Comparing performance of primary care clinicians in the interpretation of SPIROmetry with or without Artificial Intelligence Decision support software (SPIRO-AID): a protocol for a randomised controlled trial.

INTRODUCTION: Spirometry is a point-of-care lung function test that helps support the diagnosis and monitoring of chronic lung disease. The quality and interpretation accuracy of spirometry is variable in primary care. This study aims to evaluate whether artificial intelligence (AI) decision support software improves the performance of primary care clinicians in the interpretation of spirometry, against reference standard (expert interpretation). METHODS AND ANALYSIS: A parallel, two-group, statistician-blinded, randomised controlled trial of primary care clinicians in the UK, who refer for, or interpret, spirometry. People with specialist training in respiratory medicine to consultant level were excluded. A minimum target of 228 primary care clinician participants will be randomised with a 1:1 allocation to assess fifty de-identified, real-world patient spirometry sessions through an online platform either with (intervention group) or without (control group) AI decision support software report. Outcomes will cover primary care clinicians' spirometry interpretation performance including measures of technical quality assessment, spirometry pattern recognition and diagnostic prediction, compared with reference standard. Clinicians' self-rated confidence in spirometry interpretation will also be evaluated. The primary outcome is the proportion of the 50 spirometry sessions where the participant's preferred diagnosis matches the reference diagnosis. Unpaired t-tests and analysis of covariance will be used to estimate the difference in primary outcome between intervention and control groups. ETHICS AND DISSEMINATION: This study has been reviewed and given favourable opinion by Health Research Authority Wales (reference: 22/HRA/5023). Results will be submitted for publication in peer-reviewed journals, presented at relevant national and international conferences, disseminated through social media, patient and public routes and directly shared with stakeholders. TRIAL REGISTRATION NUMBER: NCT05933694.

Free-breathing 3D whole-heart joint T1/T2 mapping and water/fat imaging at 0.55 T.

PURPOSE: To develop and validate a highly efficient motion compensated free-breathing isotropic resolution 3D whole-heart joint T1/T2 mapping sequence with anatomical water/fat imaging at 0.55 T. METHODS: The proposed sequence takes advantage of shorter T1 at 0.55 T to acquire three interleaved water/fat volumes with inversion-recovery preparation, no preparation, and T2 preparation, respectively. Image navigators were used to facilitate nonrigid motion-compensated image reconstruction. T1 and T2 maps were jointly calculated by a dictionary matching method. Validations were performed with simulation, phantom, and in vivo experiments on 10 healthy volunteers and 1 patient. The performance of the proposed sequence was compared with conventional 2D mapping sequences including modified Look-Locker inversion recovery and T2-prepared balanced steady-SSFP sequence. RESULTS: The proposed sequence has a good T1 and T2 encoding sensitivity in simulation, and excellent agreement with spin-echo reference T1 and T2 values was observed in a standardized T1/T2 phantom (R2 = 0.99). In vivo experiments provided good-quality co-registered 3D whole-heart T1 and T2 maps with 2-mm isotropic resolution in a short scan time of about 7 min. For healthy volunteers, left-ventricle T1 mean and SD measured by the proposed sequence were both comparable with those of modified Look-Locker inversion recovery (640 ± 35 vs. 630 ± 25 ms [p = 0.44] and 49.9 ± 9.3 vs. 54.4 ± 20.5 ms [p = 0.42]), whereas left-ventricle T2 mean and SD measured by the proposed sequence were both slightly lower than those of T2-prepared balanced SSFP (53.8 ± 5.5 vs. 58.6 ± 3.3 ms [p < 0.01] and 5.2 ± 0.9 vs. 6.1 ± 0.8 ms [p = 0.03]). Myocardial T1 and T2 in the patient measured by the proposed sequence were in good agreement with conventional 2D sequences and late gadolinium enhancement. CONCLUSION: The proposed sequence simultaneously acquires 3D whole-heart T1 and T2 mapping with anatomical water/fat imaging at 0.55 T in a fast and efficient 7-min scan. Further investigation in patients with cardiovascular disease is now warranted.

Evaluation of Deep Learning Estimation of Whole Heart Anatomy from Automated Cardiovascular Magnetic Resonance Short- and Long-Axis Analyses in UK Biobank.

AIMS: Standard methods of heart chamber volume estimation in cardiovascular magnetic resonance (CMR) typically utilize simple geometric formulae based on a limited number of slices. We aimed to evaluate whether an automated deep learning neural network prediction of 3D anatomy of all four chambers would show stronger associations with cardiovascular risk factors and disease than standard volume estimation methods in the UK Biobank. METHODS: A deep learning network was adapted to predict 3D segmentations of left and right ventricles (LV, RV) and atria (LA, RA) at ∼1mm isotropic resolution from CMR short and long axis 2D segmentations obtained from a fully automated machine learning pipeline in 4723 individuals with cardiovascular disease (CVD) and 5733 without in the UK Biobank. Relationships between volumes at end-diastole (ED) and end-systole (ES) and risk/disease factors were quantified using univariate, multivariate and logistic regression analyses. Strength of association between deep learning volumes and standard volumes was compared using the area under the receiving operator characteristic curve (AUC). RESULTS: Univariate and multivariate associations between deep learning volumes and most risk and disease factors were stronger than for standard volumes (higher R2 and more significant P values), particularly for sex, age, and body mass index. AUC for all logistic regressions were higher for deep learning volumes than standard volumes (p<0.001 for all four chambers at ED and ES). CONCLUSIONS: Neural network reconstructions of whole heart volumes had significantly stronger associations with cardiovascular disease and risk factors than standard volume estimation methods in an automatic processing pipeline.

Inversion recovery and saturation recovery pulmonary vein MR angiography using an image based navigator fluoro trigger and variable-density 3D cartesian sampling with spiral-like order.

Contrast enhanced pulmonary vein magnetic resonance angiography (PV CE-MRA) has value in atrial ablation pre-procedural planning. We aimed to provide high fidelity, ECG gated PV CE-MRA accelerated by variable density Cartesian sampling (VD-CASPR) with image navigator (iNAV) respiratory motion correction acquired in under 4 min. We describe its use in part during the global iodinated contrast shortage. VD-CASPR/iNAV framework was applied to ECG-gated inversion and saturation recovery gradient recalled echo PV CE-MRA in 65 patients (66 exams) using .15 mmol/kg Gadobutrol. Image quality was assessed by three physicians, and anatomical segmentation quality by two technologists. Left atrial SNR and left atrial/myocardial CNR were measured. 12 patients had CTA within 6 months of MRA. Two readers assessed PV ostial measurements versus CTA for intermodality/interobserver agreement. Inter-rater/intermodality reliability, reproducibility of ostial measurements, SNR/CNR, image, and anatomical segmentation quality was compared. The mean acquisition time was 3.58 ± 0.60 min. Of 35 PV pre-ablation datasets (34 patients), mean anatomical segmentation quality score was 3.66 ± 0.54 and 3.63 ± 0.55 as rated by technologists 1 and 2, respectively (p = 0.7113). Good/excellent anatomical segmentation quality (grade 3/4) was seen in 97% of exams. Each rated one exam as moderate quality (grade 2). 95% received a majority image quality score of good/excellent by three physicians. Ostial PV measurements correlated moderate to excellently with CTA (ICCs range 0.52-0.86). No difference in SNR was observed between IR and SR. High quality PV CE-MRA is possible in under 4 min using iNAV bolus timing/motion correction and VD-CASPR.

Improving potential energy surfaces using measured Feshbach resonance states.

The structure and dynamics of a molecular system is governed by its potential energy surface (PES), representing the total energy as a function of the nuclear coordinates. Obtaining accurate potential energy surfaces is limited by the exponential scaling of Hilbert space, restricting quantitative predictions of experimental observables from first principles to small molecules with just a few electrons. Here, we present an explicitly physics-informed approach for improving and assessing the quality of families of PESs by modifying them through linear coordinate transformations based on experimental data. We demonstrate this "morphing" of the PES for the He - H2+ complex using recent comprehensive Feshbach resonance (FR) measurements for reference PESs at three different levels of quantum chemistry. In all cases, the positions and intensities of peaks in the energy distributions are improved. We find these observables to be mainly sensitive to the long-range part of the PES.

Solid-Fluid Equilibria of Atoms with Soft Repulsive and Short-Range Cohesive Interactions.

We report molecular simulations using a relatively soft interatomic potential to determine the nature of melting under extreme conditions. At temperatures and pressures above the triple point of normal materials, the density range of two-phase solid and fluid equilibria is bounded by freezing and melting curves. We address the unresolved issue of the termination of these boundaries, i.e., whether the melting curve of a solid terminates in a critical point, exhibits a maximum, goes to an asymptotic limit, or continues indefinitely. Significantly, we observe a negative change in volume upon melting at high pressures, which is normally observed only for water. We provide unequivocal evidence that the densities of the meeting and freezing lines can merge at a melting temperature maximum point. This could be a general feature of "soft" atomic fluids at extreme pressures.

Highly efficient free-breathing 3D whole-heart imaging in 3-min: single center study in adults with congenital heart disease.

BACKGROUND: Three dimensional, whole-heart (3DWH) MRI is an established non-invasive imaging modality in patients with congenital heart disease (CHD) for the diagnosis of cardiovascular morphology and for clinical decision making. Current techniques utilise diaphragmatic navigation (dNAV) for respiratory motion correction and gating and are frequently limited by long acquisition times. This study proposes and evaluates the diagnostic performance of a respiratory gating-free framework, which considers respiratory image-based navigation (iNAV), and highly accelerated variable density Cartesian sampling in concert with non-rigid motion correction and low-rank patch-based denoising (iNAV-3DWH-PROST). The method is compared to the clinical dNAV-3DWH sequence in adult patients with CHD. METHODS: In this prospective single center study, adult patients with CHD who underwent the clinical dNAV-3DWH MRI were also scanned with the iNAV-3DWH-PROST. Diagnostic confidence (4-point Likert scale) and diagnostic accuracy for common cardiovascular lesions was assessed by three readers. Scan times and diagnostic confidence were compared using the Wilcoxon-signed rank test. Co-axial vascular dimensions at three anatomic landmarks were measured, and agreement between the research and the corresponding clinical sequence was assessed with Bland-Altman analysis. RESULTS: The study included 60 participants (mean age ± [SD]: 33 ± 14 years; 36 men). The mean acquisition time of iNAV-3DWH-PROST was significantly lower compared with the conventional clinical sequence (3.1 ± 0.9 min vs 13.9 ± 3.9 min, p < 0.0001). Diagnostic confidence was higher for the iNAV-3DWH-PROST sequence compared with the clinical sequence (3.9 ± 0.2 vs 3.4 ± 0.8, p < 0.001), however there was no significant difference in diagnostic accuracy. Narrow limits of agreement and mean bias less than 0.08 cm were found between the research and the clinical vascular measurements. CONCLUSIONS: The iNAV-3DWH-PROST framework provides efficient, high quality and robust 3D whole-heart imaging in significantly shorter scan time compared to the standard clinical sequence.

Referenceless Nyquist ghost correction outperforms standard navigator-based method and improves efficiency of in vivo diffusion tensor cardiovascular magnetic resonance.

PURPOSE: The study aims to assess the potential of referenceless methods of EPI ghost correction to accelerate the acquisition of in vivo diffusion tensor cardiovascular magnetic resonance (DT-CMR) data using both computational simulations and data from in vivo experiments. METHODS: Three referenceless EPI ghost correction methods were evaluated on mid-ventricular short axis DT-CMR spin echo and STEAM datasets from 20 healthy subjects at 3T. The reduced field of view excitation technique was used to automatically quantify the Nyquist ghosts, and DT-CMR images were fit to a linear ghost model for correction. RESULTS: Numerical simulation showed the singular value decomposition (SVD) method is the least sensitive to noise, followed by Ghost/Object method and entropy-based method. In vivo experiments showed significant ghost reduction for all correction methods, with referenceless methods outperforming navigator methods for both spin echo and STEAM sequences at b = 32, 150, 450, and 600 smm - 2 $$ {\mathrm{smm}}^{-2} $$ . It is worth noting that as the strength of the diffusion encoding increases, the performance gap between the referenceless method and the navigator-based method diminishes. CONCLUSION: Referenceless ghost correction effectively reduces Nyquist ghost in DT-CMR data, showing promise for enhancing the accuracy and efficiency of measurements in clinical practice without the need for any additional reference scans.

Non-rigid motion-compensated 3D whole-heart T2 mapping in a hybrid 3T PET-MR system.

PURPOSE: Simultaneous PET-MRI improves inflammatory cardiac disease diagnosis. However, challenges persist in respiratory motion and mis-registration between free-breathing 3D PET and 2D breath-held MR images. We propose a free-breathing non-rigid motion-compensated 3D T2 -mapping sequence enabling whole-heart myocardial tissue characterization in a hybrid 3T PET-MR system and provides non-rigid respiratory motion fields to correct also simultaneously acquired PET data. METHODS: Free-breathing 3D whole-heart T2 -mapping was implemented on a hybrid 3T PET-MRI system. Three datasets were acquired with different T2 -preparation modules (0, 28, 55 ms) using 3-fold undersampled variable-density Cartesian trajectory. Respiratory motion was estimated via virtual 3D image navigators, enabling multi-contrast non-rigid motion-corrected MR reconstruction. T2 -maps were computed using dictionary-matching. Approach was tested in phantom, 8 healthy subjects, 14 MR only and 2 PET-MR patients with suspected cardiac disease and compared with spin echo reference (phantom) and clinical 2D T2 -mapping (in-vivo). RESULTS: Phantom results show a high correlation (R2 = 0.996) between proposed approach and gold standard 2D T2 mapping. In-vivo 3D T2 -mapping average values in healthy subjects (39.0 ± 1.4 ms) and patients (healthy tissue) (39.1 ± 1.4 ms) agree with conventional 2D T2 -mapping (healthy = 38.6 ± 1.2 ms, patients = 40.3 ± 1.7 ms). Bland-Altman analysis reveals bias of 1.8 ms and 95% limits of agreement (LOA) of -2.4-6 ms for healthy subjects, and bias of 1.3 ms and 95% LOA of -1.9 to 4.6 ms for patients. CONCLUSION: Validated efficient 3D whole-heart T2 -mapping at hybrid 3T PET-MRI provides myocardial inflammation characterization and non-rigid respiratory motion fields for simultaneous PET data correction. Comparable T2 values were achieved with both 3D and 2D methods. Improved image quality was observed in the PET images after MR-based motion correction.

Cardiac magnetic resonance for early atrial lesion visualization post atrial fibrillation radiofrequency catheter ablation.

BACKGROUND: Incomplete atrial lesions resulting in pulmonary vein-left atrium reconnection after pulmonary vein antrum isolation (PVAI), are related to atrial fibrillation (AF) recurrence. Unfortunately, during the PVAI procedure, fluoroscopy and electroanatomic mapping cannot accurately determine the location and size of the ablation lesions in the atrial wall and this can result in incomplete PVAI lesions (PVAI-L) after radiofrequency catheter ablation (RFCA). AIM: We seek to evaluate whether cardiac magnetic resonance (CMR), immediately after RFCA of AF, can identify PVAI-L by characterizing the left atrial tissue. METHODS: Ten patients (63.1 ± 5.7 years old, 80% male) receiving a RFCA for paroxysmal AF underwent a CMR before (<1 week) and after (<1 h) the PVAI. Two-dimensional dark-blood T2-weighted short tau inversion recovery (DB-STIR), Three-dimensional inversion-recovery prepared long inversion time (3D-TWILITE) and three-dimensional late gadolinium enhancement (3D-LGE) images were performed to visualize PVAI-L. RESULTS: The PVAI-L was visible in 10 patients (100%) using 3D-TWILITE and 3D-LGE. Conversely, On DB-STIR, the ablation core of the PAVI-L could not be identified because of a diffuse high signal of the atrial wall post-PVAI. Microvascular obstruction was identified in 7 (70%) patients using 3D-LGE. CONCLUSION: CMR can visualize PVAI-L immediately after the RFCA of AF even without the use of contrast agents. Future studies are needed to understand if the use of CMR for PVAI-L detection after RFCA can improve the results of ablation procedures.

Combining generative modelling and semi-supervised domain adaptation for whole heart cardiovascular magnetic resonance angiography segmentation.

BACKGROUND: Quantification of three-dimensional (3D) cardiac anatomy is important for the evaluation of cardiovascular diseases. Changes in anatomy are indicative of remodeling processes as the heart tissue adapts to disease. Although robust segmentation methods exist for computed tomography angiography (CTA), few methods exist for whole-heart cardiovascular magnetic resonance angiograms (CMRA) which are more challenging due to variable contrast, lower signal to noise ratio and a limited amount of labeled data. METHODS: Two state-of-the-art unsupervised generative deep learning domain adaptation architectures, generative adversarial networks and variational auto-encoders, were applied to 3D whole heart segmentation of both conventional (n = 20) and high-resolution (n = 45) CMRA (target) images, given segmented CTA (source) images for training. An additional supervised loss function was implemented to improve performance given 10%, 20% and 30% segmented CMRA cases. A fully supervised nn-UNet trained on the given CMRA segmentations was used as the benchmark. RESULTS: The addition of a small number of segmented CMRA training cases substantially improved performance in both generative architectures in both standard and high-resolution datasets. Compared with the nn-UNet benchmark, the generative methods showed substantially better performance in the case of limited labelled cases. On the standard CMRA dataset, an average 12% (adversarial method) and 10% (variational method) improvement in Dice score was obtained. CONCLUSIONS: Unsupervised domain-adaptation methods for CMRA segmentation can be boosted by the addition of a small number of supervised target training cases. When only few labelled cases are available, semi-supervised generative modelling is superior to supervised methods.

Automated detection of cardiac rest period for trigger delay calculation for image-based navigator coronary magnetic resonance angiography.

BACKGROUND: Coronary magnetic resonance angiography (coronary MRA) is increasingly being considered as a clinically viable method to investigate coronary artery disease (CAD). Accurate determination of the trigger delay to place the acquisition window within the quiescent part of the cardiac cycle is critical for coronary MRA in order to reduce cardiac motion. This is currently reliant on operator-led decision making, which can negatively affect consistency of scan acquisition. Recently developed deep learning (DL) derived software may overcome these issues by automation of cardiac rest period detection. METHODS: Thirty individuals (female, n = 10) were investigated using a 0.9 mm isotropic image-navigator (iNAV)-based motion-corrected coronary MRA sequence. Each individual was scanned three times utilising different strategies for determination of the optimal trigger delay: (1) the DL software, (2) an experienced operator decision, and (3) a previously utilised formula for determining the trigger delay. Methodologies were compared using custom-made analysis software to assess visible coronary vessel length and coronary vessel sharpness for the entire vessel length and the first 4 cm of each vessel. RESULTS: There was no difference in image quality between any of the methodologies for determination of the optimal trigger delay, as assessed by visible coronary vessel length, coronary vessel sharpness for each entire vessel and vessel sharpness for the first 4 cm of the left mainstem, left anterior descending or right coronary arteries. However, vessel length of the left circumflex was slightly greater using the formula method. The time taken to calculate the trigger delay was significantly lower for the DL-method as compared to the operator-led approach (106 ± 38.0 s vs 168 ± 39.2 s, p < 0.01, 95% CI of difference 25.5-98.1 s). CONCLUSIONS: Deep learning-derived automated software can effectively and efficiently determine the optimal trigger delay for acquisition of coronary MRA and thus may simplify workflow and improve reproducibility.

Long-read sequencing identifies a common transposition haplotype predisposing for CLCNKB deletions.

BACKGROUND: Long-read sequencing is increasingly used to uncover structural variants in the human genome, both functionally neutral and deleterious. Structural variants occur more frequently in regions with a high homology or repetitive segments, and one rearrangement may predispose to additional events. Bartter syndrome type 3 (BS 3) is a monogenic tubulopathy caused by deleterious variants in the chloride channel gene CLCNKB, a high proportion of these being large gene deletions. Multiplex ligation-dependent probe amplification, the current diagnostic gold standard for this type of mutation, will indicate a simple homozygous gene deletion in biallelic deletion carriers. However, since the phenotypic spectrum of BS 3 is broad even among biallelic deletion carriers, we undertook a more detailed analysis of precise breakpoint regions and genomic structure. METHODS: Structural variants in 32 BS 3 patients from 29 families and one BS4b patient with CLCNKB deletions were investigated using long-read and synthetic long-read sequencing, as well as targeted long-read sequencing approaches. RESULTS: We report a ~3 kb duplication of 3'-UTR CLCNKB material transposed to the corresponding locus of the neighbouring CLCNKA gene, also found on ~50 % of alleles in healthy control individuals. This previously unknown common haplotype is significantly enriched in our cohort of patients with CLCNKB deletions (45 of 51 alleles with haplotype information, 2.2 kb and 3.0 kb transposition taken together, p=9.16×10-9). Breakpoint coordinates for the CLCNKB deletion were identifiable in 28 patients, with three being compound heterozygous. In total, eight different alleles were found, one of them a complex rearrangement with three breakpoint regions. Two patients had different CLCNKA/CLCNKB hybrid genes encoding a predicted CLCNKA/CLCNKB hybrid protein with likely residual function. CONCLUSIONS: The presence of multiple different deletion alleles in our cohort suggests that large CLCNKB gene deletions originated from many independently recurring genomic events clustered in a few hot spots. The uncovered associated sequence transposition haplotype apparently predisposes to these additional events. The spectrum of CLCNKB deletion alleles is broader than expected and likely still incomplete, but represents an obvious candidate for future genotype/phenotype association studies. We suggest a sensitive and cost-efficient approach, consisting of indirect sequence capture and long-read sequencing, to analyse disease-relevant structural variant hotspots in general.

The quest for good genes: Epigamic traits, fitness, MHC and multilocus heterozygosity in the guppy.

The 'good genes' hypothesis for the evolution of male secondary sexual traits poses that female preferences for such traits are driven by indirect genetic benefits. However, support for the hypothesis remains ambiguous, and, in particular, the genetic basis for the benefits has rarely been investigated. Here, we use seminatural populations of Trinidadian guppies to investigate whether sexually selected traits (orange, black and iridescent colouration, gonopodium length and body size) predict fitness measured as the number of grandoffspring, a metric that integrates across fitness components and sexes. Furthermore, we tested whether two potential sources of genetic benefits-major histocompatibility complex (MHC) genotypes and multilocus heterozygosity (MLH)-are significant predictors of fitness and of the size of sexually selected traits. We found a significant, nonlinear effect of the area of black pigmentation and male body size on the number of grandoffspring, suggesting stabilizing selection on black area, and nonlinear selection favouring small body size. MLH was heritable (h2 = 0.14) and significantly predicted the number of grandoffspring, indicating the potential for genetic benefits based on heterozygosity. We also found support for local heterozygosity effects, which may reflect a noneven distribution of genetic load across the genome. MHC genotype was not significantly associated with any tested fitness component, or with the load of Gyrodactylus parasites. Neither MHC nor MLH was significant predictor of sexually selected traits. Overall, our results highlight the role of heterozygosity in determining fitness, but do not provide support for male sexually selected traits being indicators of genetic quality.

Insulin Resistance Is the Main Characteristic of Metabolically Unhealthy Obesity (MUO) Associated with NASH in Patients Undergoing Bariatric Surgery.

(1) Background: Metabolically healthy obesity (MHO) is a concept that applies to obese patients without any elements of metabolic syndrome (metS). In turn, metabolically unhealthy obesity (MUO) defines the presence of elements of metS in obese patients. The components of MUO can be divided into subgroups regarding the elements of inflammation, lipid and glucose metabolism and cardiovascular disease. MUO patients appear to be at greater risk of developing non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) compared to MHO patients. The aim of this study was to evaluate the influence of different MUO components on NAFLD and NASH in patients with morbid obesity undergoing bariatric surgery. (2) Methods: 141 patients undergoing bariatric surgery from September 2015 and October 2021 at RWTH Aachen university hospital (Germany) were included. Patients were evaluated pre-operatively for characteristics of metS and MUO (HbA1c, HOMA, CRP, BMI, fasting glucose, LDL, TG, HDL and the presence of arterial hypertension). Intraoperatively, a liver biopsy was taken from the left liver lobe and evaluated for the presence of NAFLD or NASH. In ordinal regression analyses, different factors were evaluated for their influence on NAFLD and NASH. (3) Results: Mean BMI of the patients was 52.3 kg/m2 (36-74.8, SD 8.4). Together, the parameters HbA1c, HOMA, CRP, BMI, fasting glucose, LDL, TG, HDL and the presence of arterial hypertension accounted for a significant amount of variance in the outcome, with a likelihood ratio of χ2 (9) = 41.547, p < 0.001, for predicting the presence of NASH. Only HOMA was an independent predictor of NASH (B = 0.102, SE = 0.0373, p = 0.007). Evaluation of steatosis showed a similar trend (likelihood ratio χ2 (9) = 40.272, p < 0.001). Independent predictors of steatosis were HbA1c (B = 0.833, SE = 0.343, p = 0.015) and HOMA (B = 0.136, SE = 0.039, p < 0.001). (4) Conclusions: The above-mentioned model, including components of MUO, was significant for diagnosing NASH in patients with morbid obesity undergoing bariatric surgery. Out of the different subitems, HOMA independently predicted the presence of NASH and steatosis, while HbA1c independently predicted steatosis and fibrosis. Taken together, the parameter of glucose metabolism appears to be more accurate for the prediction of NASH than the parameters of lipid metabolism, inflammation or the presence of cardiovascular disease.

Prognostic role of minute ventilation/carbon dioxide production slope for perioperative morbidity and long-term survival in resectable patients with nonsmall-cell lung cancer: a prospective study using propensity score overlap weighting.

BACKGROUND: The role of minute ventilation/carbon dioxide production ( / CO 2 ) slope, a ventilation efficiency marker, in predicting short-term and long-term health outcomes for patients with nonsmall-cell lung cancer (NSCLC) undergoing lung resection has not been well investigated. MATERIAL AND METHODS: This prospective cohort study consecutively enrolled NSCLC patients who underwent a presurgical cardiopulmonary exercise test from November 2014 to December 2019. The association of / CO 2 slope with relapse-free survival (RFS), overall survival (OS), and perioperative mortality was evaluated using the Cox proportional hazards and logistic models. Covariates were adjusted using propensity score overlap weighting. The optimal cut-off point of the E/ CO 2 slope was estimated using the receiver operating characteristics curve. Internal validation was completed through bootstrap resampling. RESULTS: A cohort of 895 patients [median age (interquartile range), 59 (13) years; 62.5% male] was followed for a median of 40 (range, 1-85) months. Throughout the study, there were 247 relapses or deaths and 156 perioperative complications. The incidence rates per 1000 person-years for relapses or deaths were 108.8 and 79.6 among patients with high and low E/ CO 2 slopes, respectively [weighted incidence rate difference per 1000 person-years, 29.21 (95% CI, 7.30-51.12)]. A E/ CO 2 slope of greater than or equal to 31 was associated with shorter RFS [hazard ratio for relapse or death, 1.38 (95% CI, 1.02-1.88), P =0.04] and poorer OS [hazard ratio for death, 1.69 (1.15-2.48), P =0.02] compared to a lower / CO 2 slope. A high E/ CO 2 slope increased the risk of perioperative morbidity compared with a low E/ CO 2 slope [odds ratio, 2.32 (1.54-3.49), P <0.001]. CONCLUSIONS: In patients with operable NSCLC, a high E/ CO 2 slope was significantly associated with elevated risks of poorer RFS, OS, and perioperative morbidity.

Tomography of Feshbach resonance states.

Feshbach resonances are fundamental to interparticle interactions and become particularly important in cold collisions with atoms, ions, and molecules. In this work, we present the detection of Feshbach resonances in a benchmark system for strongly interacting and highly anisotropic collisions: molecular hydrogen ions colliding with noble gas atoms. The collisions are launched by cold Penning ionization, which exclusively populates Feshbach resonances that span both short- and long-range parts of the interaction potential. We resolved all final molecular channels in a tomographic manner using ion-electron coincidence detection. We demonstrate the nonstatistical nature of the final-state distribution. By performing quantum scattering calculations on ab initio potential energy surfaces, we show that the isolation of the Feshbach resonance pathways reveals their distinctive fingerprints in the collision outcome.

Autumn outmigrants in brown trout (Salmo trutta) are not a demographic dead-end.

Genetic identity analysis and PIT (passive integrated transponder) tagging were used to examine the freshwater return rates and phenotypic characteristics of n = 1791 downstream migrating juvenile Salmo trutta in the Burrishoole catchment (northwest Ireland) across the period September 2017 to December 2020. In this system, juveniles out-migrate (move from freshwater into brackish or marine habitats) in every month of the year, with distinct seasonal peaks in spring (March through June; mostly silvered smolts) and autumn (September through December; mostly younger, unsilvered fry or parr). Both types exhibited a sex-bias towards females, which was stronger in spring (78% females) than in autumn outmigrants (67%). Sixty-nine returning fish were matched back to previous juvenile outmigrants, and similar return rates were found for spring outmigrants (5.0%), autumn outmigrants (3.3%) and fish that out-migrated outside of spring or autumn (2.8%). Spring and autumn outmigrants returned at similar dates (typically mid to late July), but autumn fish were away for longer periods (median = 612 days; spring outmigrants = 104 days). Autumn outmigrants were 25% smaller than spring outmigrants at outmigration and 6% smaller on their return, and within both groups smaller/younger outmigrants spent longer away than larger/older outmigrants. Autumn outmigrants were more likely to return unsilvered as "slob" trout (84%) than spring outmigrants (31%), suggesting they make greater use of brackish habitats that might be safer, but less productive, than fully marine habitats. Nonetheless, both types also produced silvered "sea trout" (≥1+ sea-age), implying neither is locked into a single life-history strategy. The findings emphasise that autumn outmigrants and the transitional habitats that support their persistence should not be overlooked in salmonid management and conservation.

Automated segmentation of long and short axis DENSE cardiovascular magnetic resonance for myocardial strain analysis using spatio-temporal convolutional neural networks.

BACKGROUND: Cine Displacement Encoding with Stimulated Echoes (DENSE) facilitates the quantification of myocardial deformation, by encoding tissue displacements in the cardiovascular magnetic resonance (CMR) image phase, from which myocardial strain can be estimated with high accuracy and reproducibility. Current methods for analyzing DENSE images still heavily rely on user input, making this process time-consuming and subject to inter-observer variability. The present study sought to develop a spatio-temporal deep learning model for segmentation of the left-ventricular (LV) myocardium, as spatial networks often fail due to contrast-related properties of DENSE images. METHODS: 2D + time nnU-Net-based models have been trained to segment the LV myocardium from DENSE magnitude data in short- and long-axis images. A dataset of 360 short-axis and 124 long-axis slices was used to train the networks, from a combination of healthy subjects and patients with various conditions (hypertrophic and dilated cardiomyopathy, myocardial infarction, myocarditis). Segmentation performance was evaluated using ground-truth manual labels, and a strain analysis using conventional methods was performed to assess strain agreement with manual segmentation. Additional validation was performed using an externally acquired dataset to compare the inter- and intra-scanner reproducibility with respect to conventional methods. RESULTS: Spatio-temporal models gave consistent segmentation performance throughout the cine sequence, while 2D architectures often failed to segment end-diastolic frames due to the limited blood-to-myocardium contrast. Our models achieved a DICE score of 0.83 ± 0.05 and a Hausdorff distance of 4.0 ± 1.1 mm for short-axis segmentation, and 0.82 ± 0.03 and 7.9 ± 3.9 mm respectively for long-axis segmentations. Strain measurements obtained from automatically estimated myocardial contours showed good to excellent agreement with manual pipelines, and remained within the limits of inter-user variability estimated in previous studies. CONCLUSION: Spatio-temporal deep learning shows increased robustness for the segmentation of cine DENSE images. It provides excellent agreement with manual segmentation for strain extraction. Deep learning will facilitate the analysis of DENSE data, bringing it one step closer to clinical routine.

General-Purpose Methods for Simulating Survival Data for Expected Value of Sample Information Calculations.

BACKGROUND: Expected value of sample information (EVSI) quantifies the expected value to a decision maker of reducing uncertainty by collecting additional data. EVSI calculations require simulating plausible data sets, typically achieved by evaluating quantile functions at random uniform numbers using standard inverse transform sampling (ITS). This is straightforward when closed-form expressions for the quantile function are available, such as for standard parametric survival models, but these are often unavailable when assuming treatment effect waning and for flexible survival models. In these circumstances, the standard ITS method could be implemented by numerically evaluating the quantile functions at each iteration in a probabilistic analysis, but this greatly increases the computational burden. Thus, our study aims to develop general-purpose methods that standardize and reduce the computational burden of the EVSI data-simulation step for survival data. METHODS: We developed a discrete sampling method and an interpolated ITS method for simulating survival data from a probabilistic sample of survival probabilities over discrete time units. We compared the general-purpose and standard ITS methods using an illustrative partitioned survival model with and without adjustment for treatment effect waning. RESULTS: The discrete sampling and interpolated ITS methods agree closely with the standard ITS method, with the added benefit of a greatly reduced computational cost in the scenario with adjustment for treatment effect waning. CONCLUSIONS: We present general-purpose methods for simulating survival data from a probabilistic sample of survival probabilities that greatly reduce the computational burden of the EVSI data-simulation step when we assume treatment effect waning or use flexible survival models. The implementation of our data-simulation methods is identical across all possible survival models and can easily be automated from standard probabilistic decision analyses. HIGHLIGHTS: Expected value of sample information (EVSI) quantifies the expected value to a decision maker of reducing uncertainty through a given data collection exercise, such as a randomized clinical trial. In this article, we address the problem of computing EVSI when we assume treatment effect waning or use flexible survival models, by developing general-purpose methods that standardize and reduce the computational burden of the EVSI data-generation step for survival data.We developed 2 methods for simulating survival data from a probabilistic sample of survival probabilities over discrete time units, a discrete sampling method and an interpolated inverse transform sampling method, which can be combined with a recently proposed nonparametric EVSI method to accurately estimate EVSI for collecting survival data.Our general-purpose data-simulation methods greatly reduce the computational burden of the EVSI data-simulation step when we assume treatment effect waning or use flexible survival models. The implementation of our data-simulation methods is identical across all possible survival models and can therefore easily be automated from standard probabilistic decision analyses.