Determining how polymer-bubble interactions impact algal separation using the novel "Posi"-dissolved air flotation process

The novel dissolved air flotation (DAF) process that uses hydrophobically-modified polymers (HMPs) to generate positively charged bubbles (PosiDAF) has been shown to separate negatively charged algal cells without the need for coagulation-flocculation. Previous research has been limited to HMPs of poly(N,N-dimethylaminoethyl methacrylate) (PDMAEMA) and, while they were effective at bench-scale, performance at pilot-scale was better using commercial poly(N,N-diallyl-N,N-dimethylammonium chloride) (PDADMAC). Hence, the aim of this research was to compare the effectiveness of PDADMAC modified with aliphatic and aromatic moieties in comparison to previously tested PDMAEMA HMPs in respect to algal cell separation and minimisation of effluent polymer concentration, as well as defining the underlying polymer-bubble interaction mechanisms. Polymer-bubble adhesion properties were measured using atomic force microscopy (AFM) while polymer concentration was monitored via zeta potential and, where possible, assays using fluorescence spectroscopy. Both PDADMAC functionalised with a fluorinated aromatic group (PDADMAC-BCF) and PDMAEMA modified with 1-bromodecane respectively, gave effective cell separation, while the treated effluent zeta potential values at maximum cell removal were lower than the other polymers trialled. The effluent polymer concentration when using PDADMAC-BCF was four times lower in comparison to another aromatically modified PDADMAC polymer. AFM studies indicated that, in contrast to the PDMAEMA-based polymers, the PDADMAC-based polymers did not adsorb closely to the bubble surface. The different polymer-bubble interactions indicate that separation mechanisms will also vary, potentially leading to differences in process effectiveness when explored at pilot scale

Pre-exposure prophylaxis to prevent the acquisition of HIV-1 infection (PROUD) : effectiveness results from the pilot phase of a pragmatic open-label randomised trial

Background Randomised placebo-controlled trials have shown that daily oral pre-exposure prophylaxis (PrEP) with tenofovir-emtricitabine reduces the risk of HIV infection. However, this benefit could be counteracted by risk compensation in users of PrEP. We did the PROUD study to assess this effect. Methods PROUD is an open-label randomised trial done at 13 sexual health clinics in England. We enrolled HIV-negative gay and other men who have sex with men who had had anal intercourse without a condom in the previous 90 days. Participants were randomly assigned (1:1) to receive daily combined tenofovir disoproxil fumarate (245 mg) and emtricitabine (200 mg) either immediately or after a deferral period of 1 year. Randomisation was done via web-based access to a central computer-generated list with variable block sizes (stratified by clinical site). Follow-up was quarterly. The primary outcomes for the pilot phase were time to accrue 500 participants and retention; secondary outcomes included incident HIV infection during the deferral period, safety, adherence, and risk compensation. The trial is registered with ISRCTN (number ISRCTN94465371) and ClinicalTrials.gov (NCT02065986). Findings We enrolled 544 participants (275 in the immediate group, 269 in the deferred group) between Nov 29, 2012, and April 30, 2014. Based on early evidence of effectiveness, the trial steering committee recommended on Oct 13, 2014, that all deferred participants be offered PrEP. Follow-up for HIV incidence was complete for 243 (94%) of 259 patient-years in the immediate group versus 222 (90%) of 245 patient-years in the deferred group. Three HIV infections occurred in the immediate group (1·2/100 person-years) versus 20 in the deferred group (9·0/100 person-years) despite 174 prescriptions of post-exposure prophylaxis in the deferred group (relative reduction 86%, 90% CI 64-96, p=0·0001; absolute difference 7·8/100 person-years, 90% CI 4·3-11·3). 13 men (90% CI 9-23) in a similar population would need access to 1 year of PrEP to avert one HIV infection. We recorded no serious adverse drug reactions; 28 adverse events, most commonly nausea, headache, and arthralgia, resulted in interruption of PrEp. We detected no difference in the occurrence of sexually transmitted infections, including rectal gonorrhoea and chlamydia, between groups, despite a suggestion of risk compensation among some PrEP recipients. Interpretation In this high incidence population, daily tenofovir-emtricitabine conferred even higher protection against HIV than in placebo-controlled trials, refuting concerns that effectiveness would be less in a real-world setting. There was no evidence of an increase in other sexually transmitted infections. Our findings strongly support the addition of PrEP to the standard of prevention for men who have sex with men at risk of HIV infection. Funding MRC Clinical Trials Unit at UCL, Public Health England, and Gilead Sciences

Human Muscle Satellite Cells as Targets of Chikungunya Virus Infection

BACKGROUND: Chikungunya (CHIK) virus is a mosquito-transmitted alphavirus that causes in humans an acute infection characterised by fever, polyarthralgia, head-ache, and myalgia. Since 2005, the emergence of CHIK virus was associated with an unprecedented magnitude outbreak of CHIK disease in the Indian Ocean. Clinically, this outbreak was characterized by invalidating poly-arthralgia, with myalgia being reported in 97.7% of cases. Since the cellular targets of CHIK virus in humans are unknown, we studied the pathogenic events and targets of CHIK infection in skeletal muscle. METHODOLOGY/PRINCIPAL FINDINGS: Immunohistology on muscle biopsies from two CHIK virus-infected patients with myositic syndrome showed that viral antigens were found exclusively inside skeletal muscle progenitor cells (designed as satelllite cells), and not in muscle fibers. To evaluate the ability of CHIK virus to replicate in human satellite cells, we assessed virus infection on primary human muscle cells; viral growth was observed in CHIK virus-infected satellite cells with a cytopathic effect, whereas myotubes were essentially refractory to infection. CONCLUSIONS/SIGNIFICANCE: This report provides new insights into CHIK virus pathogenesis, since it is the first to identify a cellular target of CHIK virus in humans and to report a selective infection of muscle satellite cells by a viral agent in humans

Signal transducer and activator of transcription 1 (STAT1) gain-of-function mutations and disseminated coccidioidomycosis and histoplasmosis

Background: Impaired signaling in the IFN-g/IL-12 pathway causes susceptibility to severe disseminated infections with mycobacteria and dimorphic yeasts. Dominant gain-of-function mutations in signal transducer and activator of transcription 1 (STAT1) have been associated with chronic mucocutaneous candidiasis. Objective: We sought to identify the molecular defect in patients with disseminated dimorphic yeast infections. Methods: PBMCs, EBV-transformed B cells, and transfected U3A cell lines were studied for IFN-g/IL-12 pathway function. STAT1 was sequenced in probands and available relatives. Interferon-induced STAT1 phosphorylation, transcriptional responses, protein-protein interactions, target gene activation, and function were investigated. Results: We identified 5 patients with disseminated Coccidioides immitis or Histoplasma capsulatum with heterozygous missense mutations in the STAT1 coiled-coil or DNA-binding domains. These are dominant gain-of-function mutations causing enhanced STAT1 phosphorylation, delayed dephosphorylation, enhanced DNA binding and transactivation, and enhanced interaction with protein inhibitor of activated STAT1. The mutations caused enhanced IFN-g–induced gene expression, but we found impaired responses to IFN-g restimulation. Conclusion: Gain-of-function mutations in STAT1 predispose to invasive, severe, disseminated dimorphic yeast infections, likely through aberrant regulation of IFN-g–mediated inflammationFil: Sampaio, Elizabeth P.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados Unidos. Instituto Oswaldo Cruz. Laboratorio de Leprologia; BrasilFil: Hsu, Amy P.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados UnidosFil: Pechacek, Joseph. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados UnidosFil: Hannelore I.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados Unidos. Erasmus Medical Center. Department of Medical Microbiology and Infectious Disease; Países BajosFil: Dias, Dalton L.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados UnidosFil: Paulson, Michelle L.. Clinical Research Directorate/CMRP; Estados UnidosFil: Chandrasekaran, Prabha. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados UnidosFil: Rosen, Lindsey B.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados UnidosFil: Carvalho, Daniel S.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados Unidos. Instituto Oswaldo Cruz, Laboratorio de Leprologia; BrasilFil: Ding, Li. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados UnidosFil: Vinh, Donald C.. McGill University Health Centre. Division of Infectious Diseases; CanadáFil: Browne, Sarah K.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados UnidosFil: Datta, Shrimati. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Allergic Diseases. Allergic Inflammation Unit; Estados UnidosFil: Milner, Joshua D.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Allergic Diseases. Allergic Inflammation Unit; Estados UnidosFil: Kuhns, Douglas B.. Clinical Services Program; Estados UnidosFil: Long Priel, Debra A.. Clinical Services Program; Estados UnidosFil: Sadat, Mohammed A.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Host Defenses. Infectious Diseases Susceptibility Unit; Estados UnidosFil: Shiloh, Michael. University of Texas. Southwestern Medical Center. Division of Infectious Diseases; Estados UnidosFil: De Marco, Brendan. University of Texas. Southwestern Medical Center. Division of Infectious Diseases; Estados UnidosFil: Alvares, Michael. University of Texas. Southwestern Medical Center. Division of Allergy and Immunology; Estados UnidosFil: Gillman, Jason W.. University of Texas. Southwestern Medical Center. Division of Infectious Diseases; Estados UnidosFil: Ramarathnam, Vivek. University of Texas. Southwestern Medical Center. Division of Infectious Diseases; Estados UnidosFil: de la Morena, Maite. University of Texas. Southwestern Medical Center. Division of Allergy and Immunology; Estados UnidosFil: Bezrodnik, Liliana. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños "Ricardo Gutierrez"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Moreira, Ileana. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños "Ricardo Gutierrez"; ArgentinaFil: Uzel, Gulbu. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados UnidosFil: Johnson, Daniel. University of Chicago. Comer Children; Estados UnidosFil: Spalding, Christine. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados UnidosFil: Zerbe, Christa S.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados UnidosFil: Wiley, Henry. National Eye Institute. Clinical Trials Branch; Estados UnidosFil: Greenberg, David E.. University of Texas. Southwestern Medical Center. Division of Infectious Diseases; Estados UnidosFil: Hoover, Susan E.. University of Arizona. College of Medicine. Valley Fever Center for Excellence; Estados UnidosFil: Rosenzweig, Sergio D.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Host Defenses Infectious Diseases Susceptibility Unit; Estados Unidos. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Primary Immunodeficiency Clinic; Estados UnidosFil: Galgiani, John N.. University of Arizona. College of Medicine. Valley Fever Center for Excellence; Estados UnidosFil: Holland, Steven M.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados Unido

Gaining Greater Insight into HCV Emergence in HIV-Infected Men Who Have Sex with Men: The HEPAIG Study

OBJECTIVES: The HEPAIG study was conducted to better understand Hepatitis C virus (HCV) transmission among human immuno-deficiency (HIV)-infected men who have sex with men (MSM) and assess incidence of HCV infection among this population in France. METHODS AND RESULTS: Acute HCV infection defined by anti-HCV or HCV ribonucleic acid (RNA) positivity within one year of documented anti-HCV negativity was notified among HIV-infected MSM followed up in HIV/AIDS clinics from a nationwide sampling frame. HIV and HCV infection characteristics, HCV potential exposures and sexual behaviour were collected by the physicians and via self-administered questionnaires. Phylogenetic analysis of the HCV-NS5B region was conducted. HCV incidence was 48/10 000 [95% Confidence Interval (CI):43-54] and 36/10 000 [95% CI: 30-42] in 2006 and 2007, respectively. Among the 80 men enrolled (median age: 40 years), 55% were HIV-diagnosed before 2000, 56% had at least one sexually transmitted infection in the year before HCV diagnosis; 55% were HCV-infected with genotype 4 (15 men in one 4d-cluster), 32.5% with genotype 1 (three 1a-clusters); five men were HCV re-infected; in the six-month preceding HCV diagnosis, 92% reported having casual sexual partners sought online (75.5%) and at sex venues (79%), unprotected anal sex (90%) and fisting (65%); using recreational drugs (62%) and bleeding during sex (55%). CONCLUSIONS: This study emphasizes the role of multiple unprotected sexual practices and recreational drugs use during sex in the HCV emergence in HIV-infected MSM. It becomes essential to adapt prevention strategies and inform HIV-infected MSM with recent acute HCV infection on risk of re-infection and on risk-reduction strategies

Crafting organization

The recent shift in attention away from organization studies as science has allowed for consideration of new ways of thinking about both organization and organizing and has led to several recent attempts to \u27bring down\u27 organizational theorizing. In this paper, we extend calls for organization to be represented as a creative process by considering organization as craft. Organizational craft, we argue, is attractive, accessible, malleable, reproducible, and marketable. It is also a tangible way of considering organization studies with irreverence. We draw on the hierarchy of distinctions among fine art, decorative art, and craft to suggest that understanding the organization of craft assists in complicating our understanding of marginality. We illustrate our argument by drawing on the case of a contemporary Australian craftworks and marketplace known initially as the Meat Market Craft Centre (\u27MMCC\u27) and then, until its recent closure, as Metro! &Dagger; Stella Minahan was a board member and then the Chief Executive Officer of the Metro! Craft Centre.<br /

High-Anxious Individuals Show Increased Chronic Stress Burden, Decreased Protective Immunity, and Increased Cancer Progression in a Mouse Model of Squamous Cell Carcinoma

In spite of widespread anecdotal and scientific evidence much remains to be understood about the long-suspected connection between psychological factors and susceptibility to cancer. The skin is the most common site of cancer, accounting for nearly half of all cancers in the US, with approximately 2–3 million cases of non-melanoma cancers occurring each year worldwide. We hypothesized that a high-anxious, stress-prone behavioral phenotype would result in a higher chronic stress burden, lower protective-immunity, and increased progression of the immuno-responsive skin cancer, squamous cell carcinoma. SKH1 mice were phenotyped as high- or low-anxious at baseline, and subsequently exposed to ultraviolet-B light (1 minimal erythemal dose (MED), 3 times/week, 10-weeks). The significant strengths of this cancer model are that it uses a normal, immunocompetent, outbred strain, without surgery/injection of exogenous tumor cells/cell lines, and produces lesions that resemble human tumors. Tumors were counted weekly (primary outcome), and tissues collected during early and late phases of tumor development. Chemokine/cytokine gene-expression was quantified by PCR, tumor-infiltrating helper (Th), cytolytic (CTL), and regulatory (Treg) T cells by immunohistochemistry, lymph node T and B cells by flow cytometry, adrenal and plasma corticosterone and tissue vascular-endothelial-growth-factor (VEGF) by ELISA. High-anxious mice showed a higher tumor burden during all phases of tumor development. They also showed: higher corticosterone levels (indicating greater chronic stress burden), increased CCL22 expression and Treg infiltration (increased tumor-recruited immuno-suppression), lower CTACK/CCL27, IL-12, and IFN-γ gene-expression and lower numbers of tumor infiltrating Th and CTLs (suppressed protective immunity), and higher VEGF concentrations (increased tumor angiogenesis/invasion/metastasis). These results suggest that the deleterious effects of high trait anxiety could be: exacerbated by life-stressors, accentuated by the stress of cancer diagnosis/treatment, and mediate increased tumor progression and/or metastasis. Therefore, it may be beneficial to investigate the use of chemotherapy-compatible anxiolytic treatments immediately following cancer diagnosis, and during cancer treatment/survivorship

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

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. FUNDING: Bill & Melinda Gates Foundation