Methods and Protocols最新文献

Intrinsically disordered proteins (IDPs), such as the Alzheimer's-associated tau protein, pose challenges for conventional drug discovery. This study applied the Informational Spectrum Method for Small Molecules (ISM-SM), a computational technique utilizing electron-ion interaction potentials (EIIPs), to identify potential tau modulators. Characteristic interaction frequencies derived from known ligands and conserved mammalian tau sequences were used to screen DrugBank and the COCONUT natural product database. The screening identified approved drugs previously reported to indirectly influence tau pathology or Alzheimer's disease pathways, alongside natural products including Bryostatin-14, which is known to modulate kinases involved in tau phosphorylation. These findings suggest that ISM-SM can serve as an in silico tool to identify candidate small molecules, including repurposed drugs and natural products, with potential relevance to tau function and pathology, complementing other IDP drug discovery strategies.

Objective: Motivational Interviewing may be an ideal communication style to use in conjunction with Collaborative Care to address opioid risk, as it can facilitate the discussion of alternative pain care strategies (APCSs) that are pharmacological (APCS-P; e.g., the use of non-opioid pain relievers) or non-pharmacological (APCS-NP; e.g., yoga). This study developed and piloted a coding system (MI Skills Code-APCS) for these discussions.

Method: Sessions (n = 119) from a completed randomized controlled trial comparing Collaborative Care Motivational Interviewing (CCMI) or Attention Control Psychoeducation (ACP) delivered by care managers over 12 weeks to veterans with chronic pain and high-risk opioid use enrolled in VA primary care (N = 44).

Results: Coders were able to reliably code the client utterances related to APCSs in the sessions (ICCs = 0.58-0.81). The APCS-P and APCS-NP codes were positively correlated with each other. There were two significant relationships between the MISC-APCS codes (motivational states) and the pain interference and endorsement of non-pharmacological pain care goals at 20-week follow-up.

Conclusions: The MISC-APCS has promise as a coding system that can reliably record client utterances regarding different types of pain care strategies. These utterances may be associated with post-treatment reports of pain and efforts to reduce opioid risk. The rapid development of artificial intelligence applications to healthcare can utilize this coding system to assist with the assessment and treatment of chronic pain.

Machine learning (ML) excels over regression by automatically capturing complex, non-linear relationships and interactions, enabling more flexible and accurate predictions without strict assumptions. This study focuses on developing ML-based predictive models for key post-PCI outcomes: 30-day mortality, in-hospital major bleeding, and one-year mortality. Data from 104,665 consecutive PCI cases in the Victorian Cardiac Outcomes Registry (VCOR), collected between 2013 and 2022, will be analyzed. Candidate variables, informed by prior systematic reviews and dataset availability, will undergo multiple imputations for missing values. The Boruta method will be applied to identify influential predictors. Risk-adjusted models will be developed using sophisticated ML algorithms, with performance compared across standard metrics for validation. The dataset will be split, optimized via 10-fold cross-validation, and class imbalance addressed using Adaptive Synthetic resampling technique. SHapley Additive exPlanations will interpret the most influential predictors. The variables from the best model will be converted into simplified numeric scores. External validation will be performed using the Tasmanian dataset or equivalent datasets. This study is expected to identify the most influential variables associated with 30-day all-cause mortality, in-hospital major bleeding, and long-term mortality post-PCI. These variables will form the basis for developing robust risk-scoring models to support clinical decision-making and outcome prediction.

The increasing frequency, duration, and intensity of wildfires over the past decade have raised significant concerns about widespread exposure to wildfire smoke. Inhalation of wildfire smoke poses a substantial risk to human health, with epidemiological studies linking exposure to cardiovascular, respiratory, and neurological dysfunction. Wildfire smoke contains hundreds of chemical compounds across diverse classes, with concentrations varying by fuel type and combustion conditions. Phenolic compounds are prominent constituents of wood smoke, and catechol is especially abundant under smoldering conditions that produce dense smoke. In this study, ¹⁴C-labeled catechol was spiked into smoldering eucalyptus wood smoke extract (WSE) and administered to rats via intranasal instillation. Plasma was collected at 5 min and 2 h post-exposure. Samples were analyzed using parallel accelerator and molecular mass spectrometry (PAMMS). Major catechol-derived metabolites identified included benzene oxide, catechol-cysteine conjugate, and catechol-glutamine conjugate; the parent compound was not detected. These results indicate that inhaled catechol in wood smoke is quickly metabolized upon entry into circulation. PAMMS enabled both identification and relative quantification of circulating catechol metabolites, demonstrating feasibility for biomarker discovery and exposure assessment.

A straightforward whole-mount approach has been developed that uses fluorescence imaging, mouse trachea, and a range of off-the-shelf reagents for rapidly evaluating substance toxicity within the ciliated respiratory epithelium. Using this protocol, the lumen of control trachea samples displays a typical cobblestone epithelial structure, a high density of ciliated cells, and minimal evidence of cell death, as visualized by phalloidin, acetylated tubulin, and fixable live/dead staining, respectively. In contrast, trachea subjected to treatments that induce injury show disrupted epithelial architecture and increased cell death, indicating substance toxicity. These results support the utility of this protocol for rapidly detecting and quantifying respiratory epithelial toxicity and differential cell-type susceptibility.

Drug discovery remains a time-consuming and costly process, necessitating innovative computational approaches to accelerate early stage target identification and compound development. We introduce AgentMol, a modular multimodel AI system that integrates large language models, chemical language modeling, and deep learning-based affinity prediction to automate the discovery pipeline. AgentMol begins with disease-related queries processed through a Retrieval-Augmented Generation system using the Large Language Model to identify protein targets. Protein sequences are then used to condition a GPT-2-based chemical language model, which generates corresponding small-molecule candidates in SMILES format. Finally, a regression convolutional neural network (RCNN) predicts the drug-target interaction by estimating binding affinities (pKi). Models were trained and validated on 470,560 ligand-protein pairs from the BindingDB database. The chemical language model achieved high validity (1.00), uniqueness (0.96), and diversity (0.89), whereas the RCNN model demonstrated robust predictive performance with R² > 0.6 and Pearson's R > 0.8. By leveraging LangGraph for orchestration, AgentMol delivers a scalable, interpretable pipeline, effectively enabling the end-to-end generation and evaluation of drug candidates conditioned on protein targets. This system represents a significant step toward practical AI-driven molecular discovery with accessible computational demands.

We previously developed a bead-coupled ligase detection reaction (LDR) assay that enables simple and rapid detection of single-nucleotide variations (SNVs) using synthetic oligonucleotide templates. In the present study, this approach was extended to genomic DNA extracted from colorectal cancer cell lines to evaluate its applicability to clinically relevant samples. Targeting codon 12 of the KRAS gene, PCR-amplified products served as templates for bead-coupled LDR, and fluorescence excitation-emission matrix (EEM) analysis was employed for signal readout. The four fluorophores used in the assay exhibited distinct spectral properties, allowing their signals to be clearly resolved within the EEM profiles. This mapping provided characteristic fluorescence signatures that revealed the underlying genotypes, enabling not only the distinction between homozygous and heterozygous states but also the precise identification of allele compositions, as exemplified by G/A, T/T, G/G, and G/C in colorectal cancer cell lines. The single-tube workflow, integrating magnetic bead capture with fluorescence-based detection, demonstrated robustness, speed, and cost-effectiveness compared with conventional mutation detection methods. These findings confirm that the LDR-EEM platform can be successfully applied to genomic DNA analysis, underscoring its potential as an accessible and reliable tool for SNV detection in both research and diagnostic contexts.

Investigation of molecular mechanisms that underlie the toxicity of reactive oxidants requires the usage of reductionist cellular models, where laboratory cultures are treated by known doses of the target compounds in strictly controlled conditions. In recent years, much attention has been focused on hypothiocyanous acid (HOSCN), a pseudohypohalous acid that is one of the main products of chordata heme peroxidases. Due to its instability, HOSCN cannot be purchased and stored, so it has to be enzymatically synthesized before each experiment. For the first time, we systematically classified the published protocols for HOSCN synthesis, compared them by product yield, and found that the highest achievable concentration was about 1.9 mM. This value is not convenient for large-scale experiments with high cell density. Therefore, we developed an improved protocol for HOSCN preparation by optimizing reagent ratios, incubation times, and temperature. The current paper describes all steps from scratch, namely lactoperoxidase purification via a combination of cation exchange, hydrophobic interaction, and size exclusion chromatography, HOSCN synthesis from SCN^- and H₂O₂, as well as HOSCN concentration measurement. The main advantage of the current protocol is that the product yield reaches 2.9 mM, which is 60% higher than published alternatives.

L-Carnitine (L-CAR) and acetyl-L-carnitine (Acetyl L-CAR) are the essential cofactor compounds in lipid metabolism and are used in the treatment of various diseases. The European Food Safety Authority (EFSA) has reported that Acetyl-L-CAR contributes to normal cognitive function and has a beneficial physiological effect. Therefore, the sensitive separation and determination of L-CAR and Acetyl-L-CAR in foodstuffs can provide critical information. A notable trend in modern food analysis is the increasing use of miniaturized analytical columns with a narrow inner diameter (ID). In this study, a new, green analytical method for food analysis was developed to analyze L-CAR and Acetyl-L-CAR in food samples by nano-LC/UV with a hydrophilic monolithic 100 µm ID capillary. This is the first time that the preparation and application of a hydrophilic monolithic nano-column for the analysis of L-CAR and Acetyl-L-CAR in food samples by nano LC/UV has been reported. The hydrophilic monolith was prepared using in situ co-polymerization of glyceryl methacrylate (GMM) and ethylene dimethacrylate (EDMA). Following preparation and characterization, the hydrophilic monolith was used to analyze L-CAR and Acetyl-L-CAR in food samples, including three infant powdered milk samples and five supplements using nano LC/UV. The developed method was validated in terms of precision, sensitivity, linearity, recovery, and repeatability. The LOD and LOQ values were found to be in the range of 0.04-0.09 µg/kg, respectively. In short, the proposed method proved to be suitable for the routine analysis of L-CAR and Acetyl-L-CAR in food samples.

Retinal detachment (RD) disrupts the eye's immune-privileged status, causing a local inflammatory response that contributes to adverse clinical outcomes, including proliferative vitreoretinopathy and suboptimal visual recovery. Comprehensive profiling of intraocular immune cells will offer mechanistic insights and support the development of personalized immunomodulatory strategies. Here, we describe a robust and standardized protocol for the collection and high-dimensional analysis of the intraocular immune infiltrate from patients undergoing RD surgery, using state-of-the-art spectral cytometry. Vitreous and retinal tissue samples were obtained during standard surgical procedures, without the need for additional invasive interventions. Our approach integrates two complementary protocols: one that enables selective isolation of immune cells by sorting for CD45⁺ populations, and a second one that applies a 39-color spectral cytometry panel to profile the general landscape of immune subpopulations. The panel can identify up to 62 distinct viable immune subsets per sample, along with their functional status, as it includes expression of 13 functional markers. Hence, we hereby detail sample preparation, staining, and acquisition workflow, as well as the gating strategy and essential steps necessary for reproducible immunophenotyping. Our protocol, which enables high-dimensional immune profiling from minimal biological material, provides a valuable platform for studying ocular inflammation in RD and other retinal diseases.