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Curso de Especial interés: Psicología y sexualidadEl presente trabajo es una investigación descriptiva mediante la aplicación de instrumentos como lo son encuestas. Tiene como objetivo promover por medio de un espacio virtual informar a niños, niñas, jóvenes y adolescentes acerca de la menarquia y la torarquia, que les permita comprender los procesos y cambios físicos y psicológicos que se presentan en el desarrollo del ser humano durante la etapa de la pubertad. El producto consistió en el diseño y validación de una página web en la cual los niños, niñas y adolescentes encontrarán información sobre varios procesos, principalmente sobre la menarquia y la torarquia, allí también podrán enviar sus dudas y estas serán resueltas por profesionales en el tema. La muestra se encuentra conformada por jóvenes estudiantes entre los 18 a 29 años de la ciudad de Bogotá. Uno de los hallazgos más relevantes es que las participantes tuvieron su primera menstruación (menarquia) en un rango de edad entre los 8 a 16 años, siendo esto una variable muy importante para la investigación, mientras que en el caso de los hombres se evidenció que tuvieron su primera eyaculación entre los 9 a 16 años de edad.RESUMEN 1. JUSTIFICACIÓN 2. MARCO TEÓRICO 3. METODOLOGÍA 4. OBJETIVOS 5. DISEÑO 6. INSTRUMENTOS 7. PROCEDIMIENTO 8. ASPECTOS ÉTICOS 9. ESTUDIO DE MERCADEO 10. RESULTADOS CONCLUSIONES REFERENCIAS ANEXOSPregradoPsicólog

Use of Dispersive Liquid-Liquid Microextraction and UV-Vis Spectrophotometry for the Determination of Cadmium in Water Samples

A simple and inexpensive method for cadmium determination in water using dispersive liquid-liquid microextraction and ultraviolet-visible spectrophotometry was developed. In order to obtain the best experimental conditions, experimental design was applied. Calibration was made in the range of 10–100 μg/L, obtaining good linearity (R2 = 0.9947). The obtained limit of detection based on calibration curve was 8.5 μg/L. Intra- and interday repeatability were checked at two levels, obtaining relative standard deviation values from 9.0 to 13.3%. The enrichment factor had a value of 73. Metal interferences were also checked and tolerable limits were evaluated. Finally, the method was applied to cadmium determination in real spiked water samples. Therefore, the method showed potential applicability for cadmium determination in highly contaminated liquid samples

Investigating the potential for a limited quantum speedup on protein lattice problems

Protein folding, the determination of the lowest-energy configuration of a protein, is an unsolved computational problem. If protein folding could be solved, it would lead to significant advances in molecular biology, and technological development in areas such as drug discovery and catalyst design. As a hard combinatorial optimisation problem, protein folding has been studied as a potential target problem for adiabatic quantum computing. Although several experimental implementations have been discussed in the literature, the computational scaling of these approaches has not been elucidated. In this article, we present a numerical study of the (stoquastic) adiabatic quantum algorithm applied to protein lattice folding. Using exact numerical modelling of small systems, we find that the time-to-solution metric scales exponentially with peptide length, even for small peptides. However, comparison with classical heuristics for optimisation indicates a potential limited quantum speedup. Overall, our results suggest that quantum algorithms may well offer improvements for problems in the protein folding and structure prediction realm

The prospects of quantum computing in computational molecular biology

Quantum computers can in principle solve certain problems exponentially more quickly than their classical counterparts. We have not yet reached the advent of useful quantum computation, but when we do, it will affect nearly all scientific disciplines. In this review, we examine how current quantum algorithms could revolutionize computational biology and bioinformatics. There are potential benefits across the entire field, from the ability to process vast amounts of information and run machine learning algorithms far more efficiently, to algorithms for quantum simulation that are poised to improve computational calculations in drug discovery, to quantum algorithms for optimization that may advance fields from protein structure prediction to network analysis. However, these exciting prospects are susceptible to “hype,” and it is also important to recognize the caveats and challenges in this new technology. Our aim is to introduce the promise and limitations of emerging quantum computing technologies in the areas of computational molecular biology and bioinformatics

High-resolution mass spectrometry applied to the identification of transformation products of quinolones from stability studies and new metabolites of enrofloxacin in chicken muscle tissues

The aim of this work was the identification of new metabolites and transformation products (TPs) in chicken muscle from Enrofloxacin (ENR), Ciprofloxacin (CIP), Difloxacin (DIF) and Sarafloxacin (SAR), which are antibiotics that belong to the fluoroquinolones family. The stability of ENR, CIP, DIF and SAR standard solutions versus pH degradation process (from pH 1.5 to 8.0, simulating the pH since the drug is administered until its excretion) and freeze-thawing (F/T) cycles was tested. In addition, chicken muscle samples from medicated animals with ENR were analyzed in order to identify new metabolites and TPs. The identification of the different metabolites and TPs was accomplished by comparison of mass spectral data from samples and blanks, using liquid chromatography coupled to quadrupole time-of-flight (LC-QqToF) and Multiple Mass Defect Filter (MMDF) technique as a pre-filter to remove most of the background noise and endogenous components. Confirmation and structure elucidation was performed by liquid chromatography coupled to linear ion trap quadrupole Orbitrap (LC-LTQ-Orbitrap), due to its mass accuracy and MS/MS capacity for elemental composition determination. As a result, 21 TPs from ENR, 6 TPs from CIP, 14 TPs from DIF and 12 TPs from SAR were identified due to the pH shock and F/T cycles. On the other hand, 14 metabolites were identified from the medicated chicken muscle samples. Formation of CIP and SAR, from ENR and DIF, respectively, and the formation of desethylene-quinolone were the most remarkable identified compounds

Deep learning for segmentation of the cervical cancer gross tumor volume on magnetic resonance imaging for brachytherapy

Abstract Background Segmentation of the Gross Tumor Volume (GTV) is a crucial step in the brachytherapy (BT) treatment planning workflow. Currently, radiation oncologists segment the GTV manually, which is time-consuming. The time pressure is particularly critical for BT because during the segmentation process the patient waits immobilized in bed with the applicator in place. Automatic segmentation algorithms can potentially reduce both the clinical workload and the patient burden. Although deep learning based automatic segmentation algorithms have been extensively developed for organs at risk, automatic segmentation of the targets is less common. The aim of this study was to automatically segment the cervical cancer GTV on BT MRI images using a state-of-the-art automatic segmentation framework and assess its performance. Methods A cohort of 195 cervical cancer patients treated between August 2012 and December 2021 was retrospectively collected. A total of 524 separate BT fractions were included and the axial T2-weighted (T2w) MRI sequence was used for this project. The 3D nnU-Net was used as the automatic segmentation framework. The automatic segmentations were compared with the manual segmentations used for clinical practice with Sørensen–Dice coefficient (Dice), 95th Hausdorff distance (95th HD) and mean surface distance (MSD). The dosimetric impact was defined as the difference in D98 (ΔD98) and D90 (ΔD90) between the manual segmentations and the automatic segmentations, evaluated using the clinical dose distribution. The performance of the network was also compared separately depending on FIGO stage and on GTV volume. Results The network achieved a median Dice of 0.73 (interquartile range (IQR) = 0.50–0.80), median 95th HD of 6.8 mm (IQR = 4.2–12.5 mm) and median MSD of 1.4 mm (IQR = 0.90–2.8 mm). The median ΔD90 and ΔD98 were 0.18 Gy (IQR = -1.38–1.19 Gy) and 0.20 Gy (IQR =-1.10–0.95 Gy) respectively. No significant differences in geometric or dosimetric performance were observed between tumors with different FIGO stages, however significantly improved Dice and dosimetric performance was found for larger tumors. Conclusions The nnU-Net framework achieved state-of-the-art performance in the segmentation of the cervical cancer GTV on BT MRI images. Reasonable median performance was achieved geometrically and dosimetrically but with high variability among patients

Strategies for tackling the class imbalance problem of oropharyngeal primary tumor segmentation on magnetic resonance imaging

Background and purpose: Contouring oropharyngeal primary tumors in radiotherapy is currently done manually which is time-consuming. Autocontouring techniques based on deep learning methods are a desirable alternative, but these methods can render suboptimal results when the structure to segment is considerably smaller than the rest of the image. The purpose of this work was to investigate different strategies to tackle the class imbalance problem in this tumor site. Materials and methods: A cohort of 230 oropharyngeal cancer patients treated between 2010 and 2018 was retrospectively collected. The following magnetic resonance imaging (MRI) sequences were available: T1-weighted, T2-weighted, 3D T1-weighted after gadolinium injection. Two strategies to tackle the class imbalance problem were studied: training with different loss functions (namely: Dice loss, Generalized Dice loss, Focal Tversky loss and Unified Focal loss) and implementing a two-stage approach (i.e. splitting the task in detection and segmentation). Segmentation performance was measured with Sørensen–Dice coefficient (Dice), 95th Hausdorff distance (HD) and Mean Surface Distance (MSD). Results: The network trained with the Generalized Dice Loss yielded a median Dice of 0.54, median 95th HD of 10.6 mm and median MSD of 2.4 mm but no significant differences were observed among the different loss functions (p-value > 0.7). The two-stage approach resulted in a median Dice of 0.64, median HD of 8.7 mm and median MSD of 2.1 mm, significantly outperforming the end-to-end 3D U-Net (p-value < 0.05). Conclusion: No significant differences were observed when training with different loss functions. The two-stage approach outperformed the end-to-end 3D U-Net