The potential of serum extracellular vesicles (EVs) as non-invasive biomarkers for diagnosing colorectal cancer (CRC) remains elusive. We employed an in-depth 4D-DIA proteomics and machine learning (ML) pipeline to identify key proteins, PF4 and AACT, for CRC diagnosis in serum EV samples from a discovery cohort of 37 cases. PF4 and AACT outperform traditional biomarkers, CEA and CA19-9, detected by ELISA in 912 individuals. Furthermore, we developed an EV-related random forest (RF) model with the highest diagnostic efficiency, achieving AUC values of 0.960 and 0.963 in the train and test sets, respectively. Notably, this model demonstrated reliable diagnostic performance for early-stage CRC and distinguishing CRC from benign colorectal diseases. Additionally, multi-omics approaches were employed to predict the functions and potential sources of serum EV-derived proteins. Collectively, our study identified the crucial proteomic signatures in serum EVs and established a promising EV-related RF model for CRC diagnosis in the clinic.

Introduction: Interleukin‐6(IL‐6) measurement is used as a biomarker in medical diagnosis, therapy, and prognosis in various diseases. However, several pre‐analytical factors may yield a false IL‐6 result. In this study, we set out to investigate the effects of corrected blood sample handling procedures on measurable IL‐6.Method: EDTA plasma and serum samples were collected from 45 healthy individuals. The participants were divided into three groups to perform different handling procedures. Different centrifugal timing, storage temperature, and time were executed on the samples. The changed trends of IL‐6 levels were analyzed.Results: At baseline, while the paired plasma and serum IL‐6 values had a good correlation, the plasma levels were higher than serum. In general, the unseparated EDTA plasma kept steady with time. With the increase in storage temperature and time, a more pronounced rise in unseparated serum IL‐6 was observed. Nevertheless, the samples in Group 3 which centrifuged and separated immediately kept stable after a different temperature and longtime storage.Conclusion: Sample types, centrifugal timing, storage temperature, and time may affect the IL‐6 levels. A standard blood sample handling procedure should be performed to ensure the accuracy and stability of IL‐6 values.

Skin wound repair involves myofibroblasts crucial for tissue integrity. This study utilized single-cell RNA sequencing to explore myofibroblast diversity in various wound healing scenarios. Analysis of 89,148 cells from skin ulcers, keloids, and normal scars identified 13 cell clusters. Myofibroblast subcluster analysis unveiled 11 subsets, with subclusters 1 and 9 predominant in ulcers. Subcluster 1 exhibited heightened matrix metalloproteinase expression and involvement in bacterial response and angiogenesis, crucial in inflammation. Tissue validation confirmed subcluster 1 significance., while animal models supported upregulated CA12, TDO2, and IL-7R in chronic ulcers. These findings illuminate myofibroblast heterogeneity and their impact on wound healing, offering insights into potential therapeutic targets.

Background: Airway mucus hypersecretion (AMH) can occur in children with acute respiratory diseases, but its underlying mechanisms and relationship with the lower respiratory tract microbiota (LRTM) are not yet fully understood. This study investigates the characteristics of LRTM in children with Mycoplasma pneumoniae pneumonia (MPP) and its impact on AMH.Methods: We collected bronchoalveolar lavage fluid and related clinical indicators from 202 children with MPP. 16S rRNA gene amplicon sequencing was used for detection and identification. Microbial diversity and characteristic genera were compared, and their abundance was analyzed for correlations with clinical factors.Results: As the disease course (days from onset to bronchoscopy, grouped into T1, T2, T3) extended, α-diversity of the LRTM gradually increased, particularly in the T3 hypersecretion group. Moreover, significant differences were observed in the incidence of AMH, co-infection rates, peripheral white blood cell (WBC) count, and C-reactive protein levels. In AMH, Mycoplasmoides and Veillonella abundance and peripheral neutrophils were risk factors for increased secretions. In addition, in the T3 co-infection group, Streptococcus and Prevotella increased, replacing Stenotrophomonas as the dominant genus, possibly due to β-lactam antibiotic use. Prevotella abundance was strongly correlated with WBC.Conclusion: The composition and structure of LRTM in children with MPP played a crucial role in AMH and disease progression.