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MRI annotation using an inversion-based preprocessing for CT model adaptation.
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323921.pdf (Publisher’s version ) (Open Access)BACKGROUND: Annotating new classes in MRI images is time-consuming. Refining presegmented structures can accelerate this process. Many target classes lacking in MRI are supported by computed tomography (CT) models, but translating MRI to synthetic CT images is challenging. We demonstrate that CT segmentation models can create accurate MRI presegmentations, with or without image inversion. MATERIALS AND METHODS: We retrospectively investigated the performance of two CT-trained models on MRI images: a general multiclass model (TotalSegmentator); and a specialized renal tumor model trained in-house. Both models were applied to 100 T1-weighted (T1w) and 100 T2-weighted fat-saturated (T2wfs) MRI sequences from 100 patients (50 male). Segmentation quality was evaluated on both raw and intensity-inverted sequences using Dice similarity coefficients (DSC), with reference annotations comprising manual kidney tumor annotations and automatically generated segmentations for 24 abdominal structures. RESULTS: Segmentation quality varied by MRI sequence and anatomical structure. Both models accurately segmented kidneys in T2wfs sequences without preprocessing (TotalSegmentator DSC 0.60), but TotalSegmentator failed to segment blood vessels and muscles. In T1w sequences, intensity inversion significantly improved TotalSegmentator performance, increasing the mean DSC across 24 structures from 0.04 to 0.56 (p < 0.001). Kidney tumor segmentation demonstrated poor performance in T2wfs sequences regardless of preprocessing. In T1w sequences, inversion improved tumor segmentation DSC from 0.04 to 0.42 (p < 0.001). CONCLUSION: CT-trained models can generalize to MRI when supported by image augmentation. Inversion preprocessing enabled segmentation of renal cell carcinoma in T1w MRI using a CT-trained model. CT models might be transferable to the MRI domain. RELEVANCE STATEMENT: CT-trained artificial intelligence models can be adapted for MRI segmentation using simple preprocessing, potentially reducing manual annotation efforts and accelerating the development of AI-assisted tools for MRI analysis in research and future clinical practice. KEY POINTS: CT segmentation models can create presegmentations for many structures in MRI scans. T1w MRI scans require an additional inversion step before segmenting with a CT model. Results were consistent for a large multiclass model (i.e., TotalSegmentator) and a smaller model for renal cell carcinoma
Greater fatigue is more strongly associated with reduced reward sensitivity in the long-term phase of coronavirus disease (COVID-19) than in the early phase
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321514.pdf (Publisher’s version ) (Open Access)Background
Fatigue and depressive mood is inherent to acute disease, but a substantial group of people report persisting disabling fatigue and depressive symptoms long after a COVID-19 infection. Infections have been shown to change decisions about engaging in effortful and rewarding activities, but it is currently unclear whether fatigue and depressive symptoms are similarly associated with decision making during early and persistent phases after a COVID-19 infection. Here, we investigated whether fatigue and depressive mood are associated with altered weighting of reward and effort in decision making at different timepoints after COVID-19 infection.
Methods
We conducted an online cross-sectional study between March 2021 and March 2022, in which 242 participants (18–65 years) with COVID-19 12 weeks ago (n = 81), or no prior COVID-19 (n = 90; self-reported) performed an effort-based decision-making task. In this task, participants accepted or rejected offers in which they could exert physical effort (ticking boxes on screen, 5 levels) to gain rewards (money to be gained in a voucher-lottery, 5 levels). State fatigue and depressive mood were measured with the Profile of Mood States (POMS) prior to the task. We used mixed binomial regression analysis to test whether fatigue and depressive mood were related to acceptance rates for reward and effort levels and whether this differed between the groups.
Results
Compared with no COVID-19 and COVID-19 12 weeks group reported higher state fatigue (mean ± SD: 20 ± 7 vs. 14 ± 7 and 12 ± 6 POMS-score, respectively; both p 12 weeks group, fatigue was more negatively associated with reward sensitivity compared to the COVID-19 12 weeks group (Age∗Reward: OR 0.30 (95 %CI 0.19, 0.48), p < 0.001; BMI∗Reward: OR 1.43 (95 %CI 1.01, 2.00), p = 0.047); Lifestyle∗Reward: OR 1.50 (95 %CI 1.06, 2.14), p = 0.022; Worrying∗Reward: OR 0.59 (95 %CI 0.38, 0.94), p = 0.025, respectively).
Conclusion
The finding that fatigue is related to lower reward sensitivity > 12 weeks after COVID-19 suggests potential reward deficits in post-COVID-19 fatigue. Moreover, higher age, unhealthy lifestyle, and worrying during the early phase of COVID-19 are potential risk factors for developing lower reward sensitivity. These findings are in line with previous observations that long-term inflammation induces dysregulations in neural reward processing, which should be investigated in future studies.14 p
Nitric oxide-forming nitrite reductases in the anaerobic ammonium oxidizer Kuenenia stuttgartiensis
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324369.pdf (Publisher’s version ) (Open Access)18 p
Prediction of cell states and key transcription factors of the human cornea through integrated single-cell omics analyses.
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322455.pdf (Publisher’s version ) (Open Access)The cornea, a transparent tissue composed of multiple layers, allows light to enter the eye. Several single-cell RNA-seq (scRNA-seq) analyses have been performed to explore the cell states and to understand the cellular composition of the human cornea. However, inconsistences in cell state annotations between these studies complicate the application of these findings in corneal studies. To address this, we integrated scRNA-seq data from four published studies and created a human corneal cell state meta-atlas. This meta-atlas was subsequently evaluated in two applications. First, we developed a machine learning pipeline cPredictor, using the human corneal cell state meta-atlas as input, to annotate corneal cell states. We demonstrated the accuracy of cPredictor and its ability to identify novel marker genes and rare cell states in the human cornea. Furthermore, cPredictor revealed the differences of the cell states between pluripotent stem cell-derived corneal organoids and the human cornea. Second, we integrated the scRNA-seq-based cell state meta-atlas with chromatin accessibility data, conducting motif-focused and gene regulatory network analyses. These approaches identified distinct transcription factors (TFs) driving cell states of the human cornea. The novel marker genes and TFs were validated by immunohistochemistry. Overall, this study offers a reliable and accessible reference for profiling corneal cell states, which facilitates future research in cornea development, disease, and regeneration.01 augustus 202
Induction therapy with BRAF/MEK inhibitors versus upfront ipilimumab/nivolumab in poor prognostic advanced melanoma.
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325429.pdf (Publisher’s version ) (Open Access)AIM: BRAF/MEK inhibitor induction therapy (BRAF/MEK-i) followed by ipilimumab/nivolumab (IPI/NIVO) did not show benefit over upfront IPI/NIVO in unselected advanced melanoma patients in clinical trial setting. We investigated BRAF/MEK-i in subgroups of patients with advanced melanoma and poor prognostic characteristics. METHODS: Patients with BRAF-mutant advanced melanoma treated with BRAF/MEK inhibitors (500U/L, symptomatic and asymptomatic brain metastases (BMs), liver metastases, ≥ 3 metastatic sites, and ECOG PS ≥ 2). RESULTS: We included 709 patients (187 BRAF/MEK-i and 522 upfront IPI/NIVO). In the matched cohort (n = 280), median PFS and OS were not statistically significantly different: 6.5 (95 %CI 5.3-8.1) and 20.0 (95 %CI 17.2-35.3) months for BRAF/MEK-i vs. 6.6 (95 %CI 4.3-11.3) and 54.0 (95 %CI 21.7-64.4) months for upfront IPI/NIVO. Upfront IPI/NIVO showed significantly improved PFS compared to BRAF/MEK-i for asymptomatic BMs (median 9.4 months (95 %CI 6.3-28.2) vs. 4.8 months (95 %CI 4.1-5.3); p < 0.01), and significantly improved OS for asymptomatic BMs (median 60.4 months (95 %CI 39.1-NR) vs. 16.2 months (95 %CI 11.7-31.8); p < 0.01), liver metastases (median 49.8 months (95 %CI 32.1-60.3) vs. 14.1 months (95 %CI 12.1-20.5); p < 0.01), LDH 250-500 U/L (median 52.7 months (95 %CI 35.1-NR) vs. 20.1 months (95 %CI 13.9-35.5); p = 0.03), and ≥ 3 metastatic sites (median 54.0 months (95 %CI 39.1-NR) vs. 16.7 months (95 %CI 13.8-24.8); p < 0.01). CONCLUSIONS: BRAF/MEK-i showed no significant benefit over upfront IPI/NIVO in matched and stratified analyses according to prognostic characteristics
'Une lumiere qui ecarte les nuages': Bibliometric Perspectives on Lucretius's Eighteenth-Century Modernity
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Glucose variability measured by continuous glucose monitoring is associated with skin autofluorescence: the Maastricht Study.
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323034.pdf (Publisher’s version ) (Open Access)AIMS/HYPOTHESIS: Glucose variability in people with type 2 diabetes has been associated with increased risk of CVD, and AGEs might be an underlying mechanism. Therefore, this study investigates associations of glucose variability with AGEs in the skin in people with and without impaired fasting glucose, impaired glucose tolerance or diabetes. METHODS: We used data from the Maastricht Study, a population-based cohort study. Glucose variability and AGEs in skin were measured by continuous glucose monitoring (CGM) and skin autofluorescence (SAF), respectively. Multiple linear regression was used to test the association of CGM-metrics CV and SD with SAF and adjusted for age, sex, CVD risk factors, nutritional factors and educational level. Interaction analysis was used to test the effect of glucose metabolism status on the association of CV and SD with SAF. RESULTS: We included 795 participants (mean ± SD age 59 ± 8.7 years; 49% were female). Glucose metabolism status was stratified into normal glucose metabolism (n = 459), prediabetes (n = 174) and type 2 diabetes (n = 162). Individuals with type 2 diabetes had higher values of SAF (mean ± SD 2.3 ± 0.6 arbitrary units [AU]) than those with prediabetes (2.1 ± 0.4 AU, p = 0.014) and normal glucose metabolism (2.0 ± 0.4 AU, p = 0.007). In the cohort, both SD (0.152 AU [IQR 0.088-0.217]) and CV (0.014 AU [IQR 0.005-0.017]) were significantly associated with SAF in fully adjusted analyses. Glucose metabolism status did not modify the associations of SD and CV with SAF. CONCLUSIONS/INTERPRETATION: A higher glucose variability is associated with higher levels of SAF, suggesting that glucose variability plays a role in the formation of AGEs.01 september 202
Geopolitiek en prudentieel toezicht. Geopolitieke risico’s in het prudentieel toezicht
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Science and Education in the Age of AI
No full textItem does not contain fulltextScience Cafe, 11 juni 202
Deploying projected utility to predict health behaviour in health economics: a quantitative study.
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322099.pdf (Publisher’s version ) (Open Access)Expected utility is increasingly deployed as a predictor of health behaviour within the broader domain of health economics and health sciences in general. However, research shows that this concept only explains limited variance in health behaviour. This limited explained variance is often attributed to the questionable theoretical axioms underlying the concept. Due to these limitations it was hypothesised that the concept of utility should not be conceptualized in terms of preferences for future health states (expected utility), but as realistic approximations of future health states (projected utility). Therefore, this study examines whether deployment of projected utility separately or in combination with expected utility enhances predictions of health behaviour as compared to expected utility. Online questionnaires were disseminated among a nationally representative panel of Dutch citizens (N = 2,550). The questionnaire encompassed items capturing demographic characteristics alongside instruments measuring expected utility, projected utility and health behaviour. Data analysis entailed descriptive, reliability, validity and model statistics. The results suggest that projected utility has a larger significant direct effect on and explains more variance in health behaviour than expected utility. The results subsequently indicate that expected utility and projected utility combined have a larger significant direct effect on and explain more variance in health behaviour than each type of utility separately. Health economists, policy makers and other public health practitioners are well advised to at least consider the separate or even combined deployment of projected utility in health economics in order to enhance predictions of health behaviour