Modeling interactions between Heparan sulfate and proteins based on the Heparan sulfate microarray analysis.

Heparan sulfate (HS), a sulfated polysaccharide abundant in the extracellular matrix, plays pivotal roles in various physiological and pathological processes by interacting with proteins. Investigating the binding selectivity of HS oligosaccharides to target proteins is essential, but the exhaustive inclusion of all possible oligosaccharides in microarray experiments is impractical. To address this challenge, we present a hybrid pipeline that integrates microarray and in silico techniques to design oligosaccharides with desired protein affinity. Using fibroblast growth factor 2 (FGF2) as a model protein, we assembled an in-house dataset of HS oligosaccharides on microarrays and developed two structural representations: a standard representation with all atoms explicit and a simplified representation with disaccharide units as "quasi-atoms." Predictive Quantitative Structure-Activity Relationship (QSAR) models for FGF2 affinity were developed using the Random Forest (RF) algorithm. The resulting models, considering the applicability domain, demonstrated high predictivity, with a correct classification rate of 0.81-0.80 and improved positive predictive values (PPV) up to 0.95. Virtual screening of 40 new oligosaccharides using the simplified model identified 15 computational hits, 11 of which were experimentally validated for high FGF2 affinity. This hybrid approach marks a significant step toward the targeted design of oligosaccharides with desired protein interactions, providing a foundation for broader applications in glycobiology.

Hydrophobization of Reduced Graphene Oxide Aerogel Using Soy Wax to Improve Sorption Properties.

A special technique has been developed for producing a composite aerogel which consists of graphene oxide and soy wax (GO/wax). The reduction of graphene oxide was carried out by the stepwise heating of this aerogel to 250 °C. The aerogel obtained in the process of the stepwise thermal treatment of rGO/wax was studied by IR and Raman spectroscopy, scanning electron microscopy, and thermogravimetry. The heat treatment led to an increase in the wax fraction accompanied by an increase in the contact angle of the rGO/wax aerogel surface from 136.2 °C to 142.4 °C. The SEM analysis has shown that the spatial structure of the aerogel was formed by sheets of graphene oxide, while the wax formed rather large (200-1000 nm) clumps in the folds of graphene oxide sheets and small (several nm) deposits on the flat surface of the sheets. The sorption properties of the rGO/wax aerogel were studied with respect to eight solvent, oil, and petroleum products, and it was found that dichlorobenzene (85.8 g/g) and hexane (41.9 g/g) had the maximum and minimum sorption capacities, respectively. In the case of oil and petroleum products, the indicators were in the range of 52-63 g/g. The rGO/wax aerogel was found to be highly resistant to sorption-desorption cycles. The cyclic tests also revealed a swelling effect that occurred differently for different parts of the aerogel.

STOPLIGHT: A Hit Scoring Calculator.

We introduce STOPLIGHT, a web portal to assist medicinal chemists in prioritizing hits from screening campaigns and the selection of compounds for optimization. STOPLIGHT incorporates services to assess 6 physiochemical and structural properties, 6 assay liabilities, and 11 pharmacokinetic properties, for any small molecule represented by its SMILES string. We briefly describe each service and illustrate the utility of this portal with a case study. The STOPLIGHT portal provides a user-friendly tool to guide hit selection in early drug discovery campaigns, whereby compounds with unfavorable properties can be quickly recognized and eliminated.

Matrix-assisted laser desorption/ionization matrix incorporation evaluation algorithm for improved peak coverage and signal-to-noise ratio in mass spectrometry imaging.

RATIONALE: Despite decades of implementation, the selection of optimal sample preparation conditions for matrix-assisted laser desorption/ionization (MALDI) imaging is still ambiguous due to the lack of a universal and comprehensive evaluation methodology. Thus, numerous experiments with different matrix application conditions accompany a translation of the method to novel sample types and matrices. METHODS: Mouse brain tissues were covered with 9-aminoacridine through sublimation, followed by recrystallization in vapors of 5% (v/v) methanol solution in water. The samples were analyzed by MALDI time-of-flight mass spectrometry, and the efficiency of lipid and small-molecule ionization was evaluated with different metrics. RESULTS: We first investigate the dependency of matrix density and recrystallization conditions on the thickness of an analyte-empty matrix layer to roughly evaluate the laser shot number required to obtain an intense signal with minimal noise. Then, we introduce metrics for the analysis of small imaging datasets (small sample regions) of model samples based on median quantity of peaks in spectra (medQP) and weighted median signal-to-noise ratio (wmSNR). The evaluation of small regions and taking median values for metrics help overcome the sample heterogeneity and allow for the simultaneous comparison of different acquisition parameters. CONCLUSIONS: Here, we propose a methodology based on gradual laser ablation of small regions of sample and further implementation of weighted signal-to-noise ratio to assess various matrix application conditions. The proposed approach helps reduce the number of test samples required to determine optimal sample preparation conditions and improve the overall quality of images.

Pharmacokinetics Profiler (PhaKinPro): Model Development, Validation, and Implementation as a Web Tool for Triaging Compounds with Undesired Pharmacokinetics Profiles.

Computational models that predict pharmacokinetic properties are critical to deprioritize drug candidates that emerge as hits in high-throughput screening campaigns. We collected, curated, and integrated a database of compounds tested in 12 major end points comprising over 10,000 unique molecules. We then employed these data to build and validate binary quantitative structure-activity relationship (QSAR) models. All trained models achieved a correct classification rate above 0.60 and a positive predictive value above 0.50. To illustrate their utility in drug discovery, we used these models to predict the pharmacokinetic properties for drugs in the NCATS Inxight Drugs database. In addition, we employed the developed models to predict the pharmacokinetic properties of all compounds in the DrugBank. All models described in this paper have been integrated and made publicly available via the PhaKinPro Web-portal that can be accessed at https://phakinpro.mml.unc.edu/.

Cheminformatics-Guided Cell-Free Exploration of Peptide Natural Products.

There have been significant advances in the flexibility and power of in vitro cell-free translation systems. The increasing ability to incorporate noncanonical amino acids and complement translation with recombinant enzymes has enabled cell-free production of peptide-based natural products (NPs) and NP-like molecules. We anticipate that many more such compounds and analogs might be accessed in this way. To assess the peptide NP space that is directly accessible to current cell-free technologies, we developed a peptide parsing algorithm that breaks down peptide NPs into building blocks based on ribosomal translation logic. Using the resultant data set, we broadly analyze the biophysical properties of these privileged compounds and perform a retrobiosynthetic analysis to predict which peptide NPs could be directly synthesized in augmented cell-free translation reactions. We then tested these predictions by preparing a library of highly modified peptide NPs. Two macrocyclases, PatG and PCY1, were used to effect the head-to-tail macrocyclization of candidate NPs. This retrobiosynthetic analysis identified a collection of high-priority building blocks that are enriched throughout peptide NPs, yet they had not previously been tested in cell-free translation. To expand the cell-free toolbox into this space, we established, optimized, and characterized the flexizyme-enabled ribosomal incorporation of piperazic acids. Overall, these results demonstrate the feasibility of cell-free translation for peptide NP total synthesis while expanding the limits of the technology. This work provides a novel computational tool for exploration of peptide NP chemical space, that could be expanded in the future to allow design of ribosomal biosynthetic pathways for NPs and NP-like molecules.

Surface Treatment Effect on the Mechanical and Thermal Behavior of the Glass Fabric Reinforced Polysulfone.

The chemical structure of the surface of glass fibers, including silanized fibers, was studied. Highly efficient heat-resistant composites were obtained by impregnating silanized glass fiber with a polysulfone solution, and the effect of modification of the surface of glass fibers on the physical, mechanical and thermophysical properties of the composite materials was studied. As a result of the study, it was found that the fiber-to-polymer ratio of 70/30 wt.% showed the best mechanical properties for composites reinforced with pre-heat-treated and silanized glass fibers. It has been established that the chemical treatment of the glass fibers with silanes makes it possible to increase the mechanical properties by 1.5 times compared to composites reinforced with initial fibers. It was found that the use of silane coupling agents made it possible to increase the thermal stability of the composites. Mechanisms that improve the interfacial interaction between the glass fibers and the polymer matrix have been identified. It has been shown that an increase in adhesion occurs both due to the uniform distribution of the polymer on the surface of the glass fibers and due to the improved wettability of the fibers by the polymer. An interpenetrating network was formed in the interfacial region, providing a chemical bond between the functional groups on the surface of the glass fiber and the polymer matrix, which was formed as a result of treating the glass fiber surface with silanes, It has been shown that when treated with aminopropyltriethoxysilane, significant functional unprotonated amino groups NH+/NH2+ are formed on the surface of the fibers; such free amino groups, oriented in the direction from the fiber surface, form strong bonds with the matrix polymer. Based on experimental data, the chemical structure of the polymer/glass fiber interface was identified.

Exclusion of Axionlike-Particle Cogenesis Dark Matter in a Mass Window above 100 µeV.

We report the results of Phase 1b of the ORGAN experiment, a microwave cavity haloscope searching for dark matter axions in the 107.42-111.93 µeV mass range. The search excludes axions with two-photon coupling g_{aγγ}≥4×10^{-12} GeV^{-1} with 95% confidence interval, setting the best upper bound to date and with the required sensitivity to exclude the axionlike particle cogenesis model for dark matter in this range. This result was achieved using a tunable rectangular cavity, which mitigated several practical issues that become apparent when conducting high-mass axion searches, and was the first such axion search to be conducted with such a cavity. It also represents the most sensitive axion haloscope experiment to date in the ∼100 µeV mass region.

ExEmPLAR (Extracting, Exploring, and Embedding Pathways Leading to Actionable Research): a user-friendly interface for knowledge graph mining.

SUMMARY: Knowledge graphs are being increasingly used in biomedical research to link large amounts of heterogenous data and facilitate reasoning across diverse knowledge sources. Wider adoption and exploration of knowledge graphs in the biomedical research community is limited by requirements to understand the underlying graph structure in terms of entity types and relationships, represented as nodes and edges, respectively, and learn specialized query languages for graph mining and exploration. We have developed a user-friendly interface dubbed ExEmPLAR (Extracting, Exploring, and Embedding Pathways Leading to Actionable Research) to aid reasoning over biomedical knowledge graphs and assist with data-driven research and hypothesis generation. We explain the key functionalities of ExEmPLAR and demonstrate its use with a case study considering the relationship of Trypanosoma cruzi, the etiological agent of Chagas disease, to frequently associated cardiovascular conditions. AVAILABILITY AND IMPLEMENTATION: ExEmPLAR is freely accessible at https://www.exemplar.mml.unc.edu/. For code and instructions for the using the application, see: https://github.com/beasleyjonm/AOP-COP-Path-Extractor.

A Curious Case of Vanishing Hepatocellular Carcinoma (HCC).

Hepatocellular carcinoma (HCC) is the third most common cause of cancer-related mortality worldwide. Spontaneous regression of HCC is rare with few documented cases in literature. The mechanism of this phenomenon is unknown, but tumor hypoxia and systemic inflammatory response have been suggested as possible etiologies. This article aims to shed more light on this rare phenomenon and provides an opportunity to review the proposed pathophysiology of spontaneous HCC regression. In this case report, we describe an interesting case of a 39-year-old male with HCC who underwent spontaneous regression.

Hospitalization Trends and Outcomes of Eosinophilic Esophagitis in the United States: A Decade-Long Nationwide Analysis.

Background Eosinophilic esophagitis (EoE) is a chronic antigen-mediated esophageal disease characterized by infiltration of the esophageal mucosa by eosinophils. The prevalence of EoE continues to rise worldwide. However, certain aspects of the epidemiology and pathogenesis remain unclear. Methods This study examined the hospitalization trends of EoE using an extensive inpatient database in the United States, the National (Nationwide) Inpatient Sample (NIS), to identify hospitalizations between 2010 and 2019. We assessed patient demographics as well as hospital-specific variables using the NIS. We obtained the prevalence rate of EoE for each year and used joinpoint regression analysis to obtain trends after adjusting the rate for age and gender. We also sought to characterize the outcomes of these hospitalizations by obtaining the mortality rate, length of stay (LOS), and total hospital charges (THC). Results Of 305 million hospitalizations included in the study, 33,878 were for EoE. The prevalence rate per 100,000 hospitalizations of EoE increased from 6.6 in 2010 to 15.5 in 2019. The annual percentage change obtained from the joinpoint regression analysis was 13.3% from 2010 to 2014 and 7.2% from 2014 to 2019. Most of the hospitalizations were among the male gender and young adults. Almost 95% of hospitalizations across the study period were seen in urban hospitals. We did not notice any significant trend in the mortality rates or length of stay over the study period. The THC increased significantly across the study period. Conclusion There has been an upward trend in the average prevalence rate of EoE over the decade from 2010 to 2019 which almost parallels that of inflammatory bowel disease. This represents a significant burden of disease for a condition that was initially recognized in the late 20th century.

Lies and Liabilities: Computational Assessment of High-Throughput Screening Hits to Identify Artifact Compounds.

Hits from high-throughput screening (HTS) of chemical libraries are often false positives due to their interference with assay detection technology. In response, we generated the largest publicly available library of chemical liabilities and developed "Liability Predictor," a free web tool to predict HTS artifacts. More specifically, we generated, curated, and integrated HTS data sets for thiol reactivity, redox activity, and luciferase (firefly and nano) activity and developed and validated quantitative structure-interference relationship (QSIR) models to predict these nuisance behaviors. The resulting models showed 58-78% external balanced accuracy for 256 external compounds per assay. QSIR models developed and validated herein identify nuisance compounds among experimental hits more reliably than do popular PAINS filters. Both the models and the curated data sets were implemented in "Liability Predictor," publicly available at https://liability.mml.unc.edu/. "Liability Predictor" may be used as part of chemical library design or for triaging HTS hits.

Multitask learning-driven identification of novel antitrypanosomal compounds.

Background: Chagas disease and human African trypanosomiasis cause substantial death and morbidity, particularly in low- and middle-income countries, making the need for novel drugs urgent. Methodology & results: Therefore, an explainable multitask pipeline to profile the activity of compounds against three trypanosomes (Trypanosoma brucei brucei, Trypanosoma brucei rhodesiense and Trypanosoma cruzi) were created. These models successfully discovered four new experimental hits (LC-3, LC-4, LC-6 and LC-15). Among them, LC-6 showed promising results, with IC50 values ranging 0.01-0.072 µM and selectivity indices >10,000. Conclusion: These results demonstrate that the multitask protocol offers predictivity and interpretability in the virtual screening of new antitrypanosomal compounds and has the potential to improve hit rates in Chagas and human African trypanosomiasis projects.

Cheminformatics-driven discovery of hit compounds against Paracoccidioides spp.

Aims: The development of safe and effective therapies for treating paracoccidioidomycosis using computational strategies were employed to discover anti-Paracoccidioides compounds. Materials & methods: We 1) collected, curated and integrated the largest library of compounds tested against Paracoccidioides spp.; 2) employed a similarity search to virtually screen the ChemBridge database and select nine compounds for experimental evaluation; 3) performed an experimental evaluation to determine the minimum inhibitory concentration and minimum fungicidal concentration as well as cytotoxicity; and 4) employed computational tools to identify potential targets for the most active compounds. Seven compounds presented activity against Paracoccidioides spp. Conclusion: These compounds are new hits with a predicted mechanisms of action, making them potentially attractive to develop new compounds.

Praemonitus praemunitus: can we forecast and prepare for future viral disease outbreaks?

Understanding the origins of past and present viral epidemics is critical in preparing for future outbreaks. Many viruses, including SARS-CoV-2, have led to significant consequences not only due to their virulence, but also because we were unprepared for their emergence. We need to learn from large amounts of data accumulated from well-studied, past pandemics and employ modern informatics and therapeutic development technologies to forecast future pandemics and help minimize their potential impacts. While acknowledging the complexity and difficulties associated with establishing reliable outbreak predictions, herein we provide a perspective on the regions of the world that are most likely to be impacted by future outbreaks. We specifically focus on viruses with epidemic potential, namely SARS-CoV-2, MERS-CoV, DENV, ZIKV, MAYV, LASV, noroviruses, influenza, Nipah virus, hantaviruses, Oropouche virus, MARV, and Ebola virus, which all require attention from both the public and scientific community to avoid societal catastrophes like COVID-19. Based on our literature review, data analysis, and outbreak simulations, we posit that these future viral epidemics are unavoidable, but that their societal impacts can be minimized by strategic investment into basic virology research, epidemiological studies of neglected viral diseases, and antiviral drug discovery.

Cannabis Use and Associated Gastrointestinal Disorders: A Literature Review.

Cannabis, commonly known as marijuana, is used by at least 18% of the United States (US) population, which makes it the most commonly used federally illegal drug in the United States. It is widely used for recreational purposes, while its therapeutic benefits have been extensively explored in the US. For several years, cannabis has been used for the treatment of diverse health conditions, including pain management, anti-inflammatory effects, and spasticity associated with multiple sclerosis and other neurodegenerative diseases. However, cannabis use has been associated with some acute and chronic adverse effects. This review sheds light on gastrointestinal disorders, gastroesophageal reflux disease, pancreatitis, and peptic ulcer disease that have been associated with cannabis use.

Phytochemicals and bioactive compounds effective against acute myeloid leukemia: A systematic review.

This systematic review identified various bioactive compounds which have the potential to serve as novel drugs or leads against acute myeloid leukemia. Acute myeloid leukemia (AML) is a heterogeneous hematopoietic malignancy that arises from the dysregulation of cell differentiation, proliferation, and cell death. The risk factors associated with the onset of AML include long-term exposure to radiation and chemicals such as benzene, smoking, genetic disorders, blood disorders, advancement in age, and others. Although novel strategies to manage AML, including a refinement of the conventional chemotherapy regimens, hypomethylating agents, and molecular targeted drugs, have been developed in recent years, resistance and relapse remain the main clinical problems. In this study, three databases, PubMed/MEDLINE, ScienceDirect, and Google Scholar, were systematically searched to identify various bioactive compounds with antileukemic properties. A total of 518 articles were identified, out of which 59 were viewed as eligible for the current report. From the data extracted, over 60 bioactive compounds were identified and divided into five major groups: flavonoids, alkaloids, organosulfur compounds, terpenes, and terpenoids, and other known and emerging bioactive compounds. The mechanism of actions of the analyzed individual bioactive molecules differs remarkably and includes disrupting chromatin structure, upregulating the synthesis of certain DNA repair proteins, inducing cell cycle arrest and apoptosis, and inhibiting/regulating Hsp90 activities, DNA methyltransferase 1, and histone deacetylase 1.

Wnt/ß-catenin signaling pathway inhibitors, glycyrrhizic acid, solanine, polyphyllin I, crocin, hypericin, tubeimoside-1, diosmin, and rutin in medicinal plants have better binding affinities and anticancer properties: Molecular docking and ADMET study.

Wnt/ß-catenin signaling pathway plays a role in cancer development, organogenesis, and embryogenesis. The abnormal activation promotes cancer stem cell renewal, proliferation, and differentiation. In the present study, molecular docking simulation and ADMET studies were carried out on selected bioactive compounds in search of ß-catenin protein inhibitors for drug discovery against cancer. Blind docking simulation was performed using PyRx software on Autodock Vina. ß-catenin protein (PDB ID: 1jdh) and 313 bioactive compounds (from PubChem database) with selected standard anticancer drugs were used for molecular docking. The ADMET properties of the best-performing compounds were calculated using SwissADME and pkCMS web servers. The results obtained from the molecular docking study showed that glycyrrhizic acid, solanine, polyphyllin I, crocin, hypericin, tubeimoside-1, diosmin, and rutin had the best binding interactions with ß-catenin protein based on their binding affinities. Glycyrrhizic acid and solanine had the same and lowest binding energy of -8.5 kcal/mol. This was followed by polyphyllin I with -8.4 kcal/mol, and crocin, hypericin, and tubeimoside-1 which all had a binding energy of 8.1 kcal/mol. Other top-performing compounds include diosmin and rutin with binding energy of -8.0 kcal/mol. The ADMET study revealed that the following compounds glycyrrhizic acid, solanine, polyphyllin I, crocin, hypericin, tubeimoside-1, diosmin, rutin, and baicalin all violated Lipinski's rule of 5 which implies poor oral bioavailability. However, based on the binding energy score, it was suggested that these pharmacologically active compounds are potential molecules to be tested against cancer.

Development and validation of a prognostic model for death 30 days after adult emergency laparotomy.

The probability of death after emergency laparotomy varies greatly between patients. Accurate pre-operative risk prediction is fundamental to planning care and improving outcomes. We aimed to develop a model limited to a few pre-operative factors that performed well irrespective of surgical indication: obstruction; sepsis; ischaemia; bleeding; and other. We derived a model with data from the National Emergency Laparotomy Audit for patients who had emergency laparotomy between December 2016 and November 2018. We tested the model on patients who underwent emergency laparotomy between December 2018 and November 2019. There were 4077/40,816 (10%) deaths 30 days after surgery in the derivation cohort. The final model had 13 pre-operative variables: surgical indication; age; blood pressure; heart rate; respiratory history; urgency; biochemical markers; anticipated malignancy; anticipated peritoneal soiling; and ASA physical status. The predicted mortality probability deciles ranged from 0.1% to 47%. There were 1888/11,187 deaths in the test cohort. The scaled Brier score, integrated calibration index and concordance for the model were 20%, 0.006 and 0.86, respectively. Model metrics were similar for the five surgical indications. In conclusion, we think that this prognostic model is suitable to support decision-making before emergency laparotomy as well as for risk adjustment for comparing organisations.

3'UTR of mRNA Encoding CPEB Protein Orb2 Plays an Essential Role in Intracellular Transport in Neurons.

Intracellular trafficking plays a critical role in the functioning of highly polarized cells, such as neurons. Transport of mRNAs, proteins, and other molecules to synaptic terminals maintains contact between neurons and ensures the transmission of nerve impulses. Cytoplasmic polyadenylation element binding (CPEB) proteins play an essential role in long-term memory (LTM) formation by regulating local translation in synapses. Here, we show that the 3'UTR of the Drosophila CPEB gene orb2 is required for targeting the orb2 mRNA and protein to synapses and that this localization is important for LTM formation. When the orb2 3'UTR is deleted, the orb2 mRNAs and proteins fail to localize in synaptic fractions, and pronounced LTM deficits arise. We found that the phenotypic effects of the orb2 3'UTR deletion were rescued by introducing the 3'UTR from the orb, another Drosophila CPEB gene. In contrast, the phenotypic effects of the 3'UTR deletion were not rescued by the 3'UTR from one of the Drosophila α-tubulin genes. Our results show that the orb2 mRNAs must be targeted to the correct locations in neurons and that proper targeting depends upon sequences in the 3'UTR.