Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients

Patients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predict risks at different stages of management and thereby provide insights into drivers and prognostic markers of disease progression and death. From a cohort of approx. 2.6 million citizens in Denmark, SARS-CoV-2 PCR tests were performed on subjects suspected for COVID-19 disease; 3944 cases had at least one positive test and were subjected to further analysis. SARS-CoV-2 positive cases from the United Kingdom Biobank was used for external validation. The ML models predicted the risk of death (Receiver Operation Characteristics—Area Under the Curve, ROC-AUC) of 0.906 at diagnosis, 0.818, at hospital admission and 0.721 at Intensive Care Unit (ICU) admission. Similar metrics were achieved for predicted risks of hospital and ICU admission and use of mechanical ventilation. Common risk factors, included age, body mass index and hypertension, although the top risk features shifted towards markers of shock and organ dysfunction in ICU patients. The external validation indicated fair predictive performance for mortality prediction, but suboptimal performance for predicting ICU admission. ML may be used to identify drivers of progression to more severe disease and for prognostication patients in patients with COVID-19. We provide access to an online risk calculator based on these findings.

Efficacy of Retreatment After Failed Direct-acting Antiviral Therapy in Patients With HCV Genotype 1-3 Infections

Hepatitis C virus infection is causing chronic liver disease, cirrhosis, and hepatocellular carcinoma. By combining direct-acting antivirals (DAAs), high sustained virologic response rates (SVRs) can be achieved. Resistance-associated substitutions (RASs) are commonly observed after DAA failure, and especially nonstructural protein 5A (NS5A) RASs may impact retreatment options.^1-3 Data on retreatment of DAA failure patients using first-generation DAAs are limited.^4-7 Recently, a second-generation protease- and NS5A-inhibitor plus sofosbuvir (voxilaprevir/velpatasvir/sofosbuvir [VOX/VEL/SOF]) was approved for retreatment after DAA failure.⁸ However, this and other second-generation regimens are not available in many resource-limited countries or are not reimbursed by regular insurance, and recommendations regarding the selection of retreatment regimens using first-generation DAAs are very important. This study aimed to analyze patients who were re-treated with first-generation DAAs after failure of a DAA combination therapy.

nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation

Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning. While semantic segmentation algorithms enable image analysis and quantification in many applications, the design of respective specialized solutions is non-trivial and highly dependent on dataset properties and hardware conditions. We developed nnU-Net, a deep learning-based segmentation method that automatically configures itself, including preprocessing, network architecture, training and post-processing for any new task. The key design choices in this process are modeled as a set of fixed parameters, interdependent rules and empirical decisions. Without manual intervention, nnU-Net surpasses most existing approaches, including highly specialized solutions on 23 public datasets used in international biomedical segmentation competitions. We make nnU-Net publicly available as an out-of-the-box tool, rendering state-of-the-art segmentation accessible to a broad audience by requiring neither expert knowledge nor computing resources beyond standard network training.

Prognosis of short- and long-term dialysis in patients with infective endocarditis: a nationwide study

Background

Infective endocarditis (IE) may be complicated by acute kidney injury, yet data on the use of dialysis and subsequent reversibility are sparse. We set out to examine the prognosis of short- and long-term dialysis in patients with IE.

Methods

Using Danish nationwide registries we identified patients with first-time IE from 2000 to 2017. Dialysis naïve patients were grouped into: those who were treated with dialysis during admission with IE and those who were not. The cumulative incidence of continuous use of dialysis was examined one year post-discharge Multivariable adjusted Cox proportional hazard analysis was used to examine one-year mortality for patients surviving IE based on use of dialysis.

Results

We included 7,307 patients with IE; 416 patients (5.7%) initiated dialysis treatment during admission with IE and these were younger, had more comorbidities and more often underwent cardiac valve surgery during admission with IE compared with non-dialysis patients (47.4% vs. 20.9%). In patients with both surgical intervention and dialysis treatment, 153 (77.7%) initiated dialysis on- or after the date of surgery. The in-hospital mortality was 40.4% and 19.0% for patients with and without dialysis, respectively (p<0.0001). Of those who started dialysis and survived hospitalisation, 78.4% became dialysis-free within one year after discharge. Among those who survived one week subsequent to IE discharge, we identified 5,520 who never had dialysis, 204 patients without continued use of dialysis, and 40 patients with a continued use of dialysis. The corresponding mortality risk at one year was 15.2%, 13.5%, and 41.6% (Figure), respectively. Compared with patients not treated with dialysis, those who became dialysis-free at discharge showed no increased risk of one year mortality in adjusted analysis (HR=1.45, 95% CI: 0.97–2.20), while patients who continued dialysis had an increased associated risk of mortality (HR=2.00, 95% CI: 1.20–3.33).

Conclusion

In dialysis-naïve patients with IE, more than 1 in 20 patients initiated dialysis treatment during admission. Dialysis identified a high-risk group with an in-hospital mortality of 40%–twice as high as their counterparts. In dialysis patients surviving admission with IE, almost 80% became dialysis-free and showed better long-term survival than those who continued dialysis after discharge.

Figure 1

Funding Acknowledgement

Type of funding source: None

Do Xylylenes Isomerize in Pyrolysis?

We report infrared spectra of xylylene isomers in the gas phase, using free electron laser (FEL) radiation. All xylylenes were generated by flash pyrolysis. The IR spectra were obtained by monitoring the ion dip signal, using a IR/UV double resonance scheme. A gas phase IR spectrum of para-xylylene  was recorded, whereas ortho- and meta-xylylene were found to partially rearrange to benzocyclobutene and styrene. Computations of the UV oscillator strength  for all molecules were carried out and provde an explanation for the observation of the isomerization products.

Harnessing data science to advance radiation oncology

Radiation oncology, a major treatment modality in the care of patients with malignant disease, is a technology‐ and computer‐intensive medical specialty. As such, it should lend itself ideally to data science methods, where computer science, statistics, and clinical knowledge are combined to advance state‐of‐the‐art care. Nevertheless, data science methods in radiation oncology research are still in their infancy and successful applications leading to improved patient care remain scarce. Here, we discuss data interoperability issues within and across organizational boundaries that hamper the introduction of big data and data science techniques in radiation oncology. At the semantic level, creating common underlying models and codification of the data, including the use of data elements with standardized definitions, an ontology, remains a work in progress. Methodological issues in data science and in the use of large population‐based health data registries are identified. We show that data science methods and big data cannot replace randomized clinical trials in comparative effectiveness research by reviewing a series of instances where the outcomes of big data analyses and randomized trials are at odds. We also discuss the modern wave of machine learning and artificial intelligence as represented by deep learning and convolutional neural networks. Finally, we identify promising research avenues and remain optimistic that the data sources in radiation oncology can be linked to yield important insights in the near future. We argue that data science will be a valuable complement to, but not a replacement of, the traditional hypothesis‐driven translational research chain and the randomized clinical trials that form the backbone of evidence‐based medicine.

Segmentation of roots in soil with U-Net

BACKGROUND

Plant root research can provide a way to attain stress-tolerant crops that produce greater yield in a diverse array of conditions. Phenotyping roots in soil is often challenging due to the roots being difficult to access and the use of time consuming manual methods. Rhizotrons allow visual inspection of root growth through transparent surfaces. Agronomists currently manually label photographs of roots obtained from rhizotrons using a line-intersect method to obtain root length density and rooting depth measurements which are essential for their experiments. We investigate the effectiveness of an automated image segmentation method based on the U-Net Convolutional Neural Network (CNN) architecture to enable such measurements. We design a data-set of 50 annotated chicory (Cichorium intybus L.) root images which we use to train, validate and test the system and compare against a baseline built using the Frangi vesselness filter. We obtain metrics using manual annotations and line-intersect counts.

RESULTS

Our results on the held out data show our proposed automated segmentation system to be a viable solution for detecting and quantifying roots. We evaluate our system using 867 images for which we have obtained line-intersect counts, attaining a Spearman rank correlation of 0.9748 and an r 2 of 0.9217. We also achieve an F 1 of 0.7 when comparing the automated segmentation to the manual annotations, with our automated segmentation system producing segmentations with higher quality than the manual annotations for large portions of the image.

CONCLUSION

We have demonstrated the feasibility of a U-Net based CNN system for segmenting images of roots in soil and for replacing the manual line-intersect method. The success of our approach is also a demonstration of the feasibility of deep learning in practice for small research groups needing to create their own custom labelled dataset from scratch.

Automated volumetric assessment with artificial neural networks might enable a more accurate assessment of disease burden in patients with multiple sclerosis

OBJECTIVES

Patients with multiple sclerosis (MS) regularly undergo MRI for assessment of disease burden. However, interpretation may be time consuming and prone to intra- and interobserver variability. Here, we evaluate the potential of artificial neural networks (ANN) for automated volumetric assessment of MS disease burden and activity on MRI.

METHODS

A single-institutional dataset with 334 MS patients (334 MRI exams) was used to develop and train an ANN for automated identification and volumetric segmentation of T2/FLAIR-hyperintense and contrast-enhancing (CE) lesions. Independent testing was performed in a single-institutional longitudinal dataset with 82 patients (266 MRI exams). We evaluated lesion detection performance (F1 scores), lesion segmentation agreement (DICE coefficients), and lesion volume agreement (concordance correlation coefficients [CCC]). Independent evaluation was performed on the public ISBI-2015 challenge dataset.

RESULTS

The F1 score was maximized in the training set at a detection threshold of 7 mm³ for T2/FLAIR lesions and 14 mm³ for CE lesions. In the training set, mean F1 scores were 0.867 for T2/FLAIR lesions and 0.636 for CE lesions, as compared to 0.878 for T2/FLAIR lesions and 0.715 for CE lesions in the test set. Using these thresholds, the ANN yielded mean DICE coefficients of 0.834 and 0.878 for segmentation of T2/FLAIR and CE lesions in the training set (fivefold cross-validation). Corresponding DICE coefficients in the test set were 0.846 for T2/FLAIR lesions and 0.908 for CE lesions, and the CCC was ≥ 0.960 in each dataset.

CONCLUSIONS

Our results highlight the capability of ANN for quantitative state-of-the-art assessment of volumetric lesion load on MRI and potentially enable a more accurate assessment of disease burden in patients with MS.

KEY POINTS

• Artificial neural networks (ANN) can accurately detect and segment both T2/FLAIR and contrast-enhancing MS lesions in MRI data. • Performance of the ANN was consistent in a clinically derived dataset, with patients presenting all possible disease stages in MRI scans acquired from standard clinical routine rather than with high-quality research sequences. • Computer-aided evaluation of MS with ANN could streamline both clinical and research procedures in the volumetric assessment of MS disease burden as well as in lesion detection.

Epidemiology of hidradenitis suppurativa in Germany - an observational cohort study based on a multisource approach

BACKGROUND

Hidradenitis suppurativa (HS) is a chronic, debilitating inflammatory skin disease. In Germany, there are no population-based data on the epidemiology of HS.

OBJECTIVES

The objective of this study was to examine the occurrence of HS in inpatient as well as in outpatient settings.

METHODS

We used three data sources for analysing the prevalence, incidence and case-related occurrence of HS in different settings: data of two German statutory health insurance (SHI) companies and hospital discharge data provided by the Federal Statistical Office. The studied period was from 2010 to 2015.

RESULTS

In a representative sample of about 2.3 million insurees (out of 5.9 million total persons) of the SHI DAK-Gesundheit, 791 were diagnosed with HS in 2010. This coding prevalence of 0.03% is in accordance with the data of another SHI (Barmer) on about 9 million total insurees. In 2015, at least 34.7% of incident persons with HS had one potential misdiagnosis in 2014.

CONCLUSION

This population-based study analyses the prevalence and incidence of HS in Germany. The coding prevalence of 0.03% observed in two independent SHI data sets is lower than expected. The findings of considerable potential misdiagnosis add to the underdiagnosis of HS in general and underline the need for future strategies to early detection and valid diagnosis of HS.

P5992Why aortic valve disease may persist after surgery? A joint basic science - Clinical effort

Background

Diseases of the aortic valve are a common reason for heart surgery. Aortic stenosis (AS) is associated with pressure and aortic regurgitation (AR) with a volume overload of the left ventricle (LV). Over time both pathologies lead to systolic and diastolic heart failure, while progressive downregulation of β-adrenoceptors occurs. While LV re-remodeling occurs in the majority of patients after aortic valve surgery, LV dysfunction persists in one fourth of such patients and leads to a terminal heart failure. We aimed to investigate whether differential remodeling in the protein kinase A (PKA) dependent inotropic response in myocytes and myocardial tissue obtained from patients undergoing aortic valve surgery is associated with the LV re-remodeling after surgery.

Methods

Preoperatively, pro BNP levels were measured and left ventricular strain analysis via echocardiography was performed. Interventricular septal biopsy was obtained intraoperatively in 10 patients who underwent aortic valve surgery. In-vitro contractility was analyzed in myocardial tissue paced with 4 Hz at 37 °C. Freshly isolated cells were transduced with an adenovirus expressing a cytosolic Förster resonance energy transfer (FRET) based cAMP biosensor (Epac1-camps). After 48 hours of culture, Föster-resonance energy transfer (FRET) was used for the first time to measure cAMP in 60 isolated human ventricular myocytes. Isoprenaline (10 nM – 10 μM) was used for β-adrenoceptor activation and forskolin (10 μmol) to activate adenylyl cyclase directly.

Results

We found a significantly downregulated β-adrenergic sensitivity in cardiomyocytes of patients with aortic valve disease, although contractile response to forskolin was maintained. Furthermore, we found a clear association between reduced sensitivity to isoprenaline (i.e., high EC50 values) and low maximum effect size to isoprenaline in myocardial tissue of patients with aortic valve disease, pointing out relevant β-adrenoceptor dysfunction. There were no significant differences in basal myocardial force between tissue samples of patients with AR and AS.

Conclusion

Collectively, our data show a profound remodelling in the cAMP/PKA pathway in patients with aortic valve disease. These disturbances may have an impact on the postoperative ventricular function and possibly on the long-term LV re-remodelling after aortic valve surgery.

Optogenetic manipulation of stomatal kinetics improves carbon assimilation, water use, and growth

Stomata serve dual and often conflicting roles, facilitating carbon dioxide influx into the plant leaf for photosynthesis and restricting water efflux via transpiration. Strategies for reducing transpiration without incurring a cost for photosynthesis must circumvent this inherent coupling of carbon dioxide and water vapor diffusion. We expressed the synthetic, light-gated K⁺ channel BLINK1 in guard cells surrounding stomatal pores in Arabidopsis to enhance the solute fluxes that drive stomatal aperture. BLINK1 introduced a K⁺ conductance and accelerated both stomatal opening under light exposure and closing after irradiation. Integrated over the growth period, BLINK1 drove a 2.2-fold increase in biomass in fluctuating light without cost in water use by the plant. Thus, we demonstrate the potential of enhancing stomatal kinetics to improve water use efficiency without penalty in carbon fixation.

Increasing Accuracy of Optimal Surfaces Using Min-Marginal Energies

Optimal surface methods are a class of graph cut methods posing surface estimation as an n-ary ordered labeling problem. They are used in medical imaging to find interacting and layered surfaces optimally and in low order polynomial time. Representing continuous surfaces with discrete sets of labels, however, leads to discretization errors and, if graph representations are made dense, excessive memory usage. Limiting memory usage and computation time of graph cut methods are important and graphs that locally adapt to the problem has been proposed as a solution. Min-marginal energies computed using dynamic graph cuts offer a way to estimate solution uncertainty and these uncertainties have been used to decide where graphs should be adapted. Adaptive graphs, however, introduce extra parameters, complexity, and heuristics. We propose a way to use min-marginal energies to estimate continuous solution labels that does not introduce extra parameters and show empirically on synthetic and medical imaging datasets that it leads to improved accuracy. The increase in accuracy was consistent and in many cases comparable with accuracy otherwise obtained with graphs up to eight times denser, but with proportionally less memory usage and improvements in computation time.

Untargeted metabolic profiling of dogs with a suspected toxic mitochondrial myopathy using liquid chromatography-mass spectrometry

'Go Slow myopathy' (GSM) is a suspected toxic myopathy in dogs that primarily occurs in the North Island of New Zealand, and affected dogs usually have a history of consuming meat, offal or bones from wild pigs (including previously frozen and/or cooked meat). Previous epidemiological and pathological studies on GSM have demonstrated that changes in mitochondrial structure and function are most likely caused by an environmental toxin that dogs are exposed to through the ingestion of wild pig. The disease has clinical, histological and biochemical similarities to poisoning in people and animals from the plant Ageratina altissima (white snakeroot). Aqueous and lipid extracts were prepared from liver samples of 24 clinically normal dogs and 15 dogs with GSM for untargeted liquid chromatography-mass spectrometry. Group-wise comparisons of mass spectral data revealed 38 features that were significantly different (FDR<0.05) between normal dogs and those with GSM in aqueous extracts, and 316 significantly different features in lipid extracts. No definitive cause of the myopathy was identified, but alkaloids derived from several plant species were among the possible identities of features that were more abundant in liver samples from affected dogs compared to normal dogs. Mass spectral data also revealed that dogs with GSM have reduced hepatic phospholipid and sphingolipid concentrations relative to normal dogs. In addition, affected dogs had changes in the abundance of kynurenic acid, various dicarboxylic acids and N-acetylated branch chain amino acids, suggestive of mitochondrial dysfunction.

H1N1 hemagglutinin-specific HLA-DQ6-restricted CD4+ T cells can be readily detected in narcolepsy type 1 patients and healthy controls

Following the 2009 H1N1 influenza pandemic, an increased risk of narcolepsy type 1 was observed. Homology between an H1N1 hemagglutinin and two hypocretin sequences has been reported. T cell reactivity to these peptides was assessed in 81 narcolepsy type 1 patients and 19 HLA-DQ6-matched healthy controls. HLA-DQ6-restricted H1N1 hemagglutinin-specific T cell responses were detected in 28.4% of patients and 15.8% of controls. Despite structural homology between HLA-DQ6-hypocretin and -H1N1 peptide complexes, T cell cross-reactivity was not detected. These results indicate that it is unlikely that cross-reactivity between H1N1 hemagglutinin and hypocretin peptides presented by HLA-DQ6 is involved in the development of narcolepsy.

Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study

BACKGROUND

The Response Assessment in Neuro-Oncology (RANO) criteria and requirements for a uniform protocol have been introduced to standardise assessment of MRI scans in both clinical trials and clinical practice. However, these criteria mainly rely on manual two-dimensional measurements of contrast-enhancing (CE) target lesions and thus restrict both reliability and accurate assessment of tumour burden and treatment response. We aimed to develop a framework relying on artificial neural networks (ANNs) for fully automated quantitative analysis of MRI in neuro-oncology to overcome the inherent limitations of manual assessment of tumour burden.

METHODS

In this retrospective study, we compiled a single-institution dataset of MRI data from patients with brain tumours being treated at Heidelberg University Hospital (Heidelberg, Germany; Heidelberg training dataset) to develop and train an ANN for automated identification and volumetric segmentation of CE tumours and non-enhancing T2-signal abnormalities (NEs) on MRI. Independent testing and large-scale application of the ANN for tumour segmentation was done in a single-institution longitudinal testing dataset from the Heidelberg University Hospital and in a multi-institutional longitudinal testing dataset from the prospective randomised phase 2 and 3 European Organisation for Research and Treatment of Cancer (EORTC)-26101 trial (NCT01290939), acquired at 38 institutions across Europe. In both longitudinal datasets, spatial and temporal tumour volume dynamics were automatically quantified to calculate time to progression, which was compared with time to progression determined by RANO, both in terms of reliability and as a surrogate endpoint for predicting overall survival. We integrated this approach for fully automated quantitative analysis of MRI in neuro-oncology within an application-ready software infrastructure and applied it in a simulated clinical environment of patients with brain tumours from the Heidelberg University Hospital (Heidelberg simulation dataset).

FINDINGS

For training of the ANN, MRI data were collected from 455 patients with brain tumours (one MRI per patient) being treated at Heidelberg hospital between July 29, 2009, and March 17, 2017 (Heidelberg training dataset). For independent testing of the ANN, an independent longitudinal dataset of 40 patients, with data from 239 MRI scans, was collected at Heidelberg University Hospital in parallel with the training dataset (Heidelberg test dataset), and 2034 MRI scans from 532 patients at 34 institutions collected between Oct 26, 2011, and Dec 3, 2015, in the EORTC-26101 study were of sufficient quality to be included in the EORTC-26101 test dataset. The ANN yielded excellent performance for accurate detection and segmentation of CE tumours and NE volumes in both longitudinal test datasets (median DICE coefficient for CE tumours 0·89 [95% CI 0·86-0·90], and for NEs 0·93 [0·92-0·94] in the Heidelberg test dataset; CE tumours 0·91 [0·90-0·92], NEs 0·93 [0·93-0·94] in the EORTC-26101 test dataset). Time to progression from quantitative ANN-based assessment of tumour response was a significantly better surrogate endpoint than central RANO assessment for predicting overall survival in the EORTC-26101 test dataset (hazard ratios ANN 2·59 [95% CI 1·86-3·60] vs central RANO 2·07 [1·46-2·92]; p<0·0001) and also yielded a 36% margin over RANO (p<0·0001) when comparing reliability values (ie, agreement in the quantitative volumetrically defined time to progression [based on radiologist ground truth vs automated assessment with ANN] of 87% [266 of 306 with sufficient data] compared with 51% [155 of 306] with local vs independent central RANO assessment). In the Heidelberg simulation dataset, which comprised 466 patients with brain tumours, with 595 MRI scans obtained between April 27, and Sept 17, 2018, automated on-demand processing of MRI scans and quantitative tumour response assessment within the simulated clinical environment required 10 min of computation time (average per scan).

INTERPRETATION

Overall, we found that ANN enabled objective and automated assessment of tumour response in neuro-oncology at high throughput and could ultimately serve as a blueprint for the application of ANN in radiology to improve clinical decision making. Future research should focus on prospective validation within clinical trials and application for automated high-throughput imaging biomarker discovery and extension to other diseases.

FUNDING

Medical Faculty Heidelberg Postdoc-Program, Else Kröner-Fresenius Foundation.

Treatment of migraine attacks and prevention of migraine: Guidelines by the German Migraine and Headache Society and the German Society of Neurology

In collaboration with some of the leading headache centres in Germany, Switzerland and Austria, we have established new guidelines for the treatment of migraine attacks and the prevention of migraine. A thorough literature research of the last 10 years has been the basis of the current recommendations. At the beginning, we present therapeutic novelties, followed by a summary of all recommendations. After an introduction, we cover topics like drug therapy and practical experience, non-effective medication, migraine prevention, interventional methods, non-medicational and psychological methods for prevention and therapies without proof of efficacy.

Management and prognosis of atrial fibrillation in diabetic patients: an EORP-AF General Pilot Registry report

Aims

Diabetes mellitus (DM) is one of the most important cardiovascular risk factors. The aim of this study was to evaluate clinical correlates of DM, including management and outcomes, in the EURObservational Research Programme (EORP)-Atrial Fibrillation (AF) General Pilot (EORP-AF) Registry of the European Society of Cardiology.

Methods and results

We studied consecutive patients (N = 3101) enrolled in 70 centres of nine European countries between February 2012 and March 2013, and compared diabetics with non-diabetics during a 1-year follow-up. In the overall cohort, the prevalence of DM was 20.6%. Diabetics were older (71 ± 9 vs. 68 ± 12 years, P < 0.0001) and had more comorbidities, higher CHA2DS2-VASc score (4.6 ± 1.6 vs. 2.9 ± 1.7, P < 0.0001) and higher prevalence of permanent AF (21.5 vs. 16.0%, P = 0.0022). Quality of life amongst DM patients was significantly worse [atrial fibrillation quality of life questionnaire (AF-QoL) score 45.2 ± 19.2 vs. 49.3 ± 20.1, P < 0.0001]. Amongst diabetics, the use of electrical cardioversion (16.2 vs. 24.6%, P < 0.0001) and catheter ablation (3.3 vs. 8.6%, P < 0.0001) was lower, whilst oral anticoagulants were more often prescribed (84.3 vs. 78.9%, P = 0.0027). After one year, diabetic patients had significantly higher all-cause (11.9 vs. 4.9%, P < 0.0001), cardiovascular (6.2 vs. 1.9%, P < 0.0001), and non-cardiovascular mortality (2.3 vs. 1.1%, P = 0.0356).

Conclusion

In AF patients, DM is associated with a higher prevalence of comorbidities and a worse quality of life. After one year, all-cause, cardiovascular, and non-cardiovascular mortality were significantly higher in diabetic subjects.

Excited state dynamics and time-resolved photoelectron spectroscopy of para-xylylene

We investigated the excited-state dynamics of para-xylylene using a combination of field-induced surface hopping (FISH) simulations and time-resolved ionisation experiments. Our simulations predict an ultrafast decay of the initially excited bright state (S2/S3) to the S1 state on a sub-100 fs time scale, followed by return to the ground state within ∼1 ps. This is accompanied by a transient change of the biradical character of the molecule, as monitored by calculating natural orbital occupation numbers. Specifically, the initially low biradicality is increased by electronic excitation as well as by vibrational activation. Experimentally, para-xylylene was generated by pyrolysis from [2,2]paracyclophane and excited with 266 nm radiation into the S2/S3 bright state. The subsequent dynamics were followed using ionisation as the probe step, with both mass spectra and photoelectron spectra recorded as a function of pump-probe delay. The observed decay of photoelectron and photoion intensities closely matches the theoretical predictions and is consistent with the sequential mechanism found in the simulations. This mechanism exhibits characteristic signatures in both time-resolved mass and photoelectron spectra, in particular in the appearance of fragment ions that are exclusively generated from the S1 state. This allows for a separation of the S2 and S1 dynamics in the photoelectron and mass spectra. An excellent agreement between the observed and the simulated ion signal is observed.