| 1. |
- Klionsky, Daniel J., et al.
(författare)
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Guidelines for the use and interpretation of assays for monitoring autophagy
- 2012
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Ingår i: Autophagy. - : Informa UK Limited. - 1554-8635 .- 1554-8627. ; 8:4, s. 445-544
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Forskningsöversikt (refereegranskat)abstract
- In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. A key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process vs. those that measure flux through the autophagy pathway (i.e., the complete process); thus, a block in macroautophagy that results in autophagosome accumulation needs to be differentiated from stimuli that result in increased autophagic activity, defined as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (in most higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field.
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| 2. |
- Boissin, Constance, et al.
(författare)
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Development and evaluation of deep learning algorithms for assessment of acute burns and the need for surgery
- 2023
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Assessment of burn extent and depth are critical and require very specialized diagnosis. Automated image-based algorithms could assist in performing wound detection and classification. We aimed to develop two deep-learning algorithms that respectively identify burns, and classify whether they require surgery. An additional aim assessed the performances in different Fitzpatrick skin types. Annotated burn (n = 1105) and background (n = 536) images were collected. Using a commercially available platform for deep learning algorithms, two models were trained and validated on 70% of the images and tested on the remaining 30%. Accuracy was measured for each image using the percentage of wound area correctly identified and F1 scores for the wound identifier; and area under the receiver operating characteristic (AUC) curve, sensitivity, and specificity for the wound classifier. The wound identifier algorithm detected an average of 87.2% of the wound areas accurately in the test set. For the wound classifier algorithm, the AUC was 0.885. The wound identifier algorithm was more accurate in patients with darker skin types; the wound classifier was more accurate in patients with lighter skin types. To conclude, image-based algorithms can support the assessment of acute burns with relatively good accuracy although larger and different datasets are needed.
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| 3. |
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| 4. |
- Falk-Delgado, Anna, et al.
(författare)
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Improved clinical outcome 3 months after endovascular treatment, including thrombectomy, in patients with acute ischemic stroke : a meta-analysis
- 2016
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Ingår i: Journal of neurointerventional surgery. - : BMJ. - 1759-8486 .- 1759-8478. ; 8:7
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND AND PURPOSE: Intravenous thrombolysis with tissue plasminogen activator is standard treatment in acute stroke today. The benefit of endovascular treatment has been questioned. Recently, studies evaluating endovascular treatment and intravenous thrombolysis compared with intravenous thrombolysis alone, have reported improved outcome for the intervention group. The aim of this study was to perform a meta-analysis of randomized controlled trials comparing endovascular treatment in addition to intravenous thrombolysis with intravenous thrombolysis alone.METHODS: Databases were searched for eligible randomized controlled trials. The primary outcome was a functional neurological outcome after 90 days. A secondary outcome was severe disability and death. Data were pooled in the control and intervention groups, and OR was calculated on an intention to treat basis with 95% CIs. Outcome heterogeneity was evaluated with Cochrane's Q test (significance level cut-off value at <0.10) and I(2) (significance cut-off value >50%) with the Mantel-Haenszel method for dichotomous outcomes. A p value <0.05 was regarded as statistically significant.RESULTS: Six studies met the eligibility criteria, and data from 1569 patients were analyzed. A higher probability of a functional neurological outcome after 90 days was found for the intervention group (OR 2, 95% CI 2 to 3). There was a significantly higher probability of death and severe disability in the control group compared with the intervention group.CONCLUSIONS: Endovascular treatment in addition to intravenous thrombolysis for acute ischemic stroke leads to an improved clinical outcome after 3 months, compared with patients receiving intravenous thrombolysis alone.
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| 5. |
- Fransén, Jian, et al.
(författare)
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A proof-of-concept study on mortality prediction with machine learning algorithms using burn intensive care data
- 2022
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Ingår i: Scars, Burns & Healing. - : Sage Publications. - 2059-5131.
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Tidskriftsartikel (refereegranskat)abstract
- IntroductionBurn injuries are a common traumatic injury. Large burns have high mortality requiring intensive care and accurate mortality predictions. To assess if machine learning (ML) could improve predictions, ML algorithms were tested and compared with the original and revised Baux score.MethodsAdmission data and mortality outcomes were collected from patients at Uppsala University Hospital Burn Centre from 2002 to 2019. Prognostic variables were selected, ML algorithms trained and predictions assessed by analysis of the area under the receiver operating characteristic curve (AUC). Comparison was made with Baux scores using DeLong test.ResultsA total of 17 prognostic variables were selected from 92 patients. AUCs in leave-one-out cross-validation for a decision tree model, an extreme boosting model, a random forest model, a support-vector machine (SVM) model and a generalised linear regression model (GLM) were 0.83 (95% confidence interval [CI] = 0.72–0.94), 0.92 (95% CI = 0.84–1), 0.92 (95% CI = 0.84–1), 0.92 (95% CI = 0.84–1) and 0.84 (95% CI = 0.74–0.94), respectively. AUCs for the Baux score and revised Baux score were 0.85 (95% CI = 0.75–0.95) and 0.84 (95% CI = 0.74–0.94). No significant differences were observed when comparing ML algorithms with Baux score and revised Baux score. Secondary variable selection was made to analyse model performance.ConclusionThis proof-of-concept study showed initial credibility in using ML algorithms to predict mortality in burn patients. The sample size was small and future studies are needed with larger sample sizes, further variable selections and prospective testing of the algorithms.
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| 6. |
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| 7. |
- Fransén, Jian, et al.
(författare)
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Evaluating topical opioid gel on donor site pain : A small randomised double blind controlled trial
- 2016
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Ingår i: International Journal of Surgery Open. - : Elsevier BV. - 2405-8572. ; 4, s. 5-9
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundAutologous donor skin harvested for transplantation is a common procedure in patients with burns, and patients often feel more pain at the donor site than is justified by the extent of trauma. Topical morphine gels have been thought to have an effect on peripheral opioid receptors by creating antinociceptive and anti-inflammatory effects, which could potentially reduce the systemic use of morphine-like substances and their adverse effects.MethodsWe therefore did a paired, randomised, double-blind placebo study to investigate the effect of morphine gel and placebo on dual donor sites that had been harvested in 13 patients. Pain was measured on a visual analogue scale (VAS) 15 times in a total of 5 days.ResultsThe mean (SD) VAS was 1.6 (2.3) for all sites, 1.5 (2.2) for morphine, and 2.0 (2.5) for placebo. The pain relieving effects of morphine gel were not significantly better than placebo.ConclusionThe assessment of pain at donor sites is subjective, and more systematic and objective studies are needed.
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| 8. |
- Fransén, Jian, 1986-
(författare)
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Large data and machine learning in analysis, diagnostics, and clinical decision making: applications in the treatment of burn injury
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Burn injury is a common trauma globally. Large burns require fluid resuscitation, infection control, and specialized intensive care. The size of the burn and infections caused by resistant microbes are correlated to mortality, and accurate mortality predictions are important. Errors are common when diagnosing burn depth, but early diagnosis is necessary to make correct surgical decisions. Machine learning (ML) is a set of mathematical algorithms with self-learning capabilities, which might make them suitable for medical applications.This thesis explores systematic large data analysis and ML algorithms for clinical applications in burn treatment by examining antibiotic resistance, improving mortality predictions, and automating diagnosis of burn depth.Paper I aims to find relevant trends and correlations on clinical outcomes such as mortality, microbial distribution, and antibiotic resistance from pooled data from a burn center. Data from 1570 patients and 15,006 microbiology cultures were systematically analyzed. Our results show a sustained low risk of harmful microbes, resistance, and a suggested low mortality rate.Paper II used clinical biomarkers from burn patients to train ML algorithms to predict mortality and compare it with Baux scores. When applying five types of ML algorithms, it showed no significant difference in mortality prediction compared with Baux scores.Paper III examines convolutional neural network (CNN) algorithms for two purposes. One to segment a burn wound and the other to classify whole wound images for surgery or conservative treatment. A total of 1105 diverse images were collected from patients at admission to burn centers in Sweden and South Africa. The algorithm was adequate for segmenting burn wounds and could be improved when categorizing images for surgery or conservative treatment.Paper IV further assesses CNN to automatically segment and diagnose a diverse set of early burn images for deep or superficial burn injury. A total of 1004 images were included. The algorithm proved adequate in segmenting superficial injuries but not deep injuries and performed similarly between darker and lighter skin patients.Future studies might incorporate infection variables in ML mortality predictions and larger sample sizes. Regarding automated burn image diagnosis, including multiple non-image variables might improve usability.
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| 9. |
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| 10. |
- Fransen, Jian, et al.
(författare)
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Surveillance of antibiotic susceptibility in a Swedish Burn Center 1994-2012
- 2016
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Ingår i: Burns. - : Elsevier BV. - 0305-4179 .- 1879-1409. ; 42:6, s. 1295-1303
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Tidskriftsartikel (refereegranskat)abstract
- Patients with burn trauma are at risk for infections caused by antibiotic resistant bacteria (ABR) with subsequent increase in morbidity and mortality. As part of the Swedish strategic program against antibiotic resistance in intensive care (ICU-Strama), we have surveyed the distribution of species and ABR in isolates from patients admitted to a Swedish burn center at Linkoping University Hospital from 1994 through 2012. In an international comparison Strama has been successful in reducing the antibiotic consumption among animals and humans in primary care. The aim of this study was to investigate the antibiotic consumption pressure and resistance rates in a Swedish burn unit. Methods: Microbiology data, total body surface area (TBSA), patient days, and mortality were collected from a hospital database for all patients admitted to the Burn Center at the University Hospital of Linkoping from April 1994 through December 2012. Results: A total of 1570 patients were admitted with a mean annual admission rate of 83 patients (range: 57-152). 15,006 microbiology cultures (approximately 10 per patient) were collected during the study period and of these 4531 were positive (approximately 3 per patient). The annual mean total body surface area (TBSA) was 13.4% (range 9.5-18.5) with an annual mortality rate of 5.4% (range 1-8%). The MRSA incidence was 1.7% (15/866) which corresponds to an MRSA incidence of 0.34/1000 admission days (TAD). Corresponding figures were for Escherichia coli resistant to 3rd generation cephalosporins (ESBL phenotype) 8% (13/170) and 0.3/TAD, Klebsiella spp. ESBL phenotype 5% (6/134) and 0.14/TAD, carbapenem resistant Pseudomonas aeruginosa 26% (56/209) and 1.28/TAD, and carbapenem resistant Acinetobacter spp. 3% (2/64) and 0.04/TAD. Conclusions: Our results show a sustained low risk for MRSA and high, although not increasing, risk for carbapenem resistant P. aeruginosa.
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