have not yet been developed. The animal studies reviewed here offer an excellent array of basic science research that continues to clarify mechanisms by which the microbiome may affect mental health. More evidence is needed, particularly as it relates to translating this work to human subjects. The studies presented in this paper largely demonstrate encouraging results in the treatment of depression. Limitations include small sample sizes and heterogeneous methodology. The exact mechanism by which the gut microbiota causes or alters neuropsychiatric disease states is not fully understood. In this review, we focus on recent studies investigating the relationship between gut microbiome dysbiosis and the pathogenesis of depression. This article is based on previously conducted studies and does not contain any studies with human participants or animals performed by any of the authors.

Self-reported health without clinically measurable benefits among adult users of multivitamin and multimineral supplements: a cross-sectional study.

OBJECTIVE: Multiple clinical trials fail to identify clinically measurable health benefits of daily multivitamin and multimineral (MVM) consumption in the general adult population. Understanding the determinants of widespread use of MVMs may guide efforts to better educate the public about effective nutritional practices. The objective of this study was to compare self-reported and clinically measurable health outcomes among MVM users and non-users in a large, nationally representative adult civilian non-institutionalised population in the USA surveyed on the use of complementary health practices. DESIGN: Cross-sectional analysis of the effect of MVM consumption on self-reported overall health and clinically measurable health outcomes. PARTICIPANTS: Adult MVM users and non-users from the 2012 National Health Interview Survey (n=21 603). PRIMARY AND SECONDARY OUTCOME MEASURES: Five psychological, physical, and functional health outcomes: (1) self-rated health status, (2) needing help with routine needs, (3) history of 10 chronic diseases, (4) presence of 19 health conditions in the past 12 months, and (5) Kessler 6-Item (K6) Psychological Distress Scale to measure non-specific psychological distress in the past month. RESULTS: Among 4933 adult MVM users and 16 670 adult non-users, MVM users self-reported 30% better overall health than non-users (adjusted OR 1.31; 95% CI 1.17 to 1.46; false discovery rate adjusted p<0.001). There were no differences between MVM users and non-users in history of 10 chronic diseases, number of present health conditions, severity of current psychological distress on the K6 Scale and rates of needing help with daily activities. No effect modification was observed after stratification by sex, education, and race. CONCLUSIONS: MVM users self-reported better overall health despite no apparent differences in clinically measurable health outcomes. These results suggest that widespread use of multivitamins in adults may be a result of individuals' positive expectation that multivitamin use leads to better health outcomes or a self-selection bias in which MVM users intrinsically harbour more positive views regarding their health.

Preresidency Publication Productivity of U.S. Neurosurgery Interns.

BACKGROUND: Research experience is believed to be an important component of the neurosurgery residency application process. One measure of research productivity is publication volume. The preresidency publication volume of U.S. neurosurgery interns and any potential association between applicant publication volume and the match results of top-ranked residency programs have not been well characterized. OBJECTIVE: In this study, we sought to characterize the preresidency publication volume of U.S. neurosurgery residents in the 2018-2019 intern class using the Scopus database. METHODS: For each intern, we recorded the total number of publications, total number of first or last author publications, total number of neuroscience-related publications, mean number of citations per publication, and mean impact factor of the journal per publication. Preresidency publication volumes of interns at the top-25 programs (based on a composite ranking score according to 4 different ranking metrics) were compared with those at all other programs. RESULTS: We found that 82% of neurosurgery interns included in the analysis (190 interns from 95 programs) had at least 1 publication. The average number of publications per intern among all programs was 6 ± 0.63 (mean ± standard error of the mean). We also found that interns at top-25 neurosurgery residency programs tended to have a higher number of publications (8.3 ± 1.2 vs. 4.8 ± 0.7, P = 0.0137), number of neuroscience-related publications (6.8 ± 1.1 vs. 4.1 ± 0.7, P = 0.0419), and mean number of citations per publication (9.8 ± 1.7 vs. 5.7 ± 0.8, P = 0.0267) compared with interns at all other programs. CONCLUSIONS: Our results provide a general estimate of the preresidency publication volume of U.S. neurosurgery interns and suggest a potential association between publication volume and matching in the top-25 neurosurgery residency programs.

Characterizing electronic health record usage patterns of inpatient medicine residents using event log data.

Amid growing rates of burnout, physicians report increasing electronic health record (EHR) usage alongside decreasing clinical facetime with patients. There exists a pressing need to improve physician-computer-patient interactions by streamlining EHR workflow. To identify interventions to improve EHR design and usage, we systematically characterize EHR activity among internal medicine residents at a tertiary academic hospital across various inpatient rotations and roles from June 2013 to November 2016. Logged EHR timestamps were extracted from Stanford Hospital's EHR system (Epic) and cross-referenced against resident rotation schedules. We tracked the quantity of EHR logs across 24-hour cycles to reveal daily usage patterns. In addition, we decomposed daily EHR time into time spent on specific EHR actions (e.g. chart review, note entry and review, results review).In examining 24-hour usage cycles from general medicine day and night team rotations, we identified a prominent trend in which night team activity promptly ceased at the shift's end, while day team activity tended to linger post-shift. Across all rotations and roles, residents spent on average 5.38 hours (standard deviation = 2.07) using the EHR. PGY1 (post-graduate year one) interns and PGY2+ residents spent on average 2.4 and 4.1 times the number of EHR hours on information review (chart, note, and results review) as information entry (note and order entry).Analysis of EHR event log data can enable medical educators and programs to develop more targeted interventions to improve physician-computer-patient interactions, centered on specific EHR actions.

When order sets do not align with clinician workflow: assessing practice patterns in the electronic health record.

BACKGROUND: Order sets are widely used tools in the electronic health record (EHR) for improving healthcare quality. However, there is limited insight into how well they facilitate clinician workflow. We assessed four indicators based on order set usage patterns in the EHR that reflect potential misalignment between order set design and clinician workflow needs. METHODS: We used data from the EHR on all orders of medication, laboratory, imaging and blood product items at an academic hospital and an itemset mining approach to extract orders that frequently co-occurred with order set use. We identified the following four indicators: infrequent ordering of order set items, rapid retraction of medication orders from order sets, additional a la carte ordering of items not included in order sets and a la carte ordering of items despite being listed in the order set. RESULTS: There was significant variability in workflow alignment across the 11 762 order set items used in the 77 421 inpatient encounters from 2014 to 2017. The median ordering rate was 4.1% (IQR 0.6%-18%) and median medication retraction rate was 4% (IQR 2%-10%). 143 (5%) medications were significantly less likely while 68 (3%) were significantly more likely to be retracted than if the same medication was ordered a la carte. 214 (39%) order sets were associated with least one additional item frequently ordered a la carte and 243 (45%) order sets contained at least one item that was instead more often ordered a la carte. CONCLUSION: Order sets often do not align with what clinicians need at the point of care. Quantitative insights from EHRs may inform how order sets can be optimised to facilitate clinician workflow.

Association model data learned from clinicians stratified by patient mortality outcomes at a Tertiary Academic Center.

In this data article, we learn clinical order patterns from inpatient electronic health record (EHR) data at a tertiary academic center from three different cohorts of providers: (1) Clinicians with lower-than-expected patient mortality rates, (2) clinicians with higher-than-expected patient mortality rates, and (3) an unfiltered population of clinicians. We extract and make public these order patterns learned from each clinician cohort associated with six common admission diagnoses (e.g. pneumonia, chest pain, etc.). We also share a reusable reference standard or benchmark for evaluating automatically-learned clinical order patterns for each admission diagnosis, based on a manual review of clinical practice literature. The data shared in this article can support further study, evaluation, and translation of data-driven CDS systems. Further interpretation and discussion of this data can be found in Wang et al. (2018).

Inpatient Clinical Order Patterns Machine-Learned From Teaching Versus Attending-Only Medical Services.

Clinical order patterns derived from data-mining electronic health records can be a valuable source of decision support content. However, the quality of crowdsourcing such patterns may be suspect depending on the population learned from. For example, it is unclear whether learning inpatient practice patterns from a university teaching service, characterized by physician-trainee teams with an emphasis on medical education, will be of variable quality versus an attending-only medical service that focuses strictly on clinical care. Machine learning clinical order patterns by association rule episode mining from teaching versus attending-only inpatient medical services illustrated some practice variability, but converged towards similar top results in either case. We further validated the automatically generated content by confirming alignment with external reference standards extracted from clinical practice guidelines.