Printing High Viscosity Fluids using Ultrasonic Droplet Generation

A new printing technology based on ultrasonic actuation (~1 MHz) is presented that has the potential to print high viscosity fluids. In this paper, we describe the print-head’s operating principles and construction. Acoustic focusing in the nozzles produces high pressure gradients that help eject the fluid which, under the proper conditions, forms droplets. Two types of models are presented to attempt to predict print-head behavior over a range of conditions. The first model borrows from simple fully developed, laminar flows to estimate printing conditions based on fluid properties, as well as printing pressures. The second model captures the dynamic behavior of the print-head to estimate cavity resonances that lead to acoustic focusing and potentially droplet generation. We report on experiments with several types of fluids that demonstrate the technology’s potential.Mechanical Engineerin

FcγR-mediated SARS-CoV-2 infection of monocytes activates inflammation

SARS-CoV-2 can cause acute respiratory distress and death in some patients1. Although severe COVID-19 disease is linked to exuberant inflammation, how SARS-CoV-2 triggers inflammation is not understood2. Monocytes and macrophages are sentinel cells that sense invasive infection to form inflammasomes that activate caspase-1 and gasdermin D (GSDMD), leading to inflammatory death (pyroptosis) and release of potent inflammatory mediators3. Here we show that about 6% of blood monocytes in COVID-19 patients are infected with SARS-CoV-2. Monocyte infection depends on uptake of antibody-opsonized virus by Fcγ receptors. Vaccine recipient plasma does not promote antibody-dependent monocyte infection. SARS-CoV-2 begins to replicate in monocytes, but infection is aborted, and infectious virus is not detected in infected monocyte culture supernatants. Instead, infected cells undergo inflammatory cell death (pyroptosis) mediated by activation of NLRP3 and AIM2 inflammasomes, caspase-1 and GSDMD. Moreover, tissue-resident macrophages, but not infected epithelial and endothelial cells, from COVID-19 lung autopsies have activated inflammasomes. These findings taken together suggest that antibody-mediated SARS-CoV-2 uptake by monocytes/macrophages triggers inflammatory cell death that aborts production of infectious virus but causes systemic inflammation that contributes to COVID-19 pathogenesis

Assessing the clinical utility of cancer genomic and proteomic data across tumor types

Molecular profiling of tumors promises to advance the clinical management of cancer, but the benefits of integrating molecular data with traditional clinical variables have not been systematically studied. Here we retrospectively predict patient survival using diverse molecular data (somatic copy-number alteration, DNA methylation and mRNA, miRNA and protein expression) from 953 samples of four cancer types from The Cancer Genome Atlas project. We found that incorporating molecular data with clinical variables yielded statistically significantly improved predictions (FDR < 0.05) for three cancers but those quantitative gains were limited (2.2–23.9%). Additional analyses revealed little predictive power across tumor types except for one case. In clinically relevant genes, we identified 10,281 somatic alterations across 12 cancer types in 2,928 of 3,277 patients (89.4%), many of which would not be revealed in single-tumor analyses. Our study provides a starting point and resources, including an open-access model evaluation platform, for building reliable prognostic and therapeutic strategies that incorporate molecular data

Timing Constraints of In Vivo Gag Mutations during Primary HIV-1 Subtype C Infection

Background: Aiming to answer the broad question “When does mutation occur?” this study examined the time of appearance, dominance, and completeness of in vivo Gag mutations in primary HIV-1 subtype C infection. Methods: A primary HIV-1C infection cohort comprised of 8 acutely and 34 recently infected subjects were followed frequently up to 500 days post-seroconversion (p/s). Gag mutations were analyzed by employing single-genome amplification and direct sequencing. Gag mutations were determined in relation to the estimated time of seroconversion. Time of appearance, dominance, and completeness was compared for different types of in vivo Gag mutations. Results: Reverse mutations to the wild type appeared at a median (IQR) of 62 (44;139) days p/s, while escape mutations from the wild type appeared at 234 (169;326) days p/s (p&lt;0.001). Within the subset of mutations that became dominant, reverse and escape mutations appeared at 54 (30;78) days p/s and 104 (47;198) days p/s, respectively (p&lt;0.001). Among the mutations that reached completeness, reverse and escape mutations appeared at 54 (30;78) days p/s and 90 (44;196) days p/s, respectively (p = 0.006). Time of dominance for reverse mutations to and escape mutations from the wild type was 58 (44;105) days p/s and 219 (90;326) days p/s, respectively (p&lt;0.001). Time of completeness for reverse and escape mutations was 152 (100;176) days p/s and 243 (101;370) days p/s, respectively (p = 0.001). Fitting a Cox proportional hazards model with frailties confirmed a significantly earlier time of appearance (hazard ratio (HR): 2.6; 95% CI: 2.3–3.0), dominance (4.8 (3.4–6.8)), and completeness (3.6 (2.3–5.5)) of reverse mutations to the wild type Gag than escape mutations from the wild type. Some complex mutational pathways in Gag included sequential series of reversions and escapes. Conclusions: The study identified the timing of different types of in vivo Gag mutations in primary HIV-1 subtype C infection in relation to the estimated time of seroconversion. Overall, the in vivo reverse mutations to the wild type occurred significantly earlier than escape mutations from the wild type. This shorter time to incidence of reverse mutations remained in the subsets of in vivo Gag mutations that reached dominance or completeness

HIV-1 pol Diversity among Female Bar and Hotel Workers in Northern Tanzania

A national ART program was launched in Tanzania in October 2004. Due to the existence of multiple HIV-1 subtypes and recombinant viruses co-circulating in Tanzania, it is important to monitor rates of drug resistance. The present study determined the prevalence of HIV-1 drug resistance mutations among ART-naive female bar and hotel workers, a high-risk population for HIV-1 infection in Moshi, Tanzania. A partial HIV-1 pol gene was analyzed by single-genome amplification and sequencing in 45 subjects (622 pol sequences total; median number of sequences per subject, 13; IQR 5–20) in samples collected in 2005. The prevalence of HIV-1 subtypes A1, C, and D, and inter-subtype recombinant viruses, was 36%, 29%, 9% and 27%, respectively. Thirteen different recombination patterns included D/A1/D, C/A1, A1/C/A1, A1/U/A1, C/U/A1, C/A1, U/D/U, D/A1/D, A1/C, A1/C, A2/C/A2, CRF10_CD/C/CRF10_CD and CRF35_AD/A1/CRF35_AD. CRF35_AD was identified in Tanzania for the first time. All recombinant viruses in this study were unique, suggesting ongoing recombination processes among circulating HIV-1 variants. The prevalence of multiple infections in this population was 16% (n = 7). Primary HIV-1 drug resistance mutations to RT inhibitors were identified in three (7%) subjects (K65R plus Y181C; N60D; and V106M). In some subjects, polymorphisms were observed at the RT positions 41, 69, 75, 98, 101, 179, 190, and 215. Secondary mutations associated with NNRTIs were observed at the RT positions 90 (7%) and 138 (6%). In the protease gene, three subjects (7%) had M46I/L mutations. All subjects in this study had HIV-1 subtype-specific natural polymorphisms at positions 36, 69, 89 and 93 that are associated with drug resistance in HIV-1 subtype B. These results suggested that HIV-1 drug resistance mutations and natural polymorphisms existed in this population before the initiation of the national ART program. With increasing use of ARV, these results highlight the importance of drug resistance monitoring in Tanzania

Incorporating progesterone receptor expression into the PREDICT breast prognostic model

Background: Predict Breast (www.predict.nhs.uk) is an online prognostication and treatment benefit tool for early invasive breast cancer. The aim of this study was to incorporate the prognostic effect of progesterone receptor (PR) status into a new version of PREDICT and to compare its performance to the current version (2.2).Method: The prognostic effect of PR status was based on the analysis of data from 45,088 European patients with breast cancer from 49 studies in the Breast Cancer Association Consortium. Cox proportional hazard models were used to estimate the hazard ratio for PR status. Data from a New Zealand study of 11,365 patients with early invasive breast cancer were used for external validation. Model calibration and discrimination were used to test the model performance.Results: Having a PR-positive tumour was associated with a 23% and 28% lower risk of dying from breast cancer for women with oestrogen receptor (ER)-negative and ER-positive breast cancer, respectively. The area under the ROC curve increased with the addition of PR status from 0.807 to 0.809 for patients with ER-negative tumours (p = 0.023) and from 0.898 to 0. 902 for patients with ER-positive tumours (p = 2.3 x 10(-6)) in the New Zealand cohort. Model calibration was modest with 940 observed deaths compared to 1151 predicted.Conclusion: The inclusion of the prognostic effect of PR status to PREDICT Breast has led to an improvement of model performance and more accurate absolute treatment benefit predic-tions for individual patients. Further studies should determine whether the baseline hazard function requires recalibration. (C) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Peer reviewe

Incorporating progesterone receptor expression into the PREDICT breast prognostic model

Background: Predict Breast (www.predict.nhs.uk) is an online prognostication and treatment benefit tool for early invasive breast cancer. The aim of this study was to incorporate the prognostic effect of progesterone receptor (PR) status into a new version of PREDICT and to compare its performance to the current version (2.2).Method: The prognostic effect of PR status was based on the analysis of data from 45,088 European patients with breast cancer from 49 studies in the Breast Cancer Association Consortium. Cox proportional hazard models were used to estimate the hazard ratio for PR status. Data from a New Zealand study of 11,365 patients with early invasive breast cancer were used for external validation. Model calibration and discrimination were used to test the model performance.Results: Having a PR-positive tumour was associated with a 23% and 28% lower risk of dying from breast cancer for women with oestrogen receptor (ER)-negative and ER-positive breast cancer, respectively. The area under the ROC curve increased with the addition of PR status from 0.807 to 0.809 for patients with ER-negative tumours (p = 0.023) and from 0.898 to 0. 902 for patients with ER-positive tumours (p = 2.3 x 10(-6)) in the New Zealand cohort. Model calibration was modest with 940 observed deaths compared to 1151 predicted.Conclusion: The inclusion of the prognostic effect of PR status to PREDICT Breast has led to an improvement of model performance and more accurate absolute treatment benefit predic-tions for individual patients. Further studies should determine whether the baseline hazard function requires recalibration. (C) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Peer reviewe

Germline variants and breast cancer survival in patients with distant metastases at primary breast cancer diagnosis.

Breast cancer metastasis accounts for most of the deaths from breast cancer. Identification of germline variants associated with survival in aggressive types of breast cancer may inform understanding of breast cancer progression and assist treatment. In this analysis, we studied the associations between germline variants and breast cancer survival for patients with distant metastases at primary breast cancer diagnosis. We used data from the Breast Cancer Association Consortium (BCAC) including 1062 women of European ancestry with metastatic breast cancer, 606 of whom died of breast cancer. We identified two germline variants on chromosome 1, rs138569520 and rs146023652, significantly associated with breast cancer-specific survival (P = 3.19 × 10-8 and 4.42 × 10-8). In silico analysis suggested a potential regulatory effect of the variants on the nearby target genes SDE2 and H3F3A. However, the variants showed no evidence of association in a smaller replication dataset. The validation dataset was obtained from the SNPs to Risk of Metastasis (StoRM) study and included 293 patients with metastatic primary breast cancer at diagnosis. Ultimately, larger replication studies are needed to confirm the identified associations

A likelihood ratio approach for utilizing case-control data in the clinical classification of rare sequence variants:Application to BRCA1 and BRCA2

A large number of variants identified through clinical genetic testing in disease susceptibility genes are of uncertain significance (VUS). Following the recommendations of the American College of Medical Genetics and Genomics (ACMG) and Association for Molecular Pathology (AMP), the frequency in case-control datasets (PS4 criterion) can inform their interpretation. We present a novel case-control likelihood ratio-based method that incorporates gene-specific age-related penetrance. We demonstrate the utility of this method in the analysis of simulated and real datasets. In the analysis of simulated data, the likelihood ratio method was more powerful compared to other methods. Likelihood ratios were calculated for a case-control dataset of BRCA1 and BRCA2 variants from the Breast Cancer Association Consortium (BCAC) and compared with logistic regression results. A larger number of variants reached evidence in favor of pathogenicity, and a substantial number of variants had evidence against pathogenicity findings that would not have been reached using other case-control analysis methods. Our novel method provides greater power to classify rare variants compared with classical case-control methods. As an initiative from the ENIGMA Analytical Working Group, we provide user-friendly scripts and preformatted Excel calculators for implementation of the method for rare variants in BRCA1, BRCA2, and other high-risk genes with known penetrance.</p

Rare germline copy number variants (CNVs) and breast cancer risk.

Funder: CIHRGermline copy number variants (CNVs) are pervasive in the human genome but potential disease associations with rare CNVs have not been comprehensively assessed in large datasets. We analysed rare CNVs in genes and non-coding regions for 86,788 breast cancer cases and 76,122 controls of European ancestry with genome-wide array data. Gene burden tests detected the strongest association for deletions in BRCA1 (P = 3.7E-18). Nine other genes were associated with a p-value < 0.01 including known susceptibility genes CHEK2 (P = 0.0008), ATM (P = 0.002) and BRCA2 (P = 0.008). Outside the known genes we detected associations with p-values < 0.001 for either overall or subtype-specific breast cancer at nine deletion regions and four duplication regions. Three of the deletion regions were in established common susceptibility loci. To the best of our knowledge, this is the first genome-wide analysis of rare CNVs in a large breast cancer case-control dataset. We detected associations with exonic deletions in established breast cancer susceptibility genes. We also detected suggestive associations with non-coding CNVs in known and novel loci with large effects sizes. Larger sample sizes will be required to reach robust levels of statistical significance