Chronic inflammation is a serious risk factor for cancer; however, the routes from inflammation to cancer are poorly understood. On the basis of the processes implicated by frequently mutated genes associated with inflammation and cancer in three organs (stomach, colon, and liver) extracted from the Gene Expression Omnibus, The Cancer Genome Atlas, and Gene Ontology databases, we present a multiscale model of the long-term evolutionary dynamics leading from inflammation to tumorigenesis. The model incorporates cross-talk among interactions on several scales, including responses to DNA damage, gene mutation, cell-cycle behavior, population dynamics, inflammation, and metabolism-immune balance. Model simulations revealed two stages of inflammation-induced tumorigenesis: a precancerous state and tumorigenesis. The precancerous state was mainly caused by mutations in the cell proliferation pathway; the transition from the precancerous to tumorigenic states was induced by mutations in pathways associated with apoptosis, differentiation, and metabolism-immune balance. We identified opposing effects of inflammation on tumorigenesis. Mild inflammation removed cells with DNA damage through DNA damage-induced cell death, whereas severe inflammation accelerated accumulation of mutations and hence promoted tumorigenesis. These results provide insight into the evolutionary dynamics of inflammation-induced tumorigenesis and highlight the combinatorial effects of inflammation and metabolism-immune balance. This approach establishes methods for quantifying cancer risk, for the discovery of driver pathways in inflammation-induced tumorigenesis, and has direct relevance for early detection and prevention and development of new treatment regimes. .

Introduction Breast cancer, one of the most common health threats to females worldwide, has always been a crucial topic in the medical field. With the rapid development of digital pathology, many scholars have used AI-based systems to classify breast cancer pathological images. However, most existing studies only stayed on the binary classification of breast lesions (normal vs tumor or benign vs malignant), far from meeting the clinical demand. Therefore, we established a multi-class classification system of breast digital pathology images based on AI, which is more clinically practical than the binary classification system. Methods In this paper, we adopted a two-stage architecture based on deep learning method and machine learning method for the multi-class classification (normal tissue, benign lesion, ductal carcinoma in situ, and invasive carcinoma) of breast digital pathological images. Results The proposed approach achieved an overall accuracy of 86.67% at patch-level. At WSI-level, the overall accuracies of our classification system were 88.16% on validation data and 90.43% on test data. Additionally, we used two public datasets, the BreakHis and BACH, for independent verification. The accuracies our model obtained on these two datasets were comparable to related publications. Furthermore, our model could achieve accuracies of 85.19% on multi-classification and 96.30% on binary classification (non-malignant vs malignant) using pathology images of frozen sections, which was proven to have good generalizability. Then, we used t-SNE for visualization of patch classification efficiency. Finally, we analyzed morphological characteristics of patches learned by the model. Conclusion The proposed two-stage model could be effectively applied to the multi-class classification task of breast pathology images and could be a very useful tool for assisting pathologists in diagnosing breast cancer.

The aim of this study was to assess the accuracy of Cameriere's methods on dental age estimation in the northern Chinese population. A sample of orthopantomographs of 785 healthy children (397 girls and 388 boys) aged between 5 and 15 years was collected. The seven left permanent mandibular teeth were evaluated with Cameriere's method. The sample was split into a training set to develop a Chinese-specific prediction formula and a test set to validate this novel developed formula. Following the training dataset study, the variables gender (g), x 3 (canine teeth), x 4 (first premolar), x 7 (second molar), N 0, and the first-order interaction between s and N 0 contributed significantly to the fit, yielding the following linear regression formula: Age = 10.202 + 0.826 g - 4.068x 3 - 1.536x 4 - 1.959x 7 + 0.536 N 0 - 0.219 s [Symbol: see text] N 0, where g is a variable, 1 for boys and 0 for girls. The equation explained 91.2 % (R (2) = 0.912) of the total deviance. By analyzing the test dataset, the accuracy of the European formula and Chinese formula was determined by the difference between the estimated dental age (DA) and chronological age (CA). The European formula verified on the collected Chinese children underestimated chronological age with a mean difference of around -0.23 year, while the Chinese formula underestimated the chronological age with a mean difference of -0.04 year. Significant differences in mean differences in years (DA - CA) and absolute difference (AD) between the Chinese-specific prediction formula and Cameriere's European formula were observed. In conclusion, a Chinese-specific prediction formula based on a large Chinese reference sample could ameliorate the age prediction accuracy in the age group of children.

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Tumorigenesis is a complex process that is driven by a combination of networks of genes and environmental factors; however, efficient approaches to identifying functional networks that are perturbed by the process of tumorigenesis are lacking. In this study, we provide a comprehensive network-based strategy for the systematic discovery of functional synergistic modules that are causal determinants of inflammation-induced tumorigenesis. Our approach prioritizes candidate genes selected by integrating clinical-based and network-based genome-wide gene prediction methods and identifies functional synergistic modules based on combinatorial CRISPR-Cas9 screening. On the basis of candidate genes inferred de novo from experimental and computational methods to be involved in inflammation and cancer, we used an existing TGFβ1-induced cellular transformation model in colonic epithelial cells and a new combinatorial CRISPR-Cas9 screening strategy to construct an inflammation-induced differential genetic interaction network. The inflammation-induced differential genetic interaction network that we generated yielded functional insights into the genes and functional module combinations, and showed varied responses to the inflammation agents as well as active traditional Chinese medicine compounds. We identified opposing differential genetic interactions of inflammation-induced tumorigenesis: synergistic promotion and suppression. The synergistic promotion state was primarily caused by deletions in the immune and metabolism modules; the synergistic suppression state was primarily induced by deletions in the proliferation and immune modules or in the proliferation and metabolism modules. These results provide insight into possible early combinational targets and biomarkers for inflammation-induced tumorigenesis and highlight the synergistic effects that occur among immune, proliferation, and metabolism modules. In conclusion, this approach deepens the understanding of the underlying mechanisms that cause inflammation to potentially increase the cancer risk of colonic epithelial cells and accelerate the translation into novel functional modules or synergistic module combinations that modulate complex disease phenotypes.

We aimed to evaluate the feasibility of the application of the nano-hydroxyapatite/chitosan/poly(lactide-co-glycolide) (nHA/CS/PLGA) scaffold seeded with human umbilical cord mesenchymal stem cells (hUCMSCs) in bone tissue engineering. We prepared the nHA/CS/PLGA, nHA/PLGA, CS/PLGA, and PLGA scaffolds, and tested their mechanical strength. We analyzed the surface antigen markers of hUCMSCs to determine their capability to differentiate into osteoblasts, chondrocytes, and adipocytes. The growth of hUCMSCs on the four types of scaffold was assayed using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay (MTT assay) and observed using scanning electron microscopy (SEM). Quantitative analysis of alkaline phosphatase (ALP) activity and osteocalcin (OCN) content, as well as the semi-quantitative reverse transcription-polymerase chain reaction (RT-PCR) was performed. After 21 days, the subcutaneous implantations of the scaffolds samples seeded with hUCMSCs into nude mice were analyzed using immunohistochemical staining. The results showed that the mechanical strength of the nHA/CS/PLGA scaffold was enhanced. Furthermore, the nHA/CS/PLGA scaffolds were the most suitable for the adhesion, proliferation, and osteogenic differentiation of hUCMSCs in vitro and nude mouse subcutaneous implantation. The enhanced osteogenic inductivity of the nHA/CS/PLGA scaffolds for hUCMSCs might result from the addition of nHA and CS.

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In many countries, assessment of legal age, also known as age of majority, has become increasingly important over the years. In China, individuals older than 18 years of age have full capacity regarding civil conduct and can be tried as an adult for criminal charges. Therefore, from a legal point of view, it is crucial to determine whether an individual is an adult. The developmental degree of the third molar is widely recognized as a suitable site for age estimation in late adolescence. This article uses the third molar maturity index (I) with a cutoff value of I = 0.08, which was established by Cameriere et al. in 2008, to distinguish whether an individual is a minor or an adult (≥ 18 years of age) in a northern Chinese population. A total of 840 digital orthopantomograms (OPTs) from 420 male and 420 female northern Chinese subjects aged 12 to 25 years were evaluated. It was found that an increase in I corresponded to a decrease in chronological age. In our study, I = 0.10 showed better accuracy in age discrimination in both men and women. This threshold also resulted in high sensitivity (0.929 and 0.809) and specificity (0.940 and 0.973) in males and females, respectively. The proportion of correctly classified subjects was 0.917 (95% CI, 0.898 to 0.935) in total, 0.938 (95% CI, 0.915 to 0.961) in male and 0.895 (95% CI, 0.866 to 0.925) in female subjects. Bayes post-test probabilities were 0.967 (95% CI, 0.947 to 0.986) in males and 0.983 (95% CI, 0.966 to 0.998) in females. These differences in threshold values between Chinese and Caucasian populations might be because the development of third molars is delayed in the Chinese population compared to the Caucasian population. In conclusion, I might be a useful method in legal and forensic practices to determine ages in late adolescence in northern Chinese individuals. However, a specific population should be tested before I is used for legal age estimation.