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Sexual health-related activities of nonbinary young adults.
Relatively little is known about the possible effects of personalized genetic risk information on smoking, the leading preventable cause of morbidity and mortality. We examined the acceptability and potential behavior change associated with a personalized genetically informed risk tool (RiskProfile) among current smokers. #link# Current smokers (n = 108) were enrolled in a pre-post study with three visits. At visit 1, participants completed a baseline assessment and genetic testing via 23andMe. Participants' raw genetic data (CHRNA5 variants) and smoking heaviness were used to create a tailored RiskProfile tool that communicated personalized risks of smoking-related diseases and evidence-based recommendations to promote cessation. Participants received their personalized RiskProfile intervention at visit 2, approximately 6 weeks later. Visit 3 involved a telephone-based follow-up assessment 30 days after intervention. Of enrolled participants, 83% were retained across the three visits. Immediately following intervenffectiveness and implementation research on genetically-informed behavior change interventions to enhance cancer prevention efforts.The future trajectory of the coronavirus disease 2019 (COVID-19) pandemic hinges on the dynamics of adaptive immunity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); however, salient features of the immune response elicited by natural infection or vaccination are still uncertain. We use simple epidemiological models to explore estimates for the magnitude and timing of future COVID-19 cases, given different assumptions regarding the protective efficacy and duration of the adaptive immune response to SARS-CoV-2, as well as its interaction with vaccines and nonpharmaceutical interventions. We find that variations in the immune response to primary SARS-CoV-2 infections and a potential vaccine can lead to markedly different immune landscapes and burdens of critically severe cases, ranging from sustained epidemics to near elimination. Our findings illustrate likely complexities in future COVID-19 dynamics and highlight the importance of immunological characterization beyond the measurement of active infections for adequately projecting the immune landscape generated by SARS-CoV-2 infections.Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), represents a global crisis. Key to SARS-CoV-2 therapeutic development is unraveling the mechanisms that drive high infectivity, broad tissue tropism, and severe pathology. Our 2.85-angstrom cryo-electron microscopy structure of SARS-CoV-2 spike (S) glycoprotein reveals that the receptor binding domains tightly bind the essential free fatty acid linoleic acid (LA) in three composite binding pockets. A similar pocket also appears to be present in the highly pathogenic severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV). LA binding stabilizes a locked S conformation, resulting in reduced angiotensin-converting enzyme 2 (ACE2) interaction in vitro. In human cells, LA supplementation synergizes with the COVID-19 drug remdesivir, suppressing SARS-CoV-2 replication. Our structure directly links LA and S, setting the stage for intervention strategies that target LA binding by SARS-CoV-2.Real-world evidence (RWE), conclusions derived from analysis of patients not treated in clinical trials, is increasingly recognized as an opportunity for discovery, to reduce disparities, and to contribute to regulatory approval. Maximal value of RWE may be facilitated through machine-learning techniques to integrate and interrogate large and otherwise under-utilized datasets. In cancer research, an ongoing challenge for RWE is the lack of reliable, reproducible, scalable assessment of treatment-specific outcomes. We hypothesized a deep-learning model could be trained to use radiology text reports to estimate gold-standard RECIST-defined outcomes. Using text reports from patients with non-small cell lung cancer treated with PD-1 blockade in a training cohort and two test cohorts, we developed a deep-learning model to accurately estimate best overall response and progression-free survival. Our model may be a tool to determine outcomes at scale, enabling analyses of large clinical databases. SIGNIFICANCE We developed and validated a deep-learning model trained on radiology text reports to estimate gold-standard objective response categories used in clinical trial assessments. This tool may facilitate analysis of large real-world oncology datasets using objective outcome metrics determined more reliably and at greater scale than currently possible.Cyclin-dependent kinase 4/6 (CDK4/6) and PI3K inhibitors synergize in PIK3CA-mutant ER-positive HER2-negative breast cancer models. We conducted a phase Ib trial investigating the safety and efficacy of doublet CDK4/6 inhibitor palbociclib plus selective PI3K inhibitor taselisib in advanced solid tumors, and triplet palbociclib plus taselisib plus fulvestrant in 25 patients with PIK3CA-mutant, ER-positive HER2-negative advanced breast cancer. The triplet therapy response rate in PIK3CA-mutant, ER-positive HER2-negative cancer was 37.5% [95% confidence interval (CI), 18.8-59.4]. Durable disease control was observed in PIK3CA-mutant ER-negative breast cancer and other solid tumors with doublet therapy. Both combinations were well tolerated at pharmacodynamically active doses. In the triplet group, high baseline cyclin E1 expression associated with shorter progression-free survival (PFS; HR = 4.2; 95% CI, 1.3-13.1; P = 0.02). Early circulating tumor DNA (ctDNA) dynamics demonstrated high on-treatment ctDNA association with shorter PFS (HR = 5.2; 95% CI, 1.4-19.4; P = 0.04). Longitudinal plasma ctDNA sequencing provided genomic evolution evidence during triplet therapy. SIGNIFICANCE The triplet of palbociclib, taselisib, and fulvestrant has promising efficacy in patients with heavily pretreated PIK3CA-mutant ER-positive HER2-negative advanced breast cancer. SBI-477 molecular weight of patients with PIK3CA-mutant triple-negative breast cancer derived clinical benefit from palbociclib and taselisib doublet, suggesting a potential nonchemotherapy targeted approach for this population.
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