Primary ciliary dyskinesia: evaluation using cilia beat frequency assessment via spectral analysis of digital microscopy images

Olm MA, Kogler JE Jr, Macchione M, Shoemark A, Saldiva PH, Rodrigues JC. Primary ciliary dyskinesia: evaluation using cilia beat frequency assessment via spectral analysis of digital microscopy images. J Appl Physiol 111: 295-302, 2011. First published May 5, 2011; doi:10.1152/japplphysiol.00629.2010.-Ciliary beat frequency (CBF) measurements provide valuable information for diagnosing of primary ciliary dyskinesia (PCD). We developed a system for measuring CBF, used it in association with electron microscopy to diagnose PCD, and then analyzed characteristics of PCD patients. 1 The CBF measurement system was based on power spectra measured through digital imaging. Twenty-four patients suspected of having PCD (age 1-19 yr) were selected from a group of 75 children and adolescents with pneumopathies of unknown causes. Ten healthy, nonsmoking volunteers (age >= 17 yr) served as a control group. Nasal brush samples were collected, and CBF and electron microscopy were performed. PCD was diagnosed in 12 patients: 5 had radial spoke defects, 3 showed absent central microtubule pairs with transposition, 2 had outer dynein arm defects, 1 had a shortened outer dynein arm, and 1 had a normal ultrastructure. Previous studies have reported that the most common cilia defects are in the dynein arm. As expected, the mean CBF was higher in the control group (P < 0.001) and patients with normal ultrastructure (P < 0.002), than in those diagnosed with cilia ultrastructural defects (i.e., PCD patients). An obstructive ventilatory pattern was observed in 70% of the PCD patients who underwent pulmonary function tests. All PCD patients presented bronchial wall thickening on chest computed tomography scans. The protocol and diagnostic techniques employed allowed us to diagnose PCD in 16% of patients in this study.

Independent component analysis applied to raman spectra for classification of in vitro human coronary arteries

Optical diagnostic methods, such as near-infrared Raman spectroscopy allow quantification and evaluation of human affecting diseases, which could be useful in identifying and diagnosing atherosclerosis in coronary arteries. The goal of the present work is to apply Independent Component Analysis (ICA) for data reduction and feature extraction of Raman spectra and to perform the Mahalanobis distance for group classification according to histopathology, obtaining feasible diagnostic information to detect atheromatous plaque. An 830nm Ti:sapphire laser pumped by an argon laser provides near-infrared excitation. A spectrograph disperses light scattered from arterial tissues over a liquid-nitrogen cooled CCD to detect the Raman spectra. A total of 111 spectra from arterial fragments were utilized.

Differential display and suppression subtractive hybridization analysis of the pulp of ripening banana

The fruit of banana undergoes several important physico-chemical changes during ripening. Analysis of gene expression would permit identification of important genes and regulatory elements involved in this process. Therefore, transcript profiling of preclimacteric and climacteric fruit was performed using differential display and Suppression subtractive hybridization. Our analyses resulted in the isolation of 12 differentially expressed cDNAs, which were confirmed by dot-blots and northern blots. Among the sequences identified were sequences homologous to plant aquaporins, adenine nucleotide translocator, immunophilin, legumin-like proteins, deoxyguanosine kinase and omega-3 fatty acid desaturase. Some of these cDNAs correspond to newly isolated genes involved in changes related to the respiratory climacteric, or stress-defense responses. Functional characterization of ripening-associated genes could provide information useful in controlling biochemical pathways that would have an impact on banana quality and shelf life. (C) 2009 Elsevier B.V. All rights reserved.

Study of the Extent of the Information of Cardiologists from Sao Paulo City, Brazil, Regarding a Low-Prevalence Entity: Brugada Syndrome

Objective: To determine the degree of knowledge that cardiologists from Sao Paulo, Brazil, have regarding a low-prevalent entity associated with a high rate of sudden death-Brugada syndrome. Methods: Two hundred forty-four cardiologists were interviewed by an instrument divided in two parts: in the first, we recorded gender, age, and data related to academic profile. The second-answered only by the professionals that manifested having some degree of knowledge on the syndrome-had 28 questions that evaluated their knowledge. The answers were spontaneous and they did not have a chance to consult. We used uni- and multivariate analysis on the average percentage of right and wrong answers, and the influence of the academic profile. Results: The predominant gender was the male gender (61.1%), the average age was 44.32 +/- 10.83 years, 40% with more than 20 years after obtaining their degree, 44% were educated in public institutions, 69% had a residency in cardiology, 20% had overseas practice, 12% had postdegree, 41% were linked to an educational institution, 24% with publication(s) in an indexed journal, 17.2% were authors of chapters in books, 2.5% had edited books, and 10% were linked to the Brazilian Society of Cardiac Arrhythmias. The average percentage of right answers was 45.7%. Conclusion: The sample studied revealed a little knowledge on the entity. A residency in cardiology was the factor of greater significance in the percentage of right answers. Other significant factors were the link of the interviewed person to an educational institution, or the Brazilian Society of Cardiac Arrhythmias, and having a specialist degree.

Analysis of Detergent-Insoluble and Whole Cell Lysate Fractions of Resting Neutrophils Using High-Resolution Mass Spectrometry

Neutrophilic granulocytes play a major role in the initiation and resolution of the inflammatory response, and demonstrate significant transcriptional and translational activity. Although much was known about neutrophils prior to the introduction of proteomics, the use of MS-based methodologies has provided an unprecedented tool to confirm and extend previous findings. In the present study, we performed a Gel-LC-MS/MS analysis of neutrophil detergent insoluble and whole cell lysate fractions of resting neutrophils. We achieved a set of identifications through the use of high-resolution mass spectrometry and validation of its data. We identified a total of 1249 proteins with a wide range of intensities from both detergent-insoluble and whole cell lysate fractions, allowing a mapping of proteins such as those involved in intracellular transport (Rab and Sec family proteins) and cell signaling (S100 proteins). These results represent the most comprehensive proteomic characterization of resting human neutrophils to date, and provide important information relevant for further studies of the immune system in health and disease. The methods applied here can be employed to help us understand how neutrophils respond to various physiologic and pathophysiologic conditions and could be extended to protein quantitation after cell activation.

Detection of Auditory Cortex Activity by fMRI Using a Dependent Component Analysis

Functional MRI (fMRI) data often have low signal-to-noise-ratio (SNR) and are contaminated by strong interference from other physiological sources. A promising tool for extracting signals, even under low SNR conditions, is blind source separation (BSS), or independent component analysis (ICA). BSS is based on the assumption that the detected signals are a mixture of a number of independent source signals that are linearly combined via an unknown mixing matrix. BSS seeks to determine the mixing matrix to recover the source signals based on principles of statistical independence. In most cases, extraction of all sources is unnecessary; instead, a priori information can be applied to extract only the signal of interest. Herein we propose an algorithm based on a variation of ICA, called Dependent Component Analysis (DCA), where the signal of interest is extracted using a time delay obtained from an autocorrelation analysis. We applied such method to inspect functional Magnetic Resonance Imaging (fMRI) data, aiming to find the hemodynamic response that follows neuronal activation from an auditory stimulation, in human subjects. The method localized a significant signal modulation in cortical regions corresponding to the primary auditory cortex. The results obtained by DCA were also compared to those of the General Linear Model (GLM), which is the most widely used method to analyze fMRI datasets.