Establishment of differential diagnostic model for COVID-19 and influenza A based on laboratory indicators and its clinical significance. TT -.

Objective:To explore the differences in laboratory indicators test results of coronavirus disease 2019 (COVID-19) and influenza A and to establish a differential diagnosis model for the two diseases, and to clarify the clinical significance of the model for distinguishing the two diseases. Methods :A total of 56 common COVID-19 patients and 54 influenza A patients were enrolled , and 24 common COVID-19 patients and 30 influenza A patients were used for model validation. The average values of the laboratory indicators of the patients 5 d after admission were calculated,and the elastic network model and the stepwise Logistic regression model were used to screen the indicators for identifying COVID-19 and influenza A. Elastic network models were used for the first round of selection,in which the optimal cutoff of lambda was chosen by performing 10-fold cross validations. With different random seeds,the elastic net models were fit for 200 times to select the high-frequency indexes ( frequency>90% ). A Logistic regression model with AIC as the selection criterions was used in the second round of screening uses;a nomogram was used to represent the final model;an independent data were used as an external validation set,and the area under the curve (AUC) of the validation set were calculate to evaluate the predictive the performance of the model. Results:After the first round of screening, 16 laboratory indicators were selected as the high-frequency indicators. After the second round of screening,albumin/ globulin (A/G),total bilirubin (TBIL) and erythrocyte volume (HCT) were identified as the final indicators. The model had good predictive performance , and the AUC of the verification set was 0. 844 (95% CI:0. 747-0. 941). Conclusion:A differential diagnosis model for COVID-19 and influenza A based on laboratory indicators is successfully established,and it will help clinical and timely diagnosis of both diseases.Copyright © 2022 Jilin University Press. All rights reserved.

The interactive CNY-CNH relationship: A wavelet analysis

The extant literature has explored the linkages between the onshore (CNY) and offshore (CNH) Renminbi (RMB) markets, as well as the potential factors affecting their dynamic inter-relationship. However, these efforts were made on a stand-alone basis in terms of dimensions and perspectives. This paper hence adopts the wavelet methodology to comprehensively examine the CNY-CNH interactions over 2010–2022. We find information spillovers across the two RMB markets to be bi-directional and asymmetric, with the exact pattern depending upon the particular sample period and the focal data frequency. Moreover, major macroeconomic events such as China's exchange rate reform, US-China trade tensions, COVID-19 pandemic, and more recent global uncertainty can exert distinct impacts on the flow pattern of information. We further show that the CNY-CNH exchange rate difference alone serves as a key indicator for the complex relationship between the two markets. As expected, the CNH market is more sensitive to exchange rate difference fluctuations, indicating a powerful market mechanism in the offshore RMB market, or equivalently, a substantial policy impact of the counter-cyclical adjustment by China's central bank in stabilizing the RMB rate. © 2023 Elsevier Ltd

Data-driven predictive control for smart HVAC system in IoT-integrated buildings with time-series forecasting and reinforcement learning

Optimising HVAC operations towards human wellness and energy efficiency is a major challenge for smart facilities management, especially amid COVID situations. Although IoT sensors and deep learning were applied to support HVAC operations, the loss of forecasting accuracy in recursive prediction largely hinders their applications. This study presents a data-driven predictive control method with time-series forecasting (TSF) and reinforcement learning (RL), to examine various sensor metadata for HVAC system optimisation. This involves the development and validation of 16 Long Short-Term Memory (LSTM) based architectures with bi-directional processing, convolution, and attention mechanisms. The TSF models are comprehensively evaluated under independent, short-term recursive, and long-term recursive prediction scenarios. The optimal TSF models are integrated with a Soft Actor-Critic RL agent to analyse sensor metadata and optimise HVAC operations, achieving 17.4% energy savings and 16.9% thermal comfort improvement in the surrogate environment. The results show that recursive prediction leads to a significant reduction in model accuracy, and the effect is more pronounced in the temperature-humidity prediction model. The attention mechanism significantly improves prediction performance in both recursive and independent prediction scenarios. This study contributes new data-driven methods for smart HVAC operations in IoT-enabled intelligent buildings towards a human-centric built environment. © 2023 The Authors

Efficacy and safety of traditional chinese medicine combined with western medicine for the treatment of COVID-19: A systematic review and meta-analysis

Coronavirus disease 2019 (COVID-19) has rapidly spread worldwide. Traditional Chinese Medicine (TCM) was considered important by Chinese health authorities in the fight against COVID-19. This review systematically analyzed and evaluated the safety and efficacy of TCM combined with Western Medicine (WM) for the treatment of COVID-19. We sought to provide summary evidence for clinicians when using TCM. We searched for studies in PubMed, Web of Science, Embase, Medline, the Cochrane Library, China National Knowledge Infrastructure, and Wanfang Data from database inception to June 1, 2021. Overall, 31 studies (14,579 participants) were involved in the final systematic review, including 15 randomized controlled trials and 16 observational studies. TCM combined with WM showed main outcomes of a higher clinical efficacy rate (odds ratio [OR] =2.48, 95% confidence interval [CI] =1.90-3.24, I 2 = 4%) and lower case fatality rate (OR = 0.31, 95% CI = 0.19-0.49, I 2 = 80%) compared with WM treatment alone. No significant overall adverse events were found between TCM plus WM group and WM group (OR = 1.43, 95% CI = 0.63-2.23, I 2 = 75%). Some larger randomized control trials would assist in defining the effect of TCM combined with WM on the treatment of COVID-19 complications such as cardiac injury. TCM combined with WM may be safe and effective for the treatment of COVID-19. © 2022 Heart and Mind ;Published by Wolters Kluwer - Medknow.

A microchamber-free and enzyme-free digital assay based on ultrabright fluorescent microspheres

Digital analysis is an effective single-molecule detection method and has attracted extensive attention in the field of bioassays. However, most digital assays require microchambers for signal compartmentalization. Herein, we developed a microchamber-free and enzyme-free digital assay by labeling paramagnetic beads directly with ultrabright fluorescent microspheres. In this assay, a DNA sandwich analysis was firstly performed on the bead to label a fluorescent microsphere. Then, the beads were loaded on the glass slide to form a monolayer film for signal readout. The whole analysis process does not require the participation of enzymes and the preparation of microchambers, which greatly simplifies the experimental steps and saves the costs. Furthermore, by introducing non-enzymatic hybridization chain reaction (HCR) and biotinylated DNA-conjugated gold nanoparticles (Au NPs-Bio), the capture efficiency and analytical sensitivity were improved. As a proof of concept, single-stranded DNA (ssDNA) of SARS-CoV-2 fragment was chosen as a model, and a detection limit of 1.5 fM was achieved. Spiked and recovery experiments on human serum and saliva samples validated the good performance of the proposed digital assay in real biological samples. The proposed assay provides a facile way of signal generation and readout for digital analysis. © 2023 Elsevier B.V.

Manufacturing security strategies for epidemic prevention supplies in response to the COVID-19crisis: a regional emergency manufacturing consortium design based on government regulation

The outbreak of COVID-19 poses a great threat to human life. In the early days of the COVID-19outbreak, lockdown, quarantine, movement restrictions and personal protection became the main preventive measures, which caused a surge in demand for epidemic prevention supplies. The severe shortage of epidemic prevention supplies resulted in panic, the lack of raw material supply upstream, the weakness of manufacturing capacity in midstream and the fluctuating demand downstream when the supply chain was severely disrupted. Therefore, it is imperative to quickly restore or build new manufacturing supply chains of epidemic prevention supplies. In view of this, this research proposes taking administrative regions as the basic unit, focusing on the manufacturing of epidemic prevention supplies in the supply chain, and using the mixed-integer optimization method to select partners with the the objectives of shortest manufacturing time, the highest reliability and the greatest core competitiveness, and establish for the first time a government-led system model of a regional emergency manufacturing consortium for epidemic prevention supplies, aiming to quickly restore the manufacturing supply chain when it is severely disrupted. Finally, the validity of the proposed model is demonstrated through case studies. The widespread adoption of this system model will not only solve the current crisis of short-supply of epidemic prevention supplies, but also make an important contribution to the scientific decision-making of the government in the event of similar crises in the future, which is a concrete manifestation of the overall national security. Author

Chest X-ray analysis empowered with deep learning: A systematic review

Chest radiographs are widely used in the medical domain and at present, chest X-radiation particularly plays an important role in the diagnosis of medical conditions such as pneumonia and COVID-19 disease. The recent developments of deep learning techniques led to a promising performance in medical image classification and prediction tasks. With the availability of chest X-ray datasets and emerging trends in data engineering techniques, there is a growth in recent related publications. Recently, there have been only a few survey papers that addressed chest X-ray classification using deep learning techniques. However, they lack the analysis of the trends of recent studies. This systematic review paper explores and provides a comprehensive analysis of the related studies that have used deep learning techniques to analyze chest X-ray images. We present the state-of-the-art deep learning based pneumonia and COVID-19 detection solutions, trends in recent studies, publicly available datasets, guidance to follow a deep learning process, challenges and potential future research directions in this domain. The discoveries and the conclusions of the reviewed work have been organized in a way that researchers and developers working in the same domain can use this work to support them in taking decisions on their research. (c) 2022 Elsevier B.V. All rights reserved.

AN ANALYSIS FOR THE WORK RESUMPTION UNDER THE COVID-19 EPIDEMIC BASED ON VIIRS-DNB NIGHTTIME LIGHTS IN CHINA

Timely and effective quantitative measurement of enterprises' offline resumption of work after public emergencies is conducive to the formulation and implementation of relevant policies. In this paper, we analyze the level of work resumption after the coronavirus disease 2019 (COVID-19)-influenced Chinese Spring Festival in 2020 with National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (DNB) daily data. The results demonstrate that COVID-19 has seriously affected the resumption of work after the Spring Festival holiday. Since February 10th, work has been resuming in localities. By late March, the work resumption indexes of most cities exceeded 50%, and Shanghai and Nanjing even had achieved complete resumption of work. Our method effectively estimates the resumption of work, which provides a scientific basis for local governments to formulate subsequent resumption policies. © 2021 IEEE

Clinical characteristics of COVID‑19 complicated with pleural and pericardial effusion in 10 patients

Objective: To retrospectively analyze the clinical characteristics of corona virus disease 2019 (COVID‑19) patients with pleural and pericardial effusion. Methods: We retrospectively reviewed and compared data of 60 COVID‑19 inpatients including 10 patients with pleural effusion (PLE) and pericardial effusion (PCE) and 50 cases without PLE/PCE, from January 20, 2020 to March 23, 2020 in Renmin Hospital of Wuhan University. The patients' medical history, clinical features, physical findings, laboratory test results, and chest tomographic imaging were recorded and analyzed. Statistical significance was determined using the chi‑square test, Fisher's exact test, and the Mann‑Whitney U‑test. Results: COVID‑19 patients with PLE and PCE had a higher temperature (P<0.001), a higher incidence of breath shortness (P=0.024) and faster respiratory frequency (P=0.004) than those without PLE and PCE. Laboratory findings showed that patients with PLE and PCE had higher levels of C‑reactive protein (CRP,P=0.039) and D‑dimer (P=0.038), and lower levels of lymphocytes (P=0.024), hemoglobin (P=0.003), CD4+T cell counts (P=0.016), and oxygen saturation (P=0.037). Meanwhile, patients with PLE and PCE had higher incidence of severe or critical illness and mortality rates as compared with those without PLE and PCE (all P<0.05). Conclusion: PLE and PCE were indicators for severe inflammation and poor clinical outcomes, and might be independent risk factors for critical type in COVID‑19 patients. It suggests that the treatment for the COVID‑19 patients with PLE and PCE should be more active and timely. © 2021, Editorial Board of Medical Journal of Wuhan University. All right reserved.

Reduced Intubation Rates Following Whole-Lung Low-Dose Radiation Therapy (LD-RT) in Patients With COVID-19-Related Pneumonia

Purpose/Objective(s): Low-dose radiotherapy (LD-RT) is a well-established treatment for multiple human inflammatory conditions. Whole-lung LD-RT may be effective in COVID-19-related pneumonia. Materials/Methods: Patients hospitalized with COVID-19-related pneumonia receiving supportive care, glucocorticosteroids, and/or remdesivir were administered LD-RT treatment of 0.5 or 1.5 Gy to the bilateral lungs on a prospective, combined phase I/II, multi-site, single-institution trial. Patients were followed for 28 days or until discharge and compared to controls blindly matched by age, comorbidity, duration of symptoms, and disease severity. Eligible patients were confirmed by SARS-CoV-2 positive PCR, unable to wean from oxygen at enrollment, and had radiographic consolidations. Patients were enrolled into 5 cohorts stratified by treatment variables and severity of illness: LD-RT alone vs. LD-RT with concurrent drug therapies, non-intubated vs. intubated status, and low (1.5 Gy) vs. lower (0.5 Gy) radiation dose. Qualitative aims were to establish safety and explore efficacy. Quantitative endpoints were continuous, categorical, and time-to-event, and included clinical recovery, intubation, radiographic changes, and biomarker responses. Intubation endpoints are reported for all cohorts using the log-rank test and Kaplan-Meier method. Results: Outcomes of 80 patients were available for analysis at study closure. In total, 40 of 70 planned patients (57% trial enrollment) received whole-lung LD-RT between April 24 and December 7, 2020 and were compared to 40 matched controls. Cohorts 1&2: Ten non-intubated patients received 1.5 Gy without concurrent COVID-directed drug therapies (10 of 10 planned, 100% cohort enrollment) and were compared to matched controls. Intubation rates were 40% in controls compared to 10% following LD-RT (P = 0.11). Cohort 3: One intubated patient received 1.5 Gy (1 of 20 planned, 5% cohort enrollment). Cohort 4: Twenty separate non-intubated patients received 1.5 Gy with concurrent dexamethasone/remdesivir (20 of 20 planned, 100% cohort enrollment) and were compared to matched controls. Intubation rates were 32% in controls compared to 14% following LD-RT (P = 0.09). Cohort 5: Nine patients received 0.5 Gy with concurrent drug therapies (9 of 20 planned, 45% cohort enrollment) and were compared to matched controls. Zero controls required intubation compared to 11% following LD-RT (P = 0.32). Among all non-intubated patients and matched controls combined (n = 78), mechanical ventilation was required in 28% of controls compared to 12% following LD-RT (reduced 57%, P = 0.05). The trial was prematurely closed due to observed reproducibility of efficacy. A randomized trial is now ongoing. Conclusion: In the first, prospective, phase I/II trial of radiotherapy for COVID-19-related pneumonia, a single treatment of whole-lung LD-RT reduced intubation rates by 57% compared to controls in patients receiving supportive care with or without drug therapies (P = 0.05).

Analysis of laboratory findings in recovered and non-recovered patients with COVID-19: A longitudinal observational study in Jilin Province of China

Background: We aimed to describe the laboratory characteristics of patients with laboratory confirmed coronavirus disease 2019 (COVID-19), and to compare the laboratory data between recovered and non-recovered patients. Methods: This is a multi-center study and 68 COVID-19 patients were recruited in Jilin Province, China, from January 21, 2020 to February 21, 2020. Laboratory tests were conducted at admission. Outcomes were followed up until February 21, 2020. Results: Of the 68 patients, 63 were diagnosed as mild and 5 as moderate or severe. After follow-up, there are 28 and 40 patients in recovered and non-recovered group, respectively. Lymphocytes, including immature leukocyte subpopulation, were significantly increased after day 10 in recovered patients. Platelets and thrombocytocrit were significantly increased, while mean platelet volume was reduced markedly from day 6 in recovered patients. High-sensitivity C-reactive protein was elevated at onset and continued to decline at day 7 in recovered patients. Cardiac troponin I is always higher from onset to recovery in recovered patients, yet it is sharply declined below the upper limit of reference interval after day 10. Cholinesterase and alanine aminotransferase were higher during the recovery process in recovered patients than in non-recovered patients. Both eosinophil and age were identified as independent predictors for recovery. Conclusions: Some markers had different change patterns between the recovered and non-recovered patients. Eosinophil may serve as an independent predictor for recovery in addition to age. The monitoring of the dynamic level of markers can give more effective clues for the judgment of the progress in COVID-19 patients. © 2021 AME Publishing Company. All rights reserved.

SepINav (Sepsis ICU Navigator): A data-driven software tool for sepsis monitoring and intervention using Bayesian Online Change Point Detection

The alarmingly aggravating incidents of sepsis and septic shock, and associated mortality, morbidity, and annual treatment costs among ICU admissions are an increasing concern. SepINav is a medical informatics endeavor that helps ICU practitioners and researchers to monitor and intervene on the existing sepsis patients more efficiently and interactively and conduct retrospective studies to seek rationales to different sepsis scenarios in the ICU. Moreover, Bayesian Online Changepoint Detection will help the practitioners understand the structural changes in patients’ vital sign regimes that may harbinger prior to septic shock. Besides, several additional features are added to this data-driven software tool to promise efficient monitoring and intervention and address confounding medical interventions in the ICU. © 2021 The Author(s)

Coronavirus disease 2019: a bibliometric analysis and review.

OBJECTIVE: On December 8, 2019, many cases of pneumonia with unknown etiology were first reported in Wuhan, China, subsequently identified as a novel coronavirus infection aroused worldwide concern. As the outbreak is ongoing, more and more researchers focused interest on the COVID-19. Therefore, we retrospectively analyzed the publications about COVID-19 to summarize the research hotspots and make a review, to provide reference for researchers in the world. MATERIALS AND METHODS: We conducted a search in PubMed using the keywords "COVID-19" from inception to March 1, 2020. Identified and analyzed the data included title, corresponding author, language, publication time, publication type, research focus. RESULTS: 183 publications published from 2020 January 14 to 2020 February 29 were included in the study. The first corresponding authors of the publications were from 20 different countries. Among them, 78 (42.6%) from the hospital, 64 (35%) from the university and 39 (21.3%) from the research institution. All the publications were published in 80 different journals. Journal of Medical Virology published most of them (n=25). 60 (32.8%) were original research, 29 (15.8%) were review, 20 (10.9%) were short communications. 68 (37.2%) epidemiology, 49 (26.8%) virology and 26 (14.2%) clinical features. CONCLUSIONS: According to our review, China has provided a large number of research data for various research fields, during the outbreak of COVID-19. Most of the findings play an important role in preventing and controlling the epidemic around the world. With research on the COVID-19 still booming, new vaccine and effective medicine for COVID-19 will be expected to come out in the near future with the joint efforts of researchers worldwide.