Objective To detect specific polymorphisms in Toll-interleukin 1 receptor domain containing adaptor protein(TIRAP) coding region for Chinese Han population, and verify whether they are associated with susceptibility to tuberculosis. Methods Search TIRAP polymorphisms by sequencing in small sample; detect single nucleotide polymorphism(SNP) by ligase detection reaction technique in large sample; analyze whether polymorphisms are related to tuberculosis by statistic methods. Results Four polymorphisms were present in the TIRAP coding region. 394A had higher frequencies in the tuberculosis(TB)group than the control. But allelic and genotypic analysis showed that there were no significant difference in statistic between TB patients and controls(P>0.05). The SNP G164A mutation related with TB patient's condition. Comparing to controls, retreatment patients' allelic frequencies had significant difference in statistic(P<0.05), sputum positive patients and lung cavitation patients had lower 164A frequencies. Conclusion TIRAP coding region polymorphisms may be risk factors for TB occurrence and development in Chinese Han population.

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.

Objective To detect the expression of Mycobacterium tuberculosis secreted protein Ag85B in paraffin-embedded tissues by immunohistochemistry (IHC), and to evaluate its application in the pathological diagnosis of tuberculosis.Methods One hundred and five tuberculosis specimens (54 pulmonary tuberculosis, 51 lymph nodal tuberculosis ) and 51 specimens of other diseases (8 lung cancer, 10 pulmonary abscess, 10 bronchiectasis, 7 lymphoma, 5 necrotizing lymphadenitis, 4 reactive hyperplasia lymphoid , and 7 sarcoidosis ) were collected from January 2012 to July 2013 from Beijing Chest Hospital, Capital Medical University.One-step IHC was performed on paraffin-embedded tissues using antibody directed against Ag85B.Results IHC and Ziehl-Neelsen ( ZN) acid-fast staining showed that distribution and intensity of Ag85B expression were concordant with the distribution and number of acid-fast bacilli.IHC showed significantly higher sensitivity than ZN staining (50.5%,53/105 vs.31.4%,33/105;χ2 =7.877, P=0.005).The combined sensitivity of IHC and ZN staining was 59.0%.Moreover, oil immersion was not necessary for IHC , allowing more rapid diagnosis.Conclusion IHC detection of Ag85B is a simple method with higher sensitivity than ZN staining , and demonstrated good value in the pathological diagnosis of tuberculosis.

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:An artificial intelligence(AI)model based on deep learning algorithms was constructed using clinical data to evaluate the feasibility of predicting the efficacy of neoadjuvant immunotherapy combined with chemotherapy for non-small cell lung cancer(NSCLC).Methods:Clinical and pathological data of 132 patients with NSCLC who were diagnosed and treated with neoadjuvant immunotherapy combined with chemotherapy between January 2020 and January 2024 at Beijing Chest Hospital/Beijing Tuberculosis and Thoracic Tumor Research Institute were collected.Statistical analysis was conducted to identify the main factors affecting the efficacy of neoadjuvant im-munotherapy combined with chemotherapy.Variables were selected based on statistical results and relevant literature,and a variable data-set was constructed.A deep learning model was established using a multi-layer perceptron(MLP)algorithm with 5-fold cross-validation,and the performance of the model was evaluated using receiver operating characteristic curve(ROC).Results:Among the 132 patients,univari-ate analysis demonstrated statistically significant differences in sex(P=0.020),smoking history(P=0.004),carcinoembryonic antigen(CEA)(P=0.038)and programmed death-ligand 1(PD-L1)≥1%(P=0.038)between the major pathological response(MPR)and non-MPR groups.Patients in the complete pathological response(pCR)group and non-pCR groups showed statistical differences in tumor size(P=0.007)and CEA levels(P=0.010).After 5-fold cross-validation,the average area under the curve(AUC)of the MPR prediction model in the validation and test sets was 0.72 and 0.71,respectively.Conclusions:The deep learning model can effectively predict the efficacy of neoadjuvant chemoim-munotherapy in patients with NSCLC.

OBJECTIVETo detect the expression of Mycobacterium tuberculosis secreted protein Ag85B in paraffin-embedded tissues by immunohistochemistry (IHC), and to evaluate its application in the pathological diagnosis of tuberculosis.

METHODSOne hundred and five tuberculosis specimens (54 pulmonary tuberculosis, 51 lymph nodal tuberculosis) and 51 specimens of other diseases (8 lung cancer, 10 pulmonary abscess, 10 bronchiectasis, 7 lymphoma, 5 necrotizing lymphadenitis, 4 reactive hyperplasia lymphoid, and 7 sarcoidosis) were collected from January 2012 to July 2013 from Beijing Chest Hospital, Capital Medical University. One-step IHC was performed on paraffin-embedded tissues using antibody directed against Ag85B.

RESULTSIHC and Ziehl-Neelsen (ZN) acid-fast staining showed that distribution and intensity of Ag85B expression were concordant with the distribution and number of acid-fast bacilli. IHC showed significantly higher sensitivity than ZN staining (50.5%, 53/105 vs. 31.4%, 33/105; χ² = 7.877, P = 0.005). The combined sensitivity of IHC and ZN staining was 59.0%. Moreover, oil immersion was not necessary for IHC, allowing more rapid diagnosis.

CONCLUSIONIHC detection of Ag85B is a simple method with higher sensitivity than ZN staining, and demonstrated good value in the pathological diagnosis of tuberculosis.

Acyltransferases ; metabolism ; Antigens, Bacterial ; metabolism ; Biomarkers ; metabolism ; Bronchiectasis ; diagnosis ; immunology ; Humans ; Immunohistochemistry ; Lymphadenitis ; diagnosis ; immunology ; Mycobacterium tuberculosis ; immunology ; Sarcoidosis ; diagnosis ; Staining and Labeling ; Tuberculosis, Lymph Node ; diagnosis ; immunology ; Tuberculosis, Pulmonary ; diagnosis ; immunology