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Young ballet dancers are at risk of health issues associated with altered nutritional status and of relative energy deficiency in sport compared to the general population.


To evaluate the nutritional status and body composition in ballet dancers.

Materials and methods

The study group consisted of 40 young ballet dancers (mean age 19.97 years). Height and weight were measured and body mass index was calculated in all subjects (mean BMI value 19.79 kg/m2, SD: 2.051). Body composition was estimated using the bioelectrical impedance method.


The dancers’ fat-free mass was 47.33 kg (SD: 5.064) and, on the average, body fat represented the 15.92% (SD: 16.91) of their body weight.


Ballet dancers, who usually show significantly lower BMI values compared to the general population, also displayed body fat values under the suggested range. Some screening for altered nutritional status should be performed. In addition, education programs should be recommended in young ballet dancers, in order to inform about energy and nutrition requirements for health and training and to prevent malnutrition-related problems.


There is a strong need for a non-invasive measurement technique that is capable of accurately identifying the physiological condition change or heterogeneity of subcutaneous adipose tissue (SAT) by localizing the abnormalities within the compartment. This paper aims to investigate the feasibility of Electrical Impedance Tomography (EIT) to assess the interstitial fluid in subcutaneous adipose tissue as an enhancement method of bioelectrical impedance spectroscopy (BIS). Here, we demonstrate the preliminary result of EIT with a wearable 16 electrodes sensor. The image-based reference EIT with fat weighted threshold method is proposed. In order to evaluate the performance of our novel method, a physiological swelling experiment is conducted, and Multi-Frequency Bioelectrical Impedance Analysis (MFBIA) is also applied as a comparison with EIT results. The experimental results showed that the proposed method was able to distinguish the physiological swelling condition and effectively to remove the unexpected background noise. Furthermore, the conductivity variation in the subcutaneous layer had a good correlation with extracellular water volume change from MFBIA data; the correlation coefficient R2 = 0.927. It is concluded that the proposed method provides a significant prospect for SAT assessment.


Silver nanoparticles (AgNPs) are nanomaterials obtained by nanotechnology and due to their antimicrobial properties have a major importance in the control of various species of bacteria, fungi and viruses, with applications in medicine, cosmetics or food industry. The goal of the paper was to present the results of the research carried out on rapid extracellular biosynthesis of silver nanoparticles mediated by culture filtrate of lactic acid bacteria Lactobacillus sp. strain LCM5 and to assess the antimicrobial activity. Analysis of transmission electron microscopy (TEM) micrographs evidenced that the size of AgNPs synthesized using culture filtrates of lactic acid bacteria strain LCM5 ranged between 3 and 35 nm diameter, with an average particle size of 13.84±4.56 nm. AgNPs presented a good dispersion, approximately spherical shape, with parallel stripes certifying crystal structure. Frequency distribution revealed that preponderant dimensions of biosynthesized AgNPs were below 20 nm (94%). Antimicrobial activity of AgNPs was variable depending on both species and group of test microorganisms (bacteria or fungi) involved. Diameter of growth inhibition zone of Aspergillus flavus and Aspergillus ochraceus caused by silver nanoparticles synthesized by lactic acid bacteria strain LCM5 were similar (12.39 ± 0.61mm and 12.86 ± 0.78 mm) but significant stronger inhibition was registered against Penicillium expansum (15.87 ± 1.01mm). The effectiveness of biosynthesized silver nanoparticles was more pronounced against Gram-negative bacteria Chromobacterium violaceum with larger zone of inhibition (18 ± 0.69 mm diameter) when compared to those from fungi. Results recommend the silver nanoparticles biosynthesized using culture filtrate of the lactic acid bacteria Lactobacillus sp. strain LCM5 for biotechnological purposes, as promising antimicrobial agents.


Hyperparathyroidism-Jaw Tumor (HPT-JT) is an autosomal dominant disorder with variable expression, with an estimated prevalence of 6.7 per 1,000 population. Genetic testing for predisposing CDC73 (HRPT2) mutations has been an important clinical advance, aimed at early detection and/or treatment to prevent advanced disease. The aim of this study is to assess the most deleterious SNPs mutations on CDC73 gene and to predict their influence on the functional and structural levels using different bioinformatics tools. Method: Computational analysis using twelve different in-silico tools including SIFT, PROVEAN, PolyPhen-2, SNAP2, PhD-SNP, SNPs&GO, P-Mut, I-Mutant ,Project Hope, Chimera, COSMIC and dbSNP Short Genetic Variations were used to identify the impact of mutations in CDC73 gene that might be causing jaw tumor. Results: From (733) SNPs identified in the CDC73 gene we found that only Eleven SNPs (G49C, L63P, L64P, D90H, R222G, W231R, P360S, R441C, R441H, R504S and R504H) has deleterious effect on the function and structure of protein and expected to cause the syndrome. Conclusion: Eleven substantial genetic/molecular aberrations in CDC73 gene identified that could serve as diagnostic markers for hyperparathyroidism-jaw tumor (HPT-JT).


A new and simple micelles-rich restricted access supramolecular solvent-based liquid phase microextraction method (RASUPRASs-LPME) based on the ion-pair complex formation of phosphate (PO4 3-) ions with ammonium heptamolybdate and malachite green in acidic medium was developed. The phosphate ion concentration after microextraction of the ion-pair complex to the hexagonal aggregates of decanoic acid (DA) was measured with micro-volume UV-Vis spectrophotometer at 625 nm. All analytical parameters which are effective on the method such as acid type and concentration, supramolecular solvent volume, amount of the components forming the complex, sample volume, were optimized. The preconcentration factor (PF), limit of detection (LOD) and limit of quantification (LOQ) for the developed method was found to be 15, 9.6 and 32.1, respectively. The RA-SUPRAs-LPME method was finally applied for the analysis of the phosphate content of different types of water samples.


The objective of this work is numerical simulation of the membrane by direct analysis at micro, meso and macro level. This approach includes first a defining and modeling of a basic structural unit, after that simulation of a fragment as a representative element of the membrane structure. Then the results obtained to transfer for the entire membrane module and finally modeling of the membrane as porous media with calculated permeability. The numerical simulation was done with Ansys CFX, using the Darcy’s equation for flow through porous media with configuration of the membrane and second order backward Euler transient scheme for solving the Navier-Stokes equations.

The permeability of the membrane is determined at a micro and macro level by computer simulation for different fluids, which allows to evaluating the influence of the viscosity on the flow passing through the membrane. This micro-macro approach is quite efficient and cost-effective because it saves time and requires less computer capacity and allows direct analysis of the complex structure of the membrane modules.


Around 70 infectious agents are possible threats for blood safety.

The risk for blood recipients is increasing because of new emergent agents like West Nile, Zika and Chikungunya viruses, or parasites such as Plasmodium and Trypanosoma cruzi in non-endemic regions, for instance.

Screening programmes of the donors are more and more implemented in several Countries, but these cannot prevent completely infections, especially when they are caused by new agents.

Pathogen inactivation (PI) methods might overcome the limits of the screening and different technologies have been set up in the last years.

This review aims to describe the most widely used methods focusing on their efficacy as well as on the preservation integrity of blood components.


Plants have been seen to possess the potential to be excellent biological matrices to serve as a basis for investigating the presence of promising therapeutic agents for cancer treatment. Several successful anti-cancer medicines - or their analogues - nowadays in use are plant derived and many more are under clinical trials. Under current circumstances, the purpose of this work was to test aqueous and ethanolic extracts of five aromatic and medicinal plants from arid zones on some tumor cell lines. These plants, Cymbopogon schoenanthus (L.) Spreng, Crithmum maritimum (L.) Spreng, Hammada scoparia (Pomel) Iljin, Retama raetam (Forssk.) and Zizyphus lotus (L.) Desf., widely used in Tunisian ethnomedicine, were assessed for their phenolic compounds, antioxidants and anticancer activities in aqueous and ethanol extracts. Total polyphenols, flavonoid and tannin contents were determined colorimetrically and some of these molecules were identified using RP-HPLC. A significant difference on phenolic contents and composition were found among the investigated plants. Cymbopogon schoenanthus was the richest in phenolic compounds (approx. 72%) with quercetine-3-o-rhamnoside (approx. 33%) as main contributor. For all the tested plants, the highest antioxidant capacity was detected in the ethanolic extracts rather than in the aqueous ones. The highest antiproliferative potential was observed for the ethanolic extracts. Hammada scoparia, Retama raetam and Zizyphus lotus exhibited important antiproliferative effect that reached 67% at a 1% extract concentration. Taken together, the present study supports the potential development of chemotherapeutic agents from, at least, four of the five studied Tunisian ethnomedicinal plants.


Apnea is one of the deadliest diseases that can be prevented and cured if it is detected in time. In this paper, we propose a precise method for early detection of the obstructive sleep apnea (OSA) disease using the latest feature selection and extraction methods. The feature selection in this paper is based on the Dual tree complex wavelet (DT-CWT) coefficients of the ECG signals of several patients. The feature extraction from these coefficients is done using frequency and time techniques. The Feature selection is done using the spectral regression discriminant analysis (SRDA) algorithm and the classification is performed using the hybrid RBF network. A hybrid RBF neural network is introduced in this paper for detecting apnea that is much less computationally demanding than the previously presented SVM networks. Our findings showed a 3 percent improvement in the detection and at least a 30 percent reduction in the computational complexity in comparison with methods that have been presented recently.