Contributors to high left ventricular ejection fraction in women with ischemia and no obstructive coronary artery disease: Results from the Women's Ischemia Syndrome Evaluation - Coronary Vascular Dysfunction (WISE-CVD) Study.

BACKGROUND: There are sex differences in left ventricular ejection fraction (LVEF) relevant to prognosis where women experience greater mortality at relatively higher LVEF compared to men, yet mechanistic understanding of this adverse prognosis is limited. Women with suspected ischemia with no obstructive coronary disease (INOCA) develop heart failure with preserved ejection fraction (HFpEF), yet contributors to LVEF remain largely unknown. METHODS: In 370 women with suspected ischemia with no obstructive coronary disease (INOCA) who prospectively underwent cardiac magnetic resonance imaging (CMRI), we investigated the contributions of LV morphology, function, and myocardial perfusion reserve on LVEF using univariate and multiple linear regression. RESULTS: A majority 71% of participants had high LVEF (>65%), followed by 24% having normal LVEF (55-65%), and only 5% having low EF (<55%). Baseline characteristics were comparable among the three groups, with the exception of age which was six years higher in the high LVEF group (p<0.01). Women in the high LVEF group also had the lowest LV cavity volume, greatest LV mass-volume ratio, and highest LV end-systolic elastance (all p < 0.05, adjusted for age, BMI, diabetes, and blood pressure). Myocardial perfusion reserve index was low in all groups (mean MPRI < 2.1) but was not significantly different across the spectrum of LVEF (p=0.458). CONCLUSIONS: Taken together, these data demonstrate that the majority of women with suspected INOCA have elevated LVEF related to smaller, thicker ventricles with greater contractility. Future work is needed to better understand the specific mechanisms driving morphologic and functional changes in women with INOCA, and relations to longer-term HFpEF and mortality. CLINICAL TRIALS REGISTRATION: NCT02582021.

Impact of epicardial fat on coronary vascular function, cardiac morphology, and cardiac function in women with suspected INOCA.

INTRODUCTION: Epicardial fat is a metabolically active adipose tissue depot situated between the myocardium and visceral pericardium that covers ∼80% of the heart surface. While epicardial fat has been associated with the development of atherosclerotic coronary artery disease (CAD), less is known about the relationship between epicardial fat and coronary vascular function. Moreover, the relations between excess epicardial fat and cardiac morphology and function remains incompletely understood. METHODS AND RESULTS: To address these knowledge gaps, we retrospectively analyzed data from 294 individuals from our database of women with suspected ischemia with no obstructive coronary disease (INOCA) who underwent both invasive coronary function testing and cardiac magnetic resonance imaging (cMRI). Epicardial fat area, biventricular morphology, and function, as well as left atrial function, were assessed from cine images, per established protocols. The major novel findings were twofold: First, epicardial fat area was not associated with coronary vascular dysfunction. Second, epicardial fat was associated with increased left ventricular concentricity (ß= 0.15, p= 0.01), increased septal thickness (ß= 0.17, p= 0.002), and reduced left atrial conduit fraction (ß= -0.15, p= 0.02), even after accounting for age, BMI, and history of hypertension. CONCLUSIONS: Taken together, these data do not support a measurable relationship between epicardial fat and coronary vascular dysfunction but does suggest that epicardial fat may be related to concentric remodeling and diastolic dysfunction in women with suspected INOCA. Prospective studies are needed to elucidate the long-term impact of epicardial fat in this patient population.

Identifying the Mechanisms of a Peripherally Limited Exercise Phenotype in Patients With Heart Failure With Preserved Ejection Fraction.

BACKGROUND: We identified peripherally limited patients using cardiopulmonary exercise testing and measured skeletal muscle oxygen transport and utilization during invasive single leg exercise testing to identify the mechanisms of the peripheral limitation. METHODS: Forty-five patients with heart failure with preserved ejection fraction (70±7 years, 27 females) completed seated upright cardiopulmonary exercise testing and were defined as having a (1) peripheral limitation to exercise if cardiac output/oxygen consumption (VO2) was elevated (≥6) or 5 to 6 with a stroke volume reserve >50% (n=31) or (2) a central limitation to exercise if cardiac output/VO2 slope was ≤5 or 5 to 6 with stroke volume reserve <50% (n=14). Single leg knee extension exercise was used to quantify peak leg blood flow (Doppler ultrasound), arterial-to-venous oxygen content difference (femoral venous catheter), leg VO2, and muscle oxygen diffusive conductance. In a subset of participants (n=36), phosphocreatine recovery time was measured by magnetic resonance spectroscopy to determine skeletal muscle oxidative capacity. RESULTS: Peak VO2 during cardiopulmonary exercise testing was not different between groups (central: 13.9±5.7 versus peripheral: 12.0±3.1 mL/min per kg; P=0.135); however, the peripheral group had a lower peak arterial-to-venous oxygen content difference (central: 13.5±2.0 versus peripheral: 11.1±1.6 mLO2/dL blood; P<0.001). During single leg knee extension, there was no difference in peak leg VO2 (P=0.306), but the peripherally limited group had greater blood flow/VO2 ratio (P=0.024), lower arterial-to-venous oxygen content difference (central: 12.3±2.5 versus peripheral: 10.3±2.2 mLO2/dL blood; P=0.013), and lower muscle oxygen diffusive conductance (P=0.021). A difference in magnetic resonance spectroscopy-derived phosphocreatine recovery time was not detected (P=0.199). CONCLUSIONS: Peripherally limited patients with heart failure with preserved ejection fraction identified by cardiopulmonary exercise testing have impairments in oxygen transport and utilization at the level of the skeletal muscle quantified by invasive knee extension exercise testing, which includes an increased blood flow/V̇O2 ratio and poor muscle diffusive capacity. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT04068844.

Organ-specific immune setpoints underlie divergent immune profiles across metastatic sites in breast cancer.

Immune composition within the tumor microenvironment (TME) plays a central role in the propensity of cancer to metastasize and to respond to therapy. Previous studies suggested that the metastatic TME is immune suppressed. However, limited accessibility to multiple metastatic sites within patients has made assessment of the immune TME in the context of multi-organ metastases difficult. We utilized a rapid postmortem tissue collection protocol to assess immune composition in numerous sites of breast cancer metastasis and paired tumor-free tissues. Metastases were found to have comparable immune cell densities and composition to paired tumor-free tissues of the same organ type. In contrast, immune cell densities in both metastatic and tumor-free tissues were significantly different between organ types, with lung immune infiltration consistently greater than liver. These immune profiling results were consistent between both flow cytometry and multiplex immunofluorescence-based spatial analysis. Furthermore, we found granulocytes were a predominant tumor-infiltrating immune cell in both lung and liver metastases and these granulocytes made up the majority of PD-L1-expressing cells in many tissue sites. We also identified distinct potential mechanisms of immunosuppression in lung and liver metastases, with lung having increased expression of PD-L1+ antigen-presenting cells and liver having higher numbers of activated regulatory T cells and HLA-DRlow monocytes. Together these results demonstrate that immune contexture of metastases is dictated by organ type, and that immunotherapy strategies may benefit from unique tailoring to tissue-specific features of the immune TME.

Is the A-1 Pigment in Photosystem I Part of P700? A (P700+-P700) FTIR Difference Spectroscopy Study of A-1 Mutants.

The involvement of the second pair of chlorophylls, termed A-1A and A-1B, in light-induced electron transfer in photosystem I (PSI) is currently debated. Asparagines at PsaA600 and PsaB582 are involved in coordinating the A-1B and A-1A pigments, respectively. Here we have mutated these asparagine residues to methionine in two single mutants and a double mutant in PSI from Synechocystis sp. PCC 6803, which we term NA600M, NB582M, and NA600M/NB582M mutants. (P700+-P700) FTIR difference spectra (DS) at 293 K were obtained for the wild-type and the three mutant PSI samples. The wild-type and mutant FTIR DS differ considerably. This difference indicates that the observed changes in the (P700+-P700) FTIR DS cannot be due to only the PA and PB pigments of P700. Comparison of the wild-type and mutant FTIR DS allows the assignment of different features to both A-1 pigments in the FTIR DS for wild-type PSI and assesses how these features shift upon cation formation and upon mutation. While the exact role the A-1 pigments play in the species we call P700 is unclear, we demonstrate that the vibrational modes of the A-1A and A-1B pigments are modified upon P700+ formation. Previously, we showed that the A-1 pigments contribute to P700 in green algae. In this manuscript, we demonstrate that this is also the case in cyanobacterial PSI. The nature of the mutation-induced changes in algal and cyanobacterial PSI is similar and can be considered within the same framework, suggesting a universality in the nature of P700 in different photosynthetic organisms.

Modelling and assessment of glucose-lactate kinetics in youth with overweight, obesity and metabolic dysfunction-associated steatotic liver disease: A pilot study.

AIM: In this study, we investigated glucose and lactate kinetics during a 75 g oral glucose tolerance test (OGTT) in 23 overweight and obese adolescents and assessed putative differences among participants with and without metabolic dysfunction-associated steatotic liver disease (MASLD). METHODS: We enrolled 23 young people (six girls) with obesity [body mass index 33 (29-37)]. Glucose-lactate kinetics parameters (disposal glucose insulin sensitivity, SID; fraction of glucose converted into lactate, fr; fractional lactate clearance rate, kL) and lactate production rate (LPR) were estimated using the oral glucose-lactate minimal model. MASLD presence was assessed using the proton density fat fraction. We analysed glucose, lactate and LPR time to peak, peak values and area under the curve and evaluated differences using the Wilcoxon test. MASLD and no-MASLD participants were compared using the Mann-Whitney test. Correlations between parameters were assessed using the Spearman correlation coefficient (ρ). We also tested the performance of two (4 or 3 h OGTT) protocols in estimating oral glucose-lactate minimal model and LPR parameters. RESULTS: Glucose peaks 30 min earlier than lactate (p = .0019). This pattern was present in the no-MASLD group (p < .001). LPR peaks 30 min later in the MASLD group (p = .02). LPR and kL were higher in MASLD, suggesting higher glycolysis and lactate utilization. SID and fr correlate significantly (ρ = -0.55, p = .008). SID and fr were also correlated with the body mass index, (ρ = -0.45, p = .04; and ρ = 0.45; p = .03). The protocol duration did not influence the estimates of the parameters. DISCUSSION: Youth with MASLD showed a delayed glucose metabolism, possibly because of greater utilization of the underlying substrates. A 3-h OGTT may be used to assess lactate metabolism effectively.

Proteogenomic Gene Structure Validation in the Pineapple Genome.

MD2 pineapple (Ananas comosus) is the second most important tropical crop that preserves crassulacean acid metabolism (CAM), which has high water-use efficiency and is fast becoming the most consumed fresh fruit worldwide. Despite the significance of environmental efficiency and popularity, until very recently, its genome sequence has not been determined and a high-quality annotated proteome has not been available. Here, we have undertaken a pilot proteogenomic study, analyzing the proteome of MD2 pineapple leaves using liquid chromatography-mass spectrometry (LC-MS/MS), which validates 1781 predicted proteins in the annotated F153 (V3) genome. In addition, a further 603 peptide identifications are found that map exclusively to an independent MD2 transcriptome-derived database but are not found in the standard F153 (V3) annotated proteome. Peptide identifications derived from these MD2 transcripts are also cross-referenced to a more recent and complete MD2 genome annotation, resulting in 402 nonoverlapping peptides, which in turn support 30 high-quality gene candidates novel to both pineapple genomes. Many of the validated F153 (V3) genes are also supported by an independent proteomics data set collected for an ornamental pineapple variety. The contigs and peptides have been mapped to the current F153 genome build and are available as bed files to display a custom gene track on the Ensembl Plants region viewer. These analyses add to the knowledge of experimentally validated pineapple genes and demonstrate the utility of transcript-derived proteomics to discover both novel genes and genetic structure in a plant genome, adding value to its annotation.

Application of machine learning algorithms to predict 30-day hospital readmission following cement augmentation for osteoporotic vertebral compression fractures.

Objective: Osteoporosis is a common skeletal disease that greatly increases the risk of pathologic fractures and accounts for approximately 700,000 vertebral compression fractures (VCFs) annually in the United States. Cement augmentation procedures such as balloon kyphoplasty (KP) and percutaneous vertebroplasty (VP) have demonstrated efficacy in the treatment of VCFs, however, some studies report rates of readmission as high as 10.8% following such procedures. The purpose of this study was to employ Machine Learning (ML) algorithms to predict 30-day hospital readmission following cement augmentation procedures for the treatment of VCFs using the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database. Methods: ACS-NSQIP was queried to identify patients undergoing either KP or VP from 2011 to 2014. Three ML algorithms were constructed and tasked with predicting post-operative readmissions within this cohort of patients. Results: Postoperative pneumonia, ASA Class 2 designation, age, partially-dependent functional status, and a history of smoking were independently identified as highly predictive of readmission by all ML algorithms. Among these variables postoperative pneumonia (p < 0.01), ASA Class 2 designation (p < 0.01), age (p = 0.002), and partially-dependent functional status (p < 0.01) were found to be statistically significant. Predictions were generated with an average AUC value of 0.757 and an average accuracy of 80.5%. Conclusions: Postoperative pneumonia, ASA Class 2 designation, partially-dependent functional status, and age are perioperative variables associated with 30-day readmission following cement augmentation procedures. The use of ML allows for quantification of the relative contributions of these variables toward producing readmission.

Real-world predictors of relapse in patients with schizophrenia and schizoaffective disorder in a large health system.

Schizophrenia is often characterized by recurring relapses, which are associated with a substantial clinical and economic burden. Early identification of individuals at the highest risk for relapse in real-world treatment settings could help improve outcomes and reduce healthcare costs. Prior work has identified a few consistent predictors of relapse in schizophrenia, however, studies to date have been limited to insurance claims data or small patient populations. Thus, this study used a large sample of health systems electronic health record (EHR) data to analyze relationships between patient-level factors and relapse and model a set of factors that can be used to identify the increased prevalence of relapse, a severe and preventable reality of schizophrenia. This retrospective, observational cohort study utilized EHR data extracted from the largest Midwestern U.S. non-profit healthcare system to identify predictors of relapse. The study included patients with a diagnosis of schizophrenia (ICD-10 F20) or schizoaffective disorder (ICD-10 F25) who were treated within the system between October 15, 2016, and December 31, 2021, and received care for at least 12 months. A relapse episode was defined as an emergency room or inpatient encounter with a pre-determined behavioral health-related ICD code. Patients' baseline characteristics, comorbidities and healthcare utilization were described. Modified log-Poisson regression (i.e. log Poisson regression with a robust variance estimation) analyses were utilized to estimate the prevalence of relapse across patient characteristics, comorbidities and healthcare utilization and to ultimately identify an adjusted model predicting relapse. Among the 8119 unique patients included in the study, 2478 (30.52%) experienced relapse and 5641 (69.48%) experienced no relapse. Patients were primarily male (54.72%), White Non-Hispanic or Latino (54.23%), with Medicare insurance (51.40%), and had baseline diagnoses of substance use (19.24%), overweight/obesity/weight gain (13.06%), extrapyramidal symptoms (48.00%), lipid metabolism disorder (30.66%), hypertension (26.85%), and diabetes (19.08%). Many differences in patient characteristics, baseline comorbidities, and utilization were revealed between patients who relapsed and patients who did not relapse. Through model building, the final adjusted model with all significant predictors of relapse included the following variables: insurance, age, race/ethnicity, substance use diagnosis, extrapyramidal symptoms, number of emergency room encounters, behavioral health inpatient encounters, prior relapses episodes, and long-acting injectable prescriptions written. Prevention of relapse is a priority in schizophrenia care. Challenges related to historical health record data have limited the knowledge of real-world predictors of relapse. This study offers a set of variables that could conceivably be used to construct algorithms or models to proactively monitor demographic, comorbidity, medication, and healthcare utilization parameters which place patients at risk for relapse and to modify approaches to care to avoid future relapse.

DCS blood flow index underestimates skeletal muscle perfusion in vivo: rationale and early evidence for the NIRS-DCS perfusion index.

Significance: Diffuse correlation spectroscopy (DCS) permits non-invasive assessment of skeletal muscle blood flow but may misestimate changes in muscle perfusion. Aim: We aimed to highlight recent evidence that DCS blood flow index (BFI) misestimates changes in muscle blood flow during physiological perturbation and to introduce a novel approach that adjusts BFI for estimated changes in vasodilation. Approach: We measured changes in muscle BFI during quadriceps and forearm exercises using DCS, the latter of which were adjusted for estimated changes in microvascular flow area and then compared to Doppler ultrasound in the brachial artery. Then, we compared adjusted BFI- and arterial spin labeling (ASL) MRI measures of gastrocnemius blood flow during reactive hyperemia and plantar flexion exercise. Results: We observed little-to-no change in quadriceps BFI during maximal-effort exercise. Similarly, forearm BFI was modestly increased during handgrip exercise, but the magnitude was significantly lower than measured by Doppler ultrasound in the brachial artery. However, this difference was ameliorated after adjusting BFI for estimated changes in microvascular flow area. Similar observations were also observed in the gastrocnemius when directly comparing the adjusted BFI values to ASL-MRI. Conclusions: Adjusting BFI for estimated changes in microvascular flow area may improve DCS estimates of muscle blood flow, but further study is needed to validate these methods moving forward.

Prediction of In-Hospital Mortality Following Vertebral Fracture Fixation in Patients With Ankylosing Spondylitis or Diffuse Idiopathic Skeletal Hyperostosis: Machine Learning Analysis.

BACKGROUND: Ankylosing spondylitis (AS) and diffuse idiopathic skeletal hyperostosis (DISH) are distinct pathological entities that similarly increase the risk of vertebral fractures. Such fractures can be clinically devastating and frequently portend significant neurological injury, thus making their prevention a critical focus. Of particular significance, spinal fractures in patients with AS or DISH carry a considerable risk of mortality, with reports on 1-year injury-related deaths ranging from 24% to 33%. As such, the purpose of this study was to conduct machine learning (ML) analysis to predict postoperative mortality in patients with AS or DISH using the Nationwide Inpatient Sample Healthcare Cost and Utilization Project (HCUP-NIS) database. METHODS: HCUP-NIS was queried to identify adult patients carrying a diagnosis of AS or DISH who were admitted for spinal fractures and underwent subsequent fusion or corpectomy between 2016 and 2018. Predictions of in-hospital mortality in this cohort were then generated by three independent ML algorithms. RESULTS: An in-hospital mortality rate of 5.40% was observed in our selected population, including a rate of 6.35% in patients with AS, 2.81% in patients with DISH, and 8.33% in patients with both diagnoses. Increasing age, hypertension with end-organ complications, spinal cord injury, and cervical spinal fractures each carried considerable predictive importance across the algorithms utilized in our analysis. Predictions were generated with an average area under the curve of 0.758. CONCLUSIONS: This study's application of ML algorithms to predict in-hospital mortality among patients with AS or DISH identified a number of clinical risk factors relevant to this outcome. CLINICAL RELEVANCE: These findings may serve to provide physicians with an awareness of risk factors for in-hospital mortality and, subsequently, guide management and shared decision-making among patients with AS or DISH.

3T 31P/1H calf muscle coil for 1H and 31P MRI/MRS integrated with NIRS data acquisition.

PURPOSE: Our aim was to design and build a 3T 31P/1H calf coil that is capable of providing both good 31P and 1H transmit and receive performance, as well as being capable of accommodating a near-infrared spectroscopy (NIRS) device for simultaneous NIRS data and MRI/MRS acquisition. METHOD: In this work, we propose a new 3T 31P/1H birdcage combination design consisting of two co-centrically positioned birdcages on the same surface to maximize transmit efficiency and sensitivity for both nuclei. The 31P birdcage is a high-pass birdcage, whereas the 1H birdcage is a low-pass one to minimize coupling. The diameter of the 31P/1H birdcage combination was designed to be large enough to accommodate a NIRS device for simultaneous NIRS data and MRI/MRS acquisition. RESULTS: The one-layer coil structure of the birdcage combination significantly streamlines the mechanical design and coil assembly process. Full-wave simulation results show that the 31P and 1H are very well decoupled with each other, and the 1H and 31P SNR surpasses that of their standalone counterparts in the central area. Experiment results show that the inclusion of a NIRS device does not significantly affect the performance of the coil, thus enabling simultaneous NIRS and MRI readouts during exercise. CONCLUSION: Our findings demonstrate the feasibility and effectiveness of this dual-tuned coil design for combined NIRS and MRS measurements, offering potential benefits for studying metabolic and functional changes in the skeletal muscle in vivo.

Community-developed checklists for publishing images and image analyses.

Images document scientific discoveries and are prevalent in modern biomedical research. Microscopy imaging in particular is currently undergoing rapid technological advancements. However, for scientists wishing to publish obtained images and image-analysis results, there are currently no unified guidelines for best practices. Consequently, microscopy images and image data in publications may be unclear or difficult to interpret. Here, we present community-developed checklists for preparing light microscopy images and describing image analyses for publications. These checklists offer authors, readers and publishers key recommendations for image formatting and annotation, color selection, data availability and reporting image-analysis workflows. The goal of our guidelines is to increase the clarity and reproducibility of image figures and thereby to heighten the quality and explanatory power of microscopy data.

Alpha-Gal IgE Prevalence Patterns in the United States: An Investigation of 3,000 Military Recruits.

BACKGROUND: IgE to the oligosaccharide galactose-alpha-1,3-galactose (alpha-gal) is an important cause of allergic reactions to mammalian meat. The "alpha-gal syndrome" is strongly associated with a preceding history of tick bites and in the United States is most commonly reported in parts of the southeast, but there has been limited investigation into national alpha-gal sensitization patterns and the relevance of other risk factors. OBJECTIVE: To systematically investigate alpha-gal IgE prevalence, regional patterns, and risk factors. METHODS: Alpha-gal IgE was measured by ImmunoCAP in biobanked serum samples collected from 3000 service members who presented for intake to 1 of 10 military bases in the central/eastern United States. Alpha-gal IgE sensitization (cutoff 0.1 international units/mL) was related to home of record at enlistment. RESULTS: Of the cohort, 2456 (81.9%) subjects were male, median age was 19 years (interquartile range: 18-22 years), and alpha-gal IgE was detected in 179 (6.0%). Home of record spanned all 50 states, with a median of 36 recruits per state (range: 3-261). The highest prevalence rates were in Arkansas (39%), Oklahoma (35%), and Missouri (29%), with several other southeastern states >10%. Granular mapping revealed sensitization patterns that closely mimicked county-level Amblyomma americanum reports and Ehrlichia chaffeensis infections. Sensitization was associated with male sex, rural residence, and White race in univariate and multivariable models. CONCLUSIONS: In this systematic survey, the prevalence of alpha-gal IgE among incoming military personnel was 6.0%. There were significant regional differences, with an overall pattern consistent with the known range of the lone star tick (A. americanum) and highest frequency in an area including Arkansas, Oklahoma, and Missouri.

Exercise intolerance in heart failure with preserved ejection fraction: Causes, consequences and the journey towards a cure.

Heart failure with preserved ejection fraction (HFpEF) accounts for over 50% of all heart failure cases nationwide and continues to rise in its prevalence. The complex, multi-organ involvement of the HFpEF clinical syndrome requires clinicians and investigators to adopt an integrative approach that considers the contribution of both cardiac and non-cardiac function to HFpEF pathophysiology. Thus, this symposium review outlines the key points from presentations covering the contributions of disease-related changes in cardiac function, arterial stiffness, peripheral vascular function, and oxygen delivery and utilization to exercise tolerance in patients with HFpEF. While many aspects of HFpEF pathophysiology remain poorly understood, there is accumulating evidence for a decline in vascular health in this patient group that may be remediable through pharmacological and lifestyle interventions and could improve outcomes and clinical status in this ever-growing patient population.

Myocardial steatosis across the spectrum of human health and disease.

Preclinical data strongly suggest that myocardial steatosis leads to adverse cardiac remodelling and left ventricular dysfunction. Using 1 H cardiac magnetic resonance spectroscopy, similar observations have been made across the spectrum of health and disease. The purpose of this brief review is to summarize these recent observations. We provide a brief overview of the determinants of myocardial triglyceride accumulation, summarize the current evidence that myocardial steatosis contributes to cardiac dysfunction, and identify opportunities for further research.

Acute effects of ß-hydroxybutyrate on left ventricular function in young, healthy adults.

Interest in ketones as a cardiac "super fuel" has grown significantly following reports of a marked increase in cardiac output after exogenous ketone administration in heart failure. However, the extent to which this increase in cardiac output is related to changes in cardiac contractility, and dependent on the presence of heart failure, remains incompletely understood. Therefore, we performed a randomized, double-blind, placebo-controlled study of oral ketone ester in young healthy volunteers. Baseline cardiac magnetic resonance imaging was performed and repeated every 15 min for 60 min after ketone and placebo ingestion to assess changes in left ventricular function. As expected, circulating ß-hydroxybutyrate increased rapidly after ketone ingestion, but did not change with placebo (interaction: P < 0.001). Consistent with prior investigations, ketone ingestion resulted in an average 1 L/min increase in cardiac output after 60 min that did not occur with placebo (interaction: P = 0.026). This increase in cardiac output was primarily driven by an increase in heart rate after ketone ingestion (interaction: P = 0.018), with only a modest increase in stroke volume (interaction: P = 0.037). Changes in left ventricular strain and twist mechanics were limited. Taken together, the increase in cardiac output following an acute elevation in circulating ß-hydroxybutyrate is primarily driven by changes in cardiac chronotropy, with minimal inotropic contribution.NEW & NOTEWORTHY In this randomized, double-blind, placebo-controlled study of oral ketone ester in young healthy volunteers, we show a marked increase in cardiac output (∼1 L/min), driven primarily by changes in chronotropy. The cardiac magnetic resonance imaging data support the limited role for inotropy.

Collagen fiber centerline tracking in fibrotic tissue via deep neural networks with variational autoencoder-based synthetic training data generation.

The role of fibrillar collagen in the tissue microenvironment is critical in disease contexts ranging from cancers to chronic inflammations, as evidenced by many studies. Quantifying fibrillar collagen organization has become a powerful approach for characterizing the topology of collagen fibers and studying the role of collagen fibers in disease progression. We present a deep learning-based pipeline to quantify collagen fibers' topological properties in microscopy-based collagen images from pathological tissue samples. Our method leverages deep neural networks to extract collagen fiber centerlines and deep generative models to create synthetic training data, addressing the current shortage of large-scale annotations. As a part of this effort, we have created and annotated a collagen fiber centerline dataset, with the hope of facilitating further research in this field. Quantitative measurements such as fiber orientation, alignment, density, and length can be derived based on the centerline extraction results. Our pipeline comprises three stages. Initially, a variational autoencoder is trained to generate synthetic centerlines possessing controllable topological properties. Subsequently, a conditional generative adversarial network synthesizes realistic collagen fiber images from the synthetic centerlines, yielding a synthetic training set of image-centerline pairs. Finally, we train a collagen fiber centerline extraction network using both the original and synthetic data. Evaluation using collagen fiber images from pancreas, liver, and breast cancer samples collected via second-harmonic generation microscopy demonstrates our pipeline's superiority over several popular fiber centerline extraction tools. Incorporating synthetic data into training further enhances the network's generalizability. Our code is available at https://github.com/uw-loci/collagen-fiber-metrics.