Surfactant protein A and D polymorphisms and methylprednisolone pharmacogenetics in donor lungs

Differential Regulation of Human Surfactant Protein A Genes, SFTPA1 and SFTPA2, and Their Corresponding Variants

The human SFTPA1 and SFTPA2 genes encode the surfactant protein A1 (SP-A1) and SP-A2, respectively, and they have been identified with significant genetic and epigenetic variability including sequence, deletion/insertions, and splice variants. The surfactant proteins, SP-A1 and SP-A2, and their corresponding variants play important roles in several processes of innate immunity as well in surfactant-related functions as reviewed elsewhere [1]. The levels of SP-A have been shown to differ among individuals both under baseline conditions and in response to various agents or disease states. Moreover, a number of agents have been shown to differentially regulate SFTPA1 and SFTPA2 transcripts. The focus in this review is on the differential regulation of SFTPA1 and SFTPA2 with primary focus on the role of 5′ and 3′ untranslated regions (UTRs) and flanking sequences on this differential regulation as well molecules that may mediate the differential regulation.

Human Surfactant Protein SP-A1 and SP-A2 Variants Differentially Affect the Alveolar Microenvironment, Surfactant Structure, Regulation and Function of the Alveolar Macrophage, and Animal and Human Survival Under Various Conditions

The human innate host defense molecules, SP-A1 and SP-A2 variants, differentially affect survival after infection in mice and in lung transplant patients. SP-A interacts with the sentinel innate immune cell in the alveolus, the alveolar macrophage (AM), and modulates its function and regulation. SP-A also plays a role in pulmonary surfactant-related aspects, including surfactant structure and reorganization. For most (if not all) pulmonary diseases there is a dysregulation of host defense and inflammatory processes and/or surfactant dysfunction or deficiency. Because SP-A plays a role in both of these general processes where one or both may become aberrant in pulmonary disease, SP-A stands to be an important molecule in health and disease. In humans (unlike in rodents) SP-A is encoded by two genes (SFTPA1 and SFTPA2) and each has been identified with extensive genetic and epigenetic complexity. In this review, we focus on functional, structural, and regulatory differences between the two SP-A gene-specific products, SP-A1 and SP-A2, and among their corresponding variants. We discuss the differential impact of these variants on the surfactant structure, the alveolar microenvironment, the regulation of epithelial type II miRNome, the regulation and function of the AM, the overall survival of the organism after infection, and others. Although there have been a number of reviews on SP-A, this is the first review that provides such a comprehensive account of the differences between human SP-A1 and SP-A2.

Transmission State Prediction from MAC in Cognitive Radio via Optimized Deep Learning Architecture

Cognitive Radio (CR) is the hottest network paradigm, which permits the Secondary Users (SUs) like wireless devices/users for intelligent accessing of unallocated radio spectrum. Such accessing happens by enabling interference-free transmission of Primary Users (PUs), who are allotted with some deserved radio spectrum portions. This radio communication paradigm has effective usage in vehicular networks, where communication should be established from vehicles to static stations (vehicle-to-infrastructure) or within vehicles (vehicle-to-vehicle), without allotting dedicated frequencies. Nevertheless, the major issue in designing CR is that it must be built to aid in efficient transmitting and sensing of data through the available radio spectrum channels. This paper proposes a Model Predictive Control (MPC)-based prediction model, via a Deep Learning approach. Here, a Deep Belief Network (DBN) allows in predicting the PU transmission state as idle or busy. Moreover, this paper comes out with a new optimization concept that achieves more accurate and precise prediction. The weight of DBN is optimally selected to pave way for effective performance. Further, a new hybrid algorithm named as Cuckoo Search-Grasshopper Optimization Algorithm (CS-GOA) is proposed. The performance of the proposed model is compared over the other conventional models, in terms of channel utilization and back off, and proved for supremacy. The throughput of the proposed model, even at 50 SUs is better, when compared to other methods. CS-GOA achieved better channel utilization and backoff rate, as compared to ProMAC and NN, when the numbers of SUs and PUs in the architecture are varied with time.

Weight optimized neural network for heart disease prediction using hybrid lion plus particle swarm algorithm

Heart disease remains one of the significantcauses ofmortality and morbidity amongst the world's population. Predicting heart disease is considered as one of the vital issues in clinical data analysis. Since the number of data is rising gradually, it is muchcomplicatedforanalyzing and processing, and especially, it becomes difficult to maintain the e-healthcare data. Moreover, the prediction model under machine learning seems to be anessentialfacet in this research area. In this scenario, this paper aims to propose a new heart disease prediction model with the inclusion of specificprocesses like Feature Extraction, Record, Attribute minimization, and Classification. Initially, both statistical and higher-order statistical features are extracted under feature extraction. Subsequently, the record and attribute minimization carried out, where Component Analysis PCA plays its major role in solving the "curse of dimensionality."Finally, the prediction process takes place by the Neural Network (NN) model that intake the dimensionally reduced features. Moreover, the major intention of this paper deals with the accurate prediction. Hence, it is planned to influence the utility of meta-heuristic algorithms for the weight optimization of NN. This paper introduces a new hybrid algorithm termed Particle Swarm Optimization (PSO) merged LA update (PM-LU) algorithm that solves the above-mentioned optimization crisis, which hybrids the concept of Lion Algorithm (LA) and PSO algorithm. Finally, the efficiency of proposed work is compared over other conventional approaches and its superiority is proven with respect to certain performance measures. From the analysis, the presented PM-LU-NN scheme with regards to accuracy is 3.85%, 12.5%, 12.5%, 3.85%, and 7.41% better than LM-NN, WOA-NN, FF-NN, PSO-NN and LA-NN algorithms.

The Importance of Redox Status in the Frame of Lifestyle Approaches and the Genetics of the Lung Innate Immune Molecules, SP-A1 and SP-A2, on Differential Outcomes of COVID-19 Infection

The pandemic of COVID-19 is of great concern to the scientific community. This mainly affects the elderly and people with underlying diseases. People with obesity are more likely to experience unpleasant disease symptoms and increased mortality. The severe oxidative environment that occurs in obesity due to chronic inflammation permits viral activation of further inflammation leading to severe lung disease. Lifestyle affects the levels of inflammation and oxidative stress. It has been shown that a careful diet rich in antioxidants, regular exercise, and fasting regimens, each and/or together, can reduce the levels of inflammation and oxidative stress and strengthen the immune system as they lead to weight loss and activate cellular antioxidant mechanisms and reduce oxidative damage. Thus, a lifestyle change based on the three pillars: antioxidants, exercise, and fasting could act as a proactive preventative measure against the adverse effects of COVID-19 by maintaining redox balance and well-functioning immunity. Moreover, because of the observed diversity in the expression of COVID-19 inflammation, the role of genetics of innate immune molecules, surfactant protein A (SP-A)1 and SP-A2, and their differential impact on the local lung microenvironment and host defense is reviewed as genetics may play a major role in the diverse expression of the disease.

Donor surfactant protein A2 polymorphism and lung transplant survival

PURPOSE

Gene polymorphisms of surfactant proteins, key players in lung innate immunity, have been associated with various lung diseases. The aim of this study was to investigate the potential association between variations within the surfactant protein (SP)-A gene of the donor lung allograft and recipient post-transplant outcome.

METHODS

Lung-transplant patients (n=192) were prospectively followed-up with pulmonary function tests, bronchoscopies with bronchoalveolar lavage and biopsies. Donor lungs were assayed for SP-A1 (6Aⁿ) and SP-A2 (1Aⁿ) gene polymorphism using the pyrosequencing method. Unadjusted and adjusted stratified Cox survival models are reported.

RESULTS

SP-A1 and SP-A2 genotype frequency and lung transplant recipient and donor characteristics as well as cause of death are noted. Recipients were grouped per donor SP-A2 variants. Individuals that received lungs from donors with the SP-A2 1A⁰ (n=102) versus 1A¹ variant (n=68) or SP-A2 genotype 1A⁰1A⁰ (n=54) versus 1A⁰A¹ (n=38) had greater survival at 1 year (log-rank p<0.025). No significant association was noted for SP-A1 variants. Stratified adjusted survival models for 1-year survival and diagnosis showed a reduced survival for 1A¹ variant and the 1A⁰1A¹ genotype. Furthermore, when survival was conditional on 1-year survival no significance was observed, indicating that the survival difference was due to the first year's outcome associated with the 1A¹ variant.

CONCLUSION

Donor lung SP-A gene polymorphisms are associated with post-transplant clinical outcome. Lungs from donors with the SP-A2 variant 1A¹ had a reduced survival at 1 year. The observed donor genetic differences, via innate immunity relate to the post-transplant clinical outcome.