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BackgroundIn Drosophila early post-meiotic spermatids, mitochondria undergo dramatic shaping into the Nebenkern, a spherical body with complex internal structure that contains two interwrapped giant mitochondrial derivatives. The purpose of this study was to elucidate genetic and molecular mechanisms underlying the shaping of this structure.ResultsThe knotted onions (knon) gene encodes an unconventionally large testis-specific paralog of ATP synthase subunit d and is required for internal structure of the Nebenkern as well as its subsequent disassembly and elongation. Knon localizes to spermatid mitochondria and, when exogenously expressed in flight muscle, alters the ratio of ATP synthase complex dimers to monomers. By RNAi knockdown we uncovered mitochondrial shaping roles for other testis-expressed ATP synthase subunits.ConclusionsWe demonstrate the first known instance of a tissue-specific ATP synthase subunit affecting tissue-specific mitochondrial morphogenesis. Since ATP synthase dimerization is known to affect the degree of inner mitochondrial membrane curvature in other systems, the effect of Knon and other testis-specific paralogs of ATP synthase subunits may be to mediate differential membrane curvature within the Nebenkern.

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Objectives Describe the general process used to develop and implement an EHR-based mortality risk stratification model. Assess the impact and implications of implementation an EHR-based mortality risk stratification model on clinical, quality, and financial metrics. Background. The frequency and timing of palliative care consultation is highly variable among inpatients with life-limiting illnesses despite evidence of its benefits. Objectives. To develop an EHR-based risk stratification model and evaluate its impact as a trigger for inpatient palliative care consultation. Methods. We performed a retrospective cohort study at three urban hospitals among 65,045 admissions in 2016. Using a randomly split sample and a machine learning approach, we developed and validated an EHR-based model (Palliative Connect) to predict risk of death within six months of admission. We then determined a risk threshold of $30% based on expert chart review and prospectively piloted Palliative Connect in a 4month pre-post study of triggered palliative care consultation on a general medicine service at a large academic hospital. Primary clinicians could decline the consult. We performed an intention-to-treat analysis to evaluate impact on care delivery, quality metrics, and costs. Original Research Background. While outpatient palliative care (PC) began primarily in cancer centers, outpatient PC increasingly serves patients with a wide range of diagnoses. Research Objectives. Compare characteristics of patients with cancer and non-cancer diagnoses referred to clinic-based PC, and the care they receive. Methods. Data were extracted from the Palliative Care Quality Network database regarding 3,569 patients seen by 27 clinic-based PC teams between 01/ 15/2016 and 07/17/2018. Results. Overall 79.3% (n¼2,766) of all patients referred to outpatient PC had cancer. Compared to patients with non-cancer diagnoses, patients with

Objectives Compare palliative care interventions used in randomized clinical trials for cancer versus non-cancer patients by content and structure. Discuss opportunities for future research regarding models of palliative care, particularly in non-malignant illnesses. Original Research Background. The majority of palliative care (PC) interventions are cancer-specific; fewer trials target individuals with non-malignant illnesses. Research Objectives. Compare the content and structure of PC interventions tested in cancer versus non-cancer populations. Methods. Secondary analysis of a systematic review Research Objectives. Describe the (1) frequency and severity of symptoms reported by patients with advanced cancer and (2) content of associated shared care plans.

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Technology and big data have changed the way we live…and die. Human life is being captured, analyzed, and impacted like never before. We're at the crux of a revolution, and understand very little about the long-term impacts individually and societally. What does this all mean for research, innovation, and design? How do we manage the influx of data, the power and the responsibility of this new future that currently exists without clear ethical guidelines?