Food Safety Risks of Harvesting Dropped and Drooping Produce: A Review.

ABSTRACT: The Produce Safety Rule of the Food Safety Modernization Act (FSMA) sets forth minimum standards for fruit and vegetable production in the United States. One provision states that growers must not harvest dropped produce because damage or ground contact may contaminate produce. In an unpublished survey of 2020 food safety inspections conducted by the Northeast Center to Advance Food Safety, handling of dropped produce covered by the FSMA was a common misunderstood and noncompliance issue among growers in the Northeast. In consideration of this provision's on-farm practicality, this review was conducted to evaluate the risks associated with dropped and drooping produce, to guide growers in making informed risk management decisions, and to answer the following questions: (i) what are the risk factors that influence transferability of pathogens from touching the ground to produce and (ii) what are the risks associated with harvesting dropped or drooping produce covered under the Produce Safety Rule? A search of online databases revealed 12 relevant publications, which highlighted moisture, contact time, and crop features as affecting contamination rates from a ground surface to a crop surface. Soil and mulch posed a differential risk, with bare soil generally presenting a lower risk than plastic mulch. The effects of other mulch types are unclear. Mulches may promote pathogen persistence in soil, although they may also protect produce from contaminated soils. These studies were limited in their scope and applicability and most did not directly address dropped produce. Research is needed to clarify the various effects of dropped and drooping produce, the impact of ground surface type on pathogen survivability and transfer, soil and crop features that facilitate contamination, and postharvest risks of harvesting dropped or drooping produce. A comprehensive understanding of these issues will guide growers in implementing preventive measures and better managing risk in a way practicable to each farm's unique conditions.

A genomics-informed computational biology platform prospectively predicts treatment responses in AML and MDS patients.

Patients with myelodysplastic syndromes (MDS) or acute myeloid leukemia (AML) are generally older and have more comorbidities. Therefore, identifying personalized treatment options for each patient early and accurately is essential. To address this, we developed a computational biology modeling (CBM) and digital drug simulation platform that relies on somatic gene mutations and gene CNVs found in malignant cells of individual patients. Drug treatment simulations based on unique patient-specific disease networks were used to generate treatment predictions. To evaluate the accuracy of the genomics-informed computational platform, we conducted a pilot prospective clinical study (NCT02435550) enrolling confirmed MDS and AML patients. Blinded to the empirically prescribed treatment regimen for each patient, genomic data from 50 evaluable patients were analyzed by CBM to predict patient-specific treatment responses. CBM accurately predicted treatment responses in 55 of 61 (90%) simulations, with 33 of 61 true positives, 22 of 61 true negatives, 3 of 61 false positives, and 3 of 61 false negatives, resulting in a sensitivity of 94%, a specificity of 88%, and an accuracy of 90%. Laboratory validation further confirmed the accuracy of CBM-predicted activated protein networks in 17 of 19 (89%) samples from 11 patients. Somatic mutations in the TET2, IDH1/2, ASXL1, and EZH2 genes were discovered to be highly informative of MDS response to hypomethylating agents. In sum, analyses of patient cancer genomics using the CBM platform can be used to predict precision treatment responses in MDS and AML patients.