The airway smooth muscle (ASM) plays an indispensable role in airway structure and function. Dysfunction in ASM plays a central role in the pathogenesis of chronic obstructive pulmonary disease (COPD) and contributes to alterations of contractility, inflammatory response, immunoreaction, phenotype, quantity, and size of airways. ASM makes a key contribution in COPD by various mechanisms including altered contractility and relaxation induce by [Ca2+]i, cell proliferation and hypertrophy, production and modulation of extracellular cytokines, and release of pro-and-anti-inflammatory mediators. Multiple dysfunctions of ASM contribute to modulating airway responses to stimuli, remodeling, and fibrosis, as well as influence the compliance of lungs. The present review highlights regulatory roles of multiple factors in the development of ASM dysfunction in COPD, aims to understand the regulatory mechanism by which ASM dysfunctions are initiated, and explores the clinical significance of ASM on alterations of airway structure and function in COPD and development of novel therapeutic strategies for COPD.

Background: The purpose of this study was to explore the applicability of application effect of head-mounted mixed reality (MR) equipment combined with a three-dimensional (3D) printed model in neurosurgical ventricular and haematoma puncture training. Methods: Digital Imaging and Communications in Medicine (DICOM) format image data of two patients with common neurosurgical diseases (hydrocephalus and basal ganglia haemorrhage) were imported into 3D Slicer software for 3D reconstruction, saved, and printed using 3D printing to produce a 1:1-sized head model with real person characteristics. The required model (brain ventricle, haematoma, puncture path, etc.) was constructed and imported into the head-mounted MR device, HoloLens, and a risk-free, visual, and repeatable system was designed for the training of junior physicians. A total of 16 junior physicians who studied under this specialty from September 2020 to March 2022 were selected as the research participants, and the applicability of the equipment and model during training was evaluated with assessment score sheets and questionnaires after training. Results: According to results of the assessment and questionnaire, the doctors trained by this system are more familiar with the localization of the lateral anterior ventricle horn puncture and the common endoscopic surgery for basal ganglia haemorrhage, as well as more confident in the mastery of these two operations than the traditional training methods. Conclusions: The use of head-mounted MR equipment combined with 3D printing models can provide an ideal platform for the operation training of young doctors. Through holographic images created from the combination of virtual and real images, operators can be better immersed in the operation process and deepen their understanding of the operation and related anatomical structures. The 3D printed model can be repeatedly reproduced so that doctors can master the technology, learn from mistakes, better achieve the purpose of teaching and training, and improve the effect of training.

Digestive system cancers are prevalent diseases with a high mortality rate, posing a significant threat to public health and economic burden. The diagnosis and treatment of digestive system cancer confront conventional cancer problems, such as tumor heterogeneity and drug resistance. Single-cell sequencing (SCS) emerged at times required and has developed from single-cell RNA-seq (scRNA-seq) to the single-cell multi-omics era represented by single-cell spatial transcriptomics (ST). This article comprehensively reviews the advances of single-cell omics technology in the study of digestive system tumors. While analyzing and summarizing the research cases, vital details on the sequencing platform, sample information, sampling method, and key findings are provided. Meanwhile, we summarize the commonly used SCS platforms and their features, as well as the advantages of multi-omics technologies in combination. Finally, the development trends and prospects of the application of single-cell multi-omics technology in digestive system cancer research are prospected.

Background: Aspiration pneumonia (AP) is a type of lung inflammation caused by the aspiration of food, oropharyngeal secretions, or gastric contents. This condition is particularly common in older adults and individuals with impaired swallowing or consciousness. While the diagnosis of AP relies on clinical history, swallowing assessments, and imaging, these methods have significant limitations, often leading to underdiagnosis or misdiagnosis. Reliable biomarkers for AP diagnosis are lacking, making early detection and treatment challenging. Methods: Nineteen patients diagnosed with pneumonia were included in this study, divided into two groups: AP (n = 10) and non-AP (n = 9). Biological fluid samples, including bronchoalveolar lavage fluid (BALF), saliva, serum, sputum, and urine, were analyzed using non-targeted liquid chromatography with tandem mass spectrometry (LC-MS/MS). Differential metabolites were identified using fold change analysis, statistical significance, and receiver operating characteristic (ROC) curve analysis to evaluate their diagnostic potential. Spearman correlation was used to examine the relationship between selected metabolites and clinical parameters. Results: Significant metabolic differences were found between AP and non-AP patients, with many different metabolites identified across biological fluids. Dehydroepiandrosterone sulfate (DHEAS), Androstenediol-3-sulfate (ADIOLS), and beta-muricholic acid were identified as key biomarkers through fold change analysis and ROC curve analysis, showing consistent increasing or decreasing trends in BALF, sputum, and serum samples. DHEAS was found to be negatively correlated with the Acute Physiology and Chronic Health Evaluation II (APACHE II) (r = − 0.619, p = 0.005) in BALF sample. The area under curve (AUC) values showed that these molecules could serve as effective biomarkers for AP. Conclusions: This study identifies DHEAS, ADIOLS and beta-muricholic acid as promising biomarkers for AP, with the potential to improve early diagnosis and treatment. These findings underscore the clinical value of metabolomics in developing diagnostic tools for AP, facilitating better clinical management and patient outcomes. Further research is required to validate these biomarkers in larger cohorts and explore their mechanistic roles in AP pathophysiology.

Background: Aspiration pneumonia (AP) is a common disease, particularly among the elderly, and is diagnostically challenging due to its non-specific presentation and lack of a diagnostic gold standard or reliable biomarkers. Metabolomics may enable accurate non-invasive diagnosis. To address this gap, this study aims to develop and validate metabolomic machine learning models for AP diagnosis within a multicenter prospective observational cohort. Methods: We enrolled 121 patients (Cohort 1) with AP or non-aspiration pneumonia (NonAP), nearly all of whom were from the intensive care unit. Non-targeted metabolomic profiling of serum, bronchoalveolar lavage fluid, and sputum was performed using liquid chromatography–mass spectrometry. Machine learning models were constructed for each biofluid. We also measured peripheral blood cytokines and built corresponding diagnostic models, against which the performance of the metabolomic models was compared. The optimal serum model was subsequently validated using targeted metabolomics in 175 patients. Results: In Cohort 1, distinct metabolic profiles distinguished AP from NonAP patients across all sample types. The serum-based model demonstrated superior diagnostic performance (area under the receiver operating characteristic curve (AUROC) 0.973) compared to bronchoalveolar lavage fluid and sputum models. A cytokine-only model performed poorly (AUROC 0.700), and integrating cytokines with metabolomics did not improve accuracy. Consequently, the serum biomarker panel was advanced to targeted validation. In the 175 patients, a refined model based on four serum metabolites (Galactitol, PC(18:0/20:4(5Z,8Z,11Z,14Z)), Quinolinic acid, N6,N6,N6-Trimethyl-L-lysine) retained high diagnostic accuracy (AUROC 0.900) and effectively discriminated both acute and chronic AP subtypes (AUROCs 0.905 and 0.895, respectively). Conclusions: We developed a serum metabolomics-based model with high diagnostic accuracy for AP, showcasing strong potential for clinical diagnosis.