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Key Points The glucose-phosphorylating enzyme glucokinase has a crucial role in glucose homeostasis as 'glucose sensor' of the insulin-producing pancreatic β-cells and as a regulatory step in the conversion of glucose to glycogen, as well as in gluconeogenesis in the liver. Autosomal dominant activating or inactivating mutations of glucokinase in humans and rodents cause hyperinsulinism and diabetes, respectively. The activating point mutations are clustered at a location in the enzyme structure that is distinct from the substrate binding site, which suggests that glucokinase has an allosteric activator site. These and other observations highlighted glucokinase as a potential drug target. Glucokinase activators (GKAs) have been discovered recently that stimulate the enzyme allosterically by lowering its glucose S 0.5 (the concentration of glucose that allows half-maximal activity of the enzyme) and Hill coefficient (n H ) and increasing its catalytic constant ( k cat ). At present, approximately 100 patents on low molecular-weight compounds with GKA characteristics have been disclosed by the pharmaceutical industry. GKAs lower blood glucose levels in normal laboratory animals and humans by stimulating insulin release and enhancing hepatic glucose uptake. GKAs lower blood glucose in animal models of type 2 diabetes and in humans with type 2 diabetes. An assessment of the current status of basic and clinical GKA-related research indicates that this new class of anti-diabetic drugs shows promise for monotherapy or combination drug therapy of type 2 diabetes. The glucose-phosphorylating enzyme glucokinase acts as a glucose sensor of the insulin-producing pancreatic islet β-cells, controls the conversion of glucose to glycogen in the liver and also regulates hepatic glucose production, and is therefore a potential therapeutic target for the treatment of type 2 diabetes. Here, Matschinsky discusses the physiological roles of glucokinase and the most recent progress in the development of pharmacological glucokinase activators. Glucokinase, a unique isoform of the hexokinase enzymes, which are known to phosphorylate D -glucose and other hexoses, was identified during the past three to four decades as a new, promising drug target for type 2 diabetes. Glucokinase serves as a glucose sensor of the insulin-producing pancreatic islet β-cells, controls the conversion of glucose to glycogen in the liver and regulates hepatic glucose production. Guided by this fundamental knowledge, several glucokinase activators are now being developed, and have so far been shown to lower blood glucose in several animal models of type 2 diabetes and in initial trials in humans with the disease. Here, the scientific basis and current status of this new approach to diabetes therapy are discussed.

A system of Majorana zero modes with random infinite-range interactions—the Sachdev-Ye-Kitaev (SYK) model—is thought to exhibit an intriguing relation to the horizons of extremal black holes in two-dimensional anti–de Sitter space. This connection provides a rare example of holographic duality between a solvable quantum-mechanical model and dilaton gravity. Here, we propose a physical realization of the SYK model in a solid-state system. The proposed setup employs the Fu-Kane superconductor realized at the interface between a three-dimensional topological insulator and an ordinary superconductor. The requisite N Majorana zero modes are bound to a nanoscale hole fabricated in the superconductor that is threaded by N quanta of magnetic flux. We show that when the system is tuned to the surface neutrality point (i.e., chemical potential coincident with the Dirac point of the topological insulator surface state) and the hole has sufficiently irregular shape, the Majorana zero modes are described by the SYK Hamiltonian. We perform extensive numerical simulations to demonstrate that the system indeed exhibits physical properties expected of the SYK model, including thermodynamic quantities and two-point as well as four-point correlators, and discuss ways in which these can be observed experimentally.

Dirac and Weyl semimetals form an ideal platform for testing ideas developed in high-energy physics to describe massless relativistic particles. One such quintessentially field-theoretic idea of the chiral anomaly already resulted in the prediction and subsequent observation of the pronounced negative magnetoresistance in these novel materials for parallel electric and magnetic fields. Here, we predict that the chiral anomaly occurs—and has experimentally observable consequences—when real electromagnetic fields E and B are replaced by strain-induced pseudo-electromagnetic fields e and b . For example, a uniform pseudomagnetic field b is generated when a Weyl semimetal nanowire is put under torsion. In accordance with the chiral anomaly equation, we predict a negative contribution to the wire resistance proportional to the square of the torsion strength. Remarkably, left- and right-moving chiral modes are then spatially segregated to the bulk and surface of the wire forming a “topological coaxial cable.” This produces hydrodynamic flow with potentially very long relaxation time. Another effect we predict is the ultrasonic attenuation and electromagnetic emission due to a time-periodic mechanical deformation causing pseudoelectric field e . These novel manifestations of the chiral anomaly are most striking in the semimetals with a single pair of Weyl nodes but also occur in Dirac semimetals such as Cd3As2 and Na3Bi and Weyl semimetals with unbroken time-reversal symmetry.

Second-order nonlinear spectroscopy has proven to be a powerful tool in elucidating key chemical and structural characteristics at a variety of interfaces. However, the presence of interfacial potentials may lead to complications regarding the interpretation of second harmonic and vibrational sum frequency generation responses from charged interfaces due to mixing of absorptive and dispersive contributions. Here, we examine by means of mathematical modeling how this interaction influences second-order spectral lineshapes. We discuss our findings in the context of reported nonlinear optical spectra obtained from charged water/air and solid/liquid interfaces and demonstrate the importance of accounting for the interfacial potential-dependent χ (3) term in interpreting lineshapes when seeking molecular information from charged interfaces using second-order spectroscopy. Charged interfaces are important in chemical systems, but the influence of charge on vibrational sum frequency spectra has only recently been considered. Here the authors show the importance of accounting for the interfacial potential-dependent χ (3) term in interpreting spectral lineshapes from charged interfaces.

Background This study aimed to evaluate the safety of raw vegetable products present on the German market regarding toxin-producing Bacillus cereus sensu lato ( s.l. ) group bacteria. Results A total of 147 B. cereus s.l. group strains isolated from cucumbers, carrots, herbs, salad leaves and ready-to-eat mixed salad leaves were analyzed. Their toxinogenic potential was assessed by multiplex PCR targeting the hemolysin BL ( hbl ) component D ( hblD ), non-hemolytic enterotoxin ( nhe ) component A ( nheA ), cytotoxin K-2 ( cytK-2 ) and the cereulide ( ces ) toxin genes. In addition, a serological test was used to detect Hbl and Nhe toxins. On the basis of PCR and serological results, none of the strains were positive for the cereulide protein/genes, while 91.2, 83.0 and 37.4% were positive for the Hbl, Nhe and CytK toxins or their genes, respectively. Numerous strains produced multiple toxins. Generally, strains showed resistance against the β-lactam antibiotics such as penicillin G and cefotaxim (100%), as well as amoxicillin/clavulanic acid combination and ampicillin (99.3%). Most strains were susceptible to ciprofloxacin (99.3%), chloramphenicol (98.6%), amikacin (98.0%), imipenem (93.9%), erythromycin (91.8%), gentamicin (88.4%), tetracycline (76.2%) and trimethoprim/sulfamethoxazole combination (52.4%). The genomes of eight selected strains were sequenced. The toxin gene profiles detected by PCR and serological test mostly agreed with those from whole-genome sequence data. Conclusions Our study showed that B. cereus s.l. strains encoding toxin genes occur in products sold on the German market and that these may pose a health risk to the consumer if present at elevated levels. Furthermore, a small percentage of these strains harbor antibiotic resistance genes. The presence of these bacteria in fresh produce should, therefore, be monitored to guarantee their safety.

This study aims to assess how clinical outcomes of immunotherapy in real-world (effectiveness) correspond to outcomes in clinical trials (efficacy) and to look into factors that might explain an efficacy-effectiveness (EE) gap. All patients diagnosed with stage IV non-small cell lung cancer (NSCLC) in 2015–2018 in six Dutch large teaching hospitals (Santeon network) were identified and followed-up from date of diagnosis until death or end of data collection. Progression-free survival (PFS) and overall survival (OS) from first-line (1L) pembrolizumab and second-line (2L) nivolumab were compared with clinical trial data by calculating hazard ratios (HRs). From 1950 diagnosed patients, 1005 (52%) started with any 1L treatment, of which 83 received pembrolizumab. Nivolumab was started as 2L treatment in 141 patients. For both settings, PFS times were comparable between real-world and trials (HR 1.08 (95% CI 0.75–1.55), and HR 0.91 (95% CI 0.74–1.14), respectively). OS was significantly shorter in real-world for 1L pembrolizumab (HR 1.55; 95% CI 1.07–2.25). Receiving subsequent lines of treatment was less frequent in real-world compared to trials. There is no EE gap for PFS from immunotherapy in patients with stage IV NSCLC. However, there is a gap in OS for 1L pembrolizumab. Fewer patients proceeding to a subsequent line of treatment in real-world could partly explain this.

Patients with advanced Parkinson’s disease regularly experience unstable motor states. Objective and reliable monitoring of these fluctuations is an unmet need. We used deep learning to classify motion data from a single wrist-worn IMU sensor recording in unscripted environments. For validation purposes, patients were accompanied by a movement disorder expert, and their motor state was passively evaluated every minute. We acquired a dataset of 8,661 minutes of IMU data from 30 patients, with annotations about the motor state (OFF,ON, DYSKINETIC) based on MDS-UPDRS global bradykinesia item and the AIMS upper limb dyskinesia item. Using a 1-minute window size as an input for a convolutional neural network trained on data from a subset of patients, we achieved a three-class balanced accuracy of 0.654 on data from previously unseen subjects. This corresponds to detecting the OFF, ON, or DYSKINETIC motor state at a sensitivity/specificity of 0.64/0.89, 0.67/0.67 and 0.64/0.89, respectively. On average, the model outputs were highly correlated with the annotation on a per subject scale (r = 0.83/0.84; p < 0.0001), and sustained so for the highly resolved time windows of 1 minute (r = 0.64/0.70; p < 0.0001). Thus, we demonstrate the feasibility of long-term motor-state detection in a free-living setting with deep learning using motion data from a single IMU.

Parkinson's Disease is a progressive neurodegenerative disorder marked by motor fluctuations in later disease stages that complicate treatment with levodopa. Traditional approaches to dosing often fail to capture the complex and dynamic nature of these fluctuations. In this study, we present the PD9™ algorithm, a novel approach to continuous motor state monitoring using data from a wrist-worn inertial measurement unit sensor. The algorithm provides minute-by-minute assessments of motor state severity on a unified scale quantifying bradykinesia, dyskinesia, and ON states. Data collected from 67 patients over 55,482 min were analyzed to assess levodopa response cycles. Across 218 identified levodopa cycles, the algorithm revealed reproducible patterns of symptom development based on the motor state at the time of levodopa administration. In particular, levodopa doses administered during non-ideal motor states (e.g., during dyskinesia) highlighted the limitations of fixed, empirically determined dosing regimens and underscore the need for individualized therapy, based on motor state. These findings demonstrate how AI-enabled continuous monitoring could help realize a more personalized treatment of Parkinson's disease and improve patient outcomes.

Atomic force microscopy (AFM) can visualize the dynamics of single biomolecules under near-physiological conditions. However, the scanning tip probes only the molecular surface with limited resolution, missing details required to fully deduce functional mechanisms from imaging alone. To overcome such drawbacks, we developed a computational framework to reconstruct 3D atomistic structures from AFM surface scans, employing simulation AFM and automatized fitting to experimental images. We provide applications to AFM images ranging from single molecular machines, protein filaments, to large-scale assemblies of 2D protein lattices, and demonstrate how the obtained full atomistic information advances the molecular understanding beyond the original topographic AFM image. We show that simulation AFM further allows for quantitative molecular feature assignment within measured AFM topographies. Implementation of the developed methods into the versatile interactive interface of the BioAFMviewer software, freely available at www.bioafmviewer.com , presents the opportunity for the broad Bio-AFM community to employ the enormous amount of existing structural and modeling data to facilitate the interpretation of resolution-limited AFM images.

With the emergence of multi-drug resistant bacteria the use of bacteriophages (phages) is gaining renewed interest as promising anti-microbial agents. The aim of this study was to isolate and characterize phages from human fecal samples. Three new coliphages, ɸAPCEc01, ɸAPCEc02 and ɸAPCEc03, were isolated. Their phenotypic and genomic characteristics, and lytic activity against biofilm, and in combination with ciprofloxacin, were investigated. All three phages reduced the growth of E. coli strain DPC6051 at multiplicity of infection (MOI) between 10-3 and 105. A cocktail of all three phages completely inhibited the growth of E. coli. The phage cocktail also reduced biofilm formation and prevented the emergence of phage-resistant mutants which occurred with single phage. When combined with ciprofloxacin, phage alone or in cocktail inhibited the growth of E. coli and prevented the emergence of resistant mutants. These three new phages are promising biocontrol agents for E. coli infections.