Association between negative psychology and sleep quality in dialysis patients during the COVID-19 pandemic.

AIMS AND OBJECTIVES: The aim of this study was to assess the sleep quality in dialysis patients during the COVID-19 epidemic and explore the association between negative psychology (including depression, anxiety, and stress) and sleep quality in this population. DESIGN: A cross-sectional study including three centres. METHODS (PATIENTS OR PUBLIC CONTRIBUTION): This cross-sectional study included 378 dialysis patients from April to May 2022 in three dialysis centres in Shanghai. METHODS: Depression, anxiety, stress, and sleep quality were measured by the Hospital Anxiety and Depression Scale (HADS), Perceived Stress Scale-14 (PSS-14), and Pittsburgh sleep quality index (PSQI), respectively. With a threshold of 5 to classify participants into good and poor sleep quality, with HADS/PSS-14 scores as independent variables (per standard deviation (SD) increment), respectively and binary Logistic regression model was constructed to explore the association between the three negative psychological aspects of depression, anxiety, and stress and sleep quality. RESULTS: The median PSQI score was 11.0 (mean ± SD: 11.8 ± 4.8). Among them, poor sleep quality (i.e., PSQI >5) was reported by 90.2% of participants. After adjusting for sociodemographic and disease-related information, HADS-depression was associated with a significant 49% (odds ratio (OR): 1.49; 95% CI 1.02-2.18) increase in the risk of poor sleep quality for each additional SD (2.4). Correspondingly, for each SD (7.1) increase in PSS-14, the risk of poor sleep quality was significantly increased by 95% (OR: 1.95; 95% CI 1.35-2.82). CONCLUSION: During the COVID-19 pandemic, there was a significant negative association between negative psychology, such as depression and stress, and sleep quality in dialysis patients, and this relationship was independent of the dialysis modality. RELEVANCE TO CLINICAL PRACTICE: In the context of the rampant COVID-19, the vast majority of dialysis-dependent chronic kidney disease presents with severe sleep quality problems, and negative psychology is a potential influencing factor.

Targeting RNA:protein interactions with an integrative approach leads to the identification of potent YBX1 inhibitors.

RNA-protein interactions (RPIs) are promising targets for developing new molecules of therapeutic interest. Nevertheless, challenges arise from the lack of methods and feedback between computational and experimental techniques during the drug discovery process. Here, we tackle these challenges by developing a drug screening approach that integrates chemical, structural and cellular data from both advanced computational techniques and a method to score RPIs in cells for the development of small RPI inhibitors; and we demonstrate its robustness by targeting Y-box binding protein 1 (YB-1), a messenger RNA-binding protein involved in cancer progression and resistance to chemotherapy. This approach led to the identification of 22 hits validated by molecular dynamics (MD) simulations and nuclear magnetic resonance (NMR) spectroscopy of which 11 were found to significantly interfere with the binding of messenger RNA (mRNA) to YB-1 in cells. One of our leads is an FDA-approved poly(ADP-ribose) polymerase 1 (PARP-1) inhibitor. This work shows the potential of our integrative approach and paves the way for the rational development of RPI inhibitors.

Effect of extracorporeal hemoadsorption in critically ill patients with COVID-19: A narrative review.

COVID-19 has been affecting the world unprecedentedly and will remain widely prevalent due to its elusive pathophysiological mechanism and the continuous emergence of new variants. Critically ill patients with COVID-19 are commonly associated with cytokine storm, multiple organ dysfunction, and high mortality. To date, growing evidence has shown that extracorporeal hemoadsorption can exert its adjuvant effect to standard of care by regulating immune homeostasis, reducing viremia, and decreasing endotoxin activity in critically ill COVID-19 cases. However, the selection of various hemofilters, timing of initiation and termination of hemoadsorption therapy, anticoagulation management of extracorporeal circuits, identification of target subgroups, and ultimate survival benefit remain controversial. The purpose of this narrative review is to comprehensively summarize the rationale for the use of hemoadsorption in critically ill patients with COVID-19 and to gather the latest clinical evidence in this field.

A Survey on Big Data-Enabled Innovative Online Education Systems During the COVID-19 Pandemic

With the spread of COVID-19 around the world, the education industry faces enormous challenges. Some colleges and universities have launched online teaching. Comprehensive online teaching and student health checkups help students complete the set teaching content and return to school as soon as possible. With the development of big data, combined with the epidemic risk we are facing, the rational use of big data and the internet for innovative online education has become a mainstream teaching method. Colleges and universities are not yet familiar with the development prospects and future of online education. Through the research of this paper, we can understand the combination of online education and the development of big data and promote its application in colleges and universities. Not only have innovative online education platforms such as MOOC and DingTalk been widely used, but innovative online education methods such as virtual classrooms also have been created. Based on the current epidemic background, this paper analyzes the development of online education, introduces the impact of the combination of online education and big data, and introduces innovative online education technologies and their effects. It helps online education under the influence of the new coronavirus epidemic, operating big data technology to analyze the current prospects and development of online education, showing the combination of big data technology and online education through the analysis of big data technology, and ending with more expectations on other aspects of the use of big data, which affects the online education industry as well as other industries. Finally, we summarize the combination of big data and innovative online education since the emergence of COVID-19 and introduce the concepts and methods of combining online education and big data technology in detail. The online education platform also makes a reasonable introduction. The thesis can be used to understand the problems and challenges faced by innovative online education in the context of the new coronavirus epidemic and look forward to the future on this basis.

SEIARN: Intelligent Early Warning Model of Epidemic Spread Based on LSTM Trajectory Prediction

A SEIARN compartment model with the asymptomatic infection and secondary infection is proposed to predict the trend of COVID-19 more accurately. The model is extended according to the propagation characteristics of the novel coronavirus, the concepts of the asymptomatic infected compartment and secondary infection are introduced, and the contact rate parameters of the improved model are updated in real time by using the LSTM trajectory, in order to make accurate predictions. This SEIARN model first builds on the traditional SEIR compartment model, taking into account the asymptomatic infection compartment and secondary infection. Secondly, it considers the disorder of the trajectory and uses the improved LSTM model to predict the future trajectory of the current patients and cross-track with the susceptible patients to obtain the contact rate. Then, we conduct real-time updating of exposure rates in the SEIARN model and simulation of epidemic trends in Tianjin, Xi'an, and Shijiazhuang. Finally, the comparison experiments show that the SEIARN model performs better in prediction accuracy, MSE, and RMSE.

Redox-Mediated Artificial Non-Enzymatic Antioxidant MXene Nanoplatforms for Acute Kidney Injury Alleviation.

Acute kidney injury (AKI), as a common oxidative stress-related renal disease, causes high mortality in clinics annually, and many other clinical diseases, including the pandemic COVID-19, have a high potential to cause AKI, yet only rehydration, renal dialysis, and other supportive therapies are available for AKI in the clinics. Nanotechnology-mediated antioxidant therapy represents a promising therapeutic strategy for AKI treatment. However, current enzyme-mimicking nanoantioxidants show poor biocompatibility and biodegradability, as well as non-specific ROS level regulation, further potentially causing deleterious adverse effects. Herein, the authors report a novel non-enzymatic antioxidant strategy based on ultrathin Ti3 C2 -PVP nanosheets (TPNS) with excellent biocompatibility and great chemical reactivity toward multiple ROS for AKI treatment. These TPNS nanosheets exhibit enzyme/ROS-triggered biodegradability and broad-spectrum ROS scavenging ability through the readily occurring redox reaction between Ti3 C2 and various ROS, as verified by theoretical calculations. Furthermore, both in vivo and in vitro experiments demonstrate that TPNS can serve as efficient antioxidant platforms to scavenge the overexpressed ROS and subsequently suppress oxidative stress-induced inflammatory response through inhibition of NF-κB signal pathway for AKI treatment. This study highlights a new type of therapeutic agent, that is, the redox-mediated non-enzymatic antioxidant MXene nanoplatforms in treatment of AKI and other ROS-associated diseases.

Deep learning for detecting corona virus disease 2019 (COVID-19) on high-resolution computed tomography: a pilot study.

BACKGROUND: To evaluate the diagnostic efficacy of Densely Connected Convolutional Networks (DenseNet) for detection of COVID-19 features on high resolution computed tomography (HRCT). METHODS: The Ethic Committee of our institution approved the protocol of this study and waived the requirement for patient informed consent. Two hundreds and ninety-five patients were enrolled in this study (healthy person: 149; COVID-19 patients: 146), which were divided into three separate non-overlapping cohorts (training set, n=135, healthy person, n=69, patients, n=66; validation set, n=20, healthy person, n=10, patients, n=10; test set, n=140, healthy person, n=70, patients, n=70). The DenseNet was trained and tested to classify the images as having manifestation of COVID-19 or as healthy. A radiologist also blindly evaluated all the test images and rechecked the misdiagnosed cases by DenseNet. Receiver operating characteristic curves (ROC) and areas under the curve (AUCs) were used to assess the model performance. The sensitivity, specificity and accuracy of DenseNet model and radiologist were also calculated. RESULTS: The DenseNet algorithm model yielded an AUC of 0.99 (95% CI: 0.958-1.0) in the validation set and 0.98 (95% CI: 0.972-0.995) in the test set. The threshold value was selected as 0.8, while for validation and test sets, the accuracies were 95% and 92%, the sensitivities were 100% and 97%, the specificities were 90% and 87%, and the F1 values were 95% and 93%, respectively. The sensitivity of radiologist was 94%, the specificity was 96%, while the accuracy was 95%. CONCLUSIONS: Deep learning (DL) with DenseNet can accurately classify COVID-19 on HRCT with an AUC of 0.98, which can reduce the miss diagnosis rate (combined with radiologists' evaluation) and radiologists' workload.