The features studied and the index provide measurable and applicable data for the interpretation of anterior chest wall tomography, with possible implications for prognosis and treatment of different types of pectus deformities.

Objective: To analyze reformatted sagittal sternal tomography images and classify sternal body curvature types, and compare different types of pectus populations with one another and with normal individuals. Methods: In total, 50 controls and 167 pectus patients were selected for chest CT to analyze the median sagittal plane, of whom 89 had pectus carinatum (mean age, 12 ± 10 years) and 78 pectus excavatum (mean age, 14 ± 10 years). Clinical types of pectus were classified as inferior, superior, or lateral pectus carinatum, and localized or broad pectus excavatum. The following types of sternal patterns were defined: gradual vertical curve, gradual posterior curve, gradual anterior curve, proximal third curve, middle third curve, distal third curve, anterior rectilinear, vertical rectilinear, and posterior rectilinear. Statistical analyses were performed to compare the different types of pectus with one another and with the control group. Results: Patients with different thoracic deformities, but with similar sternal curvature patterns, were observed. Some types of sternal curvature were significantly more frequent in certain types of pectus (p < 0,05). The gradual vertical curve and anterior rectilinear types prevailed in controls (p < 0,05). Conclusion: Some sternal curvature patterns were more frequent than the others in certain types of pectus and the controls. Level of Evidence II, Prognostic studies - investigating the effect of a patient characteristic on the outcome of disease.

Força muscular é uma variável comprovadamente importante de ser avaliada não somente para obter bom desempenho na prática de esportes, como também para identificar indivíduos que possam estar em um grupo de risco para lesões musculoesqueléticas. Poucos estudos descrevem valores de força para diferentes articulações em atletas de elite do futebol feminino. O objetivo deste estudo é descrever esses valores. Para isso, 23 atletas da seleção brasileira de futebol feminino, em preparação para as Olimpíadas de 2004, foram avaliadas nos movimentos de flexo-extensão de tronco, rotação interno-externa do quadril e flexo-extensão dos joelhos no dinamômetro isocinético Cybex 6000 (Lumex Inc. Ronkonkoma, NY). Foram encontrados os valores médios de torque máximo, expressos em Nm: rotação interna do quadril: 23,1; rotação externa do quadril: 25,6; flexão de tronco: 213,2; extensão de tronco: 267,7; extensão de joelho: 181,4; flexão de joelho: 102,0. Os valores encontrados devem ser considerados quando o indivíduo testado equivaler ao grupo estudado.

Background Clinical predictors of sleep quality in patients with fibromyalgia syndrome (FMS) are still unknown. By identifying these factors, we could raise new mechanistic hypotheses and guide management approaches. We aimed to describe the sleep quality of FMS patients, and to explore the clinical and quantitative sensory testing (QST) predictors of poor sleep quality and its subcomponents. Methods This study is a cross-sectional analysis of an ongoing clinical trial. We performed linear regression models between sleep quality (Pittsburgh Sleep Quality Index [PSQI]) and demographic, clinical, and QST variables, controlling for age and gender. Predictors for the total PSQI score and its seven subcomponents were found using a sequential modeling approach. Results We included 65 patients. The PSQI score was 12.78 ± 4.39, with 95.39% classified as poor sleepers. Sleep disturbance, use of sleep medications, and subjective sleep quality were the worst subdomains. We found poor PSQI scores were highly associated with symptom severity (FIQR score and PROMIS fatigue), pain severity, and higher depression levels, explaining up to 31% of the variance. Fatigue and depression scores also predicted the subjective sleep quality and daytime dysfunction subcomponents. Heart rate changes (surrogate of physical conditioning) predicted the sleep disturbance subcomponent. QST variables were not associated with sleep quality or its subcomponents. Conclusion Symptom severity, fatigue, pain, and depression (but no central sensitization) are the main predictors of poor sleep quality. Heart rate changes independently predicted the sleep disturbance subdomain (the most affected one in our sample), suggesting an essential role of physical conditioning in modulating sleep quality in FMS patients. This underscores the need for multidimensional treatments targeting depression and physical activity to improve the sleep quality of FMS patients.