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Background:Sodium hyaluronate (NaHA) is generally supplemented in products related to contact lenses for increasing comfort during wearing. The quantity of sodium hyaluronate and the material of lenses affect the retention of sodium hyaluronate on the contact lenses.Methods:We developed a convenient and sensitive but unconventional chromatographic method to quantify sodium hyaluronate and analyze its release behavior from contact lenses. The reverse-phase chromatography eluted sodium hyaluronate with high molecular masses in the shortest time and could separate salt and small compounds from sodium hyaluronate.Results:This method could accurately quantify sodium hyaluronate with diverse molecular sizes. Because sodium hyaluronate was eluted in a narrow time frame, sensitivity was significantly enhanced, and the limit of detection of this method was 0.45 μg/mL. According to this quantitation method, the attached quantity of sodium hyaluronate is related to the water content of the material. Furthermore, a material test indicated that the release efficiency of sodium hyaluronate depends on the material of lenses. Nonionic Polymacon had a longer half-life in the sodium hyaluronate release curve than negative Methafilcon A and silicone hydrogel.Conclusion:This hyaluronate quantification method is a fast, sensitive and accurate method, making it suitable for the in vitro hyaluronate research without further derivatization.

The 3GPP Narrowband Internet of Thing (NB-IoT) is the promising technology that can provide multiple types of resource unit (RU) with a special repetition mechanism to improve the scheduling flexibility and transmission reliability. Since the IoT devices need to operate for a very long time, the energy consumption becomes a critical issue. In this paper, we study how to guarantee the quality of service (QoS) while minimizing the energy consumption for IoT devices. We first model the problem and then propose an energy-efficient scheme, which consists of two stages. The first stage tries to incur the lowest energy consumption of devices and satisfy their QoS requirement. The second stage determines the scheduling order to ensure the delay constraint while maintaining energy efficiency. Simulation results show that our scheme can serve more devices while saving their energy.