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To evaluate whether short-term treatment with a selective 11β-Hydroxysteroid dehydrogenase-1 (11β-HSD1) inhibitor, AZD4017, would block hepatic cortisol production and thereby decrease hepatic fat in patients with nonalcoholic fatty liver disease (NAFLD)/nonalcoholic steatohepatitis (NASH), with or without type 2 diabetes (T2D).This was a randomized, double-blind, placebo-controlled, phase 2 study conducted at two sites. Key inclusion criteria were the presence of NAFLD or NASH on magnetic resonance imaging (MRI) or recent biopsy positive for NASH. Enrolled patients were randomly assigned (1:1) to AZD4017 or placebo for 12 weeks. Primary outcomes were between-group differences in mean change from baseline to week 12 in liver fat fraction (LFF) and conversion ofA total of 93 patients were randomized; 85 patients completed treatment. The mean (standard deviation [SD]) change in LFF was -0.667 (5.246) and 0.139 (4.323) in the AZD4017 and placebo groups (P = 0.441). For patients with NASH and T2D, the mean (SD) change in LFF was significantly improved in the AZD4017 versus the placebo group (-1.087 [5.374] vs. 1.675 [3.318]; P = 0.033). Conversion ofAlthough the study did not meet one of the primary outcomes, AZD4017 blocked the conversion of

Background: We developed a deep learning algorithm that detects coronary artery calcium (CAC) score using 12-lead electrocardiograms (CAC- ECG). We tested the hypothesis that the output from the CAC-ECG algorithm would be associated with incident atherosclerotic cardiovascular disease (ASCVD) events and that the CAC- ECG would refine the AHA/ACC Pooled Cohort Equation’s (PCE) predictive capabilities. Methods: A community-based cohort of consecutive patients seeking primary care in Olmsted County, MN, between 1998-2000 with passive follow-up via record linkage. Inclusion was identical to the PCE. The original CAC-ECG was developed in 43,210 subjects yielding an AUC of 0.83. Herein, we used the CAC-ECG output to predict a high CAC (≥ 300). Primary outcome was ASCVD defined as fatal and non-fatal myocardial infarction and ischemic stroke, secondary outcome was Major Adverse Cardiovascular Events (MACE) further including PCI, CABG, and mortality. Events were validated in duplicate. Cox proportional hazard models adjusted for variables included in the PCE and were stratified to evaluate the effect of the CAC-ECG on PCE-predicted risk. Follow-up was truncated at 10 years for PCE analyses. Results: We included 24,793 subjects, mean ± SD age 53.9 ± 12.1, 52% women, 95% white. After 16.7±3.7 yrs follow-up, 2,366 (9.5%) had ASCVD and 3,401 (13.7%) had MACE. Risk of ASCVD and MACE increased with CAC-ECG probability quintiles, independent of risk factors, p for trend Fig. A-B ). The CAC-ECG enhanced the predicted capabilities of the PCE across all ASCVD risk groups ( Fig. C ). Net reclassification improved 13.7% with comparable C-statistic from 0.77 vs. 0.78 for PCE and CAC-ECG. Conclusions: CAC-ECG was associated with ASCVD and MACE and improved PCE predicted risk. The CAC-ECG algorithm could identify individuals at risk in primary prevention; Unlike the PCE, the CAC-ECG can be applied without chart review or performing a computer tomography and may be reliably used retrospectively in cohorts with digitally stored ECGs.

To assess the diurnal patterns of postprandial glucose tolerance and insulin sensitivity, 19 subjects with type 2 diabetes (8 women; 60 ± 11 years; BMI 32 ± 5 kg/m2) and 19 anthropometrically matched subjects with no diabetes (ND; 11 women; 53 ± 12 years; BMI 29 ± 5 kg/m2) were studied during breakfast (B), lunch (L), and dinner (D) with identical mixed meals (75 g carbohydrates) on 3 consecutive days in a randomized Latin square design. Three stable isotopes of glucose were ustilized to estimate meal fluxes, and mathematical models were used in estimating indices of insulin action and β-cell function. Postmeal glucose excursions were higher at D versus B and at D versus L in type 2 diabetes (P < 0.05), while in ND they were higher at D versus B (P = 0.025) and at L versus B (P = 0.04). The insulin area under the curve was highest at B compared with L and D in type 2 diabetes, while no differences were observed in ND. Disposition index (DI) was higher at B than at L (P < 0.01) and at D (P < 0.001) in ND subjects, whereas DI was low with unchanging pattern across B-L-D in individuals with type 2 diabetes. Furthermore, between-meal differences in β-cell responsivity to glucose (F) and insulin sensitivity (SI) were concurrent with changes in the DI within groups. Fasting and postmeal glucose, insulin, and C-peptide concentrations, along with estimates of endogenous glucose production (EGP), Rd, SI, F, hepatic extraction of insulin, insulin secretion rate, extracted insulin, and DI, were altered in type 2 diabetes compared with ND (P < 0.011 for all). The data show a diurnal pattern of postprandial glucose tolerance in overweight otherwise glucose-tolerant ND individuals that differs from overweight individuals with type 2 diabetes. The results not only provide valuable insight into management strategies for better glycemic control in people with type 2 diabetes, but also improved understanding of daytime glucose metabolism in overweight individuals without impaired glucose tolerance or overt diabetes.

Coating nanoparticles with stealth epilayers increases circulation time by evading opsonization, macrophage phagocytosis, and reticuloendothelial sequestration. However, this also reduces internalization by cancer cells upon reaching the tumor. We designed gold nanorods (GNRs) with an epilayer that retains stealth properties in circulation but transforms spontaneously in the acidotic tumor microenvironment to a cell-penetrating particle. We used a customized stoichiometric ratio of l -glutamic acid and l -lysine within an amphiphilic polymer of poly( l -glutamic acid-co- l -lysine), or P(Glu-co-Lys), to effect this transformation in acidotic environments. P(Glu-co-Lys)-GNRs were internalized by cancer cells to facilitate potent in vitro radiosensitization. When administered intravenously in mice, they accumulate in the periphery and core of tumors without any signs of serum biochemical or hematological alterations, normal organ histopathological abnormalities, or overt deterioration in animal health. Furthermore, P(Glu-co-Lys)-GNRs penetrated the tumor microenvironment to accumulate in the hypoxic cores of tumors to potently radiosensitize heterotopic and orthotopic pancreatic cancers in vivo.

The diagnostic performance of radiologist readers exhibits substantial variation that cannot be explained by CT acquisition protocol differences. Studying reader detectability from CT images may help identify why certain types of lesions are missed by multiple or specific readers. Ten subspecialized abdominal radiologists marked all suspected metastases in a multi-reader-multi-case study of 102 deidentified contrast-enhanced CT liver scans at multiple radiation dose levels. A reference reader marked ground truth metastatic and benign lesions with the aid of histopathology or tumor progression on later scans. Multi-slice image patches and 3D radiomic features were extracted from the CT images. We trained deep convolutional neural networks (CNN) to predict whether an average (generalized) or individual radiologist reader would detect or miss a specific metastasis from an image patch containing it. The individualized CNN showed higher performance with an area under the receiver operating characteristic curve (AUC) of 0.82 compared to a generalized one (AUC = 0.78) in predicting reader-specific detectability. Random forests were used to build the respective versions from radiomic features. Both the individualized (AUC = 0.64) and generalized (AUC = 0.59) predictors from radiomic features showed limited ability to differentiate detected from missed lesions. This shows that CNN can identify and learn automated features that are better predictors of reader detectability of lesions than radiomic features. Individualized prediction of difficult lesions may allow targeted training of idiosyncratic weaknesses but requires substantial training data for each reader.

ImportanceReduced heart rate during exercise is common and associated with impaired aerobic capacity in heart failure with preserved ejection fraction (HFpEF), but it remains unknown if restoring exertional heart rate through atrial pacing would be beneficial.ObjectiveTo determine if implanting and programming a pacemaker for rate-adaptive atrial pacing would improve exercise performance in patients with HFpEF and chronotropic incompetence.Design, Setting, and ParticipantsSingle-center, double-blind, randomized, crossover trial testing the effects of rate-adaptive atrial pacing in patients with symptomatic HFpEF and chronotropic incompetence at a tertiary referral center (Mayo Clinic) in Rochester, Minnesota. Patients were recruited between 2014 and 2022 with 16-week follow-up (last date of follow-up, May 9, 2022). Cardiac output during exercise was measured by the acetylene rebreathe technique.InterventionsA total of 32 patients were recruited; of these, 29 underwent pacemaker implantation and were randomized to atrial rate responsive pacing or no pacing first for 4 weeks, followed by a 4-week washout period and then crossover for an additional 4 weeks.Main Outcomes and MeasuresThe primary end point was oxygen consumption (V̇o2) at anaerobic threshold (V̇o2,AT); secondary end points were peak V̇o2, ventilatory efficiency (V̇e/V̇co2 slope), patient-reported health status by the Kansas City Cardiomyopathy Questionnaire Overall Summary Score (KCCQ-OSS), and N-terminal pro-brain natriuretic peptide (NT-proBNP) levels.ResultsOf the 29 patients randomized, the mean age was 66 years (SD, 9.7) and 13 (45%) were women. In the absence of pacing, peak V̇o2 and V̇o2 at anaerobic threshold (V̇o2,AT) were both correlated with peak exercise heart rate (r = 0.46-0.51, P P P o2,AT (pacing off, 10.4 [SD, 2.9] mL/kg/min; pacing on, 10.7 [SD, 2.6] mL/kg/min; absolute difference, 0.3 [95% CI, −0.5 to 1.0] mL/kg/min; P = .46), peak V̇o2, minute ventilation (V̇e)/carbon dioxide production (V̇co2) slope, KCCQ-OSS, or NT-proBNP level. Despite the increase in heart rate, atrial pacing had no significant effect on cardiac output with exercise, owing to a decrease in stroke volume (−24 mL [95% CI, −43 to −5 mL]; P = .02). Adverse events judged to be related to the pacemaker device were observed in 6 of 29 participants (21%).Conclusions and RelevanceIn patients with HFpEF and chronotropic incompetence, implantation of a pacemaker to enhance exercise heart rate did not result in an improvement in exercise capacity and was associated with increased adverse events.Trial RegistrationClinicalTrials.gov Identifier: NCT02145351

Introduction: Conduction disturbance after transcatheter aortic valve replacement (TAVR) remains a common complication. Predictive tools to identify conduction disturbance requiring pacing are limited. Our main objective was to develop a deep learning model to predict the need for a permanent pacemaker. Hypothesis: An electrocardiogram (ECG) based deep learning algorithm can predict the need for permanent pacemaker post TAVR. Methods: We evaluated patients who had TAVR at Mayo Clinic between June 2010 and May 2021. Digital ECGs done on the TAVR date or within 24 hours post procedure were partitioned into training, validation, and testing datasets. We trained a deep learning algorithm on the training data and optimized hyperparameters against the validation data. All model performance metrics are based on the reserved test data that were estimated once model architecture was optimized. Need for permanent pacemaker, defined as pacemaker placement due to development of conduction disturbance post TAVR and at least 1% ventricular pacing on device interrogation done at least 2 weeks post pacemaker implantation, was the training target. Results: A total of 3021 patients were evaluated for inclusion and 2,407 cases were available for modeling (exclusions: prior pacemaker (n=419), unsuccessful TAVR (n=27) and no ECG within the pre-specified period (n=168)). Overall, the median age was 82 (Q1: 76, Q3: 86) and 41% were female. In the withheld test data, 15% had a permanent pacemaker with at least 1% ventricular pacing. The algorithm detected the need for pacemaker in the test set with an AUC of 0.83 (95% CI: 0.73 to 0.92). Accuracy, sensitivity, specificity, positive and negative predictive were 80%, 72%, 81%, 41% and 94%, respectively. Conclusions: This study demonstrates the ability of a deep learning model based on ECG data to assist in making the decision to implant a pacemaker following TAVR with a high negative predictive value. Larger validation studies are warranted.

Statistical techniques such as propensity score matching and instrumental variable are commonly employed to “simulate” randomization and adjust for measured confounders in comparative effectiveness research. Despite such adjustments, the results of these methods apply essentially to an “average” patient. However, as patients show significant heterogeneity in their responses to treatments, this average effect is of limited value. It does not account for individual level variabilities, which can deviate substantially from the population average. To address this critical problem, we present a framework that allows the discovery of clinically meaningful homogeneous subgroups with differential effects of plasma transfusion using unsupervised random forest clustering. Subgroup analysis using two blood transfusion datasets show that considerable variablilities exist between the subgroups and population in both the treatment effect of plasma transfusion on bleeding and mortality and risk factors for these outcomes. These results support the customization of blood transfusion therapy for the individual patient.

In blood transfusion studies, plasma transfusion (PPT) and bleeding are known to be associated with risk of prolonged ICU length of stay (ICU-LOS). However, as patients can show significant heterogeneity in response to a treatment, there might exists subgroups with differential effects. The existence and characteristics of these subpopulations in blood transfusion has not been well-studied. Further, the impact of bleeding in patients offered PPT on prolonged ICU-LOS is not known. This study presents a causal and predictive framework to examine these problems. The two-step approach first estimates the effect of bleeding in PPT patients on prolonged ICU-LOS and then estimates risks of bleeding and prolonged ICU-LOS. The framework integrates a classification model for risks prediction and a regression model to predict actual LOS. Results showed that the effect of bleeding in PPT patients significantly increases risk of prolonged ICU-LOS (55%, p=0.00) while no bleeding significantly reduces ICU-LOS (4%, p=0.046).

article i nfo Purpose: Inhaled corticosteroids (ICSs) attenuated lung injury in animal studies. We investigated the association between prehospital ICS and incidence of acute lung injury (ALI) among patients at risk. Methods: In this ancillary analysis of the large multicenter Lung Injury Prediction Study cohort, we developed a propensity score for prehospital ICS use followed by matching, for all patients and for a subgroup of patients with at least 1 risk factor for direct pulmonary injury. The primary outcome was ALI; secondary outcomes included acute respiratory distress syndrome, need for invasive mechanical ventilation, and hospital mortality. Results: Of the 5126 patients, 401 (8%) were using ICS. Acute lung injury developed in 343 (7%). The unadjusted incidence of ALI was 4.7% vs 6.9% (P = .12) among those in ICS compared with non-ICS group. In the "direct" lunginjury subgroup, the unadjusted incidenceof ALIwas 4.1%vs10.6%(P= 0.006).After propensity matching, the estimated effectfor ALIinthewhole cohortwas 0.69 (95%confidence interval, 0.39-1.2;P= .18),and thatin the direct subgroup was 0.56 (95% confidence interval, 0.22-1.46; P = .24). Conclusions: Preadmission use of ICS in a hospitalized population of patients at risk for ALI was not significantly associated with a lower incidence of ALI once controlled by comprehensive propensity-matched analysis.

The overall goal is to develop magnetic resonance elastography derived shear stiffness as a biomarker for the early identification of chemotherapy response, allowing dose, agent type and treatment regimen to be tailored on a per patient basis, improving therapeutic outcome and minimizing normal tissue toxicity. The specific purpose of this study is to test the feasibility of this novel biomarker to measure the treatment response in a well-known chemotherapy model.Tumors were grown in the right flank of genetically modified mice by subcutaneous injection of DoHH2 (non-Hodgkin's lymphoma) cells. Magnetic resonance elastography was used to quantify tumor stiffness before and after injection of a chemotherapeutic agent or saline. Histological tests were also performed on the tumors.A significant decrease (P0.0001) in magnetic resonance elastography-derived tumor shear stiffness was observed within 4 days of chemotherapy treatment, while no appreciable change was observed in saline-treated tumors. No significant change in volume occurred at this early stage, but there were decreased levels of cellular proliferation in chemotherapy-treated tumors.These results demonstrate that magnetic resonance elastography-derived estimates of shear stiffness reflect an initial response to cytotoxic therapy and suggest that this metric could be an early and sensitive biomarker of tumor response to chemotherapy.