Citations

Abstract Objective As coronavirus disease 2019 (COVID-19) started its rapid emergence and gradually transformed into an unprecedented pandemic, the need for having a knowledge repository for the disease became crucial. To address this issue, a new COVID-19 machine-readable dataset known as the COVID-19 Open Research Dataset (CORD-19) has been released. Based on this, our objective was to build a computable co-occurrence network embeddings to assist association detection among COVID-19–related biomedical entities. Materials and Methods Leveraging a Linked Data version of CORD-19 (ie, CORD-19-on-FHIR), we first utilized SPARQL to extract co-occurrences among chemicals, diseases, genes, and mutations and build a co-occurrence network. We then trained the representation of the derived co-occurrence network using node2vec with 4 edge embeddings operations (L1, L2, Average, and Hadamard). Six algorithms (decision tree, logistic regression, support vector machine, random forest, naïve Bayes, and multilayer perceptron) were applied to evaluate performance on link prediction. An unsupervised learning strategy was also developed incorporating the t-SNE (t-distributed stochastic neighbor embedding) and DBSCAN (density-based spatial clustering of applications with noise) algorithms for case studies. Results The random forest classifier showed the best performance on link prediction across different network embeddings. For edge embeddings generated using the Average operation, random forest achieved the optimal average precision of 0.97 along with a F1 score of 0.90. For unsupervised learning, 63 clusters were formed with silhouette score of 0.128. Significant associations were detected for 5 coronavirus infectious diseases in their corresponding subgroups. Conclusions In this study, we constructed COVID-19–centered co-occurrence network embeddings. Results indicated that the generated embeddings were able to extract significant associations for COVID-19 and coronavirus infectious diseases.

INTRODUCTION The cerebral cortex underlies our complex cognitive capabilities. Variations in human cortical surface area and thickness are associated with neurological, psychological, and behavioral traits and can be measured in vivo by magnetic resonance imaging (MRI). Studies in model organisms have identified genes that influence cortical structure, but little is known about common genetic variants that affect human cortical structure. RATIONALE To identify genetic variants associated with human cortical structure at both global and regional levels, we conducted a genome-wide association meta-analysis of brain MRI data from 51,665 individuals across 60 cohorts. We analyzed the surface area and average thickness of the whole cortex and 34 cortical regions with known functional specializations. RESULTS We identified 369 nominally genome-wide significant loci ( P < 5 × 10 −8 ) associated with cortical structure in a discovery sample of 33,992 participants of European ancestry. Of the 360 loci for which replication data were available, 241 loci influencing surface area and 66 influencing thickness remained significant after replication, with 237 loci passing multiple testing correction ( P < 8.3 × 10 −10 ; 187 influencing surface area and 50 influencing thickness). Common genetic variants explained 34% (SE = 3%) of the variation in total surface area and 26% (SE = 2%) in average thickness; surface area and thickness showed a negative genetic correlation ( r G = −0.32, SE = 0.05, P = 6.5 × 10 −12 ), which suggests that genetic influences have opposing effects on surface area and thickness. Bioinformatic analyses showed that total surface area is influenced by genetic variants that alter gene regulatory activity in neural progenitor cells during fetal development. By contrast, average thickness is influenced by active regulatory elements in adult brain samples, which may reflect processes that occur after mid-fetal development, such as myelination, branching, or pruning. When considered together, these results support the radial unit hypothesis that different developmental mechanisms promote surface area expansion and increases in thickness. To identify specific genetic influences on individual cortical regions, we controlled for global measures (total surface area or average thickness) in the regional analyses. After multiple testing correction, we identified 175 loci that influence regional surface area and 46 that influence regional thickness. Loci that affect regional surface area cluster near genes involved in the Wnt signaling pathway, which is known to influence areal identity. We observed significant positive genetic correlations and evidence of bidirectional causation of total surface area with both general cognitive functioning and educational attainment. We found additional positive genetic correlations between total surface area and Parkinson’s disease but did not find evidence of causation. Negative genetic correlations were evident between total surface area and insomnia, attention deficit hyperactivity disorder, depressive symptoms, major depressive disorder, and neuroticism. CONCLUSION This large-scale collaborative work enhances our understanding of the genetic architecture of the human cerebral cortex and its regional patterning. The highly polygenic architecture of the cortex suggests that distinct genes are involved in the development of specific cortical areas. Moreover, we find evidence that brain structure is a key phenotype along the causal pathway that leads from genetic variation to differences in general cognitive function. Identifying genetic influences on human cortical structure. ( A ) Measurement of cortical surface area and thickness from MRI. ( B ) Genomic locations of common genetic variants that influence global and regional cortical structure. ( C ) Our results support the radial unit hypothesis that the expansion of cortical surface area is driven by proliferating neural progenitor cells. ( D ) Cortical surface area shows genetic correlation with psychiatric and cognitive traits. Error bars indicate SE. IMAGE CREDITS: (A) K. COURTNEY; (C) M. R. GLASS

Abstract Background Carcinoembryonic antigen (CEA) levels are used in conjunction with imaging to monitor response to systemic therapy in metastatic colorectal cancer (mCRC). We sought to identify a threshold for CEA change from baseline to predict progressive disease (PD) in mCRC patients receiving first-line therapy. Methods Patients from trials collected in the ARCAD database were included if baseline CEA was at least 10 ng/mL and repeat CEA was available within 14 days of first restaging scan. Optimal cutoffs for CEA change were identified by receiver operating characteristic analysis. Prediction performance of cutoffs was evaluated by sensitivity, specificity, and negative predictive value. Analyses were conducted by treatment class: chemotherapy alone, chemotherapy with anti-VEGF antibody, and chemotherapy with anti-EGFR antibody. Results A total of 2643 mCRC patients treated with systemic therapy were included. Median percent change of CEA from baseline to first restaging for patients with complete response, partial response, or stable disease (non-PD) and PD was −53.1% and +23.6% for chemotherapy alone (n = 957) and −71.7% and −45.3% for chemotherapy with anti-VEGF antibody (n = 1355). The optimal area under the curve cutoff for differentiating PD from non-PD on first restaging was −7.5% for chemotherapy alone and −62.0% for chemotherapy with anti-VEGF antibody; chemotherapy alone, adjusted odds ratio = 6.51 (95% CI = 3.31 to 12.83, P &lt; .001), chemotherapy with anti-VEGF antibody, adjusted odds ratio = 3.45 (95% CI = 1.93 to 6.18, P &lt; .001). A 99% negative predictive value clinical cutoff for prediction of non-PD would avoid CT scan at first restaging in 21.0% of chemotherapy alone and 16.2% of chemotherapy with anti-VEGF antibody–treated patients. Among patients with stable disease on first restaging, those with decreased CEA from baseline had statistically significantly improved progression-free and overall survival. Conclusions Change in CEA from baseline to first restaging can accurately predict non-progression and correlates with long-term outcomes in patients receiving systemic chemotherapy.

3533 Background: PFS has previously been established as a surrogate for OS based on IPD from first-line mCRC trials conducted before 1999. As mCRC treatment (trt) has advanced in the last decade and OS has increased from 1 to 2 years, this surrogacy required re-examination. Methods: IPD from 16,762 pts, median age 62, 62% male, 53% ECOG PS 0 were available from 22 1st line mCRC studies conducted from 1997-2006; 12 tested targeted (anti-angiogenic and/or anti-EGFR) regimens. The relationship between PFS (first event of progression or death) and OS was evaluated at patient-, trt-arm-, and trial-levels using correlation (corr.) coefficients and R2 (closer to 1 the better) from weighted least square (WLS) regression of arm-specific survival rates and trial-specific hazard ratios (HRs), estimated using Cox and Copula bivariate models. The concordance of significance (CoS) of the treatment effects (TEs) on both endpoints was calculated. Results: 44% pts received a targeted regimen. Median PFS was 8 and OS was18 months. The corr. between PFS and OS was modest at all three levels with low CoS in TE comparisons (see Table). Analyses limited to trials testing targeted trts, non-strategy trials, or superiority trials did not improve surrogacy. Conclusions: In modern mCRC trials, where survival post-first progression exceeds time to first progression, PFS TEs do not reliably predict TEs on OS. Nonetheless, until alternative endpoints of clinical benefit can be validated, PFS remains a relevant primary endpoint for 1st line mCRC trials, as our data demonstrate that the ability for any agent to produce an OS benefit from a single line of therapy is challenging. [Table: see text]

Abstract Remediation of struggling learners is a challenge faced by all educators. In recognition of this reality, and in light of contemporary challenges facing infectious diseases (ID) fellowship program directors, the Infectious Diseases Society of America Training Program Directors’ Committee focused the 2018 National Fellowship Program Directors’ Meeting at IDWeek on “Remediation of the Struggling Fellow.” Small group discussions addressed 7 core topics, including feedback and evaluations, performance management and remediation, knowledge deficits, fellow well-being, efficiency and time management, teaching skills, and career development. This manuscript synthesizes those discussions around a competency-based framework to provide program directors and other educators with a roadmap for addressing common contemporary remediation challenges.

6 Background: Rates and survival outcomes for second-line therapy for mCRC for OA vs. YA are poorly understood. Methods: Pts with available subsequent treatment data after progression from 10 1st line trials were included. Associations between key clinical/disease characteristics, time to initial progression (TTiP) and rate of receipt of second-line therapy were evaluated. Time to progression (TTP) and overall survival (OS) were compared between OA and YA who were enrolled on second-line trials by Cox regression, adjusting for age, sex, ECOG PS, number of metastatic sites, presence of metastasis in lung/liver/peritoneum. Results: OA comprised 16.4% of first-line trials. OA and ECOG PS >0 were less likely to receive second-line therapy than YA. Odds of receiving second-line therapy decreased by 11% for each additional decade of life in multivariate analysis (p=0.0117). OA enrolled in second-line trials experience similar mTTP and mOS as YA (5.1 vs. 5.2mos; 11.6 vs 12.4mos, respectively). Conclusions: OA are less likely to receive 2nd line therapy for mCRC. We did not observe a statistical difference in survival outcomes for OA vs. YA following second-line therapy. Further study is needed to examine unmeasured factors, including comorbidity and functional status given observed inferior outcomes among adults with ECOG PS >0, and consideration given to inclusion of geriatric assessment to select OA likely to benefit from 2nd line therapy for mCRC. [Table: see text]