Citations

The enteric nervous system (ENS), a collection of neural cells contained in the wall of the gut, is of fundamental importance to gastrointestinal and systemic health. According to the prevailing paradigm, the ENS arises from progenitor cells migrating from the neural crest and remains largely unchanged thereafter. Here, we show that the lineage composition of maturing ENS changes with time, with a decline in the canonical lineage of neural-crest derived neurons and their replacement by a newly identified lineage of mesoderm-derived neurons. Single cell transcriptomics and immunochemical approaches establish a distinct expression profile of mesoderm-derived neurons. The dynamic balance between the proportions of neurons from these two different lineages in the post-natal gut is dependent on the availability of their respective trophic signals, GDNF-RET and HGF-MET. With increasing age, the mesoderm-derived neurons become the dominant form of neurons in the ENS, a change associated with significant functional effects on intestinal motility which can be reversed by GDNF supplementation. Transcriptomic analyses of human gut tissues show reduced GDNF-RET signaling in patients with intestinal dysmotility which is associated with reduction in neural crest-derived neuronal markers and concomitant increase in transcriptional patterns specific to mesoderm-derived neurons. Normal intestinal function in the adult gastrointestinal tract therefore appears to require an optimal balance between these two distinct lineages within the ENS.

Abstract The enteric nervous system (ENS), a collection of neural cells contained in the wall of the gut, is of fundamental importance to gastrointestinal and systemic health. According to the prevailing paradigm, the ENS arises from progenitor cells migrating from the neural crest and remains largely unchanged thereafter. Here, we show that the lineage composition of maturing ENS changes with time, with a decline in the canonical lineage of neural-crest derived neurons and their replacement by a newly identified lineage of mesoderm-derived neurons. Single cell transcriptomics and immunochemical approaches establish a distinct expression profile of mesoderm-derived neurons. The dynamic balance between the proportions of neurons from these two different lineages in the post-natal gut is dependent on the availability of their respective trophic signals, GDNF-RET and HGF-MET. With increasing age, the mesoderm-derived neurons become the dominant form of neurons in the ENS, a change associated with significant functional effects on intestinal motility which can be reversed by GDNF supplementation. Transcriptomic analyses of human gut tissues show reduced GDNF-RET signaling in patients with intestinal dysmotility which is associated with reduction in neural crest-derived neuronal markers and concomitant increase in transcriptional patterns specific to mesoderm-derived neurons. Normal intestinal function in the adult gastrointestinal tract therefore appears to require an optimal balance between these two distinct lineages within the ENS.

Abstract Background Immune checkpoint inhibitor combined with platinum‐etoposide is the standard first‐line therapy for patients with extensive‐stage small cell lung cancer (ES‐SCLC). The phase 3 clinical trials that led to the approval of chemoimmunotherapy in ES‐SCLC excluded patients who had an Eastern Cooperative Group (ECOG) performance status (PS) of 2–3. Therefore, data on the efficacy of chemoimmunotherapy in patients with an ECOG PS of 2–3 are limited. Methods A retrospective analysis was performed on patients diagnosed with ES‐SCLC who received chemoimmunotherapy (atezolizumab or durvalumab) within the Mayo Clinic Health System between January 2016 and January 2021. The objective of this study was to compare the overall survival (OS), progression‐free survival (PFS), and best clinical response to therapy in patients with an ECOG PS of 0–1 vs. patients with an ECOG PS of 2–3 who received chemoimmunotherapy for newly diagnosed ES‐SCLC. Results In total, 82 patients were included in the study. The mean ± standard deviation age was 68.1 ± 8.3 years. Of these, 56 patients were identified with an ECOG PS of 0–1, and 26 patients were identified with an ECOG PS of 2–3. The median PFS was similar regardless of ECOG PS (5.8 months [95% CI, 4.3–6.0 months] in the ECOG PS 0–1 group vs. 4.1 months [95% CI, 3.8–6.9 months] in the ECOG PS 2–3; p = .2994). The median OS was also similar regardless of ECOG PS (10.6 months [95% CI, 8.4–13.4 months] in the ECOG PS 0–1 group vs. 9.3 months [95% CI, 4.9–12.8 months]; p = .2718) in the ECOG PS 2‐3 group. Conclusions The study results demonstrated no significant difference in PFS or OS among the ECOG PS 2–3 and ECOG PS 0–1 groups. Therefore, chemoimmunotherapy should be considered for patients who have ES‐SCLC with an ECOG PS of 2–3.

The prevalence of Alzheimer’s disease is greater in women, but the underlying mechanisms remain to be elucidated. We herein demonstrated that α-secretase ADAM10 was downregulated and ADAM10 inhibitor sFRP1 was upregulated in 5xFAD mice. While there were no sex effects on ADAM10 protein and sFRP1 mRNA levels, female 5xFAD and age-matched non-transgenic mice exhibited higher levels of sFRP1 protein than corresponding male mice. Importantly, female 5xFAD mice accumulated more Aβ than males, and sFRP1 protein levels were positively associated with Aβ42 levels in 5xFAD mice. Our study suggests that sFRP1 is associated with amyloid pathology in a sex-dependent manner.

BACKGROUND: Cardiovascular and critical care professional societies recommend incorporating family engagement practices into routine clinical care. However, little is known about current family engagement practices in contemporary cardiac intensive care units (CICUs). METHODS: We implemented a validated 12-item family engagement practice survey among site investigators participating in the Critical Care Cardiology Trials Network, a collaborative network of CICUs in North America. The survey includes 9 items assessing specific engagement practices, 1 item about other family-centered care practices, and 2 open-ended questions on strategies and barriers concerning family engagement practice. We developed an engagement practice score by assigning 1 point for each family engagement practice partially or fully adopted at each site (max score 9). We assessed for relationships between the engagement practice score and CICU demographics. RESULTS: All sites (N=39; 100%) completed the survey. The most common family engagement practices were open visitation (95%), information and support to families (85%), structured care conferences (n=82%), and family participation in rounds (77%). The median engagement practice score was 5 (interquartile range, 4). There were no differences in engagement practice scores by geographic region or CICU type. The most commonly used strategies to promote family engagement were family presence during rounds (41%), communication (28%), and family meetings (28%). The most common barriers to family engagement were COVID-related visitation policies (38%) and resource limitations (13%). CONCLUSIONS: Family engagement practices are routinely performed in many CICUs; however, considerable variability exists. There is a need for strategies to address the variability of family engagement practices in CICUs.

BACKGROUND: Common genetic variants are associated with risk for hypertrophic cardiomyopathy and dilated cardiomyopathy and with left ventricular (LV) traits. Whether these variants are associated with myocardial fibrosis, an important pathophysiological mediator of cardiomyopathy, is unknown. METHODS: Multi-Ethnic Study of Atherosclerosis participants with T1-mapping cardiac magnetic resonance imaging in-whom extracellular volume was assessed, and genotyping information was available were included (N=1255). Log extracellular volume (%) was regressed on 50 candidate single nucleotide polymorphisms (previously identified to be associated with hypertrophic cardiomyopathy, dilated cardiomyopathy, and LV traits) adjusting for age, sex, diabetes, blood pressure, and principal components of ancestry. Ancestry-specific results were pooled by fixed-effect meta-analyses. Gene knockdown experiments were performed in human cardiac fibroblasts. RESULTS: The SMARCB1 rs2186370 intronic variant (minor allele frequency: 0.18 in White and 0.50 in Black participants), previously identified as a risk variant for dilated cardiomyopathy and hypertrophic cardiomyopathy, was significantly associated with increased extracellular volume ( P= 0.0002) after adjusting for confounding clinical variables. The SMARCB1 rs2070458 locus previously associated with increased LV wall thickness and mass was similarly significantly associated with increased extracellular volume ( P= 0.0002). The direction of effect was similar in all 4 ancestry groups, but the effect was strongest in Black participants. The variants are strong expression quantitative loci in human LV tissue and associated with genotype-dependent decreased expression of SMARCB1 ( P= 7.3×10 -22 ). SMARCB1 knockdown in human cardiac fibroblasts resulted in increased TGF (transforming growth factor)-β1–mediated α-smooth muscle actin and collagen expression. CONCLUSIONS: Common genetic variation in SMARCB1 previously associated with risk for cardiomyopathies and increased LV wall thickness is associated with increased cardiac magnetic resonance imaging–based myocardial fibrosis and increased TGF-β1 mediated myocardial fibrosis in vitro. Whether these findings suggest a pathophysiologic link between myocardial fibrosis and cardiomyopathy risk remains to be proven.

ABSTRACT Aims Takotsubo syndrome (TTS) is associated with a substantial rate of adverse events. We sought to design a machine learning (ML)-based model to predict the risk of in-hospital death and to perform a clustering of TTS patients to identify different risk profiles. Methods and results A ridge logistic regression-based ML model for predicting in-hospital death was developed on 3482 TTS patients from the International Takotsubo (InterTAK) Registry, randomly split in a train and an internal validation cohort (75% and 25% of the sample size, respectively) and evaluated in an external validation cohort (1037 patients). Thirty-one clinically relevant variables were included in the prediction model. Model performance represented the primary endpoint and was assessed according to area under the curve (AUC), sensitivity and specificity. As secondary endpoint, a K-medoids clustering algorithm was designed to stratify patients into phenotypic groups based on the 10 most relevant features emerging from the main model. The overall incidence of in-hospital death was 5.2%. The InterTAK-ML model showed an AUC of 0.89 (0.85–0.92), a sensitivity of 0.85 (0.78–0.95) and a specificity of 0.76 (0.74–0.79) in the internal validation cohort and an AUC of 0.82 (0.73–0.91), a sensitivity of 0.74 (0.61–0.87) and a specificity of 0.79 (0.77–0.81) in the external cohort for in-hospital death prediction. By exploiting the 10 variables showing the highest feature importance, TTS patients were clustered into six groups associated with different risks of in-hospital death (28.8% vs. 15.5% vs. 5.4% vs. 1.0.8% vs. 0.5%) which were consistent also in the external cohort. Conclusion A ML-based approach for the identification of TTS patients at risk of adverse short-term prognosis is feasible and effective. The InterTAK-ML model showed unprecedented discriminative capability for the prediction of in-hospital death.

Purpose: To investigate the feasibility of using cone-beam computed tomography (CBCT)-derived synthetic CTs to monitor the daily dose and trigger a plan review for adaptive proton therapy (APT) in head and neck cancer (HNC) patients. Methods: For 84 HNC patients treated with proton pencil-beam scanning (PBS), same-day CBCT and verification CT (vfCT) pairs were retrospectively collected. The ground truth CT (gtCT) was created by deforming the vfCT to the same-day CBCT, and it was then used as a dosimetric baseline and for establishing plan review trigger recommendations. Two different synthetic CT algorithms were tested; the corrected CBCT (corrCBCT) was created using an iterative image correction method and the virtual CT (virtCT) was created by deforming the planning CT to the CBCT, followed by a low-density masking process. Clinical treatment plans were recalculated on the image sets for evaluation. Results: Plan review trigger criteria for adaptive therapy were established after closely reviewing the cohort data. Compared to the vfCT, the corrCBCT and virtCT reliably produced dosimetric data more similar to the gtCT. The average discrepancy in D99 for high-risk clinical target volumes (CTV) was 1.1%, 0.7%, and 0.4% and for standard-risk CTVs was 1.8%, 0.5%, and 0.5% for the vfCT, corrCBCT, and virtCT, respectively. Conclusion: Streamlined APT has been achieved with the proposed plan review criteria and CBCT-based synthetic CT workflow.

Abstract Objective . UNet-based deep-learning (DL) architectures are promising dose engines for traditional linear accelerator (Linac) models. Current UNet-based engines, however, were designed differently with various strategies, making it challenging to fairly compare the results from different studies. The objective of this study is to thoroughly evaluate the performance of UNet-based models on magnetic-resonance (MR)-Linac-based intensity-modulated radiation therapy (IMRT) dose calculations. Approach . The UNet-based models, including the standard-UNet, cascaded-UNet, dense-dilated-UNet, residual-UNet, HD-UNet, and attention-aware-UNet, were implemented. The model input is patient CT and IMRT field dose in water, and the output is patient dose calculated by DL model. The reference dose was calculated by the Monaco Monte Carlo module. Twenty training and ten test cases of prostate patients were included. The accuracy of the DL-calculated doses was measured using gamma analysis, and the calculation efficiency was evaluated by inference time. Results . All the studied models effectively corrected low-accuracy doses in water to high-accuracy patient doses in a magnetic field. The gamma passing rates between reference and DL-calculated doses were over 86% (1%/1 mm), 98% (2%/2 mm), and 99% (3%/3 mm) for all the models. The inference times ranged from 0.03 (graphics processing unit) to 7.5 (central processing unit) seconds. Each model demonstrated different strengths in calculation accuracy and efficiency; Res-UNet achieved the highest accuracy, HD-UNet offered high accuracy with the fewest parameters but the longest inference, dense-dilated-UNet was consistently accurate regardless of model levels, standard-UNet had the shortest inference but relatively lower accuracy, and the others showed average performance. Therefore, the best-performing model would depend on the specific clinical needs and available computational resources. Significance . The feasibility of using common UNet-based models for MR-Linac-based dose calculations has been explored in this study. By using the same model input type, patient training data, and computing environment, a fair assessment of the models’ performance was present.

BACKGROUND: Premature menopause is a risk factor for accelerated cardiovascular aging, but underlying mechanisms remain incompletely understood. This study investigated the role of leukocyte telomere length (LTL), a marker of cellular aging and genomic instability, in the association of premature menopause with cardiovascular disease. METHODS: Participants from the UK Biobank and Women’s Health Initiative with complete reproductive history and LTL measurements were included. Primary analyses tested the association between age at menopause and LTL using multivariable-adjusted linear regression. Secondary analyses stratified women by history of gynecologic surgery. Mendelian randomization was used to infer causal relationships between LTL and age at natural menopause. Multivariable-adjusted Cox regression and mediation analyses tested the joint associations of premature menopause and LTL with incident coronary artery disease. RESULTS: This study included 130 254 postmenopausal women (UK Biobank: n=122 224; Women’s Health Initiative: n=8030), of whom 4809 (3.7%) had experienced menopause before age 40. Earlier menopause was associated with shorter LTL (meta-analyzed ß=−0.02 SD/5 years of earlier menopause [95% CI, −0.02 to −0.01]; P =7.2×10 -12 ). This association was stronger and significant in both cohorts for women with natural/spontaneous menopause (meta-analyzed ß=−0.04 SD/5 years of earlier menopause [95% CI, −0.04 to −0.03]; P <2.2×10 -16 ) and was independent of hormone therapy use. Mendelian randomization supported a causal association of shorter genetically predicted LTL with earlier age at natural menopause. LTL and age at menopause were independently associated with incident coronary artery disease, and mediation analyses indicated small but significant mediation effects of LTL in the association of menopausal age with coronary artery disease. CONCLUSIONS: Earlier age at menopause is associated with shorter LTL, especially among women with natural menopause. Accelerated telomere shortening may contribute to the heightened cardiovascular risk associated with premature menopause.