Leaf angle distribution in Johnsongrass, leaf thickness in sorghum and Johnsongrass, and association with response to Colletotrichum sublineola

Inoculation and Screening Methods for Major Sorghum Diseases Caused by Fungal Pathogens: Claviceps africana, Colletotrichum sublineola, Sporisorium reilianum, Peronosclerospora sorghi and Macrophomina phaseolina

Sorghum is the fifth most important crop globally. Researching interactions between sorghum and fungal pathogens is essential to further elucidate plant defense mechanisms to biotic stress, which allows breeders to employ genetic resistance to disease. A variety of creative and useful inoculation and screening methods have been developed by sorghum pathologists to study major fungal diseases. As inoculation and screening methods can be keys for successfully conducting experiments, it is necessary to summarize the techniques developed by this research community. Among many fungal pathogens of sorghum, here we summarize inoculation and screening methods for five important fungal pathogens of sorghum: Claviceps africana, Colletotrichum sublineola, Sporisorium reilianum, Peronosclerospora sorghi and Macrophomina phaseolina. The methods described within will be useful for researchers who are interested in exploring sorghum-fungal pathogen interactions. Finally, we discuss the latest biotechnologies and methods for studying plant-fungal pathogen interactions and their applicability to sorghum pathology.

Predicting EGFR mutation status by a deep learning approach in patients with non-small cell lung cancer brain metastases

PURPOSE

Non-small cell lung cancer (NSCLC) tends to metastasize to the brain. Between 10 and 60% of NSCLCs harbor an activating mutation in the epidermal growth-factor receptor (EGFR), which may be targeted with selective EGFR inhibitors. However, due to a high discordance rate between the molecular profile of the primary tumor and the brain metastases (BMs), identifying an individual patient's EGFR status of the BMs necessitates tissue diagnosis via an invasive surgical procedure. We employed a deep learning (DL) method with the aim of noninvasive detection of the EGFR mutation status in NSCLC BM.

METHODS

We retrospectively collected clinical, radiological, and pathological-molecular data of all the NSCLC patients who had been diagnosed with BMs and underwent resection of their BM during 2009-2019. The study population was then divided into two groups based upon EGFR mutational status. We further employed a DL technique to classify the two groups according to their preoperative magnetic resonance imaging features. Augmentation techniques, transfer learning approach, and post-processing of the predicted results were applied to overcome the relatively small cohort. Finally, we established the accuracy of our model in predicting EGFR mutation status of BM of NSCLC.

RESULTS

Fifty-nine patients were included in the study, 16 patients harbored EGFR mutations. Our model predicted mutational status with mean accuracy of 89.8%, sensitivity of 68.7%, specificity of 97.7%, and a receiver operating characteristic curve value of 0.91 across the 5 validation datasets.

CONCLUSION

DL-based noninvasive molecular characterization is feasible, has high accuracy and should be further validated in large prospective cohorts.