Severity prediction of pulmonary diseases using chest CT scans via cost-sensitive label multi-kernel distribution learning.

BACKGROUND AND OBJECTIVES: The progression of pulmonary diseases is a complex progress. Timely predicting whether the patients will progress to the severe stage or not in its early stage is critical to take appropriate hospital treatment. However, this task suffers from the "insufficient and incomplete" data issue since it is clinically impossible to have adequate training samples for one patient at each day. Besides, the training samples are extremely imbalanced since the patients who will progress to the severe stage is far less than those who will not progress to the non-severe stage. METHOD: We consider the severity prediction of pulmonary diseases as a time estimation problem based on CT scans. To handle the issue of "insufficient and incomplete" training samples, we introduced label distribution learning (LDL). Specifically, we generate a label distribution for each patient, making a CT image contribute to not only the learning of its chronological day, but also the learning of its neighboring days. In addition, a cost-sensitive mechanism is introduced to explore the imbalance data issue. To identify the importance of pulmonary segments in pulmonary disease severity prediction, multi-kernel learning in composite kernel space is further incorporated and particle swarm optimization (PSO) is used to find the optimal kernel weights. RESULTS: We compare the performance of the proposed CS-LD-MKSVR algorithm with several classical machine learning algorithms and deep learning (DL) algorithms. The proposed method has obtained the best classification results on the in-house data, fully indicating its effectiveness in pulmonary disease severity prediction. CONTRIBUTIONS: The severity prediction of pulmonary diseases is considered as a time estimation problem, and label distribution is introduced to describe the conversion time from non-severe stage to severe stage. The cost-sensitive mechanism is also introduced to handle the data imbalance issue to further improve the classification performance.

Relationships among Healthcare Providers' Job Demands, Leisure Involvement, Emotional Exhaustion, and Leave Intention under the COVID-19 Pandemic.

The COVID-19 pandemic has caused many medical issues. It has tested the impact of healthcare providers' job demands, emotional exhaustion, and other pressures related to the impact on organizational leave intention. Accordingly, the purpose of this study was to verify the relationship between healthcare providers' job demands, leisure involvement, emotional exhaustion, and leave intention under the COVID-19 pandemic. The questionnaire survey was used to address the issue of the present study. Convenience sampling was utilized to recruit 440 healthcare providers with a validity rate of 95%. Collected data were analyzed by structural equation modelling. Results indicated that healthcare providers' job demands do not significantly influence leisure involvement. Job demands significantly influence emotional exhaustion. Job demands significantly influence leave intention. Emotional exhaustion significantly influences leave intention. Emotional exhaustion has a significant mediating effect between job demands and leave intention. Finally, relevant practical suggestions are provided based on the study results.

Are Older People Really More Susceptible to SARS-CoV-2?

Since the outbreak, COVID-19 has spread rapidly across the globe due to its high infectivity and lethality. Age appears to be one of the key factors influencing the status and progression of SARS-CoV-2 infection, as multiple reports indicated that the majority of COVID-19 infections and severe cases are elderly. Most people simply assume that the elderly are more susceptible to SARS-CoV-2 than the young, but the mechanism behind it is still open to question. The older and younger people are at similar risk of infection because their infection process is the same and they must be exposed to the virus first. However, whether they will get sick after exposure to the virus and how their disease progresses depend on their immune mechanisms. In older populations, inflammation and immune aging reduce their ability to resist SARS-CoV-2 infection. Meanwhile, under the influence of comorbidities, ACE2 receptor and various cytokines undergo corresponding changes, thus accelerating the entry, replication, and transmission of SARS-CoV-2 in the body, promoting disease progression, and leading to severe illness and even death. In addition, the relatively fragile mental state of the elderly can also affect their timely recovery from COVID-19. Therefore, once older people are infected with SARS-CoV-2, they are more prone to severe illness and death with a poor prognosis, and they should strengthen protection to avoid exposure to the virus.

Methodology of network pharmacology for research on Chinese herbal medicine against COVID-19: A review.

Traditional Chinese medicine, as a complementary and alternative medicine, has been practiced for thousands of years in China and possesses remarkable clinical efficacy. Thus, systematic analysis and examination of the mechanistic links between Chinese herbal medicine (CHM) and the complex human body can benefit contemporary understandings by carrying out qualitative and quantitative analysis. With increasing attention, the approach of network pharmacology has begun to unveil the mystery of CHM by constructing the heterogeneous network relationship of "herb-compound-target-pathway," which corresponds to the holistic mechanisms of CHM. By integrating computational techniques into network pharmacology, the efficiency and accuracy of active compound screening and target fishing have been improved at an unprecedented pace. This review dissects the core innovations to the network pharmacology approach that were developed in the years since 2015 and highlights how this tool has been applied to understanding the coronavirus disease 2019 and refining the clinical use of CHM to combat it.

Efficacy and Safety of Lianhua Qingwen for Patients with COVID-19: A Systematic Review and Meta-Analysis.

BACKGROUND: Corona virus disease 2019 (COVID-19) has spread around the world since its outbreak, and there is no ascertained effective drug up to now. Lianhua Qingwen (LHQW) has been widely used in China and overseas Chinese, which had some advantages in the treatment of COVID-19. OBJECTIVE: To evaluate the efficacy and safety of LHQW for COVID-19 by conducting a systematic review with meta-analysis. METHODS: A comprehensive literature search was conducted in 12 electronic databases from their establishment to October 30, 2021. Note Express 3.2.0 was used for screening of trials, and the data was independently extracted in duplicate by 2 researchers. The risk of bias of randomized controlled trials (RCTs) and retrospective studies were assessed by using the Cochrane collaboration tool and Newcastle Ottawa Scale, respectively, followed by data analysis using RevMan 5.3. The RCTs or retrospective studies to treat COVID-19 using LHQW were included. The intervention measures in the experimental group were LHQW alone or combined with chemical drugs (LCWC), and that in the control group were chemical drugs (CDs). Outcome measures included computed tomography (CT) recovery rate, disappearance rates of primary (fever, cough, fatigue), respiratory, gastrointestinal and other symptoms, exacerbation rate and adverse reaction. Subgroup analysis was conducted according to whether LHQW was combined with CDs and the different treatment methods in the control group. RESULTS: Nine trials with 1,152 participants with COVID-19 were included. The CT recovery rates of LHQW and LCWC were 1.36 and 1.32 times of CDs, respectively (P<0.05). Compared with CDs, LCWC remarkably increased the disappearance rates of fever, cough, fatigue, expectoration, shortness of breath, and muscle soreness (P<0.05). LHQW also obviously decreased the exacerbation rate, which was 0.45 times of CDs alone (P<0.05). There was no obvious difference between LCWC and CDs in adverse reaction (P>0.05). CONCLUSIONS: LHQW was more suitable for treating COVID-19 patients with obvious expectoration, shortness of breath and muscle soreness. LHQW had advantages in treating COVID-19 with no obvious exacerbation. (PROSPERO No. CRD42021235937).

Clinical Characteristics, Transmissibility, Pathogenicity, Susceptible Populations, and Re-infectivity of Prominent COVID-19 Variants.

In addition to the rapid, global spread of SARS-CoV-2, new and comparatively more contagious variants are of considerable concern. These emerging mutations have become a threat to the global public health, creating COVID-19 surges in different countries. However, information on these emerging variants is limited and scattered. In this review, we discuss new variants that have emerged worldwide and identify several variants of concern, such as B.1.1.7, B.1.351, P.1, B.1.617.2 and B.1.1.529, and their basic characteristics. Other significant variants such as C.37, B.1.621, B.1.525, B.1.526, AZ.5, C.1.2, and B.1.617.1 are also discussed. This review highlights the clinical characteristics of these variants, including transmissibility, pathogenicity, susceptible population, and re-infectivity. It provides the latest information on the recent variants of SARS-CoV-2. The summary of this information will help researchers formulate reasonable strategies to curb the ongoing COVID-19 pandemic.

Efficacy and Safety of "Three Chinese Patent Medicines and Three TCM Prescriptions" for COVID-19: A Systematic Review and Network Meta-Analysis.

OBJECTIVE: To systematically evaluate the efficacy, safety, and precision of TMTP for COVID-19. METHODS: Randomized controlled trials and retrospective studies were searched in 11 electronic databases. This network meta-analysis included trials using TMTP to treat patients with COVID-19. The traditional pairwise meta-analysis was done by using Stata 15, and Bayesian network meta-analysis was done with WinBUGS. RESULTS: 18 trials were included with 2036 participants and 7 drugs. The results showed that LHQW had the most significant effects on improving expectoration, shortness of breath, sore throat, nausea, emesis, inappetence, muscle soreness, and headache, and it could produce the least adverse reactions. XBJ was the best drug for fever, fatigue, and diarrhea, which showed great advantages in lowering WBC levels. XFBD was the most effective drug for cough and chest distress, which had the least exacerbation rate. JHQG was the most effective for rhinobyon and rhinorrhea, while QFPD was the best drug in decreasing CRP levels. CONCLUSION: This study was the first most large-scale and comprehensive research of TMTP for COVID-19. The results showed that LHQW had good efficacy without obvious adverse reactions. Therefore, we believe that it should be firstly recommended for COVID-19 treatment. In addition, XBJ is recommended for patients with a severe fever, fatigue, and diarrhea, and JHQG is recommended for patients with obvious rhinobyon and rhinorrhea; then, XFBD is recommended for patients with cough and chest tightness as the main manifestation. Our findings will help experts develop new COVID-19 treatment guidelines to better guide clinical medication for protecting the health of COVID-19 patients.

Developing a Screening Procedure During the COVID-19 Pandemic: Process and Challenges Faced by a Low-Incidence Area.

Purpose: To summarize the imaging results of COVID-19 pneumonia and develop a computerized tomography (CT) screening procedure for patients at our institution with malignant tumors. Methods: Following epidemiological investigation, 1,429 patients preparing to undergo anti-tumor-treatment underwent CT scans between February 17 and April 16, 2020. When CT findings showed suspected COVID-19 pneumonia after the supervisor radiologist and the thoracic experience radiologist had double-read the initial CT images, radiologists would report the result to our hospital infection control staff. Further necessary examinations, including the RT-PCR test, in the assigned hospital was strongly recommended for patients with positive CT results. The CT examination room would perform sterilization for 30 min to 1 h. If the negative results of any suspected COVID-19 pneumonia CT findings were identified, the radiologists would upload the results to our Hospital Information Systems and inform clinicians within 2 h. Results: Fifty (0.35%, 50/1,429) suspected pneumonia cases, including 29 males and 21 females (median age: 59.5 years old; age range 27-79 years), were identified. A total of 34.0% (17/50) of the patients had a history of lung cancer and 54.0 (27/50) underwent chemotherapy or targeted therapy. Forty-six patients (92.0%) had prior CT scans, and 35 patients (76.1%) with suspected pneumonia were newly seen (median interval time: 62 days). Sub-pleura small patchy or strip-like lesions most likely due to fibrosis or hypostatic pneumonia and cluster of nodular lesions were the two main signs of suspected cases on CT images (34, 68.0%). Twenty-seven patients (54.0%) had, at least once, follow-up CT scan (median interval time: 18.0 days). Only one patient had an increase in size (interval time: 8 days), the immediately RT-PCR test result was negative. Conclusion: CT may be useful as a screening tool for COVID-19 based on imaging features. But the differential diagnosis between COVID-19 and other pulmonary infection and/or non-infectious disease is very difficult due to its overlapping imaging features.The confirmed diagnosis of the COVID-19 infection should be based on the etiologic eventually. The cancer patients at a low-incidence area would continue treatment by screening carefully before admission.

Brucellosis prevalence in yaks in China in 1980-2019: A systematic review and meta-analysis.

In the Qinghai-Tibet Plateau of China, the yak is an animal of particular economic interest, which provides protein and income for herders in daily life. Brucellosis is a bacterial disease that can infect humans and animals, including yaks. It can damage the yak reproductive system, causing miscarriage and orchitis. At the same time, brucellosis threatens the health of herders. We performed this meta-analysis using R software to explore the combined prevalence and risk factors of brucellosis in yak in China. Variability was assessed by the I2 statistic and Cochran Q statistic. We identified 52 publications of related research from four databases (Wanfang Data, VIP Chinese Journal Database, China National Knowledge Infrastructure, and of PubMed). The pooled prevalence of yak brucellosis was 8.39 %. Prevalence was highest in Southwestern China (11.1 %). The point estimate of brucellosis in yak from 2012 to 2016 was the highest (11.47 %). The point estimate of age ≤ 12 months (1.44 %) was lower than that of age > 12 months (15.6 %). This study shows that yak brucellosis is serious, and its incidence is higher than before 2012. We recommend carrying out large-scale yak brucellosis investigations in Western China and conducting comprehensive testing planning. The detection of brucellosis in adult animals should be strengthened to reduce the economic loss caused by brucellosis to herders and to improve public health.

Contribution of Total Screen/Online-Course Time to Asthenopia in Children During COVID-19 Pandemic via Influencing Psychological Stress.

Objectives: During the coronavirus disease 2019 (COVID-19) self-quarantine period, the transition to online-course has profoundly changed the learning modes of millions of school-aged children and put them at an increased risk of asthenopia. Therefore, we aimed to determine associations of the total screen/online-course time with asthenopia prevalence among that children during the COVID-19 pandemic, and whether the associations were mediated by psychological stress. Methods: Asthenopia was defined according to a validated computer vision syndrome questionnaire (CVS-Q). We used CVS-Q to collect the frequency and intensity of 16 asthenopia-related eye symptoms of 25,781 children. Demographic features, eye care habits, visual disorders, lifestyle, psychological and environmental factors, were also collected. Results: The overall asthenopia prevalence was 12.1%, varying from 5.4 to 18.2% across grade/gender-classified subgroups. A 100-h increment of total screen/online-course time were associated with an increased risk of asthenopia by 9% [odds ratio (OR) = 1.09] and 11% (OR = 1.11), respectively. Mediation analysis showed that the proportions of total effects mediated by psychological stress were 23.5 and 38.1%, respectively. Age, female gender, having myopia or astigmatism, bad habits when watching screens were also risk factors. Conversely, keeping 34-65 cm between eyes and screen, increased rest time between classes, and increased eye exercise were all associated with a decreased risk. Conclusion: Our study indicated that the influence of long total screen or online-course time on psychological stress increases asthenopia risk. The findings of this study have provided a new avenue for intervening screen-related asthenopia in addition to incorporating a reasonable schedule of online courses into educational policy.

Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19.

The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world. Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19, whereas the recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists. We hereby review the rapid responses in the community of medical imaging (empowered by AI) toward COVID-19. For example, AI-empowered image acquisition can significantly help automate the scanning procedure and also reshape the workflow with minimal contact to patients, providing the best protection to the imaging technicians. Also, AI can improve work efficiency by accurate delineation of infections in X-ray and CT images, facilitating subsequent quantification. Moreover, the computer-aided platforms help radiologists make clinical decisions, i.e., for disease diagnosis, tracking, and prognosis. In this review paper, we thus cover the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up. We particularly focus on the integration of AI with X-ray and CT, both of which are widely used in the frontline hospitals, in order to depict the latest progress of medical imaging and radiology fighting against COVID-19.

Drug repurposing for COVID-19: could vitamin C combined with glycyrrhizic acid be at play by the findings of Li et al.'s database-based network pharmacology analysis?

The outbreak and pandemic of SARS-CoV-2 in 2019 has caused a severe public health burden and will challenge global health for the future. The discovery and mechanistic investigation of drugs against Coronavirus disease 2019 (COVID-19) is in deadly demand. The paper published by Li and colleagues proposed the hypothesis that vitamin C combined with glycyrrhizic acid in treating COVID-19 and its mechanistic investigation was performed by a database-based network pharmacology. In this letter, we present critical comments on the limitations and insufficiencies involved, from both the perspective of network pharmacology and current evidence on COVID-19.

Coronary artery calcification and risk of mortality and adverse outcomes in patients with COVID-19: a Chinese multicenter retrospective cohort study.

Background: Coronary artery calcification (CAC) is an independent risk factor of major adverse cardiovascular events; however, the impact of CAC on in-hospital death and adverse clinical outcomes in patients with coronavirus disease 2019 (COVID-19) remains unclear. Objective: To explore the association between CAC and in-hospital mortality and adverse events in patients with COVID-19. Methods: This multicenter retrospective cohort study enrolled 2067 laboratory-confirmed COVID-19 patients with definitive clinical outcomes (death or discharge) admitted from 22 tertiary hospitals in China between January 3, 2020 and April 2, 2020. Demographic, clinical, laboratory results, chest CT findings, and CAC on admission were collected. The primary outcome was in-hospital death and the secondary outcome was composed of in-hospital death, admission to intensive care unit (ICU), and requiring mechanical ventilation. Multivariable Cox regression analysis and Kaplan-Meier plots were used to explore the association between CAC and in-hospital death and adverse clinical outcomes. Results: The mean age was 50 years (SD,16) and 1097 (53.1%) were male. A total of 177 patients showed high CAC level, and compared with patients with low CAC, these patients were older (mean age: 49 vs. 69 years, P < 0.001) and more likely to be male (52.0% vs. 65.0%, P = 0.001). Comorbidities, including cardiovascular disease (CVD) ([33.3%, 59/177] vs. [4.7%, 89/1890], P < 0.001), presented more often among patients with high CAC, compared with patients with low CAC. As for laboratory results, patients with high CAC had higher rates of increased D-dimer, LDH, as well as CK-MB (all P < 0.05). The mean CT severity score in high CAC group was also higher than low CAC group (12.6 vs. 11.1, P = 0.005). In multivariable Cox regression model, patients with high CAC were at a higher risk of in-hospital death (hazard ratio [HR], 1.731; 95% CI 1.010-2.971, P = 0.046) and adverse clinical outcomes (HR, 1.611; 95% CL 1.087-2.387, P = 0.018). Conclusion: High CAC is a risk factor associated with in-hospital death and adverse clinical outcomes in patients with confirmed COVID-19, which highlights the importance of calcium load testing for hospitalized COVID-19 patients and calls for attention to patients with high CAC. Supplementary Information: The online version contains supplementary material available at 10.1007/s42058-021-00072-4.

Tensor Gradient L0-Norm Minimization-Based Low-Dose CT and Its Application to COVID-19.

Methods to recover high-quality computed tomography (CT) images in low-dose cases will be of great benefit. To reach this goal, sparse-data subsampling is one of the common strategies to reduce radiation dose, which is attracting interest among the researchers in the CT community. Since analytic image reconstruction algorithms may lead to severe image artifacts, the iterative algorithms have been developed for reconstructing images from sparsely sampled projection data. In this study, we first develop a tensor gradient L0-norm minimization (TGLM) for low-dose CT imaging. Then, the TGLM model is optimized by using the split-Bregman method. The Coronavirus Disease 2019 (COVID-19) has been sweeping the globe, and CT imaging has been deployed for detection and assessing the severity of the disease. Finally, we first apply our proposed TGLM method for COVID-19 to achieve low-dose scan by incorporating the 3-D spatial information. Two COVID-19 patients (64 years old female and 56 years old man) were scanned by the [Formula: see text]CT 528 system, and the acquired projections were retrieved to validate and evaluate the performance of the TGLM.

COVID-AL: The diagnosis of COVID-19 with deep active learning.

The efficient diagnosis of COVID-19 plays a key role in preventing the spread of this disease. The computer-aided diagnosis with deep learning methods can perform automatic detection of COVID-19 using CT scans. However, large scale annotation of CT scans is impossible because of limited time and heavy burden on the healthcare system. To meet the challenge, we propose a weakly-supervised deep active learning framework called COVID-AL to diagnose COVID-19 with CT scans and patient-level labels. The COVID-AL consists of the lung region segmentation with a 2D U-Net and the diagnosis of COVID-19 with a novel hybrid active learning strategy, which simultaneously considers sample diversity and predicted loss. With a tailor-designed 3D residual network, the proposed COVID-AL can diagnose COVID-19 efficiently and it is validated on a large CT scan dataset collected from the CC-CCII. The experimental results demonstrate that the proposed COVID-AL outperforms the state-of-the-art active learning approaches in the diagnosis of COVID-19. With only 30% of the labeled data, the COVID-AL achieves over 95% accuracy of the deep learning method using the whole dataset. The qualitative and quantitative analysis proves the effectiveness and efficiency of the proposed COVID-AL framework.

Challenges of drug development during the COVID-19 pandemic: Key considerations for clinical trial designs.

There is an urgent need for targeted and effective COVID-19 treatments. Several medications, including hydroxychloroquine, remdesivir, lopinavir-ritonavir, favipiravir, tocilizumab and others have been identified as potential treatments for COVID-19. Bringing these repurposed medications to the public for COVID-19 requires robust and high-quality clinical trials that must be conducted under extremely challenging pandemic conditions. This article reviews translational science principles and strategies for conducting clinical trials in a pandemic and evaluates recent trials for different drug candidates. We hope that this knowledge will help focus efforts during this crisis and lead to the expedited development and approval of COVID-19 therapies.