TNFα decreases mitochondrial movement in human airway smooth muscle.

In airway smooth muscle (ASM) cells, excitation-contraction coupling is accomplished via a cascade of events that connect an elevation of cytosolic Ca2+ concentration ([Ca2+]cyt) with cross-bridge attachment and ATP-consuming mechanical work. Excitation-energy coupling is mediated by linkage of the elevation of [Ca2+]cyt to an increase in mitochondrial Ca2+ concentration, which in turn stimulates ATP production. Proximity of mitochondria to the sarcoplasmic reticulum (SR) and plasma membrane is thought to be an important mechanism to facilitate mitochondrial Ca2+ uptake. In this regard, mitochondrial movement in ASM cells may be key in establishing proximity. Mitochondria also move where ATP or Ca2+ buffering is needed. Mitochondrial movement is mediated through interactions with the Miro-Milton molecular complex, which couples mitochondria to kinesin motors at microtubules. We examined mitochondrial movement in human ASM cells and hypothesized that, at basal [Ca2+]cyt levels, mitochondrial movement is necessary to establish proximity of mitochondria to the SR and that, during the transient increase in [Ca2+]cyt induced by agonist stimulation, mitochondrial movement is reduced, thereby promoting transient mitochondrial Ca2+ uptake. We further hypothesized that airway inflammation disrupts basal mitochondrial movement via a reduction in Miro and Milton expression, thereby disrupting the ability of mitochondria to establish proximity to the SR and, thus, reducing transient mitochondrial Ca2+ uptake during agonist activation. The reduced proximity of mitochondria to the SR may affect establishment of transient "hot spots" of higher [Ca2+]cyt at the sites of SR Ca2+ release that are necessary for mitochondrial Ca2+ uptake via the mitochondrial Ca2+ uniporter.

Dose reduction in helical CT: dynamically adjustable z-axis X-ray beam collimation.

OBJECTIVE: The purpose of this study was to measure the dose reduction achieved with dynamically adjustable z-axis collimation. MATERIALS AND METHODS: A commercial CT system was used to acquire CT scans with and without dynamic z-axis collimation. Dose reduction was measured as a function of pitch, scan length, and position for total incident radiation in air at isocenter, accumulated dose to the center of the scan volume, and accumulated dose to a point at varying distances from a scan volume of fixed length. Image noise was measured at the beginning and center of the scan. RESULTS: The reduction in total incident radiation in air at isocenter varied between 27% and 3% (pitch, 0.5) and 46% and 8% (pitch, 1.5) for scan lengths of 20 and 500 mm, respectively. Reductions in accumulated dose to the center of the scan were 15% and 29% for pitches of 0.5 and 1.5 for 20-mm scans. For scan lengths greater than 300 mm, dose savings were less than 3% for all pitches. Dose reductions 80 mm or farther from a 100-mm scan range were 15% and 40% for pitches of 0.5 and 1.5. With dynamic z-axis collimation, noise at the extremes of a helical scan was unchanged relative to noise at the center. Estimated reductions in effective dose were 16% (0.4 mSv) for the head, 10% (0.8 and 1.4 mSv) for the chest and liver, 6% (0.8 mSv) for the abdomen and pelvis, and 4% (0.4 mSv) and 55% (1.0 mSv) for coronary CT angiography at pitches of 0.2 and 3.4. CONCLUSION: Use of dynamic z-axis collimation reduces dose in helical CT by minimizing overscanning. Percentage dose reductions are larger for shorter scan lengths and greater pitch values.

High resolution multidetector CT-aided tissue analysis and quantification of lung fibrosis.

RATIONALE AND OBJECTIVES: Volumetric high-resolution scans can be acquired of the lungs with multidetector CT (MDCT). Such scans have potential to facilitate useful visualization, characterization, and quantification of the extent of diffuse lung diseases, such as usual interstitial pneumonitis or idiopathic pulmonary fibrosis (UIP/IPF). There is a need to objectify, standardize, and improve the accuracy and repeatability of pulmonary disease characterization and quantification from such scans. This article presents a novel texture analysis approach toward classification and quantification of various pathologies present in lungs with UIP/IPF. The approach integrates a texture matching method with histogram feature analysis. MATERIALS AND METHODS: Patients with moderate UIP/IPF were scanned on a Lightspeed 8-detector GE CT scanner (140 kVp, 250 mAs). Images were reconstructed with 1.25-mm slice thickness in a high-frequency sparing algorithm (BONE) with 50% overlap and a 512 x 512 axial matrix, (0.625 mm(3) voxels). Eighteen scans were used in this study. Each dataset is preprocessed and includes segmentation of the lungs and the bronchovascular trees. Two types of analysis were performed, first an analysis of independent volume of interests (VOIs) and second an analysis of whole-lung datasets. 1) Fourteen of the 18 scans were used to create a database of independent 15 x 15 x 15 cubic voxel VOIs. The VOIs were selected by experts as having greater than 70% of the defined class. The database was composed of: honeycombing (number of VOIs 337), reticular (130), ground glass (148), normal (240), and emphysema (54). This database was used to develop our algorithm. Three progressively challenging classification experiments were designed to test our algorithm. All three experiments were performed using a 10-fold cross-validation method for error estimation. Experiment 1 consisted of a two-class discrimination: normal and abnormal. Experiment 2 consisted of a four-class discrimination: normal, reticular, honeycombing, and emphysema. Experiment 3 consisted of a five-class discrimination: normal, ground glass, reticular, honeycombing, and emphysema. 2) The remaining four scans were used to further test the algorithm on new data in the context of a whole lung analysis. Each of the four datasets was manually segmented by three experts. These datasets included normal, reticular and honeycombing regions and did not include ground glass or emphysema. The accuracy of the classification algorithm was then compared with results from experts. RESULTS: Independent VOIs: 1) two-class discrimination problem (sensitivity, specificity): normal versus abnormal (92.96%, 93.78%). 2) Four-class discrimination problem: normal (92%, 95%), reticular (86%, 87%), honeycombing (74%, 98%), and emphysema (93%, 98%). 3) Five-class discrimination problem: normal (92%, 95%), ground glass (75%, 89%), reticular (22%, 92%), honeycombing (74%, 91%), and emphysema (94%, 98%). Whole-lung datasets: 1) William's index shows that algorithm classification of lungs agrees with the experts as well as the experts agree with themselves. 2) Student t-test between overlap measures of algorithm and expert (AE) and expert and expert (EE): normal (t = -1.20, P = .230), Reticular (t = -1.44, P = .155), Honeycombing (t = -3.15, P = .003). 3) Lung volumes intraclass correlation: dataset 1 (ICC = 0.9984, F = 0.0007); dataset 2 (ICC = 0.9559, F = 0); dataset 3 (ICC = 0.8623, F= 0.0015); dataset 4 (ICC = 0.7807, F = 0.0136). CONCLUSIONS: We have demonstrated that our novel method is computationally efficient and produces results comparable to expert radiologic judgment. It is effective in the classification of normal versus abnormal tissue and performs as well as the experts in distinguishing among typical pathologies present in lungs with UIP/IPF. The continuing development of quantitative metrics will improve quantification of disease and provide objective measures of disease progression.