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PurposeTo assess the ability of quantitative pupillometry [using the Neurological Pupil index (NPi)] to predict an unfavorable neurological outcome after cardiac arrest (CA).MethodsWe performed a prospective international multicenter study (10 centers) in adult comatose CA patients. Quantitative NPi and standard manual pupillary light reflex (sPLR)—blinded to clinicians and outcome assessors—were recorded in parallel from day 1 to 3 after CA. Primary study endpoint was to compare the value of NPi versus sPLR to predict 3-month Cerebral Performance Category (CPC), dichotomized as favorable (CPC 1–2: full recovery or moderate disability) versus unfavorable outcome (CPC 3–5: severe disability, vegetative state, or death).ResultsAt any time between day 1 and 3, an NPi ≤ 2 (n = 456 patients) had a 51% (95% CI 49–53) negative predictive value and a 100% positive predictive value [PPV; 0% (0–2) false-positive rate], with a 100% (98–100) specificity and 32% (27–38) sensitivity for the prediction of unfavorable outcome. Compared with NPi, sPLR had significantly lower PPV and significantly lower specificity (p  < 0.001 at day 1 and 2; p  = 0.06 at day 3). The combination of NPi ≤ 2 with bilaterally absent somatosensory evoked potentials (SSEP; n = 188 patients) provided higher sensitivity [58% (49–67) vs. 48% (39–57) for SSEP alone], with comparable specificity [100% (94–100)].ConclusionsQuantitative NPi had excellent ability to predict an unfavorable outcome from day 1 after CA, with no false positives, and significantly higher specificity than standard manual pupillary examination. The addition of NPi to SSEP increased sensitivity of outcome prediction, while maintaining 100% specificity.

Less than 500 participants have been included in randomized trials comparing hypothermia with regular care for out-of-hospital cardiac arrest patients, and many of these trials were small and at a high risk of bias. Consequently, the accrued data on this potentially beneficial intervention resembles that of a drug following small phase II trials. A large confirmatory trial is therefore warranted. The TTM2-trial is an international, multicenter, parallel group, investigator-initiated, randomized, superiority trial in which a target temperature of 33°C after cardiac arrest will be compared with a strategy to maintain normothermia and early treatment of fever (≥37.8°C). Participants will be randomized within 3 hours of return of spontaneous circulation with the intervention period lasting 40 hours in both groups. Sedation will be mandatory for all patients throughout the intervention period. The clinical team involved with direct patient care will not be blinded to allocation group due to the inherent difficulty in blinding the intervention. Prognosticators, outcome-assessors, the steering group, the trial coordinating team, and trial statistician will be blinded. The primary outcome will be all-cause mortality at 180 days after randomization. We estimate a 55% mortality in the control group. To detect an absolute risk reduction of 7.5% with an alpha of 0.05 and 90% power, 1900 participants will be enrolled. The main secondary neurological outcome will be poor functional outcome (modified Rankin Scale 4–6) at 180 days after arrest. The TTM2-trial will compare hypothermia to 33°C with normothermia and early treatment of fever (≥37.8°C) after out-of-hospital cardiac arrest.

Long-term analyses of biodiversity data highlight a ‘biodiversity conservation paradox’: biological communities show substantial species turnover over the past century 1 , 2 , but changes in species richness are marginal 1 , 3 – 5 . Most studies, however, have focused only on the incidence of species, and have not considered changes in local abundance. Here we asked whether analysing changes in the cover of plant species could reveal previously unrecognized patterns of biodiversity change and provide insights into the underlying mechanisms. We compiled and analysed a dataset of 7,738 permanent and semi-permanent vegetation plots from Germany that were surveyed between 2 and 54 times from 1927 to 2020, in total comprising 1,794 species of vascular plants. We found that decrements in cover, averaged across all species and plots, occurred more often than increments; that the number of species that decreased in cover was higher than the number of species that increased; and that decrements were more equally distributed among losers than were gains among winners. Null model simulations confirmed that these trends do not emerge by chance, but are the consequence of species-specific negative effects of environmental changes. In the long run, these trends might result in substantial losses of species at both local and regional scales. Summarizing the changes by decade shows that the inequality in the mean change in species cover of losers and winners diverged as early as the 1960s. We conclude that changes in species cover in communities represent an important but understudied dimension of biodiversity change that should more routinely be considered in time-series analyses. Time-series data including 1,794 plant species from 7,738 vegetation plots in Germany between 1927 and 2020 reveal patterns of change in biodiversity, and suggest that more species declined than increased in abundance during this period.

Background In patients who recover consciousness after cardiac arrest (CA), a subsequent death from non-neurological causes may confound the assessment of long-term neurological outcome. We investigated the prevalence and causes of death after awakening (DAA) in a multicenter cohort of CA patients. Methods Observational multicenter cohort study on patients resuscitated from CA in eight European intensive care units (ICUs) from January 2007 to December 2014. DAA during the hospital stay was extracted retrospectively from patient medical records. Demographics, comorbidities, initial CA characteristics, concomitant therapies, prognostic tests (clinical examination, electroencephalography (EEG), somatosensory evoked potentials (SSEPs)), and cause of death were identified. Results From a total 4646 CA patients, 2478 (53%) died in-hospital, of whom 196 (4.2%; ranges 0.6–13.0%) had DAA. DAA was less frequent among out-of-hospital than in-hospital CA (82/2997 [2.7%] vs. 114/1649 [6.9%]; p  < 0.001). Median times from CA to awakening and from awakening to death were 2 [1–5] and 9 [3–18] days, respectively. The main causes of DAA were multiple organ failure ( n  = 61), cardiogenic shock ( n  = 61), and re-arrest ( n  = 26). At day 3 from admission, results from EEG ( n  = 56) and SSEPs ( n  = 60) did not indicate poor outcome. Conclusions In this large multicenter cohort, DAA was observed in 4.2% of non-survivors. Information on DAA is crucial since it may influence epidemiology and the design of future CA studies evaluating neuroprognostication and neuroprotection.

P-Type ATPases are part of the regulatory system of the cell where they are responsible for transporting ions and lipids through the cell membrane. These pumps are found in all eukaryotes and their malfunction has been found to cause several severe diseases. Knowing which substrate is pumped by a certain P-Type ATPase is therefore vital. The P-Type ATPases can be divided into 11 subtypes based on their specificity, that is, the substrate that they pump. Determining the subtype experimentally is time-consuming. Thus it is of great interest to be able to accurately predict the subtype based on the amino acid sequence only. We present an approach to P-Type ATPase sequence classification based on the k-nearest neighbors, similar to a homology search, and show that this method provides performs very well and, to the best of our knowledge, better than any existing method despite its simplicity. The classifier is made available as a web service at http://services.birc.au.dk/patbox/ which also provides access to a database of potential P-Type ATPases and their predicted subtypes.

The pressure-induced structural phase transitions in the lanthanide elements provide insight into changes in their electronic structures at high densities. After a series of transitions via close-packed structures, the regular trivalent lanthanides (La to Lu, excluding Ce, Eu and Yb) undergo first-order transitions to so-called collapsed phases, the structures of which have long been reported as monoclinic. However, the diffraction data from these phases are not well fitted by this monoclinic structure, and the patterns from Nd and Sm are distinctly different to those from the higher-Z lanthanides (Gd→). Here we present results from recent diffraction studies on Tb, Gd, Sm, Nd and Y to above 300 GPa, which reveal that there are two different collapsed structures, neither of which is monoclinic. High-precision equation of state studies of the same elements reveal distinct changes in compressibility once the collapsed phases are obtained. We also show that these new structures are strikingly similar to those observed in the higher-Z actinides at high pressure, greatly strengthening the structural systematics of the 4f and 5f elements.

To establish a deep learning model for the detection of hypoxic-ischemic encephalopathy (HIE) features on CT scans and to compare various networks to determine the best input data format. 168 head CT scans of patients after cardiac arrest were retrospectively identified and classified into two categories: 88 (52.4%) with radiological evidence of severe HIE and 80 (47.6%) without signs of HIE. These images were randomly divided into a training and a test set, and five deep learning models based on based on Densely Connected Convolutional Networks (DenseNet121) were trained and validated using different image input formats (2D and 3D images). All optimized stacked 2D and 3D networks could detect signs of HIE. The networks based on the data as 2D image data stacks provided the best results ( AUC: 94%, ACC: 79%, AUC: 93%, ACC: 79%). We provide visual explainability data for the decision making of our AI model using Gradient-weighted Class Activation Mapping. Our proof-of-concept deep learning model can accurately identify signs of HIE on CT images. Comparing different 2D- and 3D-based approaches, most promising results were achieved by 2D image stack models. After further clinical validation, a deep learning model of HIE detection based on CT images could be implemented in clinical routine and thus aid clinicians in characterizing imaging data and predicting outcome.

PurposeTo obtain initial data on the effect of different levels of targeted temperature management (TTM) in out-of-hospital cardiac arrest (OHCA).MethodsWe designed a multicentre pilot trial with 1:1:1 randomization to either 32 °C (n = 52), 33 °C (n = 49) or 34 °C (n = 49), via endovascular cooling devices during a 24-h period in comatose survivors of witnessed OHCA and initial shockable rhythm. The primary endpoint was the percentage of subjects surviving with good neurologic outcome defined by a modified Rankin Scale (mRS) score of ≤ 3, blindly assessed at 90 days.ResultsAt baseline, different proportions of patients who had received defibrillation administered by a bystander were assigned to groups of 32 °C (13.5%), 33 °C (34.7%) and 34 °C (28.6%; p = 0.03). The percentage of patients with an mRS ≤ 3 at 90 days (primary endpoint) was 65.3, 65.9 and 65.9% in patients assigned to 32, 33 and 34 °C, respectively, non-significant (NS). The multivariate Cox proportional hazards model identified two variables significantly related to the primary outcome: male gender and defibrillation by a bystander. Among the 43 patients who died before 90 days, 28 died following withdrawal of life-sustaining therapy, as follows: 7/16 (43.8%), 10/13 (76.9%) and 11/14 (78.6%) of patients assigned to 32, 33 and 34 °C, respectively (trend test p = 0.04). All levels of cooling were well tolerated.ConclusionsThere were no statistically significant differences in neurological outcomes among the different levels of TTM. However, future research should explore the efficacy of TTM at 32 °C.Clinical trial registrationClinicalTrials.gov unique identifier: NCT02035839 (http://clinicaltrials.gov).

The transparent hP 4 phase of dense sodium (Na), stable above 200 GPa, has been computed to be an electride in which valence electrons are localised on interstitial lattice sites within the structure. However, there is no experimental evidence for this interstitial electron localisation in Na, or indeed in other high-density electride phases. Using static compression and single-crystal X-ray diffraction techniques, we have grown and studied a single-crystal sample of Na in the hP 4 phase at 223 GPa. Using atomic form factors for hP 4-Na derived from quantum crystallography techniques, we present experimental results to support the electride nature of this phase. The electride nature of dense sodium in the transparent hP 4 phase, where valence electrons are interstitially localised, lacks experimental confirmation. Here, the authors use single-crystal X-ray diffraction and quantum crystallographic atomic form factors to provide experimental indication of interstitial electron localisation in hP 4-Na at 223 GPa, advancing our understanding of high density electrides.

Management of cardiac arrest patients includes active body temperature control and strict prevention of fever to avoid further neurological damage. Cold-shock proteins RNA-binding motif 3 (RBM3) and cold inducible RNA-binding protein (CIRP) expressions are induced in vitro in response to hypothermia and play a key role in hypothermia-induced neuroprotection. To measure gene expressions of RBM3, CIRP, and inflammatory biomarkers in whole blood samples from targeted temperature management (TTM)-treated post-cardiac arrest patients for the potential application as clinical biomarkers for the efficacy of TTM treatment. A prospective single center trial with the inclusion of 22 cardiac arrest patients who were treated with TTM (33°C for 24 hours) after ROSC was performed. RBM3, CIRP, interleukin 6 (IL-6), monocyte chemotactic protein 1 (MCP-1), and inducible nitric oxide synthase (iNOS) mRNA expressions were quantified by RT-qPCR. Serum RBM3 protein concentration was quantified using an enzyme-linked immunosorbent assay (ELISA). RBM3 mRNA expression was significantly induced in post-cardiac arrest patients in response to TTM. RBM3 mRNA was increased 2.2-fold compared to before TTM. A similar expression kinetic of 1.4-fold increase was observed for CIRP mRNA, but did not reached significancy. Serum RBM3 protein was not increased in response to TTM. IL-6 and MCP-1 expression peaked after ROSC and then significantly decreased. iNOS expression was significantly increased 24h after return of spontaneous circulation (ROSC) and TTM. RBM3 is temperature regulated in patients treated with TTM after CA and ROSC. RBM3 is a possible biomarker candidate to ensure the efficacy of TTM treatment in post-cardiac arrest patients and its pharmacological induction could be a potential future intervention strategy that warrants further research.