This review explores the current applications and future prospects of artificial intelligence (AI) in thoracic imaging, with a particular focus on chest radiography (chest X-ray, CXR) and computed tomography (CT).Current Concepts: Recently developed CXR AI algorithms have improved the efficiency, accuracy, and consistency of radiologists' routine clinical workflows by assisting in the detection of a wide range of thoracic diseases on CXR. These AI systems demonstrate diagnostic performance comparable to that of radiology residents who have limited interpretive experience. Furthermore, generative CXR AI technologies are capable of not only automatically detecting abnormalities such as pulmonary nodules, pneumonia, pneumothorax, and tuberculosis, but also generating radiology reports. These advancements represent a paradigm-shifting innovation that may significantly alter the current landscape of CXR interpretation in thoracic radiology. Although performance varies depending on the specific algorithm and dataset, AI applied to low-dose chest CT has demonstrated diagnostic accuracy ranging from 0.81 to 0.98 for nodule detection and malignancy assessment, with sensitivity ranging from 0.88 to 0.99 and specificity from 0.82 to 0.93. Incorporating AI as a second reader in CT interpretation can reduce reading time by approximately 20%, while also improving sensitivity for pulmonary nodule detection by 5% to 20% and malignant nodule diagnosis by 3% to 15%.Discussion and Conclusion: Both CXR AI and chest CT AI streamline image interpretation by assisting with simple and repetitive tasks. Simultaneously, they provide novel diagnostic insights that are expected to influence and potentially reshape the interpretative patterns of radiologists in the near future.

This review explores the current applications and future prospects of artificial intelligence (AI) in thoracic imaging, with a particular focus on chest radiography (chest X-ray, CXR) and computed tomography (CT).Current Concepts: Recently developed CXR AI algorithms have improved the efficiency, accuracy, and consistency of radiologists' routine clinical workflows by assisting in the detection of a wide range of thoracic diseases on CXR. These AI systems demonstrate diagnostic performance comparable to that of radiology residents who have limited interpretive experience. Furthermore, generative CXR AI technologies are capable of not only automatically detecting abnormalities such as pulmonary nodules, pneumonia, pneumothorax, and tuberculosis, but also generating radiology reports. These advancements represent a paradigm-shifting innovation that may significantly alter the current landscape of CXR interpretation in thoracic radiology. Although performance varies depending on the specific algorithm and dataset, AI applied to low-dose chest CT has demonstrated diagnostic accuracy ranging from 0.81 to 0.98 for nodule detection and malignancy assessment, with sensitivity ranging from 0.88 to 0.99 and specificity from 0.82 to 0.93. Incorporating AI as a second reader in CT interpretation can reduce reading time by approximately 20%, while also improving sensitivity for pulmonary nodule detection by 5% to 20% and malignant nodule diagnosis by 3% to 15%.Discussion and Conclusion: Both CXR AI and chest CT AI streamline image interpretation by assisting with simple and repetitive tasks. Simultaneously, they provide novel diagnostic insights that are expected to influence and potentially reshape the interpretative patterns of radiologists in the near future.

Purpose: This study analyzed the pathological complete response (pCR) rates, long-term outcomes, and biological features of human epidermal growth factor receptor 2 (HER2)-zero, HER2-low, and HER2-positive breast cancer patients undergoing neoadjuvant treatment. Methods: This single-center study included 1,667 patients who underwent neoadjuvant chemotherapy from 2008 to 2014. Patients were categorized by HER2 status, and their clinicopathological characteristics, chemotherapy responses, and recurrence-free survival (RFS) rates were analyzed. Results: Patients with HER2-low tumors were more likely to be older (p = 0.081), have a lower histological grade (p < 0.001), and have hormone receptor (HorR)-positive tumors (p < 0.001). The HER2-positive group exhibited the highest pCR rate (23.3%), followed by the HER2-zero (15.5%) and HER2-low (10.9%) groups. However, the pCR rate did not differ between HER2-low and HER2-zero tumors in the HorR-positive or HorR-negative subgroups.The 5-year RFS rates increased in the following order: HER2-low, HER2-positive, and HER2-zero (80.0%, 77.5%, and 74.5%, respectively) (log-rank test p = 0.017). A significant survival difference between patients with HER2-low and HER2-zero tumors was only identified in HorR-negative tumors (5-year RFS for HER2-low, 74.5% vs. HER2-zero, 66.0%; log-rank test p-value = 0.04). Multivariate survival analysis revealed that achieving a pCR was the most significant factor associated with improved survival (hazard ratio [HR], 4.279; p < 0.001).Compared with HER2-zero, the HRs for HER2-low and HER2-positive tumors were 0.787 (p = 0.042) and 0.728 (p = 0.005), respectively. After excluding patients who received HER2-targeted therapy, patients with HER2-low tumors exhibited better RFS than those with HER2-zero (HR 0.784, p = 0.04), whereas those with HER2-positive tumors exhibited no significant difference compared with those with HER2-low tumors (HR, 0.975; p = 0.953). Conclusion Patients with HER2-low tumors had no significant difference in pCR rate compared to HER2-zero but showed better survival, especially in HorR-negative tumors.Further investigation into biological differences is warranted.

Purpose: This study analyzed the pathological complete response (pCR) rates, long-term outcomes, and biological features of human epidermal growth factor receptor 2 (HER2)-zero, HER2-low, and HER2-positive breast cancer patients undergoing neoadjuvant treatment. Methods: This single-center study included 1,667 patients who underwent neoadjuvant chemotherapy from 2008 to 2014. Patients were categorized by HER2 status, and their clinicopathological characteristics, chemotherapy responses, and recurrence-free survival (RFS) rates were analyzed. Results: Patients with HER2-low tumors were more likely to be older (p = 0.081), have a lower histological grade (p < 0.001), and have hormone receptor (HorR)-positive tumors (p < 0.001). The HER2-positive group exhibited the highest pCR rate (23.3%), followed by the HER2-zero (15.5%) and HER2-low (10.9%) groups. However, the pCR rate did not differ between HER2-low and HER2-zero tumors in the HorR-positive or HorR-negative subgroups.The 5-year RFS rates increased in the following order: HER2-low, HER2-positive, and HER2-zero (80.0%, 77.5%, and 74.5%, respectively) (log-rank test p = 0.017). A significant survival difference between patients with HER2-low and HER2-zero tumors was only identified in HorR-negative tumors (5-year RFS for HER2-low, 74.5% vs. HER2-zero, 66.0%; log-rank test p-value = 0.04). Multivariate survival analysis revealed that achieving a pCR was the most significant factor associated with improved survival (hazard ratio [HR], 4.279; p < 0.001).Compared with HER2-zero, the HRs for HER2-low and HER2-positive tumors were 0.787 (p = 0.042) and 0.728 (p = 0.005), respectively. After excluding patients who received HER2-targeted therapy, patients with HER2-low tumors exhibited better RFS than those with HER2-zero (HR 0.784, p = 0.04), whereas those with HER2-positive tumors exhibited no significant difference compared with those with HER2-low tumors (HR, 0.975; p = 0.953). Conclusion Patients with HER2-low tumors had no significant difference in pCR rate compared to HER2-zero but showed better survival, especially in HorR-negative tumors.Further investigation into biological differences is warranted.

Purpose: This study analyzed the pathological complete response (pCR) rates, long-term outcomes, and biological features of human epidermal growth factor receptor 2 (HER2)-zero, HER2-low, and HER2-positive breast cancer patients undergoing neoadjuvant treatment. Methods: This single-center study included 1,667 patients who underwent neoadjuvant chemotherapy from 2008 to 2014. Patients were categorized by HER2 status, and their clinicopathological characteristics, chemotherapy responses, and recurrence-free survival (RFS) rates were analyzed. Results: Patients with HER2-low tumors were more likely to be older (p = 0.081), have a lower histological grade (p < 0.001), and have hormone receptor (HorR)-positive tumors (p < 0.001). The HER2-positive group exhibited the highest pCR rate (23.3%), followed by the HER2-zero (15.5%) and HER2-low (10.9%) groups. However, the pCR rate did not differ between HER2-low and HER2-zero tumors in the HorR-positive or HorR-negative subgroups.The 5-year RFS rates increased in the following order: HER2-low, HER2-positive, and HER2-zero (80.0%, 77.5%, and 74.5%, respectively) (log-rank test p = 0.017). A significant survival difference between patients with HER2-low and HER2-zero tumors was only identified in HorR-negative tumors (5-year RFS for HER2-low, 74.5% vs. HER2-zero, 66.0%; log-rank test p-value = 0.04). Multivariate survival analysis revealed that achieving a pCR was the most significant factor associated with improved survival (hazard ratio [HR], 4.279; p < 0.001).Compared with HER2-zero, the HRs for HER2-low and HER2-positive tumors were 0.787 (p = 0.042) and 0.728 (p = 0.005), respectively. After excluding patients who received HER2-targeted therapy, patients with HER2-low tumors exhibited better RFS than those with HER2-zero (HR 0.784, p = 0.04), whereas those with HER2-positive tumors exhibited no significant difference compared with those with HER2-low tumors (HR, 0.975; p = 0.953). Conclusion Patients with HER2-low tumors had no significant difference in pCR rate compared to HER2-zero but showed better survival, especially in HorR-negative tumors.Further investigation into biological differences is warranted.

This review explores the current applications and future prospects of artificial intelligence (AI) in thoracic imaging, with a particular focus on chest radiography (chest X-ray, CXR) and computed tomography (CT).Current Concepts: Recently developed CXR AI algorithms have improved the efficiency, accuracy, and consistency of radiologists' routine clinical workflows by assisting in the detection of a wide range of thoracic diseases on CXR. These AI systems demonstrate diagnostic performance comparable to that of radiology residents who have limited interpretive experience. Furthermore, generative CXR AI technologies are capable of not only automatically detecting abnormalities such as pulmonary nodules, pneumonia, pneumothorax, and tuberculosis, but also generating radiology reports. These advancements represent a paradigm-shifting innovation that may significantly alter the current landscape of CXR interpretation in thoracic radiology. Although performance varies depending on the specific algorithm and dataset, AI applied to low-dose chest CT has demonstrated diagnostic accuracy ranging from 0.81 to 0.98 for nodule detection and malignancy assessment, with sensitivity ranging from 0.88 to 0.99 and specificity from 0.82 to 0.93. Incorporating AI as a second reader in CT interpretation can reduce reading time by approximately 20%, while also improving sensitivity for pulmonary nodule detection by 5% to 20% and malignant nodule diagnosis by 3% to 15%.Discussion and Conclusion: Both CXR AI and chest CT AI streamline image interpretation by assisting with simple and repetitive tasks. Simultaneously, they provide novel diagnostic insights that are expected to influence and potentially reshape the interpretative patterns of radiologists in the near future.

This study predicts the quality markers(Q-markers) for the cough-relieving and phlegm-expelling effects of Kening Granules based on pharmacodynamics, plasma drug chemistry, network pharmacology, and pharmacokinetics. Strong ammonia solution spray and phenol red secretion assays were employed to evaluate the cough-relieving and phlegm-expelling effects of Kening Granules. Twentysix absorbed prototype components of Kening Granules were identified by ultra high performance liquid chromatography coupled with QExactive Plus quadrupole/Orbitrap high resolution mass spectrometry(UHPLC-Q-Exactive Plus Orbitrap HRMS). Through network pharmacology, 11 potential active components were screened out for the cough-relieving and phlegm-expelling effects of Kening Granules. The 11 components acted on 40 common targets such as IL6, TLR4, and STAT3, which mainly participated in PI3K/Akt, HIF-1, and EGFR signaling pathways. Pharmacokinetic quantitative analysis was performed for 7 prototype components. Three compounds including azelaic acid, caffeic acid, and vanillin were identified as Q-markers for the cough-relieving and phlegm-expelling effects of Kening Granules based on their effectiveness, transmissibility, and measurability. The results of this study are of great significance for clarifying the pharmacological substance basis, optimizing the quality standards, and promoting the clinical application of Kening Granules.
Drugs, Chinese Herbal/administration & dosage* ; Network Pharmacology ; Cough/blood* ; Male ; Humans ; Animals ; Rats ; Rats, Sprague-Dawley ; Biomarkers/blood* ; Quality Control ; Chromatography, High Pressure Liquid ; Antitussive Agents/chemistry*

OBJECTIVE To screen the potential active components of Polygonum orientale flower against myocardial ischemia- reperfusion injury （MIRI） in rats based on physiological and pathological states. METHODS SD rats were divided into normal control group， normal administration group， MIRI control group and MIRI administration group， with 5 rats in each group. After drug intervention or modeling and drug intervention， chromatographic separation plasma samples were collected， and chromatographic separation and mass spectrometry data collection were performed by using UPLC-Q-TOF/MS. The prototype components and metabolites were analyzed by comparing the reference substance maps， the maps of each plasma sample， and the relevant literature. At the same time， the common peaks in plasma samples of rats in normal administration group and MIRI administration group were identified. Combined with principal component analysis and orthogonal partial least square-discriminant analysis， the differential transitional components were screened out according to the value of variable importance in the projection （VIP）＞1， to speculate the potential active components of P. orientale flower in rats under physiological and pathological states. The SD rats were divided into control group， MIRI group， positive control group （Compound danshen tablets 0.2 g/kg， 3 times a day）， and potentially active compound groups （10 mg/kg， twice a day）， with 5 rats in each group. The rats in administration groups were given relevant medicine intragastrically， for 3 consecutive days. The activity of superoxide dismutase （SOD）， the leakages of lactate dehydrogenase （LDH）， creatine kinase isoenzyme-MB （CK-MB） and cardiac troponin Ⅰ （cTnⅠ） in plasma were detected after the last administration. RESULTS Twenty-six main chromatographic peaks were obtained from the total ion chromatogram of the extract of P. orientale flower， and 14 of them were determined， including gallic acid， catechin， protocatechuic acid and so on. There were fifteen （including 6 absorbed prototype components and 9 metabolites） and nineteen transitional components （including 6 absorbed prototype components and 13 metabolites） in the plasma sample of normal rats and MIRI rats. Eight transitional components were detected in both normal rats and MIRI rats， and the VIP values of kaempferol glucuronidation metabolites， quercetin carbonylation metabolites and N-p-paprazine to the corresponding peak were higher than 1. Compared with MIRI group， the activities of SOD were increased significantly in the plasma of MIRI rats in each potential active compound group （P＜0.01）， and the leakages of LDH， CK-MB， and cTnⅠ in the plasma of MIRI rats were reduced significantly （P＜0.01）. CONCLUSIONS The potential anti-MIRI active components in extract of P. orientale flower are N-p-paprazine， quercetin， kaempferol and kaempferol-3-O-β-D-glucoside.

Objective:To analyze the mutation of T-cell and B-cell epitopes derived from HBV PreS-S protein in occult hepatitis B virus (OHBV) and investigate the biological mechanisms of occult hepatitis B virus infection (OBI) and HBsAb positive OBI.Methods:The PreS-S region of OBI samples were amplified by nested PCR, the products were sequenced and HBV genotypes were determined. The mutations of T-cell and B-cell epitopes derived from HBV PreS-S protein were analyzed and compared among groups of HBV genotypes and the presence of HBsAb. The affinity of the high frequency of T-cell epitope substitutions were analyzed by SYF PEITHI, the changes of antigenic characteristics of high frequency of B-cell epitope substitutions were analyzed by Ab Designer, Expasy ProtParam tool, Epitope Prediction and Analysis Tools.Results:The PreS-S region of HBV was amplified in 21 samples, including 4 HBsAb+ OBI B, 6 HBsAb-OBI B, 6 HBsAb+ OBI C, 5 HBsAb-OBI C. The mutation rates in PreS-S region of OBI were significantly higher than wild type HBV strains(OBI Bvs. WT B: 2.64%: 0.66%, P<0.001; OBI Cvs. WT C: 3.67%: 1.19%, P<0.001). The mutation rates of the immunoreactive area were significantly higher than non-immunoreactive area in OBI (OBI B: 3.57%: 1.86%, P=0.005; OBI C: 4.78%: 2.65%, P<0.001). The mutation rates of the immunoreactive and non-immunoreactive area in OBI C were higher than OBI B, but there was no statistically significant difference (immunoreactive area: 4.78%: 3.57%, P=0.107; non-immunoreactive area: 2.65%: 1.86%, P=0.142). The mutation rates of T-cell and B-cell epitopes of HBsAb-OBI were higher than HBsAb+ OBI, although there was no significant difference (HBsAb-OBI Bvs. HBsAb+ OBI B: 4.17∶3.01, P=0.303; HBsAb-OBI Cvs. HBsAb+ OBI C: 5.65∶4.26, P=0.207). The affinity analysis of 4 high frequency T-cell epitope substitutions, including T47A/K, S174N, L175S, V177A, showed that the changes of affinity of most mutation sites were not obvious; the antigenicity analysis of 3 high frequency B-cell epitope substitutions, including G73S, K122R, I126M/T, did not show noticeable changes and the hydrophilicity, surface accessibility of some mutation sites were even better than wild strain. Conclusions:The mutation rates in PreS-S region of OBI were significantly higher than wild type HBV strains. The mutation rates of the immunoreactive area were higher than non-immunoreactive area in OBI. The variant activity of OBI C was higher than OBI B. The mutations of OBI might occur randomly and were not selected by antibody pressure. Single epitope and multi-epitopes combinational mutations might be a reason for OBI.