Bolton freeware: aplicativo de avaliação de discrepância de massa dentária desenvolvido pelo programa de pós-graduação em odontologia/UFRN

The calculation of tooth mass discrepancy, essential for good planning and a proper orthodontic finishing, when performed manually, besides being laborious, requires considerable time consumption. The aim of this study was to develop and test Bolton Freeware, a software for analysis of the tooth mass discrepancy of Bolton, aiming to minimize the consumption of time in a less onerous way. The digital analysis of the software was done by means of two-dimensional scanning of plaster study models and compared to manual evaluation (gold standard), using 75 pairs of stone plaster study models divided into two groups according to the magnitude of the Curve of Spee (group I from 0 to 2 mm, group II greater than 2 to 3mm). All the models had permanent dentition and were in perfect condition. The manual evaluation was performed with a digital caliper and a calculator, and the time required to perform the analysis for both methods was recorded and compared. In addition, the software was evaluated by orthodontists regarding its use, by means of questionnaires developed specifically for this purpose. Calibration was performed prior to manual analysis, and excellent levels of inter-rater agreement were achieved, with ICC > 0.75 and r > 0.9 for total and anterior proportion. It was observed in the evaluation of error of the digital method that some teeth showed a significant systematic error, being the highest measured at 0.08 mm. The analysis of total tooth mass discrepancy performed by Bolton Freeware, for those cases in which the curve of Spee is mild and moderate, differ from manual analysis, on average, 0.09 mm and 0.07 mm respectively, for each tooth evaluated, with r> 0, 8 for total and anterior proportion. According to the specificity and sensitivity test, Bolton Freeware has an improved ability to detect true negatives, i.e. the presence of discrepancy. The Bolton analysis digitally performed was faster, with an average difference of time consumed to perform the analysis of Bolton between the two methods of approximately 6 minutes. Most experts interviewed (93%) approved the usability of the software

Graph analysis of verbal fluency test discriminate between patients with Alzheimer’s disease, Mild Cognitive Impairment and normal elderly controls

Verbal fluency is the ability to produce a satisfying sequence of spoken words during a given time interval. The core of verbal fluency lies in the capacity to manage the executive aspects of language. The standard scores of the semantic verbal fluency test are broadly used in the neuropsychological assessment of the elderly, and different analytical methods are likely to extract even more information from the data generated in this test. Graph theory, a mathematical approach to analyze relations between items, represents a promising tool to understand a variety of neuropsychological states. This study reports a graph analysis of data generated by the semantic verbal fluency test by cognitively healthy elderly (NC), patients with Mild Cognitive Impairment – subtypes amnestic(aMCI) and amnestic multiple domain (a+mdMCI) - and patients with Alzheimer’s disease (AD). Sequences of words were represented as a speech graph in which every word corresponded to a node and temporal links between words were represented by directed edges. To characterize the structure of the data we calculated 13 speech graph attributes (SGAs). The individuals were compared when divided in three (NC – MCI – AD) and four (NC – aMCI – a+mdMCI – AD) groups. When the three groups were compared, significant differences were found in the standard measure of correct words produced, and three SGA: diameter, average shortest path, and network density. SGA sorted the elderly groups with good specificity and sensitivity. When the four groups were compared, the groups differed significantly in network density, except between the two MCI subtypes and NC and aMCI. The diameter of the network and the average shortest path were significantly different between the NC and AD, and between aMCI and AD. SGA sorted the elderly in their groups with good specificity and sensitivity, performing better than the standard score of the task. These findings provide support for a new methodological frame to assess the strength of semantic memory through the verbal fluency task, with potential to amplify the predictive power of this test. Graph analysis is likely to become clinically relevant in neurology and psychiatry, and may be particularly useful for the differential diagnosis of the elderly.

Metalização mecânica de ZrO2 com Ti para brasagem ZrO2/Aço com ligas de adição sem metal ativo

Metal/ceramic interfaces using zirconia have dominated the industrial applications in the last decade, due to the high mechanical strength and fracture toughness of zirconia, especially at temperatures below 300 ºC. Also noteworthy is the good ionic conductivity in high temperatures of this component. In this work joining between ZrO2 Y-TZP and ZrO2 Mg-PSZ with austenitic stainless steel was studied. These joints were brazed at high-vacuum after mechanical metallization with Ti using filler alloys composed by Ag-Cu and Ag-Cu-Ni. The influence of the metallization, and the affinity between the different groups (ceramic / filler alloys) was evaluated, in order to achieve strong metal/ceramic joints. Evaluation of joints and interfaces, also the characterization of base materials was implemented using various techniques, such as: x-ray diffraction, leak test, three-point flexural test and scanning electron microscopy with chemical analysis. The microstructural analysis revealed physical and chemical bonds in the metal/ceramic interfaces, providing superior leak proof joints and stress cracking, in order to a good joint in all brazed samples. Precipitation zones and reaction layers with eutetic characteristics were observed between the steel and the filler metal

Graph analysis of verbal fluency test discriminate between patients with Alzheimer’s disease, Mild Cognitive Impairment and normal elderly controls

Verbal fluency is the ability to produce a satisfying sequence of spoken words during a given time interval. The core of verbal fluency lies in the capacity to manage the executive aspects of language. The standard scores of the semantic verbal fluency test are broadly used in the neuropsychological assessment of the elderly, and different analytical methods are likely to extract even more information from the data generated in this test. Graph theory, a mathematical approach to analyze relations between items, represents a promising tool to understand a variety of neuropsychological states. This study reports a graph analysis of data generated by the semantic verbal fluency test by cognitively healthy elderly (NC), patients with Mild Cognitive Impairment – subtypes amnestic(aMCI) and amnestic multiple domain (a+mdMCI) - and patients with Alzheimer’s disease (AD). Sequences of words were represented as a speech graph in which every word corresponded to a node and temporal links between words were represented by directed edges. To characterize the structure of the data we calculated 13 speech graph attributes (SGAs). The individuals were compared when divided in three (NC – MCI – AD) and four (NC – aMCI – a+mdMCI – AD) groups. When the three groups were compared, significant differences were found in the standard measure of correct words produced, and three SGA: diameter, average shortest path, and network density. SGA sorted the elderly groups with good specificity and sensitivity. When the four groups were compared, the groups differed significantly in network density, except between the two MCI subtypes and NC and aMCI. The diameter of the network and the average shortest path were significantly different between the NC and AD, and between aMCI and AD. SGA sorted the elderly in their groups with good specificity and sensitivity, performing better than the standard score of the task. These findings provide support for a new methodological frame to assess the strength of semantic memory through the verbal fluency task, with potential to amplify the predictive power of this test. Graph analysis is likely to become clinically relevant in neurology and psychiatry, and may be particularly useful for the differential diagnosis of the elderly.