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In this paper, a CNN-MLP model (CMM) is proposed for COVID-19 lesion segmentation and severity grading in CT images. The CMM starts by lung segmentation using UNet, and then segmenting the lesion from the lung region using a multi-scale deep supervised UNet (MDS-UNet), finally implementing the severity grading by a multi-layer preceptor (MLP). In MDS-UNet, shape prior information is fused with the input CT image to reduce the searching space of the potential segmentation outputs. The multi-scale input compensates for the loss of edge contour information in convolution operations. In order to enhance the learning of multiscale features, the multi-scale deep supervision extracts supervision signals from different upsampling points on the network. In addition, it is empirical that the lesion which has a whiter and denser appearance tends to be more severe in the COVID-19 CT image. So, the weighted mean gray-scale value (WMG) is proposed to depict this appearance, and together with the lung and lesion area to serve as input features for the severity grading in MLP. To improve the precision of lesion segmentation, a label refinement method based on the Frangi vessel filter is also proposed. Comparative experiments on COVID-19 public datasets show that our proposed CMM achieves high accuracy on COVID-19 lesion segmentation and severity grading.

Objective: Medication non-adherence to immunosuppressants threatens allograft survival and function maintenance among solid organ transplant (SOT) recipients. This study aimed to investigate the prevalence of immunosuppressant medication non-adherence and associated factors during the COVID-19 reopening period among Chinese SOT recipients., Design: Cross-sectional study., Setting: South-central China., Population: Adult patients who received SOT with functioning graft., Methods: Sociodemographic questionnaire and scales to measure physical activity, depression and medication non-adherence were used to collect data. Logistic regression analysis was conducted to identify factors associated with medication non-adherence. Mediation and moderated mediation analyses were performed to examine the potential mechanisms influencing medication behaviour during the pandemic reopening period using SPSS PROCESS macro 4.3 software., Results: A total of 1121 participants were recruited and the prevalence of medication non-adherence was 36.3% in this study. Recipients who were men, had a higher monthly income, lived alone, had received transplantation for a minimum of 3 years, had received COVID-19 vaccination and experienced depressive symptoms exhibited an increased risk of non-adherence. Contrarily, those who engaged in high-intensity physical activity exhibited a decreased risk. Physical activity was negatively associated with medication non-adherence (r=-0.124, p<0.001) with depression fully mediating this relationship (B=-0.014, 95% CI: -0.032 to -0.003). COVID-19 vaccination significantly moderated the relationship between physical activity and depression (B=-0.303, 95% CI: -0.515 to -0.090)., Conclusion: This study investigated the prevalence of medication non-adherence among SOT recipients during the COVID-19 reopening period in China, its associated factors and a potential mechanism. Depression fully mediated the association between physical activity and medication non-adherence, and COVID-19 vaccination moderated the relationship between physical activity and depression. These findings provide some insights for managing medication behaviour when confronting public health emergencies. However, relationships displayed in the moderated mediation model should be tracked after returning to normal life and other potential relationships should be explored to deeply understand medication non-adherent behaviour., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Wastewater surveillance (WWS) of SARS-CoV-2 has become a crucial tool for monitoring COVID-19 cases and outbreaks. Previous studies have indicated that SARS-CoV-2 RNA measurement from testing solid-rich primary sludge yields better sensitivity compared to testing wastewater influent. Furthermore, measurement of pepper mild mottle virus (PMMoV) signal in wastewater allows for precise normalization of SARS-CoV-2 viral signal based on solid content, enhancing disease prevalence tracking. However, despite the widespread adoption of WWS, a knowledge gap remains regarding the impact of ferric sulfate coagulation, commonly used in enhanced primary clarification, the initial stage of wastewater treatment where solids are sedimented and removed, on SARS-CoV-2 and PMMoV quantification in wastewater-based epidemiology. This study examines the effects of ferric sulfate addition, along with the associated pH reduction, on the measurement of SARS-CoV-2 and PMMoV viral measurements in wastewater primary clarified sludge through jar testing. Results show that the addition of Fe 3+ concentrations in the conventional 0 to 60 mg/L range caused no effect on SARS-CoV-2 N1 and N2 gene region measurements in wastewater solids. However, elevated Fe 3+ concentrations were shown to be associated with a statistically significant increase in PMMoV viral measurements in wastewater solids, which consequently resulted in the underestimation of PMMoV-normalized SARS-CoV-2 viral signal measurements (N1 and N2 copies/copies of PMMoV). The observed pH reduction from coagulant addition did not contribute to the increased PMMoV measurements, suggesting that this phenomenon arises from the partitioning of PMMoV viral particles into wastewater solids., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

When an outbreak of COVID-19 occurs, it will cause a shortage of medical resources and the surge of demand for hospital beds. Predicting the length of stay (LOS) of COVID-19 patients is helpful to the overall coordination of hospital management and improves the utilization rate of medical resources. The purpose of this paper is to predict LOS for patients with COVID-19, so as to provide hospital management with auxiliary decision-making of medical resource scheduling. We collected the data of 166 COVID-19 patients in a hospital in Xinjiang from July 19, 2020, to August 26, 2020, and carried out a retrospective study. The results showed that the median LOS was 17.0 days, and the average of LOS was 18.06 days. Demographic data and clinical indicators were included as predictive variables to construct a model for predicting the LOS using gradient boosted regression trees (GBRT). The MSE, MAE and MAPE of the model are 23.84, 4.12 and 0.76 respectively. The importance of all the variables involved in the prediction of the model was analyzed, and the clinical indexes creatine kinase-MB (CK-MB), C-reactive protein (CRP), creatine kinase (CK), white blood cell count (WBC) and the age of patients had a higher contribution to the LOS. We found our GBRT model can accurately predict the LOS of COVID-19 patients, which will provide good assistant decision-making for medical management.

Medical images are used as an important basis for diagnosing diseases, among which CT images are seen as an important tool for diagnosing lung lesions. However, manual segmentation of infected areas in CT images is time-consuming and laborious. With its excellent feature extraction capabilities, a deep learning-based method has been widely used for automatic lesion segmentation of COVID-19 CT images. However, the segmentation accuracy of these methods is still limited. To effectively quantify the severity of lung infections, we propose a Sobel operator combined with multi-attention networks for COVID-19 lesion segmentation (SMA-Net). In our SMA-Net method, an edge feature fusion module uses the Sobel operator to add edge detail information to the input image. To guide the network to focus on key regions, SMA-Net introduces a self-attentive channel attention mechanism and a spatial linear attention mechanism. In addition, the Tversky loss function is adopted for the segmentation network for small lesions. Comparative experiments on COVID-19 public datasets show that the average Dice similarity coefficient (DSC) and joint intersection over union (IOU) of the proposed SMA-Net model are 86.1% and 77.8%, respectively, which are better than those in most existing segmentation networks.

Since the start of 2020, the outbreak of the Coronavirus disease (COVID-19) has been a global public health emergency, and it has caused unprecedented economic and social disaster. In order to improve the diagnosis efficiency of COVID-19 patients, a number of researchers have conducted extensive studies on applying artificial intelligence techniques to the analysis of COVID-19-related medical images. The automatic segmentation of lesions from computed tomography (CT) images using deep learning provides an important basis for the quantification and diagnosis of COVID-19 cases. For a deep learning-based CT diagnostic method, a few of accurate pixel-level labels are essential for the training process of a model. However, the translucent ground-glass area of the lesion usually leads to mislabeling while performing the manual labeling operation, which weakens the accuracy of the model. In this work, we propose a method for correcting rough labels; that is, to hierarchize these rough labels into precise ones by performing an analysis on the pixel distribution of the infected and normal areas in the lung. The proposed method corrects the incorrectly labeled pixels and enables the deep learning model to learn the infected degree of each infected pixel, with which an aiding system (named DLShelper) for COVID-19 CT image diagnosis using the hierarchical labels is also proposed. The DLShelper targets lesion segmentation from CT images, as well as the severity grading. The DLShelper assists medical staff in efficient diagnosis by providing rich auxiliary diagnostic information (including the severity grade, the proportions of the lesion and the visualization of the lesion area). A comprehensive experiment based on a public COVID-19 CT image dataset is also conducted, and the experimental results show that the DLShelper significantly improves the accuracy of segmentation for the lesion areas and also achieves a promising accuracy for the severity grading task.

Wastewater surveillance (WWS) of SARS-CoV-2 was proven to be a reliable and complementary tool for population-wide monitoring of COVID-19 disease incidence but was not as rigorously explored as an indicator for disease burden throughout the pandemic. Prior to global mass immunization campaigns and during the spread of the wildtype COVID-19 and the Alpha variant of concern (VOC), viral measurement of SARS-CoV-2 in wastewater was a leading indicator for both COVID-19 incidence and disease burden in communities. As the two-dose vaccination rates escalated during the spread of the Delta VOC in Jul. 2021 through Dec. 2021, relations weakened between wastewater signal and community COVID-19 disease incidence and maintained a strong relationship with clinical metrics indicative of disease burden (new hospital admissions, ICU admissions, and deaths). Further, with the onset of the vaccine-resistant Omicron BA.1 VOC in Dec. 2021 through Mar. 2022, wastewater again became a strong indicator of both disease incidence and burden during a period of limited natural immunization (no recent infection), vaccine escape, and waned vaccine effectiveness. Lastly, with the populations regaining enhanced natural and vaccination immunization shortly prior to the onset of the Omicron BA.2 VOC in mid-Mar 2022, wastewater is shown to be a strong indicator for both disease incidence and burden. Hospitalization-to-wastewater ratio is further shown to be a good indicator of VOC virulence when widespread clinical testing is limited. In the future, WWS is expected to show moderate indication of incidence and strong indication of disease burden in the community during future potential seasonal vaccination campaigns., Competing Interests: Declaration of competing interest The authors declare that no competing financial interests or personal relationships influenced the work reported in this manuscript., (Copyright © 2022. Published by Elsevier B.V.)

Clinical testing has been the cornerstone of public health monitoring and infection control efforts in communities throughout the COVID-19 pandemic. With the anticipated reduction of clinical testing as the disease moves into an endemic state, SARS-CoV-2 wastewater surveillance (WWS) will have greater value as an important diagnostic tool. An in-depth analysis and understanding of the metrics derived from WWS is required to interpret and utilize WWS-acquired data effectively (McClary-Gutierrez et al., 2021; O'Keeffe, 2021). In this study, the SARS-CoV-2 wastewater signal to clinical cases (WC) ratio was investigated across seven cities in Canada over periods ranging from 8 to 21 months. This work demonstrates that significant increases in the WC ratio occurred when clinical testing eligibility was modified to appointment-only testing, identifying a period of insufficient clinical testing (resulting in a reduction to testing access and a reduction in the number of daily tests) in these communities, despite increases in the wastewater signal. Furthermore, the WC ratio decreased significantly in 6 of the 7 studied locations, serving as a potential signal of the emergence of the Alpha variant of concern (VOC) in a relatively non-immunized community (40-60 % allelic proportion), while a more muted decrease in the WC ratio signaled the emergence of the Delta VOC in a relatively well-immunized community (40-60 % allelic proportion). Finally, a significant decrease in the WC ratio signaled the emergence of the Omicron VOC, likely because of the variant's greater effectiveness at evading immunity, leading to a significant number of new reported clinical cases, even when community immunity was high. The WC ratio, used as an additional monitoring metric, could complement clinical case counts and wastewater signals as individual metrics in its potential ability to identify important epidemiological occurrences, adding value to WWS as a diagnostic technology during the COVID-19 pandemic and likely for future pandemics., Competing Interests: Declaration of competing interest The authors declare that no known competing financial interests or personal relationships influenced the work reported in this manuscript., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Several observational studies have confirmed the relationship between thyroid hormones and coronavirus disease 2019 (COVID-19), but this correlation remains controversial. We performed a two-sample Mendelian randomization (MR) analysis based on the largest publicly available summary datasets. Summary statistics with 49 269 individuals for free thyroxine (FT4) and 54 288 for thyroid stimulating hormone (TSH) were used as exposure instruments. Genome-wide association studies of susceptibility (cases = 38 984; controls = 1 644 784), hospitalization (cases: 9986 = controls = 1 877 672), and very severe disease (cases = 5101; controls = 1 383 241) of COVID-19 were used as the outcome. We used the inverse-variance weighted (IVW) method as the primary analysis, and utilized MR-Egger regression, weighted median, and robust adjusted profile score (RAPS) for sensitivity analysis. Genetic predisposition to higher serum levels of FT4 within the normal range was negatively associated with the risk of COVID-19 hospitalization (odds ratio [OR] = 0.818; 95% CI, 0.718-0.932; P = 2.6 × 10-3) and very severe disease (OR = 0.758; 95% CI, 0.626-0.923; P = 5.8 × 10-3), but not susceptibility. There is no evidence that genetically predicted circulating TSH levels are associated with COVID-19 susceptibility and severity risk. Neither apparent pleiotropy nor heterogeneity were detected in the sensitivity analysis. In summary, we found that higher FT4 levels may reduce the risk of COVID-19 severity, suggesting that thyroid function testing may be required for patients with COVID-19., (© The Author(s) 2022. Published by Oxford University Press on behalf of Endocrine Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Recurrent influenza epidemics and pandemic potential are significant risks to global health. Public health authorities use clinical surveillance to locate and monitor influenza and influenza-like cases and outbreaks to mitigate hospitalizations and deaths. Currently, global integration of clinical surveillance is the only reliable method for reporting influenza types and subtypes to warn of emergent pandemic strains. The utility of wastewater surveillance (WWS) during the COVID-19 pandemic as a less resource intensive replacement or complement for clinical surveillance has been predicated on analyzing viral fragments in wastewater. We show here that influenza virus targets are stable in wastewater and partitions favorably to the solids fraction. By quantifying, typing, and subtyping the virus in municipal wastewater and primary sludge during a community outbreak, we forecasted a citywide flu outbreak with a 17-day lead time and provided population-level viral subtyping in near real-time to show the feasibility of influenza virus WWS at the municipal and neighbourhood levels in near real time using minimal resources and infrastructure., (© 2022. The Author(s).)

Background: Observational studies have reported an association between coronavirus disease 2019 (COVID-19) risk and thyroid dysfunction, but without a clear causal relationship. We attempted to evaluate the association between thyroid function and COVID-19 risk using a bidirectional two-sample Mendelian randomization (MR) analysis., Methods: Summary statistics on the characteristics of thyroid dysfunction (hypothyroidism and hyperthyroidism) were obtained from the ThyroidOmics Consortium. Genome-wide association study statistics for COVID-19 susceptibility and its severity were obtained from the COVID-19 Host Genetics Initiative, and severity phenotypes included hospitalization and very severe disease in COVID-19 participants. The inverse variance-weighted (IVW) method was used as the primary analysis method, supplemented by the weighted-median (WM), MR-Egger, and MR-PRESSO methods. Results were adjusted for Bonferroni correction thresholds., Results: The forward MR estimates show no effect of thyroid dysfunction on COVID-19 susceptibility and severity. The reverse MR found that COVID-19 susceptibility was the suggestive risk factor for hypothyroidism (IVW: OR = 1.577, 95% CI = 1.065-2.333, P = 0.022; WM: OR = 1.527, 95% CI = 1.042-2.240, P = 0.029), and there was lightly association between COVID-19 hospitalized and hypothyroidism (IVW: OR = 1.151, 95% CI = 1.004-1.319, P = 0.042; WM: OR = 1.197, 95% CI = 1.023-1.401, P = 0.023). There was no evidence supporting the association between any phenotype of COVID-19 and hyperthyroidism., Conclusion: Our results identified that COVID-19 might be the potential risk factor for hypothyroidism. Therefore, patients infected with SARS-CoV-2 should strengthen the monitoring of thyroid function., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zhang, Fang and Lv.)

Information release is a key to the macro-economy during the outbreak of the Coronavirus Diosease-2019 (COVID-19). To explore the relationship between information supply by the government and public information demand in the pandemic, this study collected over 4,000 posts published on the most popular social media platform, i.e., WeChat. Many approaches, such as text mining, are employed to explore the information at different stages during the pandemic. According to the results, the government attached great importance to the information related to the pandemic. The main topics of information released by the government included the latest situation of the pandemic, announcements by the State Council, and prevention policies for COVID-19. Information mismatch between the public and Chinese governments contributed to the economic depression caused by the pandemic. Specifically, the topics of "the latest situation" and "popular scientific knowledge regarding the pandemic" have gained the most attention of the public. The information demand of the public has changed from the pandemic itself to the recovery of social life and industrial activities after the authority announced the control of the pandemic. However, during the recession phase, the information demand has shifted to asymptomatic infections and global pandemic trends. By contrast, some of the main topics provided by the government, such as "How beautiful you are," were excessive because the public demand is insufficient. Therefore, severe mismatches existed between information release of the government and public information demand during the pandemic, which impeded the recovery of the economy. The results in this study provide strategical suggestions of information release and opinion guidance for the authorities., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Tang, Wu, Chen, Lu, Zhang, Xu and Zhang.)

Wastewater-based epidemiology/wastewater surveillance has been a topic of significant interest over the last year due to its application in SARS-CoV-2 surveillance to track prevalence of COVID-19 in communities. Although SARS-CoV-2 surveillance has been applied in more than 50 countries to date, the application of this surveillance has been largely focused on relatively affluent urban and peri-urban communities. As such, there is a knowledge gap regarding the implementation of reliable wastewater surveillance in small and rural communities for the purpose of tracking rates of incidence of COVID-19 and other pathogens or biomarkers. This study examines the relationships existing between SARS-CoV-2 viral signal from wastewater samples harvested from an upstream pumping station and from an access port at a downstream wastewater treatment lagoon with the community's COVID-19 rate of incidence (measured as percent test positivity) in a small, rural community in Canada. Real-time quantitative polymerase chain reaction (RT-qPCR) targeting the N1 and N2 genes of SARS-CoV-2 demonstrate that all 24-h composite samples harvested from the pumping station over a period of 5.5 weeks had strong viral signal, while all samples 24-h composite samples harvested from the lagoon over the same period were below the limit of quantification. RNA concentrations and integrity of samples harvested from the lagoon were both lower and more variable than from samples from the upstream pumping station collected on the same date, indicating a higher overall stability of SARS-CoV-2 RNA upstream of the lagoon. Additionally, measurements of PMMoV signal in wastewater allowed normalizing SARS-CoV-2 viral signal for fecal matter content, permitting the detection of actual changes in community prevalence with a high level of granularity. As a result, in sewered small and rural communities or low-income regions operating wastewater lagoons, samples for wastewater surveillance should be harvested from pumping stations or the sewershed as opposed to lagoons., Competing Interests: Declaration of competing interest The authors declare that no known competing financial interests or personal relationships could appear to influence the work reported in this manuscript., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Recently, various fully supervised learning methods are successfully applied for lung CT image segmentation. However, pixel-wise annotations are extremely expert-demanding and labor-intensive, but the performance of unsupervised learning methods are failed to meet the demands of practical applications. To achieve a reasonable trade-off between the performance and label dependency, a novel weakly supervised inpainting-based learning method is introduced, in which only bounding box labels are required for accurate segmentation. Specifically, lesion regions are first detected by an object detection network, then we crop them out of the input image and recover the missing holes to normal regions using a progressive CT inpainting network (PCIN). Finally, a post-processing method is designed to get the accurate segmentation mask from the difference image of input and recovered images. In addition, real information (i.e., number, location and size) of the bounding boxes of lesions from the dataset guides us to make the training dataset for PCIN. We apply a multi-scale supervised strategy to train PCIN for a progressive and stable inpainting. Moreover, to remove the visual artifacts resulted from the invalid features of missing holes, an initial patch generation network (IPGN) is proposed for holes initialization with generated pseudo healthy image patches. Experiments on the public COVID-19 dataset demonstrate that PCIN is outstanding in lung CT images inpainting, and the performance of our proposed weakly supervised method is comparable to fully supervised methods. [Display omitted] • We proposed a weakly supervised inpainting-based learning method for COVID-19 lesion segmentation only using bounding box labels. • A progressive CT inpainting network (PCIN) is proposed to recover CT images with rectangle missing holes. • An initial patch generation network (IPGN) is proposed to generate pseudo healthy image patches for holes initialization. • Experiments demonstrate that PCIN is outstanding in CT images inpainting, and the performance of our proposed weakly method is comparable to fully-supervised methods. [ABSTRACT FROM AUTHOR]