Themes, Rates, and Risk of Adverse Events of the Artificial Pancreas in the United States Using MAUDE.

Three manufacturers sell artificial pancreas systems in the United States for management of Type 1 Diabetes. Given the life-saving task required of an artificial pancreas there needs to be a high level of trust and safety in the devices. This evaluation sought to find the adjusted safety event reporting rate and themes along with device-associated risk in events reported utilizing the MAUDE database. We searched device names in the MAUDE database over the period from 2016 until August 2023 (the date of retrieval). Thematic analysis was performed using dual-reviewer examination with a 96% concurrence. Relative risk (RR) was calculated for injury, malfunction, and overall, for each manufacturer, as well as adjusted event rate per manufacturer. Most events reported related to defects in the manufacturing of the casing materials which resulted in non-delivery of therapy. Tandem Diabetes Care, Inc. had an adjusted event rate of 50 per 100,000 units and RR of 0.0225. Insulet had an adjusted event rate of 300 per 100,000 units and RR of 0.1684. Medtronic has an adjusted event rate of 2771.43 per 100,000 units and RR of 20.7857. The newer Medtronic devices show improvements in likely event rate. While the artificial pancreas is still in its infancy, these event rates are not at an acceptable level for a device which can precipitate death from malfunctions. Further exploration into safety events and much more research and development is needed for devices to reduce the event rates. Improved manufacturing practices, especially the casing materials, are highly recommended. The artificial pancreas holds promise for millions but must be improved before it becomes a true life-saving device that it has the potential to become.

The Excited-State Lifetime of Poly(NDI2OD-T2) Is Intrinsically Short.

Conjugated polymers composed of alternating electron donor and acceptor segments have come to dominate the materials being considered for organic photoelectrodes and solar cells, in large part because of their favorable near-infrared absorption. The prototypical electron-transporting push-pull polymer poly(NDI2OD-T2) (N2200) is one such material. While reasonably efficient organic solar cells can be fabricated with N2200 as the acceptor, it generally fails to contribute as much photocurrent from its absorption bands as the donor with which it is paired. Moreover, transient absorption studies have shown N2200 to have a consistently short excited-state lifetime (∼100 ps) that is dominated by a ground-state recovery. In this paper, we investigate whether these characteristics are intrinsic to the backbone structure of this polymer or if these are extrinsic effects from ubiquitous solution-phase and thin-film aggregates. We compare the solution-phase photophysics of N2200 with those of a pair of model compounds composed of alternating bithiophene (T2) donor and naphthalene diimide (NDI) acceptor units, NDI-T2-NDI and T2-NDI-T2, in a dilute solution. We find that the model compounds have even faster ground-state recovery dynamics (τ = 45, 27 ps) than the polymer (τ = 133 ps), despite remaining molecularly isolated in solution. In these molecules, as in the case of the N2200 polymer, the lowest excited state has a T2 to NDI charge-transfer (CT) character. Electronic-structure calculations indicate that the short lifetime of this state is due to fast nonradiative decay to the ground state (GS) promoted by strong CT-GS electronic coupling and strong electron-vibrational coupling with high-frequency (quantum) normal modes.

Assembly of short amphiphilic peptoids into nanohelices with controllable supramolecular chirality.

A long-standing challenge in bioinspired materials is to design and synthesize synthetic materials that mimic the sophisticated structures and functions of natural biomaterials, such as helical protein assemblies that are important in biological systems. Herein, we report the formation of a series of nanohelices from a type of well-developed protein-mimetics called peptoids. We demonstrate that nanohelix structures and supramolecular chirality can be well-controlled through the side-chain chemistry. Specifically, the ionic effects on peptoids from varying the polar side-chain groups result in the formation of either single helical fiber or hierarchically stacked helical bundles. We also demonstrate that the supramolecular chirality of assembled peptoid helices can be controlled by modifying assembling peptoids with a single chiral amino acid side chain. Computational simulations and theoretical modeling predict that minimizing exposure of hydrophobic domains within a twisted helical form presents the most thermodynamically favorable packing of these amphiphilic peptoids and suggests a key role for both polar and hydrophobic domains on nanohelix formation. Our findings establish a platform to design and synthesize chiral functional materials using sequence-defined synthetic polymers.

A shared neoantigen vaccine combined with immune checkpoint blockade for advanced metastatic solid tumors: phase 1 trial interim results.

Therapeutic vaccines that elicit cytotoxic T cell responses targeting tumor-specific neoantigens hold promise for providing long-term clinical benefit to patients with cancer. Here we evaluated safety and tolerability of a therapeutic vaccine encoding 20 shared neoantigens derived from selected common oncogenic driver mutations as primary endpoints in an ongoing phase 1/2 study in patients with advanced/metastatic solid tumors. Secondary endpoints included immunogenicity, overall response rate, progression-free survival and overall survival. Eligible patients were selected if their tumors expressed one of the human leukocyte antigen-matched tumor mutations included in the vaccine, with the majority of patients (18/19) harboring a mutation in KRAS. The vaccine regimen, consisting of a chimp adenovirus (ChAd68) and self-amplifying mRNA (samRNA) in combination with the immune checkpoint inhibitors ipilimumab and nivolumab, was shown to be well tolerated, with observed treatment-related adverse events consistent with acute inflammation expected with viral vector-based vaccines and immune checkpoint blockade, the majority grade 1/2. Two patients experienced grade 3/4 serious treatment-related adverse events that were also dose-limiting toxicities. The overall response rate was 0%, and median progression-free survival and overall survival were 1.9 months and 7.9 months, respectively. T cell responses were biased toward human leukocyte antigen-matched TP53 neoantigens encoded in the vaccine relative to KRAS neoantigens expressed by the patients' tumors, indicating a previously unknown hierarchy of neoantigen immunodominance that may impact the therapeutic efficacy of multiepitope shared neoantigen vaccines. These data led to the development of an optimized vaccine exclusively targeting KRAS-derived neoantigens that is being evaluated in a subset of patients in phase 2 of the clinical study. ClinicalTrials.gov registration: NCT03953235 .

Nutrition and Isolation in a Rural US Population over 80 Years Old: A Descriptive Analysis of a Vulnerable Population.

Factors allowing rural, community-dwelling 80+ year-olds to thrive remain unexplored. Isolation can impact this vulnerable population. In this study, patients were prospectively surveyed for age, gender, cohabitation (self, spouse, family) and location (suburban, rural, and isolated). Mini-nutritional assessment short form (MNA-SF) and BMI were obtained. A p < 0.05 represented statistical significance. Patients (n = 167) were mostly female (120; 71.9%) with an average overweight BMI (26.5) and low-normal MNA-SF scores (11.8). Most live alone (49.7%), followed by spousal (31.7%) and family (18.6%) cohabitation. Over 80% are rural (71) or rural-isolated (67), and of these, 83% had normal nutrition. Self-habitation correlated with lower MNA-SF scores (p = 0.02). Normal BMIs correlated with family cohabitation (OR = 0.90 [CI: 0.82-0.99]) and nourished MNA-SF scores with spousal cohabitation (OR = 1.69; CI: 1.15-2.47) rather than living alone. Self-habitation increases vulnerability to obesity and malnutrition. Interventions should aim to maintain independence while improving the effects of habitation on nutrition.

Thyroid neoplasia associated with nutritional deficiency in cultured jade perch in Hong Kong.

Non-neoplastic thyroid hyperplasia is common in terrestrial animals, secondary to nutritional imbalances or other goitrogenic compounds. Thyroid hyperplasia is relatively common in teleost fish; however, malignant thyroid neoplasia is rarely reported. We diagnosed cases of thyroid neoplasia in a population of jade perch (Scortum barcoo). The 3,000 affected fish had grossly apparent, bilateral pharyngeal swellings. Histologic examination confirmed proliferative thyroid lesions ranging from hyperplasia to well-differentiated follicular cell carcinoma. In addition, the younger population of animals on the farm also had bacterial septicemia and mild Dactylogyrus sp. gill infections. Feed analysis revealed a severe deficiency of iodine and vitamin C in the homemade fish diet used on the farm. The concentrations of other minerals, such as zinc, were also on the lower end of the recommended requirements for freshwater fish. The farm was using surface water in its recirculating aquaculture system. We recommended a switch to a commercial aquaculture diet, as well as to use well water rather than surface water to avoid any contaminants, and to treat the younger fish with an antibiotic for bacterial septicemia. Our case provides evidence of progression from nutritional-associated thyroid hyperplasia to neoplasia in farmed teleost fish.

Carrier density and delocalization signatures in doped carbon nanotubes from quantitative magnetic resonance.

High-performance semiconductor materials and devices are needed to supply the growing energy and computing demand. Organic semiconductors (OSCs) are attractive options for opto-electronic devices, due to their low cost, extensive tunability, easy fabrication, and flexibility. Semiconducting single-walled carbon nanotubes (s-SWCNTs) have been extensively studied due to their high carrier mobility, stability and opto-electronic tunability. Although molecular charge transfer doping affords widely tunable carrier density and conductivity in s-SWCNTs (and OSCs in general), a pervasive challenge for such systems is reliable measurement of charge carrier density and mobility. In this work we demonstrate a direct quantification of charge carrier density, and by extension carrier mobility, in chemically doped s-SWCNTs by a nuclear magnetic resonance approach. The experimental results are verified by a phase-space filling doping model, and we suggest this approach should be broadly applicable for OSCs. Our results show that hole mobility in doped s-SWCNT networks increases with increasing charge carrier density, a finding that is contrary to that expected for mobility limited by ionized impurity scattering. We discuss the implications of this important finding for additional tunability and applicability of s-SWCNT and OSC devices.

Molecular insight into how the position of an abasic site modifies DNA duplex stability and dynamics.

Local perturbations to DNA base-pairing stability from lesions and chemical modifications can alter the stability and dynamics of an entire oligonucleotide. End effects may cause the position of a disruption within a short duplex to influence duplex stability and structural dynamics, yet this aspect of nucleic acid modifications is often overlooked. We investigate how the position of an abasic site (AP site) impacts the stability and dynamics of short DNA duplexes. Using a combination of steady-state and time-resolved spectroscopy and molecular dynamics simulations, we unravel an interplay between AP-site position and nucleobase sequence that controls energetic and dynamic disruption to the duplex. The duplex is disrupted into two segments by an entropic barrier for base-pairing on each side of the AP site. The barrier induces fraying of the short segment when an AP site is near the termini. Shifting the AP site inward promotes a transition from short-segment fraying to fully encompassing the barrier into the thermodynamics of hybridization, leading to further destabilization of the duplex. Nucleobase sequence determines the length scale for this transition by tuning the barrier height and base-pair stability of the short segment, and certain sequences enable out-of-register base-pairing to minimize the barrier height.

Permutationally Invariant Networks for Enhanced Sampling (PINES): Discovery of Multimolecular and Solvent-Inclusive Collective Variables.

The typically rugged nature of molecular free-energy landscapes can frustrate efficient sampling of the thermodynamically relevant phase space due to the presence of high free-energy barriers. Enhanced sampling techniques can improve phase space exploration by accelerating sampling along particular collective variables (CVs). A number of techniques exist for the data-driven discovery of CVs parametrizing the important large-scale motions of the system. A challenge to CV discovery is learning CVs invariant to the symmetries of the molecular system, frequently rigid translation, rigid rotation, and permutational relabeling of identical particles. Of these, permutational invariance has proved a persistent challenge in frustrating the data-driven discovery of multimolecular CVs in systems of self-assembling particles and solvent-inclusive CVs for solvated systems. In this work, we integrate permutation invariant vector (PIV) featurizations with autoencoding neural networks to learn nonlinear CVs invariant to translation, rotation, and permutation and perform interleaved rounds of CV discovery and enhanced sampling to iteratively expand the sampling of configurational phase space and obtain converged CVs and free-energy landscapes. We demonstrate the permutationally invariant network for enhanced sampling (PINES) approach in applications to the self-assembly of a 13-atom argon cluster, association/dissociation of a NaCl ion pair in water, and hydrophobic collapse of a C45H92 n-pentatetracontane polymer chain. We make the approach freely available as a new module within the PLUMED2 enhanced sampling libraries.

Data-driven prediction of αIIbß3 integrin activation paths using manifold learning and deep generative modeling.

The integrin heterodimer is a transmembrane protein critical for driving cellular process and is a therapeutic target in the treatment of multiple diseases linked to its malfunction. Activation of integrin involves conformational transitions between bent and extended states. Some of the conformations that are intermediate between bent and extended states of the heterodimer have been experimentally characterized, but the full activation pathways remain unresolved both experimentally due to their transient nature and computationally due to the challenges in simulating rare barrier crossing events in these large molecular systems. An understanding of the activation pathways can provide new fundamental understanding of the biophysical processes associated with the dynamic interconversions between bent and extended states and can unveil new putative therapeutic targets. In this work, we apply nonlinear manifold learning to coarse-grained molecular dynamics simulations of bent, extended, and two intermediate states of αIIbß3 integrin to learn a low-dimensional embedding of the configurational phase space. We then train deep generative models to learn an inverse mapping between the low-dimensional embedding and high-dimensional molecular space and use these models to interpolate the molecular configurations constituting the activation pathways between the experimentally characterized states. This work furnishes plausible predictions of integrin activation pathways and reports a generic and transferable multiscale technique to predict transition pathways for biomolecular systems.

Data-driven discovery of innate immunomodulators via machine learning-guided high throughput screening.

The innate immune response is vital for the success of prophylactic vaccines and immunotherapies. Control of signaling in innate immune pathways can improve prophylactic vaccines by inhibiting unfavorable systemic inflammation and immunotherapies by enhancing immune stimulation. In this work, we developed a machine learning-enabled active learning pipeline to guide in vitro experimental screening and discovery of small molecule immunomodulators that improve immune responses by altering the signaling activity of innate immune responses stimulated by traditional pattern recognition receptor agonists. Molecules were tested by in vitro high throughput screening (HTS) where we measured modulation of the nuclear factor κ-light-chain-enhancer of activated B-cells (NF-κB) and the interferon regulatory factors (IRF) pathways. These data were used to train data-driven predictive models linking molecular structure to modulation of the NF-κB and IRF responses using deep representational learning, Gaussian process regression, and Bayesian optimization. By interleaving successive rounds of model training and in vitro HTS, we performed an active learning-guided traversal of a 139 998 molecule library. After sampling only ∼2% of the library, we discovered viable molecules with unprecedented immunomodulatory capacity, including those capable of suppressing NF-κB activity by up to 15-fold, elevating NF-κB activity by up to 5-fold, and elevating IRF activity by up to 6-fold. We extracted chemical design rules identifying particular chemical fragments as principal drivers of specific immunomodulation behaviors. We validated the immunomodulatory effect of a subset of our top candidates by measuring cytokine release profiles. Of these, one molecule induced a 3-fold enhancement in IFN-ß production when delivered with a cyclic di-nucleotide stimulator of interferon genes (STING) agonist. In sum, our machine learning-enabled screening approach presents an efficient immunomodulator discovery pipeline that has furnished a library of novel small molecules with a strong capacity to enhance or suppress innate immune signaling pathways to shape and improve prophylactic vaccination and immunotherapies.

ProtWave-VAE: Integrating Autoregressive Sampling with Latent-Based Inference for Data-Driven Protein Design.

Deep generative models (DGMs) have shown great success in the understanding and data-driven design of proteins. Variational autoencoders (VAEs) are a popular DGM approach that can learn the correlated patterns of amino acid mutations within a multiple sequence alignment (MSA) of protein sequences and distill this information into a low-dimensional latent space to expose phylogenetic and functional relationships and guide generative protein design. Autoregressive (AR) models are another popular DGM approach that typically lacks a low-dimensional latent embedding but does not require training sequences to be aligned into an MSA and enable the design of variable length proteins. In this work, we propose ProtWave-VAE as a novel and lightweight DGM, employing an information maximizing VAE with a dilated convolution encoder and an autoregressive WaveNet decoder. This architecture blends the strengths of the VAE and AR paradigms in enabling training over unaligned sequence data and the conditional generative design of variable length sequences from an interpretable, low-dimensional learned latent space. We evaluated the model's ability to infer patterns and design rules within alignment-free homologous protein family sequences and to design novel synthetic proteins in four diverse protein families. We show that our model can infer meaningful functional and phylogenetic embeddings within latent spaces and make highly accurate predictions within semisupervised downstream fitness prediction tasks. In an application to the C-terminal SH3 domain in the Sho1 transmembrane osmosensing receptor in baker's yeast, we subject ProtWave-VAE-designed sequences to experimental gene synthesis and select-seq assays for the osmosensing function to show that the model enables synthetic protein design, conditional C-terminus diversification, and engineering of the osmosensing function into SH3 paralogues.

DiAMoNDBack: Diffusion-Denoising Autoregressive Model for Non-Deterministic Backmapping of Cα Protein Traces.

Coarse-grained molecular models of proteins permit access to length and time scales unattainable by all-atom models and the simulation of processes that occur on long time scales, such as aggregation and folding. The reduced resolution realizes computational accelerations, but an atomistic representation can be vital for a complete understanding of mechanistic details. Backmapping is the process of restoring all-atom resolution to coarse-grained molecular models. In this work, we report DiAMoNDBack (Diffusion-denoising Autoregressive Model for Non-Deterministic Backmapping) as an autoregressive denoising diffusion probability model to restore all-atom details to coarse-grained protein representations retaining only Cα coordinates. The autoregressive generation process proceeds from the protein N-terminus to C-terminus in a residue-by-residue fashion conditioned on the Cα trace and previously backmapped backbone and side-chain atoms within the local neighborhood. The local and autoregressive nature of our model makes it transferable between proteins. The stochastic nature of the denoising diffusion process means that the model generates a realistic ensemble of backbone and side-chain all-atom configurations consistent with the coarse-grained Cα trace. We train DiAMoNDBack over 65k+ structures from the Protein Data Bank (PDB) and validate it in applications to a hold-out PDB test set, intrinsically disordered protein structures from the Protein Ensemble Database (PED), molecular dynamics simulations of fast-folding mini-proteins from DE Shaw Research, and coarse-grained simulation data. We achieve state-of-the-art reconstruction performance in terms of correct bond formation, avoidance of side-chain clashes, and the diversity of the generated side-chain configurational states. We make the DiAMoNDBack model publicly available as a free and open-source Python package.

Molecular insight into how the position of an abasic site and its sequence environment influence DNA duplex stability and dynamics.

Local perturbations to DNA base-pairing stability from lesions and chemical modifications can alter the stability and dynamics of an entire oligonucleotide. End effects may cause the position of a disruption within a short duplex to influence duplex stability and structural dynamics, yet this aspect of nucleic acid modifications is often overlooked. We investigate how the position of an abasic site (AP site) impacts the stability and dynamics of short DNA duplexes. Using a combination of steady-state and time-resolved spectroscopy and molecular dynamics simulations, we unravel an interplay between AP-site position and nucleobase sequence that controls energetic and dynamic disruption to the duplex. The duplex is disrupted into two segments by an entropic barrier for base pairing on each side of the AP site. The barrier induces fraying of the short segment when an AP site is near the termini. Shifting the AP site inward promotes a transition from short-segment fraying to fully encompassing the barrier into the thermodynamics of hybridization, leading to further destabilization the duplex. Nucleobase sequence determines the length scale for this transition by tuning the barrier height and base-pair stability of the short segment, and certain sequences enable out-of-register base pairing to minimize the barrier height.

Denoising Autoencoder Trained on Simulation-Derived Structures for Noise Reduction in Chromatin Scanning Transmission Electron Microscopy.

Scanning transmission electron microscopy tomography with ChromEM staining (ChromSTEM), has allowed for the three-dimensional study of genome organization. By leveraging convolutional neural networks and molecular dynamics simulations, we have developed a denoising autoencoder (DAE) capable of postprocessing experimental ChromSTEM images to provide nucleosome-level resolution. Our DAE is trained on synthetic images generated from simulations of the chromatin fiber using the 1-cylinder per nucleosome (1CPN) model of chromatin. We find that our DAE is capable of removing noise commonly found in high-angle annular dark field (HAADF) STEM experiments and is able to learn structural features driven by the physics of chromatin folding. The DAE outperforms other well-known denoising algorithms without degradation of structural features and permits the resolution of α-tetrahedron tetranucleosome motifs that induce local chromatin compaction and mediate DNA accessibility. Notably, we find no evidence for the 30 nm fiber, which has been suggested to serve as the higher-order structure of the chromatin fiber. This approach provides high-resolution STEM images that allow for the resolution of single nucleosomes and organized domains within chromatin dense regions comprising of folding motifs that modulate the accessibility of DNA to external biological machinery.

Thermodynamics and kinetics of DNA and RNA dinucleotide hybridization to gaps and overhangs.

Hybridization of short nucleic acid segments (<4 nt) to single-strand templates occurs as a critical intermediate in processes such as nonenzymatic nucleic acid replication and toehold-mediated strand displacement. These templates often contain adjacent duplex segments that stabilize base pairing with single-strand gaps or overhangs, but the thermodynamics and kinetics of hybridization in such contexts are poorly understood because of the experimental challenges of probing weak binding and rapid structural dynamics. Here we develop an approach to directly measure the thermodynamics and kinetics of DNA and RNA dinucleotide dehybridization using steady-state and temperature-jump infrared spectroscopy. Our results suggest that dinucleotide binding is stabilized through coaxial stacking interactions with the adjacent duplex segments as well as from potential noncanonical base-pairing configurations and structural dynamics of gap and overhang templates revealed using molecular dynamics simulations. We measure timescales for dissociation ranging from 0.2-40 µs depending on the template and temperature. Dinucleotide hybridization and dehybridization involve a significant free energy barrier with characteristics resembling that of canonical oligonucleotides. Together, our work provides an initial step for predicting the stability and kinetics of hybridization between short nucleic acid segments and various templates.

Mediating Photochemical Reaction Rates at Lewis Acidic Rare Earths by Selective Energy Loss to 4f-Electron States.

Manifesting chemical differences in individual rare earth (RE) element complexes is challenging due to the similar sizes of the tripositive cations and the corelike 4f shell. We disclose a new strategy for differentiating between similarly sized Dy3+ and Y3+ ions through a tailored photochemical reaction of their isostructural complexes in which the f-electron states of Dy3+ act as an energy sink. Complexes RE(hfac)3(NMMO)2 (RE = Dy (2-Dy) and Y (2-Y), hfac = hexafluoroacetylacetonate, and NMMO = N-methylmorpholine-N-oxide) showed variable rates of oxygen atom transfer (OAT) to triphenylphosphine under ultraviolet (UV) irradiation, as monitored by 1H and 19F NMR spectroscopies. Ultrafast transient absorption spectroscopy (TAS) identified the excited state(s) responsible for the photochemical OAT reaction or lack thereof. Competing sensitization pathways leading to excited-state deactivation in 2-Dy through energy transfer to the 4f electron manifold ultimately slows the OAT reaction at this metal cation. The measured rate differences between the open-shell Dy3+ and closed-shell Y3+ complexes demonstrate that using established principles of 4f ion sensitization may deliver new, selective modalities for differentiating the RE elements that do not depend on cation size.

Molecular Latent Space Simulators for Distributed and Multimolecular Trajectories.

All atom molecular dynamics (MD) simulations offer a powerful tool for molecular modeling, but the short time steps required for numerical stability of the integrator place many interesting molecular events out of reach of unbiased simulations. The popular and powerful Markov state modeling (MSM) approach can extend these time scales by stitching together multiple short discontinuous trajectories into a single long-time kinetic model but necessitates a configurational coarse-graining of the phase space that entails a loss of spatial and temporal resolution and an exponential increase in complexity for multimolecular systems. Latent space simulators (LSS) present an alternative formalism that employs a dynamical, as opposed to configurational, coarse graining comprising three back-to-back learning problems to (i) identify the molecular system's slowest dynamical processes, (ii) propagate the microscopic system dynamics within this slow subspace, and (iii) generatively reconstruct the trajectory of the system within the molecular phase space. A trained LSS model can generate temporally and spatially continuous synthetic molecular trajectories at orders of magnitude lower cost than MD to improve sampling of rare transition events and metastable states to reduce statistical uncertainties in thermodynamic and kinetic observables. In this work, we extend the LSS formalism to short discontinuous training trajectories generated by distributed computing and to multimolecular systems without incurring exponential scaling in computational cost. First, we develop a distributed LSS model over thousands of short simulations of a 264-residue proteolysis-targeting chimera (PROTAC) complex to generate ultralong continuous trajectories that identify metastable states and collective variables to inform PROTAC therapeutic design and optimization. Second, we develop a multimolecular LSS architecture to generate physically realistic ultralong trajectories of DNA oligomers that can undergo both duplex hybridization and hairpin folding. These trajectories retain thermodynamic and kinetic characteristics of the training data while providing increased precision of folding populations and time scales across simulation temperature and ion concentration.