One-Size-Fits-All Policies Are Unacceptable: A Sustainable Management and Decision-Making Model for Schools in the Post-COVID-19 Era.

This paper proposes a sustainable management and decision-making model for COVID-19 control in schools, which makes improvements to current policies and strategies. It is not a case study of any specific school or country. The term one-size-fits-all has two meanings: being blind to the pandemic, and conducting inflexible and harsh policies. The former strategy leads to more casualties and does potential harm to children. Conversely, under long-lasting strict policies, people feel exhausted. Therefore, some administrators pretend that they are working hard for COVID-19 control, and people pretend to follow pandemic control rules. The proposed model helps to alleviate these problems and improve management efficiency. A customized queue model is introduced to control social gatherings. An indoor-outdoor tracking system is established. Based on tracing data, we can assess people's infection risk, and allocate medical resources more effectively in case of emergency. We consider both social and technical feasibility. Test results demonstrate the improvements and effectiveness of the model. In conclusion, the model has patched up certain one-size-fits-all strategies to balance pandemic control and normal life.

A prognostic risk model based on DNA methylation levels of genes and lncRNAs in lung squamous cell carcinoma.

Background: Recurrence is a risk factor for the prognosis of lung squamous carcinoma (LUSC). DNA methylation levels of RNAs are also associated with LUSC prognosis. This study aimed to construct a prognostic model with high performance in predicting LUSC prognosis using the methylation levels of lncRNAs and genes. Methods: The differentially expressed RNAs (DERs) and differentially methylated RNAs (DMRs) between the recurrent and non-recurrent LUSC tissues in The Cancer Genome Atlas (TCGA; training dataset) were identified. Weighted correlation network analysis was performed to identify co-methylation networks. Differentially methylated genes and lncRNAs with opposite expression-methylation levels were used for the screening of prognosis-associated RNAs. The prognostic model was constructed and its performance was validated in the GSE39279 dataset. Results: A total of 664 DERs and 981 DMRs (including 972 genes) in recurrent LUSC tissues were identified. Three co-methylation modules, including 226 differentially methylated genes, were significantly associated with LUSC. Among prognosis-associated RNAs, 18 DERs/DMRs with opposite methylation-expression levels were included in the methylation prognostic risk model. LUSC patients with high risk scores had a poor prognosis compared with patients who had low risk scores (TCGA: HR = 3.856, 95% CI [2.297-6.471]; GSE39279: HR = 3.040, 95% CI [1.435-6.437]). This model had a high accuracy in predicting the prognosis (AUC = 0.903 and 0.800, respectively), equivalent to the nomogram model inclusive of clinical variables. Conclusions: Referring to the methylation levels of the 16-RNAs might help to predict the survival outcomes in LUSC.

An IoT-Based COVID-19 Prevention and Control System for Enclosed Spaces

To date, the protracted pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had widespread ramifications for the economy, politics, public health, etc. Based on the current situation, definitively stopping the spread of the virus is infeasible in many countries. This does not mean that populations should ignore the pandemic;instead, normal life needs to be balanced with disease prevention and control. This paper highlights the use of Internet of Things (IoT) for the prevention and control of coronavirus disease (COVID-19) in enclosed spaces. The proposed booking algorithm is able to control the gathering of crowds in specific regions. K-nearest neighbors (KNN) is utilized for the implementation of a navigation system with a congestion control strategy and global path planning capabilities. Furthermore, a risk assessment model is designed based on a “Sliding Window-Timer”algorithm, providing an infection risk assessment for individuals in potential contact with patients.

Safety and efficacy of tracheotomy for critically ill patients with coronavirus disease 2019 (COVID-19) in Wuhan: a case series of 14 patients.

OBJECTIVES: Coronavirus disease 2019 (COVID-19) is a global pandemic. Critically ill patients often require prolonged intubation for mechanical ventilation to support breathing; thus, the artificial airway must be managed by tracheotomy. Therefore, studies exploring appropriate and safe methods for tracheotomy that minimize the risks of nosocomial transmission are important. METHODS: A retrospective analysis of the clinical characteristics of 14 critically ill patients with COVID-19, who underwent bedside tracheotomy from March to April 2020 was conducted to summarize the indications for tracheotomy and key points related to personal protective equipment and surgical procedures. RESULTS: All 14 patients were diagnosed with COVID-19 and were critically ill. All tracheotomies were performed in the late phase of the infection course. The interval between the infection and tracheotomy was 33 days, and the median interval between intubation and tracheotomy was 25.5 days. The reverse transcription-polymerase chain reaction results of secretions from the operative incision and inside the tracheotomy tube were negative. Twelve patients improved after tracheotomy, with SpO2 levels maintained above 96%. One patient died of progressive respiratory failure; another patient died of uncontrolled septic shock. No medical staff who participated in the tracheotomy was infected. CONCLUSIONS: Tracheotomy in critically ill patients with COVID-19 who meet the indications for tracheotomy potentially represents a safer approach to manage the airway and help improve the treatment outcomes. A tracheotomy performed in the late phase of the disease has a relatively low risk of infection. Adherence to key steps in the tracheotomy procedure and donning adequate personal protection will help medical staff avoid infection.