Jove-Journal of Visualized Experiments最新文献

Toxoplasmosis is a parasitic infection commonly related to ocular lesions and neonatal malformations. Since the early 1950s, the first-line treatment of this disease has relied on the combination of sulfadiazine, pyrimethamine, and folinic acid. Over those years, only a few alternative regimens have been introduced. This highlights the need for discovering new bioactive molecules against Toxoplasma gondii. Given that this pathogen is an obligate intracellular parasite, traditional drug screening in the laboratory typically requires expensive materials and equipment, such as assays using fluorescent proteins or β-galactosidase-expressing parasites. Additionally, methods like optical microscopy quantification can be time-consuming. The plaque assay is a method that evaluates the intensity of intracellular pathogen proliferation by measuring the number of regions and area of destruction in a cell monolayer damaged by the parasite's lytic cycle. This work describes an optimized plaque assay protocol designed for screening active molecules against intracellular tachyzoites of T. gondii in vitro. This protocol utilizes inexpensive materials and a straightforward laboratory setup, yielding rapid and reproducible results that facilitate the identification of new active molecules against this parasite by various research groups.

This article delineates an integrated methodology that combines Mendelian randomization (MR), transcriptomic analysis, and network pharmacology to identify and prioritize potential therapeutic targets for osteoarthritis (OA). It is designed to guide researchers in implementing this multimodal pipeline to investigate drug repurpose and the development of novel therapeutic interventions for OA. The methodological framework comprises five sequential stages: first, MR analysis pipeline, employing two-sample MR to identify putative causal plasma proteins associated with OA, followed by Steiger filtering and phenome-wide association scanning to assess causal directionality and potential off-target effects; second, transcriptomic sequencing workflow, integrating RNA-seq data to identify and refine candidate protein targets; third, integration strategy, merging MR and transcriptomic results to prioritize candidate proteins; fourth, network pharmacology and molecular docking procedures, involving the construction of protein-protein interaction networks, functional enrichment analysis, and molecular docking to explore ligand-target interactions; and fifth, intended application, focusing on the prioritization of candidate compounds and natural products with potential therapeutic relevance. By organizing the workflow into distinct analytical phases, this framework provides a reproducible approach for transitioning from genetic and transcriptomic discovery to computational drug-target evaluation, without presenting specific experimental outcomes. The methodology facilitates systematic and hypothesis-driven investigation into OA therapeutics using publicly accessible datasets and computational tools.

Posterior lumbar interbody fusion (PLIF) is widely recognized as an effective surgical approach for treating degenerative lumbar spine conditions. To address challenges associated with the classical PLIF technique, such as the need for extensive neural retraction and the associated risk of dural tears, we developed a novel variant, the Lateral-PLIF, which optimizes cage placement to enhance outcomes and minimize risks. This article offers a detailed, step-by-step explanation of the procedure, highlighting its advantages in the operative field. Here we present a case of a posterior interbody fusion performed for a lumbar degenerative disease. The technique involves bilateral cage placement through the transition zone, between the central canal and intervertebral foramen, just above the lateral recess. This entry point is strategically chosen as it is safer for respecting the surrounding nervous structures, avoiding manipulation of the foraminal root, while reducing medial dural retraction and providing a better cage orientation. Finally, the technique keeps the advantages of the bilateral approach: direct bilateral decompression, high-quality discectomy, and endplates cleaning performed from each side, progressive distraction achieved from the disc space alternating right/left, and opportunity for massive bone grafting. Lateral-PLIF is a safe and effective surgical technique for lumbar interbody fusion. Its high fusion rate, improved functional outcomes, and low complication rates make it a promising alternative to traditional PLIF. This step-by-step article serves as a practical guide for surgeons, promoting the broader adoption and further validation of this technique in comparative studies.

Subsurface chemical, geochemical, and microbiological interactions, such as reactive mineral formation, gas generation, and contaminant sorption, play a critical role in shaping remediation strategies. However, evaluating these processes at the field scale is often limited by the expense and invasiveness of traditional sampling methods. The mineral trap sampler offers a practical, low-cost alternative for in situ monitoring of reactive mineral formation without requiring additional drilling or core extraction. Deployed within the screen interval of a monitoring well, the sampler passively collects mineral precipitates and microbial biomass over a period of at least thirty days. Once retrieved, the sampler is vacuum sealed and transported on ice to a laboratory for analysis using standard commercial techniques. This innovation enables scientists and engineers to assess both the rate and spatial distribution of mineral precipitation, providing valuable insights into subsurface conditions. By delivering actionable data on mineral formation and microbial activity, the mineral trap sampler supports performance evaluations and helps guide optimization strategies for remediation systems. Its simplicity and affordability make it a useful tool for improving site characterization and long-term management.

Growing food demand and climate stresses drive smart agriculture implementation, but existing Cyber-Physical Systems (CPS) lack dependable cross-layer integration and real-time flexibility, limiting performance in dynamic environments. This protocol aims to provide a cross-layer cyber-physical modeling and optimization strategy for intelligent greenhouse agriculture. It demonstrates potential applicability for enhancing the reliability and adaptability of agricultural Cyber-Physical Systems. The approach integrates a physical layer with the Soil-Plant-Atmosphere Continuum model and Ensemble Kalman Filter (EnKF) calibration for accurate soil moisture prediction. It includes a network layer employing multi-protocol fusion with Stochastic Petri Net modeling to evaluate communication reliability. A control layer builds on a stochastic hybrid system to coordinate joint decision-making. Reliability is further assessed through a functional-temporal-ecological indicator framework, while optimization combines multi-objective reinforcement learning with safety constraints and Bayesian meta-learning to enable rapid adaptation during crop switching. An edge-intelligent deployment ensures robust control during communication interruptions. Results from greenhouse tomato cultivation in Shouguang, China, show reproducible and stable performance in yield prediction, water use efficiency, and control latency under challenging conditions. This methodology provides a practical and replicable workflow for implementing adaptive and reliable agricultural Cyber-Physical Systems.

Transfusion adverse reactions (TARs) encompass undesirable effects occurring during or after blood transfusion, resulting from the administration of blood, blood products, or transfusion-associated materials. These reactions may cause mild to severe clinical manifestations, including allergic reactions, hemolysis, and transfusion-related acute lung injury (TRALI). Current conventional approaches -- such as ABO/Rh blood group matching and massive transfusion protocols -- can improve patient outcomes but fail to address complex patient-specific factors. Consequently, developing a dedicated TAR risk model is essential. Machine learning (ML) algorithms can leverage large-scale clinical and laboratory datasets to construct diagnostic and prognostic models, advancing personalized and precision medicine. Notably, the scikit-learn (SK-learn) ML algorithmhas not yet been directly applied to TAR risk assessment; nevertheless, its efficacy in constructing similar medical risk prediction models has gained significant recognition. This review presents an ML-based framework for predicting and preventing TAR, with a specific emphasis on the role of SK-learn across diverse clinical contexts. Our analysis lays the groundwork for future precision diagnosis of TAR and the development of SK-learn algorithms. Crucially, our synthesis of the literature indicates that sklearn offers a versatile and powerful toolkit for this task; however, its successful translation into clinical practice is contingent upon overcoming key challenges, including multi-center data heterogeneity and the need for robust model interpretability.

Intranasal delivery of mRNA therapeutics is a promising strategy for vaccination and treating respiratory diseases, offering direct immune activation at the site of pathogen entry. However, conventional aerosolization methods (e.g., ultrasonic or high-pressure nebulizers) deteriorate non-viral mRNA vectors through excessive shear forces, causing mRNAs to lose their structural integrity and biological activities. A Rayleigh breakup nasal atomizer was used to gently aerosolize polyethyleneimine (PEI)-mRNA vectors into uniform droplets. Green Fluorescent Protein (GFP)-encoding mRNA was formulated into cationic polyplexes and characterized pre- and post-aerosolization. The Rayleigh breakup process forms a continuous micro-jet of droplets with minimal shear, thereby preserving the physicochemical properties of the nanoparticles. Consistent particle size, low polydispersity index, and stable zeta potential before and after aerosolization were observed, confirming that the physicochemical properties of mRNA polyplexes were well preserved via Rayleigh breakup for aerosolization. Using an Alberta Idealized Nasal Inlet (AINI) model of the nasal airway, the PEI-mRNA aerosols were delivered. The aerosolized mRNAs were primarily deposited in the turbinate regions. Negligible fractions were found in the nasopharynx or lung-equivalent sections. In addition, the post-aerosolized mRNA polyplexes were successfully delivered to A549 human lung epithelial cells and produced detectable GFP expression. This protocol demonstrates a non-destructive intranasal mRNA delivery method using Rayleigh breakup aerosolization. It effectively maintains the physicochemical properties and biological functions of non-viral mRNA vectors, atomizing the aqueous phase into droplets of appropriate sizes for targeted nasal deposition. This protocol reveals a novel approach for effectively aerosolizing mRNAs and evaluating their regional deposition in the nasal cavity.

With increasing societal competition, mental health issues among college students have become increasingly prominent, while traditional mental health education often suffers from limited formats and effectiveness. This study investigates the mechanisms and effectiveness of horticultural therapy (HT), a nature-based intervention, in improving college students' mental health using a sequential mixed-methods explanatory design. In the quantitative phase, 112 college students were randomly assigned to an experimental group receiving HT intervention or a control group, with the experimental group participating in eight weekly HT sessions (90 min each) involving activities such as flower arrangement and herbal tea tasting. Intervention effects were assessed using standardized psychological scales and facial expression video analysis. In the qualitative phase, textual data reflecting participants' authentic experiences were collected and analyzed using grounded theory with NVivo 12. Results showed that, compared with the control group, the experimental group demonstrated significantly higher post-intervention scores in meaning in life (p = 0.017), overall affect index (p = 0.044), and life satisfaction (p = 0.046). Within the experimental group, post-intervention levels of meaning in life (p = 0.02), affect balance (p = 0.007), and positive affect (p = 0.019) were significantly higher than pre-intervention values. Facial expression analysis revealed notable differences in the distribution of seven expressions between groups, with positive expressions increasing over time, particularly during flower arranging and tea tasting activities. Qualitative analysis further identified a four-layer pathway model (Environment → Cognition → Emotion → Behavior), explaining 36%, 32%, 16%, and 15% of the variance, respectively. Overall, horticultural therapy effectively enhances college students' life satisfaction, sense of life meaning, and affect balance, thereby improving mental health through a multi-layered psychological pathway.

This protocol provides a methodological framework for conducting a meta-analysis to evaluate the effects of nutritional therapies on the nutritional status of gastric cancer patients. By following this protocol, researchers will be able to systematically identify, appraise, and synthesize evidence from randomized controlled trials and comparative studies. The steps include: formulating a comprehensive search strategy across multiple databases; dual-independent screening and selection of studies using predefined PICOS criteria (population, interference, comparison, outcome, and study design); standardized data extraction and harmonization of outcome measures; assessment of study quality using the Cochrane Risk of Bias 2.0 tool; and statistical synthesis using appropriate models, with exploration of heterogeneity and sensitivity analyses. Application of this protocol to the available evidence suggests potential benefits of specific nutritional interventions, e.g., oral supplementation for body weight, though high heterogeneity underscores the need for rigorous and standardized methodology in this field. A continuous model with fixed or random effects was used to get the mean difference (MD) with 95% confidence intervals (CIs). A total of 18 studies, involving 3,586 subjects, were selected for the meta-analysis. Oral nutritional supplementation had a significantly increased body weight (MD, 0.74; 95% CI, 0.20-1.27, p = 0.007) compared to the control in patients with gastric cancer. Early enteral nutrition had significantly improved prealbumin levels (MD, 22.53; 95% CI, 13.37-31.69, p < 0.001) compared to parenteral nutrition in patients. However, no significant differences were found between enteral immunonutrition and standard enteral nutrition for albumin (MD, 0.57, 95%CI, -0.31-1.44, p=0.20), prealbumin (MD, 0.23, 95%CI, -0.29-0.76, p=0.38), or transferrin levels (MD, 0.11, 95%CI, -0.09-0.32, p=0.28). The studied data showed that using oral nutritional supplementation had significantly increased body weight compared to control, and early enteral nutrition had significantly improved prealbumin levels compared to parenteral nutrition. However, more studies are required to validate this finding.

Endometrial cancer (EC) ranks among the least prevalent gynecological tumors worldwide, with rising incidence due to aging and obesity. Lymph node metastasis remains common in Uterine Corpus Endometrial Carcinoma (UCEC), necessitating new prognostic biomarkers to guide treatment. In this research, UCEC information from the Cancer Genome Atlas (TCGA) was analyzed, and the results were validated using the Gene Expression Omnibus (GEO). Eighteen differentially expressed genetic factors associated with nicotinamide metabolism (NMRDEGs) were identified. Gene Set Enrichment Analysis (GSEA) indicated their role in oxidative stress, hypoxia, glycolysis, and apoptosis processes. Univariate Cox regression identified six key genes (AURKA, CDKN3, FOXM1, CDKN2A, TK1, and CDK1), utilized to progress a hazard prediction framework. Protein-protein interaction (PPI) analysis uncovered additional hub genes such as CDK2, CCNA2, TP53, and FOXM1. The six key genes showed strong prognostic value, and the study's risk model could guide clinical decisions. Nicotinamide metabolism was found to be significantly linked with EC progression. This study offers new perceptions into the role of nicotinamide metabolism in EC and suggests possible avenues for treatment advancements.