Nutrient Estimation from 24-Hour Food Recalls Using Machine Learning and Database Mapping: A Case Study with Lactose.

The Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24) is a free dietary recall system that outputs fewer nutrients than the Nutrition Data System for Research (NDSR). NDSR uses the Nutrition Coordinating Center (NCC) Food and Nutrient Database, both of which require a license. Manual lookup of ASA24 foods into NDSR is time-consuming but currently the only way to acquire NCC-exclusive nutrients. Using lactose as an example, we evaluated machine learning and database matching methods to estimate this NCC-exclusive nutrient from ASA24 reports. ASA24-reported foods were manually looked up into NDSR to obtain lactose estimates and split into training (n = 378) and test (n = 189) datasets. Nine machine learning models were developed to predict lactose from the nutrients common between ASA24 and the NCC database. Database matching algorithms were developed to match NCC foods to an ASA24 food using only nutrients ("Nutrient-Only") or the nutrient and food descriptions ("Nutrient + Text"). For both methods, the lactose values were compared to the manual curation. Among machine learning models, the XGB-Regressor model performed best on held-out test data (R2 = 0.33). For the database matching method, Nutrient + Text matching yielded the best lactose estimates (R2 = 0.76), a vast improvement over the status quo of no estimate. These results suggest that computational methods can successfully estimate an NCC-exclusive nutrient for foods reported in ASA24

The Xist lncRNA interacts directly with SHARP to silence transcription through HDAC3

Many long non-coding RNAs (lncRNAs) affect gene expression, but the mechanisms by which they act are still largely unknown. One of the best-studied lncRNAs is Xist, which is required for transcriptional silencing of one X chromosome during development in female mammals. Despite extensive efforts to define the mechanism of Xist-mediated transcriptional silencing, we still do not know any proteins required for this role. The main challenge is that there are currently no methods to comprehensively define the proteins that directly interact with a lncRNA in the cell. Here we develop a method to purify a lncRNA from cells and identify proteins interacting with it directly using quantitative mass spectrometry. We identify ten proteins that specifically associate with Xist, three of these proteins—SHARP, SAF-A and LBR—are required for Xist-mediated transcriptional silencing. We show that SHARP, which interacts with the SMRT co-repressor that activates HDAC3, is not only essential for silencing, but is also required for the exclusion of RNA polymerase II (Pol II) from the inactive X. Both SMRT and HDAC3 are also required for silencing and Pol II exclusion. In addition to silencing transcription, SHARP and HDAC3 are required for Xist-mediated recruitment of the polycomb repressive complex 2 (PRC2) across the X chromosome. Our results suggest that Xist silences transcription by directly interacting with SHARP, recruiting SMRT, activating HDAC3, and deacetylating histones to exclude Pol II across the X chromosome

ImmunoPET helps predicting the efficacy of antibody-drug conjugates targeting TENB2 and STEAP1

The efficacy of antibody-drug conjugates (ADCs) targeted to solid tumors depends on biological processes that are hard to monitor in vivo. Zr-89-immunoPET of the ADC antibodies could help understand the performance of ADCs in the clinic by confirming the necessary penetration, binding, and internalization. This work studied monomethyl auristatin E (MMAE) ADCs against two targets in metastatic castration-resistant prostate cancer, TENB2 and STEAP1, in four patient-derived tumor models (LuCaP35V, LuCaP70, LuCaP77, LuCaP96.1). Three aspects of ADC biology were measured and compared: efficacy was measured in tumor growth inhibition studies; target expression was measured by immunohistochemistry and flow cytometry; and tumor antibody uptake was measured with In-111-mAbs and gamma counting or with Zr-89-immunoPET. Within each model, the mAb with the highest tumor uptake showed the greatest potency as an ADC. Sensitivity between models varied, with the LuCaP77 model showing weak efficacy despite high target expression and high antibody uptake. Ex vivo analysis confirmed the in vivo results, showing a correlation between expression, uptake and ADC efficacy. We conclude that Zr-89-immunoPET data can demonstrate which ADC candidates achieve the penetration, binding, and internalization necessary for efficacy in tumors sensitive to the toxic payload

Gait speed as predictor of transition into cognitive impairment: Findings from three longitudinal studies on aging

Objectives: Very few studies looking at slow gait speed as early marker of cognitive decline investigated the competing risk of death. The current study examines associations between slow gait speed and transitions between cognitive states and death in later life. Methods: We performed a coordinated analysis of three longitudinal studies with 9 to 25 years of follow-up. Data were used from older adults participating in H70 (Sweden; n = 441; aged ≥70 years), InCHIANTI (Italy; n = 955; aged ≥65 years), and LASA (the Netherlands; n = 2824; aged ≥55 years). Cognitive states were distinguished using the Mini-Mental State Examination. Slow gait speed was defined as the lowest sex-specific quintile at baseline. Multistate models were performed, adjusted for age, sex and education. Results: Most effect estimates pointed in the same direction, with slow gait speed predicting forward transitions. In two cohort studies, slow gait speed predicted transitioning from mild to severe cognitive impairment (InCHIANTI: HR = 2.08, 95%CI = 1.40–3.07; LASA: HR = 1.33, 95%CI = 1.01–1.75) and transitioning from a cognitively healthy state to death (H70: HR = 3.30, 95%CI = 1.74–6.28; LASA: HR = 1.70, 95%CI = 1.30–2.21). Conclusions: Screening for slow gait speed may be useful for identifying older adults at risk of adverse outcomes such as cognitive decline and death. However, once in the stage of more advanced cognitive impairment, slow gait speed does not seem to predict transitioning to death anymore

Updated Guidance Regarding The Risk ofAllergic Reactions to COVID-19 Vaccines and Recommended Evaluation and Management: A GRADE Assessment, and International Consensus Approach

This guidance updates 2021 GRADE (Grading of Recommendations Assessment, Development and Evaluation) recommendations regarding immediate allergic reactions following coronavirus disease 2019 (COVID-19) vaccines and addresses revaccinating individuals with first-dose allergic reactions and allergy testing to determine revaccination outcomes. Recent meta-analyses assessed the incidence of severe allergic reactions to initial COVID-19 vaccination, risk of mRNA-COVID-19 revaccination after an initial reaction, and diagnostic accuracy of COVID-19 vaccine and vaccine excipient testing in predicting reactions. GRADE methods informed rating the certainty of evidence and strength of recommendations. A modified Delphi panel consisting of experts in allergy, anaphylaxis, vaccinology, infectious diseases, emergency medicine, and primary care from Australia, Canada, Europe, Japan, South Africa, the United Kingdom, and the United States formed the recommendations. We recommend vaccination for persons without COVID-19 vaccine excipient allergy and revaccination after a prior immediate allergic reaction. We suggest against \u3e 15-minute postvaccination observation. We recommend against mRNA vaccine or excipient skin testing to predict outcomes. We suggest revaccination of persons with an immediate allergic reaction to the mRNA vaccine or excipients be performed by a person with vaccine allergy expertise in a properly equipped setting. We suggest against premedication, split-dosing, or special precautions because of a comorbid allergic history

Ovarian cancer molecular pathology.

Peer reviewe

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Tracking development assistance for health and for COVID-19: a review of development assistance, government, out-of-pocket, and other private spending on health for 204 countries and territories, 1990-2050

Background The rapid spread of COVID-19 renewed the focus on how health systems across the globe are financed, especially during public health emergencies. Development assistance is an important source of health financing in many low-income countries, yet little is known about how much of this funding was disbursed for COVID-19. We aimed to put development assistance for health for COVID-19 in the context of broader trends in global health financing, and to estimate total health spending from 1995 to 2050 and development assistance for COVID-19 in 2020. Methods We estimated domestic health spending and development assistance for health to generate total health-sector spending estimates for 204 countries and territories. We leveraged data from the WHO Global Health Expenditure Database to produce estimates of domestic health spending. To generate estimates for development assistance for health, we relied on project-level disbursement data from the major international development agencies' online databases and annual financial statements and reports for information on income sources. To adjust our estimates for 2020 to include disbursements related to COVID-19, we extracted project data on commitments and disbursements from a broader set of databases (because not all of the data sources used to estimate the historical series extend to 2020), including the UN Office of Humanitarian Assistance Financial Tracking Service and the International Aid Transparency Initiative. We reported all the historic and future spending estimates in inflation-adjusted 2020 US$, 2020 US$ per capita, purchasing-power parity-adjusted US$per capita, and as a proportion of gross domestic product. We used various models to generate future health spending to 2050. Findings In 2019, health spending globally reached$8. 8 trillion (95% uncertainty interval UI] 8.7-8.8) or $1132 (1119-1143) per person. Spending on health varied within and across income groups and geographical regions. Of this total,$40.4 billion (0.5%, 95% UI 0.5-0.5) was development assistance for health provided to low-income and middle-income countries, which made up 24.6% (UI 24.0-25.1) of total spending in low-income countries. We estimate that $54.8 billion in development assistance for health was disbursed in 2020. Of this,$13.7 billion was targeted toward the COVID-19 health response. $12.3 billion was newly committed and$1.4 billion was repurposed from existing health projects. $3.1 billion (22.4$2.4 billion (17.9%) was for supply chain and logistics. Only $714.4 million (7.7$1519 (1448-1591) per person in 2050, although spending across countries is expected to remain varied. Interpretation Global health spending is expected to continue to grow, but remain unequally distributed between countries. We estimate that development organisations substantially increased the amount of development assistance for health provided in 2020. Continued efforts are needed to raise sufficient resources to mitigate the pandemic for the most vulnerable, and to help curtail the pandemic for all. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd