Surgical Site Infection after Orthopedic Surgery Performed in Dong Guan Hospital of Traditional Chinese Medicine: A Descriptive Study of the Burden of Surgical Site Infection and its Risk factors with A Focus on Antimicrobial Prophylaxis and Traditional Chinese Medicine in Spinal Surgery

Background: Surgical site infection (SSI) is a serious complication after orthopedic surgery, and it is associated with high morbidity rates, high healthcare costs and in some cases poor patients outcomes. Aims: The purpose of this study was to identify the burden of SSI among orthopedic surgery and its associated risk factors of SSI among the people underwent spinal surgery in a selected hospital in China. Methods: From June 26 to November 30 in 2014, we performed a prospective surveillance study in the patients who underwent orthopedic surgery in a selected Chinese hospital. SSI was diagnosed based on the definition established by the Centers for Disease Control and Prevention (CDC) and was identified by bedside surveillance and post-discharge checkup. Detailed pre-, intra-, post-operative patient characteristics were prospectively recorded using a standardized data collection format. Results: A total of 287 orthopedic surgery cases, among them 192 cases of spinal surgery, were included, of which 8 cases developed surgical site infection. Wound contamination class, wound drains and blood transfusion were surgery-related risk factors for orthopedic spinal surgery during the hospital stay after bivariate analysis. Intravenous AMP was given in 176 of 287 (61.3%) after orthopedic surgery. The average duration of AMP administration was 2.2 days (range 1-9 days). Conclusion: In conclusion, we identified an incidence proportion of SSI after orthopedic surgery of 2.8%. The orthopedic SSI risk factors (wound contamination class, wound drains and blood transfusion) identified in present study may use to be reducing the incidence of SSI in the future

NLCUnet: Single-Image Super-Resolution Network with Hairline Details

Pursuing the precise details of super-resolution images is challenging for single-image super-resolution tasks. This paper presents a single-image super-resolution network with hairline details (termed NLCUnet), including three core designs. Specifically, a non-local attention mechanism is first introduced to restore local pieces by learning from the whole image region. Then, we find that the blur kernel trained by the existing work is unnecessary. Based on this finding, we create a new network architecture by integrating depth-wise convolution with channel attention without the blur kernel estimation, resulting in a performance improvement instead. Finally, to make the cropped region contain as much semantic information as possible, we propose a random 64$\times$64 crop inside the central 512$\times$512 crop instead of a direct random crop inside the whole image of 2K size. Numerous experiments conducted on the benchmark DF2K dataset demonstrate that our NLCUnet performs better than the state-of-the-art in terms of the PSNR and SSIM metrics and yields visually favorable hairline details.Comment: 6 pages,5 figure

High Performance of Commercial PAC on the Simultaneous Desulfurization and Denitrification of Wastewater From a Coal-Fired Heating Plant

The flue gas desulfurization wastewater is highly saline and has too many refractory pollutants to be recycled during the desulfurization process of the coal-fired heating plant. Given that waste heat is abundant in coal-fired heating plants, a thermal treatment method was developed to simultaneously remove sulfates and nitrates from the wastewater, with the production of chemical-grade natroalunite and recycled water. The results showed that sulfates and nitrates were 50.3 and 10 g/L in the wastewater, respectively, and only 2.8% and 9.1% were removed after direct treatment at 270°C for 7 h; but these rates increased to 99.3% and 99.9%, respectively, with the addition of commercial poly aluminum chloride. Mass balance summarized that the treatment of 1 ton wastewater needed 0.1 ton PAC and produced 0.11 ton natroalunite and 0.92 ton recycle water. The removal of sulfates and nitrates was mainly done by the precipitation reaction of sulfates such as natroalunite and the redox reaction between nitrates and organics, respectively. Thermodynamic analysis demonstrated that the precipitate reaction occurred at 45°C and accelerated in the temperature range of 45–270°C, but became slow with the decrease of sulfate and Al concentrations in wastewater. Four other reagents were also used for wastewater treatment in comparison with PAC and showed the following order of performance: PAC > citrate calcium > limestone > subacetate aluminum > citrate ferric. This method provided a practical route to treat wastewater from flue gas desulfurization without generating secondary waste

Incorporating Retrieval-based Causal Learning with Information Bottlenecks for Interpretable Graph Neural Networks

Graph Neural Networks (GNNs) have gained considerable traction for their capability to effectively process topological data, yet their interpretability remains a critical concern. Current interpretation methods are dominated by post-hoc explanations to provide a transparent and intuitive understanding of GNNs. However, they have limited performance in interpreting complicated subgraphs and can't utilize the explanation to advance GNN predictions. On the other hand, transparent GNN models are proposed to capture critical subgraphs. While such methods could improve GNN predictions, they usually don't perform well on explanations. Thus, it is desired for a new strategy to better couple GNN explanation and prediction. In this study, we have developed a novel interpretable causal GNN framework that incorporates retrieval-based causal learning with Graph Information Bottleneck (GIB) theory. The framework could semi-parametrically retrieve crucial subgraphs detected by GIB and compress the explanatory subgraphs via a causal module. The framework was demonstrated to consistently outperform state-of-the-art methods, and to achieve 32.71\% higher precision on real-world explanation scenarios with diverse explanation types. More importantly, the learned explanations were shown able to also improve GNN prediction performance