Object To study the chemical constituents from the fruits of Juniperus formosana Hayata. Methods The compounds were isolated and purified on liquid-liquid, silica gel, and aluminium oxide column chromatography, their structures were identified by spectroscopic methods, such as GC-MS, 1H-NMR, and 13C-NMR, especially 2D-NMR. Results Two new oxygenated diterpenoids, named labdatriene (Ⅰ) and labdadiene (Ⅱ), were isolated from the ethyl acetate extract. Their structures were determined to be 19-carboxy-8(17)-13(16)-14-labdatriene and 15, 19-dihydroxyl-8(17)-13(E)-labdadiene, respectively. Conclusion Compounds Ⅰ and Ⅱ are new diterpenoids.

Objective:To investigate the detection of bone marrow tumor cells in small cell lung cancer (SCLC) patients and their relationship with clinical features, treatment response and prognosis.Methods:A total of 113patients with newly diagnosed SCLC from January 2018 to October 2022 at Beijing Chest Hospital were prospectively enrolled. Before treatment, bone marrow was aspirated and separately submitted for tumor cells detection by liquid-based cytology and disseminated tumor cells (DTCs) detection by the substrction enrichment and immunostaining fluorescence in situ hybridization (SE-iFISH) platform. The correlation between the detection results of the two methods with patients' clinical features and treatment response was evaluated by Chi-square. Kaplan-Meier method was applied to create survival curves and the Cox regression model was used for multivariate analysis.Results:The positive rate of bone marrow liquid-based cytology in SCLC was 15.93% (18/113). The liver and bone metastases rates were significantly higher (55.56% vs 11.58% for liver metastasis, P＜0.001; 77.78% vs 16.84% for bone metastasis, P＜0.001) and thrombocytopenia was more common (16.67% vs 2.11%, P=0.033) in patients with tumor cells detected in liquid-based cytology than those without detected tumor cells. As for SE-iFISH, DTCs were detected in 92.92% of patients (105/113), the liver and bone metastasis rates were significantly higher (37.93% vs 11.90% for liver metastasis, P=0.002; 44.83% vs 20.23 % for bone metastasis, P=0.010), and the incidence of thrombocytopenia was significantly increased (13.79% vs 1.19%, P=0.020) in patients with DTCs≥111 per 3 ml than those with DTCs＜111 per 3 ml. The positive rates of bone marrow liquid-based cytology in the disease control group and the disease progression group were 12.00% (12/100) and 46.15% (6/13), respectively, and the difference was statistically significant ( P=0.002). However, the result of SE-iFISH revealed the DTCs quantities of the above two groups were 29 (8,110) and 64 (15,257) per 3 ml, and there was no statistical difference between the two groups ( P=0.329). Univariate analysis depicted that the median progression-free survival (PFS) and median overall survival (OS) of liquid-based cytology positive patients were significantly shorter than those of tumor cell negative patients (6.33 months vs 9.27 months for PFS, P=0.019; 8.03 months vs 19.50 months for OS, P=0.019, P=0.033). The median PFS and median OS in patients with DTCs≥111 per 3 ml decreased significantly than those with DTCs＜111 per 3 ml (6.83 months vs 9.50 months for PFS, P=0.004; 11.2 months vs 20.60 months for OS, P=0.019). Multivariate analysis showed that disease stage ( HR=2.806, 95% CI:1.499-5.251, P=0.001) and DTCs quantity detected by SE-iFISH ( HR=1.841, 95% CI:1.095-3.095, P=0.021) were independent factors of PFS, while disease stage was the independent factor of OS ( HR=2.538, 95% CI:1.169-5.512, P=0.019). Conclusions:Both bone marrow liquid-based cytology and SE-iFISH are clinically feasible. The positive detection of liquid-based cytology or DTCs≥111 per 3 ml was correlated with distant metastasis, and DTCs≥111 per 3 ml was an independent prognostic factor of decreased PFS in SCLC.

Objective:To investigate the detection of bone marrow tumor cells in small cell lung cancer (SCLC) patients and their relationship with clinical features, treatment response and prognosis.Methods:A total of 113patients with newly diagnosed SCLC from January 2018 to October 2022 at Beijing Chest Hospital were prospectively enrolled. Before treatment, bone marrow was aspirated and separately submitted for tumor cells detection by liquid-based cytology and disseminated tumor cells (DTCs) detection by the substrction enrichment and immunostaining fluorescence in situ hybridization (SE-iFISH) platform. The correlation between the detection results of the two methods with patients' clinical features and treatment response was evaluated by Chi-square. Kaplan-Meier method was applied to create survival curves and the Cox regression model was used for multivariate analysis.Results:The positive rate of bone marrow liquid-based cytology in SCLC was 15.93% (18/113). The liver and bone metastases rates were significantly higher (55.56% vs 11.58% for liver metastasis, P＜0.001; 77.78% vs 16.84% for bone metastasis, P＜0.001) and thrombocytopenia was more common (16.67% vs 2.11%, P=0.033) in patients with tumor cells detected in liquid-based cytology than those without detected tumor cells. As for SE-iFISH, DTCs were detected in 92.92% of patients (105/113), the liver and bone metastasis rates were significantly higher (37.93% vs 11.90% for liver metastasis, P=0.002; 44.83% vs 20.23 % for bone metastasis, P=0.010), and the incidence of thrombocytopenia was significantly increased (13.79% vs 1.19%, P=0.020) in patients with DTCs≥111 per 3 ml than those with DTCs＜111 per 3 ml. The positive rates of bone marrow liquid-based cytology in the disease control group and the disease progression group were 12.00% (12/100) and 46.15% (6/13), respectively, and the difference was statistically significant ( P=0.002). However, the result of SE-iFISH revealed the DTCs quantities of the above two groups were 29 (8,110) and 64 (15,257) per 3 ml, and there was no statistical difference between the two groups ( P=0.329). Univariate analysis depicted that the median progression-free survival (PFS) and median overall survival (OS) of liquid-based cytology positive patients were significantly shorter than those of tumor cell negative patients (6.33 months vs 9.27 months for PFS, P=0.019; 8.03 months vs 19.50 months for OS, P=0.019, P=0.033). The median PFS and median OS in patients with DTCs≥111 per 3 ml decreased significantly than those with DTCs＜111 per 3 ml (6.83 months vs 9.50 months for PFS, P=0.004; 11.2 months vs 20.60 months for OS, P=0.019). Multivariate analysis showed that disease stage ( HR=2.806, 95% CI:1.499-5.251, P=0.001) and DTCs quantity detected by SE-iFISH ( HR=1.841, 95% CI:1.095-3.095, P=0.021) were independent factors of PFS, while disease stage was the independent factor of OS ( HR=2.538, 95% CI:1.169-5.512, P=0.019). Conclusions:Both bone marrow liquid-based cytology and SE-iFISH are clinically feasible. The positive detection of liquid-based cytology or DTCs≥111 per 3 ml was correlated with distant metastasis, and DTCs≥111 per 3 ml was an independent prognostic factor of decreased PFS in SCLC.

Objective:To establish an artificial intelligence (AI)-assisted diagnostic system for lung cancer via deep transfer learning.Methods:The researchers collected 519 lung pathologic slides from 2016 to 2019, covering various lung tissues, including normal tissues, adenocarcinoma, squamous cell carcinoma and small cell carcinoma, from the Beijing Chest Hospital, the Capital Medical University. The slides were digitized by scanner, and 316 slides were used as training set and 203 as the internal test set. The researchers labeled all the training slides by pathologists and establish a semantic segmentation model based on DeepLab v3 with ResNet-50 to detect lung cancers at the pixel level. To perform transfer learning, the researchers utilized the gastric cancer detection model to initialize the deep neural network parameters. The lung cancer detection convolutional neural network was further trained by fine-tuning of the labeled data. The deep learning model was tested by 203 slides in the internal test set and 1 081 slides obtained from TCIA database, named as the external test set.Results:The model trained with transfer learning showed substantial accuracy advantage against the one trained from scratch for the internal test set [area under curve (AUC) 0.988 vs. 0.971, Kappa 0.852 vs. 0.832]. For the external test set, the transferred model achieved an AUC of 0.968 and Kappa of 0.828, indicating superior generalization ability. By studying the predictions made by the model, the researchers obtained deeper understandings of the deep learning model.Conclusions:The lung cancer histopathological diagnostic system achieves higher accuracy and superior generalization ability. With the development of histopathological AI, the transfer learning can effectively train diagnosis models and shorten the learning period, and improve the model performance.