Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge (preprint)

Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of airway trees remains prohibitively time-consuming. While significant efforts have been made towards enhancing airway modelling, current public-available datasets concentrate on lung diseases with moderate morphological variations. The intricate honeycombing patterns present in the lung tissues of fibrotic lung disease patients exacerbate the challenges, often leading to various prediction errors. To address this issue, the 'Airway-Informed Quantitative CT Imaging Biomarker for Fibrotic Lung Disease 2023' (AIIB23) competition was organized in conjunction with the official 2023 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). The airway structures were meticulously annotated by three experienced radiologists. Competitors were encouraged to develop automatic airway segmentation models with high robustness and generalization abilities, followed by exploring the most correlated QIB of mortality prediction. A training set of 120 high-resolution computerised tomography (HRCT) scans were publicly released with expert annotations and mortality status. The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients. The results have shown that the capacity of extracting airway trees from patients with fibrotic lung disease could be enhanced by introducing voxel-wise weighted general union loss and continuity loss. In addition to the competitive image biomarkers for prognosis, a strong airway-derived biomarker (Hazard ratio>1.5, p<0.0001) was revealed for survival prognostication compared with existing clinical measurements, clinician assessment and AI-based biomarkers.

Dual Multi-scale Mean Teacher Network for Semi-supervised Infection Segmentation in Chest CT Volume for COVID-19 (preprint)

Automated detecting lung infections from computed tomography (CT) data plays an important role for combating COVID-19. However, there are still some challenges for developing AI system. 1) Most current COVID-19 infection segmentation methods mainly relied on 2D CT images, which lack 3D sequential constraint. 2) Existing 3D CT segmentation methods focus on single-scale representations, which do not achieve the multiple level receptive field sizes on 3D volume. 3) The emergent breaking out of COVID-19 makes it hard to annotate sufficient CT volumes for training deep model. To address these issues, we first build a multiple dimensional-attention convolutional neural network (MDA-CNN) to aggregate multi-scale information along different dimension of input feature maps and impose supervision on multiple predictions from different CNN layers. Second, we assign this MDA-CNN as a basic network into a novel dual multi-scale mean teacher network (DM${^2}$T-Net) for semi-supervised COVID-19 lung infection segmentation on CT volumes by leveraging unlabeled data and exploring the multi-scale information. Our DM${^2}$T-Net encourages multiple predictions at different CNN layers from the student and teacher networks to be consistent for computing a multi-scale consistency loss on unlabeled data, which is then added to the supervised loss on the labeled data from multiple predictions of MDA-CNN. Third, we collect two COVID-19 segmentation datasets to evaluate our method. The experimental results show that our network consistently outperforms the compared state-of-the-art methods.

Third dose of anti-SARS-CoV-2 inactivated vaccine for patients with RA: Focusing on immunogenicity and effects of RA drugs

Objectives To evaluate the immunogenicity of the third dose of inactivated SARS-CoV-2 vaccine in rheumatoid arthritis (RA) patients and explore the effect of RA drugs on vaccine immunogenicity. Methods We recruited RA patients (n = 222) and healthy controls (HC, n = 177) who had been injected with a third dose of inactivated SARS-CoV-2 vaccine, and their neutralizing antibody (NAb) titer levels were assessed. Results RA patients and HC were age- and gender-matched, and the mean interval between 3rd vaccination and sampling was comparable. The NAb titers were significantly lower in RA patients after the third immunization compared with HC. The positive rate of NAb in HC group was 90.4%, while that in RA patients was 80.18%, and the difference was significant. Furthermore, comparison of NAb titers between RA treatment subgroups and HC showed that the patients in the conventional synthetic (cs) disease-modifying anti-rheumatic drugs (DMARDs) group exhibited no significant change in NAb titers, while in those receiving the treatment of biological DMARDs (bDMARDs), Janus Kinase (JAK) inhibitors, and prednisone, the NAb titers were significantly lower. Spearman correlation analysis revealed that NAb responses to SARS-CoV-2 in HC did differ significantly according to the interval between 3rd vaccination and sampling, but this finding was not observed in RA patients. In addition, NAb titers were not significantly correlated with RA-related laboratory indicators, including RF-IgA, RF-IgG, RF-IgM, anti-CCP antibody;C-RP;ESR;NEUT% and LYMPH%. Conclusion Serum antibody responses to the third dose of vaccine in RA patients were weaker than HC. Our study will help to evaluate the efficacy and safety of booster vaccination in RA patients and provide further guidance for adjusting vaccination strategies.

Impact of Coronavirus Disease of 2019 Vaccine on Health and Physical Activities Among Physical Education Students in China

Purpose This research focused primarily on the impact of the SARS-CoV-2 Vaccine (VeroCell) on Chinese physical education (PE) students' health and physical activity (PA) performance. Methods This study used quantitative methods and phenomenological procedures to collect and analyze data. Survey techniques were the main method used for collecting data from Chinese university students, using a self-designed questionnaire with a Cronbach's alpha α value of 0.76. To ensure the quality of the study, confirmatory factor analyses (CFA) were conducted, and the internal consistency reliability of the instrument was measured (alpha coefficient = 0.82). The determined sample size was 490 and around 90% as the minimum sample size was determined with the help of a sample size calculator. The author using factor loadings with h2 and an independent-sample t-test analyzed the responses of the remaining valid participants (n = 443 with a response rate of 90.40). Results Most participants (around 94%) did not experience any adverse reactions that impacted their daily life activities, health, or performance during physical activity. However, about 30–40% of students felt lethargy, weakness, muscle pain, or swelling. Regarding the impact of the vaccine on daily life, there was no difference in the responses between participants who had only received one shot of the coronavirus disease 2019 (COVID-19) vaccine and those who had received two shots (p > 0.05 in most cases). Conclusion The study concluded that the COVID-19 vaccine had no significant effect on PE students' daily activities, health, and PA performance. The results of this study could be used by policymakers to encourage people to get vaccinated and eradicate the isolation caused by COVID-19, which leads many people to develop various non-communicable diseases (NCDs).

Aerosol transport pathways and source attribution in China during the COVID-19 outbreak

Due to the coronavirus disease 2019 (COVID-19) pandemic, human activities and industrial productions were strictly restricted during January–March 2020 in China. Despite the fact that anthropogenic aerosol emissions largely decreased, haze events still occurred. Characterization of aerosol transport pathways and attribution of aerosol sources from specific regions are beneficial to air quality and pandemic control strategies. This study establishes source–receptor relationships in various regions covering all of China during the COVID-19 outbreak based on the Community Atmosphere Model version 5 with Explicit Aerosol Source Tagging (CAM5-EAST). Our analysis shows that PM2.5 burden over the North China Plain between 30 January and 19 February is mostly contributed by local emissions (40 %–66 %). For other regions in China, PM2.5 burden is largely contributed from nonlocal sources. During the most polluted days of the COVID-19 outbreak, local emissions within the North China Plain and eastern China contributed 66 % and 87 % to the increase in surface PM2.5 concentrations, respectively. This is associated with the anomalous mid-tropospheric high pressure at the location of the climatological East Asia trough and the consequently weakened winds in the lower troposphere, leading to the local aerosol accumulation. The emissions outside China, especially those from South Asia and Southeast Asia, contribute over 50 % to the increase in PM2.5 concentration in southwestern China through transboundary transport during the most polluted day. As the reduction in emissions in the near future is desirable, aerosols from long-range transport and unfavorable meteorological conditions are increasingly important to regional air quality and need to be taken into account in clean-air plans.

A Reactive Copper-Organophosphate-MXene Heterostructure Enabled Antibacterial, Self-Extinguishing and Mechanically Robust Polymer Nanocomposites

The ongoing Covid-19 pandemic has raised the need for urgent antibacterial requirements for many commercially important polymers, e.g., epoxy resins (EPs). Meanwhile, intrinsic flammability and poor impact toughness are two big obstacles that greatly impede the practical applications of EPs. Hence, it has been imperative but highly challenging to create advanced EPs combining satisfactory antibacterial, fire-retardant and mechanically robust performances so far. Here, we report a reactive multifunctional heterostructure, copper-organophosphate-MXene (CuP-MXene) by rational design. Our results show that with 5.0 wt% of CuP-MXene, in addition to achieving a high antibacterial efficiency above 99.9%, the resultant EP nanocomposite exhibits satisfactory flame retardancy (UL-94 V-0 rating, peak heat release rate decreased by 64.4%) and improved mechanical properties (tensile strength, elastic modulus and impact strength increased by 31.7%, 38.9%, and 25.0%, respectively) relative to virgin EP, outperforming its previous counterparts. Such a desirable performance portfolio arises from multiple synergistic effects between CuP and MXene. This work provides a general strategy for the design of multifunctional nanoadditives and advanced functional polymers, and creates more opportunities for industrial applications of EP in the areas of coatings, medical devices and furniture.

Towards Robust Cross-domain Image Understanding with Unsupervised Noise Removal (preprint)

Deep learning models usually require a large amount of labeled data to achieve satisfactory performance. In multimedia analysis, domain adaptation studies the problem of cross-domain knowledge transfer from a label rich source domain to a label scarce target domain, thus potentially alleviates the annotation requirement for deep learning models. However, we find that contemporary domain adaptation methods for cross-domain image understanding perform poorly when source domain is noisy. Weakly Supervised Domain Adaptation (WSDA) studies the domain adaptation problem under the scenario where source data can be noisy. Prior methods on WSDA remove noisy source data and align the marginal distribution across domains without considering the fine-grained semantic structure in the embedding space, which have the problem of class misalignment, e.g., features of cats in the target domain might be mapped near features of dogs in the source domain. In this paper, we propose a novel method, termed Noise Tolerant Domain Adaptation, for WSDA. Specifically, we adopt the cluster assumption and learn cluster discriminatively with class prototypes in the embedding space. We propose to leverage the location information of the data points in the embedding space and model the location information with a Gaussian mixture model to identify noisy source data. We then design a network which incorporates the Gaussian mixture noise model as a sub-module for unsupervised noise removal and propose a novel cluster-level adversarial adaptation method which aligns unlabeled target data with the less noisy class prototypes for mapping the semantic structure across domains. We conduct extensive experiments to evaluate the effectiveness of our method on both general images and medical images from COVID-19 and e-commerce datasets. The results show that our method significantly outperforms state-of-the-art WSDA methods.

Application of CT quantitative detection of pulmonary lesions in diagnosis and treatment of COVID

OBJECTIVE: To explore the value of CT quantitative detection of pulmonary lesions in diagnosis and treatment of COVID-19. METHODS: A total of 14 patients with COVID-19 who were treated in Quzhou People's Hospital from Jan 2020 to Apr 2020 were enrolled in the study and received chest scanning examination at the admission, progression stage of disease, recovery stage and absorption stage, the CT thin-slice scan data were imported into Toshiba Vitrea FX workstation, and the total volume, maximum diameter, maximum density and average density of pulmonary lesions were measured by using the quantitative detection function of lung lesions, and the distribution and scope of lesions were displayed in three dimensions by means of visualization technology. RESULTS: The results of the four times of CT examination showed that there were between-group significant differences in the volume of lesions, maximum density and maximum diameter during the different time periods. The volume of lesions, maximum density and maximum diameter of the patients who received the second CT examination were significantly higher than those of the patients who received other three times of CT examination;the volume of lesions, maximum density and maximum diameter of the patients who received the fourth CT examination were significantly lower than those of the patients who received other three times of CT examination(P < 0.05). CONCLUSION: The CT quantitative detection technology of lung lesions can quantify the information of lung lesions such as the total volume, maximum diameter, density, distribution and scope, continuously observe the course of disease and indicate the stage of lesions for clinicians so as to adjust the treatment program in a timely manner.

Savable but lost lives when ICU is overloaded: a model from 733 patients in epicenter Wuhan, China (preprint)

Background. Coronavirus Disease (COVID-19) causes a sudden turn over to bad at some check-point and thus needs intervention of intensive care unit (ICU). This resulted in urgent and large needs of ICUs posed great risks to the medical system. Estimating the mortality of critical in-patients who were not admitted to the ICU (MI-mortality) will be valuable to optimize the management and assignment of ICU.Methods. Retrospective, of the 733 in-patients diagnosed with COVD-19 at Huangpi Hospital of Traditional Chinese Medicine (Wuhan, China), as of March 18, 2020. This study aims to estimate the MI-mortality and build a model to identify the critical in-patients. Demographic, clinical and laboratory results were collected and analyzed. The mortality rate for the patients who failed to receive ICU and unfortunately died was analyzed. To this end, the key factors for prognostic of patients who may need ICU care were found. A prognostic classification model using machine learning was built to identify the patient who may need ICU. Results. Considering the shortage of ICU beds at the beginning of disease emergence, we defined the mortality for those patients who were predicted to be in needing of ICU treatment yet they did not as MI-mortality. Patients who entered the ICU and died were defined as ICU-mortality. To estimate MI-mortality, a prognostic classification model was built to identify the in-patients who may need ICU care based on the medical factors collected in-hospital. Its predictive accuracies on whole patient set (733 [25 708]), training set (586 [20 566]) and testing set (147 [5 142]) dataset were 0.8513, 0.8935 and 0.8288, with the AUC of 0.8844, 0.8941 and 0.9120, respectively. Our analysis had shown that the MI-mortality is 41% and the ICU-mortality is 32%, implying that enough bed of ICU in treating patients in critical conditions. Conclusions. On our cohort of 733 patients, 25 in-patients were admitted to ICU, among them 8 patients died. 25 in-patients who have been predicted by our model that they should need ICU care, yet they did not enter ICU due to lack of shorting ICU wards. The MI-mortality is 41%.

Coronavirus Pandemic: Opportunities or Challenges for Energy Transition? (preprint)

The coronavirus pandemic greatly shocked the global energy market, which could be clearly demonstrated by the recent collapse in crude oil prices. Using a dynamic multi-regional computable general equilibrium model, we explored the influences of the COVID-19 pandemic on energy production and consumption. The associated impacts on the macroeconomy as well as on carbon emissions are also examined. The results of this paper indicate dramatic negative shocks of the COVID-19 pandemic to energy consumption at both global and national levels, particularly for oil and oil products. However, the energy transition to renewables will be paused, as other non-oil fossil fuels can still play significant roles in economic activity. The epidemic may also temporarily terminate the more than ten-year increasing trend of the world’s total CO2 emissions, despite its limited contribution to the mitigation of global warming. However, there are still many opportunities worthy of use to promote short- or mid-term low-carbon energy transitions.

Clinical Characteristics of Non-Pneumonia COVID-19 Adults in Shanghai, China (preprint)

Background: Since December 2019, coronavirus disease 2019 (COVID-19) rapidly spread throughout the whole world , data have been needed on the clinical characteristics of the affected patients.Objectives: A total of 579 adult COVID-19 cases were enrolled in Shanghai from Jan 20 to Apr 15, 2020, in which 95 cases (16.41%) showed non-pneumonia on CT when confirmed. The characteristics of non-pneumonia cases have not been clearly described previously, and this might provide guidance to prevent and treatment of COVID-19.Method: We retrospectively collected the patient clinical dataset including demography, epidemiology, clinical manifestation, laboratory test results, diagnostic classification, treatment and clinical outcomes.Results: The average age of 95 COVID-19 cases was 31.45 ± 12.89 years old and 95.79% of them were less than 60 years old. They had mild clinical symptoms and/or laboratory abnormalities. 20 of the 95 cases occurred mild pneumonia during hospitalization, accompanied with lower lymphocyte counts, in which 60% cases were complicated with underlying condition and 15% cases were over 60 years old. All cases were cured. 16 of the 95 cases were local residents with clear epidemiological history and long incubation time, and mainly discovered as fever and respiratory symptoms. Other 79 cases were overseas imported, some had initial symptoms of diarrhea, smell or taste disorders and so on. They were mainly found at port of entry.Conclusions: Non-pneumonia COVID-19 predominantly occurred among young adults with mild clinical symptoms and possible long incubation time. The patients with underlying condition or at older age more likely developed mild pneumonia after diagnosis. Thereby, it is very important to pay attention to these patients and make reasonable diagnostic classification towards better prevention and treatment of COVID-19.

Clinical characteristics of non-pneuminia COVID-19 infection adults in Shanghai, China (preprint)

Background Adult patients diagnosed as COVID-19 in Shanghai were accepted in Shanghai Public Health Clinical Center. We found around 4.91% of cases showed non-pneumonia on CT imaging when they were confirmed. Understanding the characteristics of non-pneumonia cases is of great significance to guide clinical treatment and improve prevention and control measures.Methods All dataset of demography, epidemiology, clinical manifestation, laboratory test, diagnosis, classification, condition change, treatment and outcome were obtained by retrospective investigation.Results 16 cases were confirmed COVID-19 with non-pneumonia with clear epidemiological history. The median age of patients was 37 years old and 81.25% were female. The median incubation period was 15.25 days. 75% patients were familial clusters. These patients were presented with mild clinical manifestations, such as bronchitis, common cold and asymptomatic infection with or without laboratory abnormalities. 4(25%)cases had underlying diseases. 3 of them had mild pneumonia on chest CT imaging during hospitalization. All of the cases were cured and discharged after support treatment.Conclusions A few of adult patients after COVID-19 infection had non-pneumonia, with mild clinical manifestations and long incubation time. It usually occurred in young women and history of family aggregation. The mild clinical symptom may be caused by the decreasing pathogenicity after multiple generation of virus replication. However, we should be on alert that the virus is still contagious to human. Therefore, an intensive attention should be paid to these patients to avoid misdiagnosis and overlook, because these patients are potential viral source in infection of other people.

Clinical analysis of risk factors for severe patients with novel coronavirus pneumonia / 中华传染病杂志

Objective To analyze the clinical features of patients with coronavirus disease 2019 (COVID-19) in Shanghai and to investigate the risk factors for disease progression to severe cases. Methods The clinical data of 292 adult patients with COVID-19 hospitalized in Shanghai Public Health Clinical Center from January 20, 2020 to February 10, 2020 were retrospectively analyzed, including 21 severe patients and 271 mild patients. The demographic characteristics, epidemiological history, history of underlying diseases and laboratory examinations were compared between the two groups. Measurement data were compared using t test or Mann-Whitney U test. The count data were compared using hi-square test. The binary logistic regression equation was used to analyze the risk factors for the progression of patients to severe cases. Results Among the 292 patients, 21 were severe cases with the rate of 7.2% (21/292). One patient died, and the mortality rate was 4.8% in severe patients. The severe patients aged (65.0±15.7) years old, 19 (90.5%) were male, 11 (52.4%) had underlying diseases, 7 (33.3%) had close relatives diagnosed with COVID-19. The mild patients aged (48.7±15.7) years old, 135 (49.8%) were male, 74 (27.3%) had underlying diseases, 36 (13.3%) had close relatives diagnosed with COVID-19. The differences between two groups were all significant statistically ( t =-4.730, χ 2 =12.930, 5.938 and 4.744, respectively, all P <0.05). Compared with the mild patients, the levels of absolute numbers of neutrophils, alanine aminotransferase, aspartate aminotransferase, lactate dehydrogenase, creatinine, serum cystatin C, C reactive protein (CRP), procalcitonin , D -dimer, pro-B-type natriuretic peptide (proBNP), serum myoglobin, creatine kinase (CK), creatine kinase isoenzyme (CK-MB), serum troponin I (cTnI) in severe patients were all significantly higher ( U =2 091.5, 1 928.0, 1 215.5, 729.0, 1 580.5, 1 375.5, 917.5, 789.5, 1 209.0, 1 434.0, 638.0, 964.5, 1 258.0 and 1 747.5, respectively, all P <0.05), while the levels of lymphocyte count, albumin, transferrin, CD3 + T lymphocyte count, CD8 + T lymphocyte count and CD4 + T lymphocyte count in severe patients were all significantly lower ( U =1 263.5, t =4.716, U =1 214.0, 962.0, 1 167.5 and 988.0, respectively, all P <0.05). Further logistic regression analysis showed that the albumin (odds ratio ( OR )=0.806, 95% CI 0.675-0.961), CRP ( OR =1.016, 95% CI 1.000-1.032), serum myoglobin ( OR =1.010, 95% CI 1.004-1.016), CD3 + T lymphocyte count ( OR =0.996, 95% CI 0.991-1.000) and CD8 + T lymphocyte count ( OR =1.006, 95% CI 1.001-1.010) at admission were independent risk factors for the progression of COVID-19 patients to severe illness (all P <0.05). Conclusions Severe cases of patients with COVID-19 in Shanghai are predominantly elderly men with underlying diseases. Albumin, CRP, serum myoglobin, CD3 + T lymphocyte count and CD8 + T lymphocyte count could be used as early warning indicators for severe cases, which deserve more clinical attention.

A descriptive study of the impact of diseases control and prevention on the epidemics dynamics and clinical features of SARS-CoV-2 outbreak in Shanghai, lessons learned for metropolis epidemics prevention (preprint)

Objective: To describe and evaluate the impact of diseases control and prevention on epidemics dynamics and clinical features of SARS-CoV-2 outbreak in Shanghai. Design: A retrospective descriptive study Setting: China Participants: Epidemiology information was collected from publicly accessible database. 265 patients admitted to Shanghai Public Health Center with confirmed COVID-19 were enrolled for clinical features analysis. Main outcome measure: Prevention and control measures taken by Shanghai government, epidemiological, demographic, clinical, laboratory and radiology data were collected. Weibull distribution, Chi-square test, Fisher's exact test, t test or Mann-Whitney U test were used in statistical analysis. Results: COVID-19 transmission rate within Shanghai had reduced over 99% than previous speculated, and the exponential growth has been stopped so far. Epidemic was characterized by the first stage mainly composed of imported cases and the second stage where >50% of cases were local. The incubation period was 6.4 (95% CI 5.3 to 7.6) days and the mean onset-admission interval was 5.5 days (95% CI, 5.1 to 5.9). Median time for COVID-19 progressed to severe diseases were 8.5 days (IQR: 4.8-11.0 days). By February 11th, proportion of patients being mild, moderate, severe and critically ill were 1.9%(5/265), 89.8%(238/265), 3.8%(10/265), 4.5%(12/265), respectively; 47 people in our cohort were discharged, and 1 patient died. Conclusion: Strict controlling of the transmission rate at the early stage of an epidemic in metropolis can quickly prohibit the spread of the diseases. Controlling local clusters is the key to prevent outbreaks from imported cases. Most COVID-19 severe cases progressed within 14 days of disease onset. Multiple systemic laboratory abnormalities had been observed before significant respiratory dysfunction. Keyword: COVID-19, SARS-CoV-2, epidemics dynamics, diseases control, clinical features