Sabu JA, N D B.

In Silico Pharmacol

PMID:42005989

Free PMC article

Inflammation is an essential part in the natural healing process of body. Prolonged inflammation can slowdown the healing process and increase discomfort, making it necessary to reduce inflammation. Although the use of plants in treating inflammation is gaining significant attention, bryophytes remain largely overlooked due to their small size, low biomass, and the challenges associated with identifying and collecting large quantities of the desired species. However, recent interest in underexplored bryophytes and its ability to thrive in harsh environments highlights the need to re-evaluate such species for their bioactive compounds and biomedical relevance. The aim of the present study was to evaluate the phytochemical composition, quantify the terpenoid content, and assess the in vitro anti-inflammatory activity of the ethyl acetate extract of H. involuta . Subsequently, molecular docking studies were conducted with terpenoids, targeting the inflammation-related proteins COX-2 and 5-LOX. The study found that the ethyl acetate extract of H. involuta produced a yield of 3.5%, with 50 µg/mg of total terpenoid content. The GC-MS and LC-MS detected 12 and 53 phytocompounds respectively, including terpenoids with anti-inflammatory properties. In vitro anti-inflammatory assay of the extract demonstrated a dose-dependent inhibition of protein denaturation, achieving 57.27% inhibition at 100 μg/mL concentration followed by 37.16% and 35.23% inhibition against COX and LOX enzymes, respectively. In the molecular docking study, Jatrophone was identified as the most promising compound, exhibiting the highest binding affinity to both COX and LOX with a binding score of -10.45 and -10.47 kcal/mole respectively. In conclusion, similar to higher plants, the moss H. involuta also contains bioactive terpenoids like Jatrophone, Humulene 8 hydroperoxide, Ganosporelactone A, Furanojaponin, Neophytadiene with potential anti-inflammatory properties. This study has a significant impact on the field of natural product research by highlighting H. involuta , a commonly overlooked cement moss, as a novel source of bioactive compounds. Overall, this work opens new avenues for drug discovery, encourages the sustainable use of urban flora, and reinforces the importance of exploring underutilized plant species for pharmaceutical development. Graphical abstract

Shamli¹ AAA, Mousavizadegan¹ M, Lawati¹ HAJA.

PMID:PPR1178498

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Abstract This study investigates the phenolic composition of extra virgin olive oil (EVOO) from the Omani market and optimizes the phenolic extraction process. The addition of 10% (w/v) sodium chloride to the extraction solvent increased phenolic recovery by 20–30%. Feature importance analysis identified solvent polarity, influenced by solvent type, salt concentration, and volume, as the key factor driving extraction efficiency. A validated liquid chromatography–tandem mass spectrometry (LC–MS/MS) method quantified ten phenolic compounds in six commercial EVOO samples. Acid values ranged from 0.01% to 0.11%, and peroxide values were below 12 meq O₂ kg⁻¹, confirming compliance with EVOO quality standards. Total phenolic content ranged from 116 to 250 mg kg⁻¹. Two locally produced Omani oils exhibited phenolic profiles comparable to high-quality Mediterranean EVOOs. Elevated hydroxytyrosol levels indicate phenolic transformation during storage. This study provides baseline data supporting quality control and nutritional evaluation of EVOOs in the Omani market.

Bhattacharya K, Chanu NR, Das D, Khanal P, Bhattacharjee A.

J Mol Graph Model

PMID:42008960

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Beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) is a key enzyme in amyloid-β generation and remains an important target in Alzheimer's disease (AD) drug discovery. Here, we present NeuroBACE-ML, a reliability-aware screening framework for high-throughput prioritization of potent BACE1 inhibitors from small-molecule libraries. Human BACE1 bioactivity records were curated from ChEMBL and standardized on a pIC 50 scale using a strict binary definition to reduce label ambiguity: active (IC 50 ≤ 100 nM; pIC 50 ≥ 7) and inactive (IC 50 ≥ 1 μM; pIC 50 ≤ 6), while excluding the intermediate grey zone (100-1000 nM; 6 50 < 7). Molecules were represented using Morgan fingerprints and the primary classifier was built using XGBoost with Optuna-based hyperparameter optimization. On the fixed random held-out test set, NeuroBACE-ML showed high discriminative performance, with AUROC = 0.986, AUPRC = 0.991, MCC = 0.868 and balanced accuracy = 0.943 at the deployed operating threshold of 0.70 (TN = 468, FP = 14, FN = 71, TP = 763). To strengthen reliability for prospective screening, the framework incorporates probability calibration, scaffold-aware robustness assessment, applicability-domain-aware decision support, abstention logic and ensemble uncertainty analysis. In addition, external validation on an independent non-overlapping BindingDB dataset supported generalizability beyond the ChEMBL-derived benchmark (AUROC = 0.969, AUPRC = 0.987, MCC = 0.790). While the framework is intended for early-stage candidate prioritization rather than direct clinical translation, it provides a practical and deployable tool for identifying high-confidence BACE1 inhibitor candidates for downstream medicinal chemistry and experimental follow-up. The exclusion of intermediate compounds may limit real-world applicability by simplifying borderline activity patterns that can occur in practical screening settings. NeuroBACE-ML is available as a web application at https://neurobace-ml.streamlit.app/with supporting code and deployment resources available via GitHub at https://github.com/kunal74/NeuroBACE-ML.

Hattori H, Otsu M, Imai K, Narahara M, Kondo J, Shino A, Morishita EC.

Small Sci

PMID:42006585

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Small molecules that target RNA are emerging as a powerful therapeutic modality, although deriving structure-activity relationships (SARs) remains a major challenge. Here, we present AI-augmented Iterative Screening of Libraries Against RNA targets (AISLAR), a machine learning-driven strategy that accelerates the discovery of SAR-tractable RNA binders and enables rational analog design. We screened diverse, drug-like chemical libraries against two RNA motifs derived from human p53 mRNA and applied AISLAR within the open-source KNIME platform. The application of AISLAR yielded chemotypes suitable for SAR development. Biophysical assays confirmed direct binding of representative compounds to one RNA motif. Guided by early SAR trends, we developed a pharmacophore hypothesis and designed an analog that retained binding with lower predicted cardiac channel liability. Docking simulations using the crystal structure of the RNA motif revealed a plausible binding mode for the validated hit compound. While further validation across diverse RNA targets and compound libraries will be required, these results demonstrate how AISLAR can be used as a workflow linking RNA-targeted small molecule screening with rational analog design.

Harsha ML, Weaver K, Clarke WA, Rackow AR.

J Anal Toxicol

PMID:42001212

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Qualitative urine drug screens (UDSs) are essential in clinical toxicology for assessments of drug exposure and are commonly employed by emergency departments to provide rapid results that inform care. Fentanyl, a potent synthetic opioid, is of significant public health concern due to rising overdose rates. However, fentanyl is also administered clinically as an analgesic, which may result in positive UDS assessments for patients with iatrogenic exposure. Positive UDS findings may cause patient anxiety, provider confusion, and lead to medicolegal concerns in clinical settings such as labor and delivery and neonatology. As such, it is essential that UDSs are utilized appropriately and that the positivity threshold for the assay is rigorously evaluated to serve the clinical need. This study examined UDS utilization within a quaternary care system over a one-year period. A subset of remnant urine specimens from UDSs were further analyzed using HRMS (high resolution mass spectrometry) and LC-MS/MS (liquid chromatography-tandem mass spectrometry) to focus on the impact of fentanyl immunoassay cutoff concentrations in high-volume populations. At our institution, the adult emergency department (ED) had the highest number of specimens ordered (5,901), while neonatal units had the least (727). Notably, fentanyl positivity rates were the highest in neonatal units at 34%. Quantitative analysis of remnant urine samples from the ED, labor and delivery (L&D), and neonatal units showed higher fentanyl concentrations in the ED, indicative of illicit use, and lower concentrations in L&D and neonates, consistent with analgesic administration. Raising the immunoassay cutoff from 1.0 ng/mL to 5.0 ng/mL would convert iatrogenic positives to negatives while retaining illicit positives. These findings underscore the need to evaluate UDS cutoff values for improved clinical performance in different populations.