JURNAL AGROTEKNOLOGI
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Community-acquired bacteraemia in COVID-19 in comparison to influenza A and influenza B: a retrospective cohort study
No full textBackground
During the coronavirus disease 2019 (COVID-19) pandemic in the Netherlands it was noticed that very few blood cultures from COVID-19 patients turned positive with clinically relevant bacteria. This was particularly evident in comparison to the number of positive blood cultures during previous seasonal epidemics of influenza. This observation raised questions about the occurrence and causative microorganisms of bacteraemia in COVID-19 patients, especially in the perspective of the widely reported overuse of antibiotics and the rising rate of antibiotic resistance.
Methods
We conducted a retrospective cohort study on blood culture results in influenza A, influenza B and COVID-19 patients presenting to two hospitals in the Netherlands. Our main outcome consisted of the percentage of positive blood cultures. The percentage of clinically relevant blood cultures, isolated bacteria and 30-day all-cause mortality served as our secondary outcomes.
Results
A total of 1331 viral episodes were analysed in 1324 patients. There was no statistically significant difference (p = 0.47) in overall occurrence of blood culture positivity in COVID-19 patients (9.0, 95% CI 6.8–11.1) in comparison to influenza A (11.4, 95% CI 7.9–14.8) and influenza B patients (10.4, 95% CI 7.1–13.7,). After correcting for the high rate of contamination, the occurrence of clinically relevant bacteraemia in COVID-19 patients amounted to 1.0% (95% CI 0.3–1.8), which was statistically significantly lower (p = 0.04) compared to influenza A patients (4.0, 95% CI 1.9–6.1) and influenza B patients (3.0, 95% CI 1.2–4.9). The most frequently identified bacterial isolates in COVID-19 patients were Escherichia coli (n = 2) and Streptococcus pneumoniae (n = 2). The overall 30-day all-cause mortality for COVID-19 patients was 28.3% (95% CI 24.9–31.7), which was statistically significantly higher (p =
Onset and recurrence of psychiatric disorders associated with anti-hypertensive drug classes
No full textThe major anti-hypertensive (AHT) drug classes have been associated with differential risks of psychiatric disorders. However, existing data are limited largely to depression, and confounding variables have not always been controlled for. We sought to fill the evidence gap, using TriNetX Analytics, an electronic health records network. Amongst 58.6 million patients aged 18–90 years, patients prescribed a calcium channel blocker (CCB) were compared with those taking a diuretic, angiotensin-converting enzyme inhibitor (ACEI), angiotensin receptor blocker (ARB), or β-blocker. Cohorts were propensity score-matched for age, sex, race, and blood pressure. Over a 2-year exposure period, we measured the incidence and risk ratio of a first diagnosis (ICD-10 codes), or a recurrence, of psychotic, affective, and anxiety disorders, as well as substance use disorders and sleep disorders. Cohort sizes ranged from 33,734 to 322,814. CCBs were associated with a lower incidence of psychotic, affective, and anxiety disorders than β-blockers (risk ratios 0.69–0.99) and a higher incidence than ARBs (risk ratios 1.04–2.23) for both first and recurrent diagnoses. Comparisons of CCBs with ACEIs or diuretics showed smaller risk ratios that varied between disorders, and between first episode and recurrence. AHT classes were also associated with the incidence of substance use and sleep disorders. Results remained largely unchanged after more extensive cohort matching for additional potential confounders. In a secondary analysis, a comparison between ARBs and ACEIs showed lower rates of psychotic, affective, and substance use disorders with ARBs, but higher risks of anxiety and sleep disorders. In conclusion, AHT classes are differentially associated with the incidence of psychiatric disorders. ARBs show the most advantageous profile and β-blockers the least. The apparent beneficial effects of ARBs merit further study
Efficacy and safety of a modified Vaccinia Ankara-NP+M1 vaccine combined with QIV in people aged 65 and older: a randomised controlled clinical trial (INVICTUS)
No full textBackground: Pre-existing T cell responses to influenza have been correlated with improved clinical outcomes in natural history and human challenge studies. We aimed to determine the efficacy, safety and immunogenicity of a T-cell directed vaccine in older people.
Methods: This was a multicentre, participant- and safety assessor-blinded, randomised, placebo-controlled trial of the co-administration of Modified Vaccinia Ankara encoding nucleoprotein and matrix protein 1 (MVA-NP+M1) and annual influenza vaccine in participants ≥ 65. The primary outcome was the number of days with moderate or severe influenza-like symptoms (ILS) during the influenza season.
Results: 846 of a planned 2030 participants were recruited in the UK prior to, and throughout, the 2017/18 flu season. There was no evidence of a difference in the reported rates of days of moderate or severe ILS during influenza-like illness episodes (unadjusted OR = 0.95, 95% CI: 0.54–1.69; adjusted OR = 0.91, 95% CI: 0.51–1.65). The trial was stopped after one season due to a change in the recommended annual flu vaccine, for which safety of the new combination had not been established. More participants in the MVA-NP+M1 group had transient moderate or severe pain, redness, and systemic responses in the first seven days.
Conclusion: The MVA-NP+M1 vaccine is well tolerated in those aged 65 years and over. Larger trials would be needed to determine potential efficacy
ANFO vapour detection with conducting polymer percolation network sensors and GC/MS
No full textAmmonium nitrate mixed with fuel oil (ANFO) is commonly used in improvised explosive devices (IEDs). The development of ANFO vapour sensors that are small, inexpensive, and easy to use will enable widespread IED detection in the context of security and humanitarian demining. Because of concealment and the low vapour pressures of most explosive materials, achieving sufficiently high sensitivity and low limits of detection are some of the main challenges of explosives vapour detection. Here ANFO chemiresistive vapour sensors based on polypyrrole (PPy) percolation networks are presented and compared to gas chromatography-mass spectroscopy (GC/MS) results for ANFO. Improved sensitivities are achieved by using a polymer percolation network instead of a thin film for the gas sensors. Vapour concentrations are detected of 13–180 ppb of ammonia emitted by a variety of different ammonium nitrate-containing fertilisers and fertiliser-diesel mixtures
DeepSynth: Automata synthesis for automatic task segmentation in deep reinforcement learning
No full textThis paper proposes DeepSynth, a method for effective training of deep Reinforcement Learning (RL) agents when the reward is sparse and non-Markovian, but at the same time progress towards the reward requires achieving an unknown sequence of high-level objectives. Our method employs a novel algorithm for synthesis of compact automata to uncover this sequential structure automatically. We synthesise a humaninterpretable automaton from trace data collected by exploring the environment. The state space of the environment is then enriched with the synthesised automaton so that the generation of a control policy by deep RL is guided by the discovered structure encoded in the automaton. The proposed approach is able to cope with both high-dimensional, low-level features and unknown sparse non-Markovian rewards. We have evaluated DeepSynth’s performance in a set of experiments that includes the Atari game Montezuma’s Revenge. Compared to existing approaches, we obtain a reduction of two orders of magnitude in the number of iterations required for policy synthesis, and also a significant improvement in scalability
Performance of the UKPDS Outcomes Model 2 in a contemporary UK type 2 diabetes trial cohort
No full textObjectives: The UKPDS Outcomes Model developed using 30-year (1977-2007) data from the UKPDS is widely used for health outcomes’ projections and economic evaluations of therapies for patients with type 2 diabetes (T2D). However, its reliability for contemporary UK T2D populations is unclear. We assessed the performance of version 2 of the model (UKPDS-OM2) using data from the ASCEND study, which followed participants with diabetes in the UK between 2005 and 2017.
Methods: The UKPDS-OM2 was used to predict the occurrence of myocardial infarction (MI), other ischaemic heart disease (Other IHD), stroke, cardiovascular death and other death among the 14 569 participants with T2D in the ASCEND study, all without previous cardiovascular disease at study entry. Calibration
(comparison of predicted and observed year-on-year cumulative incidence over 10 years) and discrimination (c-statistics) of the model were assessed for each endpoint. The percentage error in event rates at year 7 (mean duration of follow-up) was used to quantify model bias.
Results: The UKPDS-OM2 substantially over-predicted MI, stroke, cardiovascular death and other death over the 10 year follow-up period (by 149%, 42%, 269% and 52% respectively, at year 7). Discrimination of the model for MI and Other IHD (c-statistics 0.58 and 0.60 respectively) was poorer than that for other outcomes (c-statistics ranging from 0.66 to 0.72).
Conclusions: The UKPDS-OM2 substantially over-predicted risks of key cardiovascular outcomes and death in people with T2D in ASCEND. Appropriate adjustments or a new model may be required for assessments of long-term effects of treatments in contemporary T2D cohorts
Classi-fly inferring aircraft categories from open data
No full textIn recent years, air traffic communication data has become easy to access, enabling novel research in many fields. Exploiting this new data source, a wide range of applications have emerged, from weather forecasting to stock market prediction, or the collection of intelligence about military and government movements. Typically, these applications require knowledge about the metadata of the aircraft, specifically its operator and the aircraft category.
armasuisse Science + Technology, the R&D agency for the Swiss Armed Forces, has been developing Classi-Fly, a novel approach to obtain metadata about aircraft based on their movement patterns. We validate Classi-Fly using several hundred thousand flights collected through open source means, in conjunction with ground truth from publicly available aircraft registries containing more than 2 million aircraft. We show that we can obtain the correct aircraft category with an accuracy of greater than 88%. In cases, where no metadata is available, this approach can be used to create the data necessary for applications working with air traffic communication. Finally, we show that it is feasible to automatically detect particular sensitive aircraft such as police and surveillance aircraft using this method
A systematic review and meta-analysis of gestational diabetes mellitus and mental health among BAME populations
No full textBackground
Gestational diabetes mellitus (GDM) is a common complication of pregnancy and is associated with an increased risk of mental health (MH) disorders including antenatal and postnatal depression (PND), anxiety and post-traumatic-stress-disorder (PTSD). We hypothesized GDM and MH disorders will disproportionately affect individuals from Black, Asian and Minority Ethnic backgrounds.
Methods
A systematic methodology was developed, and a protocol was published in PROSPERO (CRD42020210863) and a systematic review of publications between 1st January 1990 and 30th January 2021 was conducted. Multiple electronic databases were explored using keywords and MeSH terms. The finalised dataset was analysed using statistical methods such as random-effect models, subgroup analysis and sensitivity analysis. These were used to determine odds ratio (OR) and 95% confidence intervals (CI) to establish prevalence using variables of PND, anxiety, PTSD and stress to name a few.
Findings
Sixty studies were finalised from the 20,040 data pool. Forty-six studies were included systematically with 14 used to meta-analyze GDM and MH outcomes. A second meta-analysis was conducted using 7 studies to determine GDM risk among Black, Asian and Minority Ethnic women with pre-existing MH disorders. The results indicate an increased risk with pooled adjusted OR for both reflected at 1.23, 95% CI of 1.00–1.50 and 1.29, 95% CI of 1.11–1.50 respectively.
Interpretation
The available studies suggest a MH sequalae with GDM as well as a sequalae of GDM with MH among Black, Asian and Minority Ethnic populations. Our findings warrant further future exploration to better manage these patients
MOSAIC on the ELT: high-multiplex spectroscopy to unravel the physics of stars and galaxies from the dark ages to the present-day
No full textThe powerful combination of the cutting-edge multi-object spectrograph named MOSAIC with the world largest visible/near-infrared telescope, ESO’s Extremely Large Telescope (ELT), will allow us to probe deeper into the Universe than ever before. MOSAIC is an extremely efficient instrument for obtaining spectra of the numerous faint sources in the Universe, including the very first galaxies and sources of cosmic reionisation. MOSAIC has a high multiplex in the near-infrared (NIR) and in the visible, and also has multi-integral field units (Multi-IFUs) in the NIR. It is therefore perfectly suited to carrying out an inventory of dark matter (from rotation curves) and baryons in the cool–warm gas phases in galactic haloes at z = 3–4. MOSAIC will enable detailed maps of the intergalactic medium at z = 3, the evolutionary history of dwarf galaxies during a Hubble time, and the chemistry as directly measured from stars up to several Mpc. It will also measure faint features in cluster gravitational lenses or in streams surrounding nearby galactic haloes. The preliminary design of MOSAIC is expected to begin next year and its level of readiness is already high, given the instrumental studies already carried out by the team