Reinitiation of compensatory lung growth after subsequent lung resection

ObjectiveIn experimental animals, pneumonectomy results in rapid, hyperplastic compensatory growth of the remaining lung. The limits of this induced growth are unknown. We tested the hypothesis that compensatory growth can be reinitiated in the same lung after subsequent lung resection.MethodsA left thoracotomy (Sham group) or left pneumonectomy (PNX group) was performed in Sprague–Dawley rats. A third group underwent left pneumonectomy followed 4 weeks later by a bilobectomy of the right upper and middle lobes (PNX+LBX group). Four weeks after bilobectomy in the PNX+LBX group (8 weeks in the Sham and PNX groups), right ventricular pressures were measured by using the open chest technique, and total lung weight and lower plus cardiac lobe weight indices were measured. Lungs were inflation fixed at 25 cm H2O to measure lobe volume index and to perform morphometric measurements on lung sections. Right ventricle/left ventricle plus septum weight index was measured as another index of pulmonary hypertension.ResultsTotal lung weight index was similar in all groups. Pneumonectomy resulted in increased lower plus cardiac lobe weight and volume indices, which were significantly augmented in the PNX+LBX group. The PNX+LBX group underwent a significant increase in total volume of respiratory region, airspace, and tissue and a decrease in alveolar surface density versus the PNX group. The PNX+LBX group also had significantly increased right ventricular systolic pressure and right ventricle/left ventricle plus septum index.ConclusionThese results demonstrate that compensatory growth can be reinitiated in lungs that had previously undergone postpneumonectomy compensatory growth. This subsequent growth, however, is more hypertrophic, and pulmonary hypertension develops despite subsequent compensatory growth

Single-Cell, Genome-wide Sequencing Identifies Clonal Somatic Copy-Number Variation in the Human Brain

SUMMARY De novo copy-number variants (CNVs) can cause neuropsychiatric disease, but the degree to which they occur somatically, and during development, is unknown. Single-cell whole-genome sequencing (WGS) in >200 single cells, including >160 neurons from three normal and two pathological human brains, sensitively identified germline trisomy of chromosome 18 but found most (≥95%) neurons in normal brain tissue to be euploid. Analysis of a patient with hemimegalencephaly (HMG) due to a somatic CNV of chromosome 1q found unexpected tetrasomy 1q in ~20% of neurons, suggesting that CNVs in a minority of cells can cause widespread brain dysfunction. Single-cell analysis identified large (>1 Mb) clonal CNVs in lymphoblasts and in single neurons from normal human brain tissue, suggesting that some CNVs occur during neurogenesis. Many neurons contained one or more large candidate private CNVs, including one at chromosome 15q13.2-13.3, a site of duplication in neuropsychiatric conditions. Large private and clonal somatic CNVs occur in normal and diseased human brains

The sloppy model universality class and the Vandermonde matrix

In a variety of contexts, physicists study complex, nonlinear models with many unknown or tunable parameters to explain experimental data. We explain why such systems so often are sloppy; the system behavior depends only on a few `stiff' combinations of the parameters and is unchanged as other `sloppy' parameter combinations vary by orders of magnitude. We contrast examples of sloppy models (from systems biology, variational quantum Monte Carlo, and common data fitting) with systems which are not sloppy (multidimensional linear regression, random matrix ensembles). We observe that the eigenvalue spectra for the sensitivity of sloppy models have a striking, characteristic form, with a density of logarithms of eigenvalues which is roughly constant over a large range. We suggest that the common features of sloppy models indicate that they may belong to a common universality class. In particular, we motivate focusing on a Vandermonde ensemble of multiparameter nonlinear models and show in one limit that they exhibit the universal features of sloppy models.Comment: New content adde

MTA1 Interacts with MAT1, a cyclin-dependent kinase-activating kinase complex ring finger factor and regulates estrogen receptor transactivation functions

The transcriptional activity of estrogen receptor-α is controlled by coregulators. MTA1 (metastasis-associated protein1) represses estrogen receptor-α-driven transcription by recruiting Histone Deacetylases (HDACs) to the estrogen response element containing target gene chromatin in breast cancer cells. Using a yeast two-hybrid screen with the MTA1 C-terminal domain as bait, we identified MAT1 (menage a trois 1) as an MTA1-binding protein. MAT1 is an assembly/targeting factor for cyclin-dependent kinase-activating kinase (CAK), which has been shown to functionally interact with general transcriptional factor TFIIH, a known inducer of ER transactivation. We show that estrogen signaling promotes nuclear translocation of MAT1 and that MTA1 interacts with MAT1 both in vitro and in vivo. MAT1 binds to the C-terminal 389–441 amino acids GATA domain and N-terminal 1–164 amino acids bromo-domain of MTA1, whereas MTA1 binds to the N-terminal ring finger domain of the MAT1. In addition, MAT1 interacts with the activation function 2 domain of ER and colocalizes with ER in activated cells. MTA1 deregulation in breast cancer cells led to its interactions with the CAK complex components, ER and HDAC2. Accordingly, MTA1 inhibited CAK stimulation of ER transactivation that was partially relieved by HDAC inhibitor trichostatin A, suggesting that MTA1 might inhibit CAK-induced transactivation function of ER by recruiting HDAC. Furthermore, MTA1 overexpression inhibited the ability of CAK complex to phosphorylate ER. Together, these findings identified MAT1 as a target of MTA1 and provided new evidence to suggest that the transactivation functions of ER might be influenced by the regulatory interactions between CAK and MTA1 in breast cancer cells

EFBAT: exact family-based association tests

Background: Family-based association tests are important tools for investigating genetic risk factors of complex diseases. These tests are especially valuable for being robust to population structure. We introduce a tool, EFBAT, which performs exact family-based tests of association for X-chromosome and autosomal biallelic markers. Results: The program EFBAT extends a network algorithm previously applied to autosomal markers to include the X-chromosome and to perform tests of association under the null hypotheses "no association, no linkage" and "no association in the presence of linkage" under additive, dominant and recessive genetic models. These tests are valid regardless of patterns of missing familial data. Conclusion: The general framework for performing exact family-based association tests has been usefully extended to the X-chromosome, particularly for the hypothesis of "no association in the presence of linkage" and for different genetic models

Uncertainty Aware Training to Improve Deep Learning Model Calibration for Classification of Cardiac MR Images

Quantifying uncertainty of predictions has been identified as one way to develop more trustworthy artificial intelligence (AI) models beyond conventional reporting of performance metrics. When considering their role in a clinical decision support setting, AI classification models should ideally avoid confident wrong predictions and maximise the confidence of correct predictions. Models that do this are said to be well-calibrated with regard to confidence. However, relatively little attention has been paid to how to improve calibration when training these models, i.e., to make the training strategy uncertainty-aware. In this work we evaluate three novel uncertainty-aware training strategies comparing against two state-of-the-art approaches. We analyse performance on two different clinical applications: cardiac resynchronisation therapy (CRT) response prediction and coronary artery disease (CAD) diagnosis from cardiac magnetic resonance (CMR) images. The best-performing model in terms of both classification accuracy and the most common calibration measure, expected calibration error (ECE) was the Confidence Weight method, a novel approach that weights the loss of samples to explicitly penalise confident incorrect predictions. The method reduced the ECE by 17% for CRT response prediction and by 22% for CAD diagnosis when compared to a baseline classifier in which no uncertainty-aware strategy was included. In both applications, as well as reducing the ECE there was a slight increase in accuracy from 69% to 70% and 70% to 72% for CRT response prediction and CAD diagnosis respectively. However, our analysis showed a lack of consistency in terms of optimal models when using different calibration measures. This indicates the need for careful consideration of performance metrics when training and selecting models for complex high-risk applications in healthcare

Practical management of anticoagulation in patients with atrial fibrillation.

Anticoagulation for atrial fibrillation has become more complex due to the introduction of new anticoagulant agents, the number and kinds of patients requiring therapy, and the interactions of those patients in the matrix of care. The management of anticoagulation has become a team sport involving multiple specialties in multiple sites of care. The American College of Cardiology, through the College\u27s Anticoagulation Initiative, convened a roundtable of experts from multiple specialties to discuss topics important to the management of patients requiring anticoagulation and to make expert recommendations on issues such as the initiation and interruption of anticoagulation, quality of anticoagulation care, management of major and minor bleeding, and treatment of special populations. The attendees continued to work toward consensus on these topics, and present the key findings of this roundtable in a state-of- the-art review focusing on the practical aspects of anticoagulation care for the patient with atrial fibrillation

Dysregulation in Subcellular Localization of Myelin Basic Protein mRNA Does Not Result in Altered Myelination in Amyotrophic Lateral Sclerosis

Pathological hallmarks of amyotrophic lateral sclerosis (ALS), including protein misfolding, are well established in oligodendrocytes. More recently, an RNA trafficking deficit of key myelin proteins has been suggested in oligodendrocytes in ALS but the extent to which this affects myelination and the relative contribution of this to disease pathogenesis is unclear. ALS autopsy research findings showing demyelination contrasts with the routine clinical-pathological workup of ALS cases where it is rare to see white matter abnormalities other than simple Wallerian degeneration secondary to widespread neuronal loss. To begin to address this apparent variance, we undertook a comprehensive evaluation of myelination at an RNA, protein and structural level using human pathological material from sporadic ALS patients, genetic ALS patients (harboring C9orf72 mutation) and age- and sex-matched non-neurological controls. We performed (i) quantitative spatial profiling of the mRNA transcript encoding myelin basic protein (MBP), (ii) quantification of MBP protein and (iii) the first quantitative structural assessment of myelination in ALS post-mortem specimens by electron microscopy. We show no differences in MBP protein levels or ultrastructural myelination, despite a significant dysregulation in the subcellular trafficking of MBP mRNA in ALS patients compared to controls. We therefore confirm that whilst there are cell autonomous mRNA trafficking deficits affecting oligodendrocytes in ALS, this has no effect on myelin structure

Leadless biventricular left bundle and endocardial lateral wall pacing versus left bundle only pacing in left bundle branch block patients

Biventricular endocardial (BIV-endo) pacing and left bundle pacing (LBP) are novel delivery methods for cardiac resynchronization therapy (CRT). Both pacing methods can be delivered through leadless pacing, to avoid risks associated with endocardial or transvenous leads. We used computational modelling to quantify synchrony induced by BIV-endo pacing and LBP through a leadless pacing system, and to investigate how the right-left ventricle (RV-LV) delay, RV lead location and type of left bundle capture affect response. We simulated ventricular activation on twenty-four four-chamber heart meshes inclusive of His-Purkinje networks with left bundle branch block (LBBB). Leadless biventricular (BIV) pacing was simulated by adding an RV apical stimulus and an LV lateral wall stimulus (BIV-endo lateral) or targeting the left bundle (BIV-LBP), with an RV-LV delay set to 5 ms. To test effect of prolonged RV-LV delays and RV pacing location, the RV-LV delay was increased to 35 ms and/or the RV stimulus was moved to the RV septum. BIV-endo lateral pacing was less sensitive to increased RV-LV delays, while RV septal pacing worsened response compared to RV apical pacing, especially for long RV-LV delays. To investigate how left bundle capture affects response, we computed 90% BIV activation times (BIVAT-90) during BIV-LBP with selective and non-selective capture, and left bundle branch area pacing (LBBAP), simulated by pacing 1 cm below the left bundle. Non-selective LBP was comparable to selective LBP. LBBAP was worse than selective LBP (BIVAT-90: 54.2 ± 5.7 ms vs. 62.7 ± 6.5, p < 0.01), but it still significantly reduced activation times from baseline. Finally, we compared leadless LBP with RV pacing against optimal LBP delivery through a standard lead system by simulating BIV-LBP and selective LBP alone with and without optimized atrioventricular delay (AVD). Although LBP alone with optimized AVD was better than BIV-LBP, when AVD optimization was not possible BIV-LBP outperformed LBP alone, because the RV pacing stimulus shortened RV activation (BIVAT-90: 54.2 ± 5.7 ms vs. 66.9 ± 5.1 ms, p < 0.01). BIV-endo lateral pacing or LBP delivered through a leadless system could potentially become an alternative to standard CRT. RV-LV delay, RV lead location and type of left bundle capture affect leadless pacing efficacy and should be considered in future trial designs