In this study, a deep learning model is used to classify post-traumatic stress disorder patients through novel markers to assist in finding candidate biomarkers for the disorder.
Read More...Post-Traumatic Stress Disorder (PTSD) biomarker identification using a deep learning model
In this study, a deep learning model is used to classify post-traumatic stress disorder patients through novel markers to assist in finding candidate biomarkers for the disorder.
Read More...Development of a novel machine learning platform to identify structural trends among NNRTI HIV-1 reverse transcriptase inhibitors
With advancements in machine learning a large data scale, high throughput virtual screening has become a more attractive method for screening drug candidates. This study compared the accuracy of molecular descriptors from two cheminformatics Mordred and PaDEL, software libraries, in characterizing the chemo-structural composition of 53 compounds from the non-nucleoside reverse transcriptase inhibitors (NNRTI) class. The classification model built with the filtered set of descriptors from Mordred was superior to the model using PaDEL descriptors. This approach can accelerate the identification of hit compounds and improve the efficiency of the drug discovery pipeline.
Read More...A new therapy against MDR bacteria by in silico virtual screening of Pseudomonas aeruginosa LpxC inhibitors
Here, seeking to address the growing threat of multidrug-resistant bacteria (MDR). the authors used in silico virtual screening to target MDR Pseudomonas aeruginosa. They considered a key protein in its biosynthesis and virtually screened 20,000 candidates and 30 derivatives of brequinar. In the end, they identified a possible candidate with the highest degree of potential to inhibit the pathogen's lipid A synthesis.
Read More...OTGP: An innovative biometric authentication system with on-the-go passwords using a novel typing signature
This manuscript describes a new method of on-the-go passwords using typing characteristics. The authors developed a keyboard and keystroke recording setup and tested it with 30 participants. The results indicated the five chosen parameters are distinct across participants yet consistent across time for each participant, making it a plausible candidate for a behavior-based password system.
Read More...Computational analysis and drug repositioning: Targeting the TDP-43 RRM using FDA-approved drugs
Molecules which bind to proteins that aggregate abnormally in neurodegenerative diseases could be promising drugs for these diseases. In this study, Zhang, Wu, Zhang, and Dang simulate the binding behavior of various molecules to screen for candidates which could be promising candidates for drug development.
Read More...Genetic underpinnings of the sex bias in autism spectrum disorder
Here, seeking to identify a possible explanation for the more frequent diagnosis of autism spectrum disorder (ASD) in males than females, they sought to investigate a potential sex bias in the expression of ASD-associated genes. Based on their analysis, they identified 17 ASD-associated candidate genes that showed stronger collective sex-dependent expression.
Read More...Computational development of aryl sulfone compounds as potential NNRTIs
Non-nucleoside reverse transcriptase inhibitors (NNRTIs) are allosteric inhibitors that bind to the HIV reverse transcriptase and prevent replication. Indolyl aryl sulfones (IAS) and IAS derivatives have been found to be highly effective against mutant strains of HIV-1 reverse transcriptase. Here, we analyzed molecules designed using aryl sulfone scaffolds paired to cyclic compounds as potential NNRTIs through the computational design and docking of 100 novel NNRTI candidates. Moreover, we explored the specific combinations of functional groups and aryl sulfones that resulted in the NNRTI candidates with the strongest binding affinity while testing all compounds for carcinogenicity. We hypothesized that the combination of an IAS scaffold and pyrimidine would produce the compounds with the best binding affinity. Our hypothesis was correct as the series of molecules with an IAS scaffold and pyrimidine exhibited the best average binding affinity. Additionally, this study found 32 molecules designed in this procedure with higher or equal binding affinities to the previously successful IAS derivative 5-bromo-3-[(3,5-dimethylphenyl)sulfonyl]indole-2-carboxyamide when docked to HIV-1 reverse transcriptase.
Read More...Effect of Manuka Honey and Licorice Root Extract on the Growth of Porphyromonas gingivalis: An In Vitro Study
Chronic bad breath, or halitosis, is a problem faced by nearly 50% of the general poluation, but existing treatments such as liquid mouthwash or sugar-free gum are imperfect and temporary solutions. In this study, the authors investigate potential alternative treatments using natural ingredients such as Manuka Honey and Licorice root extract. They found that Manuka honey is almost as effective as commercial mouthwashes in reducing the growth of P gingivalis (one of the main bacteria that causes bad breath), while Licorice root extract was largely ineffective. The authors' results suggest that Manuka honey is a promising candidate in the search for new and improved halitosis treatments.
Read More...The Effectiveness of Different Palate Relievers Against a Hot Chili Pepper Sauce
Cuisine with hot chili peppers can be tasty, but sometimes painful to consume because of the burning sensations caused by the capsaicin molecule. The authors wanted to find the palate reliever that decreases the burning sensation of capsaicin the most by testing water, soft drink, olive oil, milk, and ice-cream as possible candidates. The authors hypothesized that olive oil would be the best palate reliever as it is non-polar like the capsaicin molecule. The authors surveyed 12 panelists with low, medium, and high spice tolerances and found that across all levels of spice tolerance, milk and ice-cream were the best palate relievers and soft drink the worst.
Read More...Pancreatic Adenocarcinoma: An Analysis of Drug Therapy Options through Interaction Maps and Graph Theory
Cancer is often caused by improper function of a few proteins, and sometimes it takes only a few proteins to malfunction to cause drastic changes in cells. Here the authors look at the genes that were mutated in patients with a type of pancreatic cancer to identify proteins that are important in causing cancer. They also determined which proteins currently lack effective treatment, and suggest that certain proteins (named KRAS, CDKN2A, and RBBP8) are the most important candidates for developing drugs to treat pancreatic cancer.
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