RESUMO
The relationship between the immune cell and tumor occurrence and progression remains unclear. Profiling alterations in the tumor immune microenvironment (TIME) at high resolution is crucial to identify factors influencing cancer progression and enhance the effectiveness of immunotherapy. However, traditional sequencing methods, including bulk RNA sequencing, exhibit varying degrees of masking the cellular heterogeneity and immunophenotypic changes observed in early and late-stage tumors. Single-cell RNA sequencing (scRNA-seq) has provided significant and precise TIME landscapes. Consequently, this review has highlighted TIME cellular and molecular changes in tumorigenesis and progression elucidated through recent scRNA-seq studies. Specifically, we have summarized the cellular heterogeneity of TIME at different stages, including early, late, and metastatic stages. Moreover, we have outlined the related variations that may promote tumor occurrence and metastasis in the single-cell era. The widespread applications of scRNA-seq in TIME will comprehensively redefine the understanding of tumor biology and furnish more effective immunotherapy strategies.
RESUMO
OBJECTIVES: Age and sex characteristics are evident in cephalometric radiographs (CRs), yet their accurate estimation remains challenging due to the complexity of these images. This study aimed to harness deep learning to automate age and sex estimation from CRs, potentially simplifying their interpretation. STUDY DESIGN: We compared the performance of 4 deep learning models (SVM, R-net, VGG16-SingleTask, and our proposed VGG16-MultiTask) in estimating age and sex from the testing dataset, utilizing a VGG16-based multitask deep learning model on 4,557 CRs. Gradient-weighted class activation mapping (Grad-CAM) was incorporated to identify sex. Performance was assessed using mean absolute error (MAE), specificity, sensitivity, F1 score, and the area under the curve (AUC) in receiver operating characteristic analysis. RESULTS: The VGG16-MultiTask model outperformed the others, with the lowest MAE (0.864±1.602) and highest sensitivity (0.85), specificity (0.88), F1 score (0.863), and AUC (0.93), demonstrating superior efficacy and robust performance. CONCLUSIONS: The VGG multitask model demonstrates significant potential in enhancing age and sex estimation from cephalometric analysis, underscoring the role of AI in improving biomedical interpretations.
RESUMO
OBJECTIVES: The aim of this study was to obtain greater insight into the environmental and genetic factors affecting the oral microbiome. DESIGN: To this end, we investigated the oral microbiome composition in Han and Zang populations living at different altitudes. The saliva microbiome in 115 individuals from Zang and Han populations living at different altitudes was analyzed using the 16 s rRNA gene sequencing method on the Illumina MiSeq platform. The dominant species in the oral microbiome were verified by quantitative real-time polymerase chain reaction (qPCR) analysis. RESULTS: The Han population, living at an altitude of 500 m, had higher microbiome diversity than the Zang population living at altitudes of 3000-4000 m. People living at 3000 m had a higher relative abundance of Leptothrix genus, but people living at 500 m had a higher relative abundance of Capnocytophaga genus according to Lefse difference analysis (P < 0.05). Compared to the Zang population, the Han population had higher relative abundances of Porphyromonas and Treponema genus organisms, especially Porphyromonas (P < 0.001). qPCR analysis confirmed that people living at high altitudes had the highest relative abundance of Porphyromonas gingivalis (P < 0.01). CONCLUSIONS: This study showed that both genetics and the environment had significant influences on the oral microbiome composition. The study proposed a meaningful research direction to explore the relationship between different ethnic and altitude groups and oral diseases, such as periodontal diseases.
