Models for predicting coffee yield from chemical characteristics of soil and leaves using machine learning.

BACKGROUND: Coffee farming constitutes a substantial economic resource, representing a source of income for several countries due to the high consumption of coffee worldwide. Precise management of coffee crops involves collecting crop attributes (characteristics of the soil and the plant), mapping, and applying inputs according to the plants' needs. This differentiated management is precision coffee growing and it stands out for its increased yield and sustainability. RESULTS: This research aimed to predict yield in coffee plantations by applying machine learning methodologies to soil and plant attributes. The data were obtained in a field of 54.6 ha during two consecutive seasons, applying varied fertilization rates in accordance with the recommendations of soil attribute maps. Leaf analysis maps also were monitored with the aim of establishing a correlation between input parameters and yield prediction. The machine-learning models obtained from these data predicted coffee yield efficiently. The best model demonstrated predictive fit results with a Pearson correlation of 0.86. Soil chemical attributes did not interfere with the prediction models, indicating that this analysis can be dispensed with when applying these models. CONCLUSION: These findings have important implications for optimizing coffee management and cultivation, providing valuable insights for producers and researchers interested in maximizing yield using precision agriculture. © 2024 Society of Chemical Industry.

Optimizing adjuvant inhaled chemotherapy: Synergistic enhancement in paclitaxel cytotoxicity by flubendazole nanocrystals in a cycle model approach.

Lung cancer is the leading cause of cancer-related death. In addition to new innovative approaches, practical strategies that improve the efficacy of already available drugs are urgently needed. In this study, an inhalable dry powder formulation is used to repurpose flubendazole, a poorly soluble anthelmintic drug with potential against a variety of cancer lineages. Flubendazole nanocrystals were obtained through nanoprecipitation, and dry powder was produced by spray drying. Through fractional factorial design, the spray drying parameters were optimized and the impact of formulation on aerolization properties was clarified. The loading limitations were clarified through response surface methodology, and a 15% flubendazole loading was feasible through the addition of 20% L-leucine, leading to a flubendazole particle size of 388.6 nm, median mass aerodynamic diameter of 2.9 µm, 50.3% FPF, emitted dose of 83.2% and triple the initial solubility. Although the cytotoxicity of this formulation in A549 cells was limited, the formulation showed a synergistic effect when associated with paclitaxel, leading to a surprising 1000-fold reduction in the IC50. Compared to 3 cycles of paclitaxel alone, a 3-cycle model combined treatment increased the threshold of cytotoxicity by 25% for the same dose. Our study suggests, for the first time, that orally inhaled flubendazole nanocrystals show high potential as adjuvants to increase cytotoxic agents' potency and reduce adverse effects.

Adherence to intensive nutrition care in head and neck cancer patients undergoing radiotherapy.

PURPOSE: The aim of this study was to determine the prevalence and effect of adherence to intensive nutritional care on nutritional outcomes and survival in head and neck cancer patients undergoing radiotherapy. METHODS: Three-hundred and seventeen head and neck cancer patients referred to intensive nutrition support during radiotherapy were retrospectively analyzed. Patients who missed less than 25% of their appointments with the dietitian were considered adherent. Primary outcome was percentage weight loss during treatment. Secondary outcomes were overall survival and patients' capacity to accomplish their caloric and protein recommendations. Logistic regression was used to examine predictors of weight loss and Kaplan-Meier to estimate survival. RESULTS: Less than half of the patients (n = 145, 45.7%) were adherent. Statistically significant less weight loss in the adherent group (42.8% vs 55.8%; p = 0.02) was found, despite no difference in energy or protein intake. Logistic regression models after adjusting for other variables demonstrated that adherence resulted in 43% protection from significant weight loss (odds ratio 0.57, 95% CI 0.34-0.97). Overall survival was not different between groups. CONCLUSION: Findings demonstrated that patients who were adherent to weekly contacts with the dietitian had less weight loss, but not better survival or nutritional intake. Additional investigation of factors that may act as barriers or enablers for adherence could help improve the outcomes in this population.