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Most palliative care (PC) physicians will change jobs over the course of their career, yet few supports exist to guide them through this often unfamiliar process. Job transitions frequently elicit stress and uncertainty, as individuals balance personal and professional priorities, navigate complex negotiations, and assume the inherent risks of pursuing new opportunities. Yet, when approached intentionally, a job transition can also be a transformative process that allows for self-discovery, cultivation of new skills and connections, and reshaping of one’s professional identity. This article, written by a team of PC physicians who have made job transitions themselves, offers 10 tips for navigating this professional step successfully. With emphases on authentic communication, skilled mentorship, relationship building, and exploration of local culture—the article offers practical advice to help PC physicians navigate a job transition confidently, while staying grounded in their personal values and the work they find most meaningful.

BACKGROUND: Bicuspid aortic valve (BAV) is a frequent congenital heart defect with a high heritability. Despite this, only a limited number of genes have been associated with the disease, and the molecular mechanisms remain unexplained in most cases. This study aimed to further understand the genetic architecture of BAV. METHODS: A genome-wide association study meta-analysis including 9631 cases among 65 677 participants was performed. Genes were prioritized using transcriptomic analyses based on RNA sequencing in relevant tissues, including human fetal and adult aortic valves. The impact of the knockdown or knockout of 4 candidate genes on cardiac development was verified in zebrafish. A polygenic risk score was developed, its association with BAV was evaluated in an independent cohort, and its association with a wide range of phenotypes (n=976) was evaluated in UK Biobank (n=355 618 individuals). RESULTS: Thirty-six genomic loci were identified, including 32 that were not described previously. Among the prioritized genes, KANK2 and ERBB4 were identified as potentially causal through transcriptomic analyses, colocalization, and Mendelian randomization based on gene expression in human aortic valves (n=484), whereas PRDM6 and STRN were prioritized using similar analyses from aortic (n=326) and left ventricular tissues (n=326), respectively. Targeting 4 candidate genes ( WNT4 , LEF1 , STRN , and KANK2 ) in zebrafish led to disruption in cardiac development. A polygenic risk score was associated with an odds ratio of 2.07 (95% CI, 1.90–2.25; P =5.43×10 -62 ) per SD for BAV and significantly associated with thoracic aortic aneurysm and atrial fibrillation in UK Biobank. CONCLUSIONS: This study supports a significant polygenic contribution to BAV, where the combination of multiple common variants in genes involved in heart morphogenesis disrupts aortic valve development.

This cohort study evaluates the reduction in risk of high-grade serous carcinoma among women undergoing opportunistic bilateral salpingectomy in Canada.

BACKGROUND & OBJECTIVE Sleep-disordered breathing (SDB) may be a remediable contributor to abnormal cognitive outcomes for children with myelomeningocele. We investigated the frequency of SDB among neonates with fetal vs postnatal myelomeningocele repair. METHODS In this nine-center, prospective, observational study, neonates with myelomeningocele born at more than 30 weeks gestational age underwent a 10-hour bedside polysomnogram at at least 35 weeks postmenstrual age, before hospital discharge. The primary outcome was SDB severity, reflected by the apnea-hypopnea index (AHI; number of respiratory events per hour of sleep). Decisions regarding surgical timing (fetal vs postnatal), technique (open fetal vs fetoscopic approach), and SDB treatment were made by the clinical teams. RESULTS Among 173 infants, 92/173 (53%) had SDB (median AHI 22.5, IQR 12.2–44.2). In unadjusted analysis, AHI was higher among the 90 neonates with fetal repair vs 83 with postnatal surgery (median 29, IQR 14–49, vs 19, IQR 10–36, P = .008). In propensity-adjusted multivariable models, prematurity was independently associated with AHI, whereas surgical timing (fetal vs postnatal) was not. Hindbrain herniation, myelomeningocele level, and clinical need for ventriculoperitoneal shunt did not predict AHI in adjusted models. Supplemental oxygen was commonly prescribed, for 44% of fetal vs 39% of postnatal myelomeningocele repair infants (P = .5). CONCLUSIONS SDB affects more than half of neonates with myelomeningocele. The higher AHI after fetal surgery could stem from the elevated incidence of prematurity in this group. As SDB is treatable and has a potential impact on neurodevelopment, routine screening could become an integral part of a strategy to optimize long-term medical and neurodevelopmental outcomes.

Context.— The US Food and Drug Administration (FDA) Emergency Use Authorization (EUA) for SARS-CoV-2 assays encompassed both in vitro diagnostic and laboratory-developed tests. Related testing data and documents from EUAs were published on the FDA website as PDF files, constituting a rich data resource. Objective.— To highlight the value of structured descriptive and validation data and provide recommendations on improving the quality, accessibility, and shareability of the FDA’s digital resources. Design.— SARS-CoV-2 diagnostics EUAs and related files were acquired from the FDA, and data were extracted through an iterative manual or automated process with quality assurance in accordance with a set of data entry definitions to generate a data product (ie, data analysis and presentation of data into tables, graphs, and reports, optionally including different data sets) that could be disseminated. Results.— Four challenges limited the usability of FDA EUA data, including (1) distinguishing EUA tests owing to similar names or inconsistent naming conventions; (2) document location and tracking owing to inconsistent EUA document identifiers, resource locators, and versioning; (3) data location and retrieval owing to heterogeneous structure and data formatting within EUA documentation; and (4) maintenance of existing data owing to lack of clear and traceable communication around EUA amendments. Conclusions.— Converging efforts from disparate groups to extract and synthesize FDA test data reveal an unaddressed need for better organization and communication. Existing FDA data reporting systems must be improved and adapted for the demands of the connected data science era.

ABSTRACT Amidst the opioid crisis, understanding the genetic basis of opioid use disorder (OUD) is crucial for identifying biological mechanisms and intervention points. However, genome‐wide association studies (GWASs) have been hampered by inadequate sample sizes and often the use of control populations not assessed for prior opioid exposure. Because opioid exposure is a prerequisite for the development of OUD, consideration of exposure history in controls is important. Electronic health record data (EHR) paired with genomic information allow a broader sampling of patients with OUD and exposed controls. We leveraged data across two healthcare systems to evaluate the impact of using controls not screened for opioid exposure (‘generic’) versus minimally opioid‐exposed control (‘exposed’). First, at the phenotypic level, we conducted phenome‐wide association studies (PheWAS) to compare the medical comorbidity profiles of OUD cases when using generic versus exposed controls. While PheWAS results for OUD‐related comorbidities were more pronounced when using the generic group, 83% of the disease associations were overlapping and of similar effect sizes. Second, at the genetic level, we conducted GWAS (cases vs. generic; cases vs. exposed) and assessed differences in genetic correlations and degrees of phenotypic misclassification. Genetic results were concordant across control groups based on heritability (generic: 0.16 ± 0.07 vs. 0.10 ± 0.07), associations with the coding OPRM1 variant rs1799971 ( p generic = 8.83E‐03 vs. p exposed = 1.83E‐02) and genetic correlations with prior OUD GWAS ( r g‐generic = 0.83 ± 0.26 vs. r g‐exposed = 0.78 ± 0.27). Although GWASs were limited by sample size ( N generic = 6269, N exposed = 6365), compared to an independent OUD GWAS ( N = 425 944), the dilution value for the two GWAS was not different from 1, suggesting no major impact of phenotypic misclassification. This study represents the first effort to enhance OUD genetic research through optimization of control definitions using EHR data. Generic controls ascertained within the US health systems, where exposure to prescription opioids is high, offer a practical alternative for genetic studies of OUD.

Abstract Background TMEM106B encodes a lysosomal protein and is a genetic susceptibility factor for frontotemporal lobar degeneration (FTLD). Aptamer‐based proteomics quantifies TMEM106B protein levels in CSF. The associations of CSF TMEM106B with measures of disease severity have not been previously evaluated, and the value of CSF TMEM106B as a clinical biomarker is unknown. Method CSF TMEM106B and NfL protein levels were quantified with aptamer‐based proteomics in participants from two independent cohorts: sporadic FTLD with neuropathology‐confirmed FTLD‐tau and FTLD‐TDP, clinically diagnosed PSP‐Richardson Syndrome, and controls (UCSF MAC, n = 95; mean age = 69 ± 6 yrs; 48% female) and familial FTLD comprised of presymptomatic and symptomatic FTLD mutation carriers with family non‐carrier controls (ALLFTD, n = 179; mean age = 49 ± 13 yrs; 53% female). The association between biomarkers and diverse measures of disease severity, including the CDR®+NACC‐FTLD and CDR®+NACC‐FTLD sum of boxes, were determined with linear regressions or mixed linear models. Associations with brain volumes were determined within the framework of voxel‐based morphometry. Result In both cohorts, at baseline, CSF TMEM106B did not differ by sex, age, FTLD phenotype, or CSF NfL. CSF TMEM106B was lower in cases with worse disease severity (Figure 1) and in participants with the protective TMEM106B rs1990622 G/G genotype, regardless of pathological diagnosis or FTLD‐causing mutation. Low CSF TMEM106B was associated with more severe longitudinal worsening in FTLD disease severity (Figure 2), basic and instrumental function, global cognition, executive function, and depression scores. These associations were weaker than those of NfL. Low CSF TMEM106B was associated with low brain volumes in frontal, parietal, and temporal regions (FWE‐corrected p < 0.05) (Figure 3A). CSF NfL negatively correlated (FWE‐corrected p < 0.05) with brain volumes in frontotemporal regions (Figure 3B). Conclusion CSF TMEM106B concentrations are influenced by the TMEM106B rs1990622 genotype, and correlate with disease severity and brain volume in both genetic and sporadic FTLD. Lower levels of CSF TMEM106B correlate with more severe disease and brain atrophy. Disease severity is more strongly associated with CSF NfL, while brain volumes are more strongly associated with CSF TMEM106B. CSF TMEM106B may have value as a biomarker of neurodegeneration in FTLD.

Abstract Background Recent technological advances have enhanced the precision of blood‐based multiplex proteomics and their application in Alzheimer's disease. However, these approaches remain underutilized in frontotemporal lobar degeneration (FTLD). Familial FTLD cohorts offer a unique opportunity to study presymptomatic periods of disease that cannot be studied in sporadic cases. We leveraged NUcleic acid Linked Immuno‐Sandwich Assay (NULISAseq) to identify blood‐detectable proteins that change prior to symptom onset in familial FTLD. Method 120 FTLD mutation carriers (40 C9orf72 , 40 GRN , 40 MAPT ) and 40 age and sex‐matched noncarrier controls from the ALLFTD Consortium completed blood draws and clinical assessment. Targeted plasma proteomics was performed via NUcleic acid Linked Immuno‐Sandwich Assay (NULISAseq; 132 proteins). Disease age estimates measuring predicted symptom onset were derived from a validated model incorporating FTLD gene‐specific clinical, neuroimaging, and biomarker data. Mass linear regression models identified disease age ‘thresholds’ where individual proteins significantly diverged between each mutation carrier group and controls. NULISAseq targets were validated against orthogonal assays, specifically Simoa (plasma) and SomaScan (cerebrospinal fluid [CSF]). Result Compared to controls, 14 plasma proteins significantly diverged ( p <.05) in C9orf72 , 24 in GRN , and 13 in MAPT . Beyond NEFL, 10 additional proteins significantly diverged in more than one mutation group, including markers of synaptic function (NPTX2, NPTXR) and vascular integrity (PGF) that deviated prior to estimated symptom onset (disease age range: ‐30 to 0). Proteins with gene‐specific signals included GFAP (increased in GRN at disease age ‐17), ENO2 (decreased in C9orf72 at disease age ‐7), and FLT1 (increased in MAPT at disease age ‐6). Plasma NULISA‐based NEFL and GFAP showed strong concordance when measured via plasma Simoa ( r s>0.88) and were among 38 targets with significant concordance when measured via CSF SomaScan ( r range: 0.16 to 0.75, p s<.05). Conclusion Alterations in plasma protein abundance are measurable in familial FTLD prior to predicted symptom onset. The validity and brain‐based relevance of blood‐detectable NULISA targets is bolstered by the observed convergence with plasma Simoa and CSF SomaScan. Replication of top hits in larger familial and sporadic FTLD sample sizes is warranted to determine generalizability and suitability for clinical trials.

ABSTRACT Background Critically ill patients may be overexposed to valproate because altered protein binding leads to a disproportionate free valproate fraction. The Fraser equation was derived and internally validated to estimate critically ill patients' free valproate concentrations, but it requires external validation. Methods Adult intensive care unit (ICU) patients at two academic centers with concurrently measured free and total valproate concentrations were included. Free valproate concentrations were estimated using the Fraser equation which includes total valproate, albumin and BUN concentration, and whether the patient received propofol or aspirin. The primary outcome was Fraser equation performance, assessed using Bland–Altman methods. Comparative performance against estimates from the Doré equation (an alternative predictive equation), therapeutic concordance using a target free valproate range of 5–15 mg/L, and equation improvements were explored. Results Overall, 315 patients and 556 free‐total valproate concentration pairs were included. The mean (±SD) age was 58 (±17) years and 90 (29%) patients were on valproate prior to hospital admission. The Fraser equation estimated free valproate concentrations were correlated with measured concentrations ( r = 0.728) with a negative bias (mean bias −2.77 mg/L, 95% LOA –18.9, 13.4). The Fraser equation increasingly underestimated measured concentrations as measured concentrations increased. 71% of Fraser equation estimates were therapeutically concordant (e.g., estimate and measured both within reference range) compared to 61.9% of Doré estimates ( p = 0.001). Fraser equation modifications led to minor performance improvements but did not overcome worsening underestimation with higher measured free valproate concentrations. Conclusion The Fraser equation was moderately accurate and corresponded with an appropriate interpretation for 71% of free valproate concentrations. Worse underestimation with higher measured free valproate concentrations suggests direct measurement of free valproate concentrations is warranted. While the Fraser equation could complement therapeutic decision making at centers where direct free valproate measurements are unavailable or slow to return, its accuracy is currently insufficient to replace direct measurement.