Effective prevention and management of osteoporosis would require suitable methods for population screenings and early diagnosis. Current clinically-available diagnostic methods are mainly based on the use of either X-rays or ultrasound (US). All X-ray based methods provide a measure of bone mineral density (BMD), but it has been demonstrated that other structural aspects of the bone are important in determining fracture risk, such as mechanical features and elastic properties, which cannot be assessed using densitometric techniques. Among the most commonly used techniques, dual X-ray absorptiometry (DXA) is considered the current "gold standard" for osteoporosis diagnosis and fracture risk prediction. Unfortunately, as other X-ray based techniques, DXA has specific limitations (e.g., use of ionizing radiation, large size of the equipment, high costs, limited availability) that hinder its application for population screenings and primary care diagnosis. This has resulted in an increasing interest in developing reliable pre-screening tools for osteoporosis such as quantitative ultrasound (QUS) scanners, which do not involve ionizing radiation exposure and represent a cheaper solution exploiting portable and widely available devices. Furthermore, the usefulness of QUS techniques in fracture risk prediction has been proven and, with the last developments, they are also becoming a more and more reliable approach for assessing bone quality. However, the US assessment of osteoporosis is currently used only as a pre-screening tool, requiring a subsequent diagnosis confirmation by means of a DXA evaluation. Here we illustrate the state of art in the early diagnosis of this "silent disease" and show up recent advances for its prevention and improved management through early diagnosis.

Objectives: To experimentally investigate the acoustical behavior of silica nanoparticles within conventional diagnostic ultrasound fields and to determine a suitable configuration, in terms of particle size and concentration, for their employment as targetable contrast agents. We also assessed the effectiveness of a novel method for automatic detection of targeted silica nanoparticles for future tissue typing applications. Materials and Methods: Silica nanospheres of variable size (160, 330, and 660 nm in diameter) and concentration (10¹⁰–10¹³ part/mL) were dispersed in different custom-designed agarose-based gel samples and imaged at 7.5 MHz with a conventional echograph linked to a research platform for radiofrequency signal acquisition. Off-line analysis included evaluation of backscattered ultrasound amplitude, image brightness, and nanoparticle automatic detection through radiofrequency signal processing. Results: Amplitude of nanoparticle-backscattered signals linearly increased with particle number concentration, but image brightness did not show the same trend, because the logarithmic compression caused the reaching of a “plateau” where brightness remained almost constant for further increments in particle concentration. On the other hand, both backscatter amplitude and image brightness showed significant increments when particle diameter was increased. Taking into account particle size constraints for tumor targeting (pore size of tumor endothelium and trapping effects because of reticulo-endothelial system limit the dimension of effectively employable particles to less than 380 nm), a suitable compromise is represented by the employment of 330-nm silica nanospheres at a concentration of about 1 to 2 × 10¹¹ part/mL. These particles, in fact, showed the best combination of number concentration and diameter value to obtain an effective enhancement on conventional echographic images. Furthermore, also the sensitivity of the developed method for automatic nanoparticle detection had a maximum (72.8%) with 330-nm particles, whereas it was lower with both bigger and smaller particles (being equal to 64.1% and 17.5%, respectively). Conclusions: Silica nanoparticles at a diameter of about 330 nm are very promising contrast agents for ultrasound imaging and specific tumor targeting at conventional diagnostic frequencies, being in particular automatically detectable with high sensitivity already at low doses. Future studies will be carried out to assess the acoustic behavior of nanoparticles with different geometries/sizes and to improve sensitivity of the automatic detection algorithm.

Electroencephalogram (EEG) remains the most immediate, simple, and rich source of information for understanding phenomena related to brain electrical activities. It is certainly a source of basic and interesting information to be extracted using specific and appropriate techniques. The most important aspect in processing EEG signals is to use less co-lateral assets and instrumentation in order to carried out a possible diagnosis; this is the approach of early diagnosis. Advanced estimate spectral analysis can reveal new information encompassed in EEG signals by means of specific parameters or indices. The research proposes a multidimensional approach with a combined use of decimated signal diagonalization (DSD) as basis from which it is possible to work by finding appropriate signal windows for revealing expected information and overcoming signal processing limitations encountered in quantitative EEG. Important information, about the state of the patient under observation, must be extracted from calculated DSD bispectrum. For this aim, it is useful to define an assessment index about the dynamic process associated with the analyzed signal. This information is measured by means of entropy, since the degree of order/disorder of the recorded EEG signal will be reflected in the obtained DSD bispectrum. The general advantage of multidimensional approach is to reveal eventual stealth frequencies “in space and in time” giving a topological vision to be correlated to physical areas which these frequencies emerge from. Long term and sleeping EEG recorded are analyzed, and the results obtained are of interest for an accurate diagnosis of the patient's clinical condition.