Methods and Protocols最新文献

The emergence and spread of coronavirus infections have created a necessity to develop serological methods for assessing population immunity. The enzyme-linked immunosorbent assay (ELISA) remains one of the most accessible and informative approaches for these purposes. The choice of recombinant proteins plays an important role in the sensitivity and specificity of the test system, and in this regard, the creation of a domestic ELISA based on the chimeric SM protein to the SARS-CoV-2 virus is relevant. In this work, a recombinant chimeric SM protein expressed in the E. coli system and purified using metal-affinity chromatography on Ni-NTA agarose was constructed and presented for the first time. An ELISA test system was developed and tested using panels of positive and negative sera, including samples obtained before the COVID-19 pandemic. The obtained sensitivity (90.48%) and specificity (93.65%) indicators with a ROC curve AUC = 0.9623 (OD450 = 0.213) indicate the diagnostic accuracy of the test system. The positive diagnostic ratio (LR+) = 14.25.0 indicates the reliability of a positive result. The domestically developed ELISA test system can be used for serological monitoring and assessment of the immune status of the population.

The creation of bioenergy based on the biomass wood pellet industry, which accounts for the majority of the global biomass supply, is one of the most common and important ways to utilize waste wood, wood dust, and other byproducts of wood manufacturing, known as forestry residues. Pellet production processes might greatly benefit from fast monitoring systems that may allow for at least a semi-quantitative measurement of crucial parameters such as lignin and cellulose. The determination of lignin and cellulose is complicated and time-consuming because it usually requires time-demanding and labor-intensive sample preparation. This, however, might be a crucial problem. In this context, the application of Raman spectroscopic techniques is considered a promising approach, as it enables rapid, reliable, and label-free analysis of wood pellets, providing information about the chemical composition of the biomass, specifically lignin and cellulose. The purpose of this article is to report on the application of Raman spectroscopy exemplified by the detection of the lignin/cellulose ratio. In our methodological approach, we integrated the area under the selected Raman bands to avoid a large scatter of data when only the intensities of the bands were used. Moreover, the acquired Raman spectra displayed very strong signals from both substances, which contributes to the feasibility of the analysis even with a portable instrument. This study is expected to be of assistance in situations when the monitoring of the chemical changes and the quick inspection of pellets are required in near real time, online, and in situ.

Ordinary linear regression is the most common approach for modeling relationships between continuous outcomes and explanatory variables in epidemiological research. However, this method relies on restrictive assumptions-normality, homoscedasticity, and linearity-that are often violated in real-world biomedical data. When these assumptions fail, mean-based estimates may obscure important heterogeneity across the outcome distribution. This study aims to illustrate the methodological and interpretive advantages of quantile regression over ordinary regression in the analysis of epidemiological data. Secondary data were derived from a cross-sectional study of 1415 healthy Greek adults aged 25-82 years. Body mass index (BMI) served as the outcome variable, while sex, age, physical activity, dieting status, and daily energy intake were considered predictors. Both ordinary and quantile regression models were applied to estimate associations between BMI and its determinants across the 25th, 50th, 75th, and 90th quantiles. Ordinary regression identified positive associations of BMI with age and energy intake and a negative association with physical activity. Quantile regression revealed that these relationships were not constant across the BMI distribution. The inverse association with physical activity intensified at higher quantiles, and the gender effect reversed direction at the upper tail, suggesting heterogeneity was not captured by mean-based models. Quantile regression provides a distribution-sensitive alternative to ordinary regression, offering insight into covariate effects across different points of the outcome distribution and serving as both a robust analytical tool and an educational framework for applied epidemiological research.

Live imaging has been instrumental in understanding cellular dynamics in Drosophila tissues, but technical limitations have prevented the long-term visualization of cell competition in adult brains. Here, we describe a simple ex vivo protocol that enables extended live imaging of adult Drosophila brains for up to 32 h. The method relies on non-supplemented Schneider's Drosophila medium and hydrophobic interactions to maintain brain stability during imaging, eliminating the need for complex culture conditions or embedding procedures. We validate this approach by studying cell competition in the optic lobes following traumatic brain injury, where cell competition is expected to occur with a peak at 48 h after damage. We demonstrate the value of this method by visualizing the expression of the fitness checkpoint Azot in a loser cell and its subsequent elimination. This protocol offers a versatile platform for studying cell competition and other cellular processes requiring extended observation of the adult Drosophila brain.

To develop an effective strategy for in vitro mRNA production, it is crucial to evaluate the efficiency of the in vitro transcription platform. This can be accomplished using reporter genes, such as the luciferase-encoding gene. Luciferase activity assays provide a reliable means to assess the translation efficiency of in vitro transcribed mRNAs and to explore molecular dynamics associated with untranslated regions, capping, nucleotide analog incorporation, polyadenylation, and codon usage optimization. In this study, we propose a novel approach to performing the luciferase assay, offering a simpler, faster, and high-throughput method for evaluating in vitro generated transcripts to be employed for veterinary and human vaccine purposes as well as mRNA therapeutics.

Two-legged hopping is a well-established model for assessing leg stiffness; however, in existing studies, it is unclear whether the trial segment selection affects the results. This study aimed to assess if the selected hopping segment alters the value and individual variability (%CVind) of leg stiffness and kinetic performance metrics. Elite women athletes (42, volleyball, basketball, handball) and 14 non-athletic women performed barefoot two-legged hopping (130 bpm) on a force-plate (Kistler, 9286AA, sampling at 1000 Hz). Leg stiffness was estimated from the Fz registration (resonant frequency method). Four cumulative range segments (1-10, 1-20, 1-30, and 1-40 hops) and three segments of 10-hop subranges (11-20, 21-30, and 31-40) were analyzed (repeated measures one-way Anova, p ≤ 0.05, SPSS v30.0). The hopping segment did not significantly alter the leg stiffness value (segment average 30.6 to 31.2 kN/m) or its %CVind (segment average ≈ 3%). The kinetic performance metrics depicted a solid foundation for the extracted leg stiffness value, with %CVind not exceeding 6.2%. The results indicate a data collection of just 15 hops, in continuance reduced to a 10 hops segment (after excluding the first five to avoid neuromuscular adaptation) as a robust reference choice.

ESKAPE bacteria are a major global threat due to their rapid antibiotic resistance acquisition and severe healthcare-associated infections. Effective countermeasures require epidemiological surveillance and resistance transmission studies, particularly for antimicrobial-resistant (AMR) colonization in intensive care unit (ICU) patients. Whole-genome sequencing (WGS) provides critical information on resistance spread and mechanisms. In the provided protocol, rectal and oropharyngeal swabs, or endotracheal aspirate/bronchoalveolar lavage for intubated patients, are collected at ICU admission and twice weekly. Patient interviews and medical records identify risk factors for resistant microflora. Samples undergo cultivation, species identification, antibiotic susceptibility testing, and DNA extraction. Sequencing is performed using second- and third-generation platforms, with selected isolates subject to hybrid genome assembly. Resistance genes, virulence factors, and typing profiles (MLST, cgMLST) are determined. This protocol characterizes the ICU patient colonization by AMR pathogens, including species distribution, phenotypic and genotypic resistance profiles, clonal structure, and temporal changes. It estimates detection frequency and colonization patterns at each locus, identifies key risk factors, including prior community or inter-facility exposure, and analyzes associations between risk factors and admission colonization. The study aims to estimate AMR infection risk and severity in ICU patients through the comprehensive analysis of colonization dynamics, resistance patterns, and clonal characteristics using WGS data on pathogen composition and AMR trends.

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.