Unraveling Cordia myxa's anti-malarial potential: integrative insights from network pharmacology, molecular modeling, and machine learning.

Malaria is a potentially fatal infective illness caused due to parasites that belong to the Plasmodium genus, which are transferred to humans with the help of the stings of affected female Anopheles mosquitoes, and it persists as a serious public wellness problem worldwide. Cordia myxa is a medicinal plant that possesses various medicinal characteristics like antimicrobial, anti-inflammation, antioxidant, and antidiabetic activities, which makes it an important natural resource for the therapy of different maladies in traditional medicine. In this investigation, a certain network pharmacology method has been utilized to identify the potent active components, possible targets as well as signaling pathways present in C. myxa in relation to malaria therapy. The active compounds were submitted to molecular docking approaches to validate their successful activity against the potential targets. The study concluded that three constituents named cosmosiin, stigmastanol, robinetin, and quercetin were highly active and could regulate the expression of Interleukin 6 (IL6) and Cysteine-aspartic acid protease 3 (CASP3), which may act as a potential therapeutic target for malaria treatment. These analyses are validated by molecular dynamics simulation which reflects on the overall structural stability of the intermolecular conformation and interactions. These results can also be witnessed in simulation-based trajectories binding free energies, which concluded the significant role of electrostatic and van der Waals energies in total intermolecular interactions. Finally, we utilized machine learning to predict the anti-malarial activity of C. myxa compounds, comparing them with approved drugs. Using the Chemprop model and MAIP predictions, we assessed ten compounds, revealing their potential as lead anti-malarial agents. This study establishes a groundwork for comprehending the function of the anti-malaria action of C. myxa.

Improving drug discovery with a hybrid deep generative model using reinforcement learning trained on a Bayesian docking approximation.

Generative approaches to molecular design are an area of intense study in recent years as a method to generate new pharmaceuticals with desired properties. Often though, these types of efforts are constrained by limited experimental activity data, resulting in either models that generate molecules with poor performance or models that are overfit and produce close analogs of known molecules. In this paper, we reduce this data dependency for the generation of new chemotypes by incorporating docking scores of known and de novo molecules to expand the applicability domain of the reward function and diversify the compounds generated during reinforcement learning. Our approach employs a deep generative model initially trained using a combination of limited known drug activity and an approximate docking score provided by a second machine learned Bayes regression model, with final evaluation of high scoring compounds by a full docking simulation. This strategy results in molecules with docking scores improved by 10-20% compared to molecules of similar size, while being 130 × faster than a docking only approach on a typical GPU workstation. We also show that the increased docking scores correlate with (1) docking poses with interactions similar to known inhibitors and (2) result in higher MM-GBSA binding energies comparable to the energies of known DDR1 inhibitors, demonstrating that the Bayesian model contains sufficient information for the network to learn to efficiently interact with the binding pocket during reinforcement learning. This outcome shows that the combination of the learned latent molecular representation along with the feature-based docking regression is sufficient for reinforcement learning to infer the relationship between the molecules and the receptor binding site, which suggest that our method can be a powerful tool for the discovery of new chemotypes with potential therapeutic applications.

A novel near-infrared EGFR targeting probe for metastatic lymph node imaging in preclinical mouse models.

For the treatment of patients with oral squamous cell carcinoma (OSCC), the imaging of cervical lymph nodes and the evaluation of metastastic progression are of great significance. In recent years, the development of new non-radioactive lymph node tracers has been an area of intense research. Here, we report the synthesis, good biocompatibility, and in vivo evaluation of a new small molecule near-infrared (NIR) fluorescence probe by the conjugation of Lapatinib to S0456 (LP-S). We show that like Lapatinib, LP-S binds to the epidermal growth factor receptor (EGFR) resulting in high quality fluorescence imaging of metastatic lymph nodes in OSCC mouse models. After local injection of LP-S into the tumor, the lymphatic drainage pathway and lymph nodes can be clearly identified by NIR fluorescence imaging. Further, the LP-S probe shows higher contrast and longer retention in metastatic lymph nodes, allowing them to be differentiated from normal lymph nodes, and affording a new choice for fluorescence-guided surgery. Scheme. Chemical synthesis and application of EGFR targeting probe LP-S for imaging of metastatic lymph nodes (mLNs) in OSCC.

Coating influence on inner shell water exchange: An underinvestigated major contributor to SPIONs relaxation properties.

Superparamagnetic iron oxide nanoparticles (SPIONs) are heavily studied as potential MRI contrast enhancing agents. Every year, novel coatings are reported which yield large increases in relaxivity compared to similar particles. However, the reason for the increased performance is not always well understood mechanistically. In this review, we attempt to relate these advances back to fundamental models of relaxivity, developed for chelated metal ions, primarily gadolinium. We focus most closely on the three-shell model which considers the relaxation of surface-bound, entrained, and bulk water molecules as three distinct contributions to total relaxation. Because SPIONs are larger, more complex, and entrain significantly more water than gadolinium-based contrast agents, we consider how to adapt the application of classical models to SPIONs in a predictive manner. By carefully considering models and previous results, a qualitative model of entrained water interactions emerges, based primarily on the contributions of core size, coating thickness, density, and hydrophilicity.

Switchable Schottky Contacts: Simultaneously Enhanced Output Current and Reduced Leakage Current.

Metal-semiconductor contacts are key components of nanoelectronics and atomic-scale integrated circuits. In these components Schottky diodes provide a low forward voltage and a very fast switching rate but suffer the drawback of a high reverse leakage current. Improvement of the reverse bias characteristics without degrading performance of the diode at positive voltages is deemed physically impossible for conventional silicon-based Schottky diodes. However, in this work we propose that this design challenge can be overcome in the organic-based diodes by utilizing reversible transitions between distinct adsorption states of organic molecules on metal surfaces. Motivated by previous experimental observations of controllable adsorption conformations of anthradithiophene on Cu(111), herein we use density functional theory simulations to demonstrate the distinct Schottky barrier heights of the two adsorption states. The higher Schottky barrier of the reverse bias induced by a chemisorbed state results in low leakage current, while the lower barrier of the forward bias induced by a physisorbed state yields a larger output current. The rectifying behaviors are further supported by nonequilibrium Green's function transport calculations.

One-Pot Exfoliation of Graphitic C3 N4 Quantum Dots for Blue QLEDs by Methylamine Intercalation.

Here, a simplified synthesis of graphitic carbon nitride quantum dots (g-C3 N4 -QDs) with improved solution and electroluminescent properties using a one-pot methylamine intercalation-stripping method (OMIM) to hydrothermally exfoliate QDs from bulk graphitic carbon nitride (g-C3 N4 ) is presented. The quantum dots synthesized by this method retain the blue photoluminescence with extremely high fluorescent quantum yield (47.0%). As compared to previously reported quantum dots, the g-C3 N4 -QDs synthesized herein have lower polydispersity and improved solution stability due to high absolute zeta-potential (-41.23 mV), which combine to create a much more tractable material for solution processed thin film fabrication. Spin coating of these QDs yields uniform films with full coverage and low surface roughness ideal for quantum dot light-emitting diode (QLED) fabrication. When incorporated into a functional QLED with OMIM g-C3 N4 -QDs as the emitting layer, the LED demonstrates ≈60× higher luminance (605 vs 11 Cd m-2 ) at lower operating voltage (9 vs 21 V), as compared to the previously reported first generation g-C3 N4 QLEDs, though further work is needed to improve device stability.

A Machine Learning Approach for Predicting HIV Reverse Transcriptase Mutation Susceptibility of Biologically Active Compounds.

HIV resistance emerging against antiretroviral drugs represents a great threat to the continued prolongation of the lifespans of HIV-infected patients. Therefore, methods capable of predicting resistance susceptibility in the development of compounds are in great need. By targeting the major reverse transcription residues Y181, K103, and L100, we used the biological activities of compounds against these enzymes and the wild-type reverse transcriptase to create Naïve Bayes Networks. Through this machine learning approach, we could predict, with high accuracy, whether a compound would be susceptible to a loss of potency due to resistance. Also, we could perfectly predict retrospectively whether compounds would be susceptible to both a K103 mutant RT and a Y181 mutant RT. In the study presented here, our method outperformed a traditional molecular mechanics approach. This method should be of broad interest beyond drug discovery efforts, and serves to expand the utility of machine learning for the prediction of physical, chemical, or biological properties using the vast information available in the literature.

Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation Analysis Reveal Insights into the Molecular Mechanism of Cordia myxa in the Treatment of Liver Cancer.

Traditional treatments of cancer have faced various challenges, including toxicity, medication resistance, and financial burdens. On the other hand, bioactive phytochemicals employed in complementary alternative medicine have recently gained interest due to their ability to control a wide range of molecular pathways while being less harmful. As a result, we used a network pharmacology approach to study the possible regulatory mechanisms of active constituents of Cordia myxa for the treatment of liver cancer (LC). Active constituents were retrieved from the IMPPAT database and the literature review, and their targets were retrieved from the STITCH and Swiss Target Prediction databases. LC-related targets were retrieved from expression datasets (GSE39791, GSE76427, GSE22058, GSE87630, and GSE112790) through gene expression omnibus (GEO). The DAVID Gene Ontology (GO) database was used to annotate target proteins, while the Kyoto Encyclopedia and Genome Database (KEGG) was used to analyze signaling pathway enrichment. STRING and Cytoscape were used to create protein-protein interaction networks (PPI), while the degree scoring algorithm of CytoHubba was used to identify hub genes. The GEPIA2 server was used for survival analysis, and PyRx was used for molecular docking analysis. Survival and network analysis revealed that five genes named heat shot protein 90 AA1 (HSP90AA1), estrogen receptor 1 (ESR1), cytochrome P450 3A4 (CYP3A4), cyclin-dependent kinase 1 (CDK1), and matrix metalloproteinase-9 (MMP9) are linked with the survival of LC patients. Finally, we conclude that four extremely active ingredients, namely cosmosiin, rosmarinic acid, quercetin, and rubinin influence the expression of HSP90AA1, which may serve as a potential therapeutic target for LC. These results were further validated by molecular dynamics simulation analysis, which predicted the complexes with highly stable dynamics. The residues of the targeted protein showed a highly stable nature except for the N-terminal domain without affecting the drug binding. An integrated network pharmacology and docking study demonstrated that C. myxa had a promising preventative effect on LC by working on cancer-related signaling pathways.

Purine-Doped g-C3N4-Modified Fabrics for Personal Protective Masks with Rapid and Sustained Antibacterial Activity.

Protective masks are critical to impeding microorganism transmission but can propagate infection via pathogen buildup and face touching. To reduce this liability, we integrated electrospun photocatalytic graphitic carbon nitride (g-C3N4) nanoflakes into standard surgical masks to confer a self-sanitization capacity. By optimizing the purine/melamine precursor ratio during synthesis, we reduced the g-C3N4 band gap from 2.92 to 2.05 eV, eliciting a 4× increase in sterilizing hydrogen peroxide production under visible light. This narrower band gap enables robust photocatalytic generation of reactive oxygen species from environmental and breath humidity to swiftly eliminate accumulated microbes. Under ambient sunlight, the g-C3N4 nanocomposite mask layer achieved a 97% reduction in the bacterial viability during typical use. Because the optimized band gap also allows photocatalytic activity under shadowless lamp illumination, the self-cleaning functionality could mitigate infection risk from residual pathogens in routine hospital settings. Both g-C3N4 and polycaprolactone demonstrate favorable biocompatibility and biodegradability, making this approach preferable over current commercially available metal-based options. Given the abundance and low cost of these components, this scalable approach could expand global access to reusable self-sanitizing protective masks, serving as a sustainable public health preparedness measure against future pandemics, especially in resource-limited settings.

Accurate categorization and rapid pathological diagnosis correction with Micro-Raman technique in human lung adenocarcinoma infiltration level.

Background: In the context of surgical interventions for lung adenocarcinoma (LADC), precise determination of the extent of LADC infiltration plays a pivotal role in shaping the surgeon's strategic approach to the procedure. The prevailing diagnostic standard involves the expeditious intraoperative pathological diagnosis of areas infiltrated by LADC. Nevertheless, current methodologies rely on the visual interpretation of tissue images by proficient pathologists, introducing an error margin of up to 15.6%. Methods: In this study, we investigated the utilization of Micro-Raman technique on isolated specimens of human LADC with the objective of formulating and validating a workflow for the pathological diagnosis of LADC featuring diverse degrees of infiltration. Our strategy encompasses a thorough pathological characterization of LADC, spanning different tissue types and levels of infiltration. Through the integration of Raman spectroscopy with advanced deep learning models for simultaneous diagnosis, this approach offers a swift, precise, and clinically relevant means of analysis. Results: The diagnostic performance of the convolutional neural network (CNN) model, coupled with the microscopic Raman technique, was found to be exceptional and consistent, surpassing the traditional support vector machine (SVM) model. The CNN model exhibited an area under the curve (AUC) value of 96.1% for effectively distinguishing normal tissue from LADC and an impressive 99.0% for discerning varying degrees of infiltration in LADCs. To comprehensively assess its clinical utility, Raman datasets from patients with intraoperative rapid pathologic diagnostic errors were utilized as test subjects and input into the established CNN model. The results underscored the substantial corrective capacity of the Micro-Raman technique, revealing a misdiagnosis correction rate exceeding 96% in all cases. Conclusions: Ultimately, our discoveries highlight the Micro-Raman technique's potential to augment the intraoperative diagnostic precision of LADC with varying levels of infiltration. And compared to the traditional SVM model, the CNN model has better generalization ability in diagnosing different infiltration levels. This method furnishes surgeons with an objective groundwork for making well-informed decisions concerning subsequent surgical plans.

Drug Chemical Space as a Guide for New Herbicide Development: A Cheminformatic Analysis.

Herbicides are critical resources for meeting agricultural demand. While similar in structure and function to pharmaceuticals, the development of new herbicidal mechanisms of action and new scaffolds against known mechanisms of action has been much slower than in pharmaceutical sciences. We hypothesized that this may be due in part to a relative undersampling of possible herbicidal chemistries and set out to test whether this difference in sampling existed and whether increasing the diversity of possible herbicidal chemistries would be likely to result in more efficacious herbicides. To conduct this work, we first identified databases of commercially available herbicides and clinically approved pharmaceuticals. Using these databases, we created a two-dimensional embedding of the chemical, which provides a qualitative visualization of the degree to which each chemotype is distributed within the combined chemical space and shows a moderate degree of overlap between the two sets. Next, we trained several machine learning models to classify herbicides versus drugs based on physicochemical characteristics. The most accurate of these models has an accuracy of 93% with the key differentiating characteristics being the number of polar hydrogens, number of amide bonds, LogP, and polar surface area. We then used several types of scaffold decomposition to quantitatively evaluate the chemical diversity of each molecular family and showed herbicides to have considerably fewer unique structural fragments. Finally, we used molecular docking as an in silico evaluation of further structural diversification in herbicide development. To this end, we identified herbicides with well-characterized binding sites and modified those scaffolds based on similar structural subunits from the drug dataset not present in any commercial herbicide while using the machine-learned model to ensure that required herbicide properties were maintained. Redocking the original and modified scaffolds of several herbicides showed that even this simple design strategy is capable of yielding new molecules with higher predicted affinity for the target enzymes. Overall, we show that herbicides are distinct from drugs based on physicochemical properties but less diverse in their chemistry in a way not governed by these properties. We also demonstrate in silico that increasing the diversity of herbicide scaffolds has the potential to increase potency, potentially reducing the amount needed in agricultural practice.

Computational Investigations of Traditional Chinese Medicinal Compounds against the Omicron Variant of SARS-CoV-2 to Rescue the Host Immune System.

Macrodomain-I of the NSP3 (non-structural protein 3) is responsible for immune response hijacking in the SARS-CoV-2 infection known as COVID-19. In the omicron variant (B.1.1.529), this domain harbors a new mutation, V1069I, which may increase the binding of ADPr and consequently the infection severity. This macrodomain-I, due to its significant role in infection, is deemed to be an important drug target. Hence, using structural bioinformatics and molecular simulation approaches, we performed a virtual screening of the traditional Chinese medicines (TCM) database for potential anti-viral drugs. The screening of 57,000 compounds yielded the 10 best compounds with docking scores better than the control ADPr. Among the top ten, the best three hits-TCM42798, with a docking score of -13.70 kcal/mol, TCM47007 of -13.25 kcal/mol, and TCM30675 of -12.49 kcal/mol-were chosen as the best hits. Structural dynamic features were explored including stability, compactness, flexibility, and hydrogen bonding, further demonstrating the anti-viral potential of these hits. Using the MM/GBSA approach, the total binding free energy for each complex was reported to be -69.78 kcal/mol, -50.11 kcal/mol, and -47.64 kcal/mol, respectively, which consequently reflect the stronger binding and inhibitory potential of these compounds. These agents might suppress NSP3 directly, allowing the host immune system to recuperate. The current study lays the groundwork for the development of new drugs to combat SARS-CoV-2 and its variants.

A study on setting standards for near-infrared fluorescence-image guided surgery (NIRFGS) time lapse monitoring based on preoperative liver function assessment.

Background: This study aimed to explore the relationship between the fluorescence intensity of indocyanine green (ICG) in near-infrared fluorescence guided surgery (NIRFGS) and preoperative liver function indicators. Methods: A total of 12 4T1 tumor-bearing mice were used for model establishment. Intraperitoneal injection (i.p.) of 20% carbon tetrachloride (CCl4) corn oil solution (50 µL) was given to mice in the liver injury model group, 24 hours after injection, the model was established, while the control group received 0% CCl4 corn oil solution (50 µL) (n=6 for each group). Additionally, doses of 8 mg/kg and 1 mg/kg of free ICG were injected intravenously (i.v.) (n=3 in each group). Fluorescence was imaged in vivo using an NIR fluorescence imaging system at different time points (1, 2, 4, 8, 12, 24, 48, and 72 h) after injection. Results: The absolute fluorescence intensity of mice in the liver injury model group was stronger than that in the control group. Mice in the liver injury model group had the same clearance rate of ICG from the tumor as normal mice. However, the background clearance rate was slower than that of normal mice, which prolonged the optimal tumor to background ratio (TBR) time. Correlation analysis was also used to determine which preoperative liver function parameters were most correlated with hepatic ICG clearance. Conclusions: Liver injury does not significantly affect the maximum TBR, but prolongs the optimal TBR time, and at the same time, a wider and more stable surgical window will appear. This study showed that a prolonged surgical start time is feasible according to preoperative liver function testing using NIR fluorescence imaging technology.

A pilot study of near-infrared fluorescence guided surgery for primary tumor localization and lymph node mapping in colorectal cancer.

BACKGROUND: This pilot study aimed to evaluate the feasibility of near-infrared fluorescence imaging for primary tumor localization, lymph node mapping, and metastatic lymph node detection in colorectal cancer (CRC) using indocyanine green (ICG). METHODS: A total of 11 patients with CRC were prospectively enrolled. ICG (25 mg dissolved in 30 mL sterile water) was intravenously injected preoperatively, and the fluorescence intensity of the primary tumor, lymph nodes, and normal tissues, as well as the signal-to-background ratio (SBR) and contrast-to-noise ratio (CNR) were measured at 0.5, 1, 2, 4, and 24 h after ICG injection. RESULTS: The primary tumor could be located intraoperatively, and the tumor boundary was clear at 2-4 h. There was good contrast in the fluorescence intensity between tumor and normal tissues (SBR =2.11±0.36, CNR =8.74±0.35). The lymph node detection rate was 95% (38/40), and the SBR threshold of lymph nodes was 1.13. CONCLUSIONS: This pilot study showed that primary tumor localization and lymph node mapping in CRC is feasible using near-infrared fluorescence imaging technology, though metastatic lymph nodes cannot be discriminated from benign ones. In addition, cancer nodules missed by both white light mode and palpation by the surgeon were unexpectedly found, resulting in a change in the surgical prognosis in 9.1% (1/11) of patients.

Ultracompact Iron Oxide Nanoparticles with a Monolayer Coating of Succinylated Heparin: A New Class of Renal-Clearable and Nontoxic T1 Agents for High-Field MRI.

Herein, we present a new magnetic iron oxide nanoparticle (MION) with a succinylated heparin monolayer coating, which exhibits the highest T1 relaxivity at 7 T and the lowest r2/r1 reported for any MION at these high-field conditions. While the recent proliferation of 7 T MRI instruments in hospitals worldwide has enabled widespread access to higher quality, more finely detailed, diagnostic imaging, clinically available contrast agents have not kept pace due to the general phenomenon of reduced efficacy of T1 relaxation as magnetic field strength is increased. Development of new MION agents is one strategy to address this need, and to this end, we demonstrate the in vitro magnetic properties of the MIONs reported here to extend to in vivo applications, providing greatly increased contrast in tumor imaging in a murine xenograft subject at 7 T. While MION-based contrast agents can have side effects in clinical application, these are generally thought to be less than those of gadolinium-based agents and here are further reduced by the small size allowing direct glomerular filtration from the blood followed by renal-excretion. Finally, we show the succinylated heparin monolayer coating to provide class leading magnetic properties over a homologous series of particles with core size ranging from 2 to 18 nm and show the properties to be strongly related to the surface area. We suggest the increased porosity and hydrophilicity of the coating to increase water accessibility to the surface resulting in the increased magnetic properties.

Asymmetric Schottky Contacts in van der Waals Metal-Semiconductor-Metal Structures Based on Two-Dimensional Janus Materials.

Physical and electronic asymmetry plays a crucial role in rectifiers and other devices with a directionally variant current-voltage (I-V) ratio. Several strategies for practically creating asymmetry in nanoscale components have been demonstrated, but complex fabrication procedures, high cost, and incomplete mechanistic understanding have significantly limited large-scale applications of these components. In this work, we present density functional theory calculations which demonstrate asymmetric electronic properties in a metal-semiconductor-metal (MSM) interface composed of stacked van der Waals (vdW) heterostructures. Janus MoSSe has an intrinsic dipole due to its asymmetric structure and, consequently, can act as either an n-type or p-type diode depending on the face at the interior of the stacked structure (SeMoS-SMoS vs. SMoSe-SMoS). In each configuration, vdW forces dominate the interfacial interactions, and thus, Fermi level pinning is largely suppressed. Our transport calculations show that not only does the intrinsic dipole cause asymmetric I-V characteristics in the MSM structure but also that different transmission mechanisms are involved across the S-S (direct tunneling) and S-Se interface (thermionic excitation). This work illustrates a simple and practical method to introduce asymmetric Schottky barriers into an MSM structure and provides a conceptual framework which can be extended to other 2D Janus semiconductors.

Surface Strain-Induced Collective Switching of Ensembles of Molecules on Metal Surfaces.

A central difficulty in the design of molecular electronics is poor control of the contact state between the molecule and metal electrode, which may induce instability and noise in logic and memory devices and even destroy the intrinsic functionality of the device. Here, we theoretically propose a simple and effective strategy for realizing full control of the contact state of organic molecules coated on the metal surface by applying homogeneous surface strain. As exemplified by pyrazine molecules on Cu(111), application of compressive (tensile) strain causes the molecules to uniformly adopt the physisorbed (chemisorbed) state. Within the framework of non-equilibrium Green's function calculations, we show that the two distinct contact states yield simultaneous rectification and switching behaviors. Because the contact states of all surface-bound molecules are transformed uniformly via surface strain perturbations, fully controlled collective switching and rectification effects can be simultaneously achieved in this contact system.

Integrated cascade nanozyme catalyzes in vivo ROS scavenging for anti-inflammatory therapy.

Here, an integrated cascade nanozyme with a formulation of Pt@PCN222-Mn is developed to eliminate excessive reactive oxygen species (ROS). This nanozyme mimics superoxide dismutase by incorporation of a Mn-[5,10,15,20-tetrakis(4-carboxyphenyl)porphyrinato]-based metal-organic framework compound capable of transforming oxygen radicals to hydrogen peroxide. The second mimicked functionality is that of catalase by incorporation of Pt nanoparticles, which catalyze hydrogen peroxide disproportionation to water and oxygen. Both in vitro and in vivo experimental measurements reveal the synergistic ROS-scavenging capacity of such an integrated cascade nanozyme. Two forms of inflammatory bowel disease (IBD; i.e., ulcerative colitis and Crohn's disease) can be effectively relieved by treatment with the cascade nanozyme. This study not only provides a new method for constructing enzyme-like cascade systems but also illustrates their efficient therapeutic promise in the treatment of in vivo IBDs.

Deep Molecular Orbital Driven High-Temperature Hydrogen Tautomerization Switching.

Hydrogen tautomerization molecular switches, a promising class of molecular components for the construction of complex nanocircuits, have been extensively studied using low-temperature scanning tunneling microscopy. However, these molecules are generally only reliably controllable in cryogenic environments, obstructing their utility in real devices. Here, we use dispersion-inclusive density functional theory and systematically investigate the adsorption and tautomerization behaviors of porphycene on six transition-metal surfaces. Among these surfaces, we found that hydrogen tautomerization on the Pt(110) surface corresponds to the largest switching barrier, allowing a controllable transition at high temperature. The switching behavior is closely related to the exceptional degree of charge transfer in the HOMO-2 orbital, illustrating the important role of deep orbital-surface interactions in porphycene molecular switching. Our work provides an in-depth understanding of the porphycene tautomerization mechanism and highlights new research avenues toward the practical application of molecular switches.

Succinylated heparin monolayer coating vastly increases superparamagnetic iron oxide nanoparticle T2 proton relaxivity.

Superparamagnetic iron oxide nanoparticles (SPIONs) have a history of clinical use as contrast agents in T2 weighted MRI, though relatively low T2 relaxivity has caused them to fall out of favor as new faster MRI techniques have gained prominence. We demonstrate that SPIONs coated with a monolayer of succinylated heparin (Su-HP-SPIONs) exhibit over four-fold increased T2 relaxivity (460 mM-1 s-1) as compared to the clinically approved SPION-based contrast agent Feridex (98.3 mM-1 s-1) due to greatly increased water interaction from increased hydrophilicity and thinner coating as supported by our proposed parametric model. In vivo, the performance increase of the Su-HP-SPIONs in T2 MRI imaging of xenograft tumors is ten-fold that of our in-house synthesized Feridex analogue, due to better tumor localization from the smaller size imparted by the thinner coating. In addition to these significantly improved magnetic properties, the succinylated heparin coating also exhibits favorable synthetic reproducibility, solution stability, and biocompatibility. These findings demonstrate the untapped potential of SPIONs as possible high performance clinical T2 contrast agents.