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Knowledge representation and reasoning (KR&R) has been successfully implemented in many fields to enable computers to solve complex problems with AI methods. However, its application to biomedicine has been lagging in part due to the daunting complexity of molecular and cellular pathways that govern human physiology and pathology. In this article, we describe concrete uses of Scalable PrecisiOn Medicine Knowledge Engine (SPOKE), an open knowledge network that connects curated information from thirty-seven specialized and human-curated databases into a single property graph, with 3 million nodes and 15 million edges to date. Applications discussed in this article include drug discovery, COVID-19 research and chronic disease diagnosis, and management.

MotivationKnowledge graphs (KGs) are being adopted in industry, commerce and academia. Biomedical KG presents a challenge due to the complexity, size and heterogeneity of the underlying information.ResultsIn this work, we present the Scalable Precision Medicine Open Knowledge Engine (SPOKE), a biomedical KG connecting millions of concepts via semantically meaningful relationships. SPOKE contains 27 million nodes of 21 different types and 53 million edges of 55 types downloaded from 41 databases. The graph is built on the framework of 11 ontologies that maintain its structure, enable mappings and facilitate navigation. SPOKE is built weekly by python scripts which download each resource, check for integrity and completeness, and then create a 'parent table' of nodes and edges. Graph queries are translated by a REST API and users can submit searches directly via an API or a graphical user interface. Conclusions/Significance: SPOKE enables the integration of seemingly disparate information to support precision medicine efforts.Availability and implementationThe SPOKE neighborhood explorer is available at https://spoke.rbvi.ucsf.edu.Supplementary informationSupplementary data are available at Bioinformatics online.

Knowledge representation and reasoning (KR&R) has been successfully implemented in many fields to enable computers to solve complex problems with AI methods. However, its application to biomedicine has been lagging in part due to the daunting complexity of molecular and cellular pathways that govern human physiology and pathology. In this article we describe concrete uses of SPOKE, an open knowledge network that connects curated information from 37 specialized and human-curated databases into a single property graph, with 3 million nodes and 15 million edges to date. Applications discussed in this article include drug discovery, COVID-19 research and chronic disease diagnosis and management.

Memorable messages, or messages people recall for a long period of time and identify as influential in identity development or behavioral assessment, provide a framework to address both adolescent advice and development and to identify areas that were absent from pivotal conversations like "the talk." In this qualitative survey study, we asked lesbian, gay, bisexual, transgender, and queer (LGBTQ) women to design the messages they wish their parents had provided about intimate health in their adolescence in light of the messages that they did receive. Findings suggest that ideal messages from parents would include a tone of positivity and affirmation, validation of diverse sexual and gender identities, and practical information about navigating changing bodies, sexual and reproductive health and contraception, as well as information about consent and negotiating sexual relationships. Although fewer, some participants believed these conversations are better suited for education systems and teachers than parents.

Early messages about reproductive and sexual health influence personal identities, health behaviors, and ongoing perceptions of sex and sexuality. Women are often socialized with negatively-valenced messages toward understanding their reproductive and sexual health. However, scholarship emphasizes a communicative approach for socializing intimate health behaviors and needs. The present study addresses the communication that prompts a shift in perception from early, harmful memorable messages to a more comprehensive perspective on sexuality. To do so, we qualitatively analyze open-ended survey responses from 191 women. Findings reinforce previous memorable message literature by revealing messages of shame, sex, abortion, childbearing, and fears of infertility. In addition, we extend the memorable messages construct by exploring how more positively valenced messages and individual moments of intervention redirect women in how they understand their intimate health and sexuality.

Abortion is a politicized and stigmatized health procedure that has received considerable attention in popular discourse in the past year. The present study investigated how often people (N = 235) talk about abortion throughout their lifespan and within the past 12 months, with whom they have these conversations, and which conversations were most memorable. Key findings from the present study, framed through the Theory of Memorable Messages and the Social Ecological Model, include that most participants had at least one conversation about abortion in the past 12 months. Within the past 12 months, the most common conversation partners for abortion-related communication were friends, followed by mothers and romantic partners. Over the lifespan, but prior to the past 12 months, a majority of participants had at least one conversation about abortion. Similarly, friends were the most frequent source of abortion-related communication over the lifespan, followed by mothers and romantic partners. Many of the conversations within the past 12 months focused heavily on the overturn of Roe v. Wade or navigating a post-Roe world with a body that could become pregnant, or as someone who cared about people who could become pregnant. Prior to the past 12 months, participants characterized abortion communication as tense, religious, pro-choice, descriptive of the women who might have abortions, relating to specific abortion disclosures, or political. Within the past 12 months, memorable conversations about abortion were personal or relational, and were often prompted by discussions surrounding Roe. [ABSTRACT FROM AUTHOR]

The Graduate Medical Education (GME) system in the United States (US) has garnered worldwide respect, graduating over 25,000 new physicians from over 8,000 residency and fellowship programs annually. GME is the portal of entry to medical practice and licensure in the US, and the pathway through which resident physicians develop the competence to practice independently and further develop their career plans. The number and specialty distribution of available GME positions shapes the overall composition of our national workforce; however, GME is failing to provide appropriate programs that support the delivery of our society's system of healthcare. This paper, prepared by the Health Policy Education Subcommittee of the Society of General Internal Medicine (SGIM) and unanimously endorsed by SGIM's Council, outlines a set of recommendations on how to reform the GME system to best prepare a physician workforce that can provide high quality, high value, population-based, and patient-centered health care, aligned with the dynamic needs of our nation's healthcare delivery system. These recommendations include: accurate workforce needs assessment, broadened GME funding sources, increased transparency of the use of GME dollars, and implementation of incentives to increase the accountability of GME-funded programs for the preparation and specialty selection of their program graduates.

This article describes the patient-centered Scalable National Network for Effectiveness Research (pSCANNER), which is part of the recently formed PCORnet, a national network composed of learning healthcare systems and patient-powered research networks funded by the Patient Centered Outcomes Research Institute (PCORI). It is designed to be a stakeholder-governed federated network that uses a distributed architecture to integrate data from three existing networks covering over 21 million patients in all 50 states: (1) VA Informatics and Computing Infrastructure (VINCI), with data from Veteran Health Administration's 151 inpatient and 909 ambulatory care and community-based outpatient clinics; (2) the University of California Research exchange (UC-ReX) network, with data from UC Davis, Irvine, Los Angeles, San Francisco, and San Diego; and (3) SCANNER, a consortium of UCSD, Tennessee VA, and three federally qualified health systems in the Los Angeles area supplemented with claims and health information exchange data, led by the University of Southern California. Initial use cases will focus on three conditions: (1) congestive heart failure; (2) Kawasaki disease; (3) obesity. Stakeholders, such as patients, clinicians, and health service researchers, will be engaged to prioritize research questions to be answered through the network. We will use a privacy-preserving distributed computation model with synchronous and asynchronous modes. The distributed system will be based on a common data model that allows the construction and evaluation of distributed multivariate models for a variety of statistical analyses.

Brain changes reminiscent of Alzheimer disease (AD) have been previously reported in a substantial portion of elderly cognitive healthy (HC) subjects. The major aim was to evaluate the accuracy of MRI assessed regional gray matter (GM) volume, 18F-fluorodeoxyglucose positron emission tomography (FDG-PET), and neuropsychological test scores to identify those HC subjects who subsequently convert to mild cognitive impairment (MCI) or AD dementia. We obtained in 54 healthy control (HC) subjects a priori defined region of interest (ROI) values of medial temporal and parietal FDG-PET and medial temporal GM volume. In logistic regression analyses, these ROI values were tested together with neuropsychological test scores (free recall, trail making test B (TMT-B)) as predictors of HC conversion during a clinical follow-up between 3 and 4 years. In voxel-based analyses, FDG-PET and MRI GM maps were compared between HC converters and HC non-converters. Out of the 54 HC subjects, 11 subjects converted to MCI or AD dementia. Lower FDG-PET ROI values were associated with higher likelihood of conversion (p = 0.004), with the area under the curve (AUC) yielding 82.0% (95% CI = (95.5%, 68.5%)). The GM volume ROI was not a significant predictor (p = 0.07). TMT-B but not the free recall tests were a significant predictor (AUC = 71% (95% CI = 50.4%, 91.7%)). For the combination of FDG-PET and TMT-B, the AUC was 93.4% (sensitivity = 82%, specificity = 93%). Voxel-based group comparison showed reduced FDG-PET metabolism within the temporo-parietal and prefrontal cortex in HC converters. In conclusion, medial temporal and-parietal FDG-PET and executive function show a clinically acceptable accuracy for predicting clinical progression in elderly HC subjects.

Multiple reaction monitoring (MRM) mass spectrometry coupled with stable isotope dilution (SID) and liquid chromatography (LC) is increasingly used in biological and clinical studies for precise and reproducible quantification of peptides and proteins in complex sample matrices. Robust LC-SID-MRM-MS-based assays that can be replicated across laboratories and ultimately in clinical laboratory settings require standardized protocols to demonstrate that the analysis platforms are performing adequately. We developed a system suitability protocol (SSP), which employs a predigested mixture of six proteins, to facilitate performance evaluation of LC-SID-MRM-MS instrument platforms, configured with nanoflow-LC systems interfaced to triple quadrupole mass spectrometers. The SSP was designed for use with low multiplex analyses as well as high multiplex approaches when software-driven scheduling of data acquisition is required. Performance was assessed by monitoring of a range of chromatographic and mass spectrometric metrics including peak width, chromatographic resolution, peak capacity, and the variability in peak area and analyte retention time (RT) stability. The SSP, which was evaluated in 11 laboratories on a total of 15 different instruments, enabled early diagnoses of LC and MS anomalies that indicated suboptimal LC-MRM-MS performance. The observed range in variation of each of the metrics scrutinized serves to define the criteria for optimized LC-SID-MRM-MS platforms for routine use, with pass/fail criteria for system suitability performance measures defined as peak area coefficient of variation

Five years ago, we reviewed the state of memorable message scholarship in communication, with special attention to the utility of memorable message research in health communication. Since then, works advancing the theoretical and practical applicability of the construct have continued to emerge. In this article, we discuss the trends in published memorable message research over the last five years. Since 2017, thirty-four additional peer-reviewed articles focusing on memorable messages as a central concept have been published. Health communication as a subset of the field (as well as the journal Health Communication) see a higher concentration of these publications than other areas. In this review, we concentrate on the use of theory within recent memorable message work. Although much of the memorable message research continues to be atheoretical, more work utilizing control theory, the theory of memorable messages, and communicated narrative sense-making characterize the recent literature. Additionally, more research has emerged that highlights the theoretically driven investigations about the nature of memorable messages. We review the scholarship utilizing these and other theories and suggest avenues for future research. [ABSTRACT FROM AUTHOR]

Background: Long‐term follow‐up (LTFU) clinics have been developed but only some childhood cancer survivors (CCS) attend long‐term follow‐up (LTFU). Objective: To identify factors that influence LTFU attendance. Methods: Five‐year CCS treated for a solid tumor or lymphoma in Gustave Roussy before 2000, included in the FCCSS cohort (French Childhood Cancer Survivor Study), aged >18 years and alive at the date of the LTFU Clinic opening (January 2012) were invited to a LTFU visit. Factors associated with attendance at the LTFU clinic between 2012 and 2020 were estimated using logistic regression analyses. Analyses included different types of factors: clinical (tumor characteristics, cancer treatments, late effects), medical (medical expenses were used as a proxy of survivor's health status), social (deprivation index based on census‐tract data relating to income, educational level, proportion of blue‐collar workers, and unemployed people living in the area of residence), and spatial (distance to the LTFU clinic). Results: Among 2341 CCS contacted (55% males, mean age at study, 45 years; SD ± 10 years; mean age at diagnosis, 6 years; SD ± 5 years), 779 (33%) attended at least one LTFU visit. Initial cancer‐related factors associated with LTFU visit attendance were: treatment with both radiotherapy and chemotherapy (odds ratio [OR], 4.02; 95% CI, 2.11–7.70), bone sarcoma (OR, 2.43; 95% CI, 1.56–3.78), central nervous system primitive tumor (OR, 1.65; 95% CI, 1.02–2.67), and autologous hematopoietic cell transplant (OR, 2.07; 95% CI, 1.34‐3.20). Late effects (OR, 1.70; 95% CI, 1.31–2.20), highest medical expenses (OR, 1.65; 95% CI, 1.22–2.22), living in the most advantaged area (OR vs. the most deprived area = 1.60; 95% CI, 1.15–2.22), and shorter distance from LTFU care center (<12 miles) also increased attendance. Conclusions: Patients who are apparently healthy as well as socially disadvantaged and living far away from the center are less likely to attend LTFU care. Plain Language Summary: Among 2341 adult childhood cancer survivors contacted between 2012 and 2020, 33% attended at least one long‐term follow‐up visit.Clinical factors related to attendance were multimodal treatment of first cancer (combining chemotherapy and radiotherapy), stem cell transplant, type of diagnosis (bone tumor and central nervous system primitive tumor), late effects (at least one disease among second malignancy, heart disease, or stroke), and highest medical expenses.In addition, the study identified social and spatial inequalities related to attendance, with independent negative effects of distance and social deprivation on attendance, even though the medical costs related to the long‐term follow‐up examinations are covered by the French social security system. Among 2341 adult childhood cancer survivors contacted between 2012 and 2020, 33% attended at least one long‐term follow‐up visit. Clinical factors related to attendance were multimodal treatment of first cancer (combining chemotherapy and radiotherapy), stem cell transplant, type of diagnosis (bone tumor and central nervous system primitive tumor), late effects (at least one disease among second malignancy, heart disease, or stroke ascertained from a physician or a medical record) and highest medical expenses (used as a proxy of survivor's health status in adulthood). In addition, the study identified social and spatial inequalities related to attendance, with independent negative effects of distance and social deprivation on attendance, even though the medical costs related to the long‐term follow‐up examinations are covered by the French social security system. [ABSTRACT FROM AUTHOR]