Combining experimental and mathematical modeling to reveal mechanisms of macrophage-dependent left ventricular remodeling

<p>Abstract</p> <p>Background</p> <p>Progressive remodeling of the left ventricle (LV) following myocardial infarction (MI) can lead to congestive heart failure, but the underlying initiation factors remain poorly defined. The objective of this study, accordingly, was to determine the key factors and elucidate the regulatory mechanisms of LV remodeling using integrated computational and experimental approaches.</p> <p>Results</p> <p>By examining the extracellular matrix (ECM) gene expression and plasma analyte levels in C57/BL6J mice LV post-MI and ECM gene responses to transforming growth factor (TGF-β₁) in cultured cardiac fibroblasts, we found that key factors in LV remodeling included macrophages, fibroblasts, transforming growth factor-β₁, matrix metalloproteinase-9 (MMP-9), and specific collagen subtypes. We established a mathematical model to study LV remodeling post-MI by quantifying the dynamic balance between ECM construction and destruction. The mathematical model incorporated the key factors and demonstrated that TGF-β₁stimuli and MMP-9 interventions with different strengths and intervention times lead to different LV remodeling outcomes. The predictions of the mathematical model fell within the range of experimental measurements for these interventions, providing validation for the model.</p> <p>Conclusions</p> <p>In conclusion, our results demonstrated that the balance between ECM synthesis and degradation, controlled by interactions of specific key factors, determines the LV remodeling outcomes. Our mathematical model, based on the balance between ECM construction and destruction, provides a useful tool for studying the regulatory mechanisms and for predicting LV remodeling outcomes.</p

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Progressive pseudo-label framework for unsupervised hyperspectral change detection

For hyperspectral image change detection (HSI-CD) task, unsupervised learning methods based on pseudo-labels obtained from pre-classification method are promising due to its simplicity and generality. However, the inevitable noise and the lack of diversity in the pseudo-labels would degrade the performance of the network model, resulting in insufficient mining of fine change information in HSIs. To address this issue, we propose a progressive pseudo-label (PPL) framework for HSI-CD. Differing from the state-of-the-art methods of training designed networks with fixed pseudo-labels, we focus our attention on improving the quality of pseudo-labels progressively. In the PPL framework, a multi-scale pre-classification module is constructed for generating initial pseudo-labels. Subsequently, an uncertainty-aware selection strategy is designed to update the pseudo-labels. Specifically, the new pseudo-labels are selected based on the uncertainty estimated by the Bayesian network trained with pseudo-labels. The new pseudo-labels have richer diversity with quality assurance and thus called progressive pseudo-labels. Meanwhile, the Bayesian network trained by progressive pseudo-labels has increasingly better detection capability. As a result, the PPL framework can detect strong and subtle changes while suppressing false changes. Furthermore, the PPL framework additionally provides the uncertainty to evaluate the reliability of the prediction. We have implemented intensive experiments on three publicly available datasets demonstrate the outperformance and generalization of the PPL framework. The overall accuracy (OA) and Kappa metrics on the commonly used Hermiston dataset reached 0.9558 and 0.8728, respectively

Multiscale Information Fusion for Hyperspectral Image Classification Based on Hybrid 2D-3D CNN

Hyperspectral images are widely used for classification due to its rich spectral information along with spatial information. To process the high dimensionality and high nonlinearity of hyperspectral images, deep learning methods based on convolutional neural network (CNN) are widely used in hyperspectral classification applications. However, most CNN structures are stacked vertically in addition to using a onefold size of convolutional kernels or pooling layers, which cannot fully mine the multiscale information on the hyperspectral images. When such networks meet the practical challenge of a limited labeled hyperspectral image dataset—i.e., “small sample problem”—the classification accuracy and generalization ability would be limited. In this paper, to tackle the small sample problem, we apply the semantic segmentation function to the pixel-level hyperspectral classification due to their comparability. A lightweight, multiscale squeeze-and-excitation pyramid pooling network (MSPN) is proposed. It consists of a multiscale 3D CNN module, a squeezing and excitation module, and a pyramid pooling module with 2D CNN. Such a hybrid 2D-3D-CNN MSPN framework can learn and fuse deeper hierarchical spatial–spectral features with fewer training samples. The proposed MSPN was tested on three publicly available hyperspectral classification datasets: Indian Pine, Salinas, and Pavia University. Using 5%, 0.5%, and 0.5% training samples of the three datasets, the classification accuracies of the MSPN were 96.09%, 97%, and 96.56%, respectively. In addition, we also selected the latest dataset with higher spatial resolution, named WHU-Hi-LongKou, as the challenge object. Using only 0.1% of the training samples, we could achieve a 97.31% classification accuracy, which is far superior to the state-of-the-art hyperspectral classification methods

Plasma Fractionation Enriches Post-Myocardial Infarction Samples Prior to Proteomics Analysis

Following myocardial infarction (MI), matrix metalloproteinase-9 (MMP-9) levels increase, and MMP-9 deletion improves post-MI remodeling of the left ventricle (LV). We provide here a technical report on plasma-analysis from wild type (WT) and MMP-9 null mice using fractionation and mass-spectrometry-based proteomics. MI was induced by coronary artery ligation in male WT and MMP-9 null mice (4–8 months old; n=3/genotype). Plasma was collected on days 0 (pre-) and 1 post-MI. Plasma proteins were fractionated and proteins in the lowest (fraction 1) and highest (fraction 12) molecular weight fractions were separated by 1-D SDS-PAGE, digested in-gel with trypsin and analyzed by HPLC-ESI-MS/MS on an Orbitrap Velos. We tried five different fractionation protocols, before reaching an optimized protocol that allowed us to identify over 100 proteins. Serum amyloid A substantially increased post-MI in both genotypes, while alpha-2 macroglobulin increased only in the null samples. In fraction 12, extracellular matrix proteins were observed only post-MI. Interestingly, fibronectin-1, a substrate of MMP-9, was identified at both day 0 and day 1 post-MI in the MMP-9 null mice but was only identified post-MI in the WT mice. In conclusion, plasma fractionation offers an improved depletion-free method to evaluate plasma changes following MI

From lab to market: a review of commercialization and advances for binders in lithium-, zinc-, sodium-ion batteries

The paper discusses the progress and commercialization of binders for energy storage applications, such as batteries. It explains the role of binders in holding together active materials and current collectors, and highlights the challenges associated with conventional organic solvents in binders. The potential of aqueous binders is introduced as a costeffective and environmentally friendly alternative. The advantages and limitations of different types of binders are discussed, and the importance of binder selection for optimal battery performance is emphasized. The current state of commercialization of binders is reviewed, and the need for collaboration between researchers, manufacturers, and policymakers to develop and promote environmentally friendly and cost-effective binders is emphasized. The paper concludes by outlining future directions for research and development to further improve the performance and commercialization of binders, while addressing limitations such as lack of standardization, high cost, and long-term stability and reliability.ISSN:2791-0091ISSN:2790-811

Global trends in research of mitophagy in liver diseases over past two decades: A bibliometric analysis

Increasing evidence indicated that mitophagy might play a crucial role in the occurrence and progression of liver diseases. In order to enhance our understanding of the intricate relationship between mitophagy and liver diseases, a comprehensive bibliometric analysis of the existing literature in this field was conducted. This analysis aimed to identify key trends, potential areas of future research, and forecast the development of this specific field. We systematically searched the Web of Science Core Collection (WoSCC) for publications related to mitophagy in liver diseases from 2000 to 2022. We conducted the bibliometric analysis and data visualization through VOSviewer and CiteSpace. The analysis of publication growth revealed a substantial increase in articles published in this field over the past years, indicating mitophagy's growing interest and significance in liver diseases. China and USA emerged as the leading contributors in the number of papers, with 294 and 194 independent papers, respectively. Exploring the mechanism of mitophagy in the initiation and procession of liver diseases was the main content of studies in this field, and Parkin-independent mediated mitophagy has attracted much attention recently. “Lipid metabolism,” “cell death,” “liver fibrosis” and “oxidative stress” were the primary keywords clusters. Additionally, “nlrp3 inflammasome”, “toxicity” and “nonalcoholic steatohepatitis” were emerging research hotspots in this area and have the potential to continue to be focal areas of investigation in the future. This study represents the first systematic bibliometric analysis of research on mitophagy in liver diseases conducted over the past 20 years. By providing an overview of the existing literature and identifying current research trends, this analysis sheds light on the critical areas of investigation and paves the way for future studies in this field