Explore the vast range of titles available.

Cardiac progenitor cells derived from adult heart have emerged as one of the most promising stem cell types for cardiac protection and repair. Exosomes are known to mediate cell–cell communication by transporting cell-derived proteins and nucleic acids, including various microRNAs (miRNAs). Here we investigated the cardiac progenitor cell (CPC)-derived exosomal miRNAs on protecting myocardium under oxidative stress. Sca1 + CPCs-derived exosomes were purified from conditional medium, and identified by nanoparticle trafficking analysis (NTA), transmission electron microscopy and western blotting using CD63, CD9 and Alix as markers. Exosomes production was measured by NTA, the result showed that oxidative stress-induced CPCs secrete more exosomes compared with normal condition. Although six apoptosis-related miRNAs could be detected in two different treatment-derived exosomes, only miR-21 was significantly upregulated in oxidative stress-induced exosomes compared with normal exosomes. The same oxidative stress could cause low miR-21 and high cleaved caspase-3 expression in H9C2 cardiac cells. But the cleaved caspase-3 was significantly decreased when miR-21 was overexpressed by transfecting miR-21 mimic. Furthermore, miR-21 mimic or inhibitor transfection and luciferase activity assay confirmed that programmed cell death 4 (PDCD4) was a target gene of miR-21, and miR-21/PDCD4 axis has an important role in anti-apoptotic effect of H9C2 cell. Western blotting and Annexin V/PI results demonstrated that exosomes pre-treated H9C2 exhibited increased miR-21 whereas decreased PDCD4, and had more resistant potential to the apoptosis induced by the oxidative stress, compared with non-treated cells. These findings revealed that CPC-derived exosomal miR-21 had an inhibiting role in the apoptosis pathway through downregulating PDCD4. Restored miR-21/PDCD4 pathway using CPC-derived exosomes could protect myocardial cells against oxidative stress-related apoptosis. Therefore, exosomes could be used as a new therapeutic vehicle for ischemic cardiac disease.

Dapsone is an important medication for the treatment of leprosy, but a life-threatening drug hypersensitivity syndrome develops in some patients. In this report from China, an HLA-B locus is identified as a strong genetic risk factor for the syndrome. Dapsone (4-4′-sulfonyldianiline), which was first synthesized in 1908, 1 is both an antibiotic and an antiinflammatory agent. Dapsone alone or in combination with other drugs has been used for the prevention and treatment of infectious diseases (e.g., leprosy, malaria, and actinomycetoma, as well as Pneumocystis jirovecii pneumonia in persons with human immunodeficiency virus [HIV] infection) and chronic inflammatory diseases characterized by the infiltration of neutrophils or eosinophils (e.g., dermatitis herpetiformis, linear IgA dermatosis, subcorneal pustular dermatosis, and erythema elevatum diutinum). 2 , 3 About 0.5 to 3.6% of persons treated with dapsone have a drug hypersensitivity syndrome, 3 – 5 which was first described by . . .

Men with type 2 diabetes (T2D) and obesity are often characterised by low testosterone (T). We aimed to determine whether exenatide (EXE) combined metformin (MET) treatment has a better effect on serum total testosterone (TT) levels than glimepiride (GLI) combined MET treatment in men with T2D and obesity. In a multicentre, 12‐week observational study, 176 obese T2D men with failed glycaemic control were included in the study: ninety men (mean age, 43.00 ± 8.50 years) in EXE + MET group and 86 men (mean age, 44.00 ± 7.00 years) in GLI + MET group. Serum TT levels were more significantly increased in EXE + MET group than GLI + MET group (121.72 ± 56.73 ng/dl versus 34.67 ± 16.30 ng/dl). The increasement of TT levels in those patients who lost body weight ≥5% was significantly greater than those who lost weight <5% in the two groups. The changes in TT levels are closely related to the changes in waist circumference (r = −.443, p < .001). Sexual function assessment of EXE + MET group was more significantly improved than GLI + MET group (p < .001). No serious adverse events were observed. In conclusion, short‐term combined treatment with EXE and MET is superior to GLI combined MET treatment in the improvement of serum TT levels, which could lead to an improvement of sexual hypofunction in patients with obesity and T2D.

Background Hometime, the total number of days a person is living in the community (not in a healthcare institution) in a defined period of time after a hospitalization, is a patient-centred outcome metric increasingly used in healthcare research. Hometime exhibits several properties which make its statistical analysis difficult: it has a highly non-normal distribution, excess zeros, and is bounded by both a lower and upper limit. The optimal methodology for the analysis of hometime is currently unknown. Methods Using administrative data we identified adult patients diagnosed with stroke between April 1, 2010 and December 31, 2017 in Ontario, Canada. 90-day hometime and clinically relevant covariates were determined through administrative data linkage. Fifteen different statistical and machine learning models were fit to the data using a derivation sample. The models’ predictive accuracy and bias were assessed using an independent validation sample. Results Seventy-five thousand four hundred seventy-five patients were identified (divided into a derivation set of 49,402 and a test set of 26,073). In general, the machine learning models had lower root mean square error and mean absolute error than the statistical models. However, some statistical models resulted in lower (or equal) bias than the machine learning models. Most of the machine learning models constrained predicted values between the minimum and maximum observable hometime values but this was not the case for the statistical models. The machine learning models also allowed for the display of complex non-linear interactions between covariates and hometime. No model captured the non-normal bucket shaped hometime distribution. Conclusions Overall, no model clearly outperformed the others. However, it was evident that machine learning methods performed better than traditional statistical methods. Among the machine learning methods, generalized boosting machines using the Poisson distribution as well as random forests regression were the best performing. No model was able to capture the bucket shaped hometime distribution and future research on factors which are associated with extreme values of hometime that are not available in administrative data is warranted.

Background Ninety-day hometime, the number of days a patient is living in the community in the first 90 after stroke, exhibits a non-normal bucket-shaped distribution, with lower and upper constraints making its analysis difficult. In this proof-of-concept study we evaluated the performance of random forests regression in the analysis of hometime. Methods Using administrative data we identified stroke hospitalizations between 2010 and 2017 in Ontario, Canada. We used random forests regression to predict 90-day hometime using 15 covariates. Model accuracy was determined using the r-squared statistic. Variable importance in prediction and the marginal effects of each covariate were explored. Results We identified 75,745 eligible patients. Median 90-day hometime was 59 days (Q1: 2, Q3: 83). Random forests predicted hometime with reasonable accuracy (adjusted r-squared 0.3462); no implausible values were predicted but extreme values were predicted with low accuracy. Frailty, stroke severity, and age exhibited inverse non-linear relationships with hometime and patients arriving by ambulance had less hometime than those who did not. Conclusions Random forests may be a useful method for analyzing 90-day hometime and capturing the complex non-linear relationships which exist between predictors and hometime. Future work should compare random forests to other models and focus on improving the accuracy of predictions of extreme values of hometime.

Observations of aerosol hygroscopic growth and CCN activation spectra for submicron particles are reported for the T1 ground site outside of Mexico City during the MIRAGE 2006 campaign. κ-Köhler theory is used to evaluate the characteristic hygroscopicity parameter, κ*, for the CCN active aerosol population using both size-resolved HTMDA and size-resolved CCNc measurements. Organic mass fractions (forg) are evaluated from size-resolved aerosol mass spectrometer (AMS) measurements, from which predictions of the hygroscopicity parameter are compared against κ*. Strong diurnal changes in aerosol water uptake parameters and aerosol composition are observed. We find that new particle formation (NPF) events are correlated with an increased κ* and CCN-active fraction during the daytime, with greater impact on smaller particles. During NPF events, the number concentration of 40 nm particles acting as CCN at 0.51% ± 0.06% supersaturation can surpass by more than a factor of two the corresponding concentrations of 100 nm particles. We also find that at 06:00–08:00 LT throughout the campaign, fresh traffic emissions result in substantial changes to the chemical distribution of the aerosol, with on average 65% externally mixed fraction for 40 nm particles and 30% externally mixed fraction for 100 nm particles, whereas at midday nearly all particles of both sizes can be described as \"internally mixed\". Average activation spectra and growth factor distributions are analyzed for different time periods characterizing the daytime (with and without NPF events), the early morning \"rush hour\" and the entire campaign. We show that κ* derived from CCNc measurements decreases as a function of size during all time periods, while the CCN-active fraction increases as a function of size. Size-resolved AMS measurements do not predict the observed trend for κ* versus particle size, which can be attributed to unresolved mixing state and the presence of refractory material not measured by the AMS. Measured κ* typically ranges from 0.2 to 0.35, and organics typically make up 60–85 % of the aerosol mass in the size range studied. We show that κAMS is able to describe CCN concentrations reasonably well, provided mixing-state information is available, especially at the highest CCN concentrations. This is consistent with other CCN studies carried out in urban environments, and is partly due to the fact that the highest CCN concentrations occur during the daytime when the aerosol is internally mixed. During the early morning rush hour, however, failing to account for the aerosol mixing state results in systematic overestimation of CCN concentrations by as much as 50–100% on average.

Research suggests that gender inequality, measured using the gender inequality index (GII), influences stroke mortality in women compared to men. We examine how source country GII modifies the rate of ischemic stroke in women compared to men after immigration to Canada, a country with low gender inequality. We used linked health data and immigration records of 452,089, stroke-free immigrants aged 40–69 year who migrated from 123 countries. Over 15 years of follow-up, 5991 (1.3%) had an incident ischemic stroke. We demonstrate (a) a lower adjusted rate of stroke in women compared to men (hazard ratio 0.64; 95% CI 0.61–0.67); (b) that sex differences in stroke incidence were modified by source country GII, as the hazard of stroke in women vs. men attenuated by a factor of 1.06 for every 0.1 increase in the GII of the source country (P sex*GII  = 0.002); and (c) migration to a country with low GII attenuates the adverse effect of source country GII on sex differences in stroke incidence. Evaluating pathways through which source country gender inequality differentially influences stroke risk in immigrant women compared to men could help develop strategies to mitigate the effects of early-life gender inequality on stroke risk.

Background and purpose Studies of carotid endarterectomy (CEA) require stratification by symptomatic vs asymptomatic status because of marked differences in benefits and harms. In administrative datasets, this classification has been done using hospital discharge diagnosis codes of uncertain accuracy. This study aims to develop and evaluate algorithms for classifying symptomatic status using hospital discharge and physician claims data. Methods A single center’s administrative database was used to assemble a retrospective cohort of participants with CEA. Symptomatic status was ascertained by chart review prior to linkage with physician claims and hospital discharge data. Accuracy of rule-based classification by discharge diagnosis codes was measured by sensitivity and specificity. Elastic net logistic regression and random forest models combining physician claims and discharge data were generated from the training set and assessed in a test set of final year participants. Models were compared to rule-based classification using sensitivity at fixed specificity. Results We identified 971 participants undergoing CEA at the Vancouver General Hospital (Vancouver, Canada) between January 1, 2008 and December 31, 2016. Of these, 729 met inclusion/exclusion criteria ( n  = 615 training, n  = 114 test). Classification of symptomatic status using hospital discharge diagnosis codes was 32.8% (95% CI 29–37%) sensitive and 98.6% specific (96–100%). At matched 98.6% specificity, models that incorporated physician claims data were significantly more sensitive: elastic net 69.4% (59–82%) and random forest 78.8% (69–88%). Conclusion Discharge diagnoses were specific but insensitive for the classification of CEA symptomatic status. Elastic net and random forest machine learning algorithms that included physician claims data were sensitive and specific, and are likely an improvement over current state of classification by discharge diagnosis alone.

Background The economic burden of stroke on the healthcare system has been previously described, but sex differences in healthcare costs have not been well characterized. We described the direct person-level healthcare cost in men and women as well as the various health settings in which costs were incurred following stroke. Methods In this population-based cohort study of patients admitted to hospital with stroke between 2008 and 2017 in Ontario, Canada, we used linked administrative data to calculate direct person-level costs in Canadian dollars in the one-year following stroke. We used a generalized linear model with a gamma distribution and a log link function to compare costs in women and men with and without adjustment for baseline clinical differences. We also assessed for an interaction between age and sex using restricted cubic splines to model the association of age with costs. Results We identified 101,252 patients (49% were women, median age [Q1-Q3] was 76 years [65–84]). Unadjusted costs following stroke were higher in women compared to men (mean ± standard deviation cost was $54,012 ± 54,766 for women versus $52,829 ± 59,955 for men, and median cost was $36,703 [$16,496–$72,227] for women versus $32,903 [$15,485–$66,007] for men). However, after adjustment, women had 3% lower costs compared to men (relative cost ratio and 95% confidence interval 0.97 [0.96,0.98]). The lower cost in women compared to men was most prominent among people aged over 85 years (p for interaction = 0.03). Women incurred lower costs than men in outpatient care and rehabilitation, but higher costs in complex continuing care, long-term care, and home care. Conclusions Patterns of resource utilization and direct medical costs were different between men and women after stroke. Our findings inform public payers of the drivers of costs following stroke and suggest the need for sex-based cost-effectiveness evaluation of stroke interventions with consideration of costs in all care settings.

Background Health system disruptions since onset of the COVID-19 pandemic may have adversely impacted adherence to medications for common cardiovascular risk factors. Methods We examined adherence to and discontinuation of incident prescriptions for medications treating hypertension, dyslipidemia, diabetes, and atrial fibrillation in Ontario, Alberta, and Nova Scotia, Canada. We compared the recent period (April 1, 2020 through most recently available follow-up: September 30, 2021 for Ontario; March 31, 2021 for Alberta; and March 31, 2022 for Nova Scotia) to the baseline, pre-pandemic period (April 1, 2014 through March 31, 2019). In each province, people aged ≥66 years with a valid health number and corresponding incident prescription were included. For each medication class, adherence in the recent period, defined as ≥ 0.80 proportion-of-days-covered (PDC), was compared to the pre-pandemic period using modified Poisson regression with robust error variance, adjusted for patient characteristics. Similarly adjusted Cox proportional hazards models compared hazard of discontinuation over one year of follow-up between the two time periods. Results In the recent period, PDC ranged from 48.9% for dyslipidemia medications in Alberta to 82.2% for anticoagulants in Nova Scotia. Adherence was not different between periods, with the following exceptions: higher adherence in the recent period for antihypertensives (adjusted risk ratios [aRR] 1.08, 95% CI 1.06–1.10) and dyslipidemics (aRR 1.07, 95% CI 1.04–1.09) in Nova Scotia, and for antihyperglycemics (aRR 1.10, 95% CI 1.08–1.14) and anticoagulants (1.15, 95% CI 1.12, 1.18) in Alberta. Adherence was lower in the recent period only for antihypertensives in Alberta (aRR 0.95, 95% CI 0.93, 0.97). One-year rates of discontinuation ranged from 20.9% for anticoagulants in the Alberta recent period to 56.7% for antihypertensives in the Ontario baseline period. The adjusted hazard of discontinuation was lower or unchanged in the recent period for all medication classes. Conclusions Despite significant health system disruptions since 2020, recent adherence to incident cardiovascular prescriptions was similar or better than before and rates of medication discontinuation were lower. However, interventions are still needed to improve existing, suboptimal adherence.