Coarse-to-Fine Covid-19 Segmentation via Vision-Language Alignment (preprint)

Segmentation of COVID-19 lesions can assist physicians in better diagnosis and treatment of COVID-19. However, there are few relevant studies due to the lack of detailed information and high-quality annotation in the COVID-19 dataset. To solve the above problem, we propose C2FVL, a Coarse-to-Fine segmentation framework via Vision-Language alignment to merge text information containing the number of lesions and specific locations of image information. The introduction of text information allows the network to achieve better prediction results on challenging datasets. We conduct extensive experiments on two COVID-19 datasets including chest X-ray and CT, and the results demonstrate that our proposed method outperforms other state-of-the-art segmentation methods.

Diversity and independent evolutionary profiling of rodent-borne pathogens in tropical island, China (preprint)

The risk of emerging infectious diseases (EID) is increasing globally. More than 60% of EIDs worldwide are caused by animal-borne pathogens, and most viral pathogens are rodent-borne. This study aimed to characterise the virome and analyse the phylogenetic evolution and diversity of rodent-borne viruses in Hainan Province, China. We collected 588 anal and throat samples from rodents, combined them into 28 pools according to their species and location, and processed them for next-generation sequencing and bioinformatics analysis. The diverse viral reads closely related to mammals were assigned to 15 viral families. Molecular clues of the important rodent-borne viruses were further identified by polymerase chain reaction for phylogenetic analysis and annotation of genetic characteristics such as coronavirus, arenavirus, picornavirus. We identified a pestivirus in Leopoldoms edwardsi and two bocaviruses in Rattus andamanensis and Leopoldoms edwardsi from the national nature reserves of Jianfengling and Bangxi with low amino acid identity to known pathogens are proposed as the novel species, and their rodent hosts have not been previously reported to carry these viruses. These results expand our knowledge of viral classification and host range and suggest that there are highly diverse, undiscovered viruses that have evolved independently in their unique wildlife hosts in inaccessible areas, which may cause zoonosis if they cross their host barrier. Our virome and phylogenetic analyses of rodent-borne viruses provide basic data for the prevention and control of human infectious diseases caused by rodent-borne viruses in the subtropical area of China.

Serology assays used in SARS-CoV-2 seroprevalence surveys worldwide: a systematic review and meta-analysis of assay features, testing algorithms, and performance (preprint)

Background: Many SARS-CoV-2 serological assays were rapidly developed during the COVID-19 pandemic. However, differences in detection mechanism limit the comparability of assay outputs. Methods: As part of the SeroTracker global living systematic review of SARS-CoV-2 seroprevalence studies, we collated serological assays used in serosurveys between January 1, 2020 and November 19, 2021. We mapped performance metrics to the manufacturer, third-party head-to-head, and independent group evaluations, comparing the assay performance data using a mixed-effect beta regression model. Results: Among 1807 serosurveys, 192 distinctive commercial assays and 380 self-developed assays were identified. According to manufacturers, 28.6% of all commercial assays met WHO criteria for emergency use (sensitivity [Sn.] >= 90.0%, specificity [Sp.] >= 97.0%). Third-party and independent evaluations indicated that manufacturers overstated the Sn. of their assays by 5.4% and 2.8%, and Sp. by 6.3% and 1.2%. We found in simulations that inaccurate Sn. and Sp. can substantially bias seroprevalence estimates corrected for assay performance. Conclusions: The Sn. and Sp. of the serological assay are not fixed properties, but varying features depending on testing population. To achieve precise population estimates and to ensure comparability, serosurveys should select assays with strong, independently validated performance and adjust seroprevalence estimates based on assured performance data.

Protective effectiveness of prior SARS-CoV-2 infection and hybrid immunity against Omicron infection and severe disease: a systematic review and meta-regression (preprint)

Background We aimed to systematically review the magnitude and duration of the protective effectiveness of prior infection (PE) and hybrid immunity (HE) against Omicron infection and severe disease. Methods We searched pre-print and peer-reviewed electronic databases for controlled studies from January 1, 2020, to June 1, 2022. Risk of bias (RoB) was assessed using the Risk of Bias In Non-Randomized Studies of Interventions (ROBINS-I)-Tool. We used random-effects meta-regression to estimate the magnitude of protection at 1-month intervals and the average change in protection since the last vaccine dose or infection from 3 months to 6 or 12 months. We compared our estimates of PE and HE to previously published estimates of the magnitude and durability of vaccine effectiveness (VE) against Omicron. Findings Eleven studies of prior infection and 15 studies of hybrid immunity were included. For prior infection, there were 97 estimates (27 at moderate RoB and 70 at serious RoB), with the longest follow up at 15 months. PE against hospitalization or severe disease was 82.5% [71.8-89.7%] at 3 months, and 74.6% [63.1-83.5%] at 12 months. PE against reinfection was 65.2% [52.9-75.9%] at 3 months, and 24.7% [16.4-35.5%] at 12 months. For HE, there were 153 estimates (78 at moderate RoB and 75 at serious RoB), with the longest follow up at 11 months for primary series vaccination and 4 months for first booster vaccination. Against hospitalization or severe disease, HE involving either primary series vaccination or first booster vaccination was consistently >95% for the available follow up. Against reinfection, HE involving primary series vaccination was 69.0% [58.9-77.5%] at 3 months after the most recent infection or vaccination, and 41.8% [31.5-52.8%] at 12 months, while HE involving first booster vaccination was 68.6% [58.8-76.9%] at 3 months, and 46.5% [36.0-57.3%] at 6 months. Against hospitalization or severe disease at 6 months, hybrid immunity with first booster vaccination (effectiveness 95.3% [81.9-98.9%]) or with primary series alone (96.5% [90.2-98.8%]) provided significantly greater protection than prior infection alone (80.1% [70.3-87.2%]), first booster vaccination alone (76.7% [72.5-80.4%]), or primary series alone (64.6% [54.5-73.6%]). Results for protection against reinfection were similar. Interpretation Prior infection and hybrid immunity both provided greater and more sustained protection against Omicron than vaccination alone. All protection estimates waned quickly against infection but remained high for hospitalisation or severe disease. Individuals with hybrid immunity had the highest magnitude and durability of protection against all outcomes, reinforcing the global imperative for vaccination.

TFCNs: A CNN-Transformer Hybrid Network for Medical Image Segmentation (preprint)

Medical image segmentation is one of the most fundamental tasks concerning medical information analysis. Various solutions have been proposed so far, including many deep learning-based techniques, such as U-Net, FC-DenseNet, etc. However, high-precision medical image segmentation remains a highly challenging task due to the existence of inherent magnification and distortion in medical images as well as the presence of lesions with similar density to normal tissues. In this paper, we propose TFCNs (Transformers for Fully Convolutional denseNets) to tackle the problem by introducing ResLinear-Transformer (RL-Transformer) and Convolutional Linear Attention Block (CLAB) to FC-DenseNet. TFCNs is not only able to utilize more latent information from the CT images for feature extraction, but also can capture and disseminate semantic features and filter non-semantic features more effectively through the CLAB module. Our experimental results show that TFCNs can achieve state-of-the-art performance with dice scores of 83.72\% on the Synapse dataset. In addition, we evaluate the robustness of TFCNs for lesion area effects on the COVID-19 public datasets. The Python code will be made publicly available on https://github.com/HUANGLIZI/TFCNs.

SARS-CoV-2 infection in Africa: A systematic review and meta-analysis of standardised seroprevalence studies, from January 2020 to December 2021 (preprint)

IntroductionEstimating COVID-19 cumulative incidence in Africa remains problematic due to challenges in contact tracing, routine surveillance systems and laboratory testing capacities and strategies. We undertook a meta-analysis of population-based seroprevalence studies to estimate SARS-CoV-2 seroprevalence in Africa to inform evidence-based decision making on Public Health and Social Measures (PHSM) and vaccine strategy. MethodsWe searched for seroprevalence studies conducted in Africa published 01-01-2020 to 30-12-2021 in Medline, Embase, Web of Science, and Europe PMC (preprints), grey literature, media releases and early results from WHO Unity studies. All studies were screened, extracted, assessed for risk of bias and evaluated for alignment with the WHO Unity protocol for seroepidemiological investigations. We conducted descriptive analyses of seroprevalence and meta-analysed seroprevalence differences by demographic groups, place and time. We estimated the extent of undetected infections by comparing seroprevalence and cumulative incidence of confirmed cases reported to WHO. PROSPERO: CRD42020183634. ResultsWe identified 54 full texts or early results, reporting 151 distinct seroprevalence studies in Africa Of these, 95 (63%) were low/moderate risk of bias studies. SARS-CoV-2 seroprevalence rose from 3.0% [95% CI: 1.0-9.2%] in Q2 2020 to 65.1% [95% CI: 56.3-73.0%] in Q3 2021. The ratios of seroprevalence from infection to cumulative incidence of confirmed cases was large (overall: 97:1, ranging from 10:1 to 958:1) and steady over time. Seroprevalence was highly heterogeneous both within countries - urban vs. rural (lower seroprevalence for rural geographic areas), children vs. adults (children aged 0-9 years had the lowest seroprevalence) - and between countries and African sub-regions (Middle, Western and Eastern Africa associated with higher seroprevalence). ConclusionWe report high seroprevalence in Africa suggesting greater population exposure to SARS-CoV-2 and protection against COVID-19 disease than indicated by surveillance data. As seroprevalence was heterogeneous, targeted PHSM and vaccination strategies need to be tailored to local epidemiological situations.

Global epidemiology of SARS-CoV-2 infection: a systematic review and meta-analysis of standardized population-based seroprevalence studies, Jan 2020-Oct 2021 (preprint)

Background COVID-19 case data underestimates infection and immunity, especially in low- and middle-income countries (LMICs). We meta-analyzed standardized SARS-CoV-2 seroprevalence studies to estimate global seroprevalence. Objectives/Methods We conducted a systematic review and meta-analysis, searching MEDLINE, Embase, Web of Science, preprints, and grey literature for SARS-CoV-2 seroprevalence studies aligned with the WHO UNITY protocol published between 2020-01-01 and 2021-10-29. Eligible studies were extracted and critically appraised in duplicate. We meta-analyzed seroprevalence by country and month, pooling to estimate regional and global seroprevalence over time; compared seroprevalence from infection to confirmed cases to estimate under-ascertainment; meta-analyzed differences in seroprevalence between demographic subgroups; and identified national factors associated with seroprevalence using meta-regression. PROSPERO: CRD42020183634. Results We identified 396 full texts reporting 736 distinct seroprevalence studies (41% LMIC), including 355 low/moderate risk of bias studies with national/sub-national scope in further analysis. By April 2021, global SARS-CoV-2 seroprevalence was 26.1%, 95% CI [24.6-27.6%]. Seroprevalence rose steeply in the first half of 2021 due to infection in some regions (e.g., 18.2% to 45.9% in Africa) and vaccination and infection in others (e.g., 11.3% to 57.4% in the Americas high-income countries), but remained low in others (e.g., 0.3% to 1.6% in the Western Pacific). In 2021 Q1, median seroprevalence to case ratios were 1.9:1 in HICs and 61.9:1 in LMICs. Children 0-9 years and adults 60+ were at lower risk of seropositivity than adults 20-29. In a multivariate model using data pre-vaccination, more stringent public health and social measures were associated with lower seroprevalence. Conclusions Global seroprevalence has risen considerably over time and with regional variation, however much of the global population remains susceptible to SARS-CoV-2 infection. True infections far exceed reported COVID-19 cases. Standardized seroprevalence studies are essential to inform COVID-19 control measures, particularly in resource-limited regions.

SeroTracker-ROB: reproducible decision rules for risk of bias assessment of seroprevalence studies (preprint)

BackgroundEvaluating seroprevalence study risk of bias (RoB) is crucial for robust infection surveillance, but can be a time-consuming and subjective process. We aimed to develop decision rules for reproducible RoB assessment and an automated tool to implement these decision rules. MethodsWe developed the SeroTracker-RoB approach to RoB assessment. To do so, we created objective criteria for items on the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Prevalence Studies and developed decision rules for RoB based on these items. The criteria and decision rules were based on published guidance for assessing RoB for prevalence studies and expert opinion. Decision rules were validated against the SeroTracker database of seroprevalence studies, which included consensus manual RoB judgements from two independent reviewers. We measured efficiency by calculating paired-samples t-test for time to judge RoB using the automated tool versus manually for 25 randomly selected articles from the SeroTracker database, coverage as the proportion of database studies where the decision rules could evaluate RoB, and reliability by calculating intraclass correlations between automated and manual RoB assessments. ResultsWe established objective criteria for seven of nine JBI items. We developed a set of decision rules with 61 branches. The SeroTracker-RoB tool was significantly faster than manual assessment with a mean time of 0.80 vs. 2.93 minutes per article (p<0.001), classified 100% (n = 2,070) of studies, and had good reliability compared to manual review (intraclass correlation 0.77, 95% confidence interval 0.74 to 0.80). The SeroTracker-RoB Excel Tool embeds this approach in a simple data extraction sheet for use by other researchers. ConclusionsThe SeroTracker-RoB approach was faster than manual assessment, with complete coverage and good reliability compared to two independent human reviewers. This approach and tool enable rapid, transparent, and reproducible evidence synthesis of infection prevalence studies, and may support public health efforts during future outbreaks and pandemics. O_TEXTBOXWhat is new? O_LIWhat is already known: Risk of bias assessments are a core element of evidence synthesis but can be time consuming and subjective. As such, there is a need for validated and transparent tools to automate such assessments, particularly during disease outbreaks and pandemics to inform public health decision making. However, there are currently no automated tools for risk of bias assessment of prevalence studies. C_LIO_LIWhat is new: We developed a reproducible approach to risk of bias assessment for SARS-CoV-2 seroprevalence studies. The automated approach was five times faster than manual human assessment, successfully categorized all 2,070 studies that it was tested on, and had good agreement with manual review. We built a simple Excel tool so that other researchers can use this automated approach. C_LIO_LIPotential impact: The SeroTracker-RoB approach and tool enables rapid, transparent, and reproducible risk of bias assessments for SARS-CoV-2 seroprevalence studies, and could be readily adapted for other types of disease prevalence studies. This process may also be applicable to automation of critical appraisal and risk of bias assessment for other types of studies and in other scientific disciplines. C_LI C_TEXTBOX

A Chemical-Enhanced System for CRISPR-Based Nucleic Acid Detection (preprint)

The CRISPR-based nucleic acid detection systems such as SHERLOCK, DETECTR and HOLMES have shown great potential for point-of-care testing of viral pathogens, especially in the context of COVID-19 pandemic. Here we optimize several key parameters of reaction chemistry and develop a Chemical Enhanced CRISPR Detection system for nucleic acid (termed CECRID). For the Cas12a/Cas13a-based signal detection phase, we determine buffer conditions and substrate range for optimal detection performance. By comparing several chemical additives, we find that addition of L-proline can secure or enhance Cas12a/Cas13a detection capability. For isothermal amplification phase with typical LAMP and RPA methods, inclusion of L-proline can also enhance specific target amplification as determined by CRISPR detection. Using SARS-CoV-2 pseudovirus, we demonstrate CECRID has enhanced detection sensitivity over chemical additive-null method with either fluorescence or lateral flow strip readout. Thus, CECRID provides an improved detection power and system robustness towards practical application of CRISPR-based diagnostics.

A Computational Framework of Host-Based Drug Repositioning for Broad-Spectrum Antivirals against RNA Viruses (preprint)

RNA viruses are responsible for many types of zoonotic diseases that post great challenges for public health system. Effective therapeutics against these viral infections remains limited. Here we deployed a computational framework for host-based drug repositioning to predict potential antiviral drug candidates from 2352 approved drugs and 1062 natural compounds embedded in Traditional Chinese Medicine herbs. By systematically interrogating public genetic screening data, we comprehensively catalogued human-specific host dependency genes that are indispensable for the successful viral infection corresponding to 10 families and 29 species of RNA viruses. In addition, we utilized these host dependency genes as potential drug targets, and interrogated extensive drug-target interactions through multiple ways such as database retrieval, literature mining and de novo prediction using artificial intelligence-based algorithms. Repurposed drugs or natural compounds were proposed for combating many viral pathogens such as coronaviruses (e.g., SARS-CoV-2), flaviviruses (e.g., Zika virus) and influenza viruses. This study helps to prioritize promising drug candidates for further therapeutic evaluation against these viral-related diseases.