Identification of P450 Candidates Associated with the Biosynthesis of Physalin-Class Compounds in Physalis angulata.

The Physalis genus has long been used as traditional medicine in the treatment of various diseases. Physalins, the characteristic class of compounds in this genus, are major bioactive constituents. To date, the biogenesis of physalins remains largely unknown, except for the recently established knowledge that 24-methyldesmosterol is a precursor of physalin. To identify the genes encoding P450s that are putatively involved in converting 24-methyldesmosterol to physalins, a total of 306 P450-encoding unigenes were retrieved from our recently constructed P. angulata transcriptome. Extensive phylogenetic analysis proposed 21 P450s that might participate in physalin biosynthesis. To validate the candidates, we developed a virus-induced gene silencing (VIGS) system for P. angulata, and four P450 candidates were selected for the VIGS experiments. The reduction in the transcripts of the four P450 candidates by VIGS all led to decreased levels of physalin-class compounds in the P. angulata leaves. Thus, this study provides a number of P450 candidates that are likely associated with the biosynthesis of physalin-class compounds, forming a strong basis to reveal the unknown physalin biosynthetic pathway in the future.

Clinical course and predictors of 60-day mortality in 239 critically ill patients with COVID-19: a multicenter retrospective study from Wuhan, China.

BACKGROUND: The global numbers of confirmed cases and deceased critically ill patients with COVID-19 are increasing. However, the clinical course, and the 60-day mortality and its predictors in critically ill patients have not been fully elucidated. The aim of this study is to identify the clinical course, and 60-day mortality and its predictors in critically ill patients with COVID-19. METHODS: Critically ill adult patients admitted to intensive care units (ICUs) from 3 hospitals in Wuhan, China, were included. Data on demographic information, preexisting comorbidities, laboratory findings at ICU admission, treatments, clinical outcomes, and results of SARS-CoV-2 RNA tests and of serum SARS-CoV-2 IgM were collected including the duration between symptom onset and negative conversion of SARS-CoV-2 RNA. RESULTS: Of 1748 patients with COVID-19, 239 (13.7%) critically ill patients were included. Complications included acute respiratory distress syndrome (ARDS) in 164 (68.6%) patients, coagulopathy in 150 (62.7%) patients, acute cardiac injury in 103 (43.1%) patients, and acute kidney injury (AKI) in 119 (49.8%) patients, which occurred 15.5 days, 17 days, 18.5 days, and 19 days after the symptom onset, respectively. The median duration of the negative conversion of SARS-CoV-2 RNA was 30 (range 6-81) days in 49 critically ill survivors that were identified. A total of 147 (61.5%) patients deceased by 60 days after ICU admission. The median duration between ICU admission and decease was 12 (range 3-36). Cox proportional-hazards regression analysis revealed that age older than 65 years, thrombocytopenia at ICU admission, ARDS, and AKI independently predicted the 60-day mortality. CONCLUSIONS: Severe complications are common and the 60-day mortality of critically ill patients with COVID-19 is considerably high. The duration of the negative conversion of SARS-CoV-2 RNA and its association with the severity of critically ill patients with COVID-19 should be seriously considered and further studied.

A Robust Direction of Arrival Estimation Method for Uniform Circular Array.

Estimating the Direction of Arrival (DOA) is a basic and crucial problem in array signal processing. The existing DOA methods fail to obtain reliable and accurate results when noise and reverberation occur in real applications. In this paper, an accurate and robust estimation method for estimating the DOA of sources signal is proposed. Incorporating the Estimating Signal Parameters via Rotational Invariance Techniques (ESPRIT) algorithm with the RANdom SAmple Consensus (RANSAC) algorithm gives rise to the RAN-ESPRIT method, which removes outliers automatically in noise-corrupted environments. In this work, a uniform circular array (UCA) is converted into a virtual uniform linear array (ULA) to begin with. Then, the covariance matrix of the received signals of the virtual linear array is reconstructed, and the ESPRIT algorithm is deployed to estimate initial DOA of the source signal. Finally, the modified RANSAC method with automatically selected thresholds is used to fit the source signal to obtain accurate DOA. The proposed method can remove the unreliable DOA feature data and leads to more accuracy of DOA estimation of source signals in reverberation environments. Experimental results demonstrate that the proposed method is more robust and efficient compared to the traditional methods (i.e., ESPRIT, TLS-ESPRIT).

Completion Time Minimization for Multi-UAV Information Collection via Trajectory Planning.

Unmanned Aerial Vehicles (UAVs) are widely used as mobile information collectors for sensors to prolong the network time in Wireless Sensor Networks (WSNs) due to their flexible deployment, high mobility, and low cost. This paper focuses on the scenario where rotary-wing UAVs complete information collection mission cooperatively. For the first time, we study the problem of minimizing the mission completion time for a multi-UAV system in a monitoring scenario when considering the information collection quality. The mission completion time includes flying time and hovering time. By optimizing the trajectories of all UAVs, we minimize the mission completion time while ensuring that the information of each sensor is collected. This problem can be formulated as a mixed-integer non-convex one which has been proved to be NP-hard. To solve the formulated problem, we first propose a hovering point selection algorithm to select appropriate hovering points where the UAVs can sequentially collect the information from multiple sensors. We model this problem as a BS coverage problem with the information collection quality in consideration. Then, we use a min-max cycle cover algorithm to assign these hovering points and get the trajectory of each UAV. Finally, with the obtained UAVs trajectories, we further consider the UAVs can also collect information when flying and optimize the time allocations. The performance of our algorithm is verified by simulations, which show that the mission completion time is minimum compared with state-of-the-art algorithms.

Trajectory Planning for Data Collection of Energy-Constrained Heterogeneous UAVs.

Nowadays, Unmanned Aerial Vehicles (UAVs) have received growing popularity in the Internet-of-Things (IoT) which often deploys many sensors in a relatively wide region. Since the battery capacity is limited, sensors cannot transmit over a long distance. It is necessary for designing efficient sensor data collection mechanisms to prolong the lifetime of the IoT and enhance data collection efficiency. In this paper, we consider a UAV-enabled data collection scenario, where multiple heterogeneous UAVs with different energy constraints are employed to collect data from sensors. The value of data depends on the importance of the monitoring area of the sensor and the freshness of collected data. Our objective is to maximize the data collection utility by jointly optimizing the communication scheduling and trajectory of each UAV. The data collection utility is determined by the amount and value of the collected data. This problem is a variant of multiple knapsack problem, which is a classical NP-hard problem. First, we transform the initial problem into a submodular function maximization problem under energy constraints, and then we design a novel trajectory planning algorithm to maximize the data collection utility, while accounting for different values of data and different energy constraints of heterogeneous UAVs. Finally, under different network settings, the performance of the proposed trajectory planning algorithm is evaluated via extensive simulations. The results show that the proposed algorithm can obtain maximum data collection utility.

Comparative Transcriptome Analysis of Pueraria lobata Provides Candidate Genes Involved in Puerarin Biosynthesis and Its Regulation.

Pueraria lobata is a traditional Chinese herb in which an isoflavone C-glucoside, namely puerarin, has received the utmost interest due to its medicinal properties. To date, the biogenesis of puerarin, especially its C-glucosyl reaction in the pathway, remains poorly understood. Moreover, the transcription factors (TFs) that regulate puerarin biosynthesis in P. lobata have not been reported. Here, we performed phytochemical studies on the different developmental stages of the root, stem, and leaf tissues of two P. lobata cultivars, which suggested that both the roots and stems of P. lobata were the sites of puerarin biosynthesis. RNA-sequencing was conducted with the root and stem tissues of the two cultivars under different stages, and the clean reads were mapped to the recently published genome of P. lobata var. thomsonii, yielding the transcriptome dataset. A detailed analysis of the gene expression data, gene coexpression network, and phylogeny proposed several C-GTs that likely participate in puerarin biosynthesis. The first genome-wide analysis of the whole MYB superfamily in P. lobata presented here identified a total of 123 nonredundant PlMYB genes that were significantly expressed in the analyzed tissues. The phylogenetic analysis of PlMYBs with other plant MYB proteins revealed strong PlMYB candidates that may regulate the biosynthesis of isoflavones, such as puerarin.

Activity of Ligustrum vulgare L extracts against acute pancreatitis in murine models by regulation of p38 MAPK and NF-κB signaling pathways.

Pancreatitis is a fatal disease associated with significant mortality and morbidity. At present, no specific treatment is available for pancreatitis and the patients are mainly treated with supportive medication. The need for specific antipancreatitic chemotherapy is an urgent medical obligation. In the current study, protective effects of the methanolic extract of the Ligustrum vulgare berries were investigated in the rat model of acute pancreatitis. Acute pancreatitis (AP) was induced in the male Sprague-Dawley (SD) rats by cerulein injection. Fruit extract of L. vulgare L extract was prepared using the methanol. Treatment effects of L. vulgare were evaluated in AP rats. Serum levels of lipase, amylase, proinflamatory cytokines (TNF-α, IL-6, TL-1ß), lipid peroxidase (LPO), myeloperoxidase (MPO) were determined. Histological changes in the pancreas were assessed. L. vulgare treatment prevented the increase in serum amylase and lipase levels, reduced the disease progression in pancreas, and reduced the serum levels of tumour necrosis factor (TNF)-α, interleukin (IL)-6, and IL-1ß in AP rats. Moreover, L. vulgare significantly suppressed pancreatic edema, inhibited oxidative damage (MPO activity and SOD activity), and inhibited the expression of NF-κB/p65 and activation (phosphorylation) of the inhibitor of NF-κB (IκBα) and p38 MAPKs. Histological examinations showed that L. vulgare significantly reduced the inflammatory and fibrotic changes. The results indicated the potent pancreato-protective effects of L. vulgare in AP.

Pathological Mechanisms Linking Diabetes Mellitus and Alzheimer's Disease: the Receptor for Advanced Glycation End Products (RAGE).

Diabetes and Alzheimer's disease (AD) place a significant burden on health care systems in the world and its aging populations. These diseases have long been regarded as separate entities; however, advanced glycation end products (AGEs) and the receptors for AGEs (RAGE) may be a link between diabetes and AD. In our study, mice injected with AGEs through stereotaxic surgery showed significant AD-like features: behavior showed decreased memory; immunofluorescence showed increased phosphorylated tau and APP. These results suggest links between diabetes and AD. Patients with diabetes are at a higher risk of developing AD, and the possible underlying molecular components of this association are now beginning to emerge.

Extracorporeal Membrane Oxygenation for SARS-CoV-2 Acute Respiratory Distress Syndrome: A Retrospective Study From Hubei, China.

Background: The data on long-term outcomes of patients infected by SARS-CoV-2 and treated with extracorporeal membrane oxygenation (ECMO) in China are merely available. Methods: A retrospective study included 73 patients infected by SARS-CoV-2 and treated with ECMO in 21 intensive care units in Hubei, China. Data on demographic information, clinical features, laboratory tests, ECMO durations, complications, and living status were collected. Results: The 73 ECMO-treated patients had a median age of 62 (range 33-78) years and 42 (63.6%) were males. Before ECMO initiation, patients had severe respiratory failure on mechanical ventilation with a median PO2/FiO2 of 71.9 [interquartile range (IQR), 58.6-87.0] mmHg and a median PCO2 of 62 [IQR, 43-84] mmHg on arterial blood analyses. The median duration from symptom onset to invasive mechanical ventilation, and to ECMO initiation was19 [IQR, 15-25] days, and 23 [IQR, 19-31] days. Before and after ECMO initiation, the proportions of patients receiving prone position ventilation were 58.9 and 69.9%, respectively. The median duration of ECMO support was 18.5 [IQR 12-30] days. During the treatments with ECMO, major hemorrhages occurred in 31 (42.5%) patients, and oxygenators were replaced in 21 (28.8%) patients. Since ECMO initiation, the 30-day mortality and 60-day mortality were 63.0 and 80.8%, respectively. Conclusions: In Hubei, China, the ECMO-treated patients infected by SARS-CoV-2 were of a broad age range and with severe hypoxemia. The durations of ECMO support, accompanied with increased complications, were relatively long. The long-term mortality in these patients was considerably high.

Tracheostomy in 80 COVID-19 Patients: A Multicenter, Retrospective, Observational Study.

Background: The outbreak of coronavirus disease 2019 (COVID-19) has led to a large and increasing number of patients requiring prolonged mechanical ventilation and tracheostomy. The indication and optimal timing of tracheostomy in COVID-19 patients are still unclear, and the outcomes about tracheostomy have not been extensively reported. We aimed to describe the clinical characteristics and outcomes of patients with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia who underwent elective tracheostomies. Methods: The multi-center, retrospective, observational study investigated all the COVID-19 patients who underwent elective tracheostomies in intensive care units (ICUs) of 23 hospitals in Hubei province, China, from January 8, 2020 to March 25, 2020. Demographic information, clinical characteristics, treatment, details of the tracheostomy procedure, successful weaning after tracheostomy, and living status were collected and analyzed. Data were compared between early tracheostomy patients (tracheostomy performed within 14 days of intubation) and late tracheostomy patients (tracheostomy performed after 14 days). Results: A total of 80 patients were included. The median duration from endotracheal intubation to tracheostomy was 17.5 [IQR 11.3-27.0] days. Most tracheotomies were performed by ICU physician [62 (77.5%)], and using percutaneous techniques [63 (78.8%)] at the ICU bedside [76 (95.0%)]. The most common complication was tracheostoma bleeding [14 (17.5%)], and major bleeding occurred in 4 (5.0%) patients. At 60 days after intubation, 31 (38.8%) patients experienced successful weaning from ventilator, 17 (21.2%) patients discharged from ICU, and 43 (53.8%) patients had died. Higher 60 day mortality [22 (73.3%) vs. 21 (42.0%)] were identified in patients who underwent early tracheostomy. Conclusions: In patients with SARS-CoV-2 pneumonia, tracheostomies were feasible to conduct by ICU physician at bedside with few major complications. Compared with tracheostomies conducted after 14 days of intubation, tracheostomies within 14 days were associated with an increased mortality rate.

Clinical significance of plasma levels of brain natriuretic peptide and cardiac troponin T in patients with sepsis.

Sepsis is a systemic inflammatory response of the body to an antigen and can become a life-threatening condition by triggering a cascade of changes leading to multiple organ failure such as heart failure. The aim of the present study was to examine the changes of the plasma levels of N-terminal of the prohormone brain natriuretic peptide (NT-proBNP) and cardiac troponin T (cTnT) in patients with sepsis and to analyze their prognostic significance. A total of 38 hospitalized patients with sepsis were included in the present study. The patients were divided into the survival and death groups, based on their prognosis. The plasma levels of NT-proBNP and cTnT for the two groups were measured for all the patients on the 1st, 3rd and 7th day of admission. The plasma levels of NT-proBNP and cTnT between the two groups were compared and their association with prognosis were analyzed. The plasma levels of NT-proBNP and cTnT in the death group were significantly higher than those in the survival group (P<0.05). Additionally, a positive assocciation of the plasma levels of NT-proBNP and cTnT was identified (P<0.05). In conclusion, the plasma levels of NT-proBNP and cTnT may be used as routine clinical biomarkers to assess the prognosis of patients with sepsis.