The Association between the Fibrosis-4 Index and COVID-19: A Systematic Review and Meta-Analysis

Objective. The prognosis value of fibrosis-4 score (FIB-4) in COVID-19 is controversial. Hence, we conducted a systematic review and meta-analysis to investigate the association between the FIB-4 index and COVID-19 disease progression. Methods. We performed meta-analysis using the PubMed, Embase, and Cochrane databases. A fixed- or random-effects model was used for evaluating heterogeneity. Results. Thirteen studies were included. The meta-analysis of unadjusted results showed that compared to lower FIB-4 index, patients with higher FIB-4 index had increased odds of mortality (OR=5.1, 95%CI 3.67-7.09;P<0.001), ICU admission (OR=2.32, 95%CI: 1.65-3.25, P<0.00001) and need for mechanical ventilator support (OR=3.51, 95%CI: 2.1-5.85, P<0.001). In addition, the meta-analysis of adjusted results showed patients with higher FIB-4 index was associated with increased risk of mortality (OR=3.01, 95%CI: 2.21-4.09, P<0.001) and need for mechanical ventilator support (OR=3.76, 95%CI: 2.08-6.82, P<0.001) compared to patients with lower FIB-4 index. Conclusion. This meta-analysis indicated that high FIB-4 index score was associated with the severity and mortality in COVID-19 infected patients.Copyright © 2022 by the Association of Clinical Scientists, Inc.

Blockchain-Based Patient-to-Patient Health Data Sharing

The Covid-19 pandemic catalyzed many exciting forms of health data sharing. Aside from the institution-to-institution health data sharing among cooperating institutions for research and discovery of insights in healthcare, individual-to-many and individual-to-individual health data sharing also came to the fore. However, the security risks involved here are substantial since health data disclosures can lead to privacy and security breaches or complications. In this research, we present a scheme to enable individuals to share details of medical experiences with other individuals or interested groups. Our system provides the sharing entities with anonymity and thus, facilitates rapid dissemination of empirical insights during public health emergencies like Covid-19.

Analysis of operations of breast surgery during the COVID-19 epidemic

Background and Aims: Despite that the epidemic of novel coronavirus pneumonia (COVID-19) has been under effective control in China, the number of cases outside our country is continuously growing. So, the prevention and control situation is still grim. This study, taking the Department of Breast Surgery in the First Affiliated Hospital of Army Medical University as an example, was designed to analyze and summarize the operations of breast surgery in a comprehensive tertiary hospital during the epidemic, so as to provide effective reference and experiential practice for conducting safe and efficient breast surgery work during epidemic and post-epidemic periods. Methods: Thirty-seven patients with breast cancer admitted in the Department of Breast Thyroid Surgery of the First Affiliated Hospital of Army Medical University during the COVID-19 epidemic period of January 31, 2020 to February 20, 2020 were selected. The data of the patients including the clinical characteristics, protective measures and treatment effects were retrospectively analyzed. The main points of methods for patients' admission to hospital, preoperative preparation, intraoperative protection and postoperative recovery, as well as the self-protection and psychological counseling of the medical staff were summarized. The effects of specialized treatment and epidemic prevention and control were identified by follow up. The details for how to improve the cognition of potential risk for infectious diseases, and how to strengthen the prevention and control of epidemic disease and occupational protection during the post-epidemic period were analyzed from the aspects of surgical indications, aerosol management and clinic protection in combination with the diagnosis and treatment characteristics of breast surgery. Results: Thirty-seven patients received surgical treatment after the exclusion of the risk of COVID-19. The mean operative time was (152.23±46.19) min, the average intraoperative blood loss was (85.23±23.47) mL, and no intraoperative blood transfusion was required in any of the patients. Among the 37 breast cancer patients, 19 cases underwent 6 to 8 cycles of preoperative neoadjuvant therapy, of whom, complete pathological remission was obtained in 7 cases after surgery. Prolonged fever occurred in 2 patients after operation, which returned to normal after isolation, body temperature monitoring and symptomatic treatment, and COVID-19 was excluded by nucleic acid testing for SARS-CoV-2. After follow-up, no suspected COVID-19 symptoms such as fever, cough and fatigue were noted in any of the patients or their accompanying relatives, and also no suspected cases were observed among the relevant medical staff. Conclusion: During the epidemic period, on the premise of scientific prevention and control, and full avoidance of COVID-19 infection in both doctors and patients, aggressive treatment can be provided to breast cancer patients according to the local epidemic situation. The principles of "scientific decision-making, and humanistic service" should be continuously followed, and the various prevention and control guidelines and management norms issued by superiors should be strictly obeyed, and the necessary elements such as prehospital screening, preoperative preparation, intraoperative protection and postoperative recovery of the breast cancer patients should be completed according to the actual situation. Rigorous compliance with the protection measures against epidemic in all aspects of the pre-admission and perioperative period of the patients, and coordinated relations of the epidemic prevention and control with the specialized treatment, may maximally ensure the safe completion of breast surgery and protect the health of the patients and medical personnel. © 2020 by the Author(s).

Blockchain-Based Patient-to-Patient Health Data Sharing

The Covid-19 pandemic catalyzed many exciting forms of health data sharing. Aside from the institution-to-institution health data sharing among cooperating institutions for research and discovery of insights in healthcare, individual-to-many and individual-to-individual health data sharing also came to the fore. However, the security risks involved here are substantial since health data disclosures can lead to privacy and security breaches or complications. In this research, we present a scheme to enable individuals to share details of medical experiences with other individuals or interested groups. Our system provides the sharing entities with anonymity and thus, facilitates rapid dissemination of empirical insights during public health emergencies like Covid-19. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Dynamic connectedness of China's green bonds and asset classes

This paper quantifies the co-movement and time-varying integration between China's green bonds and other asset classes across different time domains using the wavelet coherence and time-frequency connectedness model based on the time-varying parameter VAR (TVP-VAR). First, we predominantly detect a strong positive co-movement of green and conventional bonds, especially in the medium and long term. Second, strong bidirectional spillovers exist between green bonds and treasury, corporate, and financial bonds regardless of the time horizon. Lastly, cross-market spillovers between the green bonds and the stock, energy, low-carbon stock market were quite limited in the short-run but strengthened towards the long-term except during the 2015 China stock market crash and the COVID-19 recession when short-term integration rose sharply. The results document some practical enlightenment for investors and policymakers with various time horizons. © 2022 Elsevier Inc.

Multi-Feature Vision Transformer via Self-Supervised Representation Learning for Improvement of COVID-19 Diagnosis

The role of chest X-ray (CXR) imaging, due to being more cost-effective, widely available, and having a faster acquisition time compared to CT, has evolved during the COVID-19 pandemic. To improve the diagnostic performance of CXR imaging a growing number of studies have investigated whether supervised deep learning methods can provide additional support. However, supervised methods rely on a large number of labeled radiology images, which is a time-consuming and complex procedure requiring expert clinician input. Due to the relative scarcity of COVID-19 patient data and the costly labeling process, self-supervised learning methods have gained momentum and has been proposed achieving comparable results to fully supervised learning approaches. In this work, we study the effectiveness of self-supervised learning in the context of diagnosing COVID-19 disease from CXR images. We propose a multifeature Vision Transformer (ViT) guided architecture where we deploy a cross-attention mechanism to learn information from both original CXR images and corresponding enhanced local phase CXR images. By using 10% labeled CXR scans, the proposed model achieves 91.10% and 96.21% overall accuracy tested on total 35,483 CXR images of healthy (8,851), regular pneumonia (6,045), and COVID-19 (18,159) scans and shows significant improvement over state-of-the-art techniques. Code is available https://github.com/endiqq/Multi-Feature-ViT.

Radiation therapy practice changes in the COVID-19 pandemic era: A pilot study in California.

PURPOSE: This study aims to investigate practice changes among Southern and Northern California's radiation oncology centers during the COVID-19 pandemic. METHODS: On the online survey platform SurveyMonkey, we designed 10 survey questions to measure changes in various aspects of medical physics practice. The questions covered patient load and travel rules; scopes to work from home; new protocols to reduce corona virus disease-2019 (COVID-19) infection risk; availability of telemedicine; and changes in fractionation schedules and/or type of treatment plans. We emailed the survey to radiation oncology centers throughout Northern and Southern California, requesting one completed survey per center. All responses were anonymized, and data were analyzed using both qualitative and quantitative research methods. RESULTS: At the end of a 4-month collection period (July 2, 2021 to October 11, 2021), we received a total of 61 responses throughout Southern and Northern California. On average, 4111 patients were treated per day across the 61 centers. New COVID-19-related department and hospital policies, along with hybrid workflow changes, infectious control policies, and changes in patient load have been reported. Results also showed changes in treatment methods during the pandemic, such as increased use of telemedicine, hypofractionation for palliative, breast cancer, and prostate cancer cases; and simultaneous boosts, compared to sequential boosts. CONCLUSION: Our California radiation oncology center population study shows changes in various aspects of radiation oncology practices during the COVID-19 pandemic. This study serves as a pilot study to identify possible correlations and new strategies that allow radiation oncology centers to continue providing quality patient care while ensuring the safety of both staff and patients.

Phase 2 study of anlotinib combined with taxanes and lobaplatin in the neoadjuvant treatment of triple-negative breast cancer: Efficacy, safety and biomarker analysis

Background: Pathological complete response (pCR) is associated with improved prognosis in triple-negative breast cancer (TNBC). Anlotinib, a novel multi-target tyrosine kinase inhibitor that effectively inhibits VEGFR, FGFR, c-KIT, c-MET, and RET, monotherapy has been proven effective in HER-2 negative metastatic breast cancer, but its efficacy in early-stage TNBC is unknown. This phase 2 study aims to evaluate the efficacy and safety of adding anlotinib to neoadjuvant chemotherapy in patients (pts) with primary TNBC. Methods: Pts with clinical stage II/III TNBC were to be treated with 5 cycles of anlotinib (12mg, d1-14, q3w) plus 6 cycles of taxanes (docetaxel 75 mg/m2 or nab-paclitaxel 260 mg/m2, d1, q3w) and lobaplatin (30 mg/m2, d1, q3w), followed by surgery. The primary endpoint was the total pCR (tpCR;ypT0/is ypN0). A Simon's two-stage optimum design was used, and > 5 of 11 pts were required to achieve tpCR in the first stage, with a pre-specified tpCR rate of 54.5% before proceeding to the second stage. A total of 31 participants was required for the study. Results: Six out of 11 pts achieved tpCR in the first stage, reaching the threshold for the second stage. From Jan 2021 to Jan 2022, a total of 22 pts were enrolled and 12 received surgery after the completion of neoadjuvant therapy, but a total of 2 pts withdrew from the study due to the COVID-19 pandemic or serious adverse events. Of the 22 eligible pts, the median age was 49 years (range, 29-64), 64% were postmenopausal, and 73% were nodal involved. At the time of surgery, 58.3% (7/12) achieved tpCR. Of the 9 pts with the node-positive disease at diagnosis, 88.9% (8/9) became ypN0. The results of FUSCC TNBC classification (IHC-based) revealed the tpCR rates were 57.1% (4/7), 100% (3/3), and 0% (0/2) for BLIS subtype, IM subtype and LAR/unknown subtypes, respectively. Biomarker analysis showed the tpCR rates were 100% (3/3) and 100% (4/4) in patients with gBRCA1 mutation and MYC amplification, respectively. The most common grade 3 or 4 treatment-related adverse events were leucopenia (6/22, 27%), neutropenia (6/22, 27%), anemia (5/22, 23%), decreased appetite (5/22, 23%), hypertension (2/22, 9%), ALT increased (1/22, 5%) and oral mucositis (1/22, 5%). No treatment-related deaths occurred. The trial is ongoing. Conclusions: The addition of anlotinib to neoadjuvant chemotherapy showed manageable toxicity and promising antitumor activity for patients with early-stage TNBC.

Effects of the COVID-19 lockdown in Hubei, China: cessation of incense burning reduces regional landscape fire

Both anthropogenic and climatic factors are important determinants of landscape fire. Because the two groups of factors are intertwined and often act simultaneously, dissecting their effects on landscape fire is challenging. We used the COVID-19 lockdown event in Hubei, in which all immediate influences of anthropogenic factors were effectively removed, to quantify the effects of anthropogenic factors on landscape fire occurrence. We hypothesized that outdoor incense burning is the main causal factor of landscape fire. To test the hypothesis, we used random forest algorithm to model fire occurrence, including fire frequency, total area burned, and area of forest burned, for the lockdown period. We then estimated the differences between historical, simulated, and observed values of landscape fire and used the differences to represent the effects of anthropogenic activities on landscape fire. Our results showed that during the lockdown, landscape fire frequency was reduced by 77%, total area burned by 80%, and area of forest burned by 63%. By month, fire frequency decreased the most in April (85%), followed by February (80%), coinciding with the Qingming and Spring Festivals of 2020. The cessation of outdoor incense burning during the festival season was likely to be the most important factor that decreased fire occurrence, confirming our hypothesis about the causal relationship between outdoor incense-burning and landscape fire. Thus, educational programs encouraging people to stop outdoor incense burning during the festival season could reduce the occurrence of landscape fire.

SARS-CoV-2 Remained Airborne for a Prolonged Time in a Lockdown Confined Space

Airborne transmission of COVID-19 plays an important role for the pandemic. However, nucleic acid based evidence of direct association of COVID-19 with environmental contamination is lacking. Here, we investigated a COVID-19 outbreak with two fast food employees infected, in which a traveler despite of a 14-day quarantine turned positive after check in with a hotel, using environmental SARS-CoV-2 sampling, epidemiological tracing, viral RNA sequence as well as surveillance method. Out of 25 positive environmental air and surface swab samples (N = 237) collected, SARS-CoV-2 was found to have remained airborne (5640–7840 RNA copies m–3 ) for more than 4 days in a female washroom. After aging for 5 days in the air, no viable virus was detected. The traveler did not have any contacts with the two employees;however, genome sequencing showed that SARS-CoV-2 variants from three patients and two environmental surface samples belonged to 20B viral clade, sharing a nucleic acid identity of more than 99.9%. We concluded that the outbreak was triggered by SARS-CoV-2 contaminated environments, where the employees inhaled the virus from the air or touching facility surfaces where the traveler did not have any physical contacts with. © The Author(s).

Complementary Blockchain-Based Privacy Protection for Covid-19 Contact Tracing

Fighting any pandemic outbreak begins with health authorities already behind time. They are required to locate, isolate and treat infected individuals while tracking possibly infected ones. In the case of Covid-19, the high reproductive number of the virus necessitates that all contacts of an infected individual be found within the shortest possible time to slow the rate of spreading. This presents multiple challenges because of the highly invasive nature of tracing activities which demand the mobility history of patients. Patients may be unwilling to cooperate or may be unable to communicate if the infection has advanced to the point where critical care is necessary. To speedily locate contacts of an infected individual, we propose using communications data logs from telecommunications operators. We employ a modified directed graph to determine which other individuals have been in close proximity to an infected individual in a specific frame of time. We then generate a contact graph and place it in a secure offline storage platform. We employ Smart Contracts to control access to the data while the blockchain keeps records of the provenance of all data and transactions. We find this method of conducting the contact tracing and protecting the resulting data more secure and pliant to the privacy laws that regulate the handling of sensitive personal data. © 2021 IEEE.

A Robot Assisted High-flow Portable Cyclone Sampler for Bacterial and SARS-CoV-2 Aerosols

Airborne transmission of infectious diseases attracts great attention since the COVID-19 pandemic. Yet, there has been an intense dispute about aerosol transmission of the disease, which is largely due to lack of qualified instruments for studying the subject. Air sampling plays a critical role in all air pollution related study, and particularly critical for airborne pathogen detection. Here, we designed and evaluated a portable and high volume (400 L min-1) cyclone sampler named as Yao-CSpler using aerosolized Polystyrene (PS) uniform microspheres, Bacillus subtilis var. niger, Pseudomonas fluorescens, and both indoor and outdoor air particles. The experimental cutoff size of the Yao-CSpler was demonstrated to be 0.58 m (while the calculated theoretical value is 1.84 m), and the sampler has shown stable microbial collection performances for bacteria, fungi, and even viruses. The sampler had a physical collection efficiency of close 100% for particles of larger than 1 m. Jet-to-liquid distance and sampling duration were shown to substantially influence the sampler performance. Given the same sampling duration, the performances of the Yao-CSpler were significantly higher than those of the traditional BioSampler (SKC Inc.) in terms of samples' bacterial diversity. The developed sampler coupled with a robot has been successfully applied to sampling airborne SARS-CoV-2 in both Wuhan and Beijing during the COVID-19 outbreaks. With a high sampling flow, the Yao-CSpler was shown to be able to collect the SARS-CoV-2 with a detectable concentration level down to 9-219 viruses m-3 in clinical settings housing COVID-19 patients. Further more efficient bioaerosol sampler, which is able to rapidly capture low level pathogenic agents, is urgently required to better understand and confront airborne transmission of infectious diseases.

Spatial epidemiology characteristics and influencing factors of confirmed COVID-19 cases in Shaanxi province

Objective  To describe the spatial distribution of COVID-19 cases in Shaanxi Province and further explore its relevant factors, so as to provide evidence for the prevention and control of COVID-19 in Shaanxi Province.  Methods  The information of confirmed COVID-19 cases and relevant socioeconomic data in Shaanxi Province were collected. The temporal and spatial distribution characteristics of confirmed cases, and the correlation between the incidence of COVID-19 and socioeconomic factors in the population were analyzed by using a generalized linear model.  Results  Four cases were first reported in Shaanxi on 23 January 2020, with the highest number of new confirmed cases reaching 23 on 4 February and no new cases after 19 February. The imported cases appeared earlier and reached the new peak than the local cases, and entered the zero stage earlier than the local cases. The spatial distribution showed that Xi′ an (120 cases) had the largest number of confirmed cases, accounting for 48.98% of the total cases, and the districts with more confirmed cases were in Lianhu, Yanta, Xincheng and Weiyang. Socioeconomic factors which significantly associated with the number of confirmed cases in each district and country were education expenditure (IRR=0.287, 95% CI: 0.134-0.612), GDP per capita (IRR=1.143, 95% CI: 1.049-1.245) and the distance from Wuhan (IRR=0.995, 95% CI: 0.992-0.998).  Conclusion  Measures should be taken in key areas and population at the early stage of the epidemic to control the spread of the epidemic as soon as possible. © 2021, Publication Centre of Anhui Medical University. All rights reserved.

Challenges faced by schools, teachers and students in distance education implementation during the COVID-19 Outbreak: Recommendations on the application of adaptive responses

The purpose of this study aims to explore the influence for schools, teachers, and students of the full implementation of the distance education model in schools during the COVID-19 outbreak. The study drew three main findings from the exploratory qualitative research: Students' learning abilities and perceptions of independent learning significantly impact distance education quality. Teachers' pedagogical perceptions influence students' adaptability to distance education. Societal bias against distance education influences student and faculty attitudes toward distance education. This study will also suggest responses in three areas: schools, teachers, and students. Our research concludes with a theoretical and managerial discussion. © 2021 ACM.

Multi-Feature Semi-Supervised Learning for COVID-19 Diagnosis from Chest X-Ray Images

Computed tomography (CT) and chest X-ray (CXR) have been the two dominant imaging modalities deployed for improved management of Coronavirus disease 2019 (COVID-19). Due to faster imaging, less radiation exposure, and being cost-effective CXR is preferred over CT. However, the interpretation of CXR images, compared to CT, is more challenging due to low image resolution and COVID-19 image features being similar to regular pneumonia. Computer-aided diagnosis via deep learning has been investigated to help mitigate these problems and help clinicians during the decision-making process. The requirement for a large amount of labeled data is one of the major problems of deep learning methods when deployed in the medical domain. To provide a solution to this, in this work, we propose a semi-supervised learning (SSL) approach using minimal data for training. We integrate local-phase CXR image features into a multi-feature convolutional neural network architecture where the training of SSL method is obtained with a teacher/student paradigm. Quantitative evaluation is performed on 8,851 normal (healthy), 6,045 pneumonia, and 3,795 COVID-19 CXR scans. By only using 7.06% labeled and 16.48% unlabeled data for training, 5.53% for validation, our method achieves 93.61% mean accuracy on a large-scale (70.93%) test data. We provide comparison results against fully supervised and SSL methods. The code and dataset will be made available after acceptance. © 2021, Springer Nature Switzerland AG.