The relationship of bottle feeding and other sucking behaviors with speech disorder in Patagonian preschoolers

<p>Abstract</p> <p>Background</p> <p>Previous studies have shown that children's nonnutritive sucking habits may lead to delayed development of their oral anatomy and functioning. However, these findings were inconsistent. We investigated associations between use of bottles, pacifiers, and other sucking behaviors with speech disorders in children attending three preschools in Punta Arenas (Patagonia), Chile.</p> <p>Methods</p> <p>Information on infant feeding and sucking behaviors, age starting and stopping breast- and bottle-feeding, pacifier use, and other sucking behaviors, was collected from self-administered questionnaires completed by parents. Evaluation of speech problems was conducted at preschools with subsequent scoring by a licensed speech pathologist using age-normative standards.</p> <p>Results</p> <p>A total of 128 three- to five-year olds were assessed, 46% girls and 54% boys. Children were breastfed for an average of 25.2 (SD 9.6) months and used a bottle 24.4 (SD 15.2) months. Fifty-three children (41.7%) had or currently used a pacifier for an average of 11.4 (SD 17.3) months; 23 children (18.3%) were reported to have sucked their fingers. Delayed use of a bottle until after 9 months appeared to be protective for subsequent speech disorders. There was less than a one-third lower relative odds of subsequent speech disorders for children with a delayed use of a bottle compared to children without a delayed use of a bottle (OR: 0.32, 95% CI: 0.10-0.98). A three-fold increase in relative odds of speech disorder was found for finger-sucking behavior (OR: 2.99, 95% CI: 1.10-8.00) and for use of a pacifier for 3 or more years (OR: 3.42, 95% CI: 1.08-10.81).</p> <p>Conclusion</p> <p>The results suggest extended use of sucking outside of breastfeeding may have detrimental effects on speech development in young children.</p

Articulación de fones en individuos clase esqueletal I,II y III Speech patterns in skeletal class I, II and III subjects

OBJETIVO: determinar los patrones de articulación de fones consonánticos en sujetos de habla española chilena clases I, II y III esqueletal; comparar las diferencias fonéticas que existan entre clases esqueletales. MÉTODOS: se seleccionaron 54 individuos que cumplían con los criterios de inclusión determinados mediante un examen clínico intraoral y a través del análisis de Ricketts, y se conformaron los grupos de estudio de pacientes clases esqueletales I, II y III. Se les realizó un examen fonoarticulatorio estandarizado para determinar los fones modificados y el patrón articulatorio compensatorio realizado. RESULTADOS: se observaron cambios en el punto de articulación de fones consonánticos en las tres clases esqueletales, con diferencias significativas en los grupos de fones anteriores y medios entre pacientes clases I y II, sólo en el grupo de los fones anteriores entre pacientes I y III. Entre pacientes clases II y III no se observaron diferencias significativas. Se reportan modificaciones y compensaciones cualitativamente distintas entre las clases esqueletales. CONCLUSIONES: en relación a pacientes clase I, los pacientes clase II o III, presentan distinto grado de modificación en el punto de articulación de fones consonánticos. Las diferencias observadas se relacionan con los patrones esqueletales propios de cada clase.<br>PURPOSE: to determine the consonant phonemes articulation patterns in Chilean skeletal class I, II and III Spanish speakers and compare their phonetic differences. METHODS: fifty-four skeletal class I, II and III subjects were selected, based on intraoral clinical examination and Ricketts cephalometric analysis, constituting the study groups. A standardized phonoarticulatory test was applied to each patient to determine the modified phonemes and their compensatory patterns. RESULTS: the findings indicate changes in articulation in all three groups. Significant differences were found in anterior and medium phonemes between skeletal class I and II and only in the anterior phonemes between skeletal class I and III. There were no significant differences between II and III skeletal groups. Qualitatively different modifications and compensations between skeletal classes are reported. CONCLUSION: different degree of modification in consonant phonemes articulation is shown in class II and III patients when compared to class I skeletal subjects. These differences are related to the skeletal patterns

Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset