[Practice and challenges of delegating medical tasks in the interprofessional everyday work of inpatient healthcare in Germany - An exploratory survey]. / Praxis und Herausforderungen der Delegation ärztlicher Tätigkeiten im interprofessionellen Arbeitsalltag der stationären Krankenversorgung in Deutschland: eine explorative Befragung.

BACKGROUND: The delegation of medical tasks (DMT) plays a significant role in the everyday practice of inpatient care but also presents a potential challenge in interprofessional collaboration. Assessing the conditions of DMT in everyday work is crucial to identify areas for optimization. METHODS: In a nationwide exploratory study, physicians, nursing and allied health professionals working for inpatient care facilities were surveyed regarding various aspects of DMT using a standardized online questionnaire. RESULTS: The majority of the 757 participants (64.9% physicians), perceived DMT to be both economically and time-efficient (88.5% agreement) and in the best interest of patients (74%). For 78.7% of the respondents, DMT represents a potential conflict in their daily work, depending on the quality of interprofessional communication. Inadequate staffing was identified as a barrier to a broader implementation of DMT by 83.8% of participants. 63.2% of the participants considered their knowledge of legal aspects related to DMT to be at least good (participants with less than 5 years of professional experience: 52.6%). Physicians primarily acquire relevant knowledge through professional practice (71.3% vs. non-physicians 39.5%). CONCLUSION: Across the different professional groups DMT was considered beneficial and serving the interests of patients. Targeted promotion of safe and cost-effective DMT should be incorporated into medical education. Achieving greater benefits from DMT requires explicit legal frameworks, effective communication within the team and, in particular, adequate staffing among the professional groups responsible for delegated tasks.

Transport Study of Charge-Carrier Scattering in Monolayer WSe_{2}.

Employing flux-grown single crystal WSe_{2}, we report charge-carrier scattering behaviors measured in h-BN encapsulated monolayer field effect transistors. We observe a nonmonotonic change of transport mobility as a function of hole density in the degenerately doped sample, which can be explained by energy dependent scattering amplitude of strong defects calculated using the T-matrix approximation. Utilizing long mean-free path (>500 nm), we also demonstrate the high quality of our electronic devices by showing quantized conductance steps from an electrostatically defined quantum point contact, showing the potential for creating ultrahigh quality quantum optoelectronic devices based on atomically thin semiconductors.

Two-dimensional heavy fermions in the van der Waals metal CeSiI.

Heavy-fermion metals are prototype systems for observing emergent quantum phases driven by electronic interactions1-6. A long-standing aspiration is the dimensional reduction of these materials to exert control over their quantum phases7-11, which remains a significant challenge because traditional intermetallic heavy-fermion compounds have three-dimensional atomic and electronic structures. Here we report comprehensive thermodynamic and spectroscopic evidence of an antiferromagnetically ordered heavy-fermion ground state in CeSiI, an intermetallic comprising two-dimensional (2D) metallic sheets held together by weak interlayer van der Waals (vdW) interactions. Owing to its vdW nature, CeSiI has a quasi-2D electronic structure, and we can control its physical dimension through exfoliation. The emergence of coherent hybridization of f and conduction electrons at low temperature is supported by the temperature evolution of angle-resolved photoemission and scanning tunnelling spectra near the Fermi level and by heat capacity measurements. Electrical transport measurements on few-layer flakes reveal heavy-fermion behaviour and magnetic order down to the ultra-thin regime. Our work establishes CeSiI and related materials as a unique platform for studying dimensionally confined heavy fermions in bulk crystals and employing 2D device fabrication techniques and vdW heterostructures12 to manipulate the interplay between Kondo screening, magnetic order and proximity effects.

Evaluation of a STAT MRI Protocol for Emergent Ophthalmology Patients.

BACKGROUND: Evaluating patients with potentially sight-threatening conditions frequently involves urgent neuroimaging, and some providers recommend expediting emergency department (ED) evaluation. However, several factors may limit the practicality of ED evaluation. This pilot study assessed the feasibility and safety of a STAT magnetic resonance imaging (MRI) protocol, designed to facilitate outpatient MRI within 48 hours of referral, compared with ED evaluation for patients with optic disc edema. METHODS: A retrospective chart review was performed. Demographics, clinical data, and baseline ophthalmic measures were compared between patients in STAT and ED groups using the t test or Fisher exact test. Multivariate analyses compared changes in visual acuity (VA), visual field mean deviation (VF MD), retinal nerve fiber layer thickness, and edema grade between presentation and follow-up using a mixed-effects model adjusting for age, sex, and baseline measures. RESULTS: A total of 70 patients met the study criteria-24 (34.3%) in the STAT MRI cohort and 46 (65.7%) in the ED cohort. Demographic variables were similar between groups. Patients referred to the ED had worse VA ( P < 0.001), larger VF MD ( P < 0.001), and higher edema grade ( P = 0.002) at presentation. Four patients in the ED group and none in the STAT group were found to have space-occupying lesions. Multivariate analyses showed that follow-up measures were significantly associated with their baseline values (all P < 0.001) but not with referral protocol (all P > 0.099). The STAT MRI protocol was associated with lower average patient charges and hospital costs. CONCLUSIONS: The STAT MRI protocol did not result in inferior visual outcomes or delay in life-threatening diagnoses. Urgent outpatient evaluation, rather than ED referral, seems safe for some patients with optic disc edema. These findings support continued utilization of the protocol and ongoing improvement efforts.

Time-reversal symmetry breaking superconductivity between twisted cuprate superconductors.

Twisted interfaces between stacked van der Waals (vdW) cuprate crystals present a platform for engineering superconducting order parameters by adjusting stacking angles. Using a cryogenic assembly technique, we construct twisted vdW Josephson junctions (JJs) at atomically sharp interfaces between Bi2Sr2CaCu2O8+x crystals, with quality approaching the limit set by intrinsic JJs. Near 45° twist angle, we observe fractional Shapiro steps and Fraunhofer patterns, consistent with the existence of two degenerate Josephson ground states related by time-reversal symmetry (TRS). By programming the JJ current bias sequence, we controllably break TRS to place the JJ into either of the two ground states, realizing reversible Josephson diodes without external magnetic fields. Our results open a path to engineering topological devices at higher temperatures.

Drug-Free Noninvasive Thermal Nerve Block: Validation of Sham Devices.

Headache is a leading cause of disability and suffering. One major challenge in developing device treatments is demonstrating their efficacy given devices' often-high placebo rate. This paper reviews the importance of validating sham devices as part of finalizing the design for larger-scale prospective randomized controlled trials in patients with chronic headache as well as the results of a prospective, single-blind trial to validate two potential sham noninvasive thermal nerve block devices. Study participants were trained to self-administer thermal nerve block treatment using sham devices in an office visit. Two different sham systems with different temperature profiles were assessed. Devices were offered for patients to use daily at-home for one week to assess the durability of sham placebo effects before participants were given active treatment in a second office visit followed by another optional week of self-administered active treatment at-home use. Sham treatments reduced pain scores by an average of 31% from 6.0 ± 2.3 to 4.3 ± 3.3, including two participants who fell asleep during the in-office treatment and woke up with no pain, but whose pain recurred after returning home during at-home use of the sham system. In-office active treatments reduced pain scores by 52% from 6.7 ± 2.1 to 3.3 ± 2.9 with sustained pain relief during optional at-home use. Successful blinding for the study was confirmed with an ideal Bang's Blinding Index of 0 and an ideal James' Blinding Index of 1. Both the sham and active treatments were viewed by participants as highly credible, and credibility increased from the beginning to end of sham treatments on average.

Computational Design of Potent and Selective d-Peptide Agonists of the Glucagon-like Peptide-2 Receptor.

Here, we designed three d-GLP-2 agonists that activated the glucagon-like peptide-2 receptor (GLP-2R) cyclic adenosine monophosphate (cAMP) accumulation without stimulating the glucagon-like peptide-1 receptor (GLP-1R). All the d-GLP-2 agonists increased the protein kinase B phosphorylated (p-AKT) expression levels in a time- and concentration-dependent manner in vitro. The most effective d-GLP-2 analogue boosted the AKT phosphorylation 2.28 times more effectively compared to the native l-GLP-2. The enhancement in the p-AKT levels induced by the d-GLP-2 analogues could be explained by GLP-2R's more prolonged activation, given that the d-GLP-2 analogues induce a lower ß-arrestin recruitment. The higher stability to protease degradation of our d-GLP-2 agonists helps us envision their potential applications in enhancing intestinal absorption and treating inflammatory bowel illness while lowering the high dosage required by the current treatments.

Operando electron microscopy investigation of polar domain dynamics in twisted van der Waals homobilayers.

Conventional antiferroelectric materials with atomic-scale anti-aligned dipoles undergo a transition to a ferroelectric (FE) phase under strong electric fields. The moiré superlattice formed in the twisted stacks of van der Waals crystals exhibits polar domains alternating in moiré length with anti-aligned dipoles. In this moiré domain antiferroelectic (MDAF) arrangement, the distribution of electric dipoles is distinguished from that of two-dimensional FEs, suggesting dissimilar domain dynamics. Here we performed an operando transmission electron microscopy investigation on twisted bilayer WSe2 to observe the polar domain dynamics in real time. We find that the topological protection, provided by the domain wall network, prevents the MDAF-to-FE transition. As one decreases the twist angle, however, this transition occurs as the domain wall network disappears. Exploiting stroboscopic operando transmission electron microscopy on the FE phase, we measure a maximum domain wall velocity of 300 µm s-1. Domain wall pinnings by various disorders limit the domain wall velocity and cause Barkhausen noises in the polarization hysteresis loop. Atomic-scale analysis of the pinning disorders provides structural insight on how to improve the switching speed of van der Waals FEs.

GDockScore: a graph-based protein-protein docking scoring function.

Summary: Protein complexes play vital roles in a variety of biological processes, such as mediating biochemical reactions, the immune response and cell signalling, with 3D structure specifying function. Computational docking methods provide a means to determine the interface between two complexed polypeptide chains without using time-consuming experimental techniques. The docking process requires the optimal solution to be selected with a scoring function. Here, we propose a novel graph-based deep learning model that utilizes mathematical graph representations of proteins to learn a scoring function (GDockScore). GDockScore was pre-trained on docking outputs generated with the Protein Data Bank biounits and the RosettaDock protocol, and then fine-tuned on HADDOCK decoys generated on the ZDOCK Protein Docking Benchmark. GDockScore performs similarly to the Rosetta scoring function on docking decoys generated using the RosettaDock protocol. Furthermore, state-of-the-art is achieved on the CAPRI score set, a challenging dataset for developing docking scoring functions. Availability and implementation: The model implementation is available at https://gitlab.com/mcfeemat/gdockscore. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Disrupting the α-synuclein-ESCRT interaction with a peptide inhibitor mitigates neurodegeneration in preclinical models of Parkinson's disease.

Accumulation of α-synuclein into toxic oligomers or fibrils is implicated in dopaminergic neurodegeneration in Parkinson's disease. Here we performed a high-throughput, proteome-wide peptide screen to identify protein-protein interaction inhibitors that reduce α-synuclein oligomer levels and their associated cytotoxicity. We find that the most potent peptide inhibitor disrupts the direct interaction between the C-terminal region of α-synuclein and CHarged Multivesicular body Protein 2B (CHMP2B), a component of the Endosomal Sorting Complex Required for Transport-III (ESCRT-III). We show that α-synuclein impedes endolysosomal activity via this interaction, thereby inhibiting its own degradation. Conversely, the peptide inhibitor restores endolysosomal function and thereby decreases α-synuclein levels in multiple models, including female and male human cells harboring disease-causing α-synuclein mutations. Furthermore, the peptide inhibitor protects dopaminergic neurons from α-synuclein-mediated degeneration in hermaphroditic C. elegans and preclinical Parkinson's disease models using female rats. Thus, the α-synuclein-CHMP2B interaction is a potential therapeutic target for neurodegenerative disorders.

Gate-based quantum computing for protein design.

Protein design is a technique to engineer proteins by permuting amino acids in the sequence to obtain novel functionalities. However, exploring all possible combinations of amino acids is generally impossible due to the exponential growth of possibilities with the number of designable sites. The present work introduces circuits implementing a pure quantum approach, Grover's algorithm, to solve protein design problems. Our algorithms can adjust to implement any custom pair-wise energy tables and protein structure models. Moreover, the algorithm's oracle is designed to consist of only adder functions. Quantum computer simulators validate the practicality of our circuits, containing up to 234 qubits. However, a smaller circuit is implemented on real quantum devices. Our results show that using [Formula: see text] iterations, the circuits find the correct results among all N possibilities, providing the expected quadratic speed up of Grover's algorithm over classical methods (i.e., [Formula: see text]).

Wafer-Scale Epitaxial Growth of an Atomically Thin Single-Crystal Insulator as a Substrate of Two-Dimensional Material Field-Effect Transistors.

As the electron mobility of two-dimensional (2D) materials is dependent on an insulating substrate, the nonuniform surface charge and morphology of silicon dioxide (SiO2) layers degrade the electron mobility of 2D materials. Here, we demonstrate that an atomically thin single-crystal insulating layer of silicon oxynitride (SiON) can be grown epitaxially on a SiC wafer at a wafer scale and find that the electron mobility of graphene field-effect transistors on the SiON layer is 1.5 times higher than that of graphene field-effect transistors on typical SiO2 films. Microscale and nanoscale void defects caused by heterostructure growth were eliminated for the wafer-scale growth of the single-crystal SiON layer. The single-crystal SiON layer can be grown on a SiC wafer with a single thermal process. This simple fabrication process, compatible with commercial semiconductor fabrication processes, makes the layer an excellent replacement for the SiO2/Si wafer.

Burden of Severe Illness Associated With Laboratory-Confirmed Influenza in Adults Aged 50-64 Years, 2010-2011 to 2016-2017.

Background: Understanding the burden of influenza is necessary to optimize recommendations for influenza vaccination. We describe the epidemiology of severe influenza in 50- to 64-year-old residents of metropolitan Toronto and Peel region, Canada, over 7 influenza seasons. Methods: Prospective population-based surveillance for hospitalization associated with laboratory-confirmed influenza was conducted from September 2010 to August 2017. Conditions increasing risk of influenza complications were as defined by Canada's National Advisory Committee on Immunization. Age-specific prevalence of medical conditions was estimated using Ontario health administrative data. Population rates were estimated using Statistics Canada data. Results: Over 7 seasons, 1228 hospitalizations occurred in patients aged 50-64 years: 40% due to A(H3N2), 30% A(H1N1), and 22% influenza B. The average annual hospitalization rate was 15.6, 20.9, and 33.2 per 100 000 in patients aged 50-54, 55-59, and 60-64 years, respectively; average annual mortality was 0.9/100 000. Overall, 33% of patients had received current season influenza vaccine; 963 (86%) had ≥1 underlying condition increasing influenza complication risk. The most common underlying medical conditions were chronic lung disease (38%) and diabetes mellitus (31%); 25% of patients were immunocompromised. The average annual hospitalization rate was 6.1/100 000 in those without and 41/100 000 in those with any underlying condition, and highest in those with renal disease or immunocompromise (138 and 281 per 100 000, respectively). The case fatality rate in hospitalized patients was 4.4%; median length of stay was 4 days (interquartile range, 2-8 days). Conclusions: The burden of severe influenza in 50- to 64-year-olds remains significant despite our universal publicly funded vaccination program. These data may assist in improving estimates of the cost-effectiveness of new strategies to reduce this burden.

HelixGAN a deep-learning methodology for conditional de novo design of α-helix structures.

MOTIVATION: Protein and peptide engineering has become an essential field in biomedicine with therapeutics, diagnostics and synthetic biology applications. Helices are both abundant structural feature in proteins and comprise a major portion of bioactive peptides. Precise design of helices for binding or biological activity is still a challenging problem. RESULTS: Here, we present HelixGAN, the first generative adversarial network method to generate de novo left-handed and right-handed alpha-helix structures from scratch at an atomic level. We developed a gradient-based search approach in latent space to optimize the generation of novel α-helical structures by matching the exact conformations of selected hotspot residues. The designed α-helical structures can bind specific targets or activate cellular receptors. There is a significant agreement between the helix structures generated with HelixGAN and PEP-FOLD, a well-known de novo approach for predicting peptide structures from amino acid sequences. HelixGAN outperformed RosettaDesign, and our previously developed structural similarity method to generate D-peptides matching a set of given hotspots in a known L-peptide. As proof of concept, we designed a novel D-GLP1_1 analog that matches the conformations of critical hotspots for the GLP1 function. MD simulations revealed a stable binding mode of the D-GLP1_1 analog coupled to the GLP1 receptor. This novel D-peptide analog is more stable than our previous D-GLP1 design along the MD simulations. We envision HelixGAN as a critical tool for designing novel bioactive peptides with specific properties in the early stages of drug discovery. AVAILABILITY AND IMPLEMENTATION: https://github.com/xxiexuezhi/helix_gan. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Computational and artificial intelligence-based methods for antibody development.

Due to their high target specificity and binding affinity, therapeutic antibodies are currently the largest class of biotherapeutics. The traditional largely empirical antibody development process is, while mature and robust, cumbersome and has significant limitations. Substantial recent advances in computational and artificial intelligence (AI) technologies are now starting to overcome many of these limitations and are increasingly integrated into development pipelines. Here, we provide an overview of AI methods relevant for antibody development, including databases, computational predictors of antibody properties and structure, and computational antibody design methods with an emphasis on machine learning (ML) models, and the design of complementarity-determining region (CDR) loops, antibody structural components critical for binding.

A few-layer covalent network of fullerenes.

The two natural allotropes of carbon, diamond and graphite, are extended networks of sp3-hybridized and sp2-hybridized atoms, respectively1. By mixing different hybridizations and geometries of carbon, one could conceptually construct countless synthetic allotropes. Here we introduce graphullerene, a two-dimensional crystalline polymer of C60 that bridges the gulf between molecular and extended carbon materials. Its constituent fullerene subunits arrange hexagonally in a covalently interconnected molecular sheet. We report charge-neutral, purely carbon-based macroscopic crystals that are large enough to be mechanically exfoliated to produce molecularly thin flakes with clean interfaces-a critical requirement for the creation of heterostructures and optoelectronic devices2. The synthesis entails growing single crystals of layered polymeric (Mg4C60)∞ by chemical vapour transport and subsequently removing the magnesium with dilute acid. We explore the thermal conductivity of this material and find it to be much higher than that of molecular C60, which is a consequence of the in-plane covalent bonding. Furthermore, imaging few-layer graphullerene flakes using transmission electron microscopy and near-field nano-photoluminescence spectroscopy reveals the existence of moiré-like superlattices3. More broadly, the synthesis of extended carbon structures by polymerization of molecular precursors charts a clear path to the systematic design of materials for the construction of two-dimensional heterostructures with tunable optoelectronic properties.

Electrically switchable anisotropic polariton propagation in a ferroelectric van der Waals semiconductor.

Tailoring of the propagation dynamics of exciton-polaritons in two-dimensional quantum materials has shown extraordinary promise to enable nanoscale control of electromagnetic fields. Varying permittivities along crystal directions within layers of material systems, can lead to an in-plane anisotropic dispersion of polaritons. Exploiting this physics as a control strategy for manipulating the directional propagation of the polaritons is desired and remains elusive. Here we explore the in-plane anisotropic exciton-polariton propagation in SnSe, a group-IV monochalcogenide semiconductor that forms ferroelectric domains and shows room-temperature excitonic behaviour. Exciton-polaritons are launched in SnSe multilayer plates, and their propagation dynamics and dispersion are studied. This propagation of exciton-polaritons allows for nanoscale imaging of the in-plane ferroelectric domains. Finally, we demonstrate the electric switching of the exciton-polaritons in the ferroelectric domains of this complex van der Waals system. The study suggests that systems such as group-IV monochalcogenides could serve as excellent ferroic platforms for actively reconfigurable polaritonic optical devices.

A universal deep-learning model for zinc finger design enables transcription factor reprogramming.

Cys2His2 zinc finger (ZF) domains engineered to bind specific target sequences in the genome provide an effective strategy for programmable regulation of gene expression, with many potential therapeutic applications. However, the structurally intricate engagement of ZF domains with DNA has made their design challenging. Here we describe the screening of 49 billion protein-DNA interactions and the development of a deep-learning model, ZFDesign, that solves ZF design for any genomic target. ZFDesign is a modern machine learning method that models global and target-specific differences induced by a range of library environments and specifically takes into account compatibility of neighboring fingers using a novel hierarchical transformer architecture. We demonstrate the versatility of designed ZFs as nucleases as well as activators and repressors by seamless reprogramming of human transcription factors. These factors could be used to upregulate an allele of haploinsufficiency, downregulate a gain-of-function mutation or test the consequence of regulation of a single gene as opposed to the many genes that a transcription factor would normally influence.

Antibodies as drugs-a Keystone Symposia report.

Therapeutic antibodies have broad indications across diverse disease states, such as oncology, autoimmune diseases, and infectious diseases. New research continues to identify antibodies with therapeutic potential as well as methods to improve upon endogenous antibodies and to design antibodies de novo. On April 27-30, 2022, experts in antibody research across academia and industry met for the Keystone symposium "Antibodies as Drugs" to present the state-of-the-art in antibody therapeutics, repertoires and deep learning, bispecific antibodies, and engineering.

Score-based generative modeling for de novo protein design.

The generation of de novo protein structures with predefined functions and properties remains a challenging problem in protein design. Diffusion models, also known as score-based generative models (SGMs), have recently exhibited astounding empirical performance in image synthesis. Here we use image-based representations of protein structure to develop ProteinSGM, a score-based generative model that produces realistic de novo proteins. Through unconditional generation, we show that ProteinSGM can generate native-like protein structures, surpassing the performance of previously reported generative models. We experimentally validate some de novo designs and observe secondary structure compositions consistent with generated backbones. Finally, we apply conditional generation to de novo protein design by formulating it as an image inpainting problem, allowing precise and modular design of protein structure.