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Background With the population aging, diabetes and cognitive function decline are increasingly common among older adults worldwide. However, the evidence about the effects and mechanism of prediabetes and diabetes on cognitive function is still limited. The purposes of this longitudinal study were to estimate the longitudinal associations of the onset of prediabetes and diabetes status with cognitive function among Chinese adults aged 45 years or older during an 8-year period; to estimate the clinical risk factors associated with cognitive function among patients with prediabetes and diabetes. Methods Participants were enrolled between 2011 and 2012, and followed up between 2018 and 2019 in the China Health and Retirement Longitudinal Study. In this study, we focused on newly diagnosed diabetic status, and those diagnosed with diabetes before or not providing fasting blood samples were excluded. Diabetic status was assessed according to the 2010 American Diabetes Association (ADA) guidelines. The general cognitive function, demographic characteristics, and clinical and biochemical factors were also measured. Results At baseline, 849 (21.3%) participants were first diagnosed with prediabetes, and 444 (11.1%) were diabetes patients. After adjusting for age, gender, marital status, education level, ever smoking, ever drinking, self-comment about health, hypertension, dyslipidemia, depressive symptoms, cognitive function, and clinical and biochemical measurements at baseline, diabetes status was a significant risk factor for subsequent cognitive decline (unstandardized β estimate=-0.47, 95% CI=-0.91~-0.04). Further stratification analyses found that only triglyceride concentrations were negatively associated with cognitive function among prediabetes patients (unstandardized β estimate=-0.004, 95% CI=-0.007~-0.001), and only creatine reactive protein was significantly associated with cognitive decline among diabetes patients (unstandardized β estimate=-0.065, 95% CI=-0.122~-0.009). Conclusions There is a positive longitudinal association between the onset of diabetes and cognitive decline among middle-aged and elderly Chinese. The management of triglycerides through lifestyle modification for prediabetes and specific adjunctive anti-inflammatory therapy for diabetes could benefit cognitive performance.

Pathogenic microorganism analysis based on next-generation sequencing technology is an important tool for clinical diagnosis, public health surveillance, and outbreak investigation. However, scientific researchers without the relevant background lack the time, training, or infrastructure to use large data sets or install and use command line tools. Therefore, the bioinformatic team at the Chinese Center for Disease Control and Prevention developed the Microbial Data Analysis Cloud Platform (MDACP) as a safe, professional, and efficient pathogen genetic data analysis platform for rapid microbial data mining, such as for candidate pathogen detection, genome typing, and traceability. MDACP is a web service system based on the Docker platform and can be used for data analysis on various operating systems. The platform focuses on pathogen analysis and continuously develops new analysis processes according to the analysis needs of the users. This platform has a friendly user interface and is easy to operate, allowing users to submit data through data pages or graphical clients, flexibly control parameters according to data conditions, and analyze data in parallel with multiple tasks. Researchers can quickly carry out bioinformatic analyses without coding work, promote follow-up research and information mining of projects, and improve the utilization of big data in the field of disease control. MDACP enables research personnel to conduct data analysis and management and assists clinicians and disease control personnel with mining information, such as pathogen identification, classification, and traceability.

Background Extracellular superoxide dismutase (Ec‐SOD) is a major scavenger of reactive oxygen species. However, its relationships with abnormal left ventricular (LV) geometry patterns and heart failure (HF) are still unknown in patients with cardiovascular disease. Methods and Results A cross‐sectional study was carried out to evaluate the association of serum Ec‐SOD activity with LV geometry, as well as HF in 1047 patients with cardiovascular disease. All participants underwent standard echocardiography examination and measurement of serum Ec‐SOD activity. Overall, we found a significantly decreased trend of serum Ec‐SOD activity from subjects with normal geometry (147.96±15.94 U/mL), subjects with abnormal LV geometry without HF (140.19±20.12 U/mL), and subjects with abnormal LV geometry and overt HF (129.32±17.92 U/mL) after adjustment for potential confounders ( P for trend Conclusions Serum Ec‐SOD activity was independently associated with abnormal LV geometry patterns with and without overt HF. Our results indicate that Ec‐SOD might be a potential link between LV structure remodeling and the development of subsequent HF in patients with cardiovascular disease. Registration URL: https://www.clinicaltrials.gov ; Unique identifier NCT03351907.

Objective: To explore the effect of bedside work-based nursing mode on psychological stress response and quality of life of patients undergoing nasal endoscopic optic nerve decompression for traumatic optic neuropathy. Methods: Thirty patients with traumatic optic neuropathy were selected from our hospital from March 2016 to October 2019. All patients underwent nasal endoscopic optic nerve decompression and were grouped according to the order of admission. The control group received routine nursing intervention and the observation group had 15 Cases were given bedside nursing intervention based on the control group. The depression (SDS), anxiety (SAS), postoperative quality of life (SF-36) scores, and postoperative clinical effects were compared between the two groups at admission and discharge. Results: The SDS and SAS scores of the observation group at the time of discharge were lower than those of the control group, and the differences were statistically significant (P < 0.05). The clinical effect of the observation group was better than the control group, and the total effective rate of the observation group was 93.33% (14/15) which was higher than the 80.00% (12/15) of the control group, the difference was statistically significant (P < 0.05). SF-36 scores of physiological function, physical function, emotional function, social function, etc. in the observation group were higher than those in the control group, and the difference was statistically significant. (P < 0.05). Conclusion: Treatment of traumatic optic neuropathy with bedside work-based nursing mode by nasal endoscopic optic nerve decompression has significant effects, which can improve their psychological stress response and quality of life with high safety.

Background: Prophylactic transfusion of platelets provides a good protection for the implementation of invasive procedures and the prevention of bleeding events during chemotherapy and hematopoietic stem cell transplantation in patients who are suffering from hematological diseases. The issues that what is the optimal dose of platelet transfusion and how to monitor platelet transfusion efficiency are important due to the short storage time of platelet products, rising clinical demands, and decreasing donors. It is worthy that there are amount of studies have been conducted in the past to explore factors that may affect the clinical efficacy of platelet transfusion and characteristics of patients under these different transfusion effects. While, previous studies failed to exactly address the problems of clinical needs for platelet transfusion. Methods: The aim of our study is to develop a model to evaluate the efficacy of platelet transfusion and the statistic method of machine learning algorithm (ML) was involved. The differences between this algorithm and traditional methods are that the former one can continuously be learning from the data and form a self-training model, therefore ML is more accurate than traditional artificial models and generally independent of the model and parameters themselves. We further take the multi-layer fully connected layer neural network model (MLNN) into our consideration because it simulates the multi-layer interconnection of human nervous systems, which is suitable for processing inaccurate and fuzzy information. In our study, the establishment of a neural network model was used to make a multiple-dimension analysis between factors affecting platelet transfusion efficacy and platelet count added value correction index (aCCI), and explore the correlation among these influencing factors as well. Results: The study utilized the data relative to 1840 platelet transfusions performed in 460 patients with hematological diseases. The participants ranged in age from 16 to 92, and the median age was 59.5. There were 199 females and 261 men. The whole data was divided to 2 parts, 2/3 of them were analyzed as a training set and the others were used for validating. We selected 30 factors (including patient-related factors and transfusion product-related characteristics) that may affect the efficacy of platelet transfusion except the storage time of platelet (all data < 2 days), and established a model for predicting platelet transfusion efficacy based on the volume of platelet transfusion. After the model was established, it was tested for goodness of fit, and the results showed that the LOSS value tended to be stable. Conclusions: The establishment of this model may not only be used for predicting the platelet count after platelet transfusions in patients and the amount of platelets that need to be transfused, but also provide supports for the solution of related problems. Disclosures No relevant conflicts of interest to declare.

Objective :At present, patients with hematopoietic stem cell transplantation (HSCT) are gradually increasing due to hematological diseases. However, there are no further studies on the factors affecting the effect of platelet transfusion during HSCT. This study analyzed the possible factors that affect the platelet transfusion efficiency. Methods: The clinical data of patients with HSCT were collected from the Department of Hematology, Zhongda Hospital Affiliated to Southeast University from March 2015 to March 2018. According to the inclusion and exclusion criteria, a retrospective statistical analysis on the factors affecting the infusion effect was taken in all patients from +1 d to the day that the peripheral blood granulocyte count was more than 0.5×109/L after transplantation. The factors are including gender, age, types of disease, whether initial treatment, platelet inventory days in the effective period, types of transplantation, duration of disease and transfusion times. Furthermore, non-conditional logistic regression analysis was performed on the mentioned factors with statistical significance. Results: A total of 52 patients, 299 platelet transfusions were included in this study. The patients involve 28 men and 24 women, and there were 233 effective infusion and 66 invalid infusion. After chi-square test, the effects of gender, age, disease types, whether initial treatment, transplantation types, duration of disease and transfusion times on platelet transfusion were statistically significant(P Conclusion: There are many factors related to poor platelet transfusion during HSCT, in which the gender, disease types and whether initial treatment are the main influencing factors. Disclosures No relevant conflicts of interest to declare.

This paper is concerned with the resource allocation for multiple input multiple output and orthogonal frequency division multiplexing access (MIMO-OFDMA) downlink cognitive radio systems where a cognitive radio MIMO-OFDMA system is under spectrum sharing with an existing primary radio (PR) network. We use the channel learning scheme to estimate the channel information from cognitive radio transmitter (CR-TX) to PR and then make beamforming to transmit signal. Considering the interference from CR-TX to PR caused by the imperfect channel learning, we intend to maximize CR throughput under the interference power constraint at PR and CR transmit power constraint. A nearly optimal subcarrier and power allocation algorithm with linear complexity is proposed. The proposed algorithm is global optimal when the maximum transmit power is beyond certain threshold. Simulation results show that the proposed algorithm has a good performance very close to the global optimal algorithm.

This paper studies the power allocation problem for a cognitive radio (CR) multiple-input-multiple-output (MIMO) system under spectrum sharing with an existing primary radio network. In particular, the ergodic capacity maximization problem is studied in the Rayleigh fading cognitive MIMO channel under both the total transmit power constraint and a set of interference power constraints where each is applied at one of the primary receivers (PRs). This paper considers that the channel from primary transmitter (PT) to secondary receiver (SR), and the channel from secondary transmitter (ST) to SR are both in short-term fading due to the mobility of SR, whereas the channels from ST to PRs are in long-term fading. By using the Lagrangian dual method, the optimal transmit precoding matrix is derived, and some nearly optimal power allocation algorithms are proposed to maximize the bounds of secondary user ergodic capacity. Performance analysis and simulation results show that the proposed schemes can approach the comparative capacity of the optimal power allocation algorithm, which has perfect instant channel state information (CSI), particularly when the antenna number of the ST exceeds the total antenna number of PRs.

In e-commerce environment, Grain Logistics Distribution is the ultimatal guarantee to realize the entire grain-transaction process. This article sets up the system model of grain Logistics Distribution based on the Web GIS, and provides more practical guidance program for Web GIS to apply in GLD_OIS.