Integrative Analysis of Metabolomic and Transcriptomic Data Reveals the Mechanism of Color Formation in Corms of Pinellia ternata.

Pinellia ternata (Thunb.) Breit. (P. ternata) is a very important plant that is commonly used in traditional Chinese medicine. Its corms can be used as medicine and function to alleviate cough, headache, and phlegm. The epidermis of P. ternata corms is often light yellow to yellow in color; however, within the range of P. ternata found in JingZhou City in Hubei Province, China, there is a form of P. ternata in which the epidermis of the corm is red. We found that the total flavonoid content of red P. ternata corms is significantly higher than that of yellow P. ternata corms. The objective of this study was to understand the molecular mechanisms behind the difference in epidermal color between the two forms of P. ternata. The results showed that a high content of anthocyanidin was responsible for the red epidermal color in P. ternata, and 15 metabolites, including cyanidin-3-O-rutinoside-5-O-glucoside, cyanidin-3-O-glucoside, and cyanidin-3-O-rutinoside, were screened as potential color markers in P. ternata through metabolomic analysis. Based on an analysis of the transcriptome, seven genes, including PtCHS1, PtCHS2, PtCHI1, PtDFR5, PtANS, PtUPD-GT2, and PtUPD-GT3, were found to have important effects on the biosynthesis of anthocyanins in the P. ternata corm epidermis. Furthermore, two transcription factors (TFs), bHLH1 and bHLH2, may have regulatory functions in the biosynthesis of anthocyanins in red P. ternata corms. Using an integrative analysis of the metabolomic and transcriptomic data, we identified five genes, PtCHI, PtDFR2, PtUPD-GT1, PtUPD-GT2, and PtUPD-GT3, that may play important roles in the presence of the red epidermis color in P. ternata corms.

Data science in the intensive care unit.

In this editorial, we comment on the current development and deployment of data science in intensive care units (ICUs). Data in ICUs can be classified into qualitative and quantitative data with different technologies needed to translate and interpret them. Data science, in the form of artificial intelligence (AI), should find the right interaction between physicians, data and algorithm. For individual patients and physicians, sepsis and mechanical ventilation have been two important aspects where AI has been extensively studied. However, major risks of bias, lack of generalizability and poor clinical values remain. AI deployment in the ICUs should be emphasized more to facilitate AI development. For ICU management, AI has a huge potential in transforming resource allocation. The coronavirus disease 2019 pandemic has given opportunities to establish such systems which should be investigated further. Ethical concerns must be addressed when designing such AI.

Early prediction of noninvasive ventilation failure after extubation: development and validation of a machine-learning model.

BACKGROUND: Noninvasive ventilation (NIV) has been widely used in critically ill patients after extubation. However, NIV failure is associated with poor outcomes. This study aimed to determine early predictors of NIV failure and to construct an accurate machine-learning model to identify patients at risks of NIV failure after extubation in intensive care units (ICUs). METHODS: Patients who underwent NIV after extubation in the eICU Collaborative Research Database (eICU-CRD) were included. NIV failure was defined as need for invasive ventilatory support (reintubation or tracheotomy) or death after NIV initiation. A total of 93 clinical and laboratory variables were assessed, and the recursive feature elimination algorithm was used to select key features. Hyperparameter optimization was conducted with an automated machine-learning toolkit called Neural Network Intelligence. A machine-learning model called Categorical Boosting (CatBoost) was developed and compared with nine other models. The model was then prospectively validated among patients enrolled in the Cardiac Surgical ICU of Zhongshan Hospital, Fudan University. RESULTS: Of 929 patients included in the eICU-CRD cohort, 248 (26.7%) had NIV failure. The time from extubation to NIV, age, Glasgow Coma Scale (GCS) score, heart rate, respiratory rate, mean blood pressure (MBP), saturation of pulse oxygen (SpO2), temperature, glucose, pH, pressure of oxygen in blood (PaO2), urine output, input volume, ventilation duration, and mean airway pressure were selected. After hyperparameter optimization, our model showed the greatest accuracy in predicting NIV failure (AUROC: 0.872 [95% CI 0.82-0.92]) among all predictive methods in an internal validation. In the prospective validation cohort, our model was also superior (AUROC: 0.846 [95% CI 0.80-0.89]). The sensitivity and specificity in the prediction group is 89% and 75%, while in the validation group they are 90% and 70%. MV duration and respiratory rate were the most important features. Additionally, we developed a web-based tool to help clinicians use our model. CONCLUSIONS: This study developed and prospectively validated the CatBoost model, which can be used to identify patients who are at risk of NIV failure. Thus, those patients might benefit from early triage and more intensive monitoring. TRIAL REGISTRATION: NCT03704324. Registered 1 September 2018, https://register. CLINICALTRIALS: gov .

Characteristics of Kawasaki Disease Before and After the COVID-19 Pandemic in a Large Pediatric Heart Disease Center.

Background: Kawasaki disease (KD) is an acute febrile systemic vasculitis of unknown etiology. After the pandemic of coronavirus disease 2019 (COVID-19), some children infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) showed clinical symptoms similar to KD, indicating a close relationship between KD and SARS-CoV-2. Therefore, we designed this retrospective study to analyze the characteristics of KD patients before and after the COVID-19 pandemic. Methods: We retrospectively collected demographic and laboratory data of KD patients in Yuying Children's Hospital of Wenzhou Medical University from 1 January 2015 to 31 December 2020. Yuying Children's Hospital of Wenzhou Medical University is located in eastern China and is the largest pediatric heart disease center in the region, which includes a population of nearly 10 million. We studied the characteristics of KD patients and analyzed the changes in these characteristics before and after the emergence of SARS-CoV-2 in this area. Results: The analysis revealed the following novel features: (1) Under the influence of the COVID-19 pandemic, the onset age of Kawasaki disease became younger. (2) After the occurrence of COVID-19, the hospitalization days of KD patients were shorter than before the pandemic. (3) After the occurrence of COVID-19, the albumin of KD patients was higher than before the pandemic. (4) The COVID-19 pandemic did not have a significant effect on the incidence of coronary artery lesions (CALs) in Kawasaki disease. Conclusion: After the COVID-19 outbreak, the characteristics of KD patients showed a younger trend of age, shorter hospitalization days and higher levels of albumin, but the incidence of CALs did not change significantly.

Evaluation of multiplex combined real-time PCR detection kit for nucleic acid detection of 22 respiratory pathogens

Objective: To evaluate the detection consistency and power of a multiplex combined real-time PCR detection kits, and provide reference for the prevention and control of influenza plus SARS-CoV-2 infection.

Tailoring glucocorticoids in patients with severe COVID-19: a narrative review.

OBJECTIVE: To discuss the pathogenesis of severe coronavirus disease 2019 (COVID-19) infection and the pharmacological effects of glucocorticoids (GCs) toward this infection. To review randomized controlled trials (RCTs) using GCs to treat patients with severe COVID-19, and investigate whether GC timing, dosage, or duration affect clinical outcomes. Finally. to discuss the use of biological markers, respiratory parameters, and radiological evidence to select patients for improved GC therapeutic precision. BACKGROUND: COVID-19 has become an unprecedented global challenge. As GCs have been used as key immunomodulators to treat inflammation-related diseases, they may play key roles in limiting disease progression by modulating immune responses, cytokine production, and endothelial function in patients with severe COVID-19, who often experience excessive cytokine production and endothelial and renin-angiotensin system (RAS) dysfunction. Current clinical trials have partially proven this efficacy, but GC timing, dosage, and duration vary greatly, with no unifying consensus, thereby creating confusion. METHODS: Publications through March 2021 were retrieved from the Web of Science and PubMed. Results from cited references in published articles were also included. CONCLUSIONS: GCs play key roles in treating severe COVID-19 infections. Pharmacologically, GCs could modulate immune cells, reduce cytokine and chemokine, and improve endothelial functions in patients with severe COVID-19. Benefits of GCs have been observed in multiple clinical trials, but the timing, dosage and duration vary across studies. Tapering as an option is not widely accepted. However, early initiation of treatment, a tailored dosage with appropriate tapering may be of particular importance, but evidence is inconclusive and more investigations are needed. Biological markers, respiratory parameters, and radiological evidence could also help select patients for specific tailored treatments.

Lymphopenia predicted illness severity and recovery in patients with COVID-19: A single-center, retrospective study.

The outbreak of SARS-CoV-2 began in December 2019 and rapidly became a pandemic. The present study investigated the significance of lymphopenia on disease severity. A total of 115 patients with confirmed COVID-19 from a tertiary hospital in Changsha, China, were enrolled. Clinical, laboratory, treatment and outcome data were gathered and compared between patients with and without lymphopenia. The median age was 42 years (1-75). Fifty-four patients (47.0%) of the 115 patients had lymphopenia on admission. More patients in the lymphopenia group had hypertension (30.8% vs. 10.0%, P = 0.006) and coronary heart disease (3.6% vs. 0%, P = 0.029) than in the nonlymphopenia group, and more patients with leukopenia (48.1% vs 14.8%, P<0.001) and eosinopenia (92.6% vs 54.1%, P<0.001) were observed. Lymphopenia was also correlated with severity grades of pneumonia (P<0.001) and C-reactive protein (CRP) level (P = 0.0014). Lymphopenia was associated with a prolonged duration of hospitalization (17.0 days vs. 14.0 days, P = 0.002). Lymphocyte recovery appeared the earliest, prior to CRP and chest radiographs, in severe cases, which suggests its predictive value for disease improvement. Our results demonstrated the clinical significance of lymphopenia for predicting the severity of and recovery from COVID-19, which emphasizes the need to dynamically monitor lymphocyte count.