Sequential surgery for the treatment of type I knee ankylosis: a series of 62 cases.

BACKGROUNDS: The lack of systematic classification and standard treatment principles for knee ankylosis prevents optimal treatments. This study explored treatments for type I (mild) knee joint ankylosis. METHOD: This retrospective study analysed patients with knee joint ankylosis admitted from March 2013 to January 2018 who underwent sequential arthroscopic release. RESULT: The 62 patients had 12-36 (average, 18) months of follow-up. Thirty-eight patients were released; of these, 18 were assisted by limited incision with partial quadriceps femoris expansion myotomy and released according to arthroscopy. Six patients underwent lengthening and release of the quadriceps femoris. All surgeries combined with full-course rehabilitation resulted in improved joint mobility. The range of motion (ROM) of the knee joint recovered to a range of 0° to 85°-140° (mean: 118.32 ± 9.42°) from the preoperative range of 30°-70° (mean: 45° ± 15.50°). The clinical effect was evaluated according to the Judet criteria at the final follow-up. The outcomes at the last follow-up (at least for 1 year) were excellent in 55 cases, good in six cases, and fair in one case. CONCLUSION: Sequential arthroscopic release, minimal selective invasion of limited incision of partial quadriceps femoris expansion myotomy, assisted by pie-crusting technique to release, or quadriceps femoris lengthening, and release surgery for type I knee joint ankylosis, accompanied by early rehabilitation training provided satisfactory results without significant complications.

Machine-Learning Assisted Handwriting Recognition Using Graphene Oxide-Based Hydrogel.

Machine-learning assisted handwriting recognition is crucial for development of next-generation biometric technologies. However, most of the currently reported handwriting recognition systems are lacking in flexible sensing and machine learning capabilities, both of which are essential for implementation of intelligent systems. Herein, assisted by machine learning, we develop a new handwriting recognition system, which can be applied as both a recognizer for written texts and an encryptor for confidential information. This flexible and intelligent handwriting recognition system combines a printed circuit board with graphene oxide-based hydrogel sensors. It offers fast response and good sensitivity and allows high-precision recognition of handwritten content from a single letter to words and signatures. By analyzing 690 acquired handwritten signatures obtained from seven participants, we successfully demonstrate a fast recognition time (less than 1 s) and a high recognition rate (∼91.30%). Our developed handwriting recognition system has great potential in advanced human-machine interactions, wearable communication devices, soft robotics manipulators, and augmented virtual reality.

A new strategy to minimize humidity influences on acoustic wave ultraviolet sensors using ZnO nanowires wrapped with hydrophobic silica nanoparticles.

Surface acoustic wave (SAW) technology has been widely developed for ultraviolet (UV) detection due to its advantages of miniaturization, portability, potential to be integrated with microelectronics, and passive/wireless capabilities. To enhance UV sensitivity, nanowires (NWs), such as ZnO, are often applied to enhance SAW-based UV detection due to their highly porous and interconnected 3D network structures and good UV sensitivity. However, ZnO NWs are normally hydrophilic, and thus, changes in environmental parameters such as humidity will significantly influence the detection precision and sensitivity of SAW-based UV sensors. To solve this issue, in this work, we proposed a new strategy using ZnO NWs wrapped with hydrophobic silica nanoparticles as the effective sensing layer. Analysis of the distribution and chemical bonds of these hydrophobic silica nanoparticles showed that numerous C-F bonds (which are hydrophobic) were found on the surface of the sensitive layer, which effectively blocked the adsorption of water molecules onto the ZnO NWs. This new sensing layer design minimizes the influence of humidity on the ZnO NW-based UV sensor within the relative humidity range of 10-70%. The sensor showed a UV sensitivity of 9.53 ppm (mW/cm2)-1, with high linearity (R 2 value of 0.99904), small hysteresis (<1.65%) and good repeatability. This work solves the long-term dilemma of ZnO NW-based sensors, which are often sensitive to humidity changes.

A simplified three-dimensional numerical simulation approach for surface acoustic wave tweezers.

Standing surface acoustic waves (SSAWs) have been extensively used as acoustic tweezers to manipulate, transport, and separate microparticles and biological cells in a microscale fluidic environment, with great potentials for biomedical sensing, genetic analysis, and therapeutics applications. Currently, there lacks an accurate, reliable, and efficient three-dimensional (3D) modeling platform to simulate behaviors of micron-size particles/cells in acoustofluidics, which is crucial to provide the guidance for the experimental studies. The major challenge for achieving this is the computational complexity of 3D modeling. Herein, a simplified but effective 3D SSAW microfluidic model was developed to investigate the separation and manipulation of particles. This model incorporates propagation attenuation of the surface waves to increase the modeling accuracy, while simplifies the modeling of piezoelectric substrates and the wall of microchannel by determining the effective propagation region of the substrate. We have simulated the SSAWs microfluidics device, and systematically analyzed effects of voltage, tilt angle, and flow rate on the separation of the particles under the SSAWs. The obtained simulation results are compared with those obtained from the experimental studies, showing good agreements. This simplified modeling platform could become a convenient tool for acoustofluidic research.