Detection and quantification of ricin-mediated 28S ribosomal depurination by digital droplet PCR.

Ricin acts to damage cells by producing a site of depurination in 28S ribosomal RNA. This depurination results in ribosome inactivation which inhibits protein synthesis and ultimately leads to cell death. We have developed a multiplexed digital droplet polymerase chain reaction assay that enables the objective measurement of toxin activity through quantitation of depurinated 28S rRNA molecules. This assay demonstrates the first use of digital PCR technology to measure ribotoxin-mediated damage. Depurination events were detected in ricin-treated lung cell cultures as early as 1 h, and within 9 h of exposure the maximum ribosomal damage of 70% was reached and was sustained for at least 24 h post-exposure.

Lung Cancer Recurrence Risk Prediction through Integrated Deep Learning Evaluation.

Background: Prognostic risk factors for completely resected stage IA non-small-cell lung cancers (NSCLCs) have advanced minimally over recent decades. Although several biomarkers have been found to be associated with cancer recurrence, their added value to TNM staging and tumor grade are unclear. Methods: Features of preoperative low-dose CT image and histologic findings of hematoxylin- and eosin-stained tissue sections of resected lung tumor specimens were extracted from 182 stage IA NSCLC patients in the National Lung Screening Trial. These features were combined to predict the risk of tumor recurrence or progression through integrated deep learning evaluation (IDLE). Added values of IDLE to TNM staging and tumor grade in progression risk prediction and risk stratification were evaluated. Results: The 5-year AUC of IDLE was 0.817 ± 0.037 as compared to the AUC = 0.561 ± 0.042 and 0.573 ± 0.044 from the TNM stage and tumor grade, respectively. The IDLE score was significantly associated with cancer recurrence (p < 0.0001) even after adjusting for TNM staging and tumor grade. Synergy between chest CT image markers and histological markers was the driving force of the deep learning algorithm to produce a stronger prognostic predictor. Conclusions: Integrating markers from preoperative CT images and pathologist's readings of resected lung specimens through deep learning can improve risk stratification of stage 1A NSCLC patients over TNM staging and tumor grade alone. Our study suggests that combining markers from nonoverlapping platforms can increase the cancer risk prediction accuracy.

Social Capital Matters for Older Bhutanese Refugees' Integration.

Older and younger refugees have different experiences integrating into host countries. In the U.S., older Bhutanese refugees may face additional barriers when integrating, due to limited ability to adapt to a new language and cultural shifts. Social capital, however, has shown to be supportive of refugees' integration, which may be important for older Bhutanese refugees. This study's aim is to examine the effect of social capital and age on integration for Bhutanese refugees living in the United States. A cross-sectional moderated multiple linear regression analysis examined the effect of social capital and age on integration outcomes for 244 Bhutanese refugees in the United States. Higher social capital may contribute to higher levels of integration for older adult Bhutanese refugees' integration. These findings bring recommendations for policy and practice, including more targeted resettlement programs for older refugees.