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OBJECTIVE: To investigate the usefulness and performance metrics of three freely-available softwares (Rayyan®, Abstrackr® and Colandr®) for title screening in systematic reviews. STUDY DESIGN AND SETTING: In this methodological study, the usefulness of softwares to screen titles in systematic reviews was investigated by the comparison between the number of titles identified by software-assisted screening and those by manual screening using a previously published systematic review. To test the performance metrics, sensitivity, specificity, false negative rate, proportion missed, workload and timing savings were calculated. A purposely built survey was used to evaluate the rater's experiences regarding the softwares' performances. RESULTS: Rayyan® was the most sensitive software and raters correctly identified 78% of the true positives. All three softwares were specific and raters correctly identified 99% of the true negatives. They also had similar values for precision, proportion missed, workload and timing savings. Rayyan®, Abstrackr® and Colandr® had 21%, 39% and 34% of false negatives rates, respectively. Rayyan presented the best performance (35/40) according to the raters. CONCLUSION: Rayyan®, Abstrackr® and Colandr® are useful tools and provided good metric performance results for systematic title screening. Rayyan® appears to be the best ranked on the quantitative and on the raters' perspective evaluation. The most important finding of this study is that the use of software to screen titles does not remove any title that would meet the inclusion criteria for the final review, being valuable resources to facilitate the screening process.

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Computed tomography is a widely used image examination in dental imaging that provides an accurate location of oral structures and features, including the dental arch, which is an important anatomical feature. This study proposes two new semi-automatic methods for arch definition in CTs, with minimal user effort. This study includes 25 CT examinations. The first method is based on the teeth pulps, and the second one is based on the whole mandible. The methods use thresholding and morphological operations to obtain the arches. The evaluation process includes two different metrics DTW and IoU. For both metrics, the initial results of M1 were very low, but the average performance of M2 can be considered high. The analysis showed that changing the input improves the M1 results substantially. The promising results presented here suggest that these methods can be used as auxiliary tools for the proposed task.

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The present chapter describes basic aspects of the main steps for data processing on mass spectrometry-based metabolomics platforms, focusing on the main objectives and important considerations of each step. Initially, an overview of metabolomics and the pivotal techniques applied in the field are presented. Important features of data acquisition and preprocessing such as data compression, noise filtering, and baseline correction are revised focusing on practical aspects. Peak detection, deconvolution, and alignment as well as missing values are also discussed. Special attention is given to chemical and mathematical normalization approaches and the role of the quality control (QC) samples. Methods for uni- and multivariate statistical analysis and data pretreatment that could impact them are reviewed, emphasizing the most widely used multivariate methods, i.e., principal components analysis (PCA), partial least squares-discriminant analysis (PLS-DA), orthogonal partial least square-discriminant analysis (OPLS-DA), and hierarchical cluster analysis (HCA). Criteria for model validation and softwares used in data processing were also approached. The chapter ends with some concerns about the minimal requirements to report metadata in metabolomics.

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Una de las etapas fundamentales en el desarrollo de un protocolo de investigación es la construcción de la base de datos. Su diseño detallado y el llenado cuidadoso ayudan a mejorar la confiabilidad de los resultados obtenidos a partir del análisis estadístico de los datos ingresados. Justamente, uno de los problemas que frecuentemente deben enfrentar los profesionales de la salud, es la construcción y la digitación de los datos. Procesos que, de no ser realizados adecuadamente, pueden generar diferentes tipos de errores. Aunque existen softwares específicos para el armando de las bases de datos, no es frecuente que los profesionales de la salud sepan utilizarlos. Es por lo anterior que el objetivo de este apartado es describir algunos aspectos básicos del uso de la planilla de cálculo de Excel para que pueda ser utilizada como base de datos.

One of the fundamental stages in the development of a research protocol is the construction of the database. Its detailed design and careful filling help improve the reliability of the results obtained from the data's statistical analysis. One of the problems that health professionals must frequently face is the construction and entry of data. Processes that, if not carried out properly, can generate different types of errors. Although there is a specific software for building databases, health professionals do not know how to use them. Because of the above, the objective of this section is to describe some essential aspects of the use of the Excel spreadsheet so that it can be used as a database.

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Introduction Specific correlations among diffusion tensor imaging (DTI)-derived metrics and magnetic resonance spectroscopy (MRS) metabolite ratios in brains with glioblastoma are still not completely understood. Patients and methods We made retrospective cohort study. MRS ratios (choline-to-N-acetyl aspartate [Cho/NAA], lipids and lactate to creatine [LL/Cr], and myo-inositol/creatine [mI/Cr]) were correlated with eleven DTI biomarkers: mean diffusivity (MD), fractional anisotropy (FA), pure isotropic diffusion (p), pure anisotropic diffusion (q), the total magnitude of the diffusion tensor (L), linear tensor (Cl), planar tensor (Cp), spherical tensor (Cs), relative anisotropy (RA), axial diffusivity (AD) and radial diffusivity (RD) at the same regions: enhanced rim, peritumoral oedema and normal-appearing white matter. Correlational analyses of 546 MRS and DTI measurements used Spearman coefficient. Results At the enhancing rim we found four significant correlations: FA ⇔ LL/Cr, Rs = -.364, p = .034; Cp ⇔ LL/Cr, Rs = .362, p = .035; q ⇔ LL/Cr, Rs = -.349, p = .035; RA ⇔ LL/Cr, Rs = -.357, p = .038. Another ten pairs of significant correlations were found in the peritumoral edema: AD ⇔ LL/Cr, AD ⇔ mI/Cr, MD ⇔ LL/Cr, MD ⇔ mI/Cr, p ⇔ LL/Cr, p ⇔ mI/ Cr, RD ⇔ mI/Cr, RD ⇔ mI/Cr, L ⇔ LL/Cr, L ⇔ mI/Cr. Conclusions DTI and MRS biomarkers answer different questions; peritumoral oedema represents the biggest challenge with at least ten significant correlations between DTI and MRS that need additional studies. The fact that DTI and MRS measures are not specific of one histologic type of tumour broadens their application to a wider variety of intracranial pathologies.

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Evaluation and comparison of odontogenic keratocysts and detigerous cysts immunoexpression and immunostaining intensities of Ki-67 antigen by assessing the whole extent of the epithelium (all epithelium layers in combination) and each layer individually. Ki-67 immunoexpression was evaluated in 15 odontogenic keratocysts and 6 dentigerous cysts using automated methods and the Aperio Technologies Inc. computer system. No statistically significant differences were observed in immunoexpression nor in immunostaining intensities between both lesions. Also, no statistically significant differences were found between odontogenic keratocysts from maxilla versus mandible nor primary versus recurrent. However, odontogenic keratocyst showed a significantly higher cellular proliferation index in the suprabasal layers compared to the basal layer. Assessment of the cellular proliferation index through a computerized system enabled the evaluation of all epithelial tissue without field selection. The increased Ki-67 immunoexpression in suprabasal layers of odontogenic keratocyst suggests a different biological behavior and more aggressive proliferation potential when compared to dentigerous cyst. The same result was found in recurrent odontogenic keratocysts when compared with primary ones. The odontogenic keratocysts of the maxilla and mandible have similar Ki-67 immunoexpression. The evaluation of cellular proliferation only by immunohistochemical analysis with Ki-67 antigen does not provide enough data to elucidate the biological behavior of odontogenic keratocyst.

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AIM: To determine existing correlates among diffusion tensor imaging (DTI)-derived metrics in healthy brains and brains with glioblastoma multiforme (GBM). METHODS: Case-control study using DTI data from brain magnetic resonance imaging of 34 controls (mean, 41.47; SD, ± 21.94 years; range, 21-80 years) and 27 patients with GBM (mean, SD; 48.41 ± 15.18 years; range, 18-78 years). Image postprocessing using FSL software calculated eleven tensor metrics: fractional (FA) and relative anisotropy; pure isotropic (p) and anisotropic diffusions (q), total magnitude of diffusion (L); linear (Cl), planar (Cp) and spherical tensors (Cs); mean (MD), axial (AD) and radial diffusivities (RD). Partial correlation analyses (controlling the effect of age and gender) and multivariate Mancova were performed. RESULTS: There was a normal distribution for all metrics. Comparing healthy brains vs brains with GBM, there were significant very strong bivariate correlations only depicted in GBM: [FA↔Cl (+)], [FA↔q (+)], [p↔AD (+)], [AD↔MD (+)], and [MD↔RD (+)]. Among 56 pairs of bivariate correlations, only seven were significantly different. The diagnosis variable depicted a main effect [F-value (11, 23) = 11.842, P ≤ 0.001], with partial eta squared = 0.850, meaning a large effect size; age showed a similar result. The age also had a significant influence as a covariate [F (11, 23) = 10.523, P < 0.001], with a large effect size (partial eta squared = 0.834). CONCLUSION: DTI-derived metrics depict significant differences between healthy brains and brains with GBM, with specific magnitudes and correlations. This study provides reference data and makes a contribution to decrease the underlying empiricism in the use of DTI parameters in brain imaging.

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A new tablet device version (IOS platform) of the Spatial Delayed Recognition Span Task (SDRST) was developed with the aim of investigating visuospatial Working Memory (WM) abilities based on touchscreen technology. This new WM testing application will be available to download for free in Apple Store app ("SDRST app"). In order to verify the feasibility of this computer-based task, we conducted three experiments with different manipulations and groups of participants. We were interested in investigating if (1) the SDRST is sensitive enough to tap into cognitive differences brought by aging and dementia; (2) different experimental manipulations work successfully; (3) cortical brain activations seen in other WM tasks are also demonstrated here; and (4) non-human primates are able to answer the task. Performance (scores and response time) was better for young than older adults and higher for the latter when compared to Alzheimer's disease (AD) patients. All groups performed better with facial stimuli than with images of scenes and with emotional than with neutral stimuli. Electrophysiology data showed activation on prefrontal and frontal areas of scalp, theta band activity on the midline area, and gamma activity in left temporal area. There are all scalp regions known to be related to attention and WM. Besides those data, our sample of adult captive capuchin monkeys (Sapajus libidinosus) answered the task above chance level. Taken together, these results corroborate the reliability of this new computer-based SDRST as a measure of visuospatial WM in clinical and non-clinical populations as well as in non-human primates. Its tablet app allows the task to be administered in a wide range of settings, including hospitals, homes, schools, laboratories, universities, and research institutions.