Graphene via Microwave Expansion of Graphite Followed by Cryo-Quenching and its Application in Electrostatic Droplet Switching.

Monoelemental atomic sheets (Xenes) and other 2D materials offer record electronic mobility, high thermal conductivity, excellent Young's moduli, optical transparency, and flexural capability, revolutionizing ultrasensitive devices and enhancing performance. The ideal synthesis of these quantum materials should be facile, fast, scalable, reproducible, and green. Microwave expansion followed by cryoquenching (MECQ) leverages thermal stress in graphite to produce high-purity graphene within minutes. MECQ synthesis of graphene is reported at 640 and 800 W for 10 min, followed by liquid nitrogen quenching for 5 and 90 min of sonication. Microscopic and spectroscopic analyses confirmed the chemical identity and phase purity of monolayers and few-layered graphene sheets (200-12 µm). Higher microwave power yields thinner layers with enhanced purity. Molecular dynamics simulations and DFT calculations support the exfoliation under these conditions. Electrostatic droplet switching is demonstrated using MECQ-synthesized graphene, observing electrorolling of a mercury droplet on a BN/graphene interface at voltages above 20 V. This technique can inspire the synthesis of other 2D materials with high purity and enable new applications.

Rapid Semiconducting Supramolecular Mg(II)-Metallohydrogel: Exploring Its Potential in Nonvolatile Resistive Switching Applications and Antiseptic Wound Healing Properties.

An effective strategy was employed for the rapid development of a supramolecular metallohydrogel of Mg(II) ion (i.e., Mg@PEHA) using pentaethylenehexamine (PEHA) as a low-molecular-weight gelator in aqueous medium under ambient conditions. The mechanical stability of the synthesized Mg@PEHA metallohydrogel was characterized by using rheological analysis, which showed its robustness across different angular frequencies and oscillator stress levels. The metallohydrogel exhibited excellent thixotropic behavior, which signifies that Mg@PEHA has a self-healing nature. Field emission scanning electron microscopy and transmission electron microscopy images were utilized to explore the rectangular pebble-like hierarchical network of the Mg@PEHA metallohydrogel. Elemental mapping through energy-dispersive X-ray spectroscopy analysis confirmed the presence of primary chemical constituents in the metallohydrogel. Fourier transform infrared spectroscopy spectroscopy provided insights into the possible formation strategy of the metallohydrogel. In this work, Schottky diode structures in a metal-semiconductor-metal geometry based on a magnesium(II) metallohydrogel (Mg@PEHA) were constructed, and the charge transport behavior was observed. Additionally, a resistive random access memory (RRAM) device was developed using Mg@PEHA, which displayed bipolar resistive switching behavior at room temperature. The researchers investigated the switching mechanism, which involved the formation or rupture of conduction filaments, to gain insights into the resistive switching process. The RRAM device demonstrated excellent performance with a high ON/OFF ratio of approximately 100 and remarkable endurance of over 5000 switching cycles. RRAM devices exhibit good endurance, meaning they can endure a large number of read and write cycles without significant degradation in performance. RRAM devices have shown promising reliability in terms of long-term performance and stability, making them suitable for critical applications that require reliable memory solutions. Significant inhibitory activity against the drug-resistant Klebsiella pneumonia strain and its biofilm formation ability was demonstrated by Mg@PEHA. The minimum inhibitory concentration value of the metallohydrogel was determined to be 3 mg/mL when it was dissolved in 1% DMSO. To study the antibiofilm activity, an MTT assay was performed, revealing that biofilm inhibition (60%) commenced at 1 mg/mL of Mg@PEHA when dissolved in 1% DMSO. Moreover, in the mouse excisional wound model, Mg@PEHA played a crucial role in preventing postoperative wound infections and promoting wound healing.

Characterization of Regolith And Trace Economic Resources (CRATER): An Orbitrap-based laser desorption mass spectrometry instrument for in situ exploration of the Moon.

RATIONALE: Characterization of Regolith And Trace Economic Resources (CRATER), an Orbitrap™-based laser desorption mass spectrometry instrument designed to conduct high-precision, spatially resolved analyses of planetary materials, is capable of answering outstanding science questions about the Moon's formation and the subsequent processes that have modified its (sub)surface. METHODS: Here, we describe the baseline design of the CRATER flight model, which requires <20 000 cm3  volume, <10 kg mass, and <60 W peak power. The analytical capabilities and performance metrics of a prototype that meets the full functionality of the flight model are demonstrated. RESULTS: The instrument comprises a high-power, solid-state, pulsed ultraviolet (213 nm) laser source to ablate the surface of the lunar sample, a custom ion optical interface to accelerate and collimate the ions produced at the ablation site, and an Orbitrap mass analyzer capable of discriminating competing isobars via ultrahigh mass resolution and high mass accuracy. The CRATER instrument can measure elemental and isotopic abundances and characterize the organic content of lunar surface samples, as well as identify economically valuable resources for future exploration. CONCLUSION: An engineering test unit of the flight model is currently in development to serve as a pathfinder for near-term mission opportunities.

Prediction of Postoperative Urinary Tract Infection Following Benign Gynecologic Surgery.

INTRODUCTION AND HYPOTHESIS: The objective was to develop a prediction model for urinary tract infection (UTI) after pelvic surgery. METHODS: We utilized data from three tertiary care centers of women undergoing pelvic surgery. The primary outcome was a UTI within 8 weeks of surgery. Additional variables collected included procedural data, severity of prolapse, use of mesh, anti-incontinence surgery, EBL, diabetes, steroid use, estrogen use, postoperative catheter use, PVR, history of recurrent UTI, operative time, comorbidities, and postoperative morbidity including venous thromboembolism, surgical site infection. Two datasets were used for internal validation, whereas a third dataset was used for external validation. Algorithms that tested included the following: multivariable logistic regression, decision trees (DTs), naive Bayes (NB), random forest (RF), gradient boosting (GB), and multilayer perceptron (MP). RESULTS: For the training dataset, containing both University of British Columbia and Mayo Clinic Rochester data, there were 1,657 patients, with 172 (10.4%) UTIs; whereas for the University of Calgary external validation data, there were a total of 392 patients with a UTI rate of 16.1% (n = 63). All models performed well; however, the GB, DT, and RF models all had an area under the curve (AUC) > 0.97. With external validation the model retained high discriminatory ability, DT: AUC = 0.88, RF: AUC = 0.88, and GB: AUC = 0.90. CONCLUSIONS: A model with high discriminatory ability can predict UTI within 8 weeks of pelvic surgery. Future studies should focus on prospective validation and application of randomized trial models to test the utility of this model in the prevention of postoperative UTI.

Are Deep Learning Structural Models Sufficiently Accurate for Virtual Screening? Application of Docking Algorithms to AlphaFold2 Predicted Structures.

Machine learning-based protein structure prediction algorithms, such as RosettaFold and AlphaFold2, have greatly impacted the structural biology field, arousing a fair amount of discussion around their potential role in drug discovery. While there are few preliminary studies addressing the usage of these models in virtual screening, none of them focus on the prospect of hit-finding in a real-world virtual screen with a model based on low prior structural information. In order to address this, we have developed an AlphaFold2 version where we exclude all structural templates with more than 30% sequence identity from the model-building process. In a previous study, we used those models in conjunction with state-of-the-art free energy perturbation methods and demonstrated that it is possible to obtain quantitatively accurate results. In this work, we focus on using these structures in rigid receptor-ligand docking studies. Our results indicate that using out-of-the-box Alphafold2 models is not an ideal scenario for virtual screening campaigns; in fact, we strongly recommend to include some post-processing modeling to drive the binding site into a more realistic holo model.

Development of a machine learning-based predictive model for prediction of success or failure of medical management for benign prostatic hyperplasia.

OBJECTIVE: To develop a novel predictive model for identifying patients who will and will not respond to the medical management of benign prostatic hyperplasia (BPH). METHODS: Using data from the Medical Therapy of Prostatic Symptoms (MTOPS) study, several models were constructed using an initial data set of 2172 patients with BPH who were treated with doxazosin (Group 1), finasteride (Group 2), and combination therapy (Group 3). K-fold stratified cross-validation was performed on each group, Within each group, feature selection and dimensionality reduction using nonnegative matrix factorization (NMF) were performed based on the training data, before several machine learning algorithms were tested; the most accurate models, boosted support vector machines (SVMs), being selected for further refinement. The area under the receiver operating curve (AUC) was calculated and used to determine the optimal operating points. Patients were classified as treatment failures or responders, based on whether they fell below or above the AUC threshold for each group and for the whole data set. RESULTS: For the entire cohort, the AUC for the boosted SVM model was 0.698. For patients in Group 1, the AUC was 0.729, for Group 2, the AUC was 0.719, and for Group 3, the AUC was 0.698. CONCLUSION: Using MTOPS data, we were able to develop a prediction model with an acceptable rate of discrimination of medical management success for BPH.

On the Relationship between Feature Selection Metrics and Accuracy.

Feature selection metrics are commonly used in the machine learning pipeline to rank and select features before creating a predictive model. While many different metrics have been proposed for feature selection, final models are often evaluated by accuracy. In this paper, we consider the relationship between common feature selection metrics and accuracy. In particular, we focus on misorderings: cases where a feature selection metric may rank features differently than accuracy would. We analytically investigate the frequency of misordering for a variety of feature selection metrics as a function of parameters that represent how a feature partitions the data. Our analysis reveals that different metrics have systematic differences in how likely they are to misorder features which can happen over a wide range of partition parameters. We then perform an empirical evaluation with different feature selection metrics on several real-world datasets to measure misordering. Our empirical results generally match our analytical results, illustrating that misordering features happens in practice and can provide some insight into the performance of feature selection metrics.

A Wide Bandgap Semiconducting Magnesium Hydrogel: Moisture Harvest, Iodine Sequestration, and Resistive Switching.

Water harvesting from the ubiquitous moisture is pivotal for delivering fresh water to earth's arid/semiarid regions, and sequestration of iodine from the solution is crucial for environmental safety due to its severe effect on human metabolic processes. In this context, herein, a multifunctional supramolecular metallohydrogel (Mg@TAEA) is synthesized through direct mixing of magnesium nitrate hexahydrate and the low molecular weight gelator tris(2-aminoethyl)amine. Electron microscopy reveals that Mg@TAEA is sculptured in vertically grown well-oriented micrometer-sized flakes. The porous crystalline material (52 m2/g) was found to be an efficacious host matrix for water harvesting from moisture (847 mg/g). Mg@TAEA shows effective (513 mg/g) iodine sequestration from solution and adsorption of carbon dioxide (15 mg/g). The wide bandgap semiconducting Mg@TAEA (3.6 eV) material is a potential candidate for building memory devices, and the Ion/Ioff ratio of the device based on the indium tin oxide (ITO)/Mg@TAEA/Ag heterostructure was found to be ∼62. We further extended our work by analyzing the charge transport properties of the system and found space charge limited conduction (SCLC) and trap-filled SCLC to be responsible for the nonlinear transport behavior observed in the device.

Are Deep Learning Structural Models Sufficiently Accurate for Free-Energy Calculations? Application of FEP+ to AlphaFold2-Predicted Structures.

The availability of AlphaFold2 has led to great excitement in the scientific community─particularly among drug hunters─due to the ability of the algorithm to predict protein structures with high accuracy. However, beyond globally accurate protein structure prediction, it remains to be determined whether ligand binding sites are predicted with sufficient accuracy in these structures to be useful in supporting computationally driven drug discovery programs. We explored this question by performing free-energy perturbation (FEP) calculations on a set of well-studied protein-ligand complexes, where AlphaFold2 predictions were performed by removing all templates with >30% identity to the target protein from the training set. We observed that in most cases, the ΔΔG values for ligand transformations calculated with FEP, using these prospective AlphaFold2 structures, were comparable in accuracy to the corresponding calculations previously carried out using crystal structures. We conclude that under the right circumstances, AlphaFold2-modeled structures are accurate enough to be used by physics-based methods such as FEP in typical lead optimization stages of a drug discovery program.

Quantum-coupled borophene-based heterolayers for excitonic and molecular sensing applications.

Borophene (B), with remarkably unique chemical binding in its crystallographic structural phases including anisotropic structures, theoretically has high Young's modulus and thermal conductivity. Moreover, it is metallic in nature, and has recently joined the family of two-dimensional (2D) materials and is poised to be employed in flexible hetero-layered devices and sensors in fast electronic gadgets and excitonic devices. Interfacial coupling helps individual atomic sheets synergistically work in tandem, and is very crucial in controllable functionality. Most of the microscopic and spectroscopic scans reveal surface information; however, information regarding interfacial coupling is difficult to obtain. Electronic signatures of dynamic inter-layer coupling in B/boron nitride (BN) and B/molybdenum disulfide (MoS2) have been detected in the form of distinct peaks in differential current signals obtained from scanning tunneling spectroscopy (STS) and conducting atomic force microscopy (CAFM). These unique sets of observed peaks represent interfacial coupling quantum states. The peaks in the electronic density of states (DOS) obtained via density functional theory (DFT) band structure calculations matched well with the electronic signatures of coupling quantum states. In our calculations, we found that the DOS peak evolves when the component layers are brought to compromised distances. While B/BN exhibits green sensitivity indicating mid-gap formation, B/MoS2 bestows red sensitivity indicating band-gap excitation of MoS2. Molecular detection of methylene blue (MB) based on surface-enhanced Raman spectroscopy (SERS) was carried out with borophene-based hetero-layered stacks as molecular anchoring platforms.

Targeted Degradation of the Oncogenic Phosphatase SHP2.

SHP2 is a protein tyrosine phosphatase that plays a critical role in the full activation of the Ras-MAPK pathway upon stimulation of receptor tyrosine kinases, which are frequently amplified or mutationally activated in human cancer. In addition, activating mutations in SHP2 result in developmental disorders and hematologic malignancies. Several allosteric inhibitors have been developed for SHP2 and are currently in clinical trials. Here, we report the development and evaluation of a SHP2 PROTAC created by conjugating RMC-4550 with pomalidomide using a PEG linker. This molecule is highly selective for SHP2, induces degradation of SHP2 in leukemic cells at submicromolar concentrations, inhibits MAPK signaling, and suppresses cancer cell growth. SHP2 PROTACs serve as an alternative strategy for targeting ERK-dependent cancers and are useful tools alongside allosteric inhibitors for dissecting the mechanisms by which SHP2 exerts its oncogenic activity.

Esthetic management of fused incisors with ceramic veneers.

This clinical report describes a patient for whom single veneers with pink staining were used on fused maxillary incisors to camouflage and improve dental appearance.

Activation loop targeting strategy for design of receptor-interacting protein kinase 2 (RIPK2) inhibitors.

Development of selective kinase inhibitors remains a challenge due to considerable amino acid sequence similarity among family members particularly in the ATP binding site. Targeting the activation loop might offer improved inhibitor selectivity since this region of kinases is less conserved. However, the strategy presents difficulties due to activation loop flexibility. Herein, we report the design of receptor-interacting protein kinase 2 (RIPK2) inhibitors based on pan-kinase inhibitor regorafenib that aim to engage basic activation loop residues Lys169 or Arg171. We report development of CSR35 that displayed >10-fold selective inhibition of RIPK2 versus VEGFR2, the target of regorafenib. A co-crystal structure of CSR35 with RIPK2 revealed a resolved activation loop with an ionic interaction between the carboxylic acid installed in the inhibitor and the side-chain of Lys169. Our data provides principle feasibility of developing activation loop targeting type II inhibitors as a complementary strategy for achieving improved selectivity.

Targeting prostate cancer with compounds possessing dual activity as androgen receptor antagonists and HDAC6 inhibitors.

While enzalutamide and abiraterone are approved for treatment of metastatic castration-resistant prostate cancer (mCRPC), approximately 20-40% of patients have no response to these agents. It has been stipulated that the lack of response and the development of secondary resistance to these drugs may be due to the presence of AR splice variants. HDAC6 has a role in regulating the androgen receptor (AR) by modulating heat shock protein 90 (Hsp90) acetylation, which controls the nuclear localization and activation of the AR in androgen-dependent and independent scenarios. With dual-acting AR-HDAC6 inhibitors it should be possible to target patients who don't respond to enzalutamide. Herein, we describe the design, synthesis and biological evaluation of dual-acting compounds which target AR and are also specific towards HDAC6. Our efforts led to compound 10 which was found to have potent dual activity (HDAC6 IC50=0.0356µM and AR binding IC50=<0.03µM). Compound 10 was further evaluated for antagonist and other cell-based activities, in vitro stability and pharmacokinetics.

The Parkinson disease-linked LRRK2 protein mutation I2020T stabilizes an active state conformation leading to increased kinase activity.

The effect of leucine-rich repeat kinase 2 (LRRK2) mutation I2020T on its kinase activity has been controversial, with both increased and decreased effects being reported. We conducted steady-state and pre-steady-state kinetic studies on LRRKtide and its analog LRRKtide(S). Their phosphorylation differs by the rate-limiting steps: product release is rate-limiting for LRRKtide and phosphoryl transfer is rate-limiting for LRRKtide(S). As a result, we observed that the I2020T mutant is more active than wild type (WT) LRRK2 for LRRKtide(S) phosphorylation, whereas it is less active than WT for LRRKtide phosphorylation. Our pre-steady-state kinetic data suggest that (i) the I2020T mutant accelerates the rates of phosphoryl transfer of both reactions by 3-7-fold; (ii) this increase is masked by a rate-limiting product release step for LRRKtide phosphorylation; and (iii) the observed lower activity of the mutant for LRRKtide phosphorylation is a consequence of its instability: the concentration of the active form of the mutant is 3-fold lower than WT. The I2020T mutant has a dramatically low KATP and therefore leads to resistance to ATP competitive inhibitors. Two well known DFG-out or type II inhibitors are also weaker toward the mutant because they inhibit the mutant in an unexpected ATP competitive mechanism. The I2020 residue lies next to the DYG motif of the activation loop of the LRRK2 kinase domain. Our modeling and metadynamic simulations suggest that the I2020T mutant stabilizes the DYG-in active conformation and creates an unusual allosteric pocket that can bind type II inhibitors but in an ATP competitive fashion.

Discovery of LRRK2 inhibitors using sequential in silico joint pharmacophore space (JPS) and ensemble docking.

Joint pharmacophore space (JPS), ensemble docking and sequential JPS-ensemble docking were used to select three panels of compounds (10 per panel) for evaluation as LRRK2 inhibitors. These computational methods identified four LRRK2 inhibitors with IC50 values <12µM. The sequential JPS-ensemble docking predicted the majority of active hits. One of the inhibitors (Z-8205) identified using this method was also found to inhibit the G2019S mutant of LRRK2 25-fold better than wild-type enzyme. This bias for the G2019S mutant is proposed to arise from an interaction with S2019 in this form of the enzyme. In addition, Z-8205 was found to only inhibit one other kinase when profiled against a panel of 97 kinases at 10µM.

Machine Learning-Assisted Exploration of Intrinsically Spin-Ordered Two-Dimensional (2D) Nanomagnets.

The existence of spontaneous spin-ordering in two-dimensional (2D) nanomagnets holds significant importance due to their several unique and promising properties that distinguish them from conventional 2D materials. In recent times, machine learning (ML) has emerged as a powerful tool for effectively exploring and identifying the optimal 2D materials for specific applications or properties within a limited span of time. Here, we have introduced ML-accelerated approaches to specifically estimate the properties, such as the HSE bandgap and magnetoanisotropic energy (MAE) of 2D magnetic materials. Supervised ML algorithms were employed to derive the descriptors that are capable of predicting the properties of intrinsic 2D magnetic materials. Furthermore, the feature selection score is also calculated to reduce the feature space complexity and improve the model accuracy. The input features were obtained from the C2DB database, and models were constructed using linear regression, Lasso, decision tree, random forest, XG Boost, and support vector machine algorithms. The random forest model predicted the HSE band gaps with an unprecedented low root-mean-square error (RMSE) of 0.22 eV, while the linear regression gives the best fit with RMSEs of 0.25 and 0.22 meV for the MAE(x) and MAE(y), respectively. Therefore, the integration of interpretable analytical models with density functional theory offers a swift and reliable approach for uncovering the properties of intrinsic 2D magnetic materials. This collaborative methodology not only ensures speed in analysis but also enriches the material space.

CrXY (X/Y = S, Se, Te) monolayers as efficient anode materials for Li and Na-ion batteries: a first-principles study.

Over the last decade, two-dimensional (2D) materials have been of great interest in the energy storage field. Large-scale electrochemical energy storage is based on the intercalation of metal ions in layered materials having van der Waals gaps. In this work, by means of first-principles calculations, we explored the use of 2D Janus transition metal dichalcogenides (TMDs) CrSSe, CrSTe and CrSeTe as anode materials for lithium and sodium-ion batteries. To examine the electronic properties and electrochemical performance, density functional theory (DFT) calculation was used. Our research shows that lithium diffuses easily with short diffusion distances and prefers to bind effectively to the monolayer. These structures are metallic in their bare phases. The highest adsorption energy shown by CrSSe, CrSTe, and CrSeTe is -1.86 eV, -1.66 eV, -2.15 eV with a low diffusion barrier of 0.3 eV, 0.6 eV, and 0.1 eV for the Li atoms and 0.54 eV, 0.32 eV and 0.15 eV for the Na atoms, respectively. At different chemical stoichiometries, we discovered negligible average open-circuit voltages of 1.0 V, 0.52 V, 0.6 V for lithium and 0.1 V, 0.49 V, and 0.51 V for sodium atoms respectively. The storage capacities shown by CrSSe, CrSTe, and CrSeTe are 348 mA h g-1, 254 mA h g-1, 208 mA h g-1 for the Li atoms and 260 mA h g-1, 198 mA h g-1, 177 mA h g-1 for the Na atoms respectively.

Twistronics in two-dimensional transition metal dichalcogenide (TMD)-based van der Waals interface.

Transition metal dichalcogenides (TMD) based heterostructures have gained significant attention lately because of their distinct physical properties and potential uses in electronics and optoelectronics. In the present work, the effects of twist on the structural, electronic, and optical properties (such as the static dielectric constant, refractive index, extinction coefficient, and absorption coefficient) of vertically stacked TMD heterostructures, namely MoSe2/WSe2, WS2/WSe2, MoSe2/WS2 and MoS2/WSe2, have been systematically studied and a thorough comparison is done among these heterostructures. In addition, the absence of negative frequency in the phonon dispersion curve and a low formation energy confirm the structural and thermodynamical stability of all the proposed TMD heterostructures. The calculations are performed using first-principles-based density functional theory (DFT) method. Beautiful Moiré patterns are formed due to the relative rotation of the layers as a consequence of the superposition of the periodic structures of the TMDs on each other. Twist engineering allows the modulation of bandgaps and a phase change from direct to indirect band gap semiconductors as well. The high optical absorption in the visible range of spectrum makes these twisted heterostructures very promising candidates in photovoltaic applications.

Development of a novel self-healing Zn(II)-metallohydrogel with wide bandgap semiconducting properties for non-volatile memory device application.

A rapid and effective strategy has been devised for the swift development of a Zn(II)-ion-based supramolecular metallohydrogel, termed Zn@PEH, using pentaethylenehexamine as a low molecular weight gelator. This process occurs in an aqueous medium at room temperature and atmospheric pressure. The mechanical strength of the synthesized Zn@PEH metallohydrogel has been assessed through rheological analysis, considering angular frequency and oscillator stress dependencies. Notably, the Zn@PEH metallohydrogel exhibits exceptional self-healing abilities and can bear substantial loads, which have been characterized through thixotropic analysis. Additionally, this metallohydrogel displays injectable properties. The structural arrangement resembling pebbles within the hierarchical network of the supramolecular Zn@PEH metallohydrogel has been explored using FESEM and TEM measurements. EDX elemental mapping has confirmed the primary chemical constituents of the metallohydrogel. The formation mechanism of the metallohydrogel has been analyzed via FT-IR spectroscopy. Furthermore, zinc(II) metallohydrogel (Zn@PEH)-based Schottky diode structure has been fabricated in a lateral metal-semiconductor-metal configuration and  it's charge transport behavior has also been studied. Notably, the zinc(II) metallohydrogel-based resistive random access memory (RRAM) device (Zn@PEH) demonstrates bipolar resistive switching behavior at room temperature. This RRAM device showcases remarkable switching endurance over 1000 consecutive cycles and a high ON/OFF ratio of approximately 270. Further, 2 × 2 crossbar array of the RRAM devices were designed to demonstrate OR and NOT logic circuit operations, which can be extended for performing higher order computing operations. These structures hold promise for applications in non-volatile memory design, neuromorphic and in-memory computing, flexible electronics, and optoelectronic devices due to their straightforward fabrication process, robust resistive switching behavior, and overall system stability.