Precision medicine for pandemics: stratification of COVID-19 molecular phenotypes defined by topological analysis of global blood gene expression. (preprint)

Precision medicine offers a promising avenue for better therapeutic responses to pandemics such as COVID-19. This study leverages independent patient cohorts in Florence and Liege gathered under the umbrella of the DRAGON consortium for the stratification of molecular phenotypes associated with COVID-19 using topological analysis of global blood gene expression. Whole blood from 173 patients was collected and RNA was sequenced on the Novaseq platform. Molecular phenotypes were defined through topological analysis of gene expression relative to the biological network using the TopMD algorithm. The two cohorts from Florence and Liege allowed for independent validation of the findings in this study. Clustering of the topological maps of differential pathway activation revealed three distinct molecular phenotypes of COVID-19 in the Florence patient cohort, which were also observed in the Liege cohort. Cluster 1, was characterised by high activation of pathways associated with ESC pluripotency, NRF2, and TGF-B; receptor signalling. Cluster 2 displayed high activation of pathways including focal adhesion-PI3K-Akt-mTOR signalling and type I interferon induction and signalling, while Cluster 3 exhibited low IRF7-related pathway activation. TopMD was also used with the Drug-Gene Interaction Database (DGIdb), revealing pharmaceutical interventions targeting mechanisms across multiple phenotypes and individuals. The data illustrates the utility of molecular phenotyping from topological analysis of blood gene expression, and holds promise for informing personalised therapeutic strategies not only for COVID-19 but also for Disease X. Its potential transferability across multiple diseases highlights the value in pandemic response efforts, offering insights before large-scale clinical studies are initiated.

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.

Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation (preprint)

Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and clinical drive for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage.

Genetically predicted high IGF-1 levels showed protective effects on COVID-19 susceptibility and hospitalization: a Mendelian randomisation study with data from 60 studies across 25 countries.

Background: Epidemiological studies observed gender differences in COVID-19 outcomes, however, whether sex hormone plays a causal in COVID-19 risk remains unclear. This study aimed to examine associations of sex hormone, sex hormones-binding globulin (SHBG), insulin-like growth factor-1 (IGF-1), and COVID-19 risk. Methods: Two-sample Mendelian randomization (TSMR) study was performed to explore the causal associations between testosterone, estrogen, SHBG, IGF-1, and the risk of COVID-19 (susceptibility, hospitalization, and severity) using genome-wide association study (GWAS) summary level data from the COVID-19 Host Genetics Initiative (N=1,348,701). Random-effects inverse variance weighted (IVW) MR approach was used as the primary MR method and the weighted median, MR-Egger, and MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test were conducted as sensitivity analyses. Results: Higher genetically predicted IGF-1 levels have nominally significant association with reduced risk of COVID-19 susceptibility and hospitalization. For one standard deviation increase in genetically predicted IGF-1 levels, the odds ratio was 0.77 (95% confidence interval [CI], 0.61-0.97, p=0.027) for COVID-19 susceptibility, 0.62 (95% CI: 0.25-0.51, p=0.018) for COVID-19 hospitalization, and 0.85 (95% CI: 0.52-1.38, p=0.513) for COVID-19 severity. There was no evidence that testosterone, estrogen, and SHBG are associated with the risk of COVID-19 susceptibility, hospitalization, and severity in either overall or sex-stratified TSMR analysis. Conclusions: Our study indicated that genetically predicted high IGF-1 levels were associated with decrease the risk of COVID-19 susceptibility and hospitalization, but these associations did not survive the Bonferroni correction of multiple testing. Further studies are needed to validate the findings and explore whether IGF-1 could be a potential intervention target to reduce COVID-19 risk. Funding: We acknowledge support from NSFC (LR22H260001), CRUK (C31250/A22804), SHLF (Hjärt-Lungfonden, 20210351), VR (Vetenskapsrådet, 2019-00977), and SCI (Cancerfonden).

An emissions inventory using flight information reveals the long-term changes of aviation CO2 emissions in China

The low-carbon development of air transport industry is of great significance for China to achieve the commitment of carbon peak and carbon neutrality goals. In order to improve the basic data of aviation CO2 emissions, this study continuously collected full flight information in China from January 2017 to December 2020, and established a flight information database and an aircraft-engine parameter database. On the basis of IPCC's Tier 3B accounting method, this study established a long-term aviation CO2 emissions inventory of China from 2017 to 2020 by calculating and accumulating CO2 emissions of each flight. And aviation CO2 emissions of various provinces and cities in China were calculated combined with spatial allocation method. The results showed that aviation CO2 emissions in China was 104.1, 120.1, 136.9, and 88.3 Mt in 2017, 2018, 2019, and 2020, respectively, with annual growth rates of 15.4%, 14.0%, and −35.3% in 2018, 2019, and 2020, respectively. Affected by the COVID-19 pandemic, aviation CO2 emissions in all 31 provinces and 93% of cities decreased in 2020 compared with 2019. China is in the stage of rapid development of air transport industry, and aviation fossil energy consumption and CO2 emissions have continued to grow in recent years.

Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation (preprint)

Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases, while its manual delineation is unduly burdensome. To alleviate this time-consuming and potentially subjective manual procedure, researchers have proposed methods to automatically segment airways from computerized tomography (CT) images. However, some small-sized airway branches (e.g., bronchus and terminal bronchioles) significantly aggravate the difficulty of automatic segmentation by machine learning models. In particular, the variance of voxel values and the severe data imbalance in airway branches make the computational module prone to discontinuous and false-negative predictions. especially for cohorts with different lung diseases. Attention mechanism has shown the capacity to segment complex structures, while fuzzy logic can reduce the uncertainty in feature representations. Therefore, the integration of deep attention networks and fuzzy theory, given by the fuzzy attention layer, should be an escalated solution for better generalization and robustness. This paper presents an efficient method for airway segmentation, comprising a novel fuzzy attention neural network and a comprehensive loss function to enhance the spatial continuity of airway segmentation. The deep fuzzy set is formulated by a set of voxels in the feature map and a learnable Gaussian membership function. Different from the existing attention mechanism, the proposed channel-specific fuzzy attention addresses the issue of heterogeneous features in different channels. Furthermore, a novel evaluation metric is proposed to assess both the continuity and completeness of airway structures. The efficiency, generalization and robustness of the proposed method have been proved by training on normal lung disease while testing on datasets of lung cancer, COVID-19 and pulmonary fibrosis.

CS$^2$: A Controllable and Simultaneous Synthesizer of Images and Annotations with Minimal Human Intervention (preprint)

The destitution of image data and corresponding expert annotations limit the training capacities of AI diagnostic models and potentially inhibit their performance. To address such a problem of data and label scarcity, generative models have been developed to augment the training datasets. Previously proposed generative models usually require manually adjusted annotations (e.g., segmentation masks) or need pre-labeling. However, studies have found that these pre-labeling based methods can induce hallucinating artifacts, which might mislead the downstream clinical tasks, while manual adjustment could be onerous and subjective. To avoid manual adjustment and pre-labeling, we propose a novel controllable and simultaneous synthesizer (dubbed CS$^2$) in this study to generate both realistic images and corresponding annotations at the same time. Our CS$^2$ model is trained and validated using high resolution CT (HRCT) data collected from COVID-19 patients to realize an efficient infections segmentation with minimal human intervention. Our contributions include 1) a conditional image synthesis network that receives both style information from reference CT images and structural information from unsupervised segmentation masks, and 2) a corresponding segmentation mask synthesis network to automatically segment these synthesized images simultaneously. Our experimental studies on HRCT scans collected from COVID-19 patients demonstrate that our CS$^2$ model can lead to realistic synthesized datasets and promising segmentation results of COVID infections compared to the state-of-the-art nnUNet trained and fine-tuned in a fully supervised manner.

Anxiety and depression in graduating university students during the COVID-19 pandemic: a longitudinal study

Objective: To investigate the prevalence of anxiety and depression in graduating university students during the COVID-19 pandemic and determine the associated factors. Methods: A total of 681 graduating university students and 620 juniors enrolled in the first stage. The Self-rating Anxiety Scale (SAS) and Self-rating Depression Scale (SDS) were used to measure anxiety and depression. In the second stage, 578 of the 681 graduating students completed the SAS and SDS questionnaires after graduation. Results: The average SAS score of the graduating university students was significantly higher than that of the juniors (47.66±12.86 vs. 43.97±10.42, P<0.001). Depression was more prevalent among the graduating university students than in the control groups (39.06% vs. 9.19%, P<0.01). The percentages of anxiety and depression significantly decreased after graduation (t=8.602, P<0.001). The anxiety of graduating university students was associated with gender (OR=1.62, 95% CI: 1.10-2.37), monthly family income (OR=0.05, 95% CI: 0.02-0.11), and weekly exercise time (OR=0.53, 95% CI: 0.35-0.08). Their depression was related to their family’s monthly income (OR=0.09, 95% CI: 0.05-0.16) and father’s educational status (OR=2.24, 95% CI: 1.17-4.30). Conclusion: Anxiety and depression were rife within the graduating Chinese university students during the period of the COVID-19 pandemic and were both associated with monthly family income. Treatments tailored to specific targets are needed for graduating university students with mental problems.

The Fluctuation Trend of Serum Anti-SARS-CoV-2 IgM/IgG Antibodies Seroprevalence in the Non-COVID-19 Infected Population and Correlation with Peripheral Blood Leukocyte Parameters in Beijing, China, 2021: A Real-World Study.

Since 2019, the coronavirus disease 2019 (COVID-19) global pandemic has caused more than 300 million cases of disease and 5 million deaths. Vaccination has been widely accepted as the most effective measure for the prevention and control of this disease. However, there is little understanding about serum anti-SARS-CoV-2 IgM/IgG levels after inactivated vaccination as well as the relationship with peripheral blood leukocytes in the non-COVID-19 infected population. A total of 16,335 male and 22,302 female participants were recruited in this study, which was conducted in the Peking University Third Hospital located in Beijing (China). The level and seroprevalence of serum anti-SARS-CoV-2 receptor-binding domain (RBD) IgM/IgG and the association with peripheral blood leukocytes classification were investigated. With an increase in the number and percentage of full immunization of COVID-19 vaccinations in Beijing, serum anti-SARS-CoV-2 IgG antibodies levels and seroprevalence were significantly elevated (p < 0.01). The serum anti-SARS-CoV-2 IgG antibodies of 60 years and older persons were significantly lower than that of individuals that are 18~60 years old (p < 0.01), and there was a positive relationship between serum anti-SARS-CoV-2 IgG antibodies levels and peripheral blood lymphocyte count. The investigation of serum anti-SARS-CoV-2 IgM/IgG antibodies and the peripheral hematological index may prompt and help understand the adaptive immune response of vaccination.

The Role of the Vascular Niche in Organ Fibrosis and COVID-19-Related Organ Damage and the Countermeasures adopted by Chinese and Western Medicine

The vascular niche is a microenvironment located around capillaries and is mainly composed of endothelial cells, pericytes, macrophages, lymphocytes, mesenchymal stem cells, and hematopoietic stem cells. Studies have found that the vascular niche not only functions to regulate cell growth and differentiation in normal tissues, but also has an important role in regulating fibrosis in various organs and tissues in disease states. Coronavirus disease 2019 (COVID-19) is a systemic disease that broke out in 2019, caused by SARS-CoV-2 infection, which results in pulmonary inflammation, systemic multi-organ damage, and an inflammatory cytokine storm. Recently, the vascular niche has been found to play a role in COVID-19-related multi-organ damage. In this review, we introduce the important role of the vascular niche in organ fibrosis and COVID-19-related organ damage, summarize some of the cellular signaling pathways in the vascular niche that promote fibrosis, and discuss the treatment of organ fibrosis in Traditional Chinese medicine and Western medicine.

COVID-19 vaccine perceptions in the initial phases of US vaccine roll-out: an observational study on reddit.

BACKGROUND: Open online forums like Reddit provide an opportunity to quantitatively examine COVID-19 vaccine perceptions early in the vaccine timeline. We examine COVID-19 misinformation on Reddit following vaccine scientific announcements, in the initial phases of the vaccine timeline. METHODS: We collected all posts on Reddit (reddit.com) from January 1 2020 - December 14 2020 (n=266,840) that contained both COVID-19 and vaccine-related keywords. We used topic modeling to understand changes in word prevalence within topics after the release of vaccine trial data. Social network analysis was also conducted to determine the relationship between Reddit communities (subreddits) that shared COVID-19 vaccine posts, and the movement of posts between subreddits. RESULTS: There was an association between a Pfizer press release reporting 90% efficacy and increased discussion on vaccine misinformation. We observed an association between Johnson and Johnson temporarily halting its vaccine trials and reduced misinformation. We found that information skeptical of vaccination was first posted in a subreddit (r/Coronavirus) which favored accurate information and then reposted in subreddits associated with antivaccine beliefs and conspiracy theories (e.g. conspiracy, NoNewNormal). CONCLUSIONS: Our findings can inform the development of interventions where individuals determine the accuracy of vaccine information, and communications campaigns to improve COVID-19 vaccine perceptions, early in the vaccine timeline. Such efforts can increase individual- and population-level awareness of accurate and scientifically sound information regarding vaccines and thereby improve attitudes about vaccines, especially in the early phases of vaccine roll-out. Further research is needed to understand how social media can contribute to COVID-19 vaccination services.

Associations of hand washing frequency with the incidence of illness: a systematic review and meta-analysis.

BACKGROUND: Hand hygiene is one of the ways to prevent the spread of diseases. Our aim was to explore the relationship between hand washing frequency and the impact on disease, and give recommendations on the number of times to wash hands. METHODS: We searched seven electronic databases from their inception to April 11, 2020, and reference lists of related reviews for all studies on hand washing frequency and disease prevention. The Review Manager 5.3. software was used to conduct a meta-analysis. We assessed the risk of bias of included studies, and quality of evidence of the main findings. RESULTS: A total of eight studies were included. The results of the meta-analysis showed that there was no statistical significance between the effect of disease prevention and washing more than 4 times/day compared to not [odds ratio (OR) =0.61, 95% confidence interval (CI): 0.37 to 1.01]. The results of a case-control study showed that compared with hand washing ≤4 times/day, hand washing 5-10 times/day (OR =0.75, 95% CI: 0.63 to 0.91) and hand washing >10 times/day (OR =0.65, 95% CI 0.53 to 0.80) could reduce the risk of disease infection. There was no statistical significance advantage to hand washing more than 10 times/day compared to 5-10 times/day (OR =0.86, 95% CI: 0.70 to 1.06). Comparing hand washing ≤10 times/day with hand washing >10 times/day, increased hand washing was a protective factor against infection (OR =0.59, 95% CI: 0.36 to 0.97). CONCLUSIONS: The more frequently hands were washed, the lower risk of disease. So far however, there is no high-quality evidence indicating the best range of hand washing frequency for disease prevention.

Sudden death of COVID-19 patients in Wuhan, China: A retrospective cohort study.

BACKGROUND: In December 2019, coronavirus disease 2019 (COVID-19) emerged in Wuhan city and rapidly spread throughout China. So far, it has caused ~ 4000 deaths in this country. We aimed to systematically characterize clinical features and determine risk factors of sudden death for COVID-19 patients. METHODS: Deceased patients with COVID-19 in Tongji hospital from January 22 to March 23, 2020 were extracted. Patients who died within 24 hours after admission were identified as sudden deaths, and the others formed non-sudden deaths. The differences in clinical characteristics between the two groups were estimated. Risk factors associated with sudden deaths were explored by logistic regression. RESULTS: 281 deceased patients were enrolled in this study. Sudden death occurred in 28 (10.0%) patients, including 4 (14.3%) admitted to the intensive care unit. Fatigue was more common in sudden deaths (11, 47.8%) than in non-sudden deaths (40, 17.2%). Both the count and percentage of eosinophils were lower in sudden deaths than that in non-sudden deaths (P = 0.006 and P = 0.004). Compared with non-sudden deaths, sudden deaths had higher plasma levels of procalcitonin, C-reactive protein, D-dimer, alanine aminotransferase, aspartate aminotransferase, gamma-glutamyl transferase, lactate dehydrogenase, alkaline phosphatase and N-terminal pro-brain natriuretic peptide. There were not significant differences in gender, age, chest CT image features and comorbidities observed. CONCLUSIONS: The differences between the two groups suggested more severe systemic inflammation, multi-organ dysfunction, especially impaired liver and heart function in COVID-19 patients who died suddenly after admission. More researches are needed to verify these points.

A small molecule compound berberine as an orally active therapeutic candidate against COVID-19 and SARS: A computational and mechanistic study.

The novel coronavirus disease, COVID-19, has grown into a global pandemic and a major public health threat since its breakout in December 2019. To date, no specific therapeutic drug or vaccine for treating COVID-19 and SARS has been FDA approved. Previous studies suggest that berberine, an isoquinoline alkaloid, has shown various biological activities that may help against COVID-19 and SARS, including antiviral, anti-allergy and inflammation, hepatoprotection against drug- and infection-induced liver injury, as well as reducing oxidative stress. In particular, berberine has a wide range of antiviral activities such as anti-influenza, anti-hepatitis C, anti-cytomegalovirus, and anti-alphavirus. As an ingredient recommended in guidelines issued by the China National Health Commission for COVID-19 to be combined with other therapy, berberine is a promising orally administered therapeutic candidate against SARS-CoV and SARS-CoV-2. The current study comprehensively evaluates the potential therapeutic mechanisms of berberine in preventing and treating COVID-19 and SARS using computational modeling, including target mining, gene ontology enrichment, pathway analyses, protein-protein interaction analysis, and in silico molecular docking. An orally available immunotherapeutic-berberine nanomedicine, named NIT-X, has been developed by our group and has shown significantly increased oral bioavailability of berberine, increased IFN-γ production by CD8+ T cells, and inhibition of mast cell histamine release in vivo, suggesting a protective immune response. We further validated the inhibition of replication of SARS-CoV-2 in lung epithelial cells line in vitro (Calu3 cells) by berberine. Moreover, the expression of targets including ACE2, TMPRSS2, IL-1α, IL-8, IL-6, and CCL-2 in SARS-CoV-2 infected Calu3 cells were significantly suppressed by NIT-X. By supporting protective immunity while inhibiting pro-inflammatory cytokines; inhibiting viral infection and replication; inducing apoptosis; and protecting against tissue damage, berberine is a promising candidate in preventing and treating COVID-19 and SARS. Given the high oral bioavailability and safety of berberine nanomedicine, the current study may lead to the development of berberine as an orally, active therapeutic against COVID-19 and SARS.

Quarantine at home may not enough!-from the epidemiological data in Shaanxi Province of China.

OBJECTIVES: A pneumonia associated with 2019 novel coronavirus (2019-nCoV, subsequently named SARS-CoV2) emerged worldwide since December, 2019. We aimed to describe the epidemiological characteristics of 2019 coronavirus disease (COVID-19) in Shaanxi province of China. RESULTS: 1. Among the 245 patients, 132 (53.9%) were males and 113 (46.1%) were females. The average age was 46.15 ± 16.43 years, ranging from 3 to 89 years. 2. For the clinical type, 1.63% (4/245) patients were mild type, 84.90% (208/245) were moderate type, 7.76% (19/245) were severe type, 5.31% (13/245) were critical type and only 0.41% (1/245) was asymptomatic. 3. Of the 245 patients, 116 (47.35%) were input case, 114 (46.53%) were non-input case, and 15 (6.12%) were unknown exposure. 4. 48.57% (119/245) cases were family cluster, involving 42 families. The most common pattern of COVID-19 family cluster was between husband and wife or between parents and children.

Clinical characteristics of 54 medical staff with COVID-19: A retrospective study in a single center in Wuhan, China.

In December 2019, an outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) infection occurred in Wuhan, and rapidly spread to worldwide, which has attracted many people's concerns about the patients. However, studies on the infection status of medical personnel is still lacking. A total of 54 cases of SARS-Cov-2 infected medical staff from Tongji Hospital between 7 January and 11 February 2020 were analyzed in this retrospective study. Clinical and epidemiological characteristics were compared between different groups by statistical method. From 7 January to 11 February 2020, 54 medical staff of Tongji Hospital were hospitalized due to coronavirus disease 2019 (COVID-19). Most of them were from other clinical departments (72.2%) rather than emergency department (3.7%) or medical technology departments (18.5%). Among the 54 patients with COVID-19, the distribution of age had a significant difference between non-severe type and severe/critical cases (median age: 47 years vs 38 years; P = .0015). However, there was no statistical difference in terms of gender distribution and the first symptoms between theses two groups. Furthermore, we observed that the lesion regions in SARS-Cov-2 infected lungs with severe-/critical-type of medical staff were more likely to exhibit lesions in the right upper lobe (31.7% vs 0%; P = .028) and right lung (61% vs 18.2%; P = .012). Based on our findings with medical staff infection data, we suggest training for all hospital staff to prevent infection and preparation of sufficient protection and disinfection materials.

Using RIGHT (Reporting Items for Practice Guidelines in Healthcare) to evaluate the reporting quality of WHO guidelines

BACKGROUND: Without adequate reporting of research, valuable time and resources are wasted. In the same vein, adequate reporting of practice guidelines to optimise patient care is equally important. Our study examines the quality of reporting of published WHO guidelines, over time, using the RIGHT (Reporting Items for Practice Guidelines in HealThcare) reporting checklist. METHODS: We examined English-language guidelines approved by the WHO Guidelines Review Committee from inception of the committee in 2007 until 31 December 2017. Pairs of independent, trained reviewers assessed the reporting quality of these guidelines. Descriptive data were summarised with frequencies and percentages. RESULTS: We included 182 eligible guidelines. Overall, 25 out of the 34 RIGHT items were reported in 75% or more of the WHO guidelines. The reporting rates improved over time. Further, 90% of the guidelines reported document type in the title. The identification of evidence, the rationale for recommendations and the review process were reported in more than 80% of guidelines. The certainty of the evidence using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) system was assessed in 81% of the guidelines assessed. While 82% of guidelines reported funding sources, only 25% mentioned the role of funders. CONCLUSIONS: WHO guidelines provide adequate reporting of many of the RIGHT items and reporting has improved over time. WHO guidelines compare favourably to guidelines produced by other organisations. However, reporting can be further improved in a number of areas.

Chest computed tomography for the diagnosis of patients with coronavirus disease 2019 (COVID-19): a rapid review and meta-analysis.

BACKGROUND: The outbreak of the coronavirus disease 2019 (COVID-19) has had a massive impact on the whole world. Computed tomography (CT) has been widely used in the diagnosis of this novel pneumonia. This study aims to understand the role of CT for the diagnosis and the main imaging manifestations of patients with COVID-19. METHODS: We conducted a rapid review and meta-analysis on studies about the use of chest CT for the diagnosis of COVID-19. We comprehensively searched databases and preprint servers on chest CT for patients with COVID-19 between 1 January 2020 and 31 March 2020. The primary outcome was the sensitivity of chest CT imaging. We also conducted subgroup analyses and evaluated the quality of evidence using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. RESULTS: A total of 103 studies with 5,673 patients were included. Using reverse transcription polymerase chain reaction (RT-PCR) results as reference, a meta-analysis based on 64 studies estimated the sensitivity of chest CT imaging in COVID-19 was 99% (95% CI, 0.97-1.00). If case reports were excluded, the sensitivity in case series was 96% (95% CI, 0.93-0.99). The sensitivity of CT scan in confirmed patients under 18 years old was only 66% (95% CI, 0.15-1.00). The most common imaging manifestation was ground-glass opacities (GGO) which was found in 75% (95% CI, 0.68-0.82) of the patients. The pooled probability of bilateral involvement was 84% (95% CI, 0.81-0.88). The most commonly involved lobes were the right lower lobe (84%, 95% CI, 0.78-0.90) and left lower lobe (81%, 95% CI, 0.74-0.87). The quality of evidence was low across all outcomes. CONCLUSIONS: In conclusion, this meta-analysis indicated that chest CT scan had a high sensitivity in diagnosis of patients with COVID-19. Therefore, CT can potentially be used to assist in the diagnosis of COVID-19.

Breastfeeding of infants born to mothers with COVID-19: a rapid review.

BACKGROUND: Existing recommendations on whether mothers with COVID-19 should continue breastfeeding are still conflicting. We aimed to conduct a rapid review of mother-to-child transmission of COVID-19 during breastfeeding. METHODS: We systematically searched Medline, Embase, Web of Science, Cochrane Library, China Biology Medicine disc, China National Knowledge Infrastructure, Wanfang, and preprint articles up to March 2020. We included studies relevant to transmission through milk and respiratory droplets during breastfeeding of mothers with COVID-19, SARS, MERS and influenza. Two reviewers independently screened studies for eligibility, extracted data, assessed risk of bias and used GRADE to assess certainty of evidence. RESULTS: A total of 4,481 records were identified in our literature search. Six studies (five case reports and one case series) involving 58 mothers (16 mothers with COVID-19, 42 mothers with influenza) and their infants proved eligible. Five case reports showed that the viral nucleic acid tests for all thirteen collected samples of breast milk from mothers with COVID-19 were negative. A case series of 42 influenza infected postpartum mothers taking precautions (hand hygiene and wearing masks) before breastfeeding showed that no neonates were infected with influenza during one-month of follow-up. CONCLUSIONS: The current evidence indicates that SARS-CoV-2 viral nucleic acid has not been detected in breast milk. The benefits of breastfeeding may outweigh the risk of SARS-CoV-2 infection in infants. Mothers with COVID-19 should take appropriate precautions to reduce the risk of transmission via droplets and close contact during breastfeeding.