Chronic obstructive pulmonary disease (COPD) is the most common chronic respiratory disease around the world, and pharmacotherapy is the foremost treatment method currently. In recent decades, with the rapid development of bronchoscopic interventional therapy, endoscopic physical ablation technology presents a therapeutic effect in treating COPD, with few treatment-related side effects, showing excellent application prospects in treating COPD. Since ablation techniques in this field are emerging technologies with low patient acceptance, they are not widely used in the clinical treatment of COPD. This article reviews the development process of physical ablation techniques. Moreover, their current application status and the prospects in the field of COPD treatment are also summarized and analyzed. We hope to promote the application of physical ablation in the clinical treatment of COPD and provide practical references and a theoretical basis for the clinical treatment of COPD.

Objective:To analyze the evaluation value of contrast-enhanced ultrasound (CEUS) combined with automatic volumetric ultrasound in the efficacy of neoadjuvant chemotherapy (NAC) for breast cancer.Methods:A retrospective study was conducted on 98 female patients with breast cancer admitted to Shaanxi Provincial Cancer Hospital from January to December 2022. All patients received 4 cycles of NAC, and underwent conventional ultrasound, automatic volumetric ultrasound, CEUS, and histopathological examination before and after treatment. Based on the post-treatment histopathological efficacy, patients were divided into the effective group ( n=67) and the ineffective group ( n=31). The CEUS and automatic volumetric ultrasound parameters before and after treatment, as well as the evaluation efficacy of these two types of parameters for NAC efficacy in breast cancer, were compared. The value of CEUS combined with automatic volumetric ultrasound in evaluating NAC efficacy for breast cancer was analyzed. Results:After NAC treatment, the CEUS parameters [time to peak (TTP) and arrival time (AT) of contrast agent] were longer than those before treatment, while the peak intensity (PI) was lower than that before treatment (all P<0.05); the automatic volumetric ultrasound parameters (tumor volume, area, thickness, length, and width) after NAC treatment were all smaller than those before treatment (all P<0.05). After treatment, the PI, volume, area, thickness, length, and width in the effective group were significantly smaller than those in the ineffective group, while the TTP and AT were significantly longer than those in the ineffective group (all P<0.05). Receiver operating characteristic (ROC) curve analysis showed that the area under the curve (AUC) of CEUS parameters and automatic volumetric ultrasound parameters for predicting effective NAC efficacy in breast cancer was 0.837(0.749-0.904) and 0.864(0.780-0.925), respectively, with no statistically significant difference between the two ( P=0.674). The AUC of the combined parameters for predicting effective NAC efficacy was 0.942(0.875-0.979), which was significantly higher than that of CEUS parameters or automatic volumetric ultrasound parameters alone ( Z=2.947, 2.135, P=0.003, 0.033). Conclusions:The combination of CEUS and automatic volumetric ultrasound parameters has high value in the efficacy evaluation of NAC for breast cancer and can be used as a clinical reference.

Objective:To explore the value of ultrasound features modified version 2022 of the Ovarian-Adnexal Imaging Reporting and Data System（O-RADS）Category 4 in the differential diagnosis of benign and malignant ovarian-adnexal tumors.Methods:Retrospective analysis was conducted in 501 cases with ovarian masses classified into 4 categories according to the 2022 version of O-RADS who were collected from 4 clinical centers［the Second Afliated Hospital of Harbin Medical University（188 cases），Zhejiang Provincial People's Hospital（146 cases），Sichuan Provincial Maternity and Child Health Care Hospital（90 cases），and Fuling Hospital of Chongqing University（77 cases）］from January 2018 to July 2024 with concomitant surgical resection.The 424 cases from 3 of the clinical centers（the Second Hospital of Harbin Medical University，Zhejiang Provincial People's Hospital，and Sichuan Maternal and Child Health Hospital）were randomly divided into a training group（339 cases）and an internal validation group（85 cases）according to an 8∶2 randomization，while the cases from the other clinical center（Fuling Hospital of Chongqing University）were selected as the external validation group（77 cases），and the pathological diagnosis was used as the “gold standard”.Univariate and multifactorial logistic regression analyses were performed on the ultrasound characteristics of the training group to screen the independent predictors associated with ovarian carcinogenesis，and to formulate the stratification rules for the 4 types of masses in O-RADS. The ROC curve of this stratification method was plotted and the area under the curve（AUC）was calculated，and it was validated in the internal validation group and the external validation group；and the diagnostic accuracy was compared with that of the 2022 version of O-RADS.Results:Univariate logistic analysis showed that cysts with solid components，≥ 4 papillary projections，smooth inner wall of the cyst，color flow score ≥ 3 points，and acoustic shadowing were independent predictors of ovarian cancer（all P < 0.05）；while multifactorial logistic analysis showed that cysts with a solid component and a color flow score ≥3 points were independent risk factors of ovarian cancer（all P < 0.05），and smooth cyst walls and acoustic shadows were independent protective factors（all P < 0.05）.The diagnostic accuracies of the modified training group，internal validation group，and external validation group were 73.7%，68.2%，70.1%，respectively，which were significantly higher than the diagnostic accuracies of the 2022 version of the O-RADS（38.9%，37.6%，33.8%）（all P < 0.05）.The diagnostic sensitivity，specificity and AUC of the training group were 0.871，0.652，0.762，respectively，while the internal validation group were 0.844，0.585，0.714，and 0.846，0.627，0.737 in the external validation group. Conclusions:Improvement of the 2022 version of O-RADS category 4 using ultrasound features may improve the identification of benign and malignant ovarian-adnexal tumors.

Objective:To construct the gemcitabine resistant cell lines of human pancreatic cancer cell line (PANC1) and mouse pancreatic cancer cell line (PANC02), and to investigate their biological behavior changes.Methods:Gemcitabine-resistant cell lines PANC1-GR of human pancreatic cancer and PANC02-GR of mouse pancreatic cancer were induced by concentration gradient increment method. Cell count assay (CCK-8), flow cytometry, cell scratch assay and Transwell assay were used to detect the drug resistance, proliferation, cell cycle, migration and invasion of the four groups of cell lines. The drug-resistant cells were also compared with the parent cells.Results:The resistance indices of PANC1-GR and PANC02-GR were 153.3 and 185.4, respectively. The results of CCK-8 showed that with the increase of gemcitabine concentration, the proliferation of resistant cells changed significantly compared with parental cells, the population doubling time of PANC1-GR was significantly shorter than that of PANC1 (1.5±0.1) d vs (2.4±0.2) d ( t=8.00, P<0.001). The proportion of cells in S and G2/M phase increased, and the proportion of cells in G0/G1 phase decreased. The cell scratch and Transwell experiments indicated that the 24h mobility of PANC1-GR and PANC02-GR was higher than that of parent cells (47.6±2.4)% vs (28.7±6.3)% and (53.6±3.2)% vs (30.1±1.4)%, the number of individual field (200 times magnification) penetrating membrane cells was also higher than that of parent cells (269.7±30.9) vs (62.7±10.1) and (172.0±30.8) vs (36.3±4.9), with statistical significance (all P<0.05). Conclusion:Concentration gradient increment method can successfully establish gemcitabine-resistant pancreatic cancer cell lines, which have stronger proliferation, migration and invasiveness, and can be used to study the mechanism of drug resistance in pancreatic cancer.

Objective:To develop and validate machine learning models for predicting the cumulative live birth rate (CLBR) following in vitro fertilization (IVF) and to analyze key predictive features using SHAP values. Methods:This retrospective study included data from patients who underwent IVF-embryo transfer at the Department of Reproductive Medicine, Baoding Maternal and Child Health Hospital, between January 2017 and December 2022. Patients were categorized into two groups based on live birth outcome: the live birth group ( n=1 036) and the non-live birth group ( n=756). The dataset was randomly divided into a training set and a validation set in a ratio of 7∶3. Five algorithms were utilized for model development: logistic regression, random forest, extreme gradient boosting (XGBoost), support vector machine, and neural networks. Model performance was assessed using the area under the receiver operating characteristic (AUC) curve, F1 score, and calibration curves. Clinical decision curve analysis (DCA) was employed to evaluate the clinical utility of the models. SHAP values were used to interpret feature importance in the XGBoost model and enhance its explainability. Results:The XGBoost model demonstrated the best performance in predicting CLBR,with accuracy of 72.44%, AUC of 0.775, and F1 score of 0.654, accuracy and F1 score outperforming logistic regression (accuracy was 70.02%, F1 score was 0.585), random forest (accuracy was 71.69%, F1 score was 0.606), support vector machine (accuracy was 70.20%, F1 score was 0.607), and neural network (accuracy was 68.72%, F1 score was 0.560). The calibration curve of XGBoost closely aligned with the diagonal line, indicating that the predicted probabilities were very close to the actual outcomes, demonstrating good calibration. DCA indicated that the XGBoost model provided higher net benefits across a wide range of clinical decision thresholds. SHAP value analysis identified number of previous IVF failures, antral follicle count, anti-Müllerian hormone level, percentage of normal sperm morphology, and sperm DNA fragmentation index as key predictors of CLBR.Conclusion:The XGBoost model exhibits excellent predictive performance and calibration for CLBR, with SHAP values providing important insights into feature importance. This model has the potential to support the development of personalized treatment strategies in clinical practice. However, its generalizability needs to be validated using external datasets to ensure its applicability to diverse populations.

The aim of this study was to qualitatively and quantitatively detect coronavirus(CoV)in the feces of bats from Qinghua Cave,Yunnan Province,China.CoV was qualitatively tested with reverse transcription polymerase chain reaction(RT-PCR),and homology and genetic evolution were analyzed with bioinformatics software.The established reverse transcription real-time fluores-cence quantitative PCR(qRT-PCR)method was applied to CoV quantification in bat feces.The positivity rate of CoV in 306 fecal samples collected from the fulvous fruit bat(Rousettus leschenaultia)was 7.8%(24/306)according to RT-PCR.All 24 strains of CoV belonged to β-CoV,and showed a similarity of 86.8%-100.0%at the nucleotide level and 95.2%-100.0%at the amino acid level,with respect to other β-CoV sequences in the NCBI database.The positivity rate of CoV was 18.6%(57/306)according to qRT-PCR,a value higher than that according to RT-PCR(χ2=25.3,P<0.05).The mean β-CoV load was 1.3×103 copies/μL.In conclusion,the bats in Qinghua Cave,Yunnan Province,carried CoV belonging to β-CoV.The established qRT-PCR method achieved good sensitiv-ity,accuracy,reproducibility,and a higher detection rate than that of RT-PCR,and can be used for rapid detection of β-CoV in bats.

Objective:To develop and validate machine learning models for predicting the cumulative live birth rate (CLBR) following in vitro fertilization (IVF) and to analyze key predictive features using SHAP values. Methods:This retrospective study included data from patients who underwent IVF-embryo transfer at the Department of Reproductive Medicine, Baoding Maternal and Child Health Hospital, between January 2017 and December 2022. Patients were categorized into two groups based on live birth outcome: the live birth group ( n=1 036) and the non-live birth group ( n=756). The dataset was randomly divided into a training set and a validation set in a ratio of 7∶3. Five algorithms were utilized for model development: logistic regression, random forest, extreme gradient boosting (XGBoost), support vector machine, and neural networks. Model performance was assessed using the area under the receiver operating characteristic (AUC) curve, F1 score, and calibration curves. Clinical decision curve analysis (DCA) was employed to evaluate the clinical utility of the models. SHAP values were used to interpret feature importance in the XGBoost model and enhance its explainability. Results:The XGBoost model demonstrated the best performance in predicting CLBR,with accuracy of 72.44%, AUC of 0.775, and F1 score of 0.654, accuracy and F1 score outperforming logistic regression (accuracy was 70.02%, F1 score was 0.585), random forest (accuracy was 71.69%, F1 score was 0.606), support vector machine (accuracy was 70.20%, F1 score was 0.607), and neural network (accuracy was 68.72%, F1 score was 0.560). The calibration curve of XGBoost closely aligned with the diagonal line, indicating that the predicted probabilities were very close to the actual outcomes, demonstrating good calibration. DCA indicated that the XGBoost model provided higher net benefits across a wide range of clinical decision thresholds. SHAP value analysis identified number of previous IVF failures, antral follicle count, anti-Müllerian hormone level, percentage of normal sperm morphology, and sperm DNA fragmentation index as key predictors of CLBR.Conclusion:The XGBoost model exhibits excellent predictive performance and calibration for CLBR, with SHAP values providing important insights into feature importance. This model has the potential to support the development of personalized treatment strategies in clinical practice. However, its generalizability needs to be validated using external datasets to ensure its applicability to diverse populations.

Alcohol abuse is a global public health problem.Excessive drinking not only damages digestive tract,cardiovascu-lar and endocrine systems,but also damages central nervous system(CNS).Recent studies have shown that alcohol interacts with neu-roimmune system to alter neuroimmune signaling and molecular expression,thereby leading to neuroinflammation and regulating a wide range of brain functions.Microglia is main cell of CNS involved in neuroimmune responses.Microglia is activated by alcohol and acts on neurons,leading to neuropsychiatric diseases,such as neuronal loss,abnormal synaptic connections,cognitive decline and motor dysfunction.Alcohol chronically stimulates digestive tract and also affects microglia along gut-brain axis.Neural properties of microglia and related immune factors and their important roles in neuroinflammation provide a new insight into neuroimmune mecha-nisms underlying alcohol-induced changes in brain function and behavior.This review discusses progress of role of microglia and their immune signaling in alcohol-induced neuroinflammation,and provides theoretical basis for further research on neurobiological mecha-nism and treatment of alcohol abuse.

Objective:To explore the value of ultrasound features modified version 2022 of the Ovarian-Adnexal Imaging Reporting and Data System（O-RADS）Category 4 in the differential diagnosis of benign and malignant ovarian-adnexal tumors.Methods:Retrospective analysis was conducted in 501 cases with ovarian masses classified into 4 categories according to the 2022 version of O-RADS who were collected from 4 clinical centers［the Second Afliated Hospital of Harbin Medical University（188 cases），Zhejiang Provincial People's Hospital（146 cases），Sichuan Provincial Maternity and Child Health Care Hospital（90 cases），and Fuling Hospital of Chongqing University（77 cases）］from January 2018 to July 2024 with concomitant surgical resection.The 424 cases from 3 of the clinical centers（the Second Hospital of Harbin Medical University，Zhejiang Provincial People's Hospital，and Sichuan Maternal and Child Health Hospital）were randomly divided into a training group（339 cases）and an internal validation group（85 cases）according to an 8∶2 randomization，while the cases from the other clinical center（Fuling Hospital of Chongqing University）were selected as the external validation group（77 cases），and the pathological diagnosis was used as the “gold standard”.Univariate and multifactorial logistic regression analyses were performed on the ultrasound characteristics of the training group to screen the independent predictors associated with ovarian carcinogenesis，and to formulate the stratification rules for the 4 types of masses in O-RADS. The ROC curve of this stratification method was plotted and the area under the curve（AUC）was calculated，and it was validated in the internal validation group and the external validation group；and the diagnostic accuracy was compared with that of the 2022 version of O-RADS.Results:Univariate logistic analysis showed that cysts with solid components，≥ 4 papillary projections，smooth inner wall of the cyst，color flow score ≥ 3 points，and acoustic shadowing were independent predictors of ovarian cancer（all P < 0.05）；while multifactorial logistic analysis showed that cysts with a solid component and a color flow score ≥3 points were independent risk factors of ovarian cancer（all P < 0.05），and smooth cyst walls and acoustic shadows were independent protective factors（all P < 0.05）.The diagnostic accuracies of the modified training group，internal validation group，and external validation group were 73.7%，68.2%，70.1%，respectively，which were significantly higher than the diagnostic accuracies of the 2022 version of the O-RADS（38.9%，37.6%，33.8%）（all P < 0.05）.The diagnostic sensitivity，specificity and AUC of the training group were 0.871，0.652，0.762，respectively，while the internal validation group were 0.844，0.585，0.714，and 0.846，0.627，0.737 in the external validation group. Conclusions:Improvement of the 2022 version of O-RADS category 4 using ultrasound features may improve the identification of benign and malignant ovarian-adnexal tumors.

Objective:To analyze the evaluation value of contrast-enhanced ultrasound (CEUS) combined with automatic volumetric ultrasound in the efficacy of neoadjuvant chemotherapy (NAC) for breast cancer.Methods:A retrospective study was conducted on 98 female patients with breast cancer admitted to Shaanxi Provincial Cancer Hospital from January to December 2022. All patients received 4 cycles of NAC, and underwent conventional ultrasound, automatic volumetric ultrasound, CEUS, and histopathological examination before and after treatment. Based on the post-treatment histopathological efficacy, patients were divided into the effective group ( n=67) and the ineffective group ( n=31). The CEUS and automatic volumetric ultrasound parameters before and after treatment, as well as the evaluation efficacy of these two types of parameters for NAC efficacy in breast cancer, were compared. The value of CEUS combined with automatic volumetric ultrasound in evaluating NAC efficacy for breast cancer was analyzed. Results:After NAC treatment, the CEUS parameters [time to peak (TTP) and arrival time (AT) of contrast agent] were longer than those before treatment, while the peak intensity (PI) was lower than that before treatment (all P<0.05); the automatic volumetric ultrasound parameters (tumor volume, area, thickness, length, and width) after NAC treatment were all smaller than those before treatment (all P<0.05). After treatment, the PI, volume, area, thickness, length, and width in the effective group were significantly smaller than those in the ineffective group, while the TTP and AT were significantly longer than those in the ineffective group (all P<0.05). Receiver operating characteristic (ROC) curve analysis showed that the area under the curve (AUC) of CEUS parameters and automatic volumetric ultrasound parameters for predicting effective NAC efficacy in breast cancer was 0.837(0.749-0.904) and 0.864(0.780-0.925), respectively, with no statistically significant difference between the two ( P=0.674). The AUC of the combined parameters for predicting effective NAC efficacy was 0.942(0.875-0.979), which was significantly higher than that of CEUS parameters or automatic volumetric ultrasound parameters alone ( Z=2.947, 2.135, P=0.003, 0.033). Conclusions:The combination of CEUS and automatic volumetric ultrasound parameters has high value in the efficacy evaluation of NAC for breast cancer and can be used as a clinical reference.