Abstract. The exogenous application of abscisic acid (ABA) to well-watered plants may be of interest in imitating the effects of salinity on shoot growth. In this paper we have determined the time course of ABA accumulation in control and saltstressed Phaseolus vulgaris plants and its possible relation to the accumulation of solutes and other physiologic conditions. The effect on shoot parameters of the application of exogenous ABA to the root system has also been checked. The addition of exogenous ABA to control plants caused a retardation of growth. The amount of ABA applied to the growth medium caused tissue ABA concentrations to become close to those of salinized plants. The addition of exogenous ABA to plants under control conditions resulted in a profile of proline and total sugar accumulation very similar to that observed in salinized plants. It was also found that NaCl treatment decreased the stomatal conductance and transpiration rate of leaves as well as the osmotic and turgor potentials. The addition of exogenous ABA also mimicked these responses, resulting in qualitatively and quantitatively similar results. These results, particularly those showing that the early transient rise in ABA upon exposure to NaCl coincides with the period of proline and total sugar accumulation, and that treatment of plants with exogenous ABA mimics these effects, are discussed around the idea that ABA stimulates the cellular processes of osmotic adjustment in P. vulgaris.Abscisic acid (ABA) is a ubiquitous molecule in higher plants. It was originally described as a dorAbbreviations: ABA, abscisic acid; HPLC, high performance liquid chromatography; DW, dry weight; FW, fresh weight.

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Objective: Assess in a sample of people with type 1 diabetes mellitus whether mood and stress influence blood glucose levels and variability. Material and Methods: Continuous glucose monitoring was performed on 10 patients with type 1 diabetes mellitus, where interstitial glucose values were recorded every 15 min. A daily survey was conducted through Google Forms, collecting information on mood and stress. The day was divided into six slots of 4-h each, asking the patient to assess each slot in relation to mood (sad, normal or happy) and stress (calm, normal or nervous). Different measures of glycemic control (arithmetic mean and percentage of time below/above the target range) and variability (standard deviation, percentage coefficient of variation, mean amplitude of glycemic excursions and mean of daily differences) were calculated to relate the mood and stress perceived by patients with blood glucose levels and glycemic variability. A hypothesis test was carried out to quantitatively compare the data groups of the different measures using the Student’s t-test. Results: Statistically significant differences (p-value < 0.05) were found between different levels of stress. In general, average glucose and variability decrease when the patient is calm. There are statistically significant differences (p-value < 0.05) between different levels of mood. Variability increases when the mood changes from sad to happy. However, the patient’s average glucose decreases as the mood improves. Conclusions: Variations in mood and stress significantly influence blood glucose levels, and glycemic variability in the patients analyzed with type 1 diabetes mellitus. Therefore, they are factors to consider for improving glycemic control. The mean of daily differences does not seem to be a good indicator for variability.

Objective: Assess in a sample of patients with type 1 diabetes mellitus whether mood and stress influence blood glucose levels and variability. Material and Methods: Continuous glucose monitoring was performed on 10 patients with type 1 diabetes, where interstitial glucose values were recorded every 15 minutes. A daily survey was conducted through Google Forms, collecting information on mood and stress. The day was divided into 6 slots of 4-hour each, asking the patient to assess each slot in relation to mood (sad, normal or happy) and stress (calm, normal or nervous). Different measures of glycemic control (arithmetic mean and percentage of time below/above the target range) and variability (standard deviation, percentage coefficient of variation, mean amplitude of glycemic excursions and mean of daily differences) were calculated to relate the mood and stress perceived by patients with blood glucose levels and glycemic variability. A hypothesis test was carried out to quantitatively compare the data groups of the different measures using the Student's t-test. Results: Statistically significant differences (p-value < 0.05) were found between different levels of stress. In general, average glucose and variability decrease when the patient is calm. There are statistically significant differences (p-value < 0.05) between different levels of mood. Variability increases when the mood changes from sad to happy. However, the patient's average glucose decreases as the mood improves. Conclusions: Variations in mood and stress significantly influence blood glucose levels, and glycemic variability in the patients analyzed with type 1 diabetes mellitus. Therefore, they are factors to consider for improving glycemic control. The mean of daily differences does not seem to be a good indicator for variability. Keywords: Diabetes mellitus, continuous glucose monitoring, glycemic variability, average glycemia, glycemic control, stress, mood.