Immunohistochemistry(IHC)technique is one of the routine detection methods of pathology.At present,the fully automatic immunohistochemistry analyzer has been widely applied in many fields included biology,basic medicine and clinical pathology,which has been a valuable testing equipment of related pathological experiments in university.The use of this kind of equipment in experimental teaching for undergraduate can increased experimental efficiency,and reduce manual errors through standardized operations.At the same time,the possessed capability of this equipment for batch processing was suitable in teaching for class with large scale.This article explored the working principle,usage technique,daily maintenance and troubleshooting for the BOND Max fully automatic immunohistochemistry analyzer at Jilin University's National Biological Experimental Teaching Demonstration Center,and it also expounded its application in experimental teaching,which aim was to improve the efficiency of fully automatic immunohistochemistry analyzer.It can provide technical support for operator.

Objective To investigate the factors influencing the development of extra-pulmonary tuberculosis(EPTB)in patients with viral hepatitis complicated by pulmonary tuberculosis(PTB).Methods A retrospective analysis was conducted on 427 patients with Hepatitis B Virus(HBV)and Hepatitis C Virus(HCV)infections complicated by PTB admitted to the tuberculosis department of Kunming Third People's Hospital from January 2015 to December 2020.Patients were divided into the EPTB complication group(n=72)and the non-EPTB complication group(n=355)based on the presence of EPTB.Clinical treatment data of patients were collected.Univariate and multivariate Logistic regression analyse were used to screen independent risk factors for EPTB as predictive factors.A nomogram prediction model was established for Extrapulmonary Tuberculosis(EPTB)complications in patients with viral hepatitis and Pulmonary Tuberculosis(PTB),evaluated using the Hosmer-Lemeshow test and ROC curve analysis.Results Among the 427 patients,292(68.3%)were male and 135(31.7%)were female,with 72 cases of EPTB,resulting in an incidence rate of 16.86%.In the EPTB group,there were 34 males(47.2%)and 38 females(52.8%).The types of EPTB included tuberculous pleuritis(21 cases,29%),tuberculous peritonitis(16 cases,22%),lymph node tuberculosis(13 cases,18%),tuberculous encephalitis(5 cases,6%),intestinal tuberculosis(6 cases,8%),bone tuberculosis(5 cases,6%),pelvic tuberculosis(3 cases,4%),and genitourinary tuberculosis(3 cases,4%).Multivariate logistic regression analysis showed that gender(OR=0.425,95%CI:0.250-0.722,P=0.02),low triglyceride(TG)levels(OR=0.837,95%CI:0.717-0.978,P=0.025),the tuberculosis-specific antigen A(ESAT-6)(OR=1.007,95%CI:1.003～1.011 were independent influencing factors for EPTB in patients with PTB complicated by HBV and HCV infections.The optimal cutoff value for the nomogram model is 0.192,with a sensitivity of 0.611,specificity of 0.710,Youden index of 0.741,positive likelihood ratio of 2.103,and negative likelihood ratio of 0.548.The Hosmer-Lemeshow test yielded χ2=2.631,P=0.955.ROC curve analysis showed an AUC of 0.693,95%CI:0.629 1～0.7574.Conclusion The prediction model based on gender,low TG levels and ESAT-6 can well predict the occurrence of EPTB to some extent,providing a reference for clinical treatment.

Objective To evaluate the relationship between nutritional parameters and the risk of venous thromboembolicism(VTE)in patients with tuberculosis so as to identify the risk factors and predictors of thrombosis and assist in the early identification of high-risk factors for VTE in patients with the pulmonary tuberculosis.Methods A total of 323 patients diagnosed with the pulmonary tuberculosis and hospitalized in Kunming Third People's Hospital from August 2021 to August 2023 were collected.According to the VTE risk assessment of non-operative patients,they were divided into the high-risk group and the low-risk group respectively with 116 and 207 in each group.The nutritional indicators with statistically significant differences between the two groups were screened by Lasso regression.Multivariate Logistic regression was used to screen the independent risk factors for high VTE risk in pulmonary tuberculosis patients,and a nomogram prediction model was constructed.The prediction model was evaluated by receiver operating characteristic curve(ROC),calibration curve,decision curve,and influence curve.Results Patients in the high-risk group were significantly older than those in the low-risk group(59 vs.41,P<0.001),hypertension,gender,and Type 2 diabetes did not differ significantly(P values were 0.084,0.724 and 0.488,respectively).9 variables were selected from the inter-group comparison and Lasso regression,including ALB,HCT,NRS2002 scores,HBDH,RDW-SD,RDW-CV,TG,CONUT scores,and NEFA.Multivariate Logistic regression analysis showed that ALB,NRS2002 scores,RDW-SD,and CONUT scores were independent influencing factors for the high risk of VTE scores in patients with tuberculosis(P<0.005).Area under the ROC curve showed that the AUC(0.892)for high-risk VTE scores in patients with the pulmonary tuberculosis was greater than that of ALB(0.803),NRS2002 score(0.735),RDW-SD(0.685),and CONUT score(0.774).Fitting prediction model:Logit(P):Y=0.433×NRS-0.136×ALB+0.411×CONUT score+0.072×RDW-SD-1.770,P=1/(1+e-Y)(Y:prediction index,P:prediction probability).Calibration curve showed that the model prediction tended to be consistent with the actual results(U:>0.05),and the decision curve and influence curve showed that the model can bring clinical benefits.Conclusion ALB,NRS2002 scores,RDW-SD,and CONUT scores are independent influencing factors for the high risk of VTE scores in patients with tuberculosis.They can guide the clinical practice,improve these indicators as soon as possible,reduce VTE scores,and reduce the thrombosis risk.At the same time,the prediction model performs well in the verification cohort,with its discrimination ability,calibration accuracy and clinical utility(decision curve analysis)all reaching a satisfactory level.

Objective:To evaluate the application of metagenomic next-generation sequencing（mNGS）in the identification of non-tuberculous mycobacteria（NTM）.Methods:A retrospective analysis was conducted on mNGS results of 358 bronchoalveolar lavage fluid（BALF）samples positive for NTM collected at the First Affiliated Hospital of Zhejiang University School of Medicine from February 2021 to January 2024. The analysis included the distribution of NTM species，the detection of mixed pathogens，and the performance of conventional mycobacterial detection methods.Results:The results showed that 362 strains of 15 NTM species were identified from 350 specimens，8 specimens were not precise to the species level. The most frequently detected species were Mycobacterium intracellulare（37.3%，135/362）， Mycobacterium abscessus（26.8%，97/362），followed by Mycobacterium avium（11.0%，40/362）， Mycobacterium kansasii（8.0%，29/362）and Mycobacterium chelonae（7.7%，28/362）. Single NTM species were detected in 339 specimens，while two or three NTM species were simultaneously detected in 11 specimens（3.1%，11/358）. Non-NTM microorganisms co-infected were detected in 53.4%（191/358）of NTM-positive BALF samples，including common pathogens such as Pseudomonas aeruginosa， Staphylococcus aureus，and Aspergillus fumigatus；and difficult-to-identify pathogens such as Legionella pneumophila and Talaromyces marneffei. In NTM-positive patients detected by mNGS，the results supported the diagnosis of NTM infection in 298 cases（298/358，83.2%）and 105 cases（105/358，29.3%）initiated anti-NTM treatment accordingly；while in 60 cases（60/358，16.8%）the positive results were considered as colonization or unrelated to clinical infection. For samples tested with acid-fast staining，mycobacterial liquid culture，and DNA microarray，the positivity rates for NTM were 31.5%（73/232），48.7%（57/117），and 43.0%（46/107），respectively. Conclusions:mNGS demonstrates advantages in identification of NTM. However，the test may detect multiple microorganisms，in that case，the interpretation with clinical and radiological results is requried to determine the main pathogens.

Objective:To analyze the influencing factors of severe dengue fever patients in the early stage, construct a early prediction model for severe dengue fever, and evaluate it.Methods:A retrospective analysis was conducted to collect early clinical data of dengue fever patients admitted to the People's Hospital of Mengla County and the Third People's Hospital of Kunming in Yunnan Province from July to December 2023. The multifactor logistic regression was used to analyze the factors affecting the severe dengue fever patients, and Nomogram prediction model was used for visualization. Receiver operating characteristic (ROC) curve and calibration curve analysis were used to evaluate the model.Results:A total of 534 dengue fever patients were included, including 291 males and 243 females, aged (39.95 ± 15.69) years. Among them, there were 59 cases (11.05%) of severe dengue fever. The results of multifactor logistic regression analysis showed that age ( OR = 1.05, 95% CI: 1.02 - 1.08, P < 0.001), cardiovascular disease ( OR = 5.28, 95% CI: 2.08 - 13.40, P < 0.001), serous effusion ( OR = 4.34, 95% CI: 1.63 - 11.57, P = 0.003), aspartate aminotransferase ( OR = 1.03, 95% CI: 1.02 - 1.04, P < 0.001), lactate dehydrogenase ( OR = 1.00, 95% CI: 1.00 - 1.01, P = 0.001), and fibrinogen ( OR = 0.46, 95% CI: 0.28 - 0.76, P = 0.003) were independent influencing factors in the early stage of severe dengue fever. The area under the ROC curve of the Nomogram prediction model constructed from the above six variables was 0.96 (0.93 - 0.98). The calibration curve analysis results showed that the mean absolute error between the predicted values of the Nomogram prediction model and the actual observed values was 0.014. Conclusions:Age, cardiovascular disease, serous effusion, aspartate aminotransferase, lactate dehydrogenase, and fibrinogen are independent influencing factors in the early stage of severe dengue fever. The Nomogram prediction model established based on these variables has good predictive ability for severe dengue fever.

Objective:To analyze the influencing factors of severe dengue fever patients in the early stage, construct a early prediction model for severe dengue fever, and evaluate it.Methods:A retrospective analysis was conducted to collect early clinical data of dengue fever patients admitted to the People's Hospital of Mengla County and the Third People's Hospital of Kunming in Yunnan Province from July to December 2023. The multifactor logistic regression was used to analyze the factors affecting the severe dengue fever patients, and Nomogram prediction model was used for visualization. Receiver operating characteristic (ROC) curve and calibration curve analysis were used to evaluate the model.Results:A total of 534 dengue fever patients were included, including 291 males and 243 females, aged (39.95 ± 15.69) years. Among them, there were 59 cases (11.05%) of severe dengue fever. The results of multifactor logistic regression analysis showed that age ( OR = 1.05, 95% CI: 1.02 - 1.08, P < 0.001), cardiovascular disease ( OR = 5.28, 95% CI: 2.08 - 13.40, P < 0.001), serous effusion ( OR = 4.34, 95% CI: 1.63 - 11.57, P = 0.003), aspartate aminotransferase ( OR = 1.03, 95% CI: 1.02 - 1.04, P < 0.001), lactate dehydrogenase ( OR = 1.00, 95% CI: 1.00 - 1.01, P = 0.001), and fibrinogen ( OR = 0.46, 95% CI: 0.28 - 0.76, P = 0.003) were independent influencing factors in the early stage of severe dengue fever. The area under the ROC curve of the Nomogram prediction model constructed from the above six variables was 0.96 (0.93 - 0.98). The calibration curve analysis results showed that the mean absolute error between the predicted values of the Nomogram prediction model and the actual observed values was 0.014. Conclusions:Age, cardiovascular disease, serous effusion, aspartate aminotransferase, lactate dehydrogenase, and fibrinogen are independent influencing factors in the early stage of severe dengue fever. The Nomogram prediction model established based on these variables has good predictive ability for severe dengue fever.

Immunohistochemistry(IHC)technique is one of the routine detection methods of pathology.At present,the fully automatic immunohistochemistry analyzer has been widely applied in many fields included biology,basic medicine and clinical pathology,which has been a valuable testing equipment of related pathological experiments in university.The use of this kind of equipment in experimental teaching for undergraduate can increased experimental efficiency,and reduce manual errors through standardized operations.At the same time,the possessed capability of this equipment for batch processing was suitable in teaching for class with large scale.This article explored the working principle,usage technique,daily maintenance and troubleshooting for the BOND Max fully automatic immunohistochemistry analyzer at Jilin University's National Biological Experimental Teaching Demonstration Center,and it also expounded its application in experimental teaching,which aim was to improve the efficiency of fully automatic immunohistochemistry analyzer.It can provide technical support for operator.

Objective:To evaluate the application of metagenomic next-generation sequencing（mNGS）in the identification of non-tuberculous mycobacteria（NTM）.Methods:A retrospective analysis was conducted on mNGS results of 358 bronchoalveolar lavage fluid（BALF）samples positive for NTM collected at the First Affiliated Hospital of Zhejiang University School of Medicine from February 2021 to January 2024. The analysis included the distribution of NTM species，the detection of mixed pathogens，and the performance of conventional mycobacterial detection methods.Results:The results showed that 362 strains of 15 NTM species were identified from 350 specimens，8 specimens were not precise to the species level. The most frequently detected species were Mycobacterium intracellulare（37.3%，135/362）， Mycobacterium abscessus（26.8%，97/362），followed by Mycobacterium avium（11.0%，40/362）， Mycobacterium kansasii（8.0%，29/362）and Mycobacterium chelonae（7.7%，28/362）. Single NTM species were detected in 339 specimens，while two or three NTM species were simultaneously detected in 11 specimens（3.1%，11/358）. Non-NTM microorganisms co-infected were detected in 53.4%（191/358）of NTM-positive BALF samples，including common pathogens such as Pseudomonas aeruginosa， Staphylococcus aureus，and Aspergillus fumigatus；and difficult-to-identify pathogens such as Legionella pneumophila and Talaromyces marneffei. In NTM-positive patients detected by mNGS，the results supported the diagnosis of NTM infection in 298 cases（298/358，83.2%）and 105 cases（105/358，29.3%）initiated anti-NTM treatment accordingly；while in 60 cases（60/358，16.8%）the positive results were considered as colonization or unrelated to clinical infection. For samples tested with acid-fast staining，mycobacterial liquid culture，and DNA microarray，the positivity rates for NTM were 31.5%（73/232），48.7%（57/117），and 43.0%（46/107），respectively. Conclusions:mNGS demonstrates advantages in identification of NTM. However，the test may detect multiple microorganisms，in that case，the interpretation with clinical and radiological results is requried to determine the main pathogens.

Objective:To understand the status and coping strategies of death anxiety among military nurses participating in emergency public health incident response, and to provide references for improving their coping abilities.Methods:A purposive sampling and snowball sampling method were used to select 15 nurses from multiple military hospitals who supported the emergency public health incident. One-on-one semi-structured interviews were conducted, and the Colaizzi's seven-step analysis method was used to analyze the interview data.Results:Four themes were identified: the occurrence of death anxiety at different time points; the presence of significant outbreaks of death anxiety; multiple factors that help military nurses alleviate death anxiety; and the intrinsic motivation and gains of military nurses participating in the response.Conclusions:It is crucial to monitor the occurrence and changes in death anxiety among nurses throughout the entire cycle of emergency public health incidents. Managers should focus on value guidance, leverage individual positive strengths to cope with death anxiety, enhance information and social support, reinforce training on infectious disease prevention and death education, and reduce the level of death anxiety among military nurses and improve their coping abilities.

As a common clinical Chinese medicine, Prunellae Spica has the effects of clearing liver-fire, improving eyesight, resolving massesand detumescence, and has strong anti-tumor effects against thyroid cancer, breast cancer, liver cancer and other cancers. Extracts of Prunellae Spica and its active components can play an anti-tumor role in a variety of ways, including cell apoptosis, inhibiting cell invasion and metastasis, inhibiting cell proliferation, inducing autophagy, anti tumor angiogenesis, reversing tumor multidrug resistance and regulating immune function, by regulating miRNA and Wnt/β-catenin, PI3/AKT, AMPK/mTOR/ULK1, RANKL/RANK/OPG and other signal pathways . In this paper, the anti-tumor mechanism of Prunellae Spica extract was reviewed, in order to provide reference for further research and application.