Effect of platelet-rich fibrin on the control of alveolar osteitis, pain, trismus, soft tissue healing, and swelling following mandibular third molar surgery: an updated systematic review and meta-analysis

The purpose of this study was to estimate the effect of platelet-rich fibrin (PRF) on the control of alveolar osteitis (AO), pain, trismus, soft tissue healing, and swelling following mandibular third molar surgery. A comprehensive search of the literature was conducted through PubMed, Embase, Web of Science, and Cochrane Library up to May 2019. Randomized controlled studies conforming to the inclusion criteria were included. The record screening and data extraction were conducted by two authors independently. The risk of bias assessment was performed according to the guidelines recommended by the Cochrane Collaboration. The quantitative analysis was performed using RevMan version 5.3. Nineteen studies were included in the systematic review and 17 studies were eligible for the meta-analysis. The use of PRF significantly reduced the incidence of AO and postoperative pain when compared to the controls (AO: relative risk 0.43, 95% confidence interval (CI) 0.28 to 0.65, Z=3.90, P<0.0001 (I²=0%); pain: day 1, standardized mean difference (SMD) -1.12, 95% CI -1.87 to -0.37, Z=2.93, P=0.003 (I²=95%); day 3, SMD -0.93, 95% CI -1.48 to -0.38, Z=3.30, P=0.001 (I²=92%); day 7, SMD -1.84, 95% CI -2.98 to -0.71, Z=3.19, P=0.001 (I²=97%)). Additionally, the result showed a better soft tissue healing when PRF was used (mean difference -0.63, 95% CI -1.08 to -0.18, Z=2.76, P=0.006 (I²=90%)). The use of PRF reduced the incidence of AO and postoperative pain following third molar surgery. Furthermore, PRF may also improve the postoperative soft tissue healing.

Deep learning enabled smart mats as a scalable floor monitoring system

Toward smart building and smart home, floor as one of our most frequently interactive interfaces can be implemented with embedded sensors to extract abundant sensory information without the video-taken concerns. Yet the previously developed floor sensors are normally of small scale, high implementation cost, large power consumption, and complicated device configuration. Here we show a smart floor monitoring system through the integration of self-powered triboelectric floor mats and deep learning-based data analytics. The floor mats are fabricated with unique "identity" electrode patterns using a low-cost and highly scalable screen printing technique, enabling a parallel connection to reduce the system complexity and the deep-learning computational cost. The stepping position, activity status, and identity information can be determined according to the instant sensory data analytics. This developed smart floor technology can establish the foundation using floor as the functional interface for diverse applications in smart building/home, e.g., intelligent automation, healthcare, and security.

MiR-122-3p regulates the osteogenic differentiation of mouse adipose-derived stem cells via Wnt/β catenin signaling pathway

OBJECTIVE

To explore the regulatory mechanism of micro-ribonucleic acid (miR)-122-3p in the osteogenic differentiation of mouse adipose-derived stem cells (mADSCs).

MATERIALS AND METHODS

The regulatory mechanism of miR-122-3p in the osteogenic differentiation of mesenchymal stem cells was investigated through its overexpression and knockdown.

RESULTS

The overexpression of miR-122-3p inhibited the osteogenic differentiation of mADSCs. On the contrary, its knockdown promoted the osteogenic differentiation of mADSCs. The further study on the molecular mechanism of miR-122-3p regulating mADSCs' osteogenic differentiation showed that the overexpression of miR-122-3p could activate the Wingless and int-1 (WNT)/β-catenin signaling pathway, but the knockdown of miR-122-3p could repress this signaling pathway.

CONCLUSIONS

MiR-122-3p influences the osteogenic differentiation of mADSCs by modulating the WNT/β-catenin signaling pathway.

Wearable Triboelectric/Aluminum Nitride Nano-Energy-Nano-System with Self-Sustainable Photonic Modulation and Continuous Force Sensing

Wearable photonics offer a promising platform to complement the thriving complex wearable electronics system by providing high-speed data transmission channels and robust optical sensing paths. Regarding the realization of photonic computation and tunable (de)multiplexing functions based on system-level integration of abundant photonic modulators, it is challenging to reduce the overwhelming power consumption in traditional current-based silicon photonic modulators. This issue is addressed by integrating voltage-based aluminum nitride (AlN) modulator and textile triboelectric nanogenerator (T-TENG) on a wearable platform to form a nano-energy-nano-system (NENS). The T-TENG transduces the mechanical stimulations into electrical signals based on the coupling of triboelectrification and electrostatic induction. The self-generated high-voltage from the T-TENG is applied to the AlN modulator and boosts its modulation efficiency regardless of AlN's moderate Pockels effect. Complementarily, the AlN modulator's capacitive nature enables the open-circuit operation mode of T-TENG, providing the integrated NENS with continuous force sensing capability which is notably uninfluenced by operation speeds. Furthermore, a physical model is proposed to describe the coupled AlN modulator/T-TENG system. With the enhanced photonic modulation and the open-circuit operation mode enabled by synergies between the AlN modulator and the T-TENG, optical Morse code transmission and continuous human motion monitoring are demonstrated for practical wearable applications.

Unveiling Stimulation Secrets of Electrical Excitation of Neural Tissue Using a Circuit Probability Theory

Electrical excitation of neural tissue has wide applications, but how electrical stimulation interacts with neural tissue remains to be elucidated. Here, we propose a new theory, named the Circuit-Probability theory, to reveal how this physical interaction happen. The relation between the electrical stimulation input and the neural response can be theoretically calculated. We show that many empirical models, including strength-duration relationship and linear-non-linear-Poisson model, can be theoretically explained, derived, and amended using our theory. Furthermore, this theory can explain the complex non-linear and resonant phenomena and fit in vivo experiment data. In this letter, we validated an entirely new framework to study electrical stimulation on neural tissue, which is to simulate voltage waveforms using a parallel RLC circuit first, and then calculate the excitation probability stochastically.

Machine Learning Glove Using Self-Powered Conductive Superhydrophobic Triboelectric Textile for Gesture Recognition in VR/AR Applications

The rapid progress of Internet of things (IoT) technology raises an imperative demand on human machine interfaces (HMIs) which provide a critical linkage between human and machines. Using a glove as an intuitive and low-cost HMI can expediently track the motions of human fingers, resulting in a straightforward communication media of human-machine interactions. When combining several triboelectric textile sensors and proper machine learning technique, it has great potential to realize complex gesture recognition with the minimalist-designed glove for the comprehensive control in both real and virtual space. However, humidity or sweat may negatively affect the triboelectric output as well as the textile itself. Hence, in this work, a facile carbon nanotubes/thermoplastic elastomer (CNTs/TPE) coating approach is investigated in detail to achieve superhydrophobicity of the triboelectric textile for performance improvement. With great energy harvesting and human motion sensing capabilities, the glove using the superhydrophobic textile realizes a low-cost and self-powered interface for gesture recognition. By leveraging machine learning technology, various gesture recognition tasks are done in real time by using gestures to achieve highly accurate virtual reality/augmented reality (VR/AR) controls including gun shooting, baseball pitching, and flower arrangement, with minimized effect from sweat during operation.

[Anti-PD-1 therapy in advanced malignant liver tumor-induced type-1 diabetes mellitus: a case report]

Immune checkpoint inhibitor (ICI) has been emerged as a major breakthrough in tumor immunotherapy, but its unique mechanism of action has also led to a number of immune-related adverse events (irAE). Type 1 diabetes mellitus (T1DM) is one of the rarest irAEs. This paper reports a case of advanced malignant liver tumor-induced T1DM who received second-line anti-PD-1 therapy and showed initial symptoms of hyperosmolar coma and hyperglycemia. In addition, the relevant literature at home and abroad was collected and reviewed, and the clinical characteristics of T1DM induced by anti-PD-1 therapy were summarized with a view to achieve early detection, diagnosis and treatment.

Haptic-feedback smart glove as a creative human-machine interface (HMI) for virtual/augmented reality applications

Human-machine interfaces (HMIs) experience increasing requirements for intuitive and effective manipulation. Current commercialized solutions of glove-based HMI are limited by either detectable motions or the huge cost on fabrication, energy, and computing power. We propose the haptic-feedback smart glove with triboelectric-based finger bending sensors, palm sliding sensor, and piezoelectric mechanical stimulators. The detection of multidirectional bending and sliding events is demonstrated in virtual space using the self-generated triboelectric signals for various degrees of freedom on human hand. We also perform haptic mechanical stimulation via piezoelectric chips to realize the augmented HMI. The smart glove achieves object recognition using machine learning technique, with an accuracy of 96%. Through the integrated demonstration of multidimensional manipulation, haptic feedback, and AI-based object recognition, our glove reveals its potential as a promising solution for low-cost and advanced human-machine interaction, which can benefit diversified areas, including entertainment, home healthcare, sports training, and medical industry.

Predicting the intervention threshold for initiating osteoporosis treatment among postmenopausal women in China: a cost-effectiveness analysis based on real-world data

This study built a micro-simulation Markov model to determine the treatment threshold of osteoporosis in postmenopausal women in Mainland China. Treatment with zoledronate is cost-effective when FRAX-based (Fracture risk assessment tool) fracture probability is over 7%.

INTRODUCTION

The purpose of this study is to estimate FRAX-based fracture probabilities in Mainland China using real-world data, at which intervention could be cost-effective.

METHODS

We developed a micro-simulation Markov model to capture osteoporosis states and relevant morbidities including hip fracture, vertebral fracture, and wrist fracture. Baseline characteristics including incidences of osteoporosis and distribution of risk factors were derived from the Peking Vertebral Fracture study, the largest prospective cohort study of postmenopausal women in Mainland China. We projected incidences of fractures and deaths by age groups under two treatment scenarios: 1) no treatment, and 2) zoledronate. We also projected total quality-adjusted life-years (QALY) and total costs including fracture management and osteoporosis drugs for cost-effectiveness analysis. Cost-effective intervention thresholds were calculated based on the Chinese FRAX model.

RESULTS

Treatment with zoledronate was cost-effective when the 10-year probability of major osteoporotic fracture based on FRAX was above 7%. The FRAX threshold increased by age from 51 to 65 years old, and decreased in elder age groups, ranging from 4% to 9%.

CONCLUSIONS

Using real-world data, our model indicated that widespread use of zoledronate was of both clinical and economic benefit among Chinese postmenopausal women. Using a FRAX-based intervention threshold of 7% with zoledronate should permit cost-effective access to therapy to patients and contribute to reducing the disease burden of osteoporosis in Mainland China.

Development Trends and Perspectives of Future Sensors and MEMS/NEMS

With the fast development of the fifth-generation cellular network technology (5G), the future sensors and microelectromechanical systems (MEMS)/nanoelectromechanical systems (NEMS) are presenting a more and more critical role to provide information in our daily life. This review paper introduces the development trends and perspectives of the future sensors and MEMS/NEMS. Starting from the issues of the MEMS fabrication, we introduced typical MEMS sensors for their applications in the Internet of Things (IoTs), such as MEMS physical sensor, MEMS acoustic sensor, and MEMS gas sensor. Toward the trends in intelligence and less power consumption, MEMS components including MEMS/NEMS switch, piezoelectric micromachined ultrasonic transducer (PMUT), and MEMS energy harvesting were investigated to assist the future sensors, such as event-based or almost zero-power. Furthermore, MEMS rigid substrate toward NEMS flexible-based for flexibility and interface was discussed as another important development trend for next-generation wearable or multi-functional sensors. Around the issues about the big data and human-machine realization for human beings' manipulation, artificial intelligence (AI) and virtual reality (VR) technologies were finally realized using sensor nodes and its wave identification as future trends for various scenarios.

Self-Sustainable Wearable Textile Nano-Energy Nano-System (NENS) for Next-Generation Healthcare Applications

Wearable electronics presage a future in which healthcare monitoring and rehabilitation are enabled beyond the limitation of hospitals, and self-powered sensors and energy generators are key prerequisites for a self-sustainable wearable system. A triboelectric nanogenerator (TENG) based on textiles can be an optimal option for scavenging low-frequency and irregular waste energy from body motions as a power source for self-sustainable systems. However, the low output of most textile-based TENGs (T-TENGs) has hindered its way toward practical applications. In this work, a facile and universal strategy to enhance the triboelectric output is proposed by integration of a narrow-gap TENG textile with a high-voltage diode and a textile-based switch. The closed-loop current of the diode-enhanced textile-based TENG (D-T-TENG) can be increased by 25 times. The soft, flexible, and thin characteristics of the D-T-TENG enable a moderate output even as it is randomly scrunched. Furthermore, the enhanced current can directly stimulate rat muscle and nerve. In addition, the capability of the D-T-TENG as a practical power source for wearable sensors is demonstrated by powering Bluetooth sensors embedded to clothes for humidity and temperature sensing. Looking forward, the D-T-TENG renders an effective approach toward a self-sustainable wearable textile nano-energy nano-system for next-generation healthcare applications.

Gut microbiota differences during metamorphosis in sick and healthy giant spiny frogs (Paa spinosa) tadpoles

Gut microbiota plays important roles in host nutrition, immunity, development and health; therefore, disruption of the gut microbiota is closely associated with development of diseases in the host. In amphibians, metamorphosis is associated not only with extensive changes in the gut microbiota, but also with high mortality. Therefore, we hypothesized that unsuccessful restructuring of the gut microbiota during metamorphosis was an important factor that caused the fatalities. To test this hypothesis, we investigated the gut microbiota of apparently sick and healthy giant spiny frog tadpoles during metamorphosis, using high-throughput sequencing of the 16S rRNA gene. Our results showed that most dominant phyla differed significantly among developmental stages of sick and healthy Paa spinosa tadpoles. The differences in the dominant genera in sick and healthy tadpoles were the highest at the stage of degeneration of cloacal tube (TDCT). After the metamorphosis, the composition of the gut microbiota was more alike between healthy and sick tadpoles at the stage of forelimb emergence than at TDCT. These results imply that failed restructuring of the gut microbiota during metamorphosis caused the death of P. spinosa tadpoles. These results provided an important reference to prevent the high actual of P. spinosa tadpoles during metamorphosis. SIGNIFICANCE AND IMPACT OF THE STUDY: We investigated the gut microbiota of apparently sick and healthy giant spiny frog (Paa spinosa) tadpoles during metamorphosis, using high-throughput sequencing of the 16S rRNA gene. Our results showed that the differences in the dominant genera in sick and healthy tadpoles were the highest at the stage of degeneration of cloacal tube. After the metamorphosis, the composition of the gut microbiota was alike between healthy and sick tadpoles. These results imply that failed restructuring of the gut microbiota during metamorphosis caused the death of P. spinosa tadpoles.

Investigation of Low-Current Direct Stimulation for Rehabilitation Treatment Related to Muscle Function Loss Using Self-Powered TENG System

Muscle function loss is characterized as abnormal or completely lost muscle capabilities, and it can result from neurological disorders or nerve injuries. The currently available clinical treatment is to electrically stimulate the diseased muscles. Here, a self-powered system of a stacked-layer triboelectric nanogenerator (TENG) and a multiple-channel epimysial electrode to directly stimulate muscles is demonstrated. Then, the two challenges regarding direct TENG muscle stimulation are further investigated. For the first challenge of improving low-current TENG stimulation efficiency, it is found that the optimum stimulation efficiency can be achieved by conducting a systematic mapping with a multiple-channel epimysial electrode. The second challenge is TENG stimulation stability. It is found that the force output generated by TENGs is more stable than using the conventional square wave stimulation and enveloped high frequency stimulation. With modelling and in vivo measurements, it is confirmed that the two factors that account for the stable stimulation using TENGs are the long pulse duration and low current amplitude. The current waveform of TENGs can effectively avoid synchronous motoneuron recruitment at the two stimulation electrodes to reduce force fluctuation. Here, after investigating these two challenges, it is believed that TENG direct muscle stimulation could be used for rehabilitative and therapeutic purpose of muscle function loss treatment.

A New Analysis Principle for EDXRF: The Monte Carlo - Library Least-Squares Analysis Principle

A new analysis principle for energy-dispersive X–ray fluorescence has been identified and investigated as to feasibility. It consists of: (1) generating the complete spectral response for a sample of known (assumed) composition by Monte Carlo simulation,(2) keeping track of the individual elemental responses within the Monte Carlo simulation for use as library spectra, (3) use of the library least–squares (linear) analysis method to obtain the elemental amounts for any unknown sample spectrum, and (4) iterating these steps if the unknown amounts are too fax from the assumed composition originally used.

This principle has been investigated for a radioisotope source excited EDXRF system consisting of a 109Cd source and a Si(Li) detector for a Cu-Ni alloy sample (CDA Alloy 715) and a stainless steel sample (304 Stainless Steel) and found to give excellent results. This analysis principle makes unique use of the Monte Carlo “forward” simulation method to provide the elemental library spectra for use in the library least-squares method of analysis.

Self-Powered and Self-Functional Cotton Sock Using Piezoelectric and Triboelectric Hybrid Mechanism for Healthcare and Sports Monitoring

Wearable devices rely on hybrid mechanisms that possess the advantages of establishing a smarter system for healthcare, sports monitoring, and smart home applications. Socks with sensing capabilities can reveal more direct sensory information on the body for longer duration in daily life. However, the limitation of suitable materials for smart textile makes the development of multifunctional socks a major challenge. In this paper, we have developed a self-powered and self-functional sock (S²-sock) to realize diversified functions including energy harvesting and sensing various physiological signals, i.e., gait, contact force, sweat level, etc., by hybrid integrating poly(3,4-ethylenedioxythiophene) polystyrenesulfonate (PEDOT:PSS)-coated fabric triboelectric nanogenerator (TENG) and lead zirconate titanate (PZT) piezoelectric chips. An output power of 1.71 mW is collected from a PEDOT:PSS-coated sock with mild jumping at 2 Hz and load resistance of 59.7 MΩ. The study shows that cotton socks worn daily can potentially be a power source for enabling self-sustained socks comprising wireless transmission modules and integrated circuits in the future. We also investigate the influences of environmental humidity, temperature, and weight variations and verify that our S²-sock can successfully achieve walking pattern recognition and motion tracking for smart home applications. On the basis of the sensor fusion concept, the outputs from TENG and PZT sensors under exercise activities are effectively merged together for quick detection of the sweat level. By leveraging the hybrid S²-sock, we can achieve more functionality in the applications of foot-based energy harvesting and monitoring the diversified physiological signals for healthcare, smart homes, etc.

Triboelectric Self-Powered Wearable Flexible Patch as 3D Motion Control Interface for Robotic Manipulator

Triboelectric nanogenerators and sensors can be applied as human-machine interfaces to the next generation of intelligent and interactive products, where flexible tactile sensors exhibit great advantages for diversified applications such as robotic control. In this paper, we present a self-powered, flexible, triboelectric sensor (SFTS) patch for finger trajectory sensing and further apply the collected information for robotic control. This innovative sensor consists of flexible and environmentally friendly materials, i. e., starch-based hydrogel, polydimethylsiloxane (PDMS), and silicone rubber. The sensor patch can be divided into a two-dimensional (2D) SFTS for in-plane robotic movement control and a one-dimensional (1D) SFTS for out-of-plane robotic movement control. The 2D-SFTS is designed with a grid structure on top of the sensing surface to track the continuous sliding information on the fingertip, e. g., trajectory, velocity, and acceleration, with four circumjacent starch-based hydrogel PDMS elastomer electrodes. Combining the 2D-SFTS with the 1D-SFTS, three-dimensional (3D) spatial information can be generated and applied to control the 3D motion of a robotic manipulator, and the real-time demonstration is successfully realized. With the facile design and very low-cost materials, the proposed SFTS shows great potential for applications in robotics control, touch screens, and electronic skins.