We are considering a facet of precision agriculture that concentrates on plant-driven crop management. By monitoring soil, crop and climate in a field and providing a decision support system that is able to learn, it is possible to deliver treatments, such as irrigation, fertilizer and pesticide application, for specific parts of a field in real time and proactively. In this context, we have applied machine learning techniques to automatically extract new knowledge in the form of generalized decision rules towards the best administration of natural resources like water. The machine learning application model suggested in this paper is based on an inductive and iterative process of discovering knowledge on the basis of which, patterns and associations having arisen initially are re-examined to expand the pre-existing knowledge. The result of this study was the creation of an effective set of decision rules used to predict the plants' state and the prevention of unpleasant impacts from the water stress in plants.

Imaging flow cytometry (ImFC) represents a significant technological advancement in the field of cytometry, effectively merging the high-throughput capabilities of flow analysis with the detailed imaging characteristics of microscopy. In our comprehensive review, we adopt a historical perspective to chart the development of ImFC, highlighting its origins and current state of the art and forecasting potential future advancements. The genesis of ImFC stemmed from merging the hydraulic system of a flow cytometer with advanced camera technology. This synergistic coupling facilitates the morphological analysis of cell populations at a high-throughput scale, effectively evolving the landscape of cytometry. Nevertheless, ImFC’s implementation has encountered hurdles, particularly in developing software capable of managing its sophisticated data acquisition and analysis needs. The scale and complexity of the data generated by ImFC necessitate the creation of novel analytical tools that can effectively manage and interpret these data, thus allowing us to unlock the full potential of ImFC. Notably, artificial intelligence (AI) algorithms have begun to be applied to ImFC, offering promise for enhancing its analytical capabilities. The adaptability and learning capacity of AI may prove to be essential in knowledge mining from the high-dimensional data produced by ImFC, potentially enabling more accurate analyses. Looking forward, we project that ImFC may become an indispensable tool, not only in research laboratories, but also in clinical settings. Given the unique combination of high-throughput cytometry and detailed imaging offered by ImFC, we foresee a critical role for this technology in the next generation of scientific research and diagnostics. As such, we encourage both current and future scientists to consider the integration of ImFC as an addition to their research toolkit and clinical diagnostic routine.

Background: Intraoperative Flow Cytometry (iFC) is a novel technique for the assessment of the grade of malignancy and the diagnosis of tumor type and resection margins during solid tumor surgery. Herein, we set out to analyze the role of iFC in the grading of gliomas and the evaluation of resection margins. Material and Methods: iFC uses a fast cell cycle analysis protocol (Ioannina Protocol) that permits the analysis of tissue samples within 5–6 min. Cell cycle analysis evaluated the G0/G1 phase, S-phase, mitosis, and tumor index (S + mitosis phase fraction) and ploidy status. In the current study, we evaluated tumor samples and samples from the peripheral borders from patients with gliomas who underwent surgery over an 8-year period. Results: Eighty-one patients were included in the study. There were sixty-eight glioblastoma cases, five anaplastic astrocytomas, two anaplastic oligodendrogliomas, one pilocytic astrocytoma, three oligodendrogliomas and two diffuse astrocytomas. High-grade gliomas had a significantly higher tumor index than low grade gliomas (median value 22 vs. 7.5, respectively, p = 0.002). Using ROC curve analysis, a cut-off value of 17% in the tumor index could differentiate low- from high-grade gliomas with a 61.4% sensitivity and 100% specificity. All low-grade gliomas were diploid. From the high-grade gliomas, 22 tumors were aneuploid. In glioblastomas, aneuploid tumors had a significantly higher tumor index (p = 0.0018). Twenty-three samples from glioma margins were evaluated. iFC verified the presence of malignant tissue in every case, using histology as the gold standard. Conclusion: iFC constitutes a promising intraoperative technique for glioma grading and resection margin assessment. Comparative studies with additional intraoperative adjuncts are necessary.

Meningiomas are the most frequent central nervous system tumors in adults. The majority of these tumors are benign. Nevertheless, the intraoperative identification of meningioma grade is important for modifying surgical strategy in order to reduce postoperative complications. Here, we set out to investigate the role of intraoperative flow cytometry for the differentiation of low-grade (grade 1) from high-grade (grade 2–3) meningiomas. The study included 59 patients. Intraoperative flow cytometry analysis was performed using the ‘Ioannina Protocol’ which evaluates the G0/G1 phase, S-phase, mitosis and tumor index (S + mitosis phase fraction) of a tumor sample. The results are available within 5 min of sample receipt. There were 41 grade 1, 15 grade 2 and 3 grade 3 meningiomas. High-grade meningiomas had significantly higher S-phase fraction, mitosis fraction and tumor index compared to low-grade meningiomas. High-grade meningiomas had significantly lower G0/G1 phase fraction compared to low-grade meningiomas. Thirty-eight tumors were diploids and twenty-one were aneuploids. No significant difference was found between ploidy status and meningioma grade. ROC analysis indicated 11.4% of tumor index as the optimal cutoff value thresholding the discrimination between low- and high-grade meningiomas with 90.2% sensitivity and 72.2% specificity. In conclusion, intraoperative flow cytometry permits the detection of high-grade meningiomas within 5 min. Thus, surgeons may modify tumor removal strategy.

Background Silicone breast implants are used in a variety of aesthetic and reconstructive procedures such as permanent implants or tissue expanders. As foreign biomaterials, they can potentially evocate either a local inflammatory reaction or a systemic immune response. This study aimed to quantify possible alterations in white blood cells and lymphocyte subpopulations in patients who underwent silicone implant-based breast augmentation through peripheral blood analysis with flow cytometry at three and 12 months postoperatively. The same patients acted as the control group with a preoperative peripheral blood collection. Methodology This retrospective study included 14 female patients (average age = 29 years; range = 18-44 years) who underwent breast augmentation with silicone implants. Flow cytometry was used to monitor their immunologic reactions. Peripheral blood samples were obtained before, three months, and 12 months after the operation. Flow cytometric analysis was performed to assess the distribution of total leukocytes and the expression of specific lymphocyte subsets using the following antibody panel: anti-CD3, anti-CD4, anti-CD8, anti-CD16/CD56, anti-CD19, and anti-CD25. Results The immunophenotypic analysis showed that the vast majority of the peripheral blood lymphocytes consisted of T lymphocytes (CD3+), followed by natural killer (NK) cells (CD3-/CD16+/CD56+, NK cells), and B lymphocytes (CD3-/CD19+) in all periods of the study. The peripheral blood CD3+ T-lymphocyte subsets were predominantly T-helper lymphocytes (CD3+/CD4+), followed by cytotoxic T lymphocytes (CTLs) (CD3+/CD8+, CTLs), and a small percentage were T-regulatory (Treg) cells (CD4+/CD25high, Treghigh cells). Interestingly, lymphocytes showed a slight increase and T-helper lymphocytes a slight decline postoperatively, with no evidence of statistically significant shifts. Overall, there was no statistically significant difference in the temporal distribution of the peripheral blood lymphocyte subpopulations. Conclusions To our knowledge, the novelty of our study is that the same patients who underwent breast augmentation with silicone implants were used as the control group. There was no evidence of an early specific systemic immune activation in these patients. Hence, flow cytometry could be a reliable method that can be used as an in vitro test to evaluate the biocompatibility of silicone implants and observe patients with immune activity for possible clinical manifestations of symptoms in the future.