ABSTRACT
Background: Postoperative recurrence impedes the curability of early-stage hepatocellular carcinoma (E-HCC). We aimed to establish a novel recurrence-related pathological prognosticator with artificial intelligence, and investigate the relationship between pathological features and the local immunological microenvironment. Methods: A total of 576 whole-slide images (WSIs) were collected from 547 patients with E-HCC in the Zhongshan cohort, which was randomly divided into a training cohort and a validation cohort. The external validation cohort comprised 147 Tumor Node Metastasis (TNM) stage I patients from The Cancer Genome Atlas (TCGA) database. Six types of HCC tissues were identified by a weakly supervised convolutional neural network. A recurrence-related histological score (HS) was constructed and validated. The correlation between immune microenvironment and HS was evaluated through extensive immunohistochemical data. Results: The overall classification accuracy of HCC tissues was 94.17%. The C-indexes of HS in the training, validation and TCGA cohorts were 0.804, 0.739 and 0.708, respectively. Multivariate analysis showed that the HS (HR= 4.05, 95% CI: 3.40-4.84) was an independent predictor for recurrence-free survival. Patients in HS high-risk group had elevated preoperative alpha-fetoprotein levels, poorer tumor differentiation and a higher proportion of microvascular invasion. The immunohistochemistry data linked the HS to local immune cell infiltration. HS was positively correlated with the expression level of peritumoral CD14+ cells (p= 0.013), and negatively with the intratumoral CD8+ cells (p< 0.001). Conclusions: The study established a novel histological score that predicted short-term and long-term recurrence for E-HCCs using deep learning, which could facilitate clinical decision making in recurrence prediction and management.
ABSTRACT
A marine algicidal bacterium N3 was isolated from a HABs area in Mirs Bay, a subtropical bay, in southern China. Algicidal activity and algicidal mode against Phaeodactylum tricornutum, Scrippsiella trochoidea, Prorocentrum micans and Skeletonema costatum were observed by the liquid infection method. The results showed that there were no algicidal activities against P. tricornutum and S. costatum. However, when the bacterial volume fractions were 2% and 10% , S. trochoidea and P. micans could be killed, respectively. S. trochoidea cells which were exposed to strain N3 became irregular in shape and the cellular components lost their integrity and were decomposed. While, the P. micans cells became inflated and the cellular components aggregated, followed by cell lysis. Strain N3 killed S. trochoidea and P. micans directly, and the algicidal activities of the bacterial strain N3 was concentration-dependent. To S. trochoidea, 2% (V/V) of bacteria in algae showed the strongest algicidal activity, all of the S. trochoidea cells were killed within 120 h. But the growth rates of cells, in the 1% and 0. 1% treatment groups, were only slightly lower than that in the control group. In all treatment groups, the densities of strain N3 were in declining trends. While, to P. micans, 10% and 5% of bacteria in algae showed strong algicidal activities, 78% and 70% of the S. trochoidea were killed within 120 h, respectively. However, the number of S. trochoidea after exposure to 1% of bacterial cultures still increased up to 5 incubation days. And in the three treatment groups, the densities of strain N3 experienced a decrease process. The isolated strain N3 was identified as Bacillus sp. by morphological observation, physiological and biochemical characterization, and homology comparisons based on 16S rRNA sequences.
