Impact of COVID-19 pandemic on emergency reperfusion characteristics in patients with ST-segment elevation myocardial infarction

Aim To explore the impact of coronavirus-2019 disease (COVID-19) pandemic on emergency reper-fusion characteristics in patients with ST-segment elevation myocardial infarction (STEMI) from non-epicenter. Methods This was a retrospective study involved STEMI patients undergoing primary percutaneous coronary intervention (PPCI), who admitted to chest pain center in our hospital during the pandemic ( from January 23 to March 29 in 2020) and the same period in 2019, excluding the patients with COVID-19. Clinical characteristics and reperfusion parameters were compared between the two groups. Results A total of 64 STEMI patients undergoing PPCI were enrolled in our study, including 13 patients during the pandemic and 51 patients during the same period in 2019. No differences occurred in admission signs, GRACE scores, arrival periods, transferred patterns,the period from door to troponin,and the period from first medical contact to dual antiplatelet between the two groups ( P>0. 05). As compared with 2019, STEMI patients undergoing PPCI had an apparent reduction. Meanwhile, significant delays appeared in reperfusion parameters, in-cluding the period from symptom onset to first medical contact (10 h vs. 3. 0 h, P<0. 001), the period from first medical contact to electrocardiogram (6 min vs. 3 min, P<0. 001), the period from door to troponin (15 min vs. 12 min, P = 0. 048), the period from door to device (76 min vs. 62 min, P = 0. 017), the period from telephone to catheter activated (15 min vs. 5 min, P<0. 001) and the period from catheter arrival to device (52 min vs. 41 min, P = 0. 033). Conclusion Even in non-epicenter, the COVID-19 outbreak still delayed mechanical reperfusion significantly. © 2022, Editorial Office of Chinese Journal of Arteriosclerosis. All rights reserved.

A regret theory-based GEDM method with heterogeneous probabilistic hesitant information and its application to emergency plans selection of COVID-19

Emergency events are happening with increasing frequency, inflicting serious damage on the economic development and human life. A reliable and effective emergency decision making method is great for reducing various potential losses. Hence, group emergency decision making (GEDM) has drawn great attention in past few years because of its advantages dealing with the emergencies. Due to the timeliness and complexity of GEDM, vagueness and regret aversion are common among decision makers (DMs), and decision information usually needs to be expressed by various mathematical forms. To this end, this paper proposes a novel GEDM method based on heterogeneous probabilistic hesitant information sets (PHISs) and regret theory (RT). Firstly, the PHISs with real numbers, interval numbers and linguistic terms are developed to depict the situation that decision group sways precariously between several projects and best retain the original assessment. In addition, the score functions, the divergence functions and some operations of the three types of PHISs are defined. Secondly, the normalization model of PHISs is presented to remove the influence of different dimensions on information aggregation. Thirdly, group satisfaction degree (GSD) based on the score functions and the divergence functions is combined with RT for completely portraying the regret perception of decision group. Then, we introduce Dempster-Shafer (DS) theory to determine the probabilities of future possible states for emergency events. Finally, an example of coronavirus disease 2019 (COVID-19) situation is given as an application for the proposed GEDM method, whose superiority, stability and validity are demonstrated by employing the comparative analysis and sensitivity analysis. © 2022 - IOS Press. All rights reserved.

Concern over global outbreak of acute severe hepatitis of unknown etiology in children

Acute severe hepatitis in children of unknown cause is a childhood liver disease of unknown etiology that emerged suddenly during the global pandemic of coronavirus disease 2019, Since it was reported in early April 2022, it has involved more than 20 countries around the world with about 450 cases, mainly in Europe and North America, and no similar ease has been reported in China.The disease occurs mostly in children under 5 years of age and is characterized mainly by liver disease manifestations such as jaundice and elevated transaminases, with rare respiratory symptoms;a few cases develop into liver failure and need liver transplantation, and most eases have a good prognosis.The disease is currently considered to be caused by infection, but the exact pathogen remains unclear.Adenovirus is detected in blood in many cases, so the possibility of infection caused by adenovirus should be considered;and factors such as laek of trained immunity and persistent inflammatory in children with COVID-19 should also be considered.The disease is no evidence of interpersonal transmission, infectivity is low, but the possibility of a pandemic outbreak in countries with a high prevalence of COVID-2019 needs to be guarded against;hand hygiene and respiratory protection may reduce the risk of morbidity.This article provides an overview of the epidemiological and clinical features of the disease and analyzes its nature, etiology and transmission risk in order to provide updated awareness of its clinical diagnosis and treatment. © 2022 by the authors.

Visual recognition technologies for complex scenarios analysis

Public security and social governance is essential to national development nowadays. It is challenged to prevent large-scale riots in communities and various city crimes for spatial and times caled social governance in corona virus disease 2019(Covid-19) like highly accurate human identity verification, highly efficient human behavior analysis and crowd flow track and trace. The core of the challenge is to use computer vision technologies to extract visual information in complex scenarios and to fully express, identify and understand the relationship between human behavior and scenes to improve the degree of social administration and governance. Complex scenarios oriented visual technologies recognition can improve the efficiency of social intelligence and accelerate the process of intelligent social governance. The main challenge of human recognition is composed of three aspects as mentioned below: 1) the diversity attack derived from mask occlusion attack, affecting the security of human identity recognition;2) the large span of time and space information has affected the accuracy of multiple ages oriented face recognition (especially tens of millions of scales retrieval);3) the complex and changeable scenarios are required for the high robustness of the system and adapt to diverse environments. Therefore, it is necessary to facilitate technologies of remote human identity verification related to the high degree of security, face recognition accuracy, human behavior analysis and scene semantic recognition. The motion analysis of individual behavior and group interaction trend are the key components of complex scenarios based human visual contexts. In detail, individual behavior analysis mainly includes video-based pedestrian re-recognition and video-based action recognition. The group interaction recognition is mainly based on video question-and-answer and dialogue. Video-based network can record the multi-source cameras derived individuals/groups image information. Multi-camera based human behavior research of group segmentation, group tracking, group behavior analysis and abnormal behavior detection. However, it is extremely complex that the individual behavior/group interaction is recorded by multiple cameras in real scenarios, and it is still a great challenge to improve the performance of multi-camera and multi-objective behavior recognition through integrated modeling of real scene structure, individual behavior and group interaction. The video-based network recognition of individual and group behavior mainly depends on visual information in related to scene, individual and group captured. Nonetheless, complex scenarios based individual behavior analysis and group interaction recognition require human knowledge and prior knowledge without visual information in common. Specifically, a crowd sourced data application has improved visual computing performance and visual question-and-answer and dialogue and visual language navigation. The inherited knowledge in crowd sourced data can develop a data-driven machine learning model for comprehensive knowledge and prior applications in individual behavior analysis and group interaction recognition, and establish a new method of data-driven and knowledge-guided visual computing. In addition, the facial expression behavior can be recognized as the human facial micro-motions like speech the voice of language. Speech emotion recognition can capture and understand human emotions and beneficial to support the learning mode of human-machine collaboration better. It is important for research to get deeper into the technology of human visual recognition. Current researches have been focused on human facial expression recognition, speech emotion recognition, expression synthesis, and speech emotion synthesis. We carried out about the contexts of complex scenarios based real-time human identification, individual behavior and group interaction understanding analysis, visual speech emotion recognition and synthesis, comprehensive utilization of knowledge and a priori mode of ma hine learning. The research and application scenarios for the visual ability is facilitated for complex scenarios. We summarize the current situations, and predict the frontier technologies and development trends. The human visual recognition technology will harness the visual ability to recognize relationship between humans, behavior and scenes. It is potential to improve the capability of standard data construction, model computing resources, and model robustness and interpretability further. © 2022, Editorial Office of Journal of Image and Graphics. All right reserved.

ANALYSIS OF BASELINE CT FINDINGS AND EVOLUTION OF SINGLE LESION IN THE EARLY STAGE OF COVID-19

Background: COVID-19 is currently the most concerned epidemic in the world. We conducted a CT follow-up study of a single lesion in the early stage to provide imaging basis for clinicians to accurately diagnose and evaluate the prognosis of COVID-19. Methods: Seventy patients with COVID-19 diagnosed in The Second Hospital of Wuhan Iron and Steel Company were retrospectively analyzed. With the first detection of early single lesion as the baseline lesion, the average interval between 4 follow-up and baseline CT was divided into four stages. The signs of the baseline lesions and their changes in the four stages were analyzed, and their evolution was summarized. Results: We found that most of the baseline lesions were ground-glass opacities (GGO) with subpleural distribution in the lower lobe of the lungs among the 70 patients, and CT signs were different at different stages. In the first stage, baseline lesions progressed in 54 cases (77%) and new lesions were found in 36 cases (51%) . No progressive lesions and new lesions were found in the third stage. In the first three stages, the proportion of fine reticulation decreased gradually, while the proportion of crazy paving pattern and thin GGO gradually increased. Fifty-four cases (77%) were complicated with consolidation, which accounted for the highest proportion in the second stage. After consolidation, the absorption of lesions became slower and the course of disease prolonged. Twenty-two cases (31%) progressed to multiple lesions of the single lobe, and 32 cases (46%) involved both lungs. Single lesion and multiple lesions of single lobe were more easily absorbed than bilateral lung lesions. We also found that patients over 50 years old tend to involve both lungs and the course of disease is relatively longer. Conclusion: The CT imaging features of COVID-19 at different stages can be used to evaluate the progression of the disease. © 2022 A. CARBONE Editore. All rights reserved.

Lag effect of temperature on the incidence of COVID-19 in Hunan province

Objective To explore the lag effect of daily average temperature on the incidence of coronavirus disease 2019 (COVID-19) in Hunan Province and to provide scientific evidences for effective prevention of COVID-19.  Methods  The meteorological factors, the air quality factors and the data conincidence of COVID-19 reported in Hunan Province during January 21, 2020 to March 2, 2020 were collected. Spearman correlation and distributed lag non-linear model analysis were performed.  Results  A total of 1 018 COVID-19 cases were reported in Hunan Province. The distribution lag non-linear model results showed that the influence of daily average temperature on the incidence of COVID-19 presented a nonlinear relationship. The cumulative relative incidence risk of COVID-19 decreased with the increase of daily average temperature, and the lowest temperature risk of the patients was 0 ℃. Both cold temperature and hot temperature increased incidence risk of COVID-19. It was indicated that the hot effects were immediate, however, the cold effects with obvious lag effect persisted up to 12 days. The highest relative risk of COVID-19 incidence was associated with lag 8-day daily average temperature of -5 ℃(RR=2.20, 95% CI=1.16-4.19). The influence of high temperature(10 ℃) was more significant than that of low temperature(6 ℃).  Conclusion  The daily average temperature, especially cold or hot temperature, was an important influencing factor of the incidence of COVID-19 in Hunan Province, which had lag influence on the incidence of COVID-19. We suggested that some related preventive measures should be adopted to protect vulnerable population and severe patients to reduce the incidence risk. © 2021, Publication Centre of Anhui Medical University. All rights reserved.

A machine learning approach to precision medicine for COVID-19 therapeutics

Introduction: COVID-19 continues to spread across the globe, and effective treatment of COVID-19 remains a crucial area of research. While corticosteroids and remdesivir are currently recommended for the treatment of COVID-19 in the United States, particularly in patients receiving supplemental oxygen, identifying patients likely to respond positively to these treatments is necessary to effectively allocate limited resources and decrease risk of adverse events. This study evaluates the potential of machine learning algorithms to identify hospitalized COVID-19 patients most likely to experience a survival benefit from treatment with either corticosteroids or remdesivir. Methods: Two machine learning models were trained separately to identify patients likely to derive a survival benefit from corticosteroid or remdesivir treatment. Models were trained and using XGBoost in Python, using data collected from 10 geographically diverse US hospitals treating COVID-19 patients between January 1 and October 18, 2020. Models were tested on a hold-out test set. Fine-Gray models for the subdistribution hazard were built to assess survival of treated as compared to untreated patients on the whole test set, on those patients being treated with supplemental oxygen, and on patients recommended for treatment by the machine learning algorithm. Inverse probability of treatment weights were used to control for confounding. Results: In the full COVID-19 population, neither corticosteroids nor remdesivir were significantly associated with increased survival;the same was true for patients being treated with supplemental oxygen (Table 1). In contrast, corticosteroids and remdesivir were both significantly associated with increased survival time in the algorithm identified sub-populations, with hazard ratios of 0.56 (p = 0.04) and 0.40 (p = 0.04), respectively. Conclusions: Machine learning methods are able to identify hospitalized COVID-19 patients for whom corticosteroids or remdesivir are associated with increased survival. These groups benefited more than patients receiving supplemental oxygen, a group for whom these treatments are currently recommended per NIH and IDSA guidelines. Machine learning methods may improve resource allocation and patient outcomes by identifying more appropriate patients for treatment.

Characteristic analysis and comparison of glycolipid metabolism in patients with coronavirus disease 2019 in common condition and severe cases

Objective•To analyze and compare the characteristics of glycolipid metabolism between common and severe patients with coronavirus disease 2019 (COVID-19). Methods•Thirty-six patients with COVID-19 were hospitalized in the general ward of Wuhan Leishenshan Hospital and fifty severe patients with COVID-19 in intensive care unit (ICU) from February to March, 2020. All the patients were divided into two groups: the common patient group and the severe patient group. Their electronic medical records were extracted and analyzed. The demographic data as well as clinical data, laboratory results, comorbidities and clinical outcomes in the two groups were collected and compared by independent sample t test, non-parametric test as well as χ2 test. From the metabolic point of view, the characteristics of glucose and lipid metabolism in COVID-19 common and severe patients and the possible related factors for patients staying in ICU were analyzed. Results•There was no significant difference between the two groups in terms of gender, number of patients with diabetes and coronary heart disease (CAD). The average age of severe patients was significantly older than that of the common patients (P<0.05). The proportion of the severe patients with hypertension (52.0%) was significantly higher than that of the common patients (22.2%) (P<0.05). The lymphocyte count of the severe patients was significantly lower than that of the common patients (P<0.05). There was no significant difference in glutamic-pyruvic transaminase (GPT), glutamic-oxaloacetic transaminase (GOT), serum creatinine (Scr) and blood uric acid (BUA) between the two groups. Blood serum albumin (ALB), adjusted calcium concentration (Cac), total cholesterol (TC), triacylglycerols (TAG), high density lipoprotein (HDL) and the low density lipoprotein (LDL) in the severe patients were significantly lower than those in the common patients (all P<0.05). Fasting blood glucose (FBG) in the severe patients was significantly higher than that in the common patients (P=0.001). Multivariate Logistic regression showed that the increase of FBG and the decrease of TC, HDL, LDL, ALB were related to COVID-19 patients staying in ICU. Conclusion•There are deteriorative disorders in terms of glucose and lipid metabolism among the severe patients with COVID-19. The FBG, TC, HDL, LDL and ALB may related to the admission of ICU.

Association of Padua prediction score with in-hospital prognosis in COVID-19 patients.

BACKGROUND: Nearly 20% novel coronavirus disease 2019 (COVID-19) patients have abnormal coagulation function. Padua prediction score (PPS) is a validated tools for venous thromboembolism (VTE) risk assessment. However, its clinical value in COVID-19 patients' evaluation was unclear. METHODS: We prospectively evaluated the VTE risk of COVID-19 patients using PPS. Demographic and clinical data were collected. Association of PPS with 28-day mortality was analyzed by multivariate logistic regression and Kaplan-Meier analysis. RESULTS: Two hundred and seventy-four continuous patients were enrolled, with total mortality of 17.2%. Patients in high PPS group, with significantly abnormal coagulation, have a higher levels of interleukin 6 (25.27 vs. 2.55 pg/ml, P < 0.001), prophylactic anticoagulation rate (60.7% vs. 6.5%, P < 0.001) and mortality (40.5% vs. 5.9%, P < 0.001) when compared with that in low PPS group. Critical patients showed higher PPS (6 vs. 2 score, P < 0.001) than that in severe patients. Multivariate logistic regression revealed the independent risk factors of in-hospital mortality included high PPS [odds ratio (OR): 7.35, 95% confidence interval (CI): 3.08-16.01], increased interleukin-6 (OR: 11.79, 95% CI: 5.45-26.20) and elevated d-dimer (OR: 4.65, 95% CI: 1.15-12.15). Kaplan-Meier analysis indicated patients with higher PPS had a significant survival disadvantage. Prophylactic anticoagulation in higher PPS patients shows a mild advantage of mortality but without statistical significance (37.1% vs. 45.7%, P = 0.42). CONCLUSION: Higher PPS associated with in-hospital poor prognosis in COVID-19 patients. Prophylactic anticoagulation showed a mild advantage of mortality in COVID-19 patients with higher PPS, but it remain to need further investigation.

[Novel coronavirus pneumonia (COVID-19) CT distribution and sign features].

Objective: To investigate the imaging findings of 2019 novel coronavirus pneumonia (COVID-19). Methods: From January 20 to February 5, 2020, a total of 130 patients diagnosed with COVID-19 from seven hospitals in China were collected. The imaging data were reviewed and analyzed in detail. Results: (1) Distribution: the lesion detected in the lung unilaterally in 14 cases (10.7%) and bilaterally in 116 cases (89.3%). According to the distribution in the lobes of the lung, all cases could be classified into subpleural distribution (102 cases, 78.4%), centrilobular distribution (99 cases, 76.1%) and diffused distribution (8 cases, 6.1%). (2) Number of lesions: single lesion 9 cases (6.9%); multiple lesions 113 cases (86.9%), diffuse lesions 8 cases (6.1%). (3) Imaging density: 70 cases (53.8%) of ground-glass opacity (GGO), 60 cases (46.2%) of GGO+consolidation. (4) Accompanying signs: 100 cases (76.9%) with vascular thickening, 98 cases (75.3%) with "pleural parallel sign" ; " intralobular septal thickening" in 100 cases (76.9%); "halo sign" in 13 cases (10%); "reversed-halo sign" in 6 cases (4.6%); pleural effusion in 3 cases (2.3%), and pneumatocele in 2 cases (1.5%); no case with pulmonary cavity. Among 35 patients that underwent follow-up CT, 21 patients (60%) improved while 14 (40%) exacerbated. Conclusions: COVID-19 imaging characteristic mainly has subpleural, centrilobular and diffused distribution. The first two distributions can overlap or progress to diffused distribution. In the later period, it was mainly manifested as organizing pneumonia and fibrosis. The most valuable characteristic is the pleural parallel sign.