RESUMO
OBJECTIVE: To determine whether Sway, a sway-based mobile application, predicts falls and to evaluate its discriminatory sensitivity and specificity relative to other clinical measures in identifying fallers in individuals with Parkinson disease (PD). DESIGN: Observational cross-sectional study. SETTING: Community. PARTICIPANTS: A convenience sample of subjects with idiopathic PD in Hoehn and Yahr levels I-III (N=59). INTERVENTIONS: Participants completed a balance assessment using Sway, the Movement Disorders Systems-Unified PD Rating Scale motor examination, Mini-BESTest, Activities-specific Balance Confidence (ABC) Scale, and reported 6-month fall history. Participants also reported falls for each of the following 6 months. Binomial logistic regression was used to identify significant predictors of future fall status. Cutoff scores, sensitivity, and specificity were based on receiver operating characteristic plots. MAIN OUTCOME MEASURES: Sway score. RESULTS: The most predictive logistic regression model included fall history, ABC Scale, and Sway (P<.001). This model explained 61% (Nagelkerke R2) of the variance in fall prediction and correctly classified 85% of fallers. However, only fall history and ABC Scale were statistically significant (P<.02). Participants were 32 times more likely to fall in the future if they fell in the past. The ABC Scale and Mini Balance Evaluation Systems Test (Mini-BESTest) demonstrated greater accuracy than Sway (area under the curve=0.76, 0.72, and 0.65, respectively). Cutoff scores to identify fallers were 85% for the ABC Scale and 21 of 28 for the Mini-BESTest. CONCLUSION: Sway did not improve the accuracy of predicting future fallers beyond common clinical measures and fall history.
Assuntos
Acidentes por Quedas , Aplicativos Móveis , Doença de Parkinson/fisiopatologia , Equilíbrio Postural , Idoso , Estudos Transversais , Avaliação da Deficiência , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Valor Preditivo dos Testes , Sensibilidade e EspecificidadeRESUMO
A proteomic characterization of one premalignant (MCF10AT1) and two malignant (MCF10CA1a and MCF10 CA1d) human breast cancer cell lines has been performed using a combination of two-dimensional liquid separations and mass spectrometry. Chromatofocusing (CF) and NPS-RP-HPLC are combined with ESI-TOF-MS to resolve and detect intact proteins. Simultaneously, fractions are collected and digested for protein identification using MALDI-MS peptide mass fingerprinting. Following protein identification a small database was compiled for use in comparison between IDs and measured masses taking into account variables such as pI, hydrophobicity and potential modifications. Out of 239 mass bands detected between pH 4.6 and 6.0, 133 have been definitively associated with identified proteins and 67 show consistent up/down regulation in two malignant breast cancer cell lines relative to the precursor premalignant cell line. Of these, 8 are also altered in the premalignant MCF10AT1 cell line by treatment with estradiol. Differentially expressed proteins indicate significant changes to the cytoskeleton, cellular metabolism, and adaptation to environmental stressors in malignant cell lines.
Assuntos
Biomarcadores Tumorais/metabolismo , Neoplasias da Mama/metabolismo , Proteínas de Neoplasias/metabolismo , Proteoma/análise , Cromatografia Líquida de Alta Pressão , Progressão da Doença , Eletroforese em Gel Bidimensional , Humanos , Focalização Isoelétrica , Mapeamento de Peptídeos , Espectrometria de Massas por Ionização por Electrospray , Espectrometria de Massas por Ionização e Dessorção a Laser Assistida por MatrizRESUMO
Intact protein masses from immortal, nontransformed MCF10A, a human breast epithelial cell line, and its malignant derivative MCF10CA1a.cl1 have been mapped using a combination of all-liquid separations and automated data interpretation. Preparative liquid isoelectric focusing combined with nonporous silica reverse-phase high-performance liquid chromatography allows efficient separation of a large number of proteins in complex mixtures such as whole-cell lysates. Molecular weight determination of these proteins is achieved using electrospray-time of flight-mass spectrometry, however, manual data analysis for these separations is both complex and time-consuming. Protein mass mapping can be significantly enhanced by automating deconvolution functions typically performed manually, with resulting reductions in hands-on analysis time from 20-30 h per chromatogram to approximately 15 min. This reduction in analysis time allows for rapid screening of cancer cell lines for potential biomarkers over a wider pI range than would otherwise be possible.
Assuntos
Biomarcadores Tumorais/análise , Neoplasias da Mama/química , Proteínas de Neoplasias/análise , Espectrometria de Massas por Ionização por Electrospray/métodos , Biomarcadores Tumorais/isolamento & purificação , Extratos Celulares/química , Linhagem Celular Tumoral , Cromatografia Líquida de Alta Pressão , Feminino , Humanos , Focalização Isoelétrica , Peso Molecular , Proteínas de Neoplasias/isolamento & purificaçãoRESUMO
A two-dimensional liquid-phase separation scheme coupled with mass spectrometry (MS) is presented for proteomic analysis of cell lysates from normal and malignant breast epithelial cell lines. Liquid-phase separations consist of isoelectric focusing as the first dimension and nonporous silica reverse-phase high-performance liquid chromatography (NPS-RP-HPLC) as the second dimension. Protein quantitation and mass measurement are performed using electrospray ionization-time of flight MS (ESI-TOF MS). Proteins are identified by peptide mass fingerprinting using matrix-assisted laser desorption ionization-time of flight MS (MALDI-TOF MS) and MALDI-quadrupole time of flight (QTOF)-tandem mass spectrometry (MS/MS). Two pH regions with 50-60 unique proteins in each pH range were chosen for analysis. Mass maps were created that allowed visualization of protein quantitation differences between normal and malignant breast epithelial cells. Of the approximately 110 unique proteins observed from mass mapping experiments over the limited pH range, 40 (36%) were positively identified by peptide mass fingerprinting and assigned to bands in the mass maps. Of these 40 proteins, 22 were more highly expressed in one or more of the malignant cell lines. These proteins represent potential breast cancer biomarkers that could aid in diagnosis, therapy, or drug development.
Assuntos
Biomarcadores Tumorais , Neoplasias da Mama/diagnóstico , Neoplasias da Mama/metabolismo , Cromatografia Líquida de Alta Pressão/métodos , Espectrometria de Massas/métodos , Espectrometria de Massas por Ionização por Electrospray/métodos , Linhagem Celular Tumoral , Eletroforese em Gel Bidimensional , Proteínas de Choque Térmico/química , Humanos , Concentração de Íons de Hidrogênio , Focalização Isoelétrica , Queratinas/química , Proteínas/química , Proteoma , Espectrometria de Massas por Ionização e Dessorção a Laser Assistida por Matriz , Tripsina/farmacologiaRESUMO
A review of two-dimensional (2D) liquid separation methods used in our laboratory to map the protein content of human cancer cells is presented herein. The methods discussed include various means of fractionating proteins according to isoelectric point (pI) in the first dimension. The proteins in each pI fraction are subsequently separated using nonporous (NPS) reversed-phase high-performance liquid chromatography (RP-HPLC). The liquid eluent of the RP-HPLC separation is directed on-line into an electrospray ionization time-of-flight (ESI-TOF) mass spectrometer where an accurate value of the protein intact M(r) can be obtained. The result is a 2D map of pI versus M(r) analogous to 2D gel electrophoresis; however the highly accurate and reproducible M(r) serves as the basis for interlysate comparisons. In addition, the use of liquid separations allows for the collection of hundreds of purified proteins in the liquid phase for further analysis via peptide mass mapping using matrix assisted laser desorption ionization TOF MS. A description of the methodology used and its applications to analysis of several types of human cancer cell lines is described. The potential of the method for differential proteomic analysis for the identification of biomarkers of disease is discussed.