Jove-Journal of Visualized Experiments最新文献

Acute respiratory distress syndrome (ARDS) is a highly heterogeneous disease with clinical manifestations that may overlap with severe pneumonia, posing challenges for accurate differentiation. Therefore, early prediction and bedside rapid subtype clustering of ARDS patients are urgently needed. This study aims to develop a web-based system, which includes validated models of early bedside diagnosis and clinical subgroup classification, for predicting the development and phenotypes of pneumonia-associated ARDS. Diagnostic and subgroup models were developed and validated from the two large databases, Medical Information Mart for Intensive Care IV (MIMIC-IV) and Telehealth Intensive Care Unit (eICU) and were incorporated into a web-based prediction system. Data from patients with pneumonia hospitalized for more than 24 h between 2008 and 2019 were analyzed. The MIMIC-IV derivation cohort included 24,987 patients with pneumonia (14,121 with pneumonia-associated ARDS); the eICU verification cohort included 20,676 patients with pneumonia (9946 with pneumonia-associated ARDS). In diagnosis, the stacking method of machine learning performed best with an AUC of 0.919, an accuracy of 70.00%, a precision of 69.88% and a recall of 82.27% in the MIMIC-IV derivation cohort. The AUC, accuracy, precision, and recall of the eICU validation cohort were 0.915, 70.87%, 69.70% and 69.70% respectively. Pneumonia-associated ARDS was classified into three clinical phenotypes with different clinical characteristics and outcomes, all of which responded differently to treatment. Among patients in clusters 0 and 1, the in-hospital mortality rates were higher among those who received early corticosteroid treatment than among those who did not, whereas among patients in cluster 2, the in-hospital mortality rate was lower among those who received corticosteroids than among those who did not. We performed a web transformation of the diagnosis prediction and clinical subgroup classification of pneumonia-associated ARDS. Our web-based models of early bedside diagnosis and clinical subgroup classification of pneumonia-associated ARDS may assist clinicians in diagnosing and treating the disease and in promoting individualized precision treatment.

The paper aims to evaluate the efficacy and impact of efgartigimod on clinical outcomes and inflammatory markers in patients with antibody-negative long-segment myelitis (LETM).This retrospective study enrolled 47 patients diagnosed with antibody-negative LETM, including 36 in the control group who received methylprednisolone alone and 11 in the observation group who received a combination of efgartigimod and methylprednisolone. Comparisons of the expanded disability status scale (EDSS), clinical frailty scale (CFS), and Health Survey Short Form-36 (SF-36) scores between the two groups were conducted. Concentrations of interleukin-6 (IL-6), IL-10, and IL-41 were detected using enzyme-linked immunosorbent assay (ELISA), and their correlations with EDSS scores were also evaluated. The potential predictors of therapeutic efficacy were identified using univariate and multivariate logistic regression analyses.Efgartigimod significantly reduced EDSS and CFS scores after 1 month of treatment, with the observation group demonstrating greater improvements than the control group. Efgartigimod improved the quality of life as assessed by the SF-36, with significant increases in physical functioning (PF) and vitality (VT) scores in the observation group (p < 0.05). Efgartigimod decreased serum cytokine (IL-41, IL-10, and IL-6) levels and EDSS scores at the time of treatment (p < 0.05). Serum cytokine levels were positively correlated with EDSS scores. The group (treatment strategy) and length of the involved segment were predictive factors of drug efficacy.Efgartigimod demonstrated significant efficacy and safety for the treatment of LETM. Efgartigimod lowered serum concentrations of IL-6, IL-41, and IL-10 in LETM patients. The levels of IL-6, IL-41, and IL-10 correlated with EDSS scores, indicating their potential as predictive biomarkers for disease severity and treatment efficacy in LETM.

Single-molecule localization microscopy (SMLM) enables nanoscale imaging of cellular structures but is typically restricted to imaging near the coverslip due to the limitations of total internal reflection fluorescence (TIRF) and highly inclined and optical sheet (HILO) illumination. Here, a protocol that leverages a spinning disk confocal microscope equipped with optical photon reassignment (SDC-OPR) to perform SMLM in whole cells is presented. This method enables high-precision single-molecule imaging throughout the full cell volume using DNA points accumulation for imaging in nanoscale topography (DNA-PAINT) without the need for custom optics or complex illumination schemes. The protocol provides a stepwise guide on configuring a commercially available spinning disk microscope (e.g., CSU-W1 SoRA) for SMLM imaging, including acquisition parameters optimized for deep imaging. Sample preparation steps are outlined for labeling intracellular targets with DNA-conjugated probes to perform DNA-PAINT imaging, including strategies for multicolor imaging of several targets. Image acquisition is followed by single-molecule localization and reconstruction using standard software packages. Critical considerations for minimizing background, optimizing lateral resolution, and ensuring imaging quality across the full cell depth are also discussed. This accessible protocol allows researchers to perform deep, whole-cell SMLM using standard confocal equipment, expanding the range of biological questions addressable by single-molecule imaging beyond the near-membrane regions.

Heart failure (HF) remains a major global health challenge, with limited effective treatments targeting its core pathophysiological mechanisms. In this study, an integrated multiomics approach combining Mendelian randomization (MR) and single-cell RNA sequencing (scRNA-seq) was used to identify potential biomarkers and therapeutic targets for HF. We utilized data from genome-wide association studies (GWASs), expression quantitative trait loci (eQTLs), methylation quantitative trait loci (mQTLs), and protein quantitative trait loci (pQTLs) to investigate the genetic mechanisms of HF. Single-cell RNA sequencing was performed to analyse gene expression in cardiac macrophages, and pseudotime analysis was used to study the dynamic regulation of DBNL during macrophage differentiation. Molecular docking and dynamics simulations identified pirinixic acid (WY-14643; PubChem CID: 4594; synonyms: 50892-23-4; WY-14643) as a potential regulator of DBNL. Multiomics analysis revealed that DBNL (Drebrin-like) is a key gene associated with HF risk. Single-cell RNA sequencing revealed that DBNL is expressed mainly in cardiac macrophages and is upregulated under pathological conditions. The expression of DBNL by macrophages is associated with immune metabolism and profibrotic pathways, particularly the IL-6/JAK/STAT3, PI3K/AKT/mTOR, and TGF-β signalling pathways. Pseudotime analysis indicated that DBNL has a dynamic regulatory effect on macrophage differentiation, especially in chronic inflammation. Molecular docking and dynamic simulations have shown that pyridine acid has a potential role in regulating DBNL. This study elucidates the role of DBNL in the progression of heart failure and suggests its potential as a therapeutic target. Importantly, the therapeutic relevance is inferred from computational predictions. These findings offer novel insights into immune metabolism and macrophage-mediated intercellular communication, establishing a theoretical basis for future applications in the optimization of DBNL-targeted drugs and functional research. Nonetheless, additional experimental validation and preclinical studies are needed to substantiate the clinical efficacy of DBNL as a therapeutic target.

Chimeric antigen receptor (CAR) T cell therapies have demonstrated remarkable efficacy in several hematological malignancies, yet their success has not been fully replicated in solid tumors. Moreover, even in hematological cancers, relapse after CAR T cell infusion continues to compromise long-term outcomes. These challenges highlight the urgent need to develop strategies that enhance CAR T cell efficacy, persistence, overcoming tumor and microenvironment-mediated resistance. Clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9-based screening platforms provide a powerful approach to systematically identify genes that regulate CAR T cell function. By linking genetic perturbations to phenotypic outcomes, these assays enable the discovery of pathways controlling activation, proliferation, memory formation, and cytotoxicity. Standard workflows involve transduction of substantial numbers of cells with a single guide RNA (sgRNA) library, Cas9-mediated editing, selection of edited cells, and PCR amplification of sgRNA cassettes from genomic DNA (gDNA) prior to sequencing. However, PCR amplification using large amounts of gDNA poses significant challenges and often fails to selectively amplify and retrieve sgRNAs. Here, we describe an optimized CRISPR-Cas9 knockout screening protocol, which we have tested on primary human CAR T cells. The method here incorporates an intermediate step during sgRNA library preparation that reduces gDNA carryover through enzymatic digestion and selective pulldown of the sgRNA cassette, thereby increasing the efficiency of the first PCR amplification. This modification allowed us to retrieve sgRNA information across our CAR T cell screens, which had remained elusive in our previous attempts using traditional 1 and 2-step PCR amplification protocols. In conclusion, this optimized workflow facilitates CRISPR screening library preparation in challenging samples and enables the identification of key genetic determinants that can be targeted to improve therapeutic efficacy.

The insertion of removable partial dentures (RPDs) in the oral cavity is associated with changes in the oral microflora over time. However, there is a paucity of literature examining the presence of red complex bacteria (RCB) in these patients. Given RCB's established role in the development of periodontitis, peri-implantitis, and various systemic diseases, investigating the impact of RPDs on these bacteria is crucial. This study aims to quantify the subgingival burden of RCB-Porphyromonas gingivalis, Tannerella forsythia, and Treponema denticola-in abutment teeth of partially edentulous patients rehabilitated with RPDs, using real-time polymerase chain reaction (RT-PCR). A secondary objective was to assess the periodontal status of abutment and non-abutment teeth and to determine the correlations between microbial load and clinical periodontal indices. Thirty participants requiring RPDs were enrolled. Deoxyribonucleic acid (DNA) was isolated from bacterial samples obtained from abutment teeth before (T0) and 3 months post-insertion (T3). RCB quantification was performed via RT-PCR and expressed as "Lg (genome equivalents/sample)". Clinical parameters-plaque index (PI), gingival index (GI), probing depth (PD), and tooth mobility (TM)-were recorded for abutment and non-abutment teeth at both timepoints. Abutment teeth demonstrated statistically significant increases in RCB loads across all three target species from T0 to T3: P. gingivalis (1.99 ± 2.01 vs 3.64 ± 2.21, p = 0.00003), T. forsythia (2.20 ± 2.17 vs 3.56 ± 2.33, p = 0.00009), and T. denticola (0.82 ± 1.41 vs 2.35 ± 2.21, p = 0.0001). While periodontal parameters were elevated in abutment versus non-abutment teeth post-treatment, differences did not reach statistical significance. Among pathogens, T. forsythia exhibited the strongest positive correlation with periodontal indices. Short-term use of RPDs is associated with a significant increase in RCB colonization of abutment teeth, suggesting early microbial and inflammatory shifts that may precede clinically evident periodontal breakdown.

As quantitative PCR (qPCR) is adopted more widely across industries, the capacity to extract and analyze DNA in remote settings without logistical hurdles is becoming essential. One clear example is the need for straightforward tools to quantify contaminant-degrading bacteria at remote sites affected by train derailments or petroleum releases, enabling timely assessment of risk and environmental impact. To reliably achieve this, the processes must be portable and lightweight, user-friendly for non-scientists, resistant to contamination, and capable of delivering rapid results while still maintaining rigorous quality control. Although options for both nucleic acid extraction and qPCR analysis outside of the laboratory have existed for years, only recently have advances in technology enabled the development of field-deployable systems capable of reliable and accurate performance. In this study, we demonstrate the use of a field DNA extraction kit coupled with portable qPCR instruments under realistic environmental conditions. Field trials conducted at ambient outdoor temperatures of approximately 30 °C demonstrated DNA yields and amplification efficiencies comparable to those obtained in controlled laboratory settings. Importantly, results obtained in the field demonstrated equivalent sensitivity and reproducibility, confirming that portable workflows can provide high-quality data without the need for conventional laboratory infrastructure. These findings highlight the potential for decentralized molecular analyses that expand the reach of qPCR into remote and resource-limited settings while maintaining the accuracy and reliability required for decision-making.

Large Language Models (LLMs) are increasingly being applied to sensitive domains such as medical care, which pose multiple technical and ethical challenges. The most immediate issues include biases built into these models, which are trained on large datasets that may reflect societal prejudices. Such biases can yield outputs that are unfair, ungeneralizable, or even harmful, rendering them clinically inapplicable in the real world and noncompliant with ethical and regulatory constraints. We examine how well bias suppression works when fine-tuned models are prompted across four critical categories (gender, race, profession, and religion). We manually curate a diverse inference dataset and create twelve prompt variants -- six of which are debiased -- to test the outputs of three open-source LLMs: Llama2-7B, Mistral-7B, and Dolly-7B. The fairness and interpretability of the outputs are evaluated using a bias-scoring metric, where lower scores indicate better fairness and interpretability. We also note that debiased prompts reduce bias, and fine-tuning the model performs even better. The results emphasize the critical importance of timely action, model robustness, and ongoing ethical scrutiny for trustworthy and fair LLM deployment in real-world settings, such as medical imaging, and the maintenance of Electronic Health Records (EHR).

This study aimed to explore the clinical value of Sanshen Fuzheng Decoction in the secondary prevention of chemotherapy-induced neutropenia in patients with malignant ovarian tumors. Sixty patients with primary ovarian cancer who received postoperative chemotherapy between January 2022 and January 2023 were enrolled. Using a random number table, they were equally divided into a treatment group and a control group (n = 30 each). The control group received standard paclitaxel plus carboplatin (TP) chemotherapy (paclitaxel + carboplatin), while the treatment group received TP combined with Sanshen Fuzheng Decoction. Baseline indicators were not significantly different between groups before chemotherapy (P > 0.05). On days 3, 7, 10, 14, and 20 post chemotherapy, both groups showed increased hemoglobin (Hb), red blood cell (RBC), neutrophil (NE) counts, interleukin-2 (IL-2), and interleukin-6 (IL-6), and decreased white blood cell (WBC), lymphocyte (LYM), and platelet (PLT) counts. Compared to the control group, the treatment group showed significantly greater increases in Hb, RBC, NE, interleukin-2 (IL-2), and interleukin-6 (IL-6), and the decrease in LYM was more obvious, while the decrease in WBC and PLT was smaller (all P < 0.05). The total dose of recombinant human granulocyte colony-stimulating factor (rhG-CSF) and the duration of leukopenia were significantly lower in the treatment group (P < 0.05). Additionally, more improvement in Traditional Chinese Medicine (TCM) symptom scores (fatigue, weakness, sweating, dizziness, pale complexion) was observed in the treatment group. The incidence of chemotherapy-related toxic side effects was significantly lower in the treatment group (P < 0.05). No significant differences were observed in liver/kidney function, febrile neutropenia, or absolute neutrophil count (ANC) reduction (P > 0.05). San shen Fu zheng Decoction, as an adjunct to chemotherapy in ovarian cancer, showed potential benefits in improving hematological recovery, reducing rhG-CSF use, and alleviating treatment-related symptoms.

Higher-level spinal cord injury (SCI) above thoracic level 6 (T6) disrupts autonomic function and contributes to secondary complications, including fluctuations in blood pressure, heart rate, and temperature. In rats, the placement of telemetric implants in the descending aorta offers a robust methodology for assessing various cardiovascular parameters, such as systolic and diastolic pressure, mean arterial pressure, and heart rate. Core body temperature and animal activity can also be recorded following telemetric implant placement. A rat with a telemetric implant is kept on the receiver plate connected to a computer system for recording various parameters. Continuous long-term recordings at defined time intervals allow for the quantification of injury-induced physiological disruptions, including day-night variations in cardiovascular function. Comparing pre-injury (baseline) and post-injury recordings enables researchers to assess the impact of SCI on these physiological metrics. This methodology paper outlines a detailed, step-by-step procedure for telemetric implant placement in the descending aorta (using mono-occlusion of the artery) of the rat, as well as continuous telemetry recording following severe-high thoracic (T3) SCI. We discuss the challenges typically encountered while performing this procedure, as well as a dedicated troubleshooting section. The data acquisition process using Ponemah software and its various applications are also discussed. Rats display SCI-associated cardiovascular, temperature, and activity abnormalities, and telemetric implants are effective devices for studying these post-injury complications and evaluating potential treatments.