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In natural conditions, plants growth and development depends on environmental conditions, including the availability of micro- and macroelements in the soil. Nutrient status should thus be examined not by establishing the effects of single nutrient deficiencies on the physiological state of the plant but by combinations of them. Differences in the nutrient content significantly affect the photochemical process of photosynthesis therefore playing a crucial role in plants growth and development. In this work, an attempt was made to find a connection between element content in (i) different soils, (ii) plant leaves, grown on these soils and (iii) changes in selected chlorophyll a fluorescence parameters, in order to find a method for early detection of plant stress resulting from the combination of nutrient status in natural conditions. To achieve this goal, a mathematical procedure was used which combines principal component analysis (a tool for the reduction of data complexity), hierarchical k-means (a classification method) and a machine-learning method—super-organising maps. Differences in the mineral content of soil and plant leaves resulted in functional changes in the photosynthetic machinery that can be measured by chlorophyll a fluorescent signals. Five groups of patterns in the chlorophyll fluorescent parameters were established: the ‘no deficiency’, Fe-specific deficiency, slight, moderate and strong deficiency. Unfavourable development in groups with nutrient deficiency of any kind was reflected by a strong increase in Fo and ΔV/Δt0 and decline in φPo, φEoδRo and φRo. The strong deficiency group showed the suboptimal development of the photosynthetic machinery, which affects both PSII and PSI. The nutrient-deficient groups also differed in antenna complex organisation. Thus, our work suggests that the chlorophyll fluorescent method combined with machine-learning methods can be highly informative and in some cases, it can replace much more expensive and time-consuming procedures such as chemometric analyses.

Background/Objectives: Peristomal skin complications (PSCs) are common among patients with ostomies, significantly impacting quality of life and increasing healthcare utilization. This study aimed to evaluate the effects of the Dermamecum protocol, a risk-stratified educational intervention, on the prevention of PSCs, self-care improvements, health-related quality of life (HRQoL), and patient satisfaction over a 90-day follow-up period. Methods: This prospective cohort study included 305 patients stratified into three risk-based groups (green, yellow, and red paths) according to the Dermamecum protocol. Primary outcomes included PSC rates at 30, 60, and 90 days. Secondary outcomes included self-care scores, HRQoL, and patient satisfaction. Comparative analyses and trend assessments were performed across groups and time points. Temporal trends in PSCs were analyzed using Poisson regression. Results: Early PSC rates were 8.5% at 30 days, with late complications at 7.9% and 6.2% at 60 and 90 days, respectively. No significant differences in PSC rates were observed between paths. Self-care scores improved over time, with stability across groups and domains. HRQoL remained stable, with minor fluctuations in physical and mental components. Patient satisfaction was high across all paths. Poisson regression identified significant temporal trends in PSC rates, with higher risks at 30, 60, and 90 days compared to baseline. Age, BMI, and path assignment (lower risk for the green path) were significant predictors of PSCs. Conclusions: The Dermamecum protocol effectively maintained low PSC rates, supported self-care, and sustained HRQoL and patient satisfaction. These findings highlight the value of risk-stratified, patient-centered interventions in ostomy care. Further studies are needed to validate these results and explore long-term outcomes.