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Chang, C.-h.

Publications and source records attributed to Chang, C.-h..

2 recordsLinked to original sources

Understanding patterns of abiotic and biotic stress resilience to unleash the potential of crop wild relatives for climate-smart legume breeding

Although new varieties are urgently needed for climate-smart legume production, legume breeding lags behind with cereals and underutilizes wild relatives. This paper provides insights in patterns of abiotic and biotic stress resilience of legume crops and wild relatives to enhance the use and conservation of these genetic resources for climate-smart legume breeding. We focus on Vigna, a pantropical genus with more than 88 taxa including important crops such as cowpea and mung bean. Sources of pest and disease resistance occur in more than 50 percent of the Vigna taxa, which were screened while sources of abiotic stress resilience occur in less than 20 percent of the taxa, which were screened. This difference suggests that Vigna taxa co-evolve with pests and diseases while taxa are more conservative to adapt to climatic changes and salinization. Twenty-two Vigna taxa are poorly conserved in genebanks or not at all. This germplasm is not available for legume breeding and requires urgent germplasm collecting before these taxa extirpate on farm and in the wild. Vigna taxa, which tolerate heat and drought stress are rare compared with taxa, which escape these stresses or tolerate salinity. These rare Vigna taxa should be prioritized for conservation and screening for multifunctional traits of combined abiotic and biotic stress resilience. The high presence of salinity tolerance compared with drought stress tolerance, suggests that Vigna taxa are good at developing salt-tolerant traits compared with drought-tolerant traits. Vigna taxa are therefore of high value for legume production in areas that suffer from salinization.

plant biology

Generating real-world tumor burden endpoints from electronic health record data: Comparison of RECIST, radiology-anchored, and clinician-anchored approaches for abstracting real-world progression in non-small cell lung cancer

Real-world evidence derived from electronic health records (EHRs) is increasingly recognized as a supplement to evidence generated from traditional clinical trials. In oncology, tumor-based Response Evaluation Criteria in Solid Tumors (RECIST) endpoints are collected in clinical trials. The best approach for collecting similar endpoints from EHRs remains unknown. We evaluated the feasibility of a traditional RECIST-based methodology to assess EHR-derived real-world progression (rwP) and explored non-RECIST-based approaches. In this retrospective study, cohorts were randomly selected from Flatiron Healths database of patient-level EHR data in advanced non-small cell lung cancer. A RECIST-based approach was tested for feasibility (N=26). Three non-RECIST abstraction approaches were tested for feasibility, reliability, and validity (N=200): (1) radiology-anchored, (2) clinician-anchored, and (3) combined. RECIST-based cancer progression could be ascertained from the EHRs of 23% of patients (6/26). In 87% of patients (173/200), at least one rwP event was identified using both the radiology- and clinician-anchored approaches. rwP dates matched 90% of the time. In 72% of patients (124/173), the first clinician-anchored rwP event was accompanied by a downstream event (e.g., treatment change); the association was slightly lower for the radiology-anchored approach (67%; 121/180). Median overall survival (OS) was 17 months (95% confidence interval [CI]: 14, 19). Median real-world progression-free survival (rwPFS) was 5.5 (95% CI: 4.6, 6.3) and 4.9 months (95% CI: 4.2, 5.6) for clinician-anchored and radiology-anchored approaches, respectively. Correlations between rwPFS and OS were similar across approaches (Spearmans rho: 0.65-0.66). Abstractors preferred the clinician-anchored approach as it provided more comprehensive context. RECIST cannot adequately assess cancer progression in EHR-derived data due to missing data and lack of clarity in radiology reports. We found a clinician-anchored approach supported by radiology report data to be the optimal, and most practical, method for characterizing tumor-based endpoints from EHR-sourced data.

bioinformatics