bluuebunny/crossref_metadata_2025 huggingface dataset:
Remdesivir use in patients with coronavirus COVID-19 disease: a systematic review and meta-analysis of the Chinese Lancet trial with the NIH trial
Authors: Alexander Paul Elias , Piticaru Joshua , Lewis Kimberley , Aryal Komal , Thomas Priya , Szczeklik Wojciech , Fronczek Jakub , Polok Kamil , Alhazzani Waleed , Mammen Manoj
Published: May 2020

Background

Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus SARS-CoV-2, has led to significant global mortality and morbidity. Until now, no treatment has proven to be effective in COVID-19. To explore whether the use of remdesivir, initially an experimental broad-spectrum antiviral, is effective in the treatment of hospitalized patients with COVID-19, we conducted a systematic review and meta-analysis of randomized, placebo-controlled trials investigating its use.

Methods

A rapid search of the MEDLINE and EMBASE medical databases was conducted for randomized controlled trials. A systematic approach was used to screen, abstract, and critically appraise the studies. Grading of Recommendations Assessment, Development, and Evaluation (GRADE) method was applied to rate the certainty and quality of the evidence reported per study.

Results

Two RCTs studies were identified (n=1,299). A fixed-effects meta-analysis revealed reductions in mortality (RR=0.69, 0.49 to 0.99), time to clinical improvement (3.95 less days, from 3.86 days less to 4.05 less days), serious adverse events (RR=0.77, 0.63 to 0.94) and all adverse events (RR=0.87, 0.79 to 0.96).

Conclusion

In this rapid systematic review, we present pooled evidence from the 2 included RCT studies that reveal that remdesivir has a modest yet significant reduction in mortality and significantly improves the time to recovery, as well as significantly reduced risk in adverse events and serious adverse events. It is more than likely that as an antiviral, remdesivir is not sufficient on its own and may be suitable in combination with other antivirals or treatments such as convalescent plasma. Research is ongoing to clarify and contextual these promising findings.
Modelling COVID-19 Transmission in the United States through Interstate and Foreign Travels and Evaluating Impact of Governmental Public Health Interventions
Authors: Shah Nita H. , Sheoran Nisha , Jayswal Ekta , Shukla Dhairya , Shukla Nehal , Shukla Jagdish , Shah Yash
Published: May 2020

Background

The first case of COVID-19 was reported in Wuhan, China in December 2019. The disease has spread to 210 countries and has been labelled as pandemic by WHO. Modelling, evaluating, and predicting the rate of disease transmission is crucial for epidemic prevention and control. Our aim is to assess the impact of interstate and foreign travel and public health interventions implemented by the United States government in response to the Covid-19 pandemic.

Methods

A disjoint mutually exclusive compartmental model is developed to study transmission dynamics of the novel coronavirus. A system of non-linear differential equations was formulated and the basic reproduction number R0 was computed. Stability of the model was evaluated at the equilibrium points. Optimal controls were applied in the form of travel restrictions and quarantine. Numerical simulations were conducted.

Results

Analysis shows that the model is locally asymptomatically stable, at endemic and foreigners free equilibrium points. Without any mitigation measures, infectivity and subsequent hospitalization of the population increases while placing interstates individuals and foreigners under quarantine, decreases the chances of exposure and subsequent infection, leading to an increase in the recovery rate.

Conclusion

Interstate and foreign travel restrictions, in addition to quarantine, help in effectively controlling the epidemic.
Reproductive number of COVID-19: A systematic review and meta-analysis based on global level evidence
Authors: Billah Md. Arif , Miah Md. Mamun , Khan Md. Nuruzzaman
Published: May 2020

Background

The coronavirus (COVID-19) is now a global concern because of its higher transmission capacity and associated adverse consequences including death. The reproductive number of COVID-19 provides an estimate on possible extent of the transmission. This study aims to provide the average reproductive number of COVID-19 based on available global level evidence.

Methods

We searched three databases (PubMed, Web of Science, and Science Direct) to find studies reported the reproductive number of COVID-19. The searches were conducted using a pre-specified search strategy that includes keywords of COVID-19 and its reproductive number related terms, which were combined using the Boolean operators. We used meta-analysis to provide average reproduction number of COVID-19.

Results

Total of 30 studies included in this review whereas 24 of them were included in the meta-analysis. The average estimated reproductive number was 2.70 (95% CI, 2.21-3.30). We found evidence of very high heterogeneity (99.5%) of the reproductive number reported in the included studies. The highest reproductive number was reported for Diamond Princes Cruise Ship, Japan (14.8). In the country-level, higher reproductive number was reported for France (R, 6.32, 95% CI, 5.72-6.98) following Germany (R, 6.07, 95% CI, 5.51-6.69) and Spain (R, 5.08, 95% CI, 4.50-5.73). We also found estimation models, methods, and the number of cases considered to estimate reproductive number were played a role in arising the heterogeneity of the estimated reproductive number.

Conclusion

The estimated reproductive number indicates an exponential increase of COVID-19 infection in coming days. Comprehensive policies and programs are important to reduce new infections as well as the associated adverse consequences including death.
Worldwide and Regional Forecasting of Coronavirus (Covid-19) Spread using a Deep Learning Model
Authors: Direkoglu Cem , Sah Melike
Published: May 2020

In December 2019, Covid-19 epidemic was identified in Wuhan, China. Covid-19 may cause fatality especially among elderly, and people with chronic health problems. After human to human transmissions of the disease, it has rapidly spread throughout China, and then the outbreak has reached to neighboring countries in Asia. Now, the spread of the virus is accelerating in the world, and increasing number of new cases has been reported daily in Europe, Middle East, Africa and America regions. Recently, World Health Organization (WHO) also announced Covid-19 as a Pandemic. As of 3 April, worldwide around more than 1 million cases and around 60,000 fatalities are reported. Thus, forecasting regional and worldwide outbreak size of Covid-19 is important in order to take necessary actions regarding to preparedness plans and mitigation interventions. In this work, we design a deep learning model, which is an effective artificial intelligence method, to provide regional and worldwide forecasts. Particularly for worldwide, our approach predicts the cumulative number of cases, cumulative number of deaths and daily new cases. For Europe and Middle East regions, we predict the cumulative number of cases, and for Mainland China we predict daily new cases and the cumulative number of deaths. We predict the next 10 days based on the previously reported actual time series data of Covid-19. For worldwide forecasts, we use the data provided by Worldometers. For Europe and Middle East forecasts, we use the data provided by World Health Organization, and for China Mainland forecasts, the data is obtained from Chinese Centre for Disease Control and Prevention. This is the first time that a deep learning model has been employed for Covid-19 spread prediction, solely based on the known reported cases of Covid-19. The proposed deep learning architecture consists of Long Short Term Memory (LSTM) layer, dropout layer, and fully connected layers to predict regional and worldwide forecasts. We evaluate our approach with Root Mean Square Error (RMSE) metric. For forecasting, we use the network models that give the minimum RMSE on the last 3 days of actual data. Networks, which achieves the minimum RMSE on the last 3 days, are used to predict the next 10 days. Every day, the spread and situations are changing. Our approach can take into account these realtime changes; the deep learning model can be re-trained with new daily data and perform real-time forecasting. Results show that the proposed deep learning model is promising, it can predict possible scenarios regionally and globally for the spread of Covid-19.
Predicting COVID-19 spread and public health needs to contain the pandemic in West-Africa
Authors: Taboe Beaugard Hémaho , Salako Kolawolé Valère , Ngonghala Calistus N. , Glèlè Kakaï Romain
Published: May 2020

The novel coronavirus (COVID-19) pandemic is causing devastating demographic, social, and economic damage globally. Understanding current patterns of the pandemic spread and forecasting its long-term trajectory is essential in guiding policies aimed at curtailing the pandemic. This is particularly important in regions with weak economies and fragile health care systems such as West-Africa. We formulate and use a deterministic compartmental model to (i) assess the current patterns of COVID-19 spread in West-Africa, (ii) evaluate the impact of currently implemented control measures, and (iii) predict the future course of the pandemic with and without currently implemented and additional control measures in West-Africa. An analytical expression for the threshold level of control measures (involving a reduction in the effective contact rate) required to eliminate the pandemic is computed. Considering currently applied health control measures, numerical simulations of the model using baseline parameter values estimated from West-African COVID-19 data project a 60% reduction in the daily number of cases when the epidemic attains its peak. More reduction in the number of cases will be achieved if additional public health control measures that result in a reduction in the effective contact rate are implemented. We found out that disease elimination is difficult when more asymptomatic individuals contribute in transmission or are not identified and isolated in a timely manner. However, maintaining a baseline level of asymptomatic isolation and a low transmission rate will lead to a significant reduction in the number of daily cases when the pandemic peaks. For example, at the baseline level of asymptomatic isolation, at least a 53% reduction in the transmission rate is required for disease elimination, while disease elimination is also possible if asymptomatic individuals are identified and isolated within 2 days (after the incubation period). Combining two or more measures is better for disease control, e.g., if asymptomatic humans are contact traced or identified and isolated in less than 3 days then only about a 31% reduction in the disease transmission rate is required for disease elimination. Furthermore, we showed that the currently implemented measures caused the time-dependent effective reproduction number to reduce by approximately 37% from February 28, to August 24, 2020. We conclude that COVID-19 elimination requires more control measures than what is currently being applied in West-Africa and that mass testing and contact tracing in order to identify and isolate asymptomatic individuals early is very important in curtailing the burden of the pandemic.
Refining clinical algorithms for a neonatal digital platform for low-income countries: a modified Delphi technique
Authors: Evans Mari , Corden Mark H. , Crehan Caroline , Fitzgerald Felicity , Heys Michelle
Published: May 2020

Objectives

To determine whether a panel of neonatal experts could address evidence gaps in neonatal guidelines by reaching a consensus on four clinical decision algorithms for a neonatal digital platform (NeoTree).

Design

Two-round, modified Delphi technique.

Setting and participants

Participants were neonatal experts from high-income and low-income countries (LICs).

Methods

This was a consensus-generating study. In round one, experts rated items for four clinical algorithms (neonatal sepsis, hypoxic ischaemic encephalopathy, respiratory distress of the newborn, hypothermia) and justified their responses. Items meeting consensus (≥80% agreement) were included. Items not meeting consensus were either excluded, included following revisions or included if they contained core elements of evidence-based guidelines. In round two, experts rated items from round one that did not reach consensus.

Results

Fourteen experts participated in round one, ten in round two. Nine were from high-income countries, five from LICs. Experts included physicians and nurse practitioners with an average neonatal experience of 20 years, 12 in LICs. After two rounds, a consensus was reached on 43 of 84 items (52%). Experts consistently stated that items must be in line with local and WHO guidelines (irrespective of the level of supporting evidence or expert opinion). As a result, the final algorithms included 53 items (62%).

Conclusion

Four algorithms in a neonatal digital platform were reviewed and refined by consensus expert opinion. Revisions to the NeoTree application were made in response to these findings and will be clinically validated in an imminent study.

STRENGTHS AND LIMITATIONS OF THIS STUDY

➢

In this study, a large number of algorithm items were reviewed and evaluated, and half met consensus for inclusion in the management pathways.

➢

The review was conducted with experts from a broad range of countries and neonatal experience who simultaneously refined the algorithms and highlighted gaps in current evidence, emphasising the need for future research to support international neonatal guidelines.

➢

Our study method meant that experts were not able to meet in person, which might have promoted dialogue that would have allowed greater clarity in their collective opinion.

➢

The representation of neonatal experts from LICs was not as robust as from high-income countries, which may have led to an uneven evaluation of the algorithms.
Computer code comprehension shares neural resources with formal logical inference in the fronto-parietal network
Authors: Liu Y. , Kim J. , Wilson C. , Bedny M.
Published: May 2020

Despite the importance of programming to modern society, the cognitive and neural bases of code comprehension are largely unknown. Programming languages might ‘recycle’ neurocognitive mechanisms originally used for natural languages. Alternatively, comprehension of code could depend on fronto-parietal networks shared with other culturally derived symbol systems, such as formal logic and math. Expert programmers (average 11 years of programming experience) performed code comprehension and memory control tasks while undergoing fMRI. The same participants also performed language, math, formal logic, and executive control localizer tasks. A left-lateralized fronto-parietal network was recruited for code comprehension. Patterns of activity within this network distinguish between “for” loops and “if” conditional code functions. Code comprehension overlapped extensively with neural basis of formal logic and to a lesser degree math. Overlap with simpler executive processes and language was low, but laterality of language and code covaried across individuals. Cultural symbol systems, including code, depend on a distinctive fronto-parietal cortical network.
Juvenile hormone affects the development and strength of circadian rhythms in young bumble bee (Bombus terrestris) workers
Authors: Pandey Atul , Motro Uzi , Bloch Guy
Published: May 2020

The circadian and endocrine systems influence many physiological processes in animals, but little is known on the ways they interact in insects. We tested the hypothesis that juvenile hormone (JH) influences circadian rhythms in the social bumble beeBombus terrestris. JH is the major gonadotropin in this species coordinating processes such as vitellogenesis, oogenesis, wax production, and behaviors associated with reproduction. It is unknown however, whether it also influences circadian processes. We topically treated newly-emerged bees with the allatoxin Precocene-I (P-I) to reduce circulating JH titers and applied the natural JH (JH-III) for replacement therapy. We repeated this experiment in three trials, each with bees from different source colonies. Measurements of ovarian activity confirmed that our JH manipulations were effective; bees treated with P-I had inactive ovaries, and this effect was fully reverted by subsequent JH treatment. We found that JH augments the strength of circadian rhythms and the pace of rhythm development in individually isolated newly emerged worker bees. JH manipulation did not affect the free-running circadian period, overall level of locomotor activity, or the amount of sleep. Given that acute manipulation at an early age produced relatively long-lasting effects, we propose that JH effect on circadian rhythms is mostly organizational, accelerating the development or integration of the circadian system.
Highly multiplexed molecular and cellular mapping of breast cancer tissue in three dimensions using mass tomography
Authors: Catena Raúl , Özcan Alaz , Kütt Laura , Plüss Alex ,   IMAXT Consortium, Schraml Peter , Moch Holger , Bodenmiller Bernd
Published: May 2020

A holistic understanding of tissue and organ structures and their functions requires the detection of molecular constituents in their original three-dimensional (3D) context. Imaging mass cytometry (IMC) makes possible the detection of up to 40 antigens and specific nucleic acids simultaneously using metal-tagged antibodies or nucleic acid probes, respectively, but has so far been restricted to two-dimensional imaging. To enable use of IMC for 3D tissue analyses, we developed mass tomography, which combines quasi deformation-free serial sectioning with novel computational methods. We utilized mass tomography to analyze a breast cancer sample. The resulting 3D representation reveals spatial and cellular heterogeneity, preferential cell-to-cell interactions, detailed tissue-architecture motifs, and the unique microenvironment of a micro-invasion, where micro-metastases clonality is examined, showing that cells arising from the same invasive area, displaying very distinct phenotypes, are all able to produce initial invasive lesions. Mass tomography will provide invaluable insights into the tissue microenvironment, cellular neighborhoods, and tissue organization.
The cryoEM structure of the fibril-forming low-complexity domain of hnRNPA2 reveals distinct differences from pathogenic amyloid and shows how mutation converts it to the pathogenic form
Authors: Lu Jiahui , Cao Qin , Hughes Michael P. , Sawaya Michael R. , Boyer David R. , Cascio Duilio , Eisenberg David S.
Published: May 2020

hnRNPA2 is one of a group of human ribonucleoproteins (RNPs) involved in RNA metabolism which form fibrils both under cellular stress and in mutated form in neurodegenerative conditions. Previous work established that the C-terminal low-complexity domain (LCD) of hnRNPA2 fibrillizes under stress, and that missense mutations in this domain are found in the disease multisystem proteinopathy (MSP) with symptoms indistinguishable from ALS and FTD. However, little is known at the atomic level about the hnRNPA2 LCD structure that is involved in those processes and how disease mutations cause structural change. Here we present the cryo-electron microscopy (cryoEM) structure of hnRNPA2 LCD fibril core and demonstrate its capability to form a reversible hydrogelin vitrocontaining amyloid-like fibrils. Whereas these fibrils, like pathogenic amyloid, are formed from protein chains stacked into β-sheets by backbone hydrogen bonds, they display distinct structural differences: the chains are kinked, enabling non-covalent cross-linking of fibrils and disfavoring formation of pathogenic steric zippers. Both their reversibility and energetic calculations suggest these fibrils are less stable than pathogenic amyloid. Moreover, the crystal structure of the disease-mutation-containing segment of hnRNPA2 suggests that the replacement fundamentally alters the fibril structure to a more stable energetic state. These findings illuminate how molecular interactions promote protein fibril networks and how mutation can transform fibril structure from functional to pathogenic form.
UHRF1 upregulation mediates exosome release and tumor progression in osteosarcoma
Authors: Wu Stephanie C. , Kim Ahhyun , Zocchi Loredana , Chen Claire , Lopez Jocelyne , Salcido Kelsey , Wu Jie , Benavente Claudia A.
Published: May 2020

Loss of function mutations at the retinoblastoma (RB1) gene are associated with increased mortality, metastasis and poor therapeutic outcome in several cancers, including osteosarcoma (OS). However, the mechanism(s) through whichRB1loss worsens clinical outcome remain to be elucidated. Ubiquitin-like with PHD and Ring Finger domains 1 (UHRF1) has been identified as a critical downstream effector of the RB/E2F signaling pathway that is overexpressed in various cancers. Here, we show that UHRF1 upregulation is critical in rendering OS cells more aggressive. Using novel OS genetically engineered mouse models, we determined that knocking-outUhrf1considerably reverses the poorer survival associated withRb1loss. We also found that highUHRF1expression correlates with increased clinical presentation of metastatic disease. Based on gain- and loss-of-function assays, we found that UHRF1 promotes cell proliferation, migration, invasion, and metastasis. UHRF1-mediated cell mobility results as a consequence of altered extracellular vesicles and their cargo, including urokinase-type plasminogen activator (uPA). Our work presents a new mechanistic insight intoRB1loss-associated poor prognosis and a novel oncogenic role of UHRF1 through regulation of exosome secretion that is critical for OS metastasis. This study provides substantial support for targeting UHRF1 or its downstream effectors as novel therapeutic options to improve current treatment for OS.
Estimating effective reproduction number using generation time versus serial interval, with application to COVID-19 in the Greater Toronto Area, Canada
Authors: Knight Jesse , Mishra Sharmistha
Published: May 2020

Background

The effective reproductive number Re(t) is a critical measure of epidemic potential. Re(t) can be calculated in near real time using an incidence time series and the generation time distribution—the time between infection events in an infector-infectee pair. In calculating Re(t), the generation time distribution is often approximated by the serial interval distribution—the time between symptom onset in an infector-infectee pair. However, while generation time must be positive by definition, serial interval can be negative if transmission can occur before symptoms, such as in covid-19, rendering such an approximation improper in some contexts.

Methods

We developed a method to infer the generation time distribution from parametric definitions of the serial interval and incubation period distributions. We then compared estimates of Re(t) for covid-19 in the Greater Toronto Area of Canada using: negative-permitting versus non-negative serial interval distributions, versus the inferred generation time distribution.

Results

We estimated the generation time of covid-19 to be Gamma-distributed with mean 3.99 and standard deviation 2.96 days. Relative to the generation time distribution, non-negative serial interval distribution caused overestimation of Re(t) due to larger mean, while negative-permitting serial interval distribution caused underestimation of Re(t) due to larger variance.

Implications

Approximation of the generation time distribution of covid-19 with non-negative or negative-permitting serial interval distributions when calculating Re(t) may result in over or underestimation of transmission potential, respectively.
Noninvasive respiratory support in acute hypoxemic respiratory failure associated with COVID-19 and other viral infections
Authors: Crimi Claudia , Noto Alberto , Cortegiani Andrea , Impellizzeri Pietro , Elliott Mark , Ambrosino Nicolino , Gregoretti Cesare
Published: May 2020

Introduction

Noninvasive respiratory support (NRS) such as noninvasive ventilation (NIV) and high flow nasal therapy (HFNT) have been used in the treatment of acute hypoxemic respiratory failure (AHRF) related to the coronavirus disease (COVID-19) and other viral infections. However, there is a lack of consensus in favor of or against NRS use due to the risks of worsening hypoxemia, intubation delay, and aerosols environmental contamination associated with the use of these tools. We aimed to summarize the evidence on the use of NRS in adult patients with COVID-19 and other viral pneumonia (i.e. H1N1, SARS, MERS) and AHRF. We also searched for studies evaluating the risk of aerosolization/contamination with these tools.

Evidence Acquisition

We searched MEDLINE, PubMed EMBASE and two major preprint servers (biorXiv and medRxiv) from inception to April 14, 2020, for studies on the use of respiratory support in AHRF and viral pneumonia.

Evidence Synthesis

The search identified 4086 records and we found only one randomized controlled trial out of 58 studies included, with great variabilities in support utilization and failure rates. Fifteen studies explored the issue of aerosolization/contamination showing a high risk of airborne transmission via droplets generation during the use of these modalities

Conclusions

Use of NRS and treatment failure in the context of COVID-19 and viral infection associated-AHRF, varied widely. Dispersion of exhaled air is different depending on the type of respiratory therapies and interfaces. Data from randomized controlled trials are lacking.
Comparison of epidemic control strategies using agent-based simulations
Authors: del Árbol Pablo Martínez Ruiz , Iglesias Lara Lloret
Published: May 2020

A simulation of the dynamics of a small population is used to assess the impact of different confinement and testing strategies in the control of an epidemic. The simulation considers individuals as agents moving randomly across the habitat according to predefined urban patterns. Agents carry a simple tracing device that identifies signals emitted by other agents, recording the position and time of the encounter. The information of every device is propagated daily to an epidemic observatory based on an online graph database. Infections are simulated as stochastic processes depending on the proximity among individuals. Different epidemic control strategies are tested with and without the information of the tracing device under several scenarios. We observe that the success of the strategies strongly depends on the duration of the period of infectiousness before the presence of symptoms and the fraction of asymptomatic agents. If these values are high, strategies based on the presence of symptoms or on testing campaigns can hardly contain the epidemic. Strategies using massive confinement of the agents are able to control the epidemic at the cost of sending a large fraction of the population into quarantine. In cases with moderate and low values for these parameters, the tracing devices can provide a slightly better performance but only if a large fraction of the agents carry the device. Otherwise, the impact of these devices is found to be negligible in comparison with other strategies not using them. Finally, we provide a methodology allowing to use the information of the graph database to estimate basic parameters of the disease such as the infection probability.
COVID-19: Impact of Obesity and Diabetes in Disease Severity
Authors: Al-Sabah Salman , Al-Haddad Mohannad , Al-Youha Sarah , Jamal Mohammad , Almazeedi Sulaiman
Published: May 2020

Background

The Coronavirus disease 2019 (COVID-19) pandemic is straining the healthcare system, particularly for patients with severe outcomes who require admittance to the intensive care unit (ICU). This study aimed to investigate the potential associations of obesity and diabetes with COVID-19 severe outcomes, assessed as ICU admittance.

Subjects

Demographic and patient characteristics from a retrospective cohort of 1158 patients hospitalized with COVID-19 in a single center in Kuwait, along with their medical history, were analyzed. Univariate and multivariate analyses were performed to explore the associations between different variables and ICU admittance.

Results

From the 1158 hospitalized patients, 271 (23.4%) had diabetes, 236 (20.4%) had hypertension and 104 (9%) required admittance into the ICU. From patients with available measurements, 157 (21.6%) had body mass index (BMI)≥25 kg/m2. Univariate analysis showed that overweight (BMI=25.0–29.9 kg/m2), obesity class I (BMI=30–34.9 kg/m2) and morbid obesity (BMI≥40 kg/m2) associated with ICU admittance (odds ratio (OR) [95% confidence intervals (CI)]: 2.45 [1.26–4.74]p-value=0.008; OR [95% CI]: 3.51 [1.60–7.69]p-value=0.002; and OR [95% CI]: 5.18 [1.50–17.85]p-value=0.009], respectively). Patients with diabetes were more likely to be admitted to ICU (OR [95% CI]: 9.38 [5.49–16.02]). Two models for multivariate regression analysis were used, assessing either BMI or diabetes on ICU outcomes. In the BMI model, class I obesity and morbid obesity were associated with ICU admittance (adjusted OR (AOR) [95% CI]: 2.7 [1.17–6.20]p-value=0.019 and AOR [95% CI]: 3.95 [1.00–15.20]p-value=0.046, respectively). In the diabetes model, diabetes was associated with higher ICU admittance (AOR [95% CI]: 5.49 [3.13–9.65]p-value<0.001) whereas hypertension had a protective effect on ICU admittance (AOR [95% CI]: 0.51 (0.28–0.91).

Conclusions

In our cohort, overweight, obesity and diabetes in patients with COVID-19 were associated with ICU admittance, putting these patients at higher risk of poor outcomes.
Novel mammogram-based measures improve breast cancer risk prediction beyond an established measure of mammographic density
Authors: Nguyen Tuong L. , Schmidt Daniel F. , Makalic Enes , Maskarinec Gertraud , Li Shuai , Dite Gillian , Aung Ye K. , Evans Christopher F. , Trinh Ho N. , Baglietto Laura , Stone Jennifer , Song Yun-Mi , Sung Joohon , MacInnis Robert J. , Dugué Pierre-Antoine , Dowty James G. , Jenkins Mark A. , Milne Roger L. , Southey Melissa C. , Giles Graham G. , Hopper John L.
Published: May 2020

Background

Mammograms contain information that predicts breast cancer risk. We recently discovered two novel mammogram-based breast cancer risk measures based on image brightness (Cirrocumulus) and texture (Cirrus). It is not known whether these measures improve risk prediction when fitted together, and with an established measure of mammographic density (Cumulus).

Methods

We used three studies consisting of: 168 interval cases and 498 matched controls; 422 screen-detected cases and 1,197 matched controls; and 354 younger-diagnosis cases and 944 frequency-matched controls. We conducted conditional and unconditional logistic regression analyses of individually-and frequency-matched studies, respectively. We reported risk gradients as change in odds ratio per standard deviation of controls after adjusting for age and body mass index (OPERA). For models involving multiple measures, we calculated the OPERA equivalent to the area under the receiver operating characteristic curve.

Results

For interval, screen-detected and younger-diagnosis cancer, the best fitting models (OPERAs [95% confidence intervals]) were: Cumulus (1.81 [1.41 to 2.31]) and Cirrus (1.7 [1.38 to 2.14]); Cirrus (1.49 [1.32 to 1.67]) and Cirrocumulus (1.16 [1.03 to 1.31]); and Cirrus (1.70 [1.48 to 1.94]) and Cirrocumulus (1.46 [1.27 to 1.68]), respectively. Their OPERA equivalents were: 2.35, 1.58, and 2.28, respectively.

Conclusions

Our mammogram-based measures improved risk prediction beyond and, except for interval cancers, negated the influence of conventional mammographic density. Combined, these new mammogram-based risk measures are at least as accurate as the current polygenetic risk scores (OPERA ~ 1.6) in predicting, on a population basis, women who will be diagnosed with breast cancer.
Characteristics and predictors of hospitalization and death in the first 9,519 cases with a positive RT-PCR test for SARS-CoV-2 in Denmark: A nationwide cohort
Authors: Reilev Mette , Kristensen Kasper Bruun , Pottegård Anton , Lund Lars Christian , Hallas Jesper , Ernst Martin Thomsen , Christiansen Christian Fynbo , Sørensen Henrik Toft , Johansen Nanna Borup , Brun Nikolai Constantin , Voldstedlund Marianne , Støvring Henrik , Thomsen Marianne Kragh , Christensen Steffen , Gubbels Sophie , Krause Tyra Grove , Mølbak Kåre , Thomsen Reimar Wernich
Published: May 2020

Objective

To provide population-level knowledge on individuals at high risk of severe and fatal coronavirus disease 2019 (COVID-19) in order to inform targeted protection strategies in the general population and appropriate triage of hospital contacts.

Design, Setting, and Participants

Nationwide population-based cohort of all 228.677 consecutive Danish individuals tested (positive or negative) for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA from the identification of the first COVID-19 case on February 27th, 2020 until April 30th, 2020.

Main Outcomes and Measures

We examined characteristics and predictors of inpatient hospitalization versus community-management, and death versus survival, adjusted for age-, sex- and number of comorbidities.

Results

We identified 9,519 SARS-CoV-2 PCR-positive cases of whom 78% were community-managed, 22% were hospitalized (3.2% at an intensive care unit) and 5.5% had died within 30 days. Median age varied from 45 years (interquartile range (IQR) 31-57) among community-managed cases to 82 years (IQR 7589) among those who died. Age was a strong predictor of fatal disease (odds ratio (OR) 14 for 70-79-year old, OR 26 for 80-89-year old, and OR 82 for cases older than 90 years, when compared to 50-59-year old and adjusted for sex and number of comorbidities). Similarly, the number of comorbidities was strongly associated with fatal disease (OR 5.2, for cases with ≥4 comorbidities versus no comorbidities), and 82% of fatal cases had at least 2 comorbidities. A wide range of major chronic diseases were associated with hospitalization with ORs ranging from 1.3-1.4 (e.g. stroke, ischemic heart disease) to 2.2-2.7 (e.g. heart failure, hospital-diagnosed kidney disease, chronic liver disease). Similarly, chronic diseases were associated with mortality with ORs ranging from 1.2-1.3 (e.g. ischemic heart disease, hypertension) to 2.4-2.7 (e.g. major psychiatric disorder, organ transplantation). In the absence of comorbidities, mortality was relatively low (5% or less) in persons aged up to 80 years.

Conclusions and Relevance

In this first nationwide population-based study, increasing age and number of comorbidities were strongly associated with hospitalization requirement and death in COVID-19. In the absence of comorbidities, the mortality was, however, lowest until the age of 80 years. These results may help in accurate identification, triage and protection of high-risk groups in general populations, i.e. when reopening societies.
AIDCOV: An Interpretable Artificial Intelligence Model for Detection of COVID-19 from Chest Radiography Images
Authors: Zokaeinikoo Maryam , Kazemian Pooyan , Mitra Prasenjit , Kumara Soundar
Published: May 2020

As the Coronavirus Disease 2019 (COVID-19) pandemic continues to grow globally, testing to detect COVID-19 and isolating individuals who test positive remains to be the primary strategy for preventing community spread of the disease. The current gold standard method of testing for COVID-19 is the reverse transcription polymerase chain reaction (RT-PCR) test. The RT-PCR test, however, has an imperfect sensitivity (around 70%), is time-consuming and labor-intensive, and is in short supply, particularly in resource-limited countries. Therefore, automatic and accurate detection of COVID-19 using medical imaging modalities such as chest X-ray and Computed Tomography, which are more widely available and accessible, can be beneficial as an alternative diagnostic tool. We develop a novel hierarchical attention neural network model to classify chest radiography images as belonging to a person with either COVID-19, other infections, or no pneumonia (i.e., normal). We refer to this model as Artificial Intelligence for Detection of COVID-19 (AIDCOV). The hierarchical structure in AIDCOV captures the dependency of features and improves model performance while the attention mechanism makes the model interpretable and transparent. Using a publicly available dataset of 5801 chest images, we demonstrate that our model achieves a mean cross-validation accuracy of 97.8%. AIDCOV has a sensitivity of 99.3%, a specificity of 99.98%, and a positive predictive value of 99.6% in detecting COVID-19 from chest radiography images. AIDCOV can be used in conjunction with or instead of RT-PCR testing (where RT-PCR testing is unavailable) to detect and isolate individuals with COVID-19 and prevent onward transmission to the general population and healthcare workers.
Intraluminal Prucalopride Increases Propulsive Motor Activities in the Human Colon
Authors: Shokrollahi Mitra , Wang Xuan-Yu , Milkova Natalija , Huizinga Jan D , Chen Ji-Hong
Published: May 2020

Background

Luminal application of 5-HT4receptor agonists can increase peristalsis in the guinea pig, mouse, rat and rabbit colon. Our aim in the present study was to test the effects of intraluminal prucalopride on motor patterns in the human colon.

Methods

Colonic motor patterns were studied in vivo in a healthy volunteer using High-Resolution Colonic Manometry (HRCM) with an 84-sensor water perfused catheter with 1cm spacing. 5-HT and 5-HT4receptor immunohistochemistry was performed on human tissue biopsies throughout the colon.

Key results

Activating mucosal 5-HT4receptors via intraluminal prucalopride enhanced propulsive motor activity in the human colon by increasing occurrence and amplitude of propulsive motor patterns including high-amplitude propagating pressure waves (HAPWs), pancolonic simultaneous pressure waves (SPWs) and HAPW-SPWs. Prucalopride-induced motor patterns had a close temporal association with a significant degree of anal sphincter relaxation and some were accompanied by a strong urge to defecate. Biopsies showed 100% colocalization of the 5-HT4receptor to enterochromaffin cells throughout the colon and rectum.

Conclusions and inferences

Activating luminal 5-HT4receptors on enterochromaffin cells by intraluminal prucalopride increased propulsive motor activity. 5-HT4receptors were found only on enterochromaffin cells and not ubiquitous on all epithelial cells. Our data support incorporation of prucalopride in colon-specific drug delivery systems as a prokinetic to treat colonic hypomotility disorders.

50 word abstract

High-resolution colonic manometry and biopsy immunohistochemistry revealed that 5-HT4receptors in the lumen of the human colon are present exclusively on enterochromaffin cells and that the 5-HT4agonist prucalopride evokes all major propulsive motor patterns, associated with significant anal sphincter relaxation, when given intraluminally.

250-character clinical message

Activating luminal 5-HT4receptors on enterochromaffin cells by intraluminal prucalopride increased propulsive motor activity in the human colon. Colon-specific delivery systems with a 5-HT4agonist may become the preferred colon prokinetic.
Epidemic Model Guided Machine Learning for COVID-19 Forecasts in the United States
Authors: Zou Difan , Wang Lingxiao , Xu Pan , Chen Jinghui , Zhang Weitong , Gu Quanquan
Published: May 2020

We propose a new epidemic model (SuEIR) for forecasting the spread of COVID-19, including numbers of confirmed and fatality cases at national and state levels in the United States. Specifically, the SuEIR model is a variant of the SEIR model by taking into account the untested/unreported cases of COVID-19, and trained by machine learning algorithms based on the reported historical data. Besides providing basic projections for confirmed and fatality cases, the proposed SuEIR model is also able to predict the peak date of active cases, and estimate the basic reproduction number (). In particular, the forecasts based on our model suggest that the peak date of the US, New York state, and California state are 06/01/2020, 05/10/2020, and 07/01/2020 respectively. In addition, the estimatedof the US, New York state, and California state are 2.5, 3.6 and 2.2 respectively. The prediction results for all states in the US can be found on our project website:https://covid19.uclaml.org, which are updated on a weekly basis, and have been adopted by the Centers for Disease Control and Prevention (CDC) for COVID-19 death forecasts (https://www.cdc.gov/coronavirus/2019-ncov/covid-data/forecasting-us.html).
Evaluating Quality Adjusted Life Years in the Absence of Standard Utility Values- A Dynamic Joint Modelling Approach
Authors: Deo Vishal , Grover Gurprit
Published: May 2020

Estimation of Quality Adjusted Life Years (QALYs) is pivotal towards cost-effectiveness analysis (CEA) of medical interventions. Most of the CEA studies employ multi-state decision analytic modelling approach, where fixed utility values are assigned to each disease state and total QALYs are calculated on the basis of total lengths of stay in each state.

In this paper, we have formulated a new approach to CEA by defining utility as a function of a longitudinal covariate which is significantly associated with disease progression. Association parameter between the longitudinal covariate and survival times is estimated through joint modelling of the longitudinal linear mixed effects model and the Weibull accelerated failure time survival model. Metropolis-Hastings algorithm and Monte Carlo integration are used to predict expected survival times of each censored case using the joint model. Fitted longitudinal model is further used to project values of the longitudinal covariate at all time points for each patient. Utility values calculated using these projected covariate values are used to evaluate QALYs for each patient.

Retrospective survival data of HIV/ AIDS patients undergoing treatment at the Antiretroviral Therapy centre of Ram Manohar Lohia hospital in New Delhi is used to demonstrate the implementation of the proposed methodology. A simulation exercise is also carried out to gauge the predictive capability of the joint model in projecting the values of the longitudinal covariate.

The proposed dynamic approach to calculate QALY provides a promising alternative to the popular multi-state decision analytic modelling approach, especially when the standard utility values for different stages of the concerned disease are not available.
Mild Behavioral Impairment and Subjective Cognitive Decline predict Mild Cognitive Impairment
Authors: Ismail Zahinoor , McGirr Alexander , Gill Sascha , Hu Sophie , Forkert Nils D. , Smith Eric E.
Published: May 2020

Objective

Better methods for detecting preclinical neuropathological change are required for prevention of dementia. Mild behavioral impairment (MBI) and subjective cognitive decline (SCD) can represent neurobehavioral and neurocognitive axes of early stage neurodegenerative processes, which are represented in Stage 2 of the NIA-AA Alzheimer’s disease research framework. Both MBI and SCD may offer an opportunity for premorbid detection. We test the hypothesis that MBI and SCD confer additive risk for incident cognitive decline.

Methods

Participants were cognitively normal older adults followed up approximately annually at Alzheimer’s Disease Centers. Logistic regression was used to determine the relationship between baseline classification (MBI+, SCD+, neither (MBI-SCD-), or both (MBI+SCD+)) and cognitive decline, defined by Clinical Dementia Rating (CDR) total score, at 3 years.

Results

Of 2769 participants (mean age=76; 63% females), 1536 were MBI-SCD-, 254 MBI-SCD+, 743 MBI+SCD-, and 236 MBI+SCD+. At 3-years, 349 individuals (12.6%) developed cognitive decline to CDR>0. Compared to SCD-MBI-, we observed an ordinal progression in risk, with ORs [95% CI] as follows: 3.61 [2.42-5.38] for MBI-SCD+ (16.5% progression), 4.76 [3.57-6.34] for MBI+SCD-, (20.7% progression) and 8.15 [5.71-11.64] for MBI+SCD+ (30.9% progression).

Conclusion

MBI in older adults alone or in combination with SCD is associated with a higher risk of incident cognitive decline at 3 years. The highest rate of progression to MCI is observed in those with both MBI and SCD. Used in conjunction, MBI and SCD could be simple and scalable methods to identify patients at high risk for cognitive decline for prevention studies.
Early Impact of COVID-19 Pandemic on Paediatric Surgical Practice in Nigeria: a National Survey of Paediatric Surgeons
Authors: Ogundele I.O. , Alakaloko F.M. , Nwokoro C.C. , Ameh E.A.
Published: May 2020

Introduction

The novel Coronavirus disease has had significant impact on healthcare globally. Knowledge of this virus is evolving, definitive care is not yet known, and mortality is increasing. We assessed its initial impact on paediatric surgical practice in Nigeria, creating a benchmark for recommendations and future reference.

Methods

Survey of 120 paediatric surgeons from 50 centres to assess socio-demographics and specific domains of impact of COVID-19 on their services and training in Nigeria. Seventy four surgeons adequately responded. Responses have been analysed. Duplicate submissions for centres were excluded by combining and averaging the responses from centres with multiple respondents.

Results

Forty-six (92%) centres had suspended elective surgeries. All centres continued emergency surgeries but volume reduced in March by 31%. Eleven (22%) centres reported 13 suspended elective cases presenting as emergencies in March, accounting for 3% of total emergency surgeries. Nine (18%) centres adopted new modalities for managing selected surgical conditions: non-operative reduction of intussusception in 1(2%), antibiotic management of uncomplicated acute appendicitis in 5(10%), more conservative management of trauma and replacement of laparoscopic appendectomy with open surgery in 3(6%) respectively. Low perception of adequacy of Personal Protective Equipment (PPE) was reported in 35(70%) centres. Forty (80%) centres did not offer telemedicine for patients follow up. Twenty-nine (58%) centres had suspended academic training. Perception of safety to operate was low in 37(50%) respondents, indifferent in 24% and high in 26%.

Conclusion

Majority of paediatric surgical centres reported cessation of elective surgeries whilst continuing emergencies. There is however an acute decline in the volume of emergency surgeries. Adequate PPE need to be provided and preparations towards handling backlog of elective surgeries once the pandemic recedes. Further study is planned to more conclusively understand the full impact of this pandemic on children’s surgery.

What is already known and what this study adds

Anecdotal evidence suggests that elective surgeries in children have been suspended due to COVID-19 pandemic.

Our study shows that most centres have suspended elective surgeries. All centres continued emergency surgeries but the volume reduced by 31% in March 2020. Moreover, 3% of the emergency surgeries were some of the suspended elective cases presenting as emergencies. Almost 20% of centres have newly adopted non-operative modalities for managing selected emergency surgical conditions.

This data shows an urgent need for consensus guidelines for emergency services and protocols for handling backlog of elective surgeries in children once the pandemic recedes. Outcome of the modifications in treatment may be subject to future research.