Second Kind Chebyshev Wavelet Analysis of Abel’s Integral Equations

This paper presents two approximations of the solution functions of Abel’s integral equations belong ing to classes Hα[0,1), Hϕ[0,1) by (λk+1 −1,M)th partial sums of their second kind Chebyshev wavelet expansion in the interval [0,1), for λ > 1. These approximations are E(1) λk+1−1,M (f), E(2) λk+1−1,M (f). Chebyshev wavelets of the second kind were used to solve Abel’s integral equations. The Chebyshev wavelet of the second kind leads to a solution that is almost identical to their exact solution. This research paper’s accomplishment in wavelet analysis is noteworthy

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic

Human leukocyte antigen (HLA)-binding epitopes dataset for the newly identified T-cell antigens of Mycobacterium immunogenum

The dataset described herein is related to our article entitled “T-cell antigens of Mycobacterium immunogenum (MI), an etiological agent of occupational hypersensitivity pneumonitis’’ (Chandra and Yadav, 2016) [1]. The data include in silico-predicted T-cell epitopes of the T-cell antigens AgA and AgD of MI predicted to bind to HLA-I or HLA-II alleles. Data on two reference T-cell antigens ESAT-6 and CFP-10 of Mycobacterium tuberculosis H37Rv are included for comparison. The data for each antigen include the predicted epitope׳s amino acid sequence, its first amino acid position, and its ability to bind HLA-I or HLA-II allele(s). Keywords: Immunoinformatics, T-cell antigen, T-cell epitopes, HLA-I, HLA-II, Mycobacterium immunogenum, Mycobacterium tuberculosis, Hypersensitivity pneumoniti

Alveolar macrophage innate response to Mycobacterium immunogenum, the etiological agent of hypersensitivity pneumonitis: role of JNK and p38 MAPK pathways.

Mycobacterium immunogenum is an emerging pathogen of the immune-mediated lung disease hypersensitivity pneumonitis (HP) reported in machinists occupationally exposed to contaminated metal working fluid (MWF). However, the mechanism of its interaction with the host lung is unclear. Considering that alveolar macrophages play a central role in host defense in the exposed lung, understanding their interaction with the pathogen could provide initial insights into the underlying immunopathogenesis events and mechanisms. In the current study, M. immunogenum 700506, a predominant genotype isolated from HP-linked fluids, was shown to multiply intracellularly, induce proinflammatory mediators (TNF-α, IL-1α, IL-1β, IL-6, GM-CSF, NO) and cause cytotoxicity/cell death in the cultured murine alveolar macrophage cell line MH-S in a dose- and time-dependent manner. The responses were detected as early as 3h post-infection. Comparison of this and four additional genotypes of M. immunogenum (MJY-3, MJY-4, MJY-12, MJY-14) using an effective dose-time combination (100 MOI for 24h) showed these macrophage responses in the following order (albeit with some variations for individual response indicators). Inflammatory: MJY-3 ≥ 700506 > MJY-4 ≥ MJY-14 ≥ MJY-12; Cytotoxic: 700506 ≥ MJY-3 > MJY-4 ≥ MJY-12 ≥ MJY-14. In general, 700506 and MJY-3 showed a more aggressive response than other genotypes. Chemical blocking of either p38 or JNK inhibited the induction of proinflammatory mediators (cytokines, NO) by 700506. However, the cellular responses showed a somewhat opposite effect. This is the first report on M. immunogenum interactions with alveolar macrophages and on the identification of JNK- and p38- mediated signaling and its role in mediating the proinflammatory responses during these interactions

Suppression of inflammatory and infection responses in lung macrophages by eucalyptus oil and its constituent 1,8-cineole: Role of pattern recognition receptors TREM-1 and NLRP3, the MAP kinase regulator MKP-1, and NFκB

<div><p>Eucalyptus oil (EO) used in traditional medicine continues to prove useful for aroma therapy in respiratory ailments; however, there is a paucity of information on its mechanism of action and active components. In this direction, we investigated EO and its dominant constituent 1,8–cineole (eucalyptol) using the murine lung alveolar macrophage (AM) cell line MH-S. In an LPS-induced AM inflammation model, pre-treatment with EO significantly reduced (P ≤0.01or 0.05) the pro-inflammatory mediators TNF-α, IL-1 (α and β), and NO, albeit at a variable rate and extent; 1,8-cineole diminished IL-1 and IL-6. In a mycobacterial-infection AM model, EO pre-treatment or post-treatment significantly enhanced (P ≤0.01) the phagocytic activity and pathogen clearance. 1,8-cineole also significantly enhanced the pathogen clearance though the phagocytic activity was not significantly altered. EO or 1,8-cineole pre-treatment attenuated LPS-induced inflammatory signaling pathways at various levels accompanied by diminished inflammatory response. Among the pattern recognition receptors (PRRs) involved in LPS signaling, the TREM pathway surface receptor (TREM-1) was significantly downregulated. Importantly, the pre-treatments significantly downregulated (P ≤0.01) the intracellular PRR receptor NLRP3 of the inflammasome, which is consistent with the decrease in IL-1β secretion. Of the shared downstream signaling cascade for these PRR pathways, there was significant attenuation of phosphorylation of the transcription factor NF-κB and p38 (but increased phosphorylation of the other two MAP kinases, ERK1/2 and JNK1/2). 1,8-cineole showed a similar general trend except for an opposite effect on NF-κB and JNK1/2. In this context, either pre-treatment caused a significant downregulation of MKP-1 phosphatase, a negative regulator of MAPKs. Collectively, our results demonstrate that the anti-inflammatory activity of EO and 1,8-cineole is modulated via selective downregulation of the PRR pathways, including PRR receptors (TREM-1 and NLRP3) and common downstream signaling cascade partners (NF-κB, MAPKs, MKP-1). To our knowledge, this is the first report on the modulatory role of TREM-1 and NLRP3 inflammasome pathways and the MAPK negative regulator MKP-1 in context of the anti-inflammatory potential of EO and its constituent 1,8-cineole.</p></div

Modulation of LPS-induced pro-inflammatory response in alveolar macrophage cells by pre-treatment with eucalyptus oil (EO).

<p>MH-S cells (1x10⁶ cells/ml culture/well) were treated with (i) Vehicle (0.1% MeOH), (ii) EO only (0.02% vol/vol), (iii) LPS only (2 μg/ml), and (iv) LPS (2 μg/ml) +EO (0.02%); EO was added 3hours before LPS addition (“pre–treatment”). NO and cytokines were periodically measured in the culture supernatant through 24 hour time-point. Values are presented as mean ± SEM based on three independent treatments. Asterisks (* and **) indicate statistically significant (<i>P</i> ≤ 0.05 and <i>P</i> ≤ 0.01, respectively) difference when compared to the positive control (LPS only).</p

Modulation of LPS-activation of MAPKs in alveolar macrophages by pre-treatment with eucalyptus oil (EO) or 1,8-cineole (Cin).

<p>(A-D) EO pre-treatment blots; (E-H) Cin pre-treatment blots for p38, SAPK/ JNK, ERK1/2, and NF-kB. MAPKs, respectively. Activation was assessed in terms of increase in both the total content and the phosphorylated form; NF-kB activation was assessed in terms of increase in its phosphorylated form. Densitometry analysis of Western blots was done using NIH software image J. Lanes1-4 represent vehicle control (VC), EO-only, LPS-only and EO+LPS for EO pre-treatment group or VC, Cin, LPS and Cin +LPS for Cin pre-treatment group, respectively. Details on the treatments and antibodies for total- and phospho- MAPKs and β-Actin are described in Materials and Methods section. Values represent means ± SEM based on three independent treatments. Asterisks (*) and (**) indicate statistically significant (<i>P</i> ≤ 0.05 and <i>P</i> ≤ 0.01, respectively) difference as compared to the LPS treatments while the number sign (#) indicates statistical significance as compared to the vehicle control. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188232#pone.0188232.s001" target="_blank">S1 File</a> for the original Western blot images.</p

Effect of eucalyptus oil (EO) and 1,8-cineole on phagocytosis of <i>Mycobacterium smegmatis</i> and its clearance during infection of alveolar macrophages.

<p>Panels A and C: Effect of EO and cineole on phagocytosis activity (1h); Panels B and D: Effect of EO and cineole on bacterial clearance (24 h). MH-S cells (1x10^6 cells/ml culture/well) were treated with EO (0.02% v/v) or cineole either 3h before phagocytosis (“pre-treatment”) or right after phagocytosis (“post-treatment”) during infection with <i>M</i>. <i>smegmatis</i>. Bacterial counting (CFU analysis) was performed on macrophage cell lysates at 1hour (phagocytosis) or 24 hours (pathogen load) post-infection challenge. Values are presented as mean ± SEM based on three independent treatments. Asterisks (*) and (**) indicate statistically significant (<i>P</i> ≤ 0.05 and P ≤ 0.01, respectively) difference as compared to the vehicle control.</p

Modulation of LPS-induced pro-inflammatory response in alveolar macrophage cells by pre-treatment with 1,8-cineole (Cin).

<p>MH-S cells (1x10⁶ cells/ml culture/well) were treated with i) vehicle (0.1% MeOH), (ii) Cin only (0.02% vol/vol), (iii) LPS only (2 μg/ml), (iv) LPS (2 μg/ml) +Cin (0.02%); Cin was added 3hours before LPS addition (“pre-treatment”). NO and cytokines were periodically measured in the culture supernatant up to 24 hour time-point. Values are presented as mean ± SEM of based on three independent treatments. Asterisks (*) and (**) indicate statistically significant (<i>P</i> ≤ 0.05 and <i>P</i> ≤ 0.01, respectively) difference as compared to the positive control (LPS only).</p

Gene-specific primers for qRT-PCR analysis in this study.

<p>Gene-specific primers for qRT-PCR analysis in this study.</p