Influencing factors of medical device-related pressure ulcers in medical personnel during the COVID-19 pandemic: A systematic review and meta-analysis.

OBJECTIVE: To determine the influencing factors of medical device related pressure injury (MDRPU) in medical staff by meta-analysis. METHODS: A comprehensive literature search was conducted by PubMed, Embase, Cochrane Library, Web of Science, CNKI, VIP, CBM, and WanFang Data (from inception to July 27, 2022). Two researchers independently performed literature screening, quality evaluation and data extraction, and meta-analysis was conducted with RevMan 5.4 and Stata12.0 software. RESULTS: Total of 11215 medical staff were included in 9 articles. Meta analysis showed that gender, occupation, sweating, wearing time, single working time, department of COVID-19, preventive measures, and level 3 PPE were the risk factors for MDRPU in medical staff (P < 0.05). CONCLUSION: The outbreak of COVID-19 led to the occurrence of MDRPU among medical staff, and the influencing factors should be focused on. The medical administrator can further improve and standardize the preventive measures of MDRPU according to the influencing factors. Medical staff should accurately identify high-risk factors in the clinical work process, implement intervention measures, and reduce the incidence of MDRPU.

Calix[6]arene functionalized lanthanide metal-organic frameworks with boosted performance in identifying an anti-epidemic pharmaceutical.

A novel composite was fabricated by hybridizing terbium 1,3,5-benzenetricarboxylic MOF (TB-MOF) with Cx[6]. The obtained composite TB-Cx[6] possessed long-term stability and dispersion stability and was used for on-site analysis of the anti-COVID-19 disinfection product Prednis via a combing remote sampling technique.

Problematic Internet Usage and Self-Esteem in Chinese Undergraduate Students: The Mediation Effects of Individual Affect and Relationship Satisfaction.

The aim of this cross-sectional study was to examine the mediating effects of individual affect and relationship satisfaction on the relationship between self-esteem and Problematic Internet Use (PIU). Affect was measured using the Positive and Negative Affect Schedule (PANAS), relationship satisfaction was assessed using a positive and negative semantic dimension scale, self-esteem was measured using the Rosenberg Self-Esteem Scale, and PIU was measured using the Problematic Internet Use scale with a sample of 507 Chinese university students (Mage = 20.41 years, SD = 2.49). The relationships between the variables were tested using structural equation modelling with a multiple mediation model. The results revealed that negative affect and the negative semantic dimensions of relationship satisfaction mediated the relationship between self-esteem and PIU. The implications of the results and the study's theoretical contributions are discussed.

Psychological responses among nurses caring for patients with COVID-19: a comparative study in China.

Frontline healthcare nurses devoted themselves to deal with the outbreak of COVID-19, saving many lives. However, they are under incredible unknown psychological pressures with a considerable risk of infection. In this study, a self-administered questionnaire was used to survey 593 frontline nurses in Wuhan City and non-Hubei provinces for psychological responses from March 1 to March 10, 2020. Compared with nurses outside Hubei Province, those working in Wuhan were more likely to feel physically and mentally exhausted. Their probable depression and anxiety were significantly higher than those of nurses outside Hubei province (31.2%, 18.3% vs. 13.8%, 5.9%). Correspondingly, the depressive symptoms were more often reported in the Wuhan group (70.8% vs. 41.4%). Although Wuhan received wishes, concerns, and abundant psychological and material resources from all of the world, the survey-based study found that frontline nurses in Wuhan still had higher depression and anxiety with less social support compared with nurses from non-Hubei provinces. Unexpectedly, only 4.0% of nurses have sought psychological assistance. These findings suggested that the short-term psychological impact of frontline nurses in Wuhan during the COVID-19 outbreak was extremely high compared with nurses outside Hubei Province. This research enlightened the efficient integration of psychological resources, the optimization of the nurse emergency psychological assistance system, and the mental health care of medical staff during the outbreak of epidemics.

Prediction of COVID-19 Severity Using Chest Computed Tomography and Laboratory Measurements: Evaluation Using a Machine Learning Approach.

BACKGROUND: Most of the mortality resulting from COVID-19 has been associated with severe disease. Effective treatment of severe cases remains a challenge due to the lack of early detection of the infection. OBJECTIVE: This study aimed to develop an effective prediction model for COVID-19 severity by combining radiological outcome with clinical biochemical indexes. METHODS: A total of 46 patients with COVID-19 (10 severe, 36 nonsevere) were examined. To build the prediction model, a set of 27 severe and 151 nonsevere clinical laboratory records and computerized tomography (CT) records were collected from these patients. We managed to extract specific features from the patients' CT images by using a recently published convolutional neural network. We also trained a machine learning model combining these features with clinical laboratory results. RESULTS: We present a prediction model combining patients' radiological outcomes with their clinical biochemical indexes to identify severe COVID-19 cases. The prediction model yielded a cross-validated area under the receiver operating characteristic (AUROC) score of 0.93 and an F1 score of 0.89, which showed a 6% and 15% improvement, respectively, compared to the models based on laboratory test features only. In addition, we developed a statistical model for forecasting COVID-19 severity based on the results of patients' laboratory tests performed before they were classified as severe cases; this model yielded an AUROC score of 0.81. CONCLUSIONS: To our knowledge, this is the first report predicting the clinical progression of COVID-19, as well as forecasting severity, based on a combined analysis using laboratory tests and CT images.

A Primary Mediastinal Large B-Cell Lymphoma Patient With COVID-19 Infection After Intensive Immunochemotherapy: A Case Report.

Background: The outbreak of coronavirus disease 2019 (COVID-19) had become a global public health event. Lymphoma patients need to be distinguished from the general population because of their deficient immune status and intensive anti-tumor treatment. The impacts of cancer subtypes and treatment on COVID-19 infection are unclear. Case Presentation: We here report the case of a primary mediastinal large B-cell lymphoma patient who was infected with COVID-19 after intensive immunochemotherapy (DA-EPOCH-R). The patient developed a neutropenic fever during chemotherapy, and fever was persistent, although antibiotics were used. Initial chest CT was negative, and the patient received a throat swab test since the second CT showed evidence of pneumonia. With treatment with Arbidol Hydrochloride and LianHuaQingWen capsule, his COVID-19 was cured. Conclusions: To the best of our knowledge, this is the first report focusing on COVID-19 infection in a lymphoma patient undergoing intensive immunochemotherapy. For those patients being treated with immunochemotherapy in epidemic areas, a reduced dose intensity of intensive chemotherapy should be considered, and the effect of immunotherapies such as rituximab on COVID-19 infection should be considered. The impacts of anti-cancer treatment on COVID-19 infection need to be explored further.