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Background: Automated estimation of Pulmonary function test (PFT) results from Computed Tomography (CT) could advance the use of CT in screening, diagnosis, and staging of restrictive pulmonary diseases. Estimating lung function per lobe, which cannot be done with PFTs, would be helpful for risk assessment for pulmonary resection surgery and bronchoscopic lung volume reduction. Purpose: To automatically estimate PFT results from CT and furthermore disentangle the individual contribution of pulmonary lobes to a patient's lung function. Methods: We propose I3Dr, a deep learning architecture for estimating global measures from an image that can also estimate the contributions of individual parts of the image to this global measure. We apply it to estimate the separate contributions of each pulmonary lobe to a patient's total lung function from CT, while requiring only CT scans and patient level lung function measurements for training. I3Dr consists of a lobe‐level and a patient‐level model. The lobe‐level model extracts all anatomical pulmonary lobes from a CT scan and processes them in parallel to produce lobe level lung function estimates that sum up to a patient level estimate. The patient‐level model directly estimates patient level lung function from a CT scan and is used to re‐scale the output of the lobe‐level model to increase performance. After demonstrating the viability of the proposed approach, the I3Dr model is trained and evaluated for PFT result estimation using a large data set of 8 433 CT volumes for training, 1 775 CT volumes for validation, and 1 873 CT volumes for testing. Results: First, we demonstrate the viability of our approach by showing that a model trained with a collection of digit images to estimate their sum implicitly learns to assign correct values to individual digits. Next, we show that our models can estimate lobe‐level quantities, such as COVID‐19 severity scores, pulmonary volume (PV), and functional pulmonary volume (FPV) from CT while only provided with patient‐level quantities during training. Lastly, we train and evaluate models for producing spirometry and diffusion capacity of carbon mono‐oxide (DLCO) estimates at the patient and lobe level. For producing Forced Expiratory Volume in one second (FEV1), Forced Vital Capacity (FVC), and DLCO estimates, I3Dr obtains mean absolute errors (MAE) of 0.377 L, 0.297 L, and 2.800 mL/min/mm Hg respectively. We release the resulting algorithms for lung function estimation to the research community at https://grand‐challenge.org/algorithms/lobe‐wise‐lung‐function‐estimation/ Conclusions: I3Dr can estimate global measures from an image, as well as the contributions of individual parts of the image to this global measure. It offers a promising approach for estimating PFT results from CT scans and disentangling the individual contribution of pulmonary lobes to a patient's lung function. The findings presented in this work may advance the use of CT in screening, diagnosis, and staging of restrictive pulmonary diseases as well as in risk assessment for pulmonary resection surgery and bronchoscopic lung volume reduction. [ABSTRACT FROM AUTHOR]

Cardiac resynchronization therapy (CRT) is an established treatment for heart failure patients with left ventricular dysfunction and a left bundle branch block. However, its impact on right ventricular (RV) function remains uncertain. This cardiac magnetic resonance imaging study found that CRT did not improve RV volumes and function, and CRT‐off during follow‐up had an immediate detrimental effect on the RV, which may suggest potential unfavorable RV remodeling with RV pacing during CRT. [ABSTRACT FROM AUTHOR]

Background: Total lung volume is an important quantitative biomarker and is used for the assessment of restrictive lung diseases. Purpose: In this study, we investigate the performance of several deep‐learning approaches for automated measurement of total lung volume from chest radiographs. Methods: About 7621 posteroanterior and lateral view chest radiographs (CXR) were collected from patients with chest CT available. Similarly, 928 CXR studies were chosen from patients with pulmonary function test (PFT) results. The reference total lung volume was calculated from lung segmentation on CT or PFT data, respectively. This dataset was used to train deep‐learning architectures to predict total lung volume from chest radiographs. The experiments were constructed in a stepwise fashion with increasing complexity to demonstrate the effect of training with CT‐derived labels only and the sources of error. The optimal models were tested on 291 CXR studies with reference lung volume obtained from PFT. Mean absolute error (MAE), mean absolute percentage error (MAPE), and Pearson correlation coefficient (Pearson's r) were computed. Results: The optimal deep‐learning regression model showed an MAE of 408 ml and an MAPE of 8.1% using both frontal and lateral chest radiographs as input. The predictions were highly correlated with the reference standard (Pearson's r = 0.92). CT‐derived labels were useful for pretraining but the optimal performance was obtained by fine‐tuning the network with PFT‐derived labels. Conclusion: We demonstrate, for the first time, that state‐of‐the‐art deep‐learning solutions can accurately measure total lung volume from plain chest radiographs. The proposed model is made publicly available and can be used to obtain total lung volume from routinely acquired chest radiographs at no additional cost. This deep‐learning system can be a useful tool to identify trends over time in patients referred regularly for chest X‐ray. [ABSTRACT FROM AUTHOR]

Introduction: Radiofrequency (RF) atrial fibrillation (AF) ablation using a catheter dragging technique may shorten procedural duration and improve durability of pulmonary vein isolation (PVI) by creating uninterrupted linear ablation lesions. We compared a novel AF ablation approach guided by Grid annotation allowing for "drag lesions" with a standard point‐by‐point ablation approach in a single‐center randomized study. Methods: Eighty‐eight paroxysmal or persistent AF patients were randomized 1:1 to undergo RF‐PVI with either a catheter dragging ablation technique guided by Grid annotation or point‐by‐point ablation guided by Ablation Index (AI) annotation. In the Grid annotation arm, ablation was visualized using 1 mm³ grid points coloring red after meeting predefined stability and contact force criteria. In the AI annotation arm, ablation lesions were created in a point‐by‐point fashion with AI target values set at 380 and 500 for posterior/inferior and anterior/roof segments, respectively. Patients were followed up for 12 months after PVI using ECGs, 24‐h Holter monitoring and a mobile‐based one‐lead ECG device. Results: Procedure time was not different between the two randomization arms (Grid annotation 71 ± 19 min, AI annotation 72 ± 26 min, p =.765). RF time was significantly longer in the Grid annotation arm compared with the AI annotation arm (49 ± 8 min vs. 37 ± 8 min, respectively, p <.001). Atrial tachyarrhythmia recurrence was documented in 10 patients (23%) in the Grid annotation arm compared with 19 patients (42%) in the AI annotation arm with time to recurrence not reaching statistical significance (p =.074). Conclusions: This study shows that a Grid annotation‐guided dragging approach provides an alternative to point‐by‐point RF‐PVI using AI annotation. [ABSTRACT FROM AUTHOR]

Scenario‐based biodiversity modelling is a powerful approach to evaluate how possible future socio‐economic developments may affect biodiversity. Here, we evaluated the changes in terrestrial biodiversity intactness, expressed by the mean species abundance (MSA) metric, resulting from three of the shared socio‐economic pathways (SSPs) combined with different levels of climate change (according to representative concentration pathways [RCPs]): a future oriented towards sustainability (SSP1xRCP2.6), a future determined by a politically divided world (SSP3xRCP6.0) and a future with continued global dependency on fossil fuels (SSP5xRCP8.5). To this end, we first updated the GLOBIO model, which now runs at a spatial resolution of 10 arc‐seconds (~300 m), contains new modules for downscaling land use and for quantifying impacts of hunting in the tropics, and updated modules to quantify impacts of climate change, land use, habitat fragmentation and nitrogen pollution. We then used the updated model to project terrestrial biodiversity intactness from 2015 to 2050 as a function of land use and climate changes corresponding with the selected scenarios. We estimated a global area‐weighted mean MSA of 0.56 for 2015. Biodiversity intactness declined in all three scenarios, yet the decline was smaller in the sustainability scenario (−0.02) than the regional rivalry and fossil‐fuelled development scenarios (−0.06 and −0.05 respectively). We further found considerable variation in projected biodiversity change among different world regions, with large future losses particularly for sub‐Saharan Africa. In some scenario‐region combinations, we projected future biodiversity recovery due to reduced demands for agricultural land, yet this recovery was counteracted by increased impacts of other pressures (notably climate change and road disturbance). Effective measures to halt or reverse the decline of terrestrial biodiversity should not only reduce land demand (e.g. by increasing agricultural productivity and dietary changes) but also focus on reducing or mitigating the impacts of other pressures. [ABSTRACT FROM AUTHOR]

An increase in serum tryptase even below 11.4 ng/mL may indicate a mast cell-mediated hypersensitivity reaction: a prospective study in Hymenoptera venom allergic patients. Tryptase reference ranges are age-dependent in a large population-based cohort To the Editor, Tryptase is a protease that is released during degranulation of mast cells and has several functions including promotion of vasodilatation and leukocyte recruitment, fibroblast proliferation and tissue repair.1 Furthermore, tryptase concentrations in the circulation are thought to give a reflection of the mast cell burden. [Extracted from the article]

The aim of a present study was to identify the genes activated or inactivated in the amygdaloid area after the exposure to cat odor. Cat odor exposure was used to induce the ethologically relevant anxiety reaction in male rats. Differential expression of genes was analyzed using the cDNA Representational Difference Analysis (cDNA RDA). Differentially expressed mRNAs were identified by sequencing combined with database search and subsequently verified by dot blot analysis. Exposure of rats to cat odor induced avoidance of odor stimulus and suppressed the exploratory activity of animals. We found that during the cat odor exposure several genes with various functions were activated in the amygdaloid area of rat. Moreover, reverse subtraction resulted in a different set of genes that are inactivated during anxiety response. These genes can be classified according to their function as the neurotransmission related, enzymes, cell cycle regulating proteins and transcription factors. We found that during anxiety response the genes participating directly or indirectly in the synthesis of neurotransmitters (carboxypeptidase E, tyrosine 3-monooxygenase/tryptophan 5-mono-oxygenase activation protein, wolframin) were up regulated. Moreover, a number of genes involved in the signal transduction (Rho GTPase, neurochondrin, Ca/calmodulin-dependent protein kinase) were also activated. Additionally, reverse subtraction in control animals identified several up regulated genes having the antagonistic action to these genes (nischarin, Rab geranylgeranyl transferase). In conclusion, we were able to define the possible pathways linked to the regulation of anxiety response. [ABSTRACT FROM AUTHOR]

Background Information: MYG1 [Melanocyte proliferating gene 1, also known as Gamm1 (NM&#95;021640)] is a recently described gene of unknown function. MYG1 orthologues are found in simple as well as complex eukaryotes. According to sequence homology, MYG1 is considered to have a metal-dependent protein hydrolase (UPF0160) domain. The purpose of the present study was to determine the expression and subcellular localization of MYG1 protein and to identify physiological processes connected to MYG1 function., Results: Human and mouse MYG1 is ubiquitously expressed, with the highest level in the testis. Analysis of mouse embryos moreover revealed a uniform Myg1 expression at E (embryonic day) 8.5, but at E11.75 expression becomes restricted predominantly to the developing brain and eye, limb buds and tail region. MYG1 exhibits a mitochondrial targeting signal in the N-terminal region and a Pat7-type nuclear localization signal in the region between amino acids 33-39 and localizes to these compartments. No active shuttling of MYG1 between the nucleus and the mitochondria was detected and the distribution of MYG1 was not dependent on the phase of the cell cycle. Immunoprecipitation of C-terminally FLAG-tagged MYG1 from HeLa cells did not identify any co-precipitated proteins. siRNA (short interfering RNA)-mediated knockdown of MYG1 mRNA was mainly followed by changes in the level of transcripts encoding factors involved in developmental tissue patterning and growth as well as immune-related processes., Conclusions: Taken together, we infer that MYG1 is a ubiquitous nucleo-mitochondrial protein, with differential pattern and level of expression during embryonic development. MYG1 expression in normal adult tissues is stable and our data suggest MYG1 involvement in early developmental processes and also in adult stress/illness conditions.