A multi-regional, hierarchical-tier mathematical model of the spread and control of COVID-19 epidemics from epicentre to adjacent regions

Epicentres are the focus of COVID-19 research, whereas emerging regions with mainly imported cases due to population movement are often neglected. Classical compartmental models are useful, however, likely oversimplify the complexity when studying epidemics. This study aimed to develop a multi-regional, hierarchical-tier mathematical model for better understanding the complexity and heterogeneity of COVID-19 spread and control. By incorporating the epidemiological and population flow data, we have successfully constructed a multi-regional, hierarchical-tier SLIHR model. With this model, we revealed insight into how COVID-19 was spread from the epicentre Wuhan to other regions in Mainland China based on the large population flow network data. By comprehensive analysis of the effects of different control measures, we identified that Level 1 emergency response, community prevention and application of big data tools significantly correlate with the effectiveness of local epidemic containment across different provinces of China outside the epicentre. In conclusion, our multi-regional, hierarchical-tier SLIHR model revealed insight into how COVID-19 spread from the epicentre Wuhan to other regions of China, and the subsequent control of local epidemics. These findings bear important implications for many other countries and regions to better understand and respond to their local epidemics associated with the ongoing COVID-19 pandemic

Is elevated SUA associated with a worse outcome in young Chinese patients with acute cerebral ischemic stroke?

<p>Abstract</p> <p>Background</p> <p>Elevated serum uric acid (SUA) levels can enhance its antioxidant prosperities and reduce the occurrence of cerebral infarction. Significantly elevated SUA levels have been associated with a better prognosis in patients with cerebral infarction; however, the results from some studies on the relationship between SUA and the prognosis of patients with cerebral infarction remain controversial.</p> <p>Methods</p> <p>We analyzed the relationship between SUA and clinical prognosis of 585 young Chinese adults with acute ischemic stroke as determined by the modified Rankin Scale at discharge. Using multivariate logistic regression modeling, we explore the relationship between SUA levels and patient's clinical prognosis.</p> <p>Results</p> <p>Lower SUA levels at time of admission were observed more frequently in the lowest quintile for patients with severe stroke (P = 0.02). Patients with cerebral infarction patients caused by small-vessel blockage had higher SUA concentrations (P = 0.01) and the lower mRS scores (P < 0.01) were observed in, while the lowest SUA concentrations and the highest mRS scores were seen in patients with cardiogenic cerebral infarction patients. Logistic regression analysis adjusted for confounders confirmed the following independent predictors for young cerebral infarction: uric acid (-0.003: 95%CI 0.994 to 0.999) and platelet (0.004, 95%CI 0.993 to 0.996).</p> <p>Conclusion</p> <p>Elevated SUA is an independent predictor for good clinical outcome of acute cerebral infarction among young adults.</p

Validation Study of Existing Gene Expression Signatures for Anti-TNF Treatment in Patients with Rheumatoid Arthritis

So far, there are no means of identifying rheumatoid arthritis (RA) patients who will fail to respond to tumour necrosis factor blocking agents (anti-TNF), prior to treatment. We set out to validate eight previously reported gene expression signatures predicting therapy outcome. Genome-wide expression profiling using Affymetrix GeneChip Exon 1.0 ST arrays was performed on RNA isolated from whole blood of 42 RA patients starting treatment with infliximab or adalimumab. Clinical response according to EULAR criteria was determined at week 14 of therapy. Genes that have been reported to be associated with anti-TNF treatment were extracted from our dataset. K-means partition clustering was performed to assess the predictive value of the gene-sets. We performed a hypothesis-driven analysis of the dataset using eight existing gene sets predictive of anti-TNF treatment outcome. The set that performed best reached a sensitivity of 71% and a specificity of 61%, for classifying the patients in the current study. We successfully validated one of eight previously reported predictive expression profile. This replicated expression signature is a good starting point for developing a prediction model for anti-TNF treatment outcome that can be used in a daily clinical setting. Our results confirm that gene expression profiling prior to treatment is a useful tool to predict anti-TNF (non) response

Hyperoxemia and excess oxygen use in early acute respiratory distress syndrome : Insights from the LUNG SAFE study

Publisher Copyright: © 2020 The Author(s). Copyright: Copyright 2020 Elsevier B.V., All rights reserved.Background: Concerns exist regarding the prevalence and impact of unnecessary oxygen use in patients with acute respiratory distress syndrome (ARDS). We examined this issue in patients with ARDS enrolled in the Large observational study to UNderstand the Global impact of Severe Acute respiratory FailurE (LUNG SAFE) study. Methods: In this secondary analysis of the LUNG SAFE study, we wished to determine the prevalence and the outcomes associated with hyperoxemia on day 1, sustained hyperoxemia, and excessive oxygen use in patients with early ARDS. Patients who fulfilled criteria of ARDS on day 1 and day 2 of acute hypoxemic respiratory failure were categorized based on the presence of hyperoxemia (PaO2 > 100 mmHg) on day 1, sustained (i.e., present on day 1 and day 2) hyperoxemia, or excessive oxygen use (FIO2 ≥ 0.60 during hyperoxemia). Results: Of 2005 patients that met the inclusion criteria, 131 (6.5%) were hypoxemic (PaO2 < 55 mmHg), 607 (30%) had hyperoxemia on day 1, and 250 (12%) had sustained hyperoxemia. Excess FIO2 use occurred in 400 (66%) out of 607 patients with hyperoxemia. Excess FIO2 use decreased from day 1 to day 2 of ARDS, with most hyperoxemic patients on day 2 receiving relatively low FIO2. Multivariate analyses found no independent relationship between day 1 hyperoxemia, sustained hyperoxemia, or excess FIO2 use and adverse clinical outcomes. Mortality was 42% in patients with excess FIO2 use, compared to 39% in a propensity-matched sample of normoxemic (PaO2 55-100 mmHg) patients (P = 0.47). Conclusions: Hyperoxemia and excess oxygen use are both prevalent in early ARDS but are most often non-sustained. No relationship was found between hyperoxemia or excessive oxygen use and patient outcome in this cohort. Trial registration: LUNG-SAFE is registered with ClinicalTrials.gov, NCT02010073publishersversionPeer reviewe

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Chinese Herbal Medicines for Rheumatoid Arthritis: Text-Mining the Classical Literature for Potentially Effective Natural Products

Background. Rheumatoid arthritis (RA) is an autoimmune disease characterized by multijoint swelling, pain, and destruction of the synovial joints. Treatments are available but new therapies are still required. One source of new therapies is natural products, including herbs used in traditional medicines. In China and neighbouring countries, natural products have been used throughout recorded history and are still in use for RA and its symptoms. This study used text-mining of a database of classical Chinese medical books to identify candidates for future clinical and experimental investigations of therapeutics for RA. Methods. The database Encyclopaedia of Traditional Chinese Medicine (Zhong Hua Yi Dian) includes the full texts of over 1,150 classical books. Eight traditional terms were searched. All citations were assessed for relevance to RA. Results and Conclusions. After removal of duplications, 3,174 citations were considered. After applying the exclusion and inclusion criteria, 548 citations of traditional formulas were included. These derived from 138 books written from 206 CE to 1948. These formulas included 5,018 ingredients (mean, 9 ingredients/formula) comprising 243 different natural products. When these text-mining results were compared to the 18 formulas recommended in a modern Chinese Medicine clinical practice guideline, 44% of the herbal formulas were the same. This suggests considerable continuity in the clinical application of these herbs between classical and modern Chinese medicine practice. Of the 15 herbs most frequently used as ingredients of the classical formulas, all have received research attention, and all have been reported to have anti-inflammatory effects. Two of these 15 herbs have already been developed into new anti-RA therapeutics—sinomenine from Sinomenium acutum (Thunb.) Rehd. & Wils and total glucosides of peony from Paeonia lactiflora Pall. Nevertheless, there remains considerable scope for further research. This text-mining approach was effective in identifying multiple natural product candidates for future research