The COVID-19 pandemic has placed an unprecedented strain on health systems, with rapidly increasing demand for healthcare in hospitals and intensive care units (ICUs) worldwide. As the pandemic ...escalates, determining the resulting needs for healthcare resources (beds, staff, equipment) has become a key priority for many countries. Projecting future demand requires estimates of how long patients with COVID-19 need different levels of hospital care.
We performed a systematic review of early evidence on length of stay (LoS) of patients with COVID-19 in hospital and in ICU. We subsequently developed a method to generate LoS distributions which combines summary statistics reported in multiple studies, accounting for differences in sample sizes. Applying this approach, we provide distributions for total hospital and ICU LoS from studies in China and elsewhere, for use by the community.
We identified 52 studies, the majority from China (46/52). Median hospital LoS ranged from 4 to 53 days within China, and 4 to 21 days outside of China, across 45 studies. ICU LoS was reported by eight studies-four each within and outside China-with median values ranging from 6 to 12 and 4 to 19 days, respectively. Our summary distributions have a median hospital LoS of 14 (IQR 10-19) days for China, compared with 5 (IQR 3-9) days outside of China. For ICU, the summary distributions are more similar (median (IQR) of 8 (5-13) days for China and 7 (4-11) days outside of China). There was a visible difference by discharge status, with patients who were discharged alive having longer LoS than those who died during their admission, but no trend associated with study date.
Patients with COVID-19 in China appeared to remain in hospital for longer than elsewhere. This may be explained by differences in criteria for admission and discharge between countries, and different timing within the pandemic. In the absence of local data, the combined summary LoS distributions provided here can be used to model bed demands for contingency planning and then updated, with the novel method presented here, as more studies with aggregated statistics emerge outside China.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract Objectives This study aims to find whether proximity to urban green spaces is associated with human mental health. Study design A cross-sectional examination of the relationship between ...access to urban green spaces and counts of anxiety/mood disorder treatments amongst residents (aged 15 years and over) in Auckland City, New Zealand. Methods Anxiety/mood disorder treatment counts by three age groups were aggregated to 3149 small area units in Auckland. Six measures of green space access were derived using GIS techniques involving total green spaces and useable green spaces. Negative binomial regression models have been fitted to test the relationship between access to green space and area-level anxiety/mood disorder treatment counts, adjusted for age and area-level deprivation. Results Anxiety/mood disorder treatment counts were associated with three green space measures. The proportion of both total and useable green space within 3 km and distance to nearest useable green space all indicated a protective effect of increased access to green space against anxiety/mood disorder treatment counts. Access to total and useable green space within 300 m did not exhibit significant associations. Conclusion This study found that decreased distance to useable green space and increased proportion of green space within the larger neighbourhood were associated with decreased anxiety/mood disorder treatment counts in an urban environment. This suggests the benefits of green space on mental health may relate both to active participation in useable green spaces near to the home and observable green space in the neighbourhood environment.
The addition of silica nanoparticles (23 nm, 74 nm, and 170 nm) to a lightly crosslinked, model epoxy resin, was studied. The effect of silica nanoparticle content and particle size on glass ...transition temperature (Tg), coefficient of thermal expansion (CTE), Young's modulus (E), yield stress (σ), fracture energy (GIC) and fracture toughness (KIC), were investigated. The toughening mechanisms were determined using scanning electron microscopy (SEM), transmission electron microscopy (TEM) and transmission optical microscopy (TOM). The experimental results revealed that the addition of silica nanoparticles did not have a significant effect on Tg or the yield stress of epoxy resin, i.e. the yield stress and Tg remained constant regardless of silica nanoparticle size. As expected, the addition of silica nanoparticles had a significant impact on CTE, modulus and fracture toughness. The CTE values of nanosilica-filled epoxies were found to decrease with increasing silica nanoparticle content, which can be attributed to the much lower CTE of the silica nanoparticles. Interestingly, the decreases in CTE showed strong particle size dependence. The Young's modulus was also found to significantly improve with addition of silica nanoparticles and increase with increasing filler content. However, the particle size did not exhibit any effect on the Young's modulus. Finally, the fracture toughness and fracture energy showed significant improvements with the addition of silica nanoparticles, and increased with increasing filler content. The effect of particle size on fracture toughness was negligible. Observation of the fracture surfaces using SEM and TOM showed evidence of debonding of silica nanoparticles, matrix void growth, and matrix shear banding, which are credited for the increases in toughness for nanosilica-filled epoxy systems. Shear banding mechanism was the dominant mechanism while the particle debonding and plastic void growth were the minor mechanisms.
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Abstract
In the last decade, over a million stars were monitored to detect transiting planets. Manual interpretation of potential exoplanet candidates is labour intensive and subject to human error, ...the results of which are difficult to quantify. Here we present a new method of detecting exoplanet candidates in large planetary search projects that, unlike current methods, uses a neural network. Neural networks, also called ‘deep learning’ or ‘deep nets’, are designed to give a computer perception into a specific problem by training it to recognize patterns. Unlike past transit detection algorithms, deep nets learn to recognize planet features instead of relying on hand-coded metrics that humans perceive as the most representative. Our convolutional neural network is capable of detecting Earth-like exoplanets in noisy time series data with a greater accuracy than a least-squares method. Deep nets are highly generalizable allowing data to be evaluated from different time series after interpolation without compromising performance. As validated by our deep net analysis of Kepler light curves, we detect periodic transits consistent with the true period without any model fitting. Our study indicates that machine learning will facilitate the characterization of exoplanets in future analysis of large astronomy data sets.
The outbreak of monkeypox across non-endemic regions confirmed in May 2022 shows epidemiological features distinct from previously imported outbreaks, most notably its observed growth and ...predominance amongst men who have sex with men (MSM). We use a transmission model fitted to empirical sexual partnership data to show that the heavy-tailed sexual partnership distribution, in which a handful of individuals have disproportionately many partners, can explain the sustained growth of monkeypox among MSM despite the absence of such patterns previously. We suggest that the basic reproduction number (
) for monkeypox over the MSM sexual network may be substantially above 1, which poses challenges to outbreak containment. Ensuring support and tailored messaging to facilitate prevention and early detection among MSM with high numbers of partners is warranted.
A severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant, VOC 202012/01 (lineage B.1.1.7), emerged in southeast England in September 2020 and is rapidly spreading toward fixation. Using ...a variety of statistical and dynamic modeling approaches, we estimate that this variant has a 43 to 90% (range of 95% credible intervals, 38 to 130%) higher reproduction number than preexisting variants. A fitted two-strain dynamic transmission model shows that VOC 202012/01 will lead to large resurgences of COVID-19 cases. Without stringent control measures, including limited closure of educational institutions and a greatly accelerated vaccine rollout, COVID-19 hospitalizations and deaths across England in the first 6 months of 2021 were projected to exceed those in 2020. VOC 202012/01 has spread globally and exhibits a similar transmission increase (59 to 74%) in Denmark, Switzerland, and the United States.
Guidelines now discourage opioid analgesics for chronic noncancer pain because the benefits frequently do not outweigh the harms. We aimed to determine the proportion of patients with chronic ...noncancer pain who are prescribed an opioid, the types prescribed and factors associated with prescribing. Database searches were conducted from inception to 29 October 2018 without language restrictions. We included observational studies of adults with chronic noncancer pain measuring opioid prescribing. Opioids were categorized as weak (e.g. codeine) or strong (e.g. oxycodone). Study quality was assessed using a risk of bias tool designed for observational studies measuring prevalence. Individual study results were pooled using a random‐effects model. Meta‐regression investigated study‐level factors associated with prescribing (e.g. sampling year, geographic region as per World Health Organization). The overall evidence quality was assessed using Grading of Recommendations Assessment, Development and Evaluation criteria. Of the 42 studies (5,059,098 participants) identified, the majority (n = 28) were from the United States of America. Eleven studies were at low risk of bias. The pooled estimate of the proportion of patients with chronic noncancer pain prescribed opioids was 30.7% (95% CI 28.7% to 32.7%, n = 42 studies, moderate‐quality evidence). Strong opioids were more frequently prescribed than weak (18.4% (95% CI 16.0–21.0%, n = 15 studies, low‐quality evidence), versus 8.5% (95% CI 7.2–9.9%, n = 15 studies, low‐quality evidence)). Meta‐regression determined that opioid prescribing was associated with year of sampling (more prescribing in recent years) (P = 0.014) and not geographic region (P = 0.056). Opioid prescribing for patients with chronic noncancer pain is common and has increased over time.
The COVID-19 pandemic has shown a markedly low proportion of cases among children
. Age disparities in observed cases could be explained by children having lower susceptibility to infection, lower ...propensity to show clinical symptoms or both. We evaluate these possibilities by fitting an age-structured mathematical model to epidemic data from China, Italy, Japan, Singapore, Canada and South Korea. We estimate that susceptibility to infection in individuals under 20 years of age is approximately half that of adults aged over 20 years, and that clinical symptoms manifest in 21% (95% credible interval: 12-31%) of infections in 10- to 19-year-olds, rising to 69% (57-82%) of infections in people aged over 70 years. Accordingly, we find that interventions aimed at children might have a relatively small impact on reducing SARS-CoV-2 transmission, particularly if the transmissibility of subclinical infections is low. Our age-specific clinical fraction and susceptibility estimates have implications for the expected global burden of COVID-19, as a result of demographic differences across settings. In countries with younger population structures-such as many low-income countries-the expected per capita incidence of clinical cases would be lower than in countries with older population structures, although it is likely that comorbidities in low-income countries will also influence disease severity. Without effective control measures, regions with relatively older populations could see disproportionally more cases of COVID-19, particularly in the later stages of an unmitigated epidemic.
Abstract Type 1 Diabetes (T1D) is an autoimmune disease characterized by the pancreatic infiltration of immune cells resulting in T cell-mediated destruction of the insulin-producing beta cells. The ...successes of the Non-Obese Diabetic (NOD) mouse model have come in multiple forms including identifying key genetic and environmental risk factors e.g. Idd loci and effects of microorganisms including the gut microbiota, respectively, and how they may contribute to disease susceptibility and pathogenesis. Furthermore, the NOD model also provides insights into the roles of the innate immune cells as well as the B cells in contributing to the T cell-mediated disease. Unlike many autoimmune disease models, the NOD mouse develops spontaneous disease and has many similarities to human T1D. Through exploiting these similarities many targets have been identified for immune-intervention strategies. Although many of these immunotherapies did not have a significant impact on human T1D, they have been shown to be effective in the NOD mouse in early stage disease, which is not equivalent to trials in newly-diagnosed patients with diabetes. However, the continued development of humanized NOD mice would enable further clinical developments, bringing T1D research to a new translational level. Therefore, it is the aim of this review to discuss the importance of the NOD model in identifying the roles of the innate immune system and the interaction with the gut microbiota in modifying diabetes susceptibility. In addition, the role of the B cells will also be discussed with new insights gained through B cell depletion experiments and the impact on translational developments. Finally, this review will also discuss the future of the NOD mouse and the development of humanized NOD mice, providing novel insights into human T1D.
Genome-wide association (GWA) studies use high-throughput genotyping technologies to assay hundreds of thousands of single-nucleotide polymorphisms (SNPs) and relate them to clinical conditions and ...measurable traits. Since 2005, nearly 100 loci for as many as 40 common diseases and traits have been identified and replicated in GWA studies, many in genes not previously suspected of having a role in the disease under study, and some in genomic regions containing no known genes. GWA studies are an important advance in discovering genetic variants influencing disease but also have important limitations, including their potential for false-positive and false-negative results and for biases related to selection of study participants and genotyping errors. Although these studies are clearly many steps removed from actual clinical use, and specific applications of GWA findings in prevention and treatment are actively being pursued, at present these studies mainly represent a valuable discovery tool for examining genomic function and clarifying pathophysiologic mechanisms. This article describes the design, interpretation, application, and limitations of GWA studies for clinicians and scientists for whom this evolving science may have great relevance.
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