| 1. |
- Eriksson, B., et al.
(författare)
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Comorbidities, risk factors and outcomes in patients with heart failure and an ejection fraction of more than or equal to 40% in primary care- and hospital care-based outpatient clinics
- 2018
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Ingår i: Scandinavian Journal of Primary Health Care. - : TAYLOR & FRANCIS LTD. - 0281-3432 .- 1502-7724. ; 36:2, s. 207-215
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Tidskriftsartikel (refereegranskat)abstract
- Objective: The aim of this study is to describe patients with heart failure and an ejection fraction (EF) of more than or equal to 40%, managed in both Primary- and Hospital based outpatient clinks separately with their prognosis, comorbidities and risk factors. Further to compare the heart failure medication in the two groups. Design: We used the prospective Swedish Heart Failure Registry to include 9654 out-patients who had HF and EF >= 40%, 1802 patients were registered in primary care and 7852 in hospital care. Descriptive statistical tests were used to analyze base line characteristics in the two groups and multivariate logistic regression analysis to assess mortality rate in the groups separately. Setting: The prospective Swedish Heart Failure Registry. Setting: The prospective Swedish Heart Failure Registry. Subjects: Patients with heart failure and an ejection fraction (EF) of more than or equal to 40%. Main outcome measures: Comorbidities, risk factors and mortality. Results: Mean-age was 77.5 (primary care) and 70.3 years (hospital care) p < 0.0001, 46.7 vs. 36.3% women respectively (p < 0.0001) and EF >= 50% 26.1 vs. 13.4% (p < 0.0001). Co-morbidities were common in both groups (97.2% vs. 92.3%), the primary care group having more atrial fibrillation, hypertension, ischemic heart disease and COPD. According to the multivariate logistic regression analysis smoking, COPD and diabetes were the most important independent risk factors in the primary care group and valvular disease in the hospital care group. All-cause mortality during mean follow-up of almost 4 years was 315% in primary care and 27.8% in hospital care. One year-mortality rates were 7.8%, and 7.0% respectively. Conclusion: Any co-morbidity was noted in 97% of the HF-patients with an EF of more than or equal to 40% managed at primary care based out-patient clinics and these patients had partly other independent risk factors than those patients managed in hospital care based outpatients clinics. Our results indicate that more attention should be payed to manage COPD in the primary care group.
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| 2. |
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| 3. |
- Zhang, Cheng, et al.
(författare)
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Discovery of therapeutic agents targeting PKLR for NAFLD using drug repositioning
- 2022
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Ingår i: eBioMedicine. - : Elsevier BV. - 2352-3964. ; 83
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Tidskriftsartikel (refereegranskat)abstract
- Background Non-alcoholic fatty liver disease (NAFLD) encompasses a wide spectrum of liver pathologies. However, no medical treatment has been approved for the treatment of NAFLD. In our previous study, we found that PKLR could be a potential target for treatment of NALFD. Here, we investigated the effect of PKLR in in vivo model and performed drug repositioning to identify a drug candidate for treatment of NAFLD. Methods Tissue samples from liver, muscle, white adipose and heart were obtained from control and PKLR knock-out mice fed with chow and high sucrose diets. Lipidomics as well as transcriptomics analyses were conducted using these tissue samples. In addition, a computational drug repositioning analysis was performed and drug candidates were identified. The drug candidates were both tested in in vitro and in vivo models to evaluate their toxicity and efficacy. Findings The Pklr KO reversed the increased hepatic triglyceride level in mice fed with high sucrose diet and partly recovered the transcriptomic changes in the liver as well as in other three tissues. Both liver and white adipose tissues exhibited dysregulated circadian transcriptomic profiles, and these dysregulations were reversed by hepatic knockout of Pklr. In addition, 10 small molecule drug candidates were identified as potential inhibitor of PKLR using our drug repositioning pipeline, and two of them significantly inhibited both the PKLR expression and triglyceride level in in vitro model. Finally, the two selected small molecule drugs were evaluated in in vivo rat models and we found that these drugs attenuate the hepatic steatosis without side effect on other tissues. Interpretation In conclusion, our study provided biological insights about the critical role of PKLR in NAFLD progression and proposed a treatment strategy for NAFLD patients, which has been validated in preclinical studies. Copyright (C) 2022 The Authors. Published by Elsevier B.V.
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| 4. |
- Gross, James, Professor, 1975-
(författare)
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Towards an Internet of Reality
- 2021
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Ingår i: 2021 IEEE 32Nd Annual International Symposium On Personal, Indoor And Mobile Radio Communications (PIMRC). - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- Driven by standardization and commercialization, digital infrastructures evolve in waves. Over the last few years, a particular focus has been on realizing ultra-reliable low-latency wireless communications (URLLC), anticipated mostly for rather specific use cases in industrial automation. Even though initial such systems finally exist today - with future network releases advancing URLLC capabilities even more - the broad market impact to date is low. We argue in this paper that an essential missing component for corresponding dependable applications like closed-loop control or human-in-the-loop are nearby compute capabilities provided within the infrastructure, aka edge computing capabilities. Only in conjunction can such future infrastructures support dependable applications to a full extent. Nevertheless, this also leads to unique challenges which will be central to the evolution of networked infrastructures during the current decade. Out of this evolution of networked infrastructures, we finally argue that a new type of networked application class will emerge, resembling the representation of various aspects of reality in the infrastructure at any point in time. We dub this development the Internet of Reality, and discuss further challenges in this context.
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| 5. |
- Howick, Jeremy, et al.
(författare)
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Effects of empathic and positive communication in healthcare consultations: a systematic review and meta-analysis
- 2018
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Ingår i: Journal of the Royal Society of Medicine. - : Sage Publications. - 0141-0768 .- 1758-1095. ; 111:7, s. 240-252
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundPractitioners who enhance how they express empathy and create positive expectations of benefit could improve patient outcomes. However, the evidence in this area has not been recently synthesised.ObjectiveTo estimate the effects of empathy and expectations interventions for any clinical condition.DesignSystematic review and meta-analysis of randomised trials.Data sourceSix databases from inception to August 2017.Study selectionRandomised trials of empathy or expectations interventions in any clinical setting with patients aged 12 years or older.Review methodsTwo reviewers independently screened citations, extracted data, assessed risk of bias and graded quality of evidence using GRADE. Random effects model was used for meta-analysis.ResultsWe identified 28 eligible (n = 6017). In seven trials, empathic consultations improved pain, anxiety and satisfaction by a small amount (standardised mean difference −0.18 [95% confidence interval −0.32 to −0.03]). Twenty-two trials tested the effects of positive expectations. Eighteen of these (n = 2014) reported psychological outcomes (mostly pain) and showed a modest benefit (standardised mean difference −0.43 [95% confidence interval −0.65 to −0.21]); 11 (n = 1790) reported physical outcomes (including bronchial function/ length of hospital stay) and showed a small benefit (standardised mean difference −0.18 [95% confidence interval −0.32 to −0.05]). Within 11 trials (n = 2706) assessing harms, there was no evidence of adverse effects (odds ratio 1.04; 95% confidence interval 0.67 to 1.63). The risk of bias was low. The main limitations were difficulties in blinding and high heterogeneity for some comparisons.ConclusionGreater practitioner empathy or communication of positive messages can have small patient benefits for a range of clinical conditions, especially pain.Protocol registrationCochrane Database of Systematic Reviews (protocol) DOI: 10.1002/14651858.CD011934.pub2.
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| 6. |
- Saqr, Mohammed, et al.
(författare)
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How learning analytics can early predict under-achieving students in a blended medical education course
- 2017
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Ingår i: Medical teacher. - 0142-159X .- 1466-187X. ; 39:7, s. 757-767
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Tidskriftsartikel (refereegranskat)abstract
- Aim: Learning analytics (LA) is an emerging discipline that aims at analyzing students' online data in order to improve the learning process and optimize learning environments. It has yet un-explored potential in the field of medical education, which can be particularly helpful in the early prediction and identification of under-achieving students. The aim of this study was to identify quantitative markers collected from students' online activities that may correlate with students' final performance and to investigate the possibility of predicting the potential risk of a student failing or dropping out of a course.Methods: This study included 133 students enrolled in a blended medical course where they were free to use the learning management system at their will. We extracted their online activity data using database queries and Moodle plugins. Data included logins, views, forums, time, formative assessment, and communications at different points of time. Five engagement indicators were also calculated which would reflect self-regulation and engagement. Students who scored below 5% over the passing mark were considered to be potentially at risk of under-achieving.Results: At the end of the course, we were able to predict the final grade with 63.5% accuracy, and identify 53.9% of at-risk students. Using a binary logistic model improved prediction to 80.8%. Using data recorded until the mid-course, prediction accuracy was 42.3%. The most important predictors were factors reflecting engagement of the students and the consistency of using the online resources.Conclusions: The analysis of students' online activities in a blended medical education course by means of LA techniques can help early predict underachieving students, and can be used as an early warning sign for timely intervention.
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