The superior performance of the proposed method was confirmed by the high correlation coefficient of 0.8075 (p less then 0.0001) of the BMD measured by DXA in a total of 150 testing cases, with only 0.12 s required for applying the computing configuration to a single X-ray image.Anterior knee pain is a commonly documented musculoskeletal disorder among badminton players. However, current biomechanical studies of badminton lunges mainly report kinetic profiles in the lower extremity with few investigations of in-vivo loadings. The objective of this study was to evaluate tissue loadings in the patellofemoral joint via musculoskeletal modelling and Finite Element simulation. The collected marker trajectories, ground reaction force and muscle activation data were used for musculoskeletal modelling to compute knee joint angles and quadricep muscle forces. These parameters were then set as boundary conditions and loads for a quasistatic simulation using the Abaqus Explicit solver. https://www.selleckchem.com/products/vt103.html Simulations revealed that the left-forward (LF) and backward lunges showed greater contact pressure (14.98-29.61%) and von Mises stress (14.17-32.02%) than the right-forward and backward lunges; while, loadings in the left-backward lunge were greater than the left-forward lunge by 13-14%. Specifically, the stress in the chondral layer was greater than the contact interface, particularly in the patellar cartilage. These findings suggest that right-side dominant badminton players load higher in the right patellofemoral joint during left-side (backhand) lunges. Knowledge of these tissue loadings may provide implications for the training of badminton footwork, such as musculature development, to reduce cartilage loading accumulation, and prevent anterior knee pain.This article presents the results of the development and study of an ultrasonic radiator (US radiator) of increased power, which is designed for control, location, cavitation processing of liquids, and coagulation of foreign particles in a gaseous media at frequencies from 30 to 90 kHz. The proposed method of vibration summing of low-power high-frequency Langevin transducers on a diametrically vibrating the summing radiating element (summator) allowed developing such a US radiator. The selecting of shape and the search of optimal dimensions allowed creating real designs of US radiator with operating frequency up to 90 kHz. It can generate exposure intensity of 45 W/cm2 at the US radiator power up to 1900 W and an operating frequency of 30 kHz or exposure intensity of 34.6 W/cm2 at the US radiator power up to 270 W and an operating frequency of 90 kHz at cavitational processing of liquids.Time-effective, unsupervised clustering techniques are exploited to discriminate nanometric metal disks patterned on a dielectric substrate. The discrimination relies on cluster analysis applied to time-resolved optical traces obtained from thermo-acoustic microscopy based on asynchronous optical sampling. The analysis aims to recognize similarities among nanopatterned disks and to cluster them accordingly. Each cluster is characterized by a fingerprint time-resolved trace, synthesizing the common features of the thermo-acoustics response of the composing elements. The protocol is robust and widely applicable, not relying on any specific knowledge of the physical mechanisms involved. The present route constitutes an alternative diagnostic tool for on-chip non-destructive testing of individual nano-objects.
Focal cortical dysplasias (FCDs) represent one of the most frequent causes of pharmaco-resistant focal epilepsies. Despite improved clinical imaging methods over the past years, FCD detection remains challenging, as FCDs vary in location, size, and shape and commonly blend into surrounding tissues without clear definable boundaries. We developed a novel convolutional neural network for FCD detection and segmentation and validated it prospectively on daily-routine MRIs.

The neural network was trained on 201 T1 and FLAIR 3 T MRI volume sequences of 158 patients with mainly FCDs, regardless of type, and 7 focal PMG. Non-FCD/PMG MRIs, drawn from 100 normal MRIs and 50 MRIs with non-FCD/PMG pathologies, were added to the training. We applied the algorithm prospectively on 100 consecutive MRIs of patients with focal epilepsy from daily clinical practice. The results were compared with corresponding neuroradiological reports and morphometric MRI analyses evaluated by an experienced epileptologist.

Best trainin developed a 3D convolutional neural network with autoencoder regularization for FCD detection and segmentation. Our algorithm employs the largest FCD training dataset to date with various types of FCDs and some focal PMG. It provided a higher sensitivity in detecting FCDs than conventional visual analyses. Despite its low specificity, the number of false positively predicted lesions per MRI was lower than with morphometric analysis. We consider our algorithm already useful for FCD pre-screening in everyday clinical practice.It is highly tempting to develop high-efficacy targeted nanotherapeutics based on FDA approved polymers like PLGA. Herein, we describe facile fabrication of robust, hyaluronic acid-surfaced and disulfide-crosslinked star-PLGA nanoparticles (HA-sPLGA XNPs) for targeted and reduction-triggered release of docetaxel (DTX), achieving markedly enhanced treatment of A549 lung tumor in vivo. HA-sPLGA XNPs carrying 5.2 wt.% DTX (DTX-HA-sPLGA XNPs) had a size of 105.5 ± 0.5 nm and great stability while almost completely released DTX under 10 mM glutathione. Confocal and flow cytometry experiments revealed fast cellular uptake of HA-sPLGA XNPs by CD44-overexpressing A549 cells. DTX-HA-sPLGA XNPs held much higher potency to A549 cells than DTX-loaded HA-surfaced and non-crosslinked star-PLGA nanoparticles (DTX-HA-sPLGA NPs), DTX-loaded HA-surfaced and non-crosslinked linear-PLGA nanoparticles (DTX-HA-lPLGA NPs), and free DTX (IC50 = 0.18 versus 0.38, 1.21 and 0.83 µg DTX equiv./mL). Intriguingly, DTX-HA-sPLGA XNPs revealnical translation. Here, we developed hyaluronic acid-surfaced and disulfide-crosslinked star-PLGA nanoparticles (HA-sPLGA XNPs) that enabled stable encapsulation and targeted delivery of docetaxel (DTX) to CD44+ A549 lung cancer cells in vitro and in vivo, affording markedly improved tumor accumulation and repression and lower side effects compared with free DTX control. Importantly, HA-sPLGA XNPs are based on fully biocompatible materials and comparably simple to fabricate. The evident tumor targetability and safety makes HA-sPLGA XNPs a unique and potentially translatable platform for chemotherapy of CD44+ cancers.