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The fourth globe-trotting installment in the exciting Secret Agents Jack and Max Stalwart series. Now Jack teams up with his older brother Max to solve a thrilling California puzzle, using their special training as Global Protection Force agents. In the foothills of California's Sierra Nevada Mountains, an unbelievable discovery is made: $10 million in rare coins dating back to the California Gold Rush! Secret Agents Jack and Max Stalwart are sent to protect the treasure. But almost as soon as they arrive, it vanishes into thin air. Little do they know that the culprit is someone from Jack's past. Unfortunately for the brothers, the criminal isn't only after the loot. He wants revenge.

PurposeThe BCL-2 family of anti-apoptotic proteins, BCL-2, BCL-XL and MCL-1, can mediate survival of some types of cancer. DT2216 is a PROteolysis-TArgeting Chimera (PROTAC) that degrades BCL-XL specifically and is in phase 1 trials. We sought to define the frequency and mechanism of resistance to DT2216 in T-cell acute lymphoblastic leukemia (T-ALL) cell lines.MethodsWe measured cell survival and protein levels of BCL-XL, BCL-2, MCL-1 and the pro-apoptotic BIM in 13 distinct T-ALL cell lines after exposure to varying concentrations of DT2216.ResultsWe identified concentrations of DT2216 which were cytotoxic to each T-ALL cell line.These concentrations have no correlation with the initial protein levels of BCL-XL, BCL-2, MCL-1 or BIM in each cell line. However, there was a correlation between survival to DT2216 and the efficiency of degradation of BCL-XL by DT2216. Only one cell line, SUP-T1, had significant resistance to DT2216, defined as an IC50 above what is achievable in murine tumors in vivo.ConclusionResistance to DT2216 is rare in a wide variety of T-ALL cells but when it occurs is correlated with decreased BCL-XL degradation. Resistance to DT2216 in T-ALL is not predicted by initial BCL-XL or BIM protein levels, or BCL-2 or MCL-1 levels before or after treatment. These data imply that a phase 2 clinical trial of DT2216 in T-ALL should be widely available and not limited to a subset of patients.

The Global Protection Force refused to share any details with Jack about his brother's whereabouts, but on two previous missions, Jack had collected clues that pointed to Max being in Egypt. Now Jack has reason to believe that King Tut's diadem--a crown thought to have magical powers--is the cause of Max's disappearance. Can Jack prevent an ancient and terrible curse of the pharaohs from wreaking havoc and finally save Max?

Fanconi anemia (FA) is a multigenic disease of bone marrow failure and cancer susceptibility stemming from a failure to remove DNA crosslinks and other chromosomal lesions. Within the FA DNA damage response pathway, DNA-dependent monoubiquitinaton of FANCD2 licenses downstream events, while timely FANCD2 deubiquitination serves to extinguish the response. Here, we show with reconstituted biochemical systems, which we developed, that efficient FANCD2 deubiquitination by the USP1-UAF1 complex is dependent on DNA and DNA binding by UAF1. Surprisingly, we find that the DNA binding activity of the UAF1-associated protein RAD51AP1 can substitute for that of UAF1 in FANCD2 deubiquitination in our biochemical system. We also reveal the importance of DNA binding by UAF1 and RAD51AP1 in FANCD2 deubiquitination in the cellular setting. Our results provide insights into a key step in the FA pathway and help define the multifaceted role of the USP1-UAF1-RAD51AP1 complex in DNA damage tolerance and genome repair. In the Fanconi anemia pathway, deubiquitination of FANCD2 is a fundamental regulatory step. Here, the authors have developed a set of biochemical tools to reconstitute FANCD2 deubiquitination by recombinant USP1-UAF1-RAD51AP1 and reveal critical mechanistic details of the process.

يتناول كتاب (جنرالات صدام : وجهات نظر عن الحرب العراقية-الإيرانية) والذي قام بتأليفه (كيفن م. وودز، وليامسن موراي، إليزابيث أ. ناثان، ليلى صبارا، آنا م. فينيغاس) في حوالي (408) صفحة من القطع المتوسط موضوع (الحرب الإيرانية العراقية) مستعرضا المحتويات التالية : الخليفة، أصول الحرب والتخطيط لشنها، تعليم صدام في الحرب، تأقلم القوات المسلحة العراقية مع واقع الحرب، السياق التاريخي ولائحة الأحداث بحسب تسلسلها الزمني، حوار مع الفريق الركن مجيد رشيد الحمداني، القيادة العامة للقوات المسلحة المساعدة الأجنبية.

Coronavirus disease 2019 (COVID-19) has rapidly affected mortality worldwide 1 . There is unprecedented urgency to understand who is most at risk of severe outcomes, and this requires new approaches for the timely analysis of large datasets. Working on behalf of NHS England, we created OpenSAFELY—a secure health analytics platform that covers 40% of all patients in England and holds patient data within the existing data centre of a major vendor of primary care electronic health records. Here we used OpenSAFELY to examine factors associated with COVID-19-related death. Primary care records of 17,278,392 adults were pseudonymously linked to 10,926 COVID-19-related deaths. COVID-19-related death was associated with: being male (hazard ratio (HR) 1.59 (95% confidence interval 1.53–1.65)); greater age and deprivation (both with a strong gradient); diabetes; severe asthma; and various other medical conditions. Compared with people of white ethnicity, Black and South Asian people were at higher risk, even after adjustment for other factors (HR 1.48 (1.29–1.69) and 1.45 (1.32–1.58), respectively). We have quantified a range of clinical factors associated with COVID-19-related death in one of the largest cohort studies on this topic so far. More patient records are rapidly being added to OpenSAFELY, we will update and extend our results regularly. OpenSAFELY, a new health analytics platform that includes data from over 17 million adult NHS patients in England, is used to examine factors associated with COVID-19-related death.

Background It has long been advised to account for baseline covariates in the analysis of confirmatory randomised trials, with the main statistical justifications being that this increases power and, when a randomisation scheme balanced covariates, permits a valid estimate of experimental error. There are various methods available to account for covariates but it is not clear how to choose among them. Methods Taking the perspective of writing a statistical analysis plan, we consider how to choose between the three most promising broad approaches: direct adjustment, standardisation and inverse-probability-of-treatment weighting. Results The three approaches are similar in being asymptotically efficient, in losing efficiency with mis-specified covariate functions and in handling designed balance. If a marginal estimand is targeted (for example, a risk difference or survival difference), then direct adjustment should be avoided because it involves fitting non-standard models that are subject to convergence issues. Convergence is most likely with IPTW. Robust standard errors used by IPTW are anti-conservative at small sample sizes. All approaches can use similar methods to handle missing covariate data. With missing outcome data, each method has its own way to estimate a treatment effect in the all-randomised population. We illustrate some issues in a reanalysis of GetTested , a randomised trial designed to assess the effectiveness of an electonic sexually transmitted infection testing and results service. Conclusions No single approach is always best: the choice will depend on the trial context. We encourage trialists to consider all three methods more routinely.

COVID-19 has disproportionately affected minority ethnic populations in the UK. Our aim was to quantify ethnic differences in SARS-CoV-2 infection and COVID-19 outcomes during the first and second waves of the COVID-19 pandemic in England. We conducted an observational cohort study of adults (aged ≥18 years) registered with primary care practices in England for whom electronic health records were available through the OpenSAFELY platform, and who had at least 1 year of continuous registration at the start of each study period (Feb 1 to Aug 3, 2020 [wave 1], and Sept 1 to Dec 31, 2020 [wave 2]). Individual-level primary care data were linked to data from other sources on the outcomes of interest: SARS-CoV-2 testing and positive test results and COVID-19-related hospital admissions, intensive care unit (ICU) admissions, and death. The exposure was self-reported ethnicity as captured on the primary care record, grouped into five high-level census categories (White, South Asian, Black, other, and mixed) and 16 subcategories across these five categories, as well as an unknown ethnicity category. We used multivariable Cox regression to examine ethnic differences in the outcomes of interest. Models were adjusted for age, sex, deprivation, clinical factors and comorbidities, and household size, with stratification by geographical region. Of 17 288 532 adults included in the study (excluding care home residents), 10 877 978 (62·9%) were White, 1 025 319 (5·9%) were South Asian, 340 912 (2·0%) were Black, 170 484 (1·0%) were of mixed ethnicity, 320 788 (1·9%) were of other ethnicity, and 4 553 051 (26·3%) were of unknown ethnicity. In wave 1, the likelihood of being tested for SARS-CoV-2 infection was slightly higher in the South Asian group (adjusted hazard ratio 1·08 [95% CI 1·07–1·09]), Black group (1·08 [1·06–1·09]), and mixed ethnicity group (1·04 [1·02–1·05]) and was decreased in the other ethnicity group (0·77 [0·76–0·78]) relative to the White group. The risk of testing positive for SARS-CoV-2 infection was higher in the South Asian group (1·99 [1·94–2·04]), Black group (1·69 [1·62–1·77]), mixed ethnicity group (1·49 [1·39–1·59]), and other ethnicity group (1·20 [1·14–1·28]). Compared with the White group, the four remaining high-level ethnic groups had an increased risk of COVID-19-related hospitalisation (South Asian group 1·48 [1·41–1·55], Black group 1·78 [1·67–1·90], mixed ethnicity group 1·63 [1·45–1·83], other ethnicity group 1·54 [1·41–1·69]), COVID-19-related ICU admission (2·18 [1·92–2·48], 3·12 [2·65–3·67], 2·96 [2·26–3·87], 3·18 [2·58–3·93]), and death (1·26 [1·15–1·37], 1·51 [1·31–1·71], 1·41 [1·11–1·81], 1·22 [1·00–1·48]). In wave 2, the risks of hospitalisation, ICU admission, and death relative to the White group were increased in the South Asian group but attenuated for the Black group compared with these risks in wave 1. Disaggregation into 16 ethnicity groups showed important heterogeneity within the five broader categories. Some minority ethnic populations in England have excess risks of testing positive for SARS-CoV-2 and of adverse COVID-19 outcomes compared with the White population, even after accounting for differences in sociodemographic, clinical, and household characteristics. Causes are likely to be multifactorial, and delineating the exact mechanisms is crucial. Tackling ethnic inequalities will require action across many fronts, including reducing structural inequalities, addressing barriers to equitable care, and improving uptake of testing and vaccination. Medical Research Council.

AbstractObjectiveTo derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults.DesignPopulation based cohort study.Setting and participantsQResearch database, comprising 1205 general practices in England with linkage to covid-19 test results, Hospital Episode Statistics, and death registry data. 6.08 million adults aged 19-100 years were included in the derivation dataset and 2.17 million in the validation dataset. The derivation and first validation cohort period was 24 January 2020 to 30 April 2020. The second temporal validation cohort covered the period 1 May 2020 to 30 June 2020.Main outcome measuresThe primary outcome was time to death from covid-19, defined as death due to confirmed or suspected covid-19 as per the death certification or death occurring in a person with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the period 24 January to 30 April 2020. The secondary outcome was time to hospital admission with confirmed SARS-CoV-2 infection. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance, including measures of discrimination and calibration, was evaluated in each validation time period.Results4384 deaths from covid-19 occurred in the derivation cohort during follow-up and 1722 in the first validation cohort period and 621 in the second validation cohort period. The final risk algorithms included age, ethnicity, deprivation, body mass index, and a range of comorbidities. The algorithm had good calibration in the first validation cohort. For deaths from covid-19 in men, it explained 73.1% (95% confidence interval 71.9% to 74.3%) of the variation in time to death (R2); the D statistic was 3.37 (95% confidence interval 3.27 to 3.47), and Harrell’s C was 0.928 (0.919 to 0.938). Similar results were obtained for women, for both outcomes, and in both time periods. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths within 97 days was 75.7%. People in the top 20% of predicted risk of death accounted for 94% of all deaths from covid-19.ConclusionThe QCOVID population based risk algorithm performed well, showing very high levels of discrimination for deaths and hospital admissions due to covid-19. The absolute risks presented, however, will change over time in line with the prevailing SARS-C0V-2 infection rate and the extent of social distancing measures in place, so they should be interpreted with caution. The model can be recalibrated for different time periods, however, and has the potential to be dynamically updated as the pandemic evolves.

Grauer's gorilla (Gorilla beringei graueri), the World's largest primate, is confined to eastern Democratic Republic of Congo (DRC) and is threatened by civil war and insecurity. During the war, armed groups in mining camps relied on hunting bushmeat, including gorillas. Insecurity and the presence of several militia groups across Grauer's gorilla's range made it very difficult to assess their population size. Here we use a novel method that enables rigorous assessment of local community and ranger-collected data on gorilla occupancy to evaluate the impacts of civil war on Grauer's gorilla, which prior to the war was estimated to number 16,900 individuals. We show that gorilla numbers in their stronghold of Kahuzi-Biega National Park have declined by 87%. Encounter rate data of gorilla nests at 10 sites across its range indicate declines of 82-100% at six of these sites. Spatial occupancy analysis identifies three key areas as the most critical sites for the remaining populations of this ape and that the range of this taxon is around 19,700 km2. We estimate that only 3,800 Grauer's gorillas remain in the wild, a 77% decline in one generation, justifying its elevation to Critically Endangered status on the IUCN Red List of Threatened Species.