One-year outcomes after transcatheter insertion of an interatrial shunt device for the management of heart failure with preserved ejection fraction

Background—Heart failure with preserved ejection fraction has a complex pathophysiology and remains a therapeutic challenge. Elevated left atrial pressure, particularly during exercise, is a key contributor to morbidity and mortality. Preliminary analyses have demonstrated that a novel interatrial septal shunt device that allows shunting to reduce the left atrial pressure provides clinical and hemodynamic benefit at 6 months. Given the chronicity of heart failure with preserved ejection fraction, evidence of longer-term benefit is required. Methods and Results—Patients (n=64) with left ventricular ejection fraction ≥40%, New York Heart Association class II–IV, elevated pulmonary capillary wedge pressure (≥15 mm Hg at rest or ≥25 mm Hg during supine bicycle exercise) participated in the open-label study of the interatrial septal shunt device. One year after interatrial septal shunt device implantation, there were sustained improvements in New York Heart Association class (P<0.001), quality of life (Minnesota Living with Heart Failure score, P<0.001), and 6-minute walk distance (P<0.01). Echocardiography showed a small, stable reduction in left ventricular end-diastolic volume index (P<0.001), with a concomitant small stable increase in the right ventricular end-diastolic volume index (P<0.001). Invasive hemodynamic studies performed in a subset of patients demonstrated a sustained reduction in the workload corrected exercise pulmonary capillary wedge pressure (P<0.01). Survival at 1 year was 95%, and there was no evidence of device-related complications. Conclusions—These results provide evidence of safety and sustained clinical benefit in heart failure with preserved ejection fraction patients 1 year after interatrial septal shunt device implantation. Randomized, blinded studies are underway to confirm these observations

Scientific Opportunities with an X-ray Free-Electron Laser Oscillator

An X-ray free-electron laser oscillator (XFELO) is a new type of hard X-ray source that would produce fully coherent pulses with meV bandwidth and stable intensity. The XFELO complements existing sources based on self-amplified spontaneous emission (SASE) from high-gain X-ray free-electron lasers (XFEL) that produce ultra-short pulses with broad-band chaotic spectra. This report is based on discussions of scientific opportunities enabled by an XFELO during a workshop held at SLAC on June 29 - July 1, 2016Comment: 21 pages, 12 figure

Evolution of microscopic heterogeneity and dynamics in choline chloride-based deep eutectic solvents

Deep eutectic solvents (DESs) are an emerging class of non-aqueous solvents that are potentially scalable, easy to prepare and functionalize for many applications ranging from biomass processing to energy storage technologies. Predictive understanding of the fundamental correlations between local structure and macroscopic properties is needed to exploit the large design space and tunability of DESs for specific applications. Here, we employ a range of computational and experimental techniques that span length-scales from molecular to macroscopic and timescales from picoseconds to seconds to study the evolution of structure and dynamics in model DESs, namely Glyceline and Ethaline, starting from the parent compounds. We show that systematic addition of choline chloride leads to microscopic heterogeneities that alter the primary structural relaxation in glycerol and ethyleneglycol and result in new dynamic modes that are strongly correlated to the macroscopic properties of the DES formed

Genome-Wide Analysis of Gene Expression in Primate Taste Buds Reveals Links to Diverse Processes

Efforts to unravel the mechanisms underlying taste sensation (gustation) have largely focused on rodents. Here we present the first comprehensive characterization of gene expression in primate taste buds. Our findings reveal unique new insights into the biology of taste buds. We generated a taste bud gene expression database using laser capture microdissection (LCM) procured fungiform (FG) and circumvallate (CV) taste buds from primates. We also used LCM to collect the top and bottom portions of CV taste buds. Affymetrix genome wide arrays were used to analyze gene expression in all samples. Known taste receptors are preferentially expressed in the top portion of taste buds. Genes associated with the cell cycle and stem cells are preferentially expressed in the bottom portion of taste buds, suggesting that precursor cells are located there. Several chemokines including CXCL14 and CXCL8 are among the highest expressed genes in taste buds, indicating that immune system related processes are active in taste buds. Several genes expressed specifically in endocrine glands including growth hormone releasing hormone and its receptor are also strongly expressed in taste buds, suggesting a link between metabolism and taste. Cell type-specific expression of transcription factors and signaling molecules involved in cell fate, including KIT, reveals the taste bud as an active site of cell regeneration, differentiation, and development. IKBKAP, a gene mutated in familial dysautonomia, a disease that results in loss of taste buds, is expressed in taste cells that communicate with afferent nerve fibers via synaptic transmission. This database highlights the power of LCM coupled with transcriptional profiling to dissect the molecular composition of normal tissues, represents the most comprehensive molecular analysis of primate taste buds to date, and provides a foundation for further studies in diverse aspects of taste biology

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

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

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Genetic stratification of depression by neuroticism: revisiting a diagnostic tradition

Background. Major depressive disorder and neuroticism share a large genetic basis. We sought to determine whether this shared basis could be decomposed to identify genetic factors that are specific to depression. Methods. We analysed summary statistics from genome-wide association studies of depression (from the Psychiatric Genomics Consortium, 23andMe, and UK Biobank) and compared them to genome-wide association studies (GWAS) of neuroticism (from UK Biobank). First, we used a pairwise GWAS analysis to classify variants as associated with only depression, with only neuroticism, or with both. Second, we estimated partial genetic correlations to test whether the depression’s genetic link with other phenotypes was explained by shared overlap with neuroticism. Results. We found evidence that most genomic regions (25/37) associated with depression are likely to be shared with neuroticism. The overlapping common genetic variance of depression and neuroticism was genetically correlated primarily with psychiatric disorders. We found that the genetic contributions to depression, that was not shared with neuroticism, was positively correlated with metabolic phenotypes and cardiovascular disease, and negatively correlated with the personality trait conscientiousness. After removing shared genetic overlap with neuroticism, depression still had a specific association with schizophrenia, bipolar disorder, coronary artery disease, and age of first birth. Independent of depression, neuroticism had specific genetic correlates in ulcerative colitis, pubertal growth, anorexia, and education. Conclusion. Our findings demonstrate that, while genetic risk factors for depression are largely shared with neuroticism, there are also non-neuroticism related features of depression that may be useful for further patient or phenotypic stratification

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

Exercise and memory

Bibliography: p. 68-89.The purpose of this study was to examine whether prior exercise had an influence on one's ability to memorize and recall a consonant vowel consonant (CVe) word list. Thirty college students were randomly assigned to one of three groups. Prior to learning the eve list the control group did no exercise, the easy and moderate exercise groups pedalled a bicycle ergometer for ten minutes at an intensity which would raise the heart rate to 110-120 beats per minute and 130-140 beats per minute, respectively. Short term recall was measured immediately after learning the eve list, and long term recall was measured after a five minute mathematical distractor task was performed. An analysis of covariance design was used to test for significance. Results showed that exercise, regardless of intensity, provided for superior performance of letter recall (p <-05). It was determined that recall time increased with exercise (p < .05). There were no significant differences between short and long term recall. A chi square analysis on the number of words recalled showed no significant differences. Overall results of this study support the "inverted U hypothesis of activation" namely, that an optimal amount of tension exists for performance on recall of a eve word list. Deviation from this optimal amount of tension in either direction results in poorer performance