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This research study proposes a classification system to identify and prevent occupational accidents in fiber storage warehouses at a pulp and paper facility. The present analysis is based on variables including pedestrian circulation, bobcat, trailer trucks, access, pedestrian circulation zones, and handrails. The proposed methodology defines and trains the system by using occupational accident event data collected at the facility. Three different predicting algorithms are used: J48 decision-making trees, Naive Bayes, and Bayesian nets. The results show that the J48 decision-making tree algorithm accurately identifies possible occupational accidents 90% of the time. It is concluded that identifying variables involved in occupational accidents allows generating a C4.5 (J48) decision-making tree that can be used as a support tool to prevent occupational accidents. [ABSTRACT FROM AUTHOR]

Through this manuscript, it is intended to achieve a normative, historical and doctrinal contextualization of Health as a fundamental right and as a public service. The foregoing based on a qualitative research methodology and under the descriptive research method, since the analysis was carried out based on the Colombian regulatory framework regarding the particular topic and the legal and medical bibliography that contextualizes health as a right and as a public service. [ABSTRACT FROM AUTHOR]

This paper presents a new methodology based on the application of Support Vector Machine algorithms, Naïve Bayes and Genetic Algorithms in diagnostics of psychosocial evaluations for the identification and prediction of the psychosocial risk level of public-school teachers in Colombia. A comparative study of the model of machine learning for prediction was carried out: Support Vector Machines (SVM) and Naïve Bayes, in two stages, first with all the variables and second, reducing the dimensionality of the database applying genetic algorithms, The best forty variables with the best efficiency in prediction accuracy were selected. The database used consisted of 3000 epidemiological records, which corresponded to teachers from public schools in the metropolitan area of a Colombian city. The use of SVM easily detected variables of physiological type and the best prediction performance was obtained with accuracy of 96.3%. [ABSTRACT FROM AUTHOR]

This paper presents a new methodology based on machine learning techniques in diagnostics of psychosocial assessments to identify the risk level in teachers of public schools in Colombia. A comparative study of three important models of machine learning for prediction was done: artificial neural networks, decision trees and naive bayes, reducing the dimensionality of the data. This was done by applying genetic algorithms, algorithm of the expected amount of information, the algorithm GainRatioAttributeEval, Pearson's relation coefficient and principal components analysis. A database was used with 5340 epidemiological records, corresponding to psychosocial evaluations of teachers from public schools in the metropolitan area of a Colombian city. The best predictive performance was obtained with the model of artificial neural networks with an accuracy 93%. [ABSTRACT FROM AUTHOR]

Purpose: Total marrow irradiation (TMI) has significantly advanced radiation conditioning for hematopoietic cell transplantation in hematologic malignancies by reducing conditioning-induced toxicities and improving survival outcomes in relapsed/refractory patients. However, the relapse rate remains high, and the lack of a preclinical TMI model has hindered scientific advancements. To accelerate TMI translation to the clinic, we developed a TMI delivery system in preclinical models.Methods and Materials: A Precision X-RAD SmART irradiator was used for TMI model development. Images acquired with whole-body contrast-enhanced computed tomography (CT) were used to reconstruct and delineate targets and vital organs for each mouse. Multiple beam and CT-guided Monte Carlo-based plans were performed to optimize doses to the targets and to vary doses to the vital organs. Long-term engraftment and reconstitution potential were evaluated by a congenic bone marrow transplantation (BMT) model and serial secondary BMT, respectively. Donor cell engraftment was measured using noninvasive bioluminescence imaging and flow cytometry.Results: Multimodal imaging enabled identification of targets (skeleton and spleen) and vital organs (eg, lungs, gut, liver). In contrast to total body irradiation (TBI), TMI treatment allowed variation of radiation dose exposure to organs relative to the target dose. Dose reduction mirrored that in clinical TMI studies. Similar to TBI, mice treated with different TMI regimens showed full long-term donor engraftment in primary BMT and second serial BMT. The TBI-treated mice showed acute gut damage, which was minimized in mice treated with TMI.Conclusions: A novel multimodal image guided preclinical TMI model is reported here. TMI conditioning maintained long-term engraftment with reconstitution potential and reduced organ damage. Therefore, this TMI model provides a unique opportunity to study the therapeutic benefit of reduced organ damage and BM dose escalation to optimize treatment regimens in BMT and hematologic malignancies. [ABSTRACT FROM AUTHOR]

Purpose: Although vascular alterations in solid tumor malignancies are known to decrease therapeutic delivery, the effects of leukemia-induced bone marrow vasculature (BMV) alterations on therapeutic delivery are not well known. Additionally, functional quantitative measurements of the leukemic BMV during chemotherapy and radiation therapy are limited, largely due to a lack of high-resolution imaging techniques available preclinically. This study develops a murine model using compartmental modeling for quantitative multiphoton microscopy (QMPM) to characterize the malignant BMV before and during treatment.Methods and Materials: Using QMPM, live time-lapsed images of dextran leakage from the local BMV to the surrounding bone marrow of mice bearing acute lymphoblastic leukemia (ALL) were taken and fit to a 2-compartment model to measure the transfer rate (Ktrans), fractional extracellular extravascular space (νec), and vascular permeability parameters, as well as functional single-vessel characteristics. In response to leukemia-induced BMV alterations, the effects of 2 to 4 Gy low-dose radiation therapy (LDRT) on the BMV, drug delivery, and mouse survival were assessed post-treatment to determine whether neoadjuvant LDRT before chemotherapy improves treatment outcome.Results: Mice bearing ALL had significantly altered Ktrans, increased νec, and increased permeability compared with healthy mice. Angiogenesis, decreased single-vessel perfusion, and decreased vessel diameter were observed. BMV alterations resulted in disease-dependent reductions in cellular uptake of Hoechst dye. LDRT to mice bearing ALL dilated BMV, increased single-vessel perfusion, and increased daunorubicin uptake by ALL cells. Consequently, LDRT administered to mice before receiving nilotinib significantly increased survival compared with mice receiving LDRT after nilotinib, demonstrating the importance of LDRT conditioning before therapeutic administration.Conclusion: The developed QMPM enables single-platform assessments of the pharmacokinetics of fluorescent agents and characterization of the BMV. Initial results suggest BMV alterations after neoadjuvant LDRT may contribute to enhanced drug delivery and increased treatment efficacy for ALL. The developed QMPM enables observations of the BMV for use in ALL treatment optimization. [ABSTRACT FROM AUTHOR]