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

Development and validation of the DIabetes Severity SCOre (DISSCO) in 139 626 individuals with type 2 diabetes: a retrospective cohort study

OBJECTIVE: Clinically applicable diabetes severity measures are lacking, with no previous studies comparing their predictive value with glycated hemoglobin (HbA1c). We developed and validated a type 2 diabetes severity score (the DIabetes Severity SCOre, DISSCO) and evaluated its association with risks of hospitalization and mortality, assessing its additional risk information to sociodemographic factors and HbA1c. RESEARCH DESIGN AND METHODS: We used UK primary and secondary care data for 139 626 individuals with type 2 diabetes between 2007 and 2017, aged ≥35 years, and registered in general practices in England. The study cohort was randomly divided into a training cohort (n=111 748, 80%) to develop the severity tool and a validation cohort (n=27 878). We developed baseline and longitudinal severity scores using 34 diabetes-related domains. Cox regression models (adjusted for age, gender, ethnicity, deprivation, and HbA1c) were used for primary (all-cause mortality) and secondary (hospitalization due to any cause, diabetes, hypoglycemia, or cardiovascular disease or procedures) outcomes. Likelihood ratio (LR) tests were fitted to assess the significance of adding DISSCO to the sociodemographics and HbA1c models. RESULTS: A total of 139 626 patients registered in 400 general practices, aged 63±12 years were included, 45% of whom were women, 83% were White, and 18% were from deprived areas. The mean baseline severity score was 1.3±2.0. Overall, 27 362 (20%) people died and 99 951 (72%) had ≥1 hospitalization. In the training cohort, a one-unit increase in baseline DISSCO was associated with higher hazard of mortality (HR: 1.14, 95% CI 1.13 to 1.15, area under the receiver operating characteristics curve (AUROC)=0.76) and cardiovascular hospitalization (HR: 1.45, 95% CI 1.43 to 1.46, AUROC=0.73). The LR tests showed that adding DISSCO to sociodemographic variables significantly improved the predictive value of survival models, outperforming the added value of HbA1c for all outcomes. Findings were consistent in the validation cohort. CONCLUSIONS: Higher levels of DISSCO are associated with higher risks for hospital admissions and mortality. The new severity score had higher predictive value than the proxy used in clinical practice, HbA1c. This reproducible algorithm can help practitioners stratify clinical care of patients with type 2 diabetes

Comorbidity Clusters and In-Hospital Outcomes in Patients Admitted with Acute Myocardial Infarction in the USA: A National Population-Based Study

BACKGROUND: The prevalence of multimorbidity in patients with acute myocardial infarction (AMI) is increasing. It is unclear whether comorbidities cluster into distinct phenogroups and whether are associated with clinical trajectories. METHODS: Survey-weighted analysis of the United States Nationwide Inpatient Sample (NIS) for patients admitted with a primary diagnosis of AMI in 2018. In-hospital outcomes included mortality, stroke, bleeding, and coronary revascularisation. Latent class analysis of 21 chronic conditions was used to identify comorbidity classes. Multivariable logistic and linear regressions were fitted for associations between comorbidity classes and outcomes. RESULTS: Among 416,655 AMI admissions included in the analysis, mean (±SD) age was 67 (±13) years, 38% were females, and 76% White ethnicity. Overall, hypertension, coronary heart disease (CHD), dyslipidaemia, and diabetes were common comorbidities, but each of the identified five classes (C) included ≥1 predominant comorbidities defining distinct phenogroups: cancer/coagulopathy/liver disease class (C1); least burdened (C2); CHD/dyslipidaemia (largest/referent group, (C3)); pulmonary/valvular/peripheral vascular disease (C4); diabetes/kidney disease/heart failure class (C5). Odds ratio (95% confidence interval [CI]) for mortality ranged between 2.11 (1.89-2.37) in C2 to 5.57 (4.99-6.21) in C1. For major bleeding, OR for C1 was 4.48 (3.78; 5.31); for acute stroke, ORs ranged between 0.75 (0.60; 0.94) in C2 to 2.76 (2.27; 3.35) in C1; for coronary revascularization, ORs ranged between 0.34 (0.32; 0.36) in C1 to 1.41 (1.30; 1.53) in C4. CONCLUSIONS: We identified distinct comorbidity phenogroups that predicted in-hospital outcomes in patients admitted with AMI. Some conditions overlapped across classes, driven by the high comorbidity burden. Our findings demonstrate the predictive value and potential clinical utility of identifying patients with AMI with specific comorbidity clustering

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