Modifier Genes as Therapeutics: The Nuclear Hormone Receptor Rev Erb Alpha (Nr1d1) Rescues Nr2e3 Associated Retinal Disease

Nuclear hormone receptors play a major role in many important biological processes. Most nuclear hormone receptors are ubiquitously expressed and regulate processes such as metabolism, circadian function, and development. They function in these processes to maintain homeostasis through modulation of transcriptional gene networks. In this study we evaluate the effectiveness of a nuclear hormone receptor gene to modulate retinal degeneration and restore the integrity of the retina. Currently, there are no effective treatment options for retinal degenerative diseases leading to progressive and irreversible blindness. In this study we demonstrate that the nuclear hormone receptor gene Nr1d1 (Rev-Erba) rescues Nr2e3- associated retinal degeneration in the rd7 mouse, which lacks a functional Nr2e3 gene. Mutations in human NR2E3 are associated with several retinal degenerations including enhanced S cone syndrome and retinitis pigmentosa. The rd7 mouse, lacking Nr2e3, exhibits an increase in S cones and slow, progressive retinal degeneration. A traditional genetic mapping approach previously identified candidate modifier loci. Here, we demonstrate that in vivo delivery of the candidate modifier gene, Nr1d1 rescues Nr2e3 associated retinal degeneration. We observed clinical, histological, functional, and molecular restoration of the rd7 retina. Furthermore, we demonstrate that the mechanism of rescue at the molecular and functional level is through the re-regulation of key genes within the Nr2e3-directed transcriptional network. Together, these findings reveal the potency of nuclear receptors as modulators of disease and specifically of NR1D1 as a novel therapeutic for retinal degenerations

OSCE best practice guidelines—applicability for nursing simulations

Background: Objective structured clinical examinations (OSCEs) have been used for many years within healthcare programmes as a measure of students’ and clinicians’ clinical performance. OSCEs are a form of simulation and are often summative but may be formative. This educational approach requires robust design based on sound pedagogy to assure practice and assessment of holistic nursing care. As part of a project testing seven OSCE best practice guidelines (BPGs) across three sites, the BPGs were applied to an existing simulation activity. The aim of this study was to determine the applicability and value of the OSCE BPGs in an existing formative simulation. Methods: A mixed methods approach was used to address the research question: in what ways do OSCE BPGs align with simulations. The BPGs were aligned and compared with all aspects of an existing simulation activity offered to first-year nursing students at a large city-based university, prior to their first clinical placement in an Australian healthcare setting. Survey questions, comprised of Likert scales and free-text responses, used at other sites were slightly modified for reference to simulation. Students’ opinions about the refined simulation activity were collected via electronic survey immediately following the simulation and from focus groups. Template analysis, using the BPGs as existing or a priori thematic codes, enabled interpretation and illumination of the data from both sources.Results: Few changes were made to the existing simulation plan and format. Students’ responses from surveys (n = 367) and four focus groups indicated that all seven BPGs were applicable for simulations in guiding their learning, particularly in the affective domain, and assisting their perceived needs in preparing for upcoming clinical practice. Discussion: Similarities were found in the intent of simulation and OSCEs informed by the BPGs to enable feedback to students about holistic practice across affective, cognitive and psychomotor domains. The similarities in this study are consistent with findings from exploring the applicability of the BPGs for OSCEs in other nursing education settings, contexts, universities and jurisdictions. The BPGs also aligned with other frameworks and standards often used to develop and deliver simulations. Conclusions: Findings from this study provide further evidence of the applicability of the seven OSCE BPGs to inform the development and delivery of, in this context, simulation activities for nurses. The manner in which simulation is offered to large cohorts requires further consideration to meet students’ needs in rehearsing the registered nurse role

OSCE Best Practice Guidelines – applicability for nursing simulations

Background: Objective structured clinical examinations (OSCEs) have been used for many years within healthcare programmes as a measure of students’ and clinicians’ clinical performance. OSCEs are a form of simulation and are often summative but may be formative. This educational approach requires robust design based on sound pedagogy to assure practice and assessment of holistic nursing care. As part of a project testing seven OSCE best practice guidelines (BPGs) across three sites, the BPGs were applied to an existing simulation activity. The aim of this study was to determine the applicability and value of the OSCE BPGs in an existing formative simulation. Methods: A mixed methods approach was used to address the research question: in what ways do OSCE BPGs align with simulations. The BPGs were aligned and compared with all aspects of an existing simulation activity offered to first-year nursing students at a large city-based university, prior to their first clinical placement in an Australian healthcare setting. Survey questions, comprised of Likert scales and free-text responses, used at other sites were slightly modified for reference to simulation. Students’ opinions about the refined simulation activity were collected via electronic survey immediately following the simulation and from focus groups. Template analysis, using the BPGs as existing or a priori thematic codes, enabled interpretation and illumination of the data from both sources.Results: Few changes were made to the existing simulation plan and format. Students’ responses from surveys (n = 367) and four focus groups indicated that all seven BPGs were applicable for simulations in guiding their learning, particularly in the affective domain, and assisting their perceived needs in preparing for upcoming clinical practice. Discussion: Similarities were found in the intent of simulation and OSCEs informed by the BPGs to enable feedback to students about holistic practice across affective, cognitive and psychomotor domains. The similarities in this study are consistent with findings from exploring the applicability of the BPGs for OSCEs in other nursing education settings, contexts, universities and jurisdictions. The BPGs also aligned with other frameworks and standards often used to develop and deliver simulations. Conclusions: Findings from this study provide further evidence of the applicability of the seven OSCE BPGs to inform the development and delivery of, in this context, simulation activities for nurses. The manner in which simulation is offered to large cohorts requires further consideration to meet students’ needs in rehearsing the registered nurse role

Why is the Winner the Best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work

Gastrointestinal function in intensive care patients: terminology, definitions and management. Recommendations of the ESICM Working Group on Abdominal Problems

Acute gastrointestinal (GI) dysfunction and failure have been increasingly recognized in critically ill patients. The variety of definitions proposed in the past has led to confusion and difficulty in comparing one study to another. An international working group convened to standardize the definitions for acute GI failure and GI symptoms and to review the therapeutic options

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Why is the winner the best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The 'typical' lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work