Crystal engineering, Bio Pharmaceutics and Cell biology of active pharmaceutical ingredient (drug) nanoparticles. Formation and cell interaction of hydrocortisone and prednisolone nanoparticles.

Nanotechnology applications have emerged enormously in recent times. Of particular interest is that area that overlaps the areas of nanotechnology, biology and medicine: nanomedicine. One advantage of nanomedicines is it that it can be used as an enabling technology by pharmaceutical researchers and industry to overcome issues associated with the low bioavailability of hydrophobic drugs. In the first part of the current study, nanosuspensions of two of hydrophobic steroid drugs: hydrocortisone and prednisolone were produced. Nanosuspensions were prepared using a bottom-up approach: the anti-solvent precipitation method using microfluidic reactors. Surface modification was carried out on these nanosuspensions using cationic surfactants to obtain nanoparticles with different levels of surface positive charge as indicated by ¿-potential values. Dynamic light scattering (DLS) and transmission electron microscope (TEM) techniques were used to characterize the prepared nanoparticles. Powder X-ray diffraction (PXRD) and differential scanning calorimetry (DSC) were also used to characterize hydrocortisone nanoparticles. In the second part, cellular uptake of both coated and uncoated nanoparticles by HaCaT keratinocytes cell line was examined and indicated by quantifying the anti- inflammatory effect of nanoparticles on the LPS-induced inflammation. Also, TEM was employed to evaluate the cellular uptake of hydrocortisone nanoparticles. Results showed higher ant-inflammatory effect of coated nanoparticles over uncoated nanoparticles. Furthermore, the anti-inflammatory effect of coated nanoparticles was correlated to the degree of positive surface charge.Libyan governmen

Oral anticoagulant prescribing among patients with cancer and atrial fibrillation in England, 2009–2019:OAC prescribing in AF patients with cancer

BACKGROUND: Anticoagulation of patients with atrial fibrillation (AF) and cancer is challenging because of their high risk for stroke and bleeding. Little is known of the variations of oral anticoagulant (OAC) prescribing in patients with AF with and without cancer.METHODS: Patients with first-time AF during 2009-2019 from the Clinical Practice Research Datalink were included. Cancer diagnosis was defined as a history of breast, prostate, colorectal, lung, or hematological cancer. Competing-risk analysis was used to assess the risk of OAC prescribing in patients with AF and cancer adjusted for clinical and sociodemographic factors.RESULTS: Of 177,065 patients with AF, 11.7% had cancer. Compared to patients without cancer, patients with cancer were less likely to receive OAC: prostate cancer (subhazard ratio [SHR], 0.95; 95% CI, 0.91-0.99), breast cancer (SHR, 0.93; 95% CI, 0.89-0.98), colorectal cancer (SHR, 0.93; 95% CI, 0.88-0.99), hematological cancer (SHR, 0.70; 95% CI, 0.65-0.75), and lung cancer (SHR, 0.44; 95% CI, 0.38-0.50). The cumulative incidence function (CIF) of OAC prescribing was lowest for patients with lung cancer and hematological cancer compared with patients without cancer. The difference between the CIF of OAC prescribing in patients with and without cancer becomes narrower in the most deprived areas. Elderly patients (aged ≥85 years) overall had the lowest CIF of OAC prescribing regardless of cancer status.CONCLUSIONS: In patients with AF, underprescribing of OAC is independently associated with certain cancer types. Patients with hematological and lung cancer are the least likely to receive anticoagulation therapy compared with patients without cancer. Underprescribing of OAC in cancer is linked to old age. Further studies of patients with AF and cancer are warranted to assess the net clinical benefit of anticoagulation in certain cancer types.</p

Assessing the severity of Type 2 diabetes using clinical data-based measures:a systematic review

Aims To identify and critically appraise measures that use clinical data to grade the severity of Type 2 diabetes. Methods We searched MEDLINE, Embase and PubMed between inception and June 2018. Studies reporting on clinical data‐based diabetes‐specific severity measures in adults with Type 2 diabetes were included. We excluded studies conducted solely in participants with other types of diabetes. After independent screening, the characteristics of the eligible measures including design and severity domains, the clinical utility of developed measures, and the relationship between severity levels and health‐related outcomes were assessed. Results We identified 6798 studies, of which 17 studies reporting 18 different severity measures (32 314 participants in 17 countries) were included: a diabetes severity index (eight studies, 44%); severity categories (seven studies, 39%); complication count (two studies, 11%); and a severity checklist (one study, 6%). Nearly 89% of the measures included diabetes‐related complications and/or glycaemic control indicators. Two of the severity measures were validated in a separate study population. More severe diabetes was associated with increased healthcare costs, poorer cognitive function and significantly greater risks of hospitalization and mortality. The identified measures differed greatly in terms of the included domains. One study reported on the use of a severity measure prospectively. Conclusions Health records are suitable for assessment of diabetes severity; however, the clinical uptake of existing measures is limited. The need to advance this research area is fundamental as higher levels of diabetes severity are associated with greater risks of adverse outcomes. Diabetes severity assessment could help identify people requiring targeted and intensive therapies and provide a major benchmark for efficient healthcare services

The comorbidity burden of type 2 diabetes mellitus: patterns, clusters and predictions from a large English primary care cohort

Background: Presence of additional chronic conditions has significant impact on the treatment and management of type-2 diabetes (T2DM). Little is known about the patterns of comorbidities in this population. The aims of this study are to quantify comorbidity patterns in people with T2DM, to estimate the prevalence of six chronic conditions in 2027 and to identify clusters of similar conditions. Methods: We used the Clinical Practice Research Datalink (CPRD) linked with the Index of Multiple Deprivation (IMD) data to identify patients diagnosed with T2DM between 2007 and 2017. 102,394 people met the study inclusion criteria. We calculated the crude and age-standardised prevalence of 18 chronic conditions present at and after the T2DM diagnosis. We analysed longitudinally the 6 most common conditions and forecasted their prevalence in 2027 using linear regression. We used agglomerative hierarchical clustering to identify comorbidity clusters. These analyses were repeated on subgroups stratified by gender and deprivation. Results: More people living in the most deprived areas had ≥1 comorbidities present at the time of diagnosis (72% of females; 64% of males) compared to the most affluent areas (67% of females; 59% of males). Depression prevalence increased in all strata, and was more common in the most deprived areas. Depression was predicted to affect 33% of females and 15% of males diagnosed with T2DM in 2027. Moderate clustering tendencies were observed, with concordant conditions grouped together and some variations between groups of different demographics. Conclusions: Comorbidities are common in this population and high between-patient variability in comorbidity patterns emphasises the need for patient-centred healthcare. Mental health is a growing concern and there is a need for interventions that target both physical and mental health in this population

Incidence of nonvalvular atrial fibrillation and oral anticoagulant prescribing in England, 2009 to 2019: A cohort study.

BACKGROUND: Atrial fibrillation (AF) is an important risk factor for ischaemic stroke, and AF incidence is expected to increase. Guidelines recommend using oral anticoagulants (OACs) to prevent the development of stroke. However, studies have reported the frequent underuse of OACs in AF patients. The objective of this study is to describe nonvalvular atrial fibrillation (NVAF) incidence in England and assess the clinical and socioeconomic factors associated with the underprescribing of OACs. METHODS AND FINDINGS: We conducted a population-based retrospective cohort study using the UK Clinical Practice Research Datalink (CPRD) database to identify patients with NVAF aged ≥18 years and registered in English general practices between 2009 and 2019. Annual incidence rate of NVAF by age, deprivation quintile, and region was estimated. OAC prescribing status was explored for patients at risk for stroke and classified into the following: OAC, aspirin only, or no treatment. We used a multivariable multinomial logistic regression model to estimate relative risk ratios (RRRs) and 95% confidence intervals (CIs) of the factors associated with OAC or aspirin-only prescribing compared to no treatment in patients with NVAF who are recommended to take OAC. The multivariable regression was adjusted for age, sex, comorbidities, socioeconomic status, baseline treatment, frailty, bleeding risk factors, and takes into account clustering by general practice. Between 2009 and 2019, 12,517,191 patients met the criteria for being at risk of developing NVAF. After a median follow-up of 4.6 years, 192,265 patients had an incident NVAF contributing a total of 647,876 person-years (PYR) of follow-up. The overall age-adjusted incidence of NVAF per 10,000 PYR increased from 20.8 (95% CI: 20.4; 21.1) in 2009 to 25.5 (25.1; 25.9) in 2019. Higher incidence rates were observed for older ages and males. Among NVAF patients eligible for anticoagulation, OAC prescribing rose from 59.8% (95% CI: 59.0; 60.6) in 2009 to 83.2% (95% CI: 83.0; 83.4) in 2019. Several conditions were associated with lower risk of OAC prescribing: dementia [RRR 0.52 (0.47; 0.59)], liver disease 0.58 (0.50; 0.67), malignancy 0.74 (0.72; 0.77), and history of falls 0.82 (0.78; 0.85). Compared to white ethnicity, patients from black and other ethnic minorities were less likely to receive OAC; 0.78 (0.65; 0.94) and 0.76 (0.64; 0.91), respectively. Patients living in the most deprived areas were less likely to receive OAC 0.85 (0.79; 0.91) than patients living in the least deprived areas. Practices located in the East of England were associated with higher risk of prescribing aspirin only over no treatment than practices in London (RRR 1.22; 95% CI 1.02 to 1.45). The main limitation of this study is that these findings depends on accurate recording of conditions by health professionals and the inevitable residual confounding due to lack of data on certain factors that could be associated with under-prescribing of OACs. CONCLUSIONS: The incidence of NVAF increased between 2009 and 2015, before plateauing. Underprescribing of OACs in NVAF is associated with a range of comorbidities, ethnicity, and socioeconomic factors, demonstrating the need for initiatives to reduce inequalities in the care for AF patients

Association between patient factors and the effectiveness of wearable trackers at increasing the number of steps per day among adults with Cardiometabolic conditions: meta-analysis of individual patient data from randomized controlled trials.

Background Current evidence supports the use of wearable trackers by people with cardiometabolic conditions. However, as the health benefits are small and confounded by heterogeneity, there remains uncertainty as to which patient groups are most helped by wearable trackers. Objective This study examined the effects of wearable trackers in patients with cardiometabolic conditions to identify subgroups of patients who most benefited and to understand interventional differences. Methods We obtained individual participant data from randomized controlled trials of wearable trackers that were conducted before December 2020 and measured steps per day as the primary outcome in participants with cardiometabolic conditions including diabetes, overweight or obesity, and cardiovascular disease. We used statistical models to account for clustering of participants within trials and heterogeneity across trials to estimate mean differences with the 95% CI. Results Individual participant data were obtained from 9 of 25 eligible randomized controlled trials, which included 1481 of 3178 (47%) total participants. The wearable trackers revealed that over the median duration of 12 weeks, steps per day increased by 1656 (95% CI 918-2395), a significant change. Greater increases in steps per day from interventions using wearable trackers were observed in men (interaction coefficient -668, 95% CI -1157 to -180), patients in age categories over 50 years (50-59 years: interaction coefficient 1175, 95% CI 377-1973; 60-69 years: interaction coefficient 981, 95% CI 222-1740; 70-90 years: interaction coefficient 1060, 95% CI 200-1920), White patients (interaction coefficient 995, 95% CI 360-1631), and patients with fewer comorbidities (interaction coefficient -517, 95% CI -1188 to -11) compared to women, those aged below 50, non-White patients, and patients with multimorbidity. In terms of interventional differences, only face-to-face delivery of the tracker impacted the effectiveness of the interventions by increasing steps per day. Conclusions In patients with cardiometabolic conditions, interventions using wearable trackers to improve steps per day mostly benefited older White men without multimorbidity. TRIAL REGISTRATION: PROSPERO CRD42019143012; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=143012

Assessing the severity of cardiovascular disease in 213 088 patients with coronary heart disease: a retrospective cohort study

Objective Most current cardiovascular disease (CVD) risk stratification tools are for people without CVD, but very few are for prevalent CVD. In this study, we developed and validated a CVD severity score in people with coronary heart disease (CHD) and evaluated the association between severity and adverse outcomes. Methods Primary and secondary care data for 213 088 people with CHD in 398 practices in England between 2007 and 2017 were used. The cohort was randomly divided into training and validation datasets (80%/20%) for the severity model. Using 20 clinical severity indicators (each assigned a weight=1), baseline and longitudinal CVD severity scores were calculated as the sum of indicators. Adjusted Cox and competing-risk regression models were used to estimate risks for all-cause and cause-specific hospitalisation and mortality. Results Mean age was 64.5±12.7 years, 46% women, 16% from deprived areas, baseline severity score 1.5±1.2, with higher scores indicating a higher burden of disease. In the training dataset, 138 510 (81%) patients were hospitalised at least once, and 39 944 (23%) patients died. Each 1-unit increase in baseline severity was associated with 41% (95% CI 37% to 45%, area under the receiver operating characteristics (AUROC) curve=0.79) risk for 1 year for all-cause mortality; 59% (95% CI 52% to 67%, AUROC=0.80) for cardiovascular (CV)/diabetes mortality; 27% (95% CI 26% to 28%) for any-cause hospitalisation and 37% (95% CI 36% to 38%) for CV/diabetes hospitalisation. Findings were consistent in the validation dataset. Conclusions Higher CVD severity score is associated with higher risks for any-cause and cause-specific hospital admissions and mortality in people with CHD. Our reproducible score based on routinely collected data can help practitioners better prioritise management of people with CHD in primary care

Investigating the links between diagnostic uncertainty, emotional exhaustion, and turnover intention in General Practitioners working in the United Kingdom

BackgroundGeneral Practitioners (GPs) report high levels of burnout, job dissatisfaction, and turnover intention. The complexity of presenting problems to general practice makes diagnostic uncertainty a common occurrence that has been linked to burnout. The interrelationship between diagnostic uncertainty with other factors such as burnout, job satisfaction and turnover intention have not been previously examined.ObjectivesTo examine associations between diagnostic uncertainty, emotional exhaustion (EE), depersonalization (DP), job satisfaction, and turnover intention in GPs.MethodsSeventy general practices in England were randomly selected through the Oxford-Royal College of General Practitioners Research and Surveillance Centre (RCGP-RSC). A total of 348 GPs within 67 these practices completed a 10-item online questionnaire which included questions on GP characteristics, work-life balance, job satisfaction, sickness presenteeism, diagnostic uncertainty, turnover intention as well as EE and DP. Associations between diagnostic uncertainty and each of EE, DP, job satisfaction, and turnover intention were evaluated in multivariate mixed-effect ordinal logistic regressions whilst adjusting for covariates, to account for the correlation in the three outcomes of interest.ResultsAlmost one-third of GPs (n = 101; 29%) reported experiencing &gt;10% of diagnostic uncertainty in their day-to-day practice over the past year. GPs reporting greater diagnostic uncertainty had higher levels of EE [OR = 3.90; 95% CI = (2.54, 5.99)], job dissatisfaction [OR = 2.01; 95% CI = (1.30, 3.13)] and turnover intention [OR = 4.51; 95% CI = (2.86, 7.11)]. GPs with no sickness presenteeism had lower levels of EE [OR = 0.53; 95% CI = (0.35, 0.82)], job dissatisfaction [OR = 0.56; 95% CI = (0.35, 0.88)], and turnover intention [OR = 0.61; 95% CI = (0.41, 0.91)].ConclusionDiagnostic uncertainty may not only negatively impact on the wellbeing of GPs, but could also have adverse implications on workforce retention in primary care