Ligand Classifier of Adaptively Boosting Ensemble Decision Stumps (LiCABEDS) and Its Application on Modeling Ligand Functionality for 5HT-Subtype GPCR Families

Advanced high-throughput screening (HTS) technologies generate great amounts of bioactivity data, and this data needs to be analyzed and interpreted with attention to understand how these small molecules affect biological systems. As such, there is an increasing demand to develop and adapt cheminformatics algorithms and tools in order to predict molecular and pharmacological properties on the basis of these large data sets. In this manuscript, we report a novel machine-learning-based ligand classification algorithm, named Ligand Classifier of Adaptively Boosting Ensemble Decision Stumps (LiCABEDS), for data-mining and modeling of large chemical data sets to predict pharmacological properties in an efficient and accurate manner. The performance of LiCABEDS was evaluated through predicting GPCR ligand functionality (agonist or antagonist) using four different molecular fingerprints, including Maccs, FP2, Unity, and Molprint 2D fingerprints. Our studies showed that LiCABEDS outperformed two other popular techniques, classification tree and Naive Bayes classifier, on all four types of molecular fingerprints. Parameters in LiCABEDS, including the number of boosting iterations, initialization condition, and a “reject option” boundary, were thoroughly explored and discussed to demonstrate the capability of handling imbalanced data sets, as well as its robustness and flexibility. In addition, the detailed mathematical concepts and theory are also given to address the principle behind statistical prediction models. The LiCABEDS algorithm has been implemented into a user-friendly software package that is accessible online at http://www.cbligand.org/LiCABEDS/

Luminescent MOF-Based Nanofibers with Visual Monitoring and Antibacterial Properties for Diabetic Wound Healing

Diabetic wound healing remains as a serious challenge for medical circles that required continuous monitoring and effective management. Herein, the glucose oxidase/carbon dots@copper-metal–organic framework-based nanofibers (GOx/CDs@MOF NFs) were proposed as a multifunctional wound dressing, aiming to visually monitor wound pH and inhibit bacterial infection. In the diabetic wound microenvironment, the GOx/CDs@MOF NFs could convert endogenous glucose into hydroxyl radial (•OH) through the cascade catalytic reaction. In vivo and vitro experimental results confirmed that the GOx/CDs@MOF NFs could efficiently kill bacteria and promote wound healing. Additionally, CDs as a pH fluorescent indicator endowed GOx/CDs@MOF NFs with sensitive and reversible fluorescent sensing behavior to wound pH, and these visual images could also be captured by smartphones and transformed into RGB color mode (red, green, blue) values, allowing for onsite evaluation of the wound status. This multifunctional wound dressing provides a smart and effective solution for diabetic wound management and takes an immeasurable step toward the development of the next generation of digitally visualized wound dressings

Residue Preference Mapping of Ligand Fragments in the Protein Data Bank

The interaction between small molecules and proteins is one of the major concerns for structure-based drug design because the principles of protein−ligand interactions and molecular recognition are not thoroughly understood. Fortunately, the analysis of protein−ligand complexes in the Protein Data Bank (PDB) enables unprecedented possibilities for new insights. Herein, we applied molecule-fragmentation algorithms to split the ligands extracted from PDB crystal structures into small fragments. Subsequently, we have developed a ligand fragment and residue preference mapping (LigFrag-RPM) algorithm to map the profiles of the interactions between these fragments and the 20 proteinogenic amino acid residues. A total of 4032 fragments were generated from 71 798 PDB ligands by a ring cleavage (RC) algorithm. Among these ligand fragments, 315 unique fragments were characterized with the corresponding fragment−residue interaction profiles by counting residues close to these fragments. The interaction profiles revealed that these fragments have specific preferences for certain types of residues. The applications of these interaction profiles were also explored and evaluated in case studies, showing great potential for the study of protein−ligand interactions and drug design. Our studies demonstrated that the fragment−residue interaction profiles generated from the PDB ligand fragments can be used to detect whether these fragments are in their favorable or unfavorable environments. The algorithm for a ligand fragment and residue preference mapping (LigFrag-RPM) developed here also has the potential to guide lead chemistry modifications as well as binding residues predictions

Molecular Fingerprint-Based Artificial Neural Networks QSAR for Ligand Biological Activity Predictions

In this manuscript, we have reported a novel 2D fingerprint-based artificial neural network QSAR (FANN-QSAR) method in order to effectively predict biological activities of structurally diverse chemical ligands. Three different types of fingerprints, namely, ECFP6, FP2 and MACCS, were used in FANN-QSAR algorithm development, and FANN-QSAR models were compared to known 3D and 2D QSAR methods using five data sets previously reported. In addition, the derived models were used to predict GPCR cannabinoid ligand binding affinities using our manually curated cannabinoid ligand database containing 1699 structurally diverse compounds with reported cannabinoid receptor subtype CB₂ activities. To demonstrate its useful applications, the established FANN-QSAR algorithm was used as a virtual screening tool to search a large NCI compound database for lead cannabinoid compounds, and we have discovered several compounds with good CB₂ binding affinities ranging from 6.70 nM to 3.75 μM. To the best of our knowledge, this is the first report for a fingerprint-based neural network approach validated with a successful virtual screening application in identifying lead compounds. The studies proved that the FANN-QSAR method is a useful approach to predict bioactivities or properties of ligands and to find novel lead compounds for drug discovery research

The cumulative retention rates of MMT participants from 2006 to 2011.

<p>The retention rate of participants in MMT with CM was significantly higher than that of MMT with CP (log-rank <i>x²</i>  =  7.490; <i>P</i><0.05).</p

Enhanced Isothermal Amplification for Ultrafast Sensing of SARS-CoV‑2 in Microdroplets

Rapid and high-throughput screening is critical to control the COVID-19 pandemic. Recombinase polymerase amplification (RPA) with highly accessible and sensitive nucleic acid amplification has been widely used for point-of-care infection diagnosis. Here, we report an integrated microdroplet array platform composed of an ultrasonic unit and minipillar array to enhance the RPA for ultrafast, high-sensitivity, and high-throughput detection of SARS-CoV-2. On such a platform, the independent microvolume reactions on individual minipillars greatly decrease the consumption of reagents. The microstreaming driven by ultrasound creates on-demand contactless microagitation in the microdroplets and promotes the interaction between RPA components, thus greatly accelerating the amplification. In the presence of microstreaming, the detection time is 6–12 min, which is 38.8–59.3% shorter than that of controls without microstreaming, and the end-point fluorescence intensity also increased 1.3–1.7 times. Furthermore, the microagitation-enhanced RPA also exhibits a lower detection limit (0.42 copy/μL) for SARS-CoV-2 in comparison to the controls. This integrated microdroplet array detection platform is expected to meet the needs for high-throughput nucleic acid testing (NAT) to improve the containment of viral transmission during the epidemic, as well as provide a potential platform for the timely detection of other pathogens or viruses

Multivariate Cox proportional hazards model for retention in MMT.

<p>Multivariate Cox proportional hazards model for retention in MMT.</p

sj-docx-1-jicm-10.1177_08850666211073582 - Supplemental material for Kidney and Mortality Outcomes Associated with Ondansetron in Critically Ill Patients

Supplemental material, sj-docx-1-jicm-10.1177_08850666211073582 for Kidney and Mortality Outcomes Associated with Ondansetron in Critically Ill Patients by Matthew Gray, Priyanka Priyanka, Sandra Kane-Gill, Lirong Wang and John A. Kellum in Journal of Intensive Care Medicine</p

Selectivity of twenty-eight PKD1 inhibitors.

<p>Inhibition of PKCα (<b>A</b>), PKCδ (<b>B</b>), or CAMKIIα (<b>C</b>) by each of the twenty-eight hits was determined at 100 nM, 1 µM and 10 µM concentrations. In the PKC assays, GF109203X, a potent PKC inhibitor was used as control.</p