Invasive Fungal Diseases in Adult Patients in Intensive Care Unit (FUNDICU): 2024 consensus definitions from ESGCIP, EFISG, ESICM, ECMM, MSGERC, ISAC, and ISHAM.

PURPOSE: The aim of this document was to develop standardized research definitions of invasive fungal diseases (IFD) in non-neutropenic, adult patients without classical host factors for IFD, admitted to intensive care units (ICUs). METHODS: After a systematic assessment of the diagnostic performance for IFD in the target population of already existing definitions and laboratory tests, consensus definitions were developed by a panel of experts using the RAND/UCLA appropriateness method. RESULTS: Standardized research definitions were developed for proven invasive candidiasis, probable deep-seated candidiasis, proven invasive aspergillosis, probable invasive pulmonary aspergillosis, and probable tracheobronchial aspergillosis. The limited evidence on the performance of existing definitions and laboratory tests for the diagnosis of IFD other than candidiasis and aspergillosis precluded the development of dedicated definitions, at least pending further data. The standardized definitions provided in the present document are aimed to speed-up the design, and increase the feasibility, of future comparative research studies.

Subspace corrected relevance learning with application in neuroimaging.

In machine learning, data often comes from different sources, but combining them can introduce extraneous variation that affects both generalization and interpretability. For example, we investigate the classification of neurodegenerative diseases using FDG-PET data collected from multiple neuroimaging centers. However, data collected at different centers introduces unwanted variation due to differences in scanners, scanning protocols, and processing methods. To address this issue, we propose a two-step approach to limit the influence of center-dependent variation on the classification of healthy controls and early vs. late-stage Parkinson's disease patients. First, we train a Generalized Matrix Learning Vector Quantization (GMLVQ) model on healthy control data to identify a "relevance space" that distinguishes between centers. Second, we use this space to construct a correction matrix that restricts a second GMLVQ system's training on the diagnostic problem. We evaluate the effectiveness of this approach on the real-world multi-center datasets and simulated artificial dataset. Our results demonstrate that the approach produces machine learning systems with reduced bias - being more specific due to eliminating information related to center differences during the training process - and more informative relevance profiles that can be interpreted by medical experts. This method can be adapted to similar problems outside the neuroimaging domain, as long as an appropriate "relevance space" can be identified to construct the correction matrix.

Segmentation-Based Analysis of T2- and T1-Weighted Dynamic Magnetic Resonance Images Provides Adequate Observer Agreement in the Evaluation of Interstitial Lung Disease.

OBJECTIVE: The aim of the study is to quantify observer agreement in the magnetic resonance imaging (MRI) classification of inflammatory or fibrotic interstitial lung disease (ILD). METHODS: Our study is a preliminary analysis of a larger prospective cohort. The MRI images of 18 patients with ILD (13 females; mean age, 65 years) were acquired in a 1.5 T scanner and included axial fat-saturated T2-weighted (T2-WI, n = 18) and coronal fat-saturated T1-weighted images before and 1, 3, 5, and 10 minutes after gadolinium administration (n = 16). The MRI studies were evaluated with 2 different methods: a qualitative evaluation (visual assessment and measurement of few regions of interest; evaluations were performed independently by 5 radiologists and 3 times by 1 radiologist) and a segmentation-based analysis with software extraction of signal intensity values (evaluations were performed independently by 2 radiologists and twice by 1 radiologist). Interstitial lung disease was classified as inflammatory or fibrotic, based on previously described imaging criteria. RESULTS: Regarding the qualitative evaluation, intraobserver agreement was excellent (κ = 0.92, P < 0.05) for T2-WI and fair (κ = 0.29, P < 0.05) for T1 dynamic study, while interobserver agreement was moderate (κ = 0.56, P < 0.05) and poor (κ = 0.11, P = 0.18), respectively. In contrast, upon segmentation-based analysis, intraobserver and interobserver agreement were excellent for T2-WI (κ = 0.886, P < 0.001; κ = 1.00, P < 0.001; respectively); for T1-WI, intraobserver agreement was excellent (κ = 0.87, P < 0.05) and interobserver agreement was good (κ = 0.75, P < 0.05). CONCLUSIONS: Segmentation-based MRI analysis is more reproducible than a qualitative evaluation with visual assessment and measurement of few regions of interest.

Lung Magnetic Resonance Imaging for Prediction of Progression in Patients With Nonidiopathic Pulmonary Fibrosis Interstitial Lung Disease: A Pilot Study.

PURPOSE: Correlate magnetic resonance imaging (MRI) parameters at baseline with disease progression in nonidiopathic pulmonary fibrosis interstitial lung disease (ILD). MATERIALS AND METHODS: Prospective observational cohort study, in which patients with non-idiopathic pulmonary fibrosis ILD underwent MRI at baseline (1.5 T). T2-weighted images (T2-WI) were acquired by axial free-breathing respiratory-gated fat-suppressed "periodically rotated overlapping parallel lines with enhanced reconstruction" and T1-weighted images (T1-WI) by coronal end-expiratory breath-hold fat-suppressed "volumetric interpolated breath-hold examination" sequences, before and at time points T1, T3, T5, and T10 minutes after gadolinium administration. After MRI segmentation, signal intensity values were extracted by dedicated software. Percentage of the ILD volume and a ratio between signal intensity of ILD (SIILD) and normal lung (SInormal lung) were calculated for T2-WI; percentage of signal intensity (%SI) at each time point, time to peak enhancement, and percent relative enhancement of ILD in comparison with normal lung (%SIILD/normal lung) were calculated for T1-WI. MRI parameters at baseline were correlated with diagnosis of disease progression and variation in percent predicted forced vital capacity (%FVC) and diffusing capacity of the lung for carbon monoxide after 12 months. RESULTS: Comprehensive MRI evaluation (T2-WI and T1-WI) was performed in 21 of the 25 patients enrolled (68% females; mean age: 62.6 y). Three of the 24 patients who completed follow-up fulfilled criteria for disease progression. Baseline T2-WI SIILD/SInormal lung was higher for the progression group (P = 0.052). T2-WI SIILD/SInormal lung and T1-WI %SIILD/normal lung at T1 were positively correlated with the 12-month variation in %FVC (r = 0.495, P = 0.014 and r = 0.489, P= 0.034, respectively). CONCLUSIONS: Baseline MRI parameters correlate with %FVC decline after 12 months.

Comparison of univariate and multivariate analyses for brain [18F]FDG PET data in α-synucleinopathies.

BACKGROUND: Brain imaging with [18F]FDG-PET can support the diagnostic work-up of patients with α-synucleinopathies. Validated data analysis approaches are necessary to evaluate disease-specific brain metabolism patterns in neurodegenerative disorders. This study compared the univariate Statistical Parametric Mapping (SPM) single-subject procedure and the multivariate Scaled Subprofile Model/Principal Component Analysis (SSM/PCA) in a cohort of patients with α-synucleinopathies. METHODS: We included [18F]FDG-PET scans of 122 subjects within the α-synucleinopathy spectrum: Parkinson's Disease (PD) normal cognition on long-term follow-up (PD - low risk to dementia (LDR); n = 28), PD who developed dementia on clinical follow-up (PD - high risk of dementia (HDR); n = 16), Dementia with Lewy Bodies (DLB; n = 67), and Multiple System Atrophy (MSA; n = 11). We also included [18F]FDG-PET scans of isolated REM sleep behaviour disorder (iRBD; n = 51) subjects with a high risk of developing a manifest α-synucleinopathy. Each [18F]FDG-PET scan was compared with 112 healthy controls using SPM procedures. In the SSM/PCA approach, we computed the individual scores of previously identified patterns for PD, DLB, and MSA: PD-related patterns (PDRP), DLBRP, and MSARP. We used ROC curves to compare the diagnostic performances of SPM t-maps (visual rating) and SSM/PCA individual pattern scores in identifying each clinical condition across the spectrum. Specifically, we used the clinical diagnoses ("gold standard") as our reference in ROC curves to evaluate the accuracy of the two methods. Experts in movement disorders and dementia made all the diagnoses according to the current clinical criteria of each disease (PD, DLB and MSA). RESULTS: The visual rating of SPM t-maps showed higher performance (AUC: 0.995, specificity: 0.989, sensitivity 1.000) than PDRP z-scores (AUC: 0.818, specificity: 0.734, sensitivity 1.000) in differentiating PD-LDR from other α-synucleinopathies (PD-HDR, DLB and MSA). This result was mainly driven by the ability of SPM t-maps to reveal the limited or absent brain hypometabolism characteristics of PD-LDR. Both SPM t-maps visual rating and SSM/PCA z-scores showed high performance in identifying DLB (DLBRP = AUC: 0.909, specificity: 0.873, sensitivity 0.866; SPM t-maps = AUC: 0.892, specificity: 0.872, sensitivity 0.910) and MSA (MSARP: AUC: 0.921, specificity: 0.811, sensitivity 1.000; SPM t-maps: AUC: 1.000, specificity: 1.000, sensitivity 1.000) from other α-synucleinopathies. PD-HDR and DLB were comparable for the brain hypo and hypermetabolism patterns, thus not allowing differentiation by SPM t-maps or SSM/PCA. Of note, we found a gradual increase of PDRP and DLBRP expression in the continuum from iRBD to PD-HDR and DLB, where the DLB patients had the highest scores. SSM/PCA could differentiate iRBD from DLB, reflecting specifically the differences in disease staging and severity (AUC: 0.938, specificity: 0.821, sensitivity 0.941). CONCLUSIONS: SPM-single subject maps and SSM/PCA are both valid methods in supporting diagnosis within the α-synucleinopathy spectrum, with different strengths and pitfalls. The former reveals dysfunctional brain topographies at the individual level with high accuracy for all the specific subtype patterns, and particularly also the normal maps; the latter provides a reliable quantification, independent from the rater experience, particularly in tracking the disease severity and staging. Thus, our findings suggest that differences in data analysis approaches exist and should be considered in clinical settings. However, combining both methods might offer the best diagnostic performance.

Hepatic Global Transcriptomic Profiles of Holstein Cows According to Parity Reveal Age-Related Changes in Early Lactation.

Cows can live for over 20 years, but their productive lifespan averages only around 3 years after first calving. Liver dysfunction can reduce lifespan by increasing the risk of metabolic and infectious disease. This study investigated the changes in hepatic global transcriptomic profiles in early lactation Holstein cows in different lactations. Cows from five herds were grouped as primiparous (lactation number 1, PP, 534.7 ± 6.9 kg, n = 41), or multiparous with lactation numbers 2-3 (MP2-3, 634.5 ± 7.5 kg, n = 87) or 4-7 (MP4-7, 686.6 ± 11.4 kg, n = 40). Liver biopsies were collected at around 14 days after calving for RNA sequencing. Blood metabolites and milk yields were measured, and energy balance was calculated. There were extensive differences in hepatic gene expression between MP and PP cows, with 568 differentially expressed genes (DEGs) between MP2-3 and PP cows, and 719 DEGs between MP4-7 and PP cows, with downregulated DEGs predominating in MP cows. The differences between the two age groups of MP cows were moderate (82 DEGs). The gene expression differences suggested that MP cows had reduced immune functions compared with the PP cows. MP cows had increased gluconeogenesis but also evidence of impaired liver functionality. The MP cows had dysregulated protein synthesis and glycerophospholipid metabolism, and impaired genome and RNA stability and nutrient transport (22 differentially expressed solute carrier transporters). The genes associated with cell cycle arrest, apoptosis, and the production of antimicrobial peptides were upregulated. More surprisingly, evidence of hepatic inflammation leading to fibrosis was present in the primiparous cows as they started their first lactation. This study has therefore shown that the ageing process in the livers of dairy cows is accelerated by successive lactations and increasing milk yields. This was associated with evidence of metabolic and immune disorders together with hepatic dysfunction. These problems are likely to increase involuntary culling, thus reducing the average longevity in dairy herds.

Occipital hypometabolism is a risk factor for conversion to Parkinson's disease in isolated REM sleep behaviour disorder.

PURPOSE: Isolated REM sleep behaviour disorder (iRBD) patients are at high risk of developing clinical syndromes of the α-synuclein spectrum. Progression markers are needed to determine the neurodegenerative changes and to predict their conversion. Brain imaging with 18F-FDG PET in iRBD is promising, but longitudinal studies are scarce. We investigated the regional brain changes in iRBD over time, related to phenoconversion. METHODS: Twenty iRBD patients underwent two consecutive 18F-FDG PET brain scans and clinical assessments (3.7 ± 0.6 years apart). Seventeen patients also underwent 123I-MIBG and 123I-FP-CIT SPECT scans at baseline. Four subjects phenoconverted to Parkinson's disease (PD) during follow-up. 18F-FDG PET scans were compared to controls with a voxel-wise single-subject procedure. The relationship between regional brain changes in metabolism and PD-related pattern scores (PDRP) was investigated. RESULTS: Individual hypometabolism t-maps revealed three scenarios: (1) normal 18F-FDG PET scans at baseline and follow-up (N = 10); (2) normal scans at baseline but occipital or occipito-parietal hypometabolism at follow-up (N = 4); (3) occipital hypometabolism at baseline and follow-up (N = 6). All patients in the last group had pathological 123I-MIBG and 123I-FP-CIT SPECT. iRBD converters (N = 4) showed occipital hypometabolism at baseline (third scenario). At the group level, hypometabolism in the frontal and occipito-parietal regions and hypermetabolism in the cerebellum and limbic regions were progressive over time. PDRP z-scores increased over time (0.54 ± 0.36 per year). PDRP expression was driven by occipital hypometabolism and cerebellar hypermetabolism. CONCLUSIONS: Our results suggest that occipital hypometabolism at baseline in iRBD implies a short-term conversion to PD. This might help in stratification strategies for disease-modifying trials.

Validation of the REM behaviour disorder phenoconversion-related pattern in an independent cohort.

BACKGROUND: A brain glucose metabolism pattern related to phenoconversion in patients with idiopathic/isolated REM sleep behaviour disorder (iRBDconvRP) was recently identified. However, the validation of the iRBDconvRP in an external, independent group of iRBD patients is needed to verify the reproducibility of such pattern, so to increase its importance in clinical and research settings. The aim of this work was to validate the iRBDconvRP in an independent group of iRBD patients. METHODS: Forty iRBD patients (70 ± 5.59 years, 19 females) underwent brain [18F]FDG-PET in Seoul National University. Thirteen patients phenoconverted at follow-up (7 Parkinson disease, 5 Dementia with Lewy bodies, 1 Multiple system atrophy; follow-up time 35 ± 20.56 months) and 27 patients were still free from parkinsonism/dementia after 62 ± 29.49 months from baseline. We applied the previously identified iRBDconvRP to validate its phenoconversion prediction power. RESULTS: The iRBDconvRP significantly discriminated converters from non-converters iRBD patients (p = 0.016; Area under the Curve 0.74, Sensitivity 0.69, Specificity 0.78), and it significantly predicted phenoconversion (Hazard ratio 4.26, C.I.95%: 1.18-15.39). CONCLUSIONS: The iRBDconvRP confirmed its robustness in predicting phenoconversion in an independent group of iRBD patients, suggesting its potential role as a stratification biomarker for disease-modifying trials.

Derivation and Validation of a Phenoconversion-Related Pattern in Idiopathic Rapid Eye Movement Behavior Disorder.

BACKGROUND: Idiopathic rapid eye movement sleep behavior disorder (iRBD) represents the prodromal stage of α-synucleinopathies. Reliable biomarkers are needed to predict phenoconversion. OBJECTIVE: The aim was to derive and validate a brain glucose metabolism pattern related to phenoconversion in iRBD (iRBDconvRP) using spatial covariance analysis (Scaled Subprofile Model and Principal Component Analysis [SSM-PCA]). METHODS: Seventy-six consecutive iRBD patients (70 ± 6 years, 15 women) were enrolled in two centers and prospectively evaluated to assess phenoconversion (30 converters, 73 ± 6 years, 14 Parkinson's disease and 16 dementia with Lewy bodies, follow-up time: 21 ± 14 months; 46 nonconverters, 69 ± 6 years, follow-up time: 33 ± 19 months). All patients underwent [18 F]FDG-PET (18 F-fluorodeoxyglucose positron emitting tomography) to investigate brain glucose metabolism at baseline. SSM-PCA was applied to obtain the iRBDconvRP; nonconverter patients were considered as the reference group. Survival analysis and Cox regression were applied to explore prediction power. RESULTS: First, we derived and validated two distinct center-specific iRBDconvRP that were comparable and significantly able to predict phenoconversion. Then, SSM-PCA was applied to the whole set, identifying the iRBDconvRP. The iRBDconvRP included positive voxel weights in cerebellum; brainstem; anterior cingulate cortex; lentiform nucleus; and middle, mesial temporal, and postcentral areas. Negative voxel weights were found in posterior cingulate, precuneus, middle frontal gyrus, and parietal areas. Receiver operating characteristic analysis showed an area under the curve of 0.85 (sensitivity: 87%, specificity: 72%), discriminating converters from nonconverters. The iRBDconvRP significantly predicted phenoconversion (hazard ratio: 7.42, 95% confidence interval: 2.6-21.4). CONCLUSIONS: We derived and validated an iRBDconvRP to efficiently discriminate converter from nonconverter iRBD patients. [18 F]FDG-PET pattern analysis has potential as a phenoconversion biomarker in iRBD patients. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Beta distribution misspecification tests with application to Covid-19 mortality rates in the United States.

The beta distribution is routinely used to model variables that assume values in the standard unit interval, (0, 1). Several alternative laws have, nonetheless, been proposed in the literature, such as the Kumaraswamy and simplex distributions. A natural and empirically motivated question is: does the beta law provide an adequate representation for a given dataset? We test the null hypothesis that the beta model is correctly specified against the alternative hypothesis that it does not provide an adequate data fit. Our tests are based on the information matrix equality, which only holds when the model is correctly specified. They are thus sensitive to model misspecification. Simulation evidence shows that the tests perform well, especially when coupled with bootstrap resampling. We model state and county Covid-19 mortality rates in the United States. The misspecification tests indicate that the beta law successfully represents Covid-19 death rates when they are computed using either data from prior to the start of the vaccination campaign or data collected when such a campaign was under way. In the latter case, the beta law is only accepted when the negative impact of vaccination reach on death rates is moderate. The beta model is rejected under data heterogeneity, i.e., when mortality rates are computed using information gathered during both time periods.

FDG-PET combined with learning vector quantization allows classification of neurodegenerative diseases and reveals the trajectory of idiopathic REM sleep behavior disorder.

BACKGROUND AND OBJECTIVES: 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) combined with principal component analysis (PCA) has been applied to identify disease-related brain patterns in neurodegenerative disorders such as Parkinson's disease (PD), Dementia with Lewy Bodies (DLB) and Alzheimer's disease (AD). These patterns are used to quantify functional brain changes at the single subject level. This is especially relevant in determining disease progression in idiopathic REM sleep behavior disorder (iRBD), a prodromal stage of PD and DLB. However, the PCA method is limited in discriminating between neurodegenerative conditions. More advanced machine learning algorithms may provide a solution. In this study, we apply Generalized Matrix Learning Vector Quantization (GMLVQ) to FDG-PET scans of healthy controls, and patients with AD, PD and DLB. Scans of iRBD patients, scanned twice with an approximate 4 year interval, were projected into GMLVQ space to visualize their trajectory. METHODS: We applied a combination of SSM/PCA and GMLVQ as a classifier on FDG-PET data of healthy controls, AD, DLB, and PD patients. We determined the diagnostic performance by performing a ten times repeated ten fold cross validation. We analyzed the validity of the classification system by inspecting the GMLVQ space. First by the projection of the patients into this space. Second by representing the axis, that span this decision space, into a voxel map. Furthermore, we projected a cohort of RBD patients, whom have been scanned twice (approximately 4 years apart), into the same decision space and visualized their trajectories. RESULTS: The GMLVQ prototypes, relevance diagonal, and decision space voxel maps showed metabolic patterns that agree with previously identified disease-related brain patterns. The GMLVQ decision space showed a plausible quantification of FDG-PET data. Distance traveled by iRBD subjects through GMLVQ space per year (i.e. velocity) was correlated with the change in motor symptoms per year (Spearman's rho =0.62, P=0.004). CONCLUSION: In this proof-of-concept study, we show that GMLVQ provides a classification of patients with neurodegenerative disorders, and may be useful in future studies investigating speed of progression in prodromal disease stages.

Two-Dimensional Supersolid Formation in Dipolar Condensates.

Dipolar condensates have recently been coaxed to form the long-sought supersolid phase. While one-dimensional supersolids may be prepared by triggering a roton instability, we find that such a procedure in two dimensions (2D) leads to a loss of both global phase coherence and crystalline order. Unlike in 1D, the 2D roton modes have little in common with the supersolid configuration. We develop a finite-temperature stochastic Gross-Pitaevskii theory that includes beyond-mean-field effects to explore the formation process in 2D and find that evaporative cooling directly into the supersolid phase-hence bypassing the first-order roton instability-can produce a robust supersolid in a circular trap. Importantly, the resulting supersolid is stable at the final nonzero temperature. We then experimentally produce a 2D supersolid in a near-circular trap through such an evaporative procedure. Our work provides insight into the process of supersolid formation in 2D and defines a realistic path to the formation of large two-dimensional supersolid arrays.

Effects of Digital Food Labels on Healthy Food Choices in Online Grocery Shopping.

In order to induce the shift in consumer behavior necessary for the mitigation of diet-related diseases, front-of-package labels (FoPL) such as the Nutri-Score that support consumers in their efforts to identify nutritionally valuable products during grocery shopping have been found to be effective; however, they remain non-compulsory in most regions. Counter-intuitively, a similar stream of research on digital web-based FoPL does not yet exist, even though such digital labels hold several advantages over physical labels. Digital FoPL can provide scalable and personalized interventions, are easier to implement than physical labels, and are especially timely due to the recent increase in online grocery shopping. The goal of this study was to demonstrate the technical feasibility and intervention potential of novel, scalable, and passively triggered health behavior interventions distributed via easy-to-install web browser extensions designed to support healthy food choices via the inclusion of digital FoPL in online supermarkets. To that end, we developed a Chrome web browser extension for a real online supermarket and evaluated the effect of this digital food label intervention (i.e., display of the Nutri-Score next to visible products) on the nutritional quality of individuals' weekly grocery shopping in a randomized controlled laboratory trial (N = 135). Compared to the control group, individuals exposed to the intervention chose products with a higher nutritional quality (e.g., 8% higher healthy trolley index (HETI), 3.3% less sugar, 7.5% less saturated fat). In particular, users with low food literacy seemed to benefit from the digital FoPL (e.g., 11% higher HETI, 10.5% less sugar, 5.5% less saturated fat). Furthermore, participants exposed to the food label advocated its introduction more strongly than the control group (p = 0.081). Consumers worldwide could easily install such applications to display digital food labels on their end devices, and would thus not have to wait for stakeholders in the food industry to eventually reach consensus on mandatory food label introduction.

Digital Solutions to Diagnose and Manage Postbariatric Hypoglycemia.

Postbariatric hypoglycemia (PBH) is an increasingly recognized late metabolic complication of bariatric surgery, characterized by low blood glucose levels 1-3 h after a meal, particularly if the meal contains rapid-acting carbohydrates. PBH can often be effectively managed through appropriate nutritional measures, which remain the cornerstone treatment today. However, their implementation in daily life continues to challenge both patients and health care providers. Emerging digital technologies may allow for more informed and improved decision-making through better access to relevant data to manage glucose levels in PBH. Examples include applications for automated food analysis from meal images, digital receipts of purchased food items or integrated platforms allowing the connection of continuously measured glucose with food and other health-related data. The resulting multi-dimensional data can be processed with artificial intelligence systems to develop prediction algorithms and decision support systems with the aim of improving glucose control, safety, and quality of life of PBH patients. Digital innovations, however, face trade-offs between user burden vs. amount and quality of data. Further challenges to their development are regulatory non-compliance regarding data ownership of the platforms acquiring the required data, as well as user privacy concerns and compliance with regulatory requirements. Through navigating these trade-offs, digital solutions could significantly contribute to improving the management of PBH.

Proportion of Concentrate in the Diet of Early Lactation Dairy Cows Has Contrasting Effects on Circulating Leukocyte Global Transcriptomic Profiles, Health and Fertility According to Parity.

The functionality of circulating leukocytes in dairy cows is suppressed after calving, with negative energy balance as a risk factor. Leukocyte transcriptomic profiles were compared separately in 44 multiparous (MP) and 18 primiparous (PP) Holstein-Friesian cows receiving diets differing in concentrate proportion to test whether immune dysfunction could be mitigated by appropriate nutrition. After calving, cows were offered either (1) low concentrate (LC); (2) medium concentrate (MC) or (3) high concentrate (HC) diets with proportions of concentrate to grass silage of 30%:70%, 50%:50% and 70%:30%, respectively. Cow phenotype data collected included circulating metabolites, milk yield and health and fertility records. RNA sequencing of circulating leukocytes at 14 days in milk was performed. The HC diet improved energy balance in both age groups. There were more differentially expressed genes in PP than MP cows (460 vs. 173, HC vs. LC comparison) with few overlaps. The MP cows on the LC diet showed upregulation of the complement and coagulation cascade and innate immune defence mechanisms against pathogens and had a trend of more cases of mastitis and poorer fertility. In contrast, the PP cows on the HC diet showed greater immune responses based on both gene expression and phenotypic data and longer interval of calving to conception. The leukocytes of MP and PP cows therefore responded differentially to the diets between age, nutrient supply and immunity affecting their health and subsequent fertility.

Rapid Eye Movement Sleep Behavior Disorder: Abnormal Cardiac Image and Progressive Abnormal Metabolic Brain Pattern.

BACKGROUND: Isolated rapid eye movement sleep behavior disorder (iRBD) is prodromal for α-synucleinopathies. OBJECTIVE: The aim of this study was to determine whether pathological cardiac [123 I]meta-iodobenzylguanidine scintigraphy ([123 I]MIBG) is associated with progression of [18 F]fluorodeoxyglucose-positron emission tomography-based Parkinson's disease (PD)-related brain pattern (PDRP) expression in iRBD. METHODS: Seventeen subjects with iRBD underwent [18 F]fluorodeoxyglucose-positron emission tomography brain imaging twice ~3.6 years apart. In addition, [123 I]MIBG and [123 I]N-ω-fluoropropyl-2ß-carbomethoxy-3ß-(4-iodophenyl)nortropane single-photon emission computed tomography ([123 I]FP-CIT-SPECT) at baseline were performed. Olfactory, cognitive, and motor functions were tested annually. RESULTS: Twelve of 17 subjects had pathological [123 I]MIBG. At baseline, 6 of 12 of these expressed the PDRP (suprathreshold PDRP z score). At follow-up, 12 of 17 subjects had suprathreshold PDRP z scores, associated with pathological [123 I]MIBG in 92% and with pathological [123 I]FP-CIT-SPECT in 75%. Subjects with pathological [123 I]MIBG had higher PDRP z score change per year (P = 0.027). Three subjects phenoconverted to PD; all had pathological [123 I]MIBG and [123 I]FP-CIT-SPECT, suprathreshold baseline PDRP z scores, and hyposmia. CONCLUSIONS: Pathological [123 I]MIBG was associated with progressive and suprathreshold PDRP z scores at follow-up. Abnormal [123 I]MIBG likely identifies iRBD as prodromal PD earlier than pathological [123 I]FP-CIT-SPECT. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Autopolyploidization affects transcript patterns and gene targeting frequencies in Physcomitrella.

KEY MESSAGE: In Physcomitrella, whole-genome duplications affected the expression of about 3.7% of the protein-encoding genes, some of them relevant for DNA repair, resulting in a massively reduced gene-targeting frequency. Qualitative changes in gene expression after an autopolyploidization event, a pure duplication of the whole genome (WGD), might be relevant for a different regulation of molecular mechanisms between angiosperms growing in a life cycle with a dominant diploid sporophytic stage and the haploid-dominant mosses. Whereas angiosperms repair DNA double-strand breaks (DSB) preferentially via non-homologous end joining (NHEJ), in the moss Physcomitrella homologous recombination (HR) is the main DNA-DSB repair pathway. HR facilitates the precise integration of foreign DNA into the genome via gene targeting (GT). Here, we studied the influence of ploidy on gene expression patterns and GT efficiency in Physcomitrella using haploid plants and autodiploid plants, generated via an artificial WGD. Single cells (protoplasts) were transfected with a GT construct and material from different time-points after transfection was analysed by microarrays and SuperSAGE sequencing. In the SuperSAGE data, we detected 3.7% of the Physcomitrella genes as differentially expressed in response to the WGD event. Among the differentially expressed genes involved in DNA-DSB repair was an upregulated gene encoding the X-ray repair cross-complementing protein 4 (XRCC4), a key player in NHEJ. Analysing the GT efficiency, we observed that autodiploid plants were significantly GT suppressed (p < 0.001) attaining only one third of the expected GT rates. Hence, an alteration of global transcript patterns, including genes related to DNA repair, in autodiploid Physcomitrella plants correlated with a drastic suppression of HR.

Circuit imaging biomarkers in preclinical and prodromal Parkinson's disease.

Parkinson's disease (PD) commences several years before the onset of motor features. Pathophysiological understanding of the pre-clinical or early prodromal stages of PD are essential for the development of new therapeutic strategies. Two categories of patients are ideal to study the early disease stages. Idiopathic rapid eye movement sleep behavior disorder (iRBD) represents a well-known prodromal stage of PD in which pathology is presumed to have reached the lower brainstem. The majority of patients with iRBD will develop manifest PD within years to decades. Another category encompasses non-manifest mutation carriers, i.e. subjects without symptoms, but with a known mutation or genetic variant which gives an increased risk of developing PD. The speed of progression from preclinical or prodromal to full clinical stages varies among patients and cannot be reliably predicted on the individual level. Clinical trials will require inclusion of patients with a predictable conversion within a limited time window. Biomarkers are necessary that can confirm pre-motor PD status and can provide information regarding lead time and speed of progression. Neuroimaging changes occur early in the disease process and may provide such a biomarker. Studies have focused on radiotracer imaging of the dopaminergic nigrostriatal system, which can be assessed with dopamine transporter (DAT) single photon emission computed tomography (SPECT). Loss of DAT binding represents an effect of irreversible structural damage to the nigrostriatal system. This marker can be used to monitor disease progression and identify individuals at specific risk for phenoconversion. However, it is known that changes in neuronal activity precede structural changes. Functional neuro-imaging techniques, such as 18F-2-fluoro-2-deoxy-D-glucose Positron Emission Tomography (18F-FDG PET) and functional magnetic resonance imaging (fMRI), can be used to model the effects of disease on brain networks when combined with advanced analytical methods. Because these changes occur early in the disease process, functional imaging studies are of particular interest in prodromal PD diagnosis. In addition, fMRI and 18F-FDG PET may be able to predict a specific future phenotype in prodromal cohorts, which is not possible with DAT SPECT. The goal of the current review is to discuss the network-level brain changes in pre-motor PD.

Biomarkers of conversion to α-synucleinopathy in isolated rapid-eye-movement sleep behaviour disorder.

Patients with isolated rapid-eye-movement sleep behaviour disorder (RBD) are commonly regarded as being in the early stages of a progressive neurodegenerative disease involving α-synuclein pathology, such as Parkinson's disease, dementia with Lewy bodies, or multiple system atrophy. Abnormal α-synuclein deposition occurs early in the neurodegenerative process across the central and peripheral nervous systems and might precede the appearance of motor symptoms and cognitive decline by several decades. These findings provide the rationale to develop reliable biomarkers that can better predict conversion to clinically manifest α-synucleinopathies. In addition, biomarkers of disease progression will be essential to monitor treatment response once disease-modifying therapies become available, and biomarkers of disease subtype will be essential to enable prediction of which subtype of α-synucleinopathy patients with isolated RBD might develop.

Efficacy of the TMPRSS2 inhibitor camostat mesilate in patients hospitalized with Covid-19-a double-blind randomized controlled trial.

BACKGROUND: The trans-membrane protease serine 2 (TMPRSS2) is essential for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cell entry and infection. Efficacy and safety of TMPRSS2 inhibitors in patients with coronavirus disease 2019 (Covid-19) have not been evaluated in randomized trials. METHODS: We conducted an investigator-initiated, double-blind, randomized, placebo-controlled multicenter trial in patients hospitalized with confirmed SARS-CoV-2 infection from April 4, to December 31, 2020. Within 48 h of admission, participants were randomly assigned in a 2:1 ratio to receive the TMPRSS2 inhibitor camostat mesilate 200 mg three times daily for 5 days or placebo. The primary outcome was time to discharge or clinical improvement measured as ≥2 points improvement on a 7-point ordinal scale. Other outcomes included 30-day mortality, safety and change in oropharyngeal viral load. FINDINGS: 137 patients were assigned to receive camostat mesilate and 68 to placebo. Median time to clinical improvement was 5 days (interquartile range [IQR], 3 to 7) in the camostat group and 5 days (IQR, 2 to 10) in the placebo group (P = 0·31). The hazard ratio for 30-day mortality in the camostat compared with the placebo group was 0·82 (95% confidence interval [CI], 0·24 to 2·79; P = 0·75). The frequency of adverse events was similar in the two groups. Median change in viral load from baseline to day 5 in the camostat group was -0·22 log10 copies/mL (p <0·05) and -0·82 log10 in the placebo group (P <0·05). INTERPRETATION: Under this protocol, camostat mesilate treatment was not associated with increased adverse events during hospitalization for Covid-19 and did not affect time to clinical improvement, progression to ICU admission or mortality. ClinicalTrials.gov Identifier: NCT04321096. EudraCT Number: 2020-001200-42.