Age, sex, and lung volume dependence of dissolved xenon-129 MRI gas exchange metrics.

PURPOSE: To characterize the dependence of Xe-MRI gas transfer metrics upon age, sex, and lung volume in a group of healthy volunteers. METHODS: Sixty-five subjects with no history of chronic lung disease were assessed with 129Xe-MRI using a four-echo 3D radial spectroscopic imaging sequence and a dose of xenon titrated according to subject height that was inhaled from a lung volume of functional residual capacity (FRC). Imaging was repeated in 34 subjects at total lung capacity (TLC). Regional maps of the fractions of dissolved xenon in red blood cells (RBC), membrane (M), and airspace (Gas) were acquired at an isotropic resolution of 2 cm, from which global averages of the ratios RBC:M, RBC:Gas, and M:Gas were computed. RESULTS: Data from 26 males and 36 females with a median age of 43 y (range: 20-69 y) were of sufficient quality to analyze. Age (p = 0.0006) and sex (p < 0.0001) were significant predictors for RBC:M, and a linear regression showed higher values and steeper decline in males: RBC:M(Males) = -0.00362 × Age + 0.60 (p = 0.01, R2 = 0.25); RBC:M(Females) = -0.00170 × Age + 0.44 (p = 0.02, R2 = 0.15). Similarly, age and sex were significant predictors for RBC:Gas but not for M:Gas. RBC:M, M:Gas and RBC:Gas were significantly lower at TLC than at FRC (plus inhaled volume), with an average 9%, 30% and 35% decrease, respectively. CONCLUSION: Expected age and sex dependence of pulmonary function concurs with 129Xe RBC:M imaging results, demonstrating that these variables must be considered when reporting Xe-MRI metrics. Xenon doses and breathing maneuvers should be controlled due to the strong dependence of Xe-MRI metrics upon lung volume.

A comparison of free amino nitrogen and yeast assimilable nitrogen measurement methods for use in alcoholic fermentation of whey.

The continued, efficient use of whey as a fermentable substrate to produce alcoholic beverages will benefit from improving the measurement of key components relevant to the fermentability of whey. One component of fermentation that is not well studied in whey is the concentration of fermentable nitrogen, either as free amino nitrogen (FAN) or yeast assimilable nitrogen (YAN). Fermentable nitrogen in media is essential for yeast cells to replicate. Insufficient concentrations of FAN or YAN to support the growing yeast population can result in sluggish or "stuck" fermentations. Three common methods of fermentable nitrogen determination were evaluated and compared for use in whey fermentation systems, based on ninhydrin, nitrogen by o-phthalaldehyde (NOPA), and formaldehyde pH (Sørensen titration) values. Each measurement method was evaluated independently using standard addition curves and compared for their overall accuracy using paired t-tests (α = 0.05). Although the formaldehyde pH method showed high precision, it did not measure nitrogen accurately as it overestimated YAN in whey by up to 6 times relative to other tests. The ninhydrin and NOPA methods both showed accuracy for fermentable nitrogen determination in whey, based on analysis of standardized nitrogen sources. We concluded that either the ninhydrin FAN or NOPA YAN method may be used to determine the fermentable nitrogen content in whey. This is expected to improve the ability of producers to use whey as a fermentation medium.

Weakened APC/C activity at mitotic exit drives cancer vulnerability to KIF18A inhibition.

The efficacy of current antimitotic cancer drugs is limited by toxicity in highly proliferative healthy tissues. A cancer-specific dependency on the microtubule motor protein KIF18A therefore makes it an attractive therapeutic target. Not all cancers require KIF18A, however, and the determinants underlying this distinction remain unclear. Here, we show that KIF18A inhibition drives a modest and widespread increase in spindle assembly checkpoint (SAC) signaling from kinetochores which can result in lethal mitotic delays. Whether cells arrest in mitosis depends on the robustness of the metaphase-to-anaphase transition, and cells predisposed with weak basal anaphase-promoting complex/cyclosome (APC/C) activity and/or persistent SAC signaling through metaphase are uniquely sensitive to KIF18A inhibition. KIF18A-dependent cancer cells exhibit hallmarks of this SAC:APC/C imbalance, including a long metaphase-to-anaphase transition, and slow mitosis overall. Together, our data reveal vulnerabilities in the cell division apparatus of cancer cells that can be exploited for therapeutic benefit.

Small-molecule inhibition of kinesin KIF18A reveals a mitotic vulnerability enriched in chromosomally unstable cancers.

Chromosomal instability (CIN) is a hallmark of cancer, caused by persistent errors in chromosome segregation during mitosis. Aggressive cancers like high-grade serous ovarian cancer (HGSOC) and triple-negative breast cancer (TNBC) have a high frequency of CIN and TP53 mutations. Here, we show that inhibitors of the KIF18A motor protein activate the mitotic checkpoint and selectively kill chromosomally unstable cancer cells. Sensitivity to KIF18A inhibition is enriched in TP53-mutant HGSOC and TNBC cell lines with CIN features, including in a subset of CCNE1-amplified, CDK4-CDK6-inhibitor-resistant and BRCA1-altered cell line models. Our KIF18A inhibitors have minimal detrimental effects on human bone marrow cells in culture, distinct from other anti-mitotic agents. In mice, inhibition of KIF18A leads to robust anti-cancer effects with tumor regression observed in human HGSOC and TNBC models at well-tolerated doses. Collectively, our results provide a rational therapeutic strategy for selective targeting of CIN cancers via KIF18A inhibition.

The influence of regional wind patterns on air quality during forest fires near Sydney, Australia.

Particulate pollution from forest fire smoke threatens the health of communities by increasing the occurrence of respiratory illnesses. Wind drives both fire behaviour and smoke dispersal. Understanding regional wind patterns would assist in effectively managing smoke risk. Sydney, Australia is prone to smoke pollution because it has a large population close to fire-prone eucalypt forests. Here we use the self-organising maps (SOM) technique to identify sixteen unique wind classes for the Sydney region from days with active fires, including identifying sea breeze occurrence. We explored differences in PM2.5 levels between classes and between hazard reduction burning (HRB) and wildfire days. For HRB days, classes with the highest PM2.5 mostly had a sea breeze, whereas better air quality days usually had winds aligned across the region (e.g. all westerly). The wind class with the most HRB days had low wind speeds and a sea breeze and was among the worst wind classes for air quality. For wildfire days, days with a sea breeze were also generally of poor air quality but many classes had at least some poor air quality days, most of which were during the 2019-2020 east coast wildfires in New South Wales. Some poor air quality days occurred in wind classes that sent strong plumes directly over air quality stations, spread smoke over a wide area or transported smoke from outside the region. The classes identified may be useful in scheduling HRBs, for example, identifying days with low pollution risk to conduct an HRB, or for assisting in understanding pollution risk and sending health warnings during HRBs and wildfires. Further development of the approach may allow the creation of multi-day classifications for fire managers to plan HRB ignitions over several days to ensure better smoke dispersal. Further research could incorporate other weather predictors or focus on other regions.

Multicenter Standardization of Phase-Resolved Functional Lung MRI in Patients With Suspected Chronic Thromboembolic Pulmonary Hypertension.

BACKGROUND: Detection of pulmonary perfusion defects is the recommended approach for diagnosing chronic thromboembolic pulmonary hypertension (CTEPH). This is currently achieved in a clinical setting using scintigraphy. Phase-resolved functional lung (PREFUL) magnetic resonance imaging (MRI) is an alternative technique for evaluating regional ventilation and perfusion without the use of ionizing radiation or contrast media. PURPOSE: To assess the feasibility and image quality of PREFUL-MRI in a multicenter setting in suspected CTEPH. STUDY TYPE: This is a prospective cohort sub-study. POPULATION: Forty-five patients (64 ± 16 years old) with suspected CTEPH from nine study centers. FIELD STRENGTH/SEQUENCE: 1.5 T and 3 T/2D spoiled gradient echo/bSSFP/T2 HASTE/3D MR angiography (TWIST). ASSESSMENT: Lung signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were compared between study centers with different MRI machines. The contrast between normally and poorly perfused lung areas was examined on PREFUL images. The perfusion defect percentage calculated using PREFUL-MRI (QDPPREFUL ) was compared to QDP from the established dynamic contrast-enhanced MRI technique (QDPDCE ). Furthermore, QDPPREFUL was compared between a patient subgroup with confirmed CTEPH or chronic thromboembolic disease (CTED) to other clinical subgroups. STATISTICAL TESTS: t-Test, one-way analysis of variance (ANOVA), Pearson's correlation. Significance level was 5%. RESULTS: Significant differences in lung SNR and CNR were present between study centers. However, PREFUL perfusion images showed a significant contrast between normally and poorly perfused lung areas (mean delta of normalized perfusion -4.2% SD 3.3) with no differences between study sites (ANOVA: P = 0.065). QDPPREFUL was significantly correlated with QDPDCE (r = 0.66), and was significantly higher in 18 patients with confirmed CTEPH or CTED (57.9 ± 12.2%) compared to subgroups with other causes of PH or with excluded PH (in total 27 patients with mean ± SD QDPPREFUL = 33.9 ± 17.2%). DATA CONCLUSION: PREFUL-MRI could be considered as a non-invasive method for imaging regional lung perfusion in multicenter studies. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 1.

PhysVENeT: a physiologically-informed deep learning-based framework for the synthesis of 3D hyperpolarized gas MRI ventilation.

Functional lung imaging modalities such as hyperpolarized gas MRI ventilation enable visualization and quantification of regional lung ventilation; however, these techniques require specialized equipment and exogenous contrast, limiting clinical adoption. Physiologically-informed techniques to map proton (1H)-MRI ventilation have been proposed. These approaches have demonstrated moderate correlation with hyperpolarized gas MRI. Recently, deep learning (DL) has been used for image synthesis applications, including functional lung image synthesis. Here, we propose a 3D multi-channel convolutional neural network that employs physiologically-informed ventilation mapping and multi-inflation structural 1H-MRI to synthesize 3D ventilation surrogates (PhysVENeT). The dataset comprised paired inspiratory and expiratory 1H-MRI scans and corresponding hyperpolarized gas MRI scans from 170 participants with various pulmonary pathologies. We performed fivefold cross-validation on 150 of these participants and used 20 participants with a previously unseen pathology (post COVID-19) for external validation. Synthetic ventilation surrogates were evaluated using voxel-wise correlation and structural similarity metrics; the proposed PhysVENeT framework significantly outperformed conventional 1H-MRI ventilation mapping and other DL approaches which did not utilize structural imaging and ventilation mapping. PhysVENeT can accurately reflect ventilation defects and exhibits minimal overfitting on external validation data compared to DL approaches that do not integrate physiologically-informed mapping.

Combined inhibition of BCL-2 and MCL-1 overcomes BAX deficiency-mediated resistance of TP53-mutant acute myeloid leukemia to individual BH3 mimetics.

TP53-mutant acute myeloid leukemia (AML) respond poorly to currently available treatments, including venetoclax-based drug combinations and pose a major therapeutic challenge. Analyses of RNA sequencing and reverse phase protein array datasets revealed significantly lower BAX RNA and protein levels in TP53-mutant compared to TP53-wild-type (WT) AML, a finding confirmed in isogenic CRISPR-generated TP53-knockout and -mutant AML. The response to either BCL-2 (venetoclax) or MCL-1 (AMG176) inhibition was BAX-dependent and much reduced in TP53-mutant compared to TP53-WT cells, while the combination of two BH3 mimetics effectively activated BAX, circumventing survival mechanisms in cells treated with either BH3 mimetic, and synergistically induced cell death in TP53-mutant AML and stem/progenitor cells. The BH3 mimetic-driven stress response and cell death patterns after dual inhibition were largely independent of TP53 status and affected by apoptosis induction. Co-targeting, but not individual targeting of BCL-2 and MCL-1 in mice xenografted with TP53-WT and TP53-R248W Molm13 cells suppressed both TP53-WT and TP53-mutant cell growth and significantly prolonged survival. Our results demonstrate that co-targeting BCL-2 and MCL-1 overcomes BAX deficiency-mediated resistance to individual BH3 mimetics in TP53-mutant cells, thus shifting cell fate from survival to death in TP53-deficient and -mutant AML. This concept warrants clinical evaluation.

Longitudinal Lung Function Assessment of Patients Hospitalized With COVID-19 Using 1H and 129Xe Lung MRI.

BACKGROUND: Microvascular abnormalities and impaired gas transfer have been observed in patients with COVID-19. The progression of pulmonary changes in these patients remains unclear. RESEARCH QUESTION: Do patients hospitalized with COVID-19 without evidence of architectural distortion on structural imaging exhibit longitudinal improvements in lung function measured by using 1H and 129Xe MRI between 6 and 52 weeks following hospitalization? STUDY DESIGN AND METHODS: Patients who were hospitalized with COVID-19 pneumonia underwent a pulmonary 1H and 129Xe MRI protocol at 6, 12, 25, and 51 weeks following hospital admission in a prospective cohort study between November 2020 and February 2022. The imaging protocol was as follows: 1H ultra-short echo time, contrast-enhanced lung perfusion, 129Xe ventilation, 129Xe diffusion-weighted, and 129Xe spectroscopic imaging of gas exchange. RESULTS: Nine patients were recruited (age 57 ± 14 [median ± interquartile range] years; six of nine patients were male). Patients underwent MRI at 6 (n = 9), 12 (n = 9), 25 (n = 6), and 51 (n = 8) weeks following hospital admission. Patients with signs of interstitial lung damage were excluded. At 6 weeks, patients exhibited impaired 129Xe gas transfer (RBC to membrane fraction), but lung microstructure was not increased (apparent diffusion coefficient and mean acinar airway dimensions). Minor ventilation abnormalities present in four patients were largely resolved in the 6- to 25-week period. At 12 weeks, all patients with lung perfusion data (n = 6) showed an increase in both pulmonary blood volume and flow compared with 6 weeks, although this was not statistically significant. At 12 weeks, significant improvements in 129Xe gas transfer were observed compared with 6-week examinations; however, 129Xe gas transfer remained abnormally low at weeks 12, 25, and 51. INTERPRETATION: 129Xe gas transfer was impaired up to 1 year following hospitalization in patients who were hospitalized with COVID-19 pneumonia, without evidence of architectural distortion on structural imaging, whereas lung ventilation was normal at 52 weeks.

A hybrid model- and deep learning-based framework for functional lung image synthesis from multi-inflation CT and hyperpolarized gas MRI.

BACKGROUND: Hyperpolarized gas MRI is a functional lung imaging modality capable of visualizing regional lung ventilation with exceptional detail within a single breath. However, this modality requires specialized equipment and exogenous contrast, which limits widespread clinical adoption. CT ventilation imaging employs various metrics to model regional ventilation from non-contrast CT scans acquired at multiple inflation levels and has demonstrated moderate spatial correlation with hyperpolarized gas MRI. Recently, deep learning (DL)-based methods, utilizing convolutional neural networks (CNNs), have been leveraged for image synthesis applications. Hybrid approaches integrating computational modeling and data-driven methods have been utilized in cases where datasets are limited with the added benefit of maintaining physiological plausibility. PURPOSE: To develop and evaluate a multi-channel DL-based method that combines modeling and data-driven approaches to synthesize hyperpolarized gas MRI lung ventilation scans from multi-inflation, non-contrast CT and quantitatively compare these synthetic ventilation scans to conventional CT ventilation modeling. METHODS: In this study, we propose a hybrid DL configuration that integrates model- and data-driven methods to synthesize hyperpolarized gas MRI lung ventilation scans from a combination of non-contrast, multi-inflation CT and CT ventilation modeling. We used a diverse dataset comprising paired inspiratory and expiratory CT and helium-3 hyperpolarized gas MRI for 47 participants with a range of pulmonary pathologies. We performed six-fold cross-validation on the dataset and evaluated the spatial correlation between the synthetic ventilation and real hyperpolarized gas MRI scans; the proposed hybrid framework was compared to conventional CT ventilation modeling and other non-hybrid DL configurations. Synthetic ventilation scans were evaluated using voxel-wise evaluation metrics such as Spearman's correlation and mean square error (MSE), in addition to clinical biomarkers of lung function such as the ventilated lung percentage (VLP). Furthermore, regional localization of ventilated and defect lung regions was assessed via the Dice similarity coefficient (DSC). RESULTS: We showed that the proposed hybrid framework is capable of accurately replicating ventilation defects seen in the real hyperpolarized gas MRI scans, achieving a voxel-wise Spearman's correlation of 0.57 ± 0.17 and an MSE of 0.017 ± 0.01. The hybrid framework significantly outperformed CT ventilation modeling alone and all other DL configurations using Spearman's correlation. The proposed framework was capable of generating clinically relevant metrics such as the VLP without manual intervention, resulting in a Bland-Altman bias of 3.04%, significantly outperforming CT ventilation modeling. Relative to CT ventilation modeling, the hybrid framework yielded significantly more accurate delineations of ventilated and defect lung regions, achieving a DSC of 0.95 and 0.48 for ventilated and defect regions, respectively. CONCLUSION: The ability to generate realistic synthetic ventilation scans from CT has implications for several clinical applications, including functional lung avoidance radiotherapy and treatment response mapping. CT is an integral part of almost every clinical lung imaging workflow and hence is readily available for most patients; therefore, synthetic ventilation from non-contrast CT can provide patients with wider access to ventilation imaging worldwide.

Implementable Deep Learning for Multi-sequence Proton MRI Lung Segmentation: A Multi-center, Multi-vendor, and Multi-disease Study.

BACKGROUND: Recently, deep learning via convolutional neural networks (CNNs) has largely superseded conventional methods for proton (1 H)-MRI lung segmentation. However, previous deep learning studies have utilized single-center data and limited acquisition parameters. PURPOSE: Develop a generalizable CNN for lung segmentation in 1 H-MRI, robust to pathology, acquisition protocol, vendor, and center. STUDY TYPE: Retrospective. POPULATION: A total of 809 1 H-MRI scans from 258 participants with various pulmonary pathologies (median age (range): 57 (6-85); 42% females) and 31 healthy participants (median age (range): 34 (23-76); 34% females) that were split into training (593 scans (74%); 157 participants (55%)), testing (50 scans (6%); 50 participants (17%)) and external validation (164 scans (20%); 82 participants (28%)) sets. FIELD STRENGTH/SEQUENCE: 1.5-T and 3-T/3D spoiled-gradient recalled and ultrashort echo-time 1 H-MRI. ASSESSMENT: 2D and 3D CNNs, trained on single-center, multi-sequence data, and the conventional spatial fuzzy c-means (SFCM) method were compared to manually delineated expert segmentations. Each method was validated on external data originating from several centers. Dice similarity coefficient (DSC), average boundary Hausdorff distance (Average HD), and relative error (XOR) metrics to assess segmentation performance. STATISTICAL TESTS: Kruskal-Wallis tests assessed significances of differences between acquisitions in the testing set. Friedman tests with post hoc multiple comparisons assessed differences between the 2D CNN, 3D CNN, and SFCM. Bland-Altman analyses assessed agreement with manually derived lung volumes. A P value of <0.05 was considered statistically significant. RESULTS: The 3D CNN significantly outperformed its 2D analog and SFCM, yielding a median (range) DSC of 0.961 (0.880-0.987), Average HD of 1.63 mm (0.65-5.45) and XOR of 0.079 (0.025-0.240) on the testing set and a DSC of 0.973 (0.866-0.987), Average HD of 1.11 mm (0.47-8.13) and XOR of 0.054 (0.026-0.255) on external validation data. DATA CONCLUSION: The 3D CNN generated accurate 1 H-MRI lung segmentations on a heterogenous dataset, demonstrating robustness to disease pathology, sequence, vendor, and center. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 1.

Imaging biomarkers of lung ventilation in interstitial lung disease from 129Xe and oxygen enhanced 1H MRI.

PURPOSE: To compare imaging biomarkers from hyperpolarised 129Xe ventilation MRI and dynamic oxygen-enhanced MRI (OE-MRI) with standard pulmonary function tests (PFT) in interstitial lung disease (ILD) patients. To evaluate if biomarkers can separate ILD subtypes and detect early signs of disease resolution or progression. STUDY TYPE: Prospective longitudinal. POPULATION: Forty-one ILD (fourteen idiopathic pulmonary fibrosis (IPF), eleven hypersensitivity pneumonitis (HP), eleven drug-induced ILD (DI-ILD), five connective tissue disease related-ILD (CTD-ILD)) patients and ten healthy volunteers imaged at visit 1. Thirty-four ILD patients completed visit 2 (eleven IPF, eight HP, ten DIILD, five CTD-ILD) after 6 or 26 weeks. FIELD STRENGTH/SEQUENCE: MRI was performed at 1.5 T, including inversion recovery T1 mapping, dynamic MRI acquisition with varying oxygen levels, and hyperpolarised 129Xe ventilation MRI. Subjects underwent standard spirometry and gas transfer testing. ASSESSMENT: Five 1H MRI and two 129Xe MRI ventilation metrics were compared with spirometry and gas transfer measurements. STATISTICAL TEST: To evaluate differences at visit 1 among subgroups: ANOVA or Kruskal-Wallis rank tests with correction for multiple comparisons. To assess the relationships between imaging biomarkers, PFT, age and gender, at visit 1 and for the change between visit 1 and 2: Pearson correlations and multilinear regression models. RESULTS: The global PFT tests could not distinguish ILD subtypes. Percentage ventilated volumes were lower in ILD patients than in HVs when measured with 129Xe MRI (HV 97.4 ± 2.6, CTD-ILD: 91.0 ± 4.8 p = 0.017, DI-ILD 90.1 ± 7.4 p = 0.003, HP 92.6 ± 4.0 p = 0.013, IPF 88.1 ± 6.5 p < 0.001), but not with OE-MRI. 129Xe reported more heterogeneous ventilation in DI-ILD and IPF than in HV, and OE-MRI reported more heterogeneous ventilation in DI-ILD and IPF than in HP or CTD-ILD. The longitudinal changes reported by the imaging biomarkers did not correlate with the PFT changes between visits. DATA CONCLUSION: Neither 129Xe ventilation nor OE-MRI biomarkers investigated in this study were able to differentiate between ILD subtypes, suggesting that ventilation-only biomarkers are not indicated for this task. Limited but progressive loss of ventilated volume as measured by 129Xe-MRI may be present as the biomarker of focal disease progresses. OE-MRI biomarkers are feasible in ILD patients and do not correlate strongly with PFT. Both OE-MRI and 129Xe MRI revealed more spatially heterogeneous ventilation in DI-ILD and IPF.

A Dual-Channel Deep Learning Approach for Lung Cavity Estimation From Hyperpolarized Gas and Proton MRI.

BACKGROUND: Hyperpolarized gas MRI can quantify regional lung ventilation via biomarkers, including the ventilation defect percentage (VDP). VDP is computed from segmentations derived from spatially co-registered functional hyperpolarized gas and structural proton (1 H)-MRI. Although acquired at similar lung inflation levels, they are frequently misaligned, requiring a lung cavity estimation (LCE). Recently, single-channel, mono-modal deep learning (DL)-based methods have shown promise for pulmonary image segmentation problems. Multichannel, multimodal approaches may outperform single-channel alternatives. PURPOSE: We hypothesized that a DL-based dual-channel approach, leveraging both 1 H-MRI and Xenon-129-MRI (129 Xe-MRI), can generate LCEs more accurately than single-channel alternatives. STUDY TYPE: Retrospective. POPULATION: A total of 480 corresponding 1 H-MRI and 129 Xe-MRI scans from 26 healthy participants (median age [range]: 11 [8-71]; 50% females) and 289 patients with pulmonary pathologies (median age [range]: 47 [6-83]; 51% females) were split into training (422 scans [88%]; 257 participants [82%]) and testing (58 scans [12%]; 58 participants [18%]) sets. FIELD STRENGTH/SEQUENCE: 1.5-T, three-dimensional (3D) spoiled gradient-recalled 1 H-MRI and 3D steady-state free-precession 129 Xe-MRI. ASSESSMENT: We developed a multimodal DL approach, integrating 129 Xe-MRI and 1 H-MRI, in a dual-channel convolutional neural network. We compared this approach to single-channel alternatives using manually edited LCEs as a benchmark. We further assessed a fully automatic DL-based framework to calculate VDPs and compared it to manually generated VDPs. STATISTICAL TESTS: Friedman tests with post hoc Bonferroni correction for multiple comparisons compared single-channel and dual-channel DL approaches using Dice similarity coefficient (DSC), average boundary Hausdorff distance (average HD), and relative error (XOR) metrics. Bland-Altman analysis and paired t-tests compared manual and DL-generated VDPs. A P value < 0.05 was considered statistically significant. RESULTS: The dual-channel approach significantly outperformed single-channel approaches, achieving a median (range) DSC, average HD, and XOR of 0.967 (0.867-0.978), 1.68 mm (37.0-0.778), and 0.066 (0.246-0.045), respectively. DL-generated VDPs were statistically indistinguishable from manually generated VDPs (P = 0.710). DATA CONCLUSION: Our dual-channel approach generated LCEs, which could be integrated with ventilated lung segmentations to produce biomarkers such as the VDP without manual intervention. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 1.

Image Phenotyping of Preterm-Born Children Using Hyperpolarized 129Xe Lung Magnetic Resonance Imaging and Multiple-Breath Washout.

Rationale: Preterm birth is associated with low lung function in childhood, but little is known about the lung microstructure in childhood. Objectives: We assessed the differential associations between the historical diagnosis of bronchopulmonary dysplasia (BPD) and current lung function phenotypes on lung ventilation and microstructure in preterm-born children using hyperpolarized 129Xe ventilation and diffusion-weighted magnetic resonance imaging (MRI) and multiple-breath washout (MBW). Methods: Data were available from 63 children (aged 9-13 yr), including 44 born preterm (⩽34 weeks' gestation) and 19 term-born control subjects (⩾37 weeks' gestation). Preterm-born children were classified, using spirometry, as prematurity-associated obstructive lung disease (POLD; FEV1 < lower limit of normal [LLN] and FEV1/FVC < LLN), prematurity-associated preserved ratio of impaired spirometry (FEV1 < LLN and FEV1/FVC ⩾ LLN), preterm-(FEV1 ⩾ LLN) and term-born control subjects, and those with and without BPD. Ventilation heterogeneity metrics were derived from 129Xe ventilation MRI and SF6 MBW. Alveolar microstructural dimensions were derived from 129Xe diffusion-weighted MRI. Measurements and Main Results: 129Xe ventilation defect percentage and ventilation heterogeneity index were significantly increased in preterm-born children with POLD. In contrast, mean 129Xe apparent diffusion coefficient, 129Xe apparent diffusion coefficient interquartile range, and 129Xe mean alveolar dimension interquartile range were significantly increased in preterm-born children with BPD, suggesting changes of alveolar dimensions. MBW metrics were all significantly increased in the POLD group compared with preterm- and term-born control subjects. Linear regression confirmed the differential effects of obstructive disease on ventilation defects and BPD on lung microstructure. Conclusion: We show that ventilation abnormalities are associated with POLD, and BPD in infancy is associated with abnormal lung microstructure.

Systematic in situ hydration neutron reflectometry study on Nafion thin films.

Reported herein is a neutron reflectometry (NR) study on hydrated Nafion thin films (∼30 nm) on a silicon substrate with native oxide. The Nafion morphology is investigated systematically across the whole relative humidity range using both H2O and D2O vapours to enable a comparative study. By utilising this systematic approach two key results have been obtained. The first is that by leveraging the strong positive scattering signal from the D2O vapour, a complete and systematic water adsorption isotherm (Type II) for a Nafion thin film is produced. Utilising the slight negative scattering signal of the H2O enabled the quantification of the hydration dependent evolution of the formation of Nafion/water lamellae near the substrate surface. The number of lamellae layers increases continuously with hydration, and does not form abruptly. We also report the effects of swelling on the thin films across the relative humidity ranges. The work reported should prove useful in quantifying other hydration dependent properties of Nafion thin films such as conductivity and understanding Nafion/semiconductor based devices, as well as showcasing a NR methodology for other hydrophilic polymers.

Large-scale investigation of deep learning approaches for ventilated lung segmentation using multi-nuclear hyperpolarized gas MRI.

Respiratory diseases are leading causes of mortality and morbidity worldwide. Pulmonary imaging is an essential component of the diagnosis, treatment planning, monitoring, and treatment assessment of respiratory diseases. Insights into numerous pulmonary pathologies can be gleaned from functional lung MRI techniques. These include hyperpolarized gas ventilation MRI, which enables visualization and quantification of regional lung ventilation with high spatial resolution. Segmentation of the ventilated lung is required to calculate clinically relevant biomarkers. Recent research in deep learning (DL) has shown promising results for numerous segmentation problems. Here, we evaluate several 3D convolutional neural networks to segment ventilated lung regions on hyperpolarized gas MRI scans. The dataset consists of 759 helium-3 (3He) or xenon-129 (129Xe) volumetric scans and corresponding expert segmentations from 341 healthy subjects and patients with a wide range of pathologies. We evaluated segmentation performance for several DL experimental methods via overlap, distance and error metrics and compared them to conventional segmentation methods, namely, spatial fuzzy c-means (SFCM) and K-means clustering. We observed that training on combined 3He and 129Xe MRI scans using a 3D nn-UNet outperformed other DL methods, achieving a mean ± SD Dice coefficient of 0.963 ± 0.018, average boundary Hausdorff distance of 1.505 ± 0.969 mm, Hausdorff 95th percentile of 5.754 ± 6.621 mm and relative error of 0.075 ± 0.039. Moreover, limited differences in performance were observed between 129Xe and 3He scans in the testing set. Combined training on 129Xe and 3He yielded statistically significant improvements over the conventional methods (p < 0.0001). In addition, we observed very strong correlation and agreement between DL and expert segmentations, with Pearson correlation of 0.99 (p < 0.0001) and Bland-Altman bias of - 0.8%. The DL approach evaluated provides accurate, robust and rapid segmentations of ventilated lung regions and successfully excludes non-lung regions such as the airways and artefacts. This approach is expected to eliminate the need for, or significantly reduce, subsequent time-consuming manual editing.

Acute extracorporeal shockwave lithotripsy for ureteric stones - 7-years' experience from a busy district general hospital.

OBJECTIVES: To demonstrate the efficacy and cost-effectiveness of acute extracorporeal shockwave lithotripsy (ESWL) for ureteric stones we present our experience of ESWL in 530 ureteric stone cases, in the largest UK series we are aware of to date. ESWL is underutilised in ureteric stone management. The Getting It Right First Time (GIRFT) report showed just four units nationally treated >10% of acute ureteric stones with ESWL. Despite guideline recommendations as a first-line treatment option, few large volume studies have been published. PATIENTS AND METHODS: Retrospective review of prospectively collected data between December 2012 and February 2020 was performed. Data relating to patient demographics, stone characteristics, skin-to-stone distance, and treatment failure were collected. Cost analysis was conducted by the Trust's surgical financial manager. Multivariable analyses were performed to assess for predictors of ESWL success. RESULTS: A success rate of 68% (95% confidence interval 64%-72%) at 6 weeks was observed (n = 530). The median (interquartile range) number of treatment sessions was 2 (1, 2). Stone diameter was observed to be a predictor of ESWL success. The small number of stones treated of >13 mm or >1250 HU had an ~50% chance of successful treatment. Acute ureteric ESWL was less costly than acute ureterorenoscopy, consistent with findings from previous NHS studies. CONCLUSION: Acute ESWL is a safe, reliable, and financially viable treatment option for a wider spectrum of patients than reflected in international guidelines based on our large, heterogenous series. In the coronavirus disease 2019 (COVID-19) era, with theatre access reduced and concerns over aerosol generating procedures, acute ESWL remains an attractive first-line treatment option.

A dual center and dual vendor comparison study of automated perfusion-weighted phase-resolved functional lung magnetic resonance imaging with dynamic contrast-enhanced magnetic resonance imaging in patients with cystic fibrosis.

For sensitive diagnosis and monitoring of pulmonary disease, ionizing radiation-free imaging methods are of great importance. A noncontrast and free-breathing proton magnetic resonance imaging (MRI) technique for assessment of pulmonary perfusion is phase-resolved functional lung (PREFUL) MRI. Since there is no validation of PREFUL MRI across different centers and scanners, the purpose of this study was to compare perfusion-weighted PREFUL MRI with the well-established dynamic contrast-enhanced (DCE) MRI across two centers on scanners from two different vendors. Sixteen patients with cystic fibrosis (CF) (Center 1: 10 patients; Center 2: 6 patients) underwent PREFUL and DCE MRI at 1.5T in the same imaging session. Normalized perfusion-weighted values and perfusion defect percentage (QDP) values were calculated for the whole lung and three central slices (dorsal, central, ventral of the carina). Obtained parameters were compared using Pearson correlation, Spearman correlation, Bland-Altman analysis, Wilcoxon signed-rank test, and Wilcoxon rank-sum test. Moderate-to-strong correlations between normalized perfusion-weighted PREFUL and DCE values were found (posterior slice: r = 0.69, p < 0.01). Spatial overlap of PREFUL and DCE QDP maps showed an agreement of 79.4% for the whole lung. Further, spatial overlap values of Center 1 were not significantly different to those of Center 2 for the three central slices (p > 0.07). The feasibility of PREFUL MRI across two different centers and two different vendors was shown in patients with CF and obtained results were in agreement with DCE MRI.

Maximal Activation of Apoptosis Signaling by Cotargeting Antiapoptotic Proteins in BH3 Mimetic-Resistant AML and AML Stem Cells.

MCL-1 is known to play a major role in resistance to BCL-2 inhibition, but the contribution of other BCL-2 family proteins has not been fully explored. We, here, demonstrate the ineffectiveness of MCL-1 inhibitor AMG176 in venetoclax-resistant, and conversely, of venetoclax in AMG176-resistant acute myelogenous leukemia (AML). Like cells with acquired resistance to venetoclax, cells with acquired resistance to AMG176 express increased MCL-1. Both cells with acquired resistance to venetoclax and to AMG176 express increased levels of BCL-2 and BCL-2A1, decreased BAX, and/or altered levels of other BCL-2 proteins. Cotargeting BCL-2 and MCL-1 was highly synergistic in AML cell lines with intrinsic or acquired resistance to BH3 mimetics or engineered to genetically overexpress BCL-2 or BCL-2A1 or downregulate BAX. The combination effectively eliminated primary AML blasts and stem/progenitor cells resistant to or relapsed after venetoclax-based therapy irrespective of mutations and cytogenetic abnormalities. Venetoclax and AMG176 combination markedly suppressed antiapoptotic BCL-2 proteins and AML stem/progenitor cells and dramatically extended mouse survival (median 336 vs. control 126 days; P < 0.0001) in a patient-derived xenograft (PDX) model developed from a venetoclax/hypomethylating agent therapy-resistant patient with AML. However, decreased BAX levels in the bone marrow residual leukemia cells after 4-week combination treatment may represent a resistance mechanism that contributed to their survival. Enhanced antileukemia activity was also observed in a PDX model of monocytic AML, known to be resistant to venetoclax therapy. Our results support codependence on multiple antiapoptotic BCL-2 proteins and suppression of BAX as mechanisms of AML resistance to individual BH3 mimetics. Cotargeting of MCL-1 and BCL-2 eliminates otherwise apoptosis-resistant cells.