Acute ischaemic stroke associated with SARS-CoV-2 infection in North America

BACKGROUND: To analyse the clinical characteristics of COVID-19 with acute ischaemic stroke (AIS) and identify factors predicting functional outcome. METHODS: Multicentre retrospective cohort study of COVID-19 patients with AIS who presented to 30 stroke centres in the USA and Canada between 14 March and 30 August 2020. The primary endpoint was poor functional outcome, defined as a modified Rankin Scale (mRS) of 5 or 6 at discharge. Secondary endpoints include favourable outcome (mRS ≤2) and mortality at discharge, ordinal mRS (shift analysis), symptomatic intracranial haemorrhage (sICH) and occurrence of in-hospital complications. RESULTS: A total of 216 COVID-19 patients with AIS were included. 68.1% (147/216) were older than 60 years, while 31.9% (69/216) were younger. Median [IQR] National Institutes of Health Stroke Scale (NIHSS) at presentation was 12.5 (15.8), and 44.2% (87/197) presented with large vessel occlusion (LVO). Approximately 51.3% (98/191) of the patients had poor outcomes with an observed mortality rate of 39.1% (81/207). Age \u3e60 years (aOR: 5.11, 95% CI 2.08 to 12.56, p\u3c0.001), diabetes mellitus (aOR: 2.66, 95% CI 1.16 to 6.09, p=0.021), higher NIHSS at admission (aOR: 1.08, 95% CI 1.02 to 1.14, p=0.006), LVO (aOR: 2.45, 95% CI 1.04 to 5.78, p=0.042), and higher NLR level (aOR: 1.06, 95% CI 1.01 to 1.11, p=0.028) were significantly associated with poor functional outcome. CONCLUSION: There is relationship between COVID-19-associated AIS and severe disability or death. We identified several factors which predict worse outcomes, and these outcomes were more frequent compared to global averages. We found that elevated neutrophil-to-lymphocyte ratio, rather than D-Dimer, predicted both morbidity and mortality

Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression

Importance Diverse models have been developed to predict psychosis in patients with clinical high-risk (CHR) states. Whether prediction can be improved by efficiently combining clinical and biological models and by broadening the risk spectrum to young patients with depressive syndromes remains unclear. Objectives To evaluate whether psychosis transition can be predicted in patients with CHR or recent-onset depression (ROD) using multimodal machine learning that optimally integrates clinical and neurocognitive data, structural magnetic resonance imaging (sMRI), and polygenic risk scores (PRS) for schizophrenia; to assess models' geographic generalizability; to test and integrate clinicians' predictions; and to maximize clinical utility by building a sequential prognostic system. Design, Setting, and Participants This multisite, longitudinal prognostic study performed in 7 academic early recognition services in 5 European countries followed up patients with CHR syndromes or ROD and healthy volunteers. The referred sample of 167 patients with CHR syndromes and 167 with ROD was recruited from February 1, 2014, to May 31, 2017, of whom 26 (23 with CHR syndromes and 3 with ROD) developed psychosis. Patients with 18-month follow-up (n = 246) were used for model training and leave-one-site-out cross-validation. The remaining 88 patients with nontransition served as the validation of model specificity. Three hundred thirty-four healthy volunteers provided a normative sample for prognostic signature evaluation. Three independent Swiss projects contributed a further 45 cases with psychosis transition and 600 with nontransition for the external validation of clinical-neurocognitive, sMRI-based, and combined models. Data were analyzed from January 1, 2019, to March 31, 2020. Main Outcomes and Measures Accuracy and generalizability of prognostic systems. Results A total of 668 individuals (334 patients and 334 controls) were included in the analysis (mean [SD] age, 25.1 [5.8] years; 354 [53.0%] female and 314 [47.0%] male). Clinicians attained a balanced accuracy of 73.2% by effectively ruling out (specificity, 84.9%) but ineffectively ruling in (sensitivity, 61.5%) psychosis transition. In contrast, algorithms showed high sensitivity (76.0%-88.0%) but low specificity (53.5%-66.8%). A cybernetic risk calculator combining all algorithmic and human components predicted psychosis with a balanced accuracy of 85.5% (sensitivity, 84.6%; specificity, 86.4%). In comparison, an optimal prognostic workflow produced a balanced accuracy of 85.9% (sensitivity, 84.6%; specificity, 87.3%) at a much lower diagnostic burden by sequentially integrating clinical-neurocognitive, expert-based, PRS-based, and sMRI-based risk estimates as needed for the given patient. Findings were supported by good external validation results. Conclusions and RelevanceThese findings suggest that psychosis transition can be predicted in a broader risk spectrum by sequentially integrating algorithms' and clinicians' risk estimates. For clinical translation, the proposed workflow should undergo large-scale international validation.Question Can a transition to psychosis be predicted in patients with clinical high-risk states or recent-onset depression by optimally integrating clinical, neurocognitive, neuroimaging, and genetic information with clinicians' prognostic estimates? Findings In this prognostic study of 334 patients and 334 control individuals, machine learning models sequentially combining clinical and biological data with clinicians' estimates correctly predicted disease transitions in 85.9% of cases across geographically distinct patient populations. The clinicians' lack of prognostic sensitivity, as measured by a false-negative rate of 38.5%, was reduced to 15.4% by the sequential prognostic model. Meaning These findings suggest that an individualized prognostic workflow integrating artificial and human intelligence may facilitate the personalized prevention of psychosis in young patients with clinical high-risk syndromes or recent-onset depression.</p

Traces of trauma – a multivariate pattern analysis of childhood trauma, brain structure and clinical phenotypes

Background: Childhood trauma (CT) is a major yet elusive psychiatric risk factor, whose multidimensional conceptualization and heterogeneous effects on brain morphology might demand advanced mathematical modeling. Therefore, we present an unsupervised machine learning approach to characterize the clinical and neuroanatomical complexity of CT in a larger, transdiagnostic context. Methods: We used a multicenter European cohort of 1076 female and male individuals (discovery: n = 649; replication: n = 427) comprising young, minimally medicated patients with clinical high-risk states for psychosis; patients with recent-onset depression or psychosis; and healthy volunteers. We employed multivariate sparse partial least squares analysis to detect parsimonious associations between combinations of items from the Childhood Trauma Questionnaire and gray matter volume and tested their generalizability via nested cross-validation as well as via external validation. We investigated the associations of these CT signatures with state (functioning, depressivity, quality of life), trait (personality), and sociodemographic levels. Results: We discovered signatures of age-dependent sexual abuse and sex-dependent physical and sexual abuse, as well as emotional trauma, which projected onto gray matter volume patterns in prefronto-cerebellar, limbic, and sensory networks. These signatures were associated with predominantly impaired clinical state- and trait-level phenotypes, while pointing toward an interaction between sexual abuse, age, urbanicity, and education. We validated the clinical profiles for all three CT signatures in the replication sample. Conclusions: Our results suggest distinct multilayered associations between partially age- and sex-dependent patterns of CT, distributed neuroanatomical networks, and clinical profiles. Hence, our study highlights how machine learning approaches can shape future, more fine-grained CT research

Effects of two-photon oxidation for the development of graphene-bio interfaces

The discovery of graphene’s excellent electronic properties established a research field towards creating graphene-based neural interfaces. Indeed, graphene can record neuronal activity, which is itself based on electrical signals. An active neuronal network is crucial for building a graphene-neuron interface, and is heavily dependent on the environment of the neuron, which consists of a gel-like matrix. Several factors, such as matrix stiffness and protein layers have been shown to affect the formation of the network. In the past decade, two-photon oxidation (2PO) of graphene was established as an all-optical, nanoscale method that introduces hydroxyl and epoxide groups on the graphene surface, while preserving the carbon network and the adjacent pristine graphene. In this thesis, the effects of 2PO of graphene on attached proteins and a supramolecular hydrogel were studied. Two well-known model proteins, horseradish peroxidase (HRP) and bovine serum albumin, were investigated regarding their noncovalent immobilization on pristine and 2PO graphene surfaces. Additionally, the enzymatic function of HRP immobilized on graphene was studied. The supramolecular hydrogel was analyzed regarding its stiffness, the incorporation of graphene oxide flakes into the gel and the surface-mediated self-assembly on pristine and 2PO graphene surfaces. The results present 2PO as a tool to tune protein immobilization and function, and its effect on the supramolecular self-assembly of an amino acid based amphiphile. Overall, this thesis contributes to the knowledge about surface-related effects towards graphene-bio interfaces.Grafeenin erinomaisten elektronisten ominaisuuksien löytäminen loi tutkimusalan grafeeniin perustuvien hermorajapintojen luomiseksi. Grafeeni voi todellakin mitata hermosolujen aktiivisuutta, joka itse perustuu sähköisiin signaaleihin. Aktiivinen hermosoluverkko on ratkaisevan tärkeä grafeeni-neuronirajapinnan rakentamiselle, ja se on voimakkaasti riippuvainen hermosolujen ympäristöstä, joka koostuu geelinkaltaisesta matriisista. Useiden tekijöiden, kuten ympäröivän matriisin jäykkyyden ja proteiinikerrosten, on osoitettu vaikuttavan verkoston muodostumiseen. Viimeisen vuosikymmenen aikana grafeenin kaksifotonihapetus (2PO) on kehittynyt täysin optiseksi, nanomittakaavan menetelmäksi, jonka avulla voidaan funktionalisoida hydroksyyli- ja epoksidiryhmiä grafeenin pinnalle säilyttäen yhtenäinen hiiliverkosto hapetetulla alueella samalla kun hapettamattoman grafeenin rakenne säilyy täysin muuttumattomana. Tässä opinnäytetyössä tutkittiin grafeenin kaksifotonihapetuksen vaikutuksia siihen kiinnitettyihin proteiineihin ja supramolekulaariseen hydrogeeliin. Kahta hyvin tunnettua malliproteiinia tutkittiin liittyen niiden ei-kovalenttiseen immobilisaatioon muokkaamattomille ja kaksifotonihapetetuille grafeenipinnoille. Lisäksi tutkittiin grafeeniin immobilisoidun piparjuuriperoksidaasin entsymaattista toimintaa. Supramolekulaarista hydrogeeliä analysoitiin sen jäykkyyden, grafeenioksidihiutaleiden sisällyttämisen geeliin ja pintavälitteisen itsejärjestäytymisen suhteen muokkaamattomilla ja kaksifotonihapetetuilla grafeenipinnoilla. Tulosten mukaan kaksifotonihapetus toimii työkaluna, jonka avulla voidaan säätää proteiinien immobilisaatiota ja toimintaa sekä aminohappopohjaisen amfifiilin supramolekulaarista itsejärjestäytymistä. Kaiken kaikkiaan tämä opinnäytetyö lisää tietoa pintailmiöistä liittyen grafeeni-biorajapintojen kehittämiseen

The Status Quo of Digital Innovation Units: “A Day Late and a Dollar Short”

This paper examines digital innovation (DI) types and digital trends that are especially addressed within digital innovation units (DIU). As research on DIUs is still scarce, we collected website data from German DAX30 incumbents to identify dependencies between different DIU setups, DI types and digital trends. Not surprisingly, our results show that DIUs primarily focus on digital products and business models related to AI, IoT/Smart x and Data Analytics. Differentiating between four DIU setups, we could not find particular digital trends being addressed by specific setups. In addition, drawing on the Gartner Hype Cycle (GHC), we show that DIUs mostly pay attention to digital trends in more mature stages. We conclude and recommend the need for DIUs to focus on radical DIs to be as innovative as they should be and to pay attention on digital trends in the earlier stages of the GHC to promote radical DIs

Catalytic Activity of Horseradish Peroxidase Immobilized on Pristine and Two‐Photon Oxidized Graphene

Abstract Biosensors based on graphene and bio‐graphene interfaces have gained momentum in recent years due to graphene's outstanding electronic and mechanical properties. By introducing the patterning of a single‐layer graphene surface by two‐photon oxidation (2PO), the surface hydrophobicity/hydrophilicity and doping can be varied at the nanoscale while preserving the carbon network, thus opening possibilities to design new devices. In this study, the effect of 2PO on the catalytic activity of the noncovalently immobilized enzyme horseradish peroxidase (HRP) on single‐layer graphene‐coated Si/SiO2 chips is presented. To monitor the activity continuously, a simple well‐plate setup is introduced. Upon controllable 1–2‐layer immobilization, the catalytic activity decreases to a maximum value of 7.5% of the free enzyme. Interestingly, the activity decreases with increasing 2PO area on the samples. Hence, the HRP catalytic activity on the graphene surface is locally controlled. This approach can enable the development of graphene‐bio interfaces with locally varying enzyme activity

Tailored Polyelectrolyte Multilayer Systems by Variation of Polyelectrolyte Composition and EDC/NHS Cross-Linking: Controlled Drug Release vs. Drug Reservoir Capabilities and Cellular Response for Improved Osseointegration

Polyelectrolyte multilayers (PEM) are versatile tools used to investigate fundamental interactions between material-related parameters and the resulting performance in stem cell differentiation, respectively, in bone tissue engineering. In the present study, we investigate the suitability of PEMs with a varying collagen content for use as drug carriers for the human bone morphogenetic protein 2 (rhBMP-2). We use three different PEM systems consisting either of the positively charged poly-L-lysine or the glycoprotein collagen type I and the negatively charged glycosaminoglycan heparin. For a specific modification of the loading capacity and the release kinetics, the PEMs were stepwise cross-linked before loading with cytokine. We demonstrate the possibility of immobilizing significant amounts of rhBMP-2 in all multilayer systems and to specifically tune its release via cross-linking. Furthermore, we prove that the drug release of rhBMP-2 plays only a minor role in the differentiation of osteoprogenitor cells. We find a significantly higher influence of the immobilized rhBMP-2 within the collagen-rich coatings that obviously represent an excellent mimicry of the native extracellular matrix. The cytokine immobilized in its bioactive form was able to achieve an increase in orders of magnitude both in the early stages of differentiation and in late calcification compared to the unloaded layers

Nanoscale Probing of the Supramolecular Assembly in a Two-Component Gel by Near-Field Infrared Spectroscopy

The design of soft biomaterials requires a deep understanding of molecular self-assembly. We introduce here a nanoscale infrared (IR) spectroscopy study of a two-component supramolecular gel to assess the system´s heterogeneity and supramolecular assembly. In contrast to far-field IR spectroscopy, near-field IR spectroscopy revealed differences in the secondary structures of the gelator molecules and non-covalent interactions at three distinct nano-locations of the gel network. A β-sheet arrangement is dominant in single and parallel fibres with a small proportion of an α-helix present, while the molecular assembly derives from strong hydrogen bonding. However, at the crossing point of two fibres, only the β-sheet motif is observed, with an intense π-π stacking contribution. Near-field nanospectroscopy can become a powerful tool for the nanoscale distinction of non-covalent interactions, while is expected to advance the existing spectroscopic assessments of supramolecular gels.peerReviewe

Process Development for Additive Manufacturing of Alumina Toughened Zirconia for 3D Structures by Means of Two-Photon Absorption Technique

Additive manufacturing is well established for plastics and metals, and it gets more and more implemented in a variety of industrial processes. Beside these well-established material platforms, additive manufacturing processes are highly interesting for ceramics, especially regarding resource conservation and for the production of complex three-dimensional shapes and structures with specific feature sizes in the µm and mm range with high accuracy. The usage of ceramics in 3D printing is, however, just at the beginning of a technical implementation in a continuously and fast rising field of research and development. The flexible fabrication of highly complex and precise 3D structures by means of light-induced photopolymerization that are difficult to realize using traditional ceramic fabrication methods such as casting and machining is of high importance. Generally, slurry-based ceramic 3D printing technologies involve liquid or semi-liquid polymeric systems dispersed with ceramic particles as feedstock (inks or pastes), depending on the solid loading and viscosity of the system. This paper includes all types of photo-curable polymer-ceramic-mixtures (feedstock), while demonstrating our own work on 3D printed alumina toughened zirconia based ceramic slurries with light induced polymerization on the basis of two-photon absorption (TPA) for the first time. As a proven exemplary on cuboids with varying edge length and double pyramids in the µm-range we state that real 3D micro-stereolithographic fabrication of ceramic products will be generally possible in the near future by means of TPA. This technology enables the fabrication of 3D structures with high accuracy in comparison to ceramic technologies that apply single-photon excitation. In sum, our work is intended to contribute to the fundamental development of this technology for the representation of oxide-ceramic components (proof-of-principle) and helps to exploit the high potential of additive processes in the field of bio-ceramics in the medium to long-term future

Tailored Polyelectrolyte Multilayer Systems by Variation of Polyelectrolyte Composition and EDC/NHS Cross-Linking: Physicochemical Characterization and In Vitro Evaluation

The layer-by-layer (LbL) self-assembly technique is an effective method to immobilize components of the extracellular matrix (ECM) such as collagen and heparin onto, e.g., implant surfaces/medical devices with the aim of forming polyelectrolyte multilayers (PEMs). Increasing evidence even suggests that cross-linking influences the physicochemical character of PEM films since mechanical cues inherent to the substrate may be as important as its chemical nature to influence the cellular behavior. In this study, for the first-time different collagen/heparin films have been prepared and cross-linked with EDC/NHS chemistry. Quartz crystal microbalance, zeta potential analyzer, diffuse reflectance Fourier transform infrared spectroscopy, atomic force microscopy and ellipsometry were used to characterize film growth, stiffness, and topography of different film systems. The analysis of all data proves a nearly linear film growth for all PEM systems, the efficacy of cross-linking and the corresponding changes in the film rigidity after cross-linking and an appropriate surface topography. Furthermore, preliminary cell culture experiments illustrated those cellular processes correlate roughly with the quantity of newly created covalent amide bonds. This allows a precise adjustment of the physicochemical properties of the selected film architecture regarding the desired application and target cells. It could be shown that collagen improves the biocompatibility of heparin containing PEMs and due to their ECM-analogue nature both molecules are ideal candidates intended to be used for any biomedical application with a certain preference to improve the performance of bone implants or bone augmentation strategies