Proteoglycan fragmentation and respiratory mechanics in mechanically ventilated healthy rats.

This research investigated whether stretching of lung tissue due to increased positive alveolar pressure swings during mechanical ventilation (MV) at various tidal volumes (V(T)) might affect the composition and/or structure of the glycosaminoglycan (GAG) components of pulmonary extracellular proteoglycans. Experiments were performed in 30 healthy rats: 1) anesthetized and immediately killed (controls, C-0); 2) anesthetized and spontaneously breathing for 4 h (C-4h); and 3) anesthetized, paralyzed, and mechanically ventilated for 4 h with air at 0-cmH(2)O end-expiratory pressure and V(T) of 8 ml/kg (MV-1), 16 ml/kg (MV-2), 24 ml/kg (MV-3), or 32 ml/kg (MV-4), adjusting respiratory rates at a minute ventilation of 270 ml/min. Compared with C-0 and C-4h, a significant reduction of dynamic and static compliance of the respiratory system and of the lung was observed only in MV-4, while extravascular lung water significantly increased in MV-3 and MV-4, but not in MV-1 and MV-2. However, even in MV-1, MV induced a significant fragmentation of pulmonary GAGs. Extraction of covalently bound GAGs and wash out of loosely bound or fragmented GAGs progressively increased with increasing V(T) and was associated with increased expression of local (matrix metalloproteinase-2) and systemic (matrix metalloproteinase-9) activated metalloproteases. We conclude that 1) MV, even at "physiological" low V(T), severely affects the pulmonary extracellular architecture, exposing the lung parenchyma to development of ventilator-induced lung injury; and 2) respiratory mechanics is not a reliable clinical tool for early detection of lung injury.     

Interspecies Gene Name Extrapolation—A New Approach

The use of animal models has facilitated numerous scientific developments, especially when employing “omics” technologies to study the effects of various environmental factors on humans. Our study presents a new bioinformatics pipeline suitable when the generated microarray data from animal models does not contain the necessary human gene name annotation. We conducted single color gene expression microarray on duodenum and spleen tissue obtained from pigs which have been exposed to zearalenone and Escherichia coli contamination, either alone or combined. By performing a combination of file format modifications and data alignments using various online tools as well as a command line environment, we performed the pig to human gene name extrapolation with an average yield of 58.34%, compared to 3.64% when applying more simple methods. In conclusion, while online data analysis portals on their own are of great importance in data management and assessment, our new pipeline provided a more effective approach for a situation which can be frequently encountered by researchers in the “omics” era.     

Identification of differentially expressed microRNAs by microarray: a possible role for microRNA genes in pituitary adenomas

MicroRNAs (miRNAs) are small non-coding RNAs that control gene expression by targeting mRNA. It has been demonstrated that miRNA expression is altered in many human cancers, suggesting that they may play a role in human neoplasia. To determine whether miRNA expression is altered in pituitary adenomas, we analyzed the entire miRNAome in 32 pituitary adenomas and in 6 normal pituitary samples by microarray and by Real-Time PCR. Here, we show that 30 miRNAs are differentially expressed between normal pituitary and pituitary adenomas. Moreover, 24 miRNAs were identified as a predictive signature of pituitary adenoma and 29 miRNAs were able to predict pituitary adenoma histotype. miRNA expression could differentiate micro- from macro-adenomas and treated from non-treated patient samples. Several of the identified miRNAs are involved in cell proliferation and apoptosis, suggesting that their deregulated expression may be involved in pituitary tumorigenesis. Predictive miRNAs could be potentially useful diagnostic markers, improving the classification of pituitary adenomas.     

Three-dimensional easy morphological (3-DEMO) classification of scoliosis, part I

Background

While scoliosis has, for a long time, been defined as a three-dimensional (3D) deformity, morphological classifications are confined to the two dimensions of radiographic assessments. The actually existing 3-D classification proposals have been developed in research laboratories and appear difficult to be understood by clinicians.

Aim of the study

The aim of this study was to use the results of a 3D evaluation to obtain a simple and clinically oriented morphological classification (3-DEMO) that might make it possible to distinguish among different populations of scoliotic patients.

Method

We used a large database of evaluations obtained through an optoelectronic system (AUSCAN) that gives a 3D reconstruction of the spine. The horizontal view was used, with a spinal reference system (Top View). An expert clinician evaluated the morphological reconstruction of 149 pathological spines in order to find parameters that could be used for classificatory ends. These were verified in a mathematical way and through computer simulations: some parameters had to be excluded. Pathological data were compared with those of 20 normal volunteers.

Results

We found three classificatory parameters, which are fully described and discussed in this paper: Direction, the angle between spinal pathological and normal AP axis; Shift, the co-ordinates of the barycentre of the Top View; Phase, the parameter describing the spatial evolution of the curve. Using these parameters it was possible to distinguish normal and pathological spines, to classify our population and to differentiate scoliotic patients with identical AP classification but different 3D behaviors.

Conclusion

The 3-DEMO classification offers a new and simple way of viewing the spine through an auxiliary plane using a spinal reference system. Further studies are currently under way to compare this new system with the existing 3-D classifications, to obtain it using everyday clinical and x-rays data, and to develop a triage for clinical use.