Efficacy and safety of post-auricular injection of methylprednisolone sodium succinate and lidocaine in the treatment of acute tinnitus, and its effect on sleep quality

Purpose: To study the efficacy and safety of post-auricular injection of methylprednisolone sodium succinate plus lidocaine in the treatment of acute tinnitus, and its effect on sleep quality.Methods: Eighty acute tinnitus patients admitted to Nanfang Hospital, Southern Medical University, Guangzhou, China from January 2020 to June 2021, served as subjects for this retrospective analysis. The patients were equally assigned to a reference group given postauricular injection of lidocaine, and a research group treated with post-auricular injection of methylprednisolone sodium succinate in combination with lidocaine. Treatment efficacy values in the two groups of patients were determined and compared.Results: Total treatment effectiveness values in patients in the research group after one course of treatment, and at three months post-treatment, were significantly higher than the corresponding values in the reference group (p < 0.05). After treatment, the two groups had markedly improved Tinnitus Handicap Inventory (THI) scores, although patients in the research group had lower scores than those in reference group (p < 0.05). There were significant decreases in post-treatment pure tone threshold and Pittsburgh sleep quality index (PSQI) in the two groups of patients, with lower values in the research group than in the reference group (p < 0.05). There were no obvious adverse drug reactions during treatment and during 3-month follow-up period.Conclusion: Post-auricular injection of methylprednisolone sodium succinate and lidocaine effectively improved the clinical efficacy and the sleep quality of acute tinnitus patients. It is a simple and highly safe operation which merits clinical application

Modified Glucose-Insulin-Potassium Regimen Provides Cardioprotection With Improved Tissue Perfusion in Patients Undergoing Cardiopulmonary Bypass Surgery

Background Laboratory studies demonstrate glucose-insulin-potassium (GIK) as a potent cardioprotective intervention, but clinical trials have yielded mixed results, likely because of varying formulas and timing of GIK treatment and different clinical settings. This study sought to evaluate the effects of modified GIK regimen given perioperatively with an insulin-glucose ratio of 1:3 in patients undergoing cardiopulmonary bypass surgery. Methods and Results In this prospective, randomized, double-blinded trial with 930 patients referred for cardiac surgery with cardiopulmonary bypass, GIK (200 g/L glucose, 66.7 U/L insulin, and 80 mmol/L KCl) or placebo treatment was administered intravenously at 1 mL/kg per hour 10 minutes before anesthesia and continuously for 12.5 hours. The primary outcome was the incidence of in-hospital major adverse cardiac events including all-cause death, low cardiac output syndrome, acute myocardial infarction, cardiac arrest with successful resuscitation, congestive heart failure, and arrhythmia. GIK therapy reduced the incidence of major adverse cardiac events and enhanced cardiac function recovery without increasing perioperative blood glucose compared with the control group. Mechanistically, this treatment resulted in increased glucose uptake and less lactate excretion calculated by the differences between arterial and coronary sinus, and increased phosphorylation of insulin receptor substrate-1 and protein kinase B in the hearts of GIK-treated patients. Systemic blood lactate was also reduced in GIK-treated patients during cardiopulmonary bypass surgery. Conclusions A modified GIK regimen administered perioperatively reduces the incidence of in-hospital major adverse cardiac events in patients undergoing cardiopulmonary bypass surgery. These benefits are likely a result of enhanced systemic tissue perfusion and improved myocardial metabolism via activation of insulin signaling by GIK. Clinical Trial Registration URL: clinicaltrials.gov. Identifier: NCT01516138

The Integrated Information System for Natural Disaster Mitigation

Supported by the World Bank, the Integrated Information System for Natural Disaster Mitigation (ISNDM), including the operational service system and network telecommunication system, has been in development for three years in the Center of Disaster Reduction, Chinese Academy of Sciences, based on the platform of the GIS software Arcview. It has five main modules: disaster background information, socio- economic information, disaster-induced factors database, disaster scenarios database, and disaster assessment. ISNDM has several significant functions, which include information collection, information processing, data storage, and information distribution. It is a simple but comprehensive demonstration system for our national center for natural disaster reduction

Hierarchical mining algorithm for high dimensional spatiotemporal big data based on association rules

The traditional data mining algorithm focuses too much on a single dimension of data time or space, ignoring the association between time and space, which leads to a large amount of computation and low processing efficiency of the mining algorithm and makes it difficult to guarantee the final data mining effect. In response to the above problems, a hierarchical mining algorithm based on association rules for high-dimensional spatio-temporal big data is proposed. Based on the traditional association rules, after establishing the association rules of spatio-temporal data, the data to be mined are cleaned for redundancy. After selecting the local linear embedding algorithm to reduce the dimensionality of the data, a hierarchical mining strategy is developed to realize high-dimensional spatio-temporal big data mining by searching frequent predicates to form a spatio-temporal transaction database. The simulation experiment results verify that the algorithm has high complexity and can effectively reduce the processing volume, which can improve the processing efficiency by at least 56.26% compared with other algorithms

Machine-learning-driven synthesis of carbon dots with enhanced quantum yields

Knowing the correlation of reaction parameters in the preparation process of carbon dots (CDs) is essential for optimizing the synthesis strategy, exploring exotic properties, and exploiting potential applications. However, the integrated screening experimental data on the synthesis of CDs are huge and noisy. Machine learning (ML) has recently been successfully used for the screening of high-performance materials. Here, we demonstrate how ML-based techniques can offer insight into the successful prediction, optimization, and acceleration of CDs' synthesis process. A regression ML model on hydrothermal-synthesized CDs is established capable of revealing the relationship between various synthesis parameters and experimental outcomes as well as enhancing the process-related properties such as the fluorescent quantum yield (QY). CDs exhibiting a strong green emission with QY up to 39.3% are obtained through the combined ML guidance and experimental verification. The mass of precursors and the volume of alkaline catalysts are identified as the most important features in the synthesis of high-QY CDs by the trained ML model. The CDs are applied as an ultrasensitive fluorescence probe for monitoring the Fe3+ ion because of their superior optical behaviors. The probe exhibits the linear response to the Fe3+ ion with a wide concentration range (0-150 μM), and its detection limit is 0.039 μM. Our findings demonstrate the great capability of ML to guide the synthesis of high-quality CDs, accelerating the development of intelligent material.Ministry of Education (MOE)Accepted versio

Graphene Quantum Dot-Mediated Atom-Layer Semiconductor Electrocatalyst for Hydrogen Evolution

Highlights The functional groups on graphene quantum dots (GQDs) for boosting the formation of MoS2 nanosheets via theoretical calculations were predicted. Near atom-layer-QD@SO3 with about 2 nm were synthesized using a functionalized GQD-induced in-situ bottom-up approach. Mechanistic insight on the role of functionalized GQDs was elaborated, namely, electron-withdrawing group functionalized GQDs facilitate the formation of nanosheet architectures of MoS2 compared to electron-donating group