Prolonged Time From Diagnosis to Breast-Conserving Surgery Is Associated With Upstaging in Hormone Receptor-Positive Invasive Ductal Breast Carcinoma

BACKGROUND: Time to surgery (TTS) has been suggested to have an association with mortality in early-stage breast cancer. OBJECTIVE: This study aims to determine the association between TTS and preoperative disease progression in tumor size or nodal status among women diagnosed with clinical T1N0M0 ductal breast cancer. METHODS: Women diagnosed with clinical T1N0M0 ductal breast cancer who had breast-conserving surgery as their first definitive treatment between 2010 and 2016 (n = 90,405) were analyzed using the National Cancer Database. Separate multivariable logistic regression models for hormone receptor (HR)-positive and HR-negative patients, adjusted for clinical and demographic variables, were used to assess the relationship between TTS and upstaging of tumor size (T-upstaging) or nodal status (N-upstaging). RESULTS: T-upstaging occurred in 6.76% of HR-positive patients and 11.00% of HR-negative patients, while N-upstaging occurred in 12.69% and 10.75% of HR-positive and HR-negative patients, respectively. Among HR-positive patients, odds of T-upstaging were higher for 61-90 days TTS (odds ratio [OR] 1.18, 95% confidence interval [CI] 1.05-1.34) and ≥91 days TTS (OR 1.47, 95% CI 1.17-1.84) compared with ≤30 days TTS, and odds of N- upstaging were higher for ≥91 days TTS (OR 1.35, 95% CI 1.13-1.62). No association between TTS and either T- or N-upstaging was found among HR-negative patients. Other clinical and demographic variables, including grade, tumor location, and race/ethnicity, were associated with both T- and N-upstaging. CONCLUSION: TTS ≥61 and ≥91 days was a significant predictor of T- and N-upstaging, respectively, in HR-positive patients; however, TTS was not associated with upstaging in HR-negative breast cancer. Delays in surgery may contribute to measurable disease progression in T1N0M0 ductal breast cancer

The Conserved Tarp Actin Binding Domain Is Important for Chlamydial Invasion

The translocated actin recruiting phosphoprotein (Tarp) is conserved among all pathogenic chlamydial species. Previous reports identified single C. trachomatis Tarp actin binding and proline rich domains required for Tarp mediated actin nucleation. A peptide antiserum specific for the Tarp actin binding domain was generated and inhibited actin polymerization in vitro and C. trachomatis entry in vivo, indicating an essential role for Tarp in chlamydial pathogenesis. Sequence analysis of Tarp orthologs from additional chlamydial species and C. trachomatis serovars indicated multiple putative actin binding sites. In order to determine whether the identified actin binding domains are functionally conserved, GST-Tarp fusions from multiple chlamydial species were examined for their ability to bind and nucleate actin. Chlamydial Tarps harbored variable numbers of actin binding sites and promoted actin nucleation as determined by in vitro polymerization assays. Our findings indicate that Tarp mediated actin binding and nucleation is a conserved feature among diverse chlamydial species and this function plays a critical role in bacterial invasion of host cells

Scleroderma and related disorders: 223. Long Term Outcome in a Contemporary Systemic Sclerosis Cohort

Background: We have previously compared outcome in two groups of systemic sclerosis (SSc) patients with disease onset a decade apart and we reported data on 5 year survival and cumulative incidence of organ disease in a contemporary SSc cohort. The present study examines longer term outcome in an additional cohort of SSc followed for 10 years. Methods: We have examined patients with disease onset between years 1995 and 1999 allowing for at least 10 years of follow-up in a group that has characteristics representative for the patients we see in contemporary clinical practice. Results: Of the 398 patients included in the study, 252 (63.3%) had limited cutaneous (lc) SSc and 146 (36.7%) had diffuse cutaneous (dc) SSc. The proportion of male patients was higher among the dcSSc group (17.1% v 9.9%, p = 0.037) while the mean age of onset was significantly higher among lcSSc patients (50 ± 13 v 46 ± 13 years ± SD, p = 0.003). During a 10 year follow-up from disease onset, 45% of the dcSSc and 21% of the lcSSc subjects developed clinically significant pulmonary fibrosis, p < 0.001. Among them approximately half reached the endpoint within the first 3 years (23% of dcSSc and 10% of lcSSc) and over three quarters within the first 5 years (34% and 16% respectively). There was a similar incidence of pulmonary hypertension (PH) in the two subsets with a steady rate of increase over time. At 10 years 13% of dcSSc and 15% of lcSSc subjects had developed PH (p=0.558), with the earliest cases observed within the first 2 years of disease. Comparison between subjects who developed PH in the first and second 5 years from disease onset demonstrated no difference in demographic or clinical characteristics, but 5-year survival from PH onset was better among those who developed this complication later in their disease (49% v 24%), with a strong trend towards statistical significance (p = 0.058). Incidence of SSc renal crisis (SRC) was significantly higher among the dcSSc patients (12% v 4% in lcSSc, p = 0.002). As previously observed, the rate of development of SRC was highest in the first 3 years of disease- 10% in dcSSc and 3% in lcSSc. All incidences of clinically important cardiac disease developed in the first 5 years from disease onset (7% in dcSSc v 1% in lcSSc, p < 0.001) and remained unchanged at 10 years. As expected, 10-year survival among lcSSc subjects was significantly higher (81%) compared to that of dcSSc patients (70%, p = 0.006). Interestingly, although over the first 5 years the death rate was much higher in the dcSSc cohort (16% v 6% in lcSSc), over the following years it became very similar for both subsets (14% and 13% between years 5 and 10, and 18% and 17% between years 10 and 15 for dcSSc and lcSSc respectively). Conclusions: Even though dcSSc patients have higher incidence for most organ complications compared to lcSSc subjects, the worse survival among them is mainly due to higher early mortality rate. Mortality rate after first 5 years of disease becomes comparable in the two disease subsets. Disclosure statement: The authors have declared no conflicts of interes

Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings: Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation: As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and developm nt investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Funding: Bill & Melinda Gates Foundation. © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licens

Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950-2019 : a comprehensive demographic analysis for the Global Burden of Disease Study 2019

Background: Accurate and up-to-date assessment of demographic metrics is crucial for understanding a wide range of social, economic, and public health issues that affect populations worldwide. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 produced updated and comprehensive demographic assessments of the key indicators of fertility, mortality, migration, and population for 204 countries and territories and selected subnational locations from 1950 to 2019. Methods: 8078 country-years of vital registration and sample registration data, 938 surveys, 349 censuses, and 238 other sources were identified and used to estimate age-specific fertility. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate age-specific fertility rates for 5-year age groups between ages 15 and 49 years. With extensions to age groups 10–14 and 50–54 years, the total fertility rate (TFR) was then aggregated using the estimated age-specific fertility between ages 10 and 54 years. 7417 sources were used for under-5 mortality estimation and 7355 for adult mortality. ST-GPR was used to synthesise data sources after correction for known biases. Adult mortality was measured as the probability of death between ages 15 and 60 years based on vital registration, sample registration, and sibling histories, and was also estimated using ST-GPR. HIV-free life tables were then estimated using estimates of under-5 and adult mortality rates using a relational model life table system created for GBD, which closely tracks observed age-specific mortality rates from complete vital registration when available. Independent estimates of HIV-specific mortality generated by an epidemiological analysis of HIV prevalence surveys and antenatal clinic serosurveillance and other sources were incorporated into the estimates in countries with large epidemics. Annual and single-year age estimates of net migration and population for each country and territory were generated using a Bayesian hierarchical cohort component model that analysed estimated age-specific fertility and mortality rates along with 1250 censuses and 747 population registry years. We classified location-years into seven categories on the basis of the natural rate of increase in population (calculated by subtracting the crude death rate from the crude birth rate) and the net migration rate. We computed healthy life expectancy (HALE) using years lived with disability (YLDs) per capita, life tables, and standard demographic methods. Uncertainty was propagated throughout the demographic estimation process, including fertility, mortality, and population, with 1000 draw-level estimates produced for each metric. Findings: The global TFR decreased from 2·72 (95% uncertainty interval [UI] 2·66–2·79) in 2000 to 2·31 (2·17–2·46) in 2019. Global annual livebirths increased from 134·5 million (131·5–137·8) in 2000 to a peak of 139·6 million (133·0–146·9) in 2016. Global livebirths then declined to 135·3 million (127·2–144·1) in 2019. Of the 204 countries and territories included in this study, in 2019, 102 had a TFR lower than 2·1, which is considered a good approximation of replacement-level fertility. All countries in sub-Saharan Africa had TFRs above replacement level in 2019 and accounted for 27·1% (95% UI 26·4–27·8) of global livebirths. Global life expectancy at birth increased from 67·2 years (95% UI 66·8–67·6) in 2000 to 73·5 years (72·8–74·3) in 2019. The total number of deaths increased from 50·7 million (49·5–51·9) in 2000 to 56·5 million (53·7–59·2) in 2019. Under-5 deaths declined from 9·6 million (9·1–10·3) in 2000 to 5·0 million (4·3–6·0) in 2019. Global population increased by 25·7%, from 6·2 billion (6·0–6·3) in 2000 to 7·7 billion (7·5–8·0) in 2019. In 2019, 34 countries had negative natural rates of increase; in 17 of these, the population declined because immigration was not sufficient to counteract the negative rate of decline. Globally, HALE increased from 58·6 years (56·1–60·8) in 2000 to 63·5 years (60·8–66·1) in 2019. HALE increased in 202 of 204 countries and territories between 2000 and 2019

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Role for chlamydial inclusion membrane proteins in inclusion membrane structure and biogenesis.

The chlamydial inclusion membrane is extensively modified by the insertion of type III secreted effector proteins. These inclusion membrane proteins (Incs) are exposed to the cytosol and share a common structural feature of a long, bi-lobed hydrophobic domain but little or no primary amino acid sequence similarity. Based upon secondary structural predictions, over 50 putative inclusion membrane proteins have been identified in Chlamydia trachomatis. Only a limited number of biological functions have been defined and these are not shared between chlamydial species. Here we have ectopically expressed several C. trachomatis Incs in HeLa cells and find that they induce the formation of morphologically distinct membranous vesicular compartments. Formation of these vesicles requires the bi-lobed hydrophobic domain as a minimum. No markers for various cellular organelles were observed in association with these vesicles. Lipid probes were incorporated by the Inc-induced vesicles although the lipids incorporated were dependent upon the specific Inc expressed. Co-expression of Inc pairs indicated that some colocalized in the same vesicle, others partially overlapped, and others did not associate at all. Overall, it appears that Incs may have an intrinsic ability to induce membrane formation and that individual Incs can induce membranous structures with unique properties

Tarp orthologs harbor multiple actin binding domains.

<p><b>A</b>) A schematic of the Tarp orthologs from <i>C. trachomatis</i> serovar L2 (L2), <i>C. trachomatis</i> serovar D (D), <i>C. trachomatis</i> serovar A (A), <i>C. muridarum</i> (MoPn), <i>C. pneumoniae</i> (Cpn), and <i>C. caviae</i> (GPIC) indicating the location of the putative actin binding domains (red boxes), a proline rich domain (blue boxes), and tyrosine rich phosphorylation domain (green boxes). <b>B</b>) ClustalW sequence alignment of the putative actin binding domains from Tarp orthologs in A. The sequence predicted to harbor the actin binding alpha helix is indicated by the open box. Identical amino acids within each alignment are in red. Similar residues are in blue. The consensus sequence shown is based on homology greater than 50%. The number indicates the amino acid residue of the amino terminus of the peptide shown. <b>C</b>) The Tarp orthologs associate with actin. GST-fusions of the Tarp orthologs described above harboring sequence similar to the <i>C. trachomatis</i> L2 (L2) actin binding domain were expressed and purified. Extracts from HeLa cells were incubated with GST or GST fusions to Tarp orthologs and specifically bound proteins were resolved by SDS-PAGE and visualized by Coomassie blue staining (CB). Samples identical to those shown in the Coomassie-stained gel were subject to immunoblotting with an actin (α actin) specific antibody. A GST fusion to the VCA domain of N-wasp (GST-VCA) served as a positive control for actin binding.</p

The <i>C. trachomatis</i> serovar A Tarp ortholog employs a spire-like actin nucleation mechanism and does not require the L2 Tarp proline rich domain for actin nucleation.

<p><i>C. trachomatis</i> serovar A Tarp fragments harboring either the three functional actin binding domains (ABDs) alone or the actin binding domains and the proline rich domain (PRD) were digested to remove the GST moiety and analyzed by gel filtration and pyrene actin polymerization assays. <b>A</b>) <i>C. trachomatis</i> serovar A GST-Tarp fusion proteins were purified and digested with protease (+/− enz) to remove the GST moiety (* indicates GST is removed). Proteins were resolved by SDS/PAGE and visualized by Coomassie blue staining. <b>B</b>) Removal of the proline rich domain from <i>C. trachomatis</i> A Tarp inhibits oligomerization. Gel filtration of proteins shown in panel <b>A</b>. Protein fractions were collected in 2-min intervals from gel filtration columns and immobilized to a nitrocellulose membrane by vacuum filtration. Membranes were subjected to immunoblotting with a Tarp specific antibody. Protein standards are indicated above the dot-blot with respective molecular weight and peak elution times. <b>C</b>) Oligomerization of <i>C. trachomatis</i> A Tarp is not required for actin nucleation. Purified Tarp (A) with and without proline rich domain increased actin polymerization compared to GST and actin alone controls in pyrene actin polymerization assays. The results are from one experiment representative of three separate experiments.</p

The Tarp actin binding domain (ABD) peptide antibody recognizes native Tarp of multiple serovars and species and does not recognize the ABD of the host cell WAVE2 protein.

<p><b>A</b>) Schematic of <i>C. trachomatis</i> GST-Tarp fusions used to examine the specificity of the peptide antibody directed toward the Tarp actin binding domain. Tarp amino acids and positions are indicated above each bar in the schematic. Purified GST fusions were immobilized to nitrocellulose and immunoblots were performed with Tarp actin binding domain (αABD) and Tarp (α Tarp) specific antibodies. <b>B</b>) The Tarp actin binding domain (α ABD) specific antisera recognizes only a single protein within chlamydia-infected host cells. Chlamydia-infected (+L2) and uninfected (−L2) host cells were suspended in protein sample buffer following a 30 min. infection. Proteins were resolved by SDS-PAGE and visualized by Coomassie blue staining (CB). Immunoblots were performed with Tarp (α Tarp) and Tarp actin binding domain (α ABD) specific antisera. <b>C</b>) The Tarp actin binding domain (α ABD) antibody recognizes a protein in lysates generated from purified <i>C. trachomatis</i> serovar L2 (LGV-434), <i>C. caviae</i> (GPIC), <i>C. pneumoniae</i> (Cpn), <i>C. trachomatis</i> serovar D (D-UW3), <i>C. trachomatis</i> serovar A (A HAR-13) and <i>C. muridarum</i> mouse pneumonitis biovar (MoPn) elementary bodies. Loading for SDS-PAGE was based upon equivalent numbers of EBs. <i>C. pneumoniae</i> Tarp was not readily visible on the original exposure but was easily visualized with longer exposures. <b>D</b>) The Tarp actin binding domain (α ABD) antibody recognizes non-reduced, non-denatured native protein immobilized to nitrocellulose. Immunoblots were performed of lysates generated from cells infected with <i>C. trachomatis</i> (HeLa +L2) and uninfected host cells (HeLa). Purified recombinant Tarp protein (C-domain Tarp) and solubilized lysates derived from elementary bodies (EBs) served as positive controls. Immunoblots to detect WAVE2 (α WAVE2) and actin (α actin) were performed as additional controls. <b>E</b>) The Tarp actin binding domain (α ABD) antibody immunoprecipitates Tarp from infected cells. Tarp was immunoprecipitated with α ABD from lysates generated from cells infected with <i>C. trachomatis</i> (HeLa +L2) and uninfected host cells (HeLa). Proteins were resolved by SDS-PAGE and immunoblotted with Tarp (α Tarp) and WAVE2 (α WAVE2) specific antibodies (arrowheads). The anti-Tarp polyclonal antibody recognizes an unknown antigen in the infected and uninfected HeLa cell lysates that is not immunoprecipitated by the α ABD antibody. The αABD antibody does not recognize this antigen in immunoblots (panels B, D, and F). Note that the IgG heavy chain is observed in both infected and uninfected lanes. <b>F</b>) Tarp and WAVE2 were immunoprecipitated from infected (+L2) and uninfected (−L2) HeLa cells, resolved by SDS-PAGE and immunoblotted with Tarp actin binding domain (α ABD) and WAVE2 (α WAVE2) specific antibodies (arrowheads). Molecular mass is in kilodaltons (kDa) for panels B, C, E & F.</p