Search

Serum quality is an important factor in the pre-analytical phase of laboratory analysis. Visual inspection of serum quality (including recognition of hemolysis, icterus, and lipemia) is widely used in clinical laboratories but is time-consuming, subjective, and prone to errors.Deep learning models were trained using a dataset of 16,427 centrifuged blood images with known serum indices values (including hemolytic index, icteric index, and lipemic index) and their performance was evaluated by five-fold cross-validation. Models were developed for recognizing qualified, unqualified and image-interfered samples, predicting serum indices values, and finally composed into a deep learning-based system for the automatic assessment of serum quality.The area under the receiver operating characteristic curve (AUC) of the developed model for recognizing qualified, unqualified and image-interfered samples was 0.987, 0.983, and 0.999 respectively. As for subclassification of hemolysis, icterus, and lipemia, the AUCs were 0.989, 0.996, and 0.993. For serum indices and total bilirubin predictions, the Pearson's correlation coefficients (PCCs) of the developed model were 0.840, 0.963, 0.854, and 0.953 respectively. Moreover, 30.8% of serum indices tests were deemed unnecessary due to the preliminary application of the deep learning-based system.The deep learning-based system is suitable for the assessment of serum quality and holds the potential to be used as an accurate, efficient, and rarely interfered solution in clinical laboratories.

The abundance and type of immune cells in the tumor microenvironment (TME) significantly influence immunotherapy and tumor progression. However, the role of immune cells in the TME of gastric cancer (GC) is poorly understood. We studied the correlations, proportion, and infiltration of immune and stromal cells in GC tumors. Data analyses showed a significant association of infiltration levels of specific immune cells with the pathological characteristics and clinical outcomes of GC. Furthermore, based on the difference in infiltration levels of immune and stromal cells, GC patients were divided into two categories, those with “immunologically hot” (hot) tumors and those with “immunologically cold” (cold) tumors. The assay for transposase‐accessible chromatin using sequencing and RNA sequencing analyses revealed that the hot and cold tumors had altered epigenomic and transcriptional profiles. Claudin‐3 (CLDN3) was found to have high expression in the cold tumors and negatively correlated with CD8+ T cells in GC. Overexpression of CLDN3 in GC cells inhibited the expression of MHC‐I and CXCL9. Finally, the differentially expressed genes between hot and cold tumors were utilized to generate a prognostic model, which predicted the overall survival of GC as well as patients with immunotherapy. Overall, we undertook a comprehensive analysis of the immune cell infiltration pattern in GC and provided an accurate model for predicting the prognosis of GC patients., Immune hot and cold tumors showed the difference in epigenetic and transcriptomic alterations. Claudin 3 (CLDN3) was negatively correlated with the infiltration of CD8+ T cells. Overexpression of CLDN3 in gastric cancer (GC) cells inhibited the expression of MHC‐I and CXCL9. The prediction model performed well in predicting overall survival of patients with GC and patients with immunotherapy treatment.

The present study was aimed to explore the functional role of microRNA (miR)-29b in colon cancer, as well as underlying mechanisms. Expressions of miR-29b and folate receptor 1 (FOLR1) were measured in both human colon tumor samples and cell lines. Colon cancer cell lines SW480 and SW620 were transfected with miR-29b mimic, antisense oligonucleotides (ASO)-miR-29b, small interfering (siRNA) against FOLR1 (si-FOLR1), or corresponding negative controls (NCs), and then were incubated with or without oxaliplatin (L-OHP). Thereafter, cell viability, cytotoxicity, cell apoptosis, and expression of FOLR1, ATP Binding Cassette Subfamily G Member 2 (ABCG2) and p-glycoprotein (p-gp) were analyzed. We found that miR-29b was significantly decreased, while FOLR1 was statistically elevated in colon cancer samples and cell lines compared to the nontumor samples and nontumourigenic immortalized human colon epithelial cell line FHC. Overexpression of miR-29b markedly inhibited cell viability, promoted sensitivity to L-OHP, stimulated cell apoptosis (all p < .05), and decreased the levels of ABCG2 and p-gp in cancer cells, whereas suppression of miR-29b showed contrary results. Moreover, we observed that FOLR1 was a direct target of miR-29b and was negatively regulated by miR-29b. In addition, the findings revealed that the effects of FOLR1 inhibition on cell viability, sensitivity to L-OHP, cell apoptosis, and the levels of ABCG2 and p-gp were similar to overexpression of miR-29b. Taken together, our study suggests that miR-29b inhibits cell growth and promotes sensitivity to L-OHP in colon cancer by targeting FOLR1.

Background: Due to the lack of effective drugs, gastric cancer(GC) has a high mortality rate among other cancers, with a low 5-year survival rate and an inferior prognosis. Thus, screening of meaningful tumor biomarkers or therapeutic targets could play a vital role in the diagnosis, treatment, prognosis, and follow-up of GC. Methods: Gene expression profiles and comprehensive clinical information of 407 patients with GC were downloaded from The Cancer Genome Atlas (TCGA) database. GC-related single-cell RNA sequencing data from the GSE118916 dataset was downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were screened from transcriptomic data in GC and normal samples by R language. The DAVID database was also used to analyze the functions and pathways of DEGs. After combining differential genes with patient survival information, target genes were identified. The interaction of DEGs in the protein-protein interaction (PPI) network was also studied. Results: Our study identified a total of 209 differential genes, which might be positively related to GC. Gene Ontology (GO) analysis indicated numerous enrichment of DEGs in the extracellular matrix organization, extracellular structure organization, and muscle contraction. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that the DEGs were mainly enriched in focal adhesion, protein digestion and absorption, AGE-RAGE signaling pathway in diabetic complications. Further analysis showed the higher expression of Carboxypeptidase vitellogenic-like gene (CPVL) was related to the better prognosis of GC patients in both TCGA and the GEO database. FAM3 metabolism regulating signaling molecule D (FAM3D) and oxidized low-density lipoprotein receptor 1 (OLR1) were significantly associated with GC patients’ prognosis only in the GEO database. Lastly, the PPI network shows the gene expression proteins that interact most closely with CPVL protein.Conclusion: Our study revealed that CPVL gene could be a promising target for the diagnosis and treatment of GC, which has a great significance for the future research on GC. In addition, we were the first to find a close relationship between FAM3D and GC.