Σύνθεση ιοντικών υγρών και βαθέων ευτηκτικών διαλυτών και εφαρμογή τους στο διαχωρισμό του αζεοτροπικού μίγματος αιθανόλης/νερού

Productivity in temperate tree crops such as apple has been lifted several-fold by research focusing on optimising a combination of canopy components including light relations, vigour control, tree architecture and crop load. This paper outlines the research behind the Small Tree-High Productivity Initiative (STHPI), which is focused on improving productivity of mango, avocado and macadamia. Preliminary results from work we are undertaking for each of the above canopy components in mango will be outlined. A rootstock screening trial to identify vigour-managing, high-productivity rootstocks is being undertaken, and we present a comparison of the best-performing low-medium vigour rootstock varieties compared with control 'Kensington Pride' (KP) rootstock at 6 months old. Comparisons between 'Keitt', 'NMBP 1243' and 'Calypso' scion cultivars with regard to tree diameter, height and canopy growth at different orchard densities and training systems will also be presented. Preliminary results from an orchard light-relations study indicate that mango yields continued to increase with light interception up to 50%, and reached a maximum of 20-30 t ha at 68% light interception in KP trees approximately 25 years old. In a crop load trial, inflorescence thinning in a 'Calypso' orchard did not significantly reduce yields when up to 90% of inflorescences were removed, but did when 95% of inflorescences were removed, as trees were unable to compensate by adjusting fruit set, size and yield. Inflorescence thinning beyond 80% increased the number of fruit set per panicle, and thinning up to and including 90% of inflorescences increased fruit weight from 340 g to >400 g per fruit. This project is still in its initial stages; however, early indications suggest there may be opportunities to improve early orchard yields through optimising light interception in an orchard's life, potentially through the use of higher densities, and that rootstocks and tree training methods, once identified, may help in the management of vigour. It is also hoped to obtain a better understanding of how crop load influences the balance between vegetative growth, flowering, fruiting, alternate bearing and fruit quality

Single‐active switch high‐voltage gain DC–DC converter using a non‐coupled inductor

A single‐active switch high‐voltage gain non‐coupled inductor DC–DC converter is presented. The introduced converter achieves high step‐up gain without using any coupled inductors or transformers, provides high efficiency, and has a simple control system. The converter also achieves low voltage stress on the switch and diodes without clamping circuits, reducing cost, conduction losses, and complexity. The input current of the introduced converter is continuous with low ripple, and is therefore suitable for renewable energy applications in which the fast dynamic response of the converter is necessary. The principle of operation and design considerations of the introduced converter are investigated. A 200 W prototype circuit with 40 kHz switching frequency, 40 V input voltage, and 250 V output voltage is implemented. The prototype operates at 93.2% efficiency, with voltage and current error of less than 4% compared to theoretical values

Identification and differentiation of Fasciola hepatica and Fasciola gigantica using a simple PCR-restriction enzyme method

Accurate morphological differentiation between the liver fluke species Fasciola hepatica and Fasciola gigantica is difficult. We evaluated PCR-restriction enzyme profiles of internal transcribed spacer 1 (ITS1) that could aid in their identification. Fifty F. hepatica and 30 F. gigantica specimens were collected from different hosts in three provinces of Iran. For DNA extraction, we crushed fragments of the worms between two glass slides as a new method to break down the cells. DNA from the crushed materials was then extracted with a conventional phenol-chloroform method and with the newly developed technique, commercial FTA cards. A primer pair was selected to amplify a 463-bp region of the ITS1 sequence. After sequencing 14 samples and in silico analysis, cutting sites of all known enzymes were predicted and TasI was selected as the enzyme that yielded the most informative profile. Crushing produced enough DNA for PCR amplification with both the phenol-chloroform and commercial FTA card method. The DNA extracted from all samples was successfully amplified and yielded a single sharp band of the expected size. Digestion of PCR products with TasI allowed us to distinguish the two species. In all samples, molecular identification was consistent with morphological identification. Our PCR-restriction enzyme profile is a simple, rapid and reliable method for differentiating F. hepatica and F. gigantica, and can be used for diagnostic and epidemiological purposes. © 2009 Elsevier Inc. All rights reserved

A Data-Driven Typology of Asthma Medication Adherence using Cluster Analysis

Asthma preventer medication non-adherence is strongly associated with poor asthma control. One-dimensional measures of adherence may ignore clinically important patterns of medication-taking behavior. We sought to construct a data-driven multi-dimensional typology of medication non-adherence in children with asthma. We analyzed data from an intervention study of electronic inhaler monitoring devices, comprising 211 patients yielding 35,161 person-days of data. Five adherence measures were extracted: the percentage of doses taken, the percentage of days on which zero doses were taken, the percentage of days on which both doses were taken, the number of treatment intermissions per 100 study days, and the duration of treatment intermissions per 100 study days. We applied principal component analysis on the measures and subsequently applied k-means to determine cluster membership. Decision trees identified the measure that could predict cluster assignment with the highest accuracy, increasing interpretability and increasing clinical utility. We demonstrate the use of adherence measures towards a three-group categorization of medication non-adherence, which succinctly describes the diversity of patient medication taking patterns in asthma. The percentage of prescribed doses taken during the study contributed to the prediction of cluster assignment most accurately (84% in out-of-sample data)

Identifying subtypes of chronic kidney disease with machine learning: development, internal validation and prognostic validation using linked electronic health records in 350,067 individuals

BACKGROUND: Although chronic kidney disease (CKD) is associated with high multimorbidity, polypharmacy, morbidity and mortality, existing classification systems (mild to severe, usually based on estimated glomerular filtration rate, proteinuria or urine albumin-creatinine ratio) and risk prediction models largely ignore the complexity of CKD, its risk factors and its outcomes. Improved subtype definition could improve prediction of outcomes and inform effective interventions. METHODS: We analysed individuals ≥18 years with incident and prevalent CKD (n = 350,067 and 195,422 respectively) from a population-based electronic health record resource (2006-2020; Clinical Practice Research Datalink, CPRD). We included factors (n = 264 with 2670 derived variables), e.g. demography, history, examination, blood laboratory values and medications. Using a published framework, we identified subtypes through seven unsupervised machine learning (ML) methods (K-means, Diana, HC, Fanny, PAM, Clara, Model-based) with 66 (of 2670) variables in each dataset. We evaluated subtypes for: (i) internal validity (within dataset, across methods); (ii) prognostic validity (predictive accuracy for 5-year all-cause mortality and admissions); and (iii) medications (new and existing by British National Formulary chapter). FINDINGS: After identifying five clusters across seven approaches, we labelled CKD subtypes: 1. Early-onset, 2. Late-onset, 3. Cancer, 4. Metabolic, and 5. Cardiometabolic. Internal validity: We trained a high performing model (using XGBoost) that could predict disease subtypes with 95% accuracy for incident and prevalent CKD (Sensitivity: 0.81-0.98, F1 score:0.84-0.97). Prognostic validity: 5-year all-cause mortality, hospital admissions, and incidence of new chronic diseases differed across CKD subtypes. The 5-year risk of mortality and admissions in the overall incident CKD population were highest in cardiometabolic subtype: 43.3% (42.3-42.8%) and 29.5% (29.1-30.0%), respectively, and lowest in the early-onset subtype: 5.7% (5.5-5.9%) and 18.7% (18.4-19.1%). MEDICATIONS: Across CKD subtypes, the distribution of prescription medication classes at baseline varied, with highest medication burden in cardiometabolic and metabolic subtypes, and higher burden in prevalent than incident CKD. INTERPRETATION: In the largest CKD study using ML, to-date, we identified five distinct subtypes in individuals with incident and prevalent CKD. These subtypes have relevance to study of aetiology, therapeutics and risk prediction. FUNDING: AstraZeneca UK Ltd, Health Data Research UK

A retrospective cohort study measured predicting and validating the impact of the COVID-19 pandemic in individuals with chronic kidney disease

Chronic kidney disease (CKD) is associated with increased risk of baseline mortality and severe COVID-19, but analyses across CKD stages, and comorbidities are lacking. In prevalent and incident CKD, we investigated comorbidities, baseline risk, COVID-19 incidence, and predicted versus observed one-year excess death. In a national dataset (NHS Digital Trusted Research Environment (NHSD TRE)) for England encompassing 56 million individuals), we conducted a retrospective cohort study (March 2020 to March 2021) for prevalence of comorbidities by incident and prevalent CKD, SARS-CoV-2 infection and mortality. Baseline mortality risk, incidence and outcome of infection by comorbidities, controlling for age, sex and vaccination were assessed. Observed versus predicted one-year mortality at varying population infection rates and pandemic-related relative risks using our published model in pre-pandemic CKD cohorts (NHSD TRE and Clinical Practice Research Datalink (CPRD)) were compared. Among individuals with CKD (prevalent:1,934,585, incident:144,969), comorbidities were common (73.5% and 71.2% with one or more condition(s) in respective data sets, and 13.2% and 11.2% with three or more conditions, in prevalent and incident CKD), and associated with SARS-CoV-2 infection, particularly dialysis/transplantation (odds ratio 2.08, 95% confidence interval 2.04-2.13) and heart failure(1.73, 1.71-1.76), but not cancer (1.01, 1.01-1.04). One-year all-cause mortality varied by age, sex, multi-morbidity and CKD stage. Compared with 34,265 observed excess deaths, in the NHSD-TRE and CPRD databases respectively, we predicted 28,746 and 24,546 deaths (infection rates 10% and relative risks 3.0), and 23,754 and 20,283 deaths (observed infection rates 6.7% and relative risks 3.7). Thus, in this largest, national-level study, individuals with CKD have a high burden of comorbidities and multi-morbidity, and high risk of pre-pandemic and pandemic mortality. Hence, treatment of comorbidities, non-pharmaceutical measures, and vaccination are priorities for people with CKD and management of long-term conditions is important during and beyond the pandemic

Vaccinations, cardiovascular drugs, hospitalisation and mortality in COVID-19 and Long COVID.

OBJECTIVE: To identify highest-risk subgroups for COVID-19 and Long COVID(LC), particularly in contexts of influenza and cardiovascular disease(CVD). METHODS: Using national, linked electronic health records for England(NHS England Secure Data Environment via CVD-COVID-UK/COVID-IMPACT Consortium), we studied individuals(of all ages) with COVID-19 and LC (2020-2023). We compared all-cause hospitalisation and mortality by prior CVD, high CV risk, vaccination status(COVID-19/influenza), and CVD drugs, investigating impact of vaccination and CVD prevention using population preventable fractions. RESULTS: Hospitalisation and mortality were 15.3% and 2.0% among 17,373,850 individuals with COVID-19(LC rate 1.3%), and 16.8% and 1.4% among 301,115 with LC. Adjusted risk of mortality and hospitalisation were reduced with COVID-19 vaccination≥2 doses(COVID-19:HR 0.36 and 0.69; LC:0.44 and 0.90). With influenza vaccination, mortality was reduced, but not hospitalisation(COVID-19:0.86 and 1.01, and LC:0.72 and 1.05). Mortality and hospitalisation were reduced by CVD prevention in those with CVD, e.g. anticoagulants- COVID:19:0.69 and 0.92; LC:0.59 and 0.88; lipid lowering- COVID-19:0.69 and 0.86; LC:0.68 and 0.90. COVID-19 vaccination averted 245044 of 321383 and 7586 of 8738 preventable deaths after COVID-19 and LC, respectively. INTERPRETATION: Prior CVD and high CV risk are associated with increased hospitalisation and mortality in COVID-19 and LC. Targeted COVID-19 vaccination and CVD prevention are priority interventions. FUNDING: NIHR. HDR UK

Evaluation of antithrombotic use and COVID-19 outcomes in a nationwide atrial fibrillation cohort

OBJECTIVE: To evaluate antithrombotic (AT) use in individuals with atrial fibrillation (AF) and at high risk of stroke (CHA2DS2-VASc score ≥2) and investigate whether pre-existing AT use may improve COVID-19 outcomes. METHODS: Individuals with AF and CHA2DS2-VASc score ≥2 on 1 January 2020 were identified using electronic health records for 56 million people in England and were followed up until 1 May 2021. Factors associated with pre-existing AT use were analysed using logistic regression. Differences in COVID-19-related hospitalisation and death were analysed using logistic and Cox regression in individuals with pre-existing AT use versus no AT use, anticoagulants (AC) versus antiplatelets (AP), and direct oral anticoagulants (DOACs) versus warfarin. RESULTS: From 972 971 individuals with AF (age 79 (±9.3), female 46.2%) and CHA2DS2-VASc score ≥2, 88.0% (n=856 336) had pre-existing AT use, 3.8% (n=37 418) had a COVID-19 hospitalisation and 2.2% (n=21 116) died, followed up to 1 May 2021. Factors associated with no AT use included comorbidities that may contraindicate AT use (liver disease and history of falls) and demographics (socioeconomic status and ethnicity). Pre-existing AT use was associated with lower odds of death (OR=0.92, 95% CI 0.87 to 0.96), but higher odds of hospitalisation (OR=1.20, 95% CI 1.15 to 1.26). AC versus AP was associated with lower odds of death (OR=0.93, 95% CI 0.87 to 0.98) and higher hospitalisation (OR=1.17, 95% CI 1.11 to 1.24). For DOACs versus warfarin, lower odds were observed for hospitalisation (OR=0.86, 95% CI 0.82 to 0.89) but not for death (OR=1.00, 95% CI 0.95 to 1.05). CONCLUSIONS: Pre-existing AT use may be associated with lower odds of COVID-19 death and, while not evidence of causality, provides further incentive to improve AT coverage for eligible individuals with AF

COVID-19 trajectories among 57 million adults in England:a cohort study using electronic health records

Background: Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework. Methods: In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status. Findings: Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1. Interpretation: Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources. Funding: British Heart Foundation Data Science Centre, led by Health Data Research UK.</p