Biomedical subjects

Giovanni Magenes

Publications and source records attributed to Giovanni Magenes.

5 recordsLinked to original sources

PubMed2006

Effects of electromagnetic stimulation on calcified matrix production by SAOS-2 cells over a polyurethane porous scaffold.

There is increasing interest in designing new biomaterials that could potentially be used in the form of scaffolds as bone substitutes. In this study we used a hydrophobic crosslinked polyurethane in a typical tissue-engineering approach, that is, the seeding and in vitro culturing of cells using a porous scaffold. Using an electromagnetic bioreactor (magnetic field intensity, 2 mT; frequency, 75 Hz), we investigated the effect of the electromagnetic stimulation on SAOS-2 human osteoblast proliferation and calcified matrix production. Cell proliferation was twice as high; expression of decorin, osteocalcin, osteopontin, type I collagen, and type III collagen was greater (1.3, 12.2, 12.1, 10.0, and 10.5 times as great, respectively); and calcium deposition was 5 times as great as under static conditions without electromagnetic stimulation. RT-PCR analysis revealed the electromagnetically upregulated transcription specific for decorin, fibronectin, osteocalcin, osteopontin, transforming growth factor-beta, type I collagen, and type III collagen. The immunolocalization of the extracellular matrix constituents showed their colocalization in the cell-rich areas. The bioreactor and the polyurethane foam were designed to obtain cell colonization and calcified matrix deposition. This cultured biomaterial could be used, in clinical applications, as an osteoinductive implant for bone repair.

Bioreactors↗

PubMed2006

Comparison of entropy-based regularity estimators: application to the fetal heart rate signal for the identification of fetal distress.

This paper considers the multiscale entropy (MSE) approach for estimating the regularity of time series at different scales. Sample entropy (SampEn) and approximate entropy (ApEn) are evaluated in MSE analysis on simulated data to enhance the main features of both estimators. We applied the approximate entropy and the sample entropy estimators to fetal heart rate signals on both single and multiple scales for an early identification of fetal sufferance antepartum. Our results show that the ApEn index significantly distinguishes suffering from normal fetuses between the 30th and the 35th week of gestation. Furthermore, our data shows that the MSE entropy values are reliable indicators of the fetal distress associated with the presence of a pathological condition at birth.

Algorithms↗

PubMed2006

Latency detection in motor responses: a model-based approach with genetic algorithm optimization.

The latency of a response is one of the most frequently reported parameters when describing the characteristics of a motor system. Such measurement provides important information both to the basic researcher investigating the neural circuitry of the underlying physiological system and to the clinician gathering information for diagnosing a patient. Our concern here is that when the latency of a response is determined on experimentally recorded data by using the most commonly referenced techniques to find the onset of a motor response, the resulting figure encompasses both the neural processing time and the dynamics of the system producing the response (e.g., the musculoskeletal apparatus). Therefore, the resulting latency measurement cumulates information relative to two substantially different sources and thus having different implications. The goal of our study is that of suggesting a technique allowing the separation of the relative contributions of neural transmission and processing time from that of the dynamics of the motor system. This is accomplished by using a technique based on fitting a model to the experimentally recorded response, thus allowing to exploit as much as is known with regards to the dynamics of the studied motor system (e.g., model order and constraints on the values of the model parameters). The optimization of the model parameters for fitting the experimental data is carried out using a real-valued genetic algorithm, allowing to avoid trapping in local, suboptimal minima. The use of this approach allows to estimate the pure delay in the response introduced by neural processing more accurately than the traditional latency detection techniques based on adaptive thresholds.

Algorithms↗

PubMed2005

Optimization of two-joint arm movements: a model technique or a result of natural selection?

The fossil record of early hominids suggests that their Arm length, and presumably stature and weight, had a tendency to increase. Using the minimum jerk principle and a related formulation of averaged specific power, ASP, with regard to selected two-joint Arm movements, the current paper explores relationships between ASP, hand trajectory length (or Arm length, or body mass) and mean movement speed, deriving relationships which indicate that ASP is proportional to cubic mean movement speed, but inversely proportional to hand trajectory length (or Arm length, or 1/3 power of body mass). Accordingly, an ;ecological niche' is modeled in a three-parameter space. Either ASP maximization for fixed movement time, or ASP minimization for fixed mean movement speed, taken as selective optimization criterion, allows the increasing of human Arm length during evolution, regardless of the arm-to-forearm length ratio.

Algorithms↗

PubMed2003

Linear and nonlinear parameters for the analysis of fetal heart rate signal from cardiotocographic recordings.

Antepartum fetal monitoring based on the classical cardiotocography (CTG) is a noninvasive and simple tool for checking fetal status. Its introduction in the clinical routine limited the occurrence of fetal problems leading to a reduction of the precocious child mortality. Nevertheless, very poor indications on fetal pathologies can be inferred from the even automatic CTG analysis methods, which are actually employed. The feeling is that fetal heart rate (FHR) signals and uterine contractions carry much more information on fetal state than is usually extracted by classical analysis methods. In particular, FHR signal contains indications about the neural development of the fetus. However, the methods actually adopted for judging a CTG trace as "abnormal" give weak predictive indications about fetal dangers. We propose a new methodological approach for the CTG monitoring, based on a multiparametric FHR analysis, which includes spectral parameters from autoregressive models and nonlinear algorithms (approximate entropy). This preliminary study considers 14 normal fetuses, eight cases of gestational (maternal) diabetes, and 13 intrauterine growth retarded fetuses. A comparison with the traditional time domain analysis is also included. This paper shows that the proposed new parameters are able to separate normal from pathological fetuses. Results constitute the first step for realizing a new clinical classification system for the early diagnosis of most common fetal pathologies.

Algorithms↗