Global, regional, and national progress towards Sustainable Development Goal 3.2 for neonatal and child health: all-cause and cause-specific mortality findings from the Global Burden of Disease Study 2019

Background Sustainable Development Goal 3.2 has targeted elimination of preventable child mortality, reduction of neonatal death to less than 12 per 1000 livebirths, and reduction of death of children younger than 5 years to less than 25 per 1000 livebirths, for each country by 2030. To understand current rates, recent trends, and potential trajectories of child mortality for the next decade, we present the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 findings for all-cause mortality and cause-specific mortality in children younger than 5 years of age, with multiple scenarios for child mortality in 2030 that include the consideration of potential effects of COVID-19, and a novel framework for quantifying optimal child survival. Methods We completed all-cause mortality and cause-specific mortality analyses from 204 countries and territories for detailed age groups separately, with aggregated mortality probabilities per 1000 livebirths computed for neonatal mortality rate (NMR) and under-5 mortality rate (USMR). Scenarios for 2030 represent different potential trajectories, notably including potential effects of the COVID-19 pandemic and the potential impact of improvements preferentially targeting neonatal survival. Optimal child survival metrics were developed by age, sex, and cause of death across all GBD location-years. The first metric is a global optimum and is based on the lowest observed mortality, and the second is a survival potential frontier that is based on stochastic frontier analysis of observed mortality and Healthcare Access and Quality Index. Findings Global U5MR decreased from 71.2 deaths per 1000 livebirths (95% uncertainty interval WI] 68.3-74-0) in 2000 to 37.1 (33.2-41.7) in 2019 while global NMR correspondingly declined more slowly from 28.0 deaths per 1000 live births (26.8-29-5) in 2000 to 17.9 (16.3-19-8) in 2019. In 2019,136 (67%) of 204 countries had a USMR at or below the SDG 3.2 threshold and 133 (65%) had an NMR at or below the SDG 3.2 threshold, and the reference scenario suggests that by 2030,154 (75%) of all countries could meet the U5MR targets, and 139 (68%) could meet the NMR targets. Deaths of children younger than 5 years totalled 9.65 million (95% UI 9.05-10.30) in 2000 and 5.05 million (4.27-6.02) in 2019, with the neonatal fraction of these deaths increasing from 39% (3.76 million 95% UI 3.53-4.021) in 2000 to 48% (2.42 million; 2.06-2.86) in 2019. NMR and U5MR were generally higher in males than in females, although there was no statistically significant difference at the global level. Neonatal disorders remained the leading cause of death in children younger than 5 years in 2019, followed by lower respiratory infections, diarrhoeal diseases, congenital birth defects, and malaria. The global optimum analysis suggests NMR could be reduced to as low as 0.80 (95% UI 0.71-0.86) deaths per 1000 livebirths and U5MR to 1.44 (95% UI 1-27-1.58) deaths per 1000 livebirths, and in 2019, there were as many as 1.87 million (95% UI 1-35-2.58; 37% 95% UI 32-43]) of 5.05 million more deaths of children younger than 5 years than the survival potential frontier. Interpretation Global child mortality declined by almost half between 2000 and 2019, but progress remains slower in neonates and 65 (32%) of 204 countries, mostly in sub-Saharan Africa and south Asia, are not on track to meet either SDG 3.2 target by 2030. Focused improvements in perinatal and newborn care, continued and expanded delivery of essential interventions such as vaccination and infection prevention, an enhanced focus on equity, continued focus on poverty reduction and education, and investment in strengthening health systems across the development spectrum have the potential to substantially improve USMR. Given the widespread effects of COVID-19, considerable effort will be required to maintain and accelerate progress. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd

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

COVID-19 Outcomes of Patients With Differentiated Thyroid Cancer: A Multicenter Los Angeles Cohort Study.

ObjectiveCancer may be a risk factor for worse outcomes in severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) infections. However, there is a significant variability across cancer types in the extent of disease burden and modalities of cancer treatment that may impact morbidity and mortality from coronavirus disease-19 (COVID-19). Therefore, we evaluated COVID-19 outcomes in patients with a differentiated thyroid cancer (DTC) history.MethodsThis is a retrospective cohort study of patients with a history of DTC and SARS-CoV2 infection from 2 academic Los Angeles healthcare systems. Demographic, thyroid cancer, and treatment data were analyzed for associations with COVID-19 outcomes.ResultsOf 21 patients with DTC and COVID-19, 8 (38.1%) were hospitalized and 2 (9.5%) died from COVID-19. Thyroid cancer initial disease burden and extent, treatment, or current response to therapy (eg, excellent vs incomplete) were not associated with COVID-19 severity in DTC patients. However, older age and the presence of a comorbidity other than DTC were significantly associated with COVID-19 hospitalization (P = .047 and P = .024, respectively). COVID-19-attributed hospitalization and mortality in DTC patients was lower than that previously reported in cancer patients, although similar to patients with nonthyroid malignancies in these centers.ConclusionThese data suggest that among patients with DTC, advanced age and comorbid conditions are significant contributors to the risk of hospitalization from SARS-CoV2 infection, rather than factors associated with thyroid cancer diagnosis, treatment, or disease burden. This multicenter report of clinical outcomes provides additional data to providers to inform DTC patients regarding their risk of COVID-19

Lumican Binds ALK5 to Promote Epithelium Wound Healing

Lumican (Lum), a small leucine-rich proteoglycan (SLRP) family member, has multiple matricellular functions both as an extracellular matrix component and as a matrikine regulating cell proliferation, gene expression and wound healing. To date, no cell surface receptor has been identified to mediate the matrikine functions of Lum. This study aimed to identify a perspective receptor that mediates Lum effects on promoting wound healing. Transforming growth factor-β receptor 1 (ALK5) was identified as a potential Lum-interacting protein through in silico molecular docking and molecular dynamics. This finding was verified by biochemical pull-down assays. Moreover, the Lum function on wound healing was abrogated by an ALK5-specific chemical inhibitor as well as by ALK5 shRNAi. Finally, we demonstrated that eukaryote-specific post-translational modifications are not required for the wound healing activity of Lum, as recombinant GST-Lum fusion proteins purified from E. coli and a chemically synthesized LumC(13) peptide (the last C-terminal 13 amino acids of Lum) have similar effects on wound healing in vitro and in vivo

A Core Complex of CPSF73, CPSF100, and Symplekin May Form Two Different Cleavage Factors for Processing of Poly(A) and Histone mRNAs

Metazoan histone mRNAs are unique: their pre-mRNAs contain no introns and the mRNAs are not polyadenylated ending instead in a conserved stem-loop structure. In Drosophila, canonical poly(A) signals are located downstream of the normal cleavage site of each histone gene, and are utilized when histone 3’end formation is inhibited. Here we define a sub-complex of poly(A) factors required for histone pre-mRNA processing. We demonstrate that Symplekin, CPSF73 and CPSF100 are present in a stable complex and interact with histone specific processing factors. We use chromatin immunoprecipitation to show that Symplekin and CPSF73, but not CstF50, cotranscriptionally associate with histone genes. Depletion of SLBP recruits CstF50 to histone genes. Knockdown of CPSF160 or CstF64 downregulates Symplekin but does not affect histone pre-mRNA processing or association of Symplekin with the histone locus. These results suggest that a common core cleavage factor is required for processing of histone and polyadenylated pre-mRNAs

Purification of recombinant Lumican and the healing of scratched HTCE cells.

<p><i>Purification of GST-Lum and GST-LumC₅₀ recombinant proteins</i> Purification of recombinant GST-Lum and GST-LumC₅₀ was monitored by Coomassie Brilliant Blue (CBB) staining and western blot anlaysis. (A) CBB staining revealed two major bands eluted with glutathione. The upper band with a Mr. ∼70 kDa was GST-Lum and the lower band with a Mr. ∼25 kDa was GST. (B) Immunostaining with an anti-LumN oilgopeptide antibody (CDDLKLKSVPMVPPGIK) only labeled the 70 kDa band (GST-Lum fusion protein) while the lower band did not react to the antibody and is likely related to GST. (C) CBB stained two bands at 30 and 25 kDa from <i>E.coli</i> transfected with GST-LumC₅₀ plasmid. (D) Immunostaining with an anti-LumC peptide antibody (NPLTQSSLPPDMYEC) labeled the 30 kDa GST-LumC₅₀ fusion protein. <i>Effect of recombinant GST-Lum and GST-LumC₅₀ on healing of scratched HTCE cells</i> Confluent HTCE cells were wounded in CM (complete medium), BM (basic medium), BM + GST (glutathione S-transferase recombinant protein), BM+recombinant GST-Lum (0.15 µM) and GSTLumC_50. The wound gap was determined by time-lapse microscopy. (E) Representative time-lapse images of the healing of scratched HTCE cells; (F) The healing followed biphasic kinetics in cells treated with BM+GST-Lum and CM, whereas those of BM and BM+GST followed monophasic kinetics. R² values were as follows: BM 0.957; CM 0.994; GST 0.985; LumC₅₀ 0.985; Lum 0.995. The rate constants are summarized in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082730#pone-0082730-t001" target="_blank">Table 1</a>.</p

Inhibition and down regulation of ALK5 abrogates the effect of Lum on healing.

<p>(A) <i>ALK5-specific chemical inhibitor (SB431542) blocked the wound healing activity of Lum.</i> SB431542 completely blocked the wound-healing activity of GST-Lum. R² values were as follows: BM 0.984; GST 0.986; GST-Lum 0.989; BM+SB431542 0.853; GST+ SB431542 0.958; Lum+ SB431542 0.920 (B) <i>Selection of ALK5 shRNAi clone.</i> A virus of scrambled control RNA did not change ALK5 mRNA level while ALK5-shRNAi down regulated the target mRNA level from 20% to 80%. The clone (42382) with an 80% knock-down efficiency was used in further experiments. (C) <i>Effects of shRNAi on wound healing of HTCE cells.</i> Down regulation of ALK5 by shRNAi did not significantly affect the biphasic healing of wounded HTCE cells in CM while it abolished the promotion of biphasic wound healing in GST-Lum. Control shRNAi did not affect Lum mediated wound healing. R² values were as follows: Lum+scamble 0.968; Lum+ALK5shRNA 0.917.The rate constants are summarized in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082730#pone-0082730-t002" target="_blank">Table 2</a>.</p

Lumican binds to ALK5: in silico analysis and pull down assays.

<p>In silico molecular dynamic analysis was performed to identify a binding partner of Lum. <i>Panels A and B represent potential binding of Kera and Lum to ALK5 (TGFβ type 1 receptor), respectively.</i> ALK5 in green and the GS domain of ALK5 (TGFbR1) in pink. (A) Keratocan interacts via its Leucine Rich Repeat domain with the extracellular domain of ALK5 with low affinity (ΔG = 30.39 kJ/mol). (B) Lum favorably interacts via its C-terminal domain with the GS domain of ALK5 (ΔG = −100 kJ/mol). The LumC domain has an even higher affinity with ALK5 (ΔG of −1086 kJ/mol). (C) <i>GST pull down assay shows in vitro interaction between ALK5 and GST-Lum.</i> ALK5-Flag was co-eluted with GST-Lum, but not GST, suggesting a direct interaction between Lum and ALK5. (D) <i>In vitro interaction between ALK5 and the C-terminal Lumican.</i> Both ALK5-Flag and GST-LumC were visible in lysates incubated with GST and GST-LumC. IP with an anti-flag antibody resulted in a GST positive band in the mixture of GST-LumC₅₀ and cell lysate, but not in the mixture of GST and lysate, indicating that GST-LumC₅₀, but not GST, is co-precipitated with ALK5-flag by anti-flag antibodies. (E) <i>C-terminal of Lum binds to ALK5.</i> ALK5-Flag was co-precipitated with LumC₅₀-myc and Lum-myc, but not LumN-myc.</p