Design, synthesis, and pharmacological characterization of sulfonylurea-based NLRP3 inhibitors: Towards an effective therapeutic strategy for Alzheimer's disease.

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder that severely diminishes the quality of life for millions. The NLRP3 inflammasome, a critical mediator of inflammation, has emerged as a promising therapeutic target for AD. In this study, we report the development and optimization of a novel series of sulfonylurea-based NLRP3 inhibitors, with a focus on compound MC1 for the treatment of AD. Utilizing the co-crystal structure of MCC950 in complex with NLRP3 as a guide, we employed a hybrid approach of computer-aided drug design and traditional medicinal chemistry to perform two iterative optimization cycles. This strategy led to the synthesis and evaluation of 40 sulfonylurea derivatives, culminating in the identification of MC1 as the lead candidate. MC1 exhibited enhanced NLRP3 inhibitory activity and demonstrated high binding affinity to NLRP3, effectively blocking NLRP3 activation induced by diverse stimuli such as ATP and Nigericin, without perturbing upstream processes like reactive oxygen species (ROS) generation. In vivo experiments in AD mouse models revealed that MC1 significantly ameliorated cognitive deficits, surpassing the performance of MCC950. Importantly, MC1 showed no signs of hepatotoxicity or adverse effects on the central nervous system. These findings suggest that MC1 holds strong potential as a lead compound for further development in AD therapy, providing a new scaffold for NLRP3 inhibition with improved safety and efficacy profiles.

Deep-Learning-Driven Discovery of SN3-1, a Potent NLRP3 Inhibitor with Therapeutic Potential for Inflammatory Diseases.

The NLRP3 inflammasome plays a central role in the pathogenesis of various intractable human diseases, making it an urgent target for therapeutic intervention. Here, we report the development of SN3-1, a novel orally potent NLRP3 inhibitor, designed through a lead compound strategy centered on deep-learning-based molecular generative models. Our strategy enables rapid fragment enumeration and takes into account the synthetic accessibility of the compounds, thereby significantly enhancing the optimization of lead compounds and facilitating the discovery of potent inhibitors. X-ray crystallography provided insights into the SN3-1 inhibitory mechanism. SN3-1 has shown a favorable safety profile in both acute and chronic toxicity assessments and exhibits robust pharmacokinetic properties. Furthermore, SN3-1 demonstrated significant therapeutic efficacy in various disease models characterized by NLRP3 activation. This study introduces a potent candidate for developing NLRP3 inhibitors and significantly expands the repertoire of tools available for the discovery of novel inhibitors.

Exploring Chemical Reaction Space with Machine Learning Models: Representation and Feature Perspective.

Chemical reactions serve as foundational building blocks for organic chemistry and drug design. In the era of large AI models, data-driven approaches have emerged to innovate the design of novel reactions, optimize existing ones for higher yields, and discover new pathways for synthesizing chemical structures comprehensively. To effectively address these challenges with machine learning models, it is imperative to derive robust and informative representations or engage in feature engineering using extensive data sets of reactions. This work aims to provide a comprehensive review of established reaction featurization approaches, offering insights into the selection of representations and the design of features for a wide array of tasks. The advantages and limitations of employing SMILES, molecular fingerprints, molecular graphs, and physics-based properties are meticulously elaborated. Solutions to bridge the gap between different representations will also be critically evaluated. Additionally, we introduce a new frontier in chemical reaction pretraining, holding promise as an innovative yet unexplored avenue.

Exploring the conformational dynamics and thermodynamics of EGFR S768I and G719X + S768I mutations in non-small cell lung cancer: An in silico approaches.

Non-small cell lung cancer (NSCLC) is often driven by mutations in the epidermal growth factor receptor (EGFR) gene. However, rare mutations such as G719X and S768I lack standard anti-EGFR targeted therapies. Understanding the structural differences between wild-type EGFR and these rare mutants is crucial for developing EGFR-targeted drugs. We performed a systematic analysis using molecular dynamics simulations, essential dynamics (ED), molecular mechanics Poisson-Boltzmann surface area, and free energy calculation methods to compare the kinetic properties, molecular motion, and free energy distribution between wild-type EGFR and the rare mutants' structures G719X-EGFR, S768I-EGFR, and G719X + S768I-EGFR. Our results showed that S768I-EGFR and G719X + S768I-EGFR have higher global and local conformational flexibility and lower thermal and global structural stability than WT-EGFR. ED analysis revealed different molecular motion patterns between S768I-EGFR, G719X + S768I-EGFR, and WT-EGFR. The A-loop and αC-helix, crucial structural elements related to the active state, showed a tendency toward active state development, providing a molecular mechanism explanation for NSCLC caused by EGFR S768I and EGFR G719C + S768I mutations. The present study may be helpful in the development of new EGFR-targeted drugs based on the structure of rare mutations. Our findings may aid in developing new targeted treatments for patients with EGFR S768I and EGFR G719X + S768I mutations.

Long-wave infrared photothermoelectric detectors with ultrahigh polarization sensitivity.

Filter-free miniaturized polarization-sensitive photodetectors have important applications in the next-generation on-chip polarimeters. However, their polarization sensitivity is thus far limited by the intrinsic low diattenuation and inefficient photon-to-electron conversion. Here, we implement experimentally a miniaturized detector based on one-dimensional tellurium nanoribbon, which can significantly improve the photothermoelectric responses by translating the polarization-sensitive absorption into a large temperature gradient together with the finite-size effect of a perfect plasmonic absorber. Our devices exhibit a zero-bias responsivity of 410 V/W and an ultrahigh polarization ratio (2.5 × 104), as well as a peak polarization angle sensitivity of 7.10 V/W•degree, which is one order of magnitude higher than those reported in the literature. Full linear polarimetry detection is also achieved with the proposed device in a simple geometrical configuration. Polarization-coded communication and optical strain measurement are demonstrated showing the great potential of the proposed devices. Our work presents a feasible solution for miniaturized room-temperature infrared photodetectors with ultrahigh polarization sensitivity.

A two-dimensional mid-infrared optoelectronic retina enabling simultaneous perception and encoding.

Infrared machine vision system for object perception and recognition is becoming increasingly important in the Internet of Things era. However, the current system suffers from bulkiness and inefficiency as compared to the human retina with the intelligent and compact neural architecture. Here, we present a retina-inspired mid-infrared (MIR) optoelectronic device based on a two-dimensional (2D) heterostructure for simultaneous data perception and encoding. A single device can perceive the illumination intensity of a MIR stimulus signal, while encoding the intensity into a spike train based on a rate encoding algorithm for subsequent neuromorphic computing with the assistance of an all-optical excitation mechanism, a stochastic near-infrared (NIR) sampling terminal. The device features wide dynamic working range, high encoding precision, and flexible adaption ability to the MIR intensity. Moreover, an inference accuracy more than 96% to MIR MNIST data set encoded by the device is achieved using a trained spiking neural network (SNN).

Photonic Majorana quantum cascade laser with polarization-winding emission.

Topological cavities, whose modes are protected against perturbations, are promising candidates for novel semiconductor laser devices. To date, there have been several demonstrations of topological lasers (TLs) exhibiting robust lasing modes. The possibility of achieving nontrivial beam profiles in TLs has recently been explored in the form of vortex wavefront emissions enabled by a structured optical pump or strong magnetic field, which are inconvenient for device applications. Electrically pumped TLs, by contrast, have attracted attention for their compact footprint and easy on-chip integration with photonic circuits. Here, we experimentally demonstrate an electrically pumped TL based on photonic analogue of a Majorana zero mode (MZM), implemented monolithically on a quantum cascade chip. We show that the MZM emits a cylindrical vector (CV) beam, with a topologically nontrivial polarization profile from a terahertz (THz) semiconductor laser.

On-chip mid-infrared photothermoelectric detectors for full-Stokes detection.

On-chip polarimeters are highly desirable for the next-generation ultra-compact optical and optoelectronic systems. Polarization-sensitive photodetectors relying on anisotropic absorption of natural/artificial materials have emerged as a promising candidate for on-chip polarimeters owing to their filterless configurations. However, these photodetectors can only be applied for detection of either linearly or circularly polarized light, not applicable for full-Stokes detection. Here, we propose and demonstrate three-ports polarimeters comprising on-chip chiral plasmonic metamaterial-mediated mid-infrared photodetectors for full-Stokes detection. By manipulating the spatial distribution of chiral metamaterials, we could convert polarization-resolved absorptions to corresponding polarization-resolved photovoltages of three ports through the photothermoelectric effect. We utilize the developed polarimeter in an imaging demonstration showing reliable ability for polarization reconstruction. Our work provides an alternative strategy for developing polarization-resolved photodetectors with a bandgap-independent operation range in the mid-infrared.

Chemical Space, Scaffolds, and Halogenated Compounds of CMNPD: A Comprehensive Chemoinformatic Analysis.

The comprehensive marine natural products database (CMNPD) is a new free access and comprehensive database developed originally by Lyu's team of our research group, including more than 30 000 marine natural products (MNPs) reported from the 1960s. In this article, we aimed to present CMNPD's value in drug discovery and to present several characteristics of MNPs based on our new comprehensive data. We used chemoinformatic analysis methods to report the molecular properties, chemical space, and several scaffold assessments of CMNPD compared with several databases. Then, we reported the characteristics of MNPs from the aspect of halogens, comparing MNPs with terrestrial natural products (TNPs) and drugs. We found that CMNPD had a low proportion (2.91%) of scaffolds utilized by drugs, and high similarities between CMNPD and NPAtlas (a microbial natural products database), which are worth further investigation. The proportion of bromides in MNPs is outstandingly higher (11.0%) in contrast to other halogens. Furthermore, the results showed great differences in halogenated structures between MNPs and drugs, especially brominated substructures. Finally, we found that many marine species (2.52%) reported only halogenated compounds. It can be concluded from these results that CMNPD is a promising source for drug discovery and has many scientific issues relative to MNPs that need to be further investigated.

Tailorable infrared emission of microelectromechanical system-based thermal emitters with NiO films for gas sensing.

Infrared gas sensors hold great promise in the internet of things and artificial intelligence. Making infrared light sources with miniaturized size, reliable and tunable emission is essential but remains challenging. Herein, we present the tailorability of radiant power and the emergence of new emission wavelength of microelectromechanical system (MEMS)-based thermal emitters with nickel oxide (NiO) films. The coating of NiO on emitters increases top surface emissivity and induces the appearance of new wavelengths between 15 and 19 µm, all of which have been justified by spectroscopic methods. Furthermore, a sensor array is assembled for simultaneous monitoring of concentrations of carbon dioxide (CO2), methane (CH4), humidity, and temperature. The platform shows selective and sensitive detection at room temperature toward CO2 and CH4 with detection limits of around 50 and 1750 ppm, respectively, and also shows fast response/recovery and good recyclability. The demonstrated emission tailorability of MEMS emitters and their usage in sensor array provide novel insights for designing and fabricating optical sensors with good performance, which is promising for mass production and commercialization.

Target Prediction Model for Natural Products Using Transfer Learning.

A large proportion of lead compounds are derived from natural products. However, most natural products have not been fully tested for their targets. To help resolve this problem, a model using transfer learning was built to predict targets for natural products. The model was pre-trained on a processed ChEMBL dataset and then fine-tuned on a natural product dataset. Benefitting from transfer learning and the data balancing technique, the model achieved a highly promising area under the receiver operating characteristic curve (AUROC) score of 0.910, with limited task-related training samples. Since the embedding distribution difference is reduced, embedding space analysis demonstrates that the model's outputs of natural products are reliable. Case studies have proved our model's performance in drug datasets. The fine-tuned model can successfully output all the targets of 62 drugs. Compared with a previous study, our model achieved better results in terms of both AUROC validation and its success rate for obtaining active targets among the top ones. The target prediction model using transfer learning can be applied in the field of natural product-based drug discovery and has the potential to find more lead compounds or to assist researchers in drug repurposing.

Radiological Society of North America/Quantitative Imaging Biomarker Alliance Shear Wave Speed Bias Quantification in Elastic and Viscoelastic Phantoms.

OBJECTIVES: To quantify the bias of shear wave speed (SWS) measurements between different commercial ultrasonic shear elasticity systems and a magnetic resonance elastography (MRE) system in elastic and viscoelastic phantoms. METHODS: Two elastic phantoms, representing healthy through fibrotic liver, were measured with 5 different ultrasound platforms, and 3 viscoelastic phantoms, representing healthy through fibrotic liver tissue, were measured with 12 different ultrasound platforms. Measurements were performed with different systems at different sites, at 3 focal depths, and with different appraisers. The SWS bias across the systems was quantified as a function of the system, site, focal depth, and appraiser. A single MRE research system was also used to characterize these phantoms using discrete frequencies from 60 to 500 Hz. RESULTS: The SWS from different systems had mean difference 95% confidence intervals of ±0.145 m/s (±9.6%) across both elastic phantoms and ± 0.340 m/s (±15.3%) across the viscoelastic phantoms. The focal depth and appraiser were less significant sources of SWS variability than the system and site. Magnetic resonance elastography best matched the ultrasonic SWS in the viscoelastic phantoms using a 140 Hz source but had a - 0.27 ± 0.027-m/s (-12.2% ± 1.2%) bias when using the clinically implemented 60-Hz vibration source. CONCLUSIONS: Shear wave speed reconstruction across different manufacturer systems is more consistent in elastic than viscoelastic phantoms, with a mean difference bias of < ±10% in all cases. Magnetic resonance elastographic measurements in the elastic and viscoelastic phantoms best match the ultrasound systems with a 140-Hz excitation but have a significant negative bias operating at 60 Hz. This study establishes a foundation for meaningful comparison of SWS measurements made with different platforms.

CMNPD: a comprehensive marine natural products database towards facilitating drug discovery from the ocean.

Marine organisms are expected to be an important source of inspiration for drug discovery after terrestrial plants and microorganisms. Despite the remarkable progress in the field of marine natural products (MNPs) chemistry, there are only a few open access databases dedicated to MNPs research. To meet the growing demand for mining and sharing for MNPs-related data resources, we developed CMNPD, a comprehensive marine natural products database based on manually curated data. CMNPD currently contains more than 31 000 chemical entities with various physicochemical and pharmacokinetic properties, standardized biological activity data, systematic taxonomy and geographical distribution of source organisms, and detailed literature citations. It is an integrated platform for structure dereplication (assessment of novelty) of (marine) natural products, discovery of lead compounds, data mining of structure-activity relationships and investigation of chemical ecology. Access is available through a user-friendly web interface at https://www.cmnpd.org. We are committed to providing a free data sharing platform for not only professional MNPs researchers but also the broader scientific community to facilitate drug discovery from the ocean.

Far-field controllable excitation of phonon polariton via nanostructure engineering.

Hexagonal boron nitride (h-BN) as a natural mid-infrared (mid-IR) hyperbolic material which supports a strong excitation of phonon polariton (PhP) has enabled a new class of photonic devices with unprecedented functionalities. The hyperbolic property of h-BN has not only brought in new physical insights but also spurred potential applications. However, most of the current h-BN devices are designed repying on near-field excitation and manipulation of PhP. For fully realizing the potentials of h-BN, research on far-field controllable excitation and control of PhP is important for future integrated photonic devices. In this work, we exploit the designs of controllable far-field excitation of PhP in nanostructure-patterned h-BN thin film for deep subwavelength focusing (FWHM∼λ0/14.9) and interference patterns of 1D (FWHM∼λ0/52) and 2D standing waves (FWHM∼λ0/36.8) which find great potential for super-resolution imaging beyond diffraction limit. These polaritonic patterns could be easily tuned remotely by manipulating the polarization and phase of incident laser. This approach provides a novel platform for practical IR nanophotonic devices and potential applications in mid-IR bio-imaging and sensing.

Association of ANRIL polymorphisms with coronary artery disease: A systemic meta-analysis.

BACKGROUND: The long noncoding RNAs have gradually been reported to be an important class of RNAs with pivotal roles in the development and progression of myocardial infarction (MI). In this study, we hypothesized that genetic variant of cyclin-dependent kinase inhibitor 2B antisense RNA (ANRIL) may affect the prognosis of MI patients. METHODS: A systematic review and meta-analysis of studies including 11,269 cases and 10,707 controls on the association of 5 ANRIL single nucleotide polymorphism and the overall risk of MI or coronary artery disease (CAD) was performed. RESULTS: In the meta-analysis, rs4977574 A > G, rs1333040 C > T, rs1333042 A > G and rs10757274 A > G ANRIL polymorphisms were correlated with overall MI or CAD risk. No significant associations were found between ANRIL rs1333049 G > C polymorphism and CAD risk. CONCLUSIONS: The results indicated that ANRIL polymorphism (rs4977574, rs1333040, rs1333042, and rs10757274) were more generally associated with CAD or MI risk. Further experimental studies to evaluate the limits of this hypothesis are warranted, and future functional studies are required to clarify the possible mechanisms.

Resonant nanostructures for highly confined and ultra-sensitive surface phonon-polaritons.

Plasmonics on metal-dielectric interfaces was widely seen as the main route for miniaturization of components and interconnect of photonic circuits. However recently, ultra-confined surface phonon-polaritonics in high-index chalcogenide films of nanometric thickness has emerged as an important alternative to plasmonics. Here, using mid-IR near-field imaging we demonstrate tunable surface phonon-polaritons in CMOS-compatible interfaces of few-nm thick germanium on silicon carbide. We show that Ge-SiC resonators with nanoscale footprint can support sheet and edge surface modes excited at the free space wavelength hundred times larger than their physical dimensions. Owing to the surface nature of the modes, the sensitivity of real-space polaritonic patterns provides pathway for local detection of the interface composition change at sub-nanometer level. Such deeply subwavelength resonators are of interest for high-density optoelectronic applications, filters, dispersion control and optical delay devices.

Electrically pumped topological laser with valley edge modes.

Quantum cascade lasers are compact, electrically pumped light sources in the technologically important mid-infrared and terahertz region of the electromagnetic spectrum1,2. Recently, the concept of topology3 has been expanded from condensed matter physics into photonics4, giving rise to a new type of lasing5-8 using topologically protected photonic modes that can efficiently bypass corners and defects4. Previous demonstrations of topological lasers have required an external laser source for optical pumping and have operated in the conventional optical frequency regime5-8. Here we demonstrate an electrically pumped terahertz quantum cascade laser based on topologically protected valley edge states9-11. Unlike topological lasers that rely on large-scale features to impart topological protection, our compact design makes use of the valley degree of freedom in photonic crystals10,11, analogous to two-dimensional gapped valleytronic materials12. Lasing with regularly spaced emission peaks occurs in a sharp-cornered triangular cavity, even if perturbations are introduced into the underlying structure, owing to the existence of topologically protected valley edge states that circulate around the cavity without experiencing localization. We probe the properties of the topological lasing modes by adding different outcouplers to the topological cavity. The laser based on valley edge states may open routes to the practical use of topological protection in electrically driven laser sources.

Radiation Enhancement by Graphene Oxide on Microelectromechanical System Emitters for Highly Selective Gas Sensing.

Infrared gas sensors have been proven promising for broad applications in Internet of Things and Industrial Internet of Things. However, the lack of miniaturized light sources with good compatibility and tunable spectral features hinders their widespread utilization. Herein, a strategy is proposed to increase the radiated power from microelectromechanical-based thermal emitters by coating with graphene oxide (GO). The radiation can be substantially enhanced, which partially stems from the high emissivity of GO coating demonstrated by spectroscopic methods. Moreover, the sp2 structure within GO may induce plasmons and thus couple with photons to produce blackbody radiation and/or new thermal emission sources. As a proof-of-concept demonstration, the GO-coated emitter is integrated into a multifunctional monitoring platform and evaluated for gas detection. The platform exhibits sensitive and highly selective detection toward CO2 at room temperature with a detection limit of 50 ppm and short response/recovery time, outperforming the state-of-the-art gas sensors. This study demonstrates the emission tailorability of thermal emitters and the feasibility of improving the associated gas sensing property, offering perspectives for designing and fabricating high-end optical sensors with cost-effectiveness and superior performance.