Effect of socks structures on plantar dynamic pressure distribution

A major purpose of investigating the plantar pressure in patients with pain or those at risk for skin injury is to reduce the pressure below metatarsal heads, specially first and second metatarsal heads. The aim of this article is to evaluate the effects of the socks structures on the changes in plantar dynamic pressure. In this study, seven socks types with different structures for the sole area were produced. The Gaitview^® AFA-50 system, a force plate, was used to measure the plantar dynamic pressure of 10 participants. The barefoot plantar dynamic pressure distribution was compared with the plantar dynamic pressure distribution with socks by two independent samples test on various zones of the foot and on different genders using SPSS software. Mann-Whitney tests were used to determine specific significant differences. The obtained results showed that the main trend was to redistribute the plantar dynamic pressure from the higher plantar pressure zones (toe and first through forth metatarsal bone regions) were decreased and as a result the plantar pressure toward the relatively lower pressure zones (fifth metatarsal bone and midfoot regions). In comparison with the barefoot condition, the cross miss structure reduced the mean pressure in the critical region of the foot (first metatarsal) for male and female subjects ( p < 0.05) and also the mock rib structure reduced the mean pressure for female subjects ( p < 0.05). In general, the results suggested wearing the socks because the socks make the plantar pressure redistributed from high to low plantar pressure zones. The results of this research indicated that wearing socks with cross miss and mock rib structures will reduce the mean plantar pressure values in forefoot area in comparison with the barefoot condition.

Evaluation of the effects of injection velocity and different gel concentrations on nanoparticles in hyperthermia therapy

BACKGROUND AND OBJECTIVE

In magnetic fluid hyperthermia therapy, controlling temperature elevation and optimizing heat generation is an immense challenge in practice. The resultant heating configuration by magnetic fluid in the tumor is closely related to the dispersion of particles, frequency and intensity of magnetic field, and biological tissue properties.

METHODS

In this study, to solve heat transfer equation, we used COMSOL Multiphysics and to verify the model, an experimental setup has been used.  To show the accuracy of the model, simulations have been compared with experimental results. In the second part, by using experimental results of nanoparticles distribution inside Agarose gel according to various gel concentration, 0.5%, 1%, 2%, and 4%, as well as the injection velocity, 4 µL/min, 10 µL/min, 20 µL/min, and 40 µL/min, for 0.3 cc magnetite fluid, power dissipation inside gel has been calculated and used for temperature prediction inside of the gel.

RESULTS

The Outcomes demonstrated that by increasing the flow rate injection at determined concentrations, mean temperature drops. In addition, 2% concentration has a higher mean temperature than semi spherical nanoparticles distribution.

CONCLUSION

The results may have implications for treatment of the tumor and any kind of cancer diseases.

Use of a pattern recognition technique to control a multifunctional prosthesis

Various kinds of command source can be used to control an above-elbow prosthesis. But none of them can be used to perform a specified task easily. The research is devoted to investigation of the potential effectiveness of applying the kinematic data of the shoulder joint to control an upper-limb prosthesis. Using these data as input signals, an appropriate signal processing technique, pattern recognition, is utilised to derive control commands. The purpose of the investigation is to use these commands to control a multifunctional prosthesis so that an amputee can perform a few tasks. For testing performance accuracy, a goniometer is worn by a subject with intact arm. It is also interfaced to a digital computer. Next, he is asked to do one of the predefined tasks for which the joint angle trajectories have already been derived and stored. Almost as soon as the shoulder joint angles are sampled and sent to the computer, the program calculates the elbow and wrist angles. These values are compared with actual elbow and wrist angles, which are monitored by the goniometer.