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 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.

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 explore the predictive value of albumin,hemoglobin and multifactorial model for poor postoperative prognosis in elderly patients with meningioma.Methods A retrospective analysis was conducted on 253 elderly patients who underwent meningioma surgery and were transferred to the neurosurgical intensive care unit(NICU)at General Hospital of Northern Theater Command from January 2019 to September 2021,serving as the modeling cohort.Another 227 elderly patients who were treated in NICU after meningioma surgery from November 2021 to June 2023 were used as the validation cohort.Patients in the modeling cohort were categorized into good prognosis group[Glasgow Coma Scale(GCS)score>7,n=161]and poor prognosis group(GCS≤7,n=92)based on the GCS.Univariate and multifactorial logistic regression analyses were performed on the modeling cohort to identify independent risk factors,and a multifactorial model for predicting poor postoperative prognosis in elderly patients with meningioma was constructed based on these factors.The predictive efficacy and accuracy of the model were evaluated using the area under the receiver operating characteristic(ROC)curve(AUC),sensitivity,specificity,Hosmer-Lemeshow goodness-of-fit test,and calibration curves.The predictive value of postoperative albumin,hemoglobin,and the multifactorial models for postoperative prognosis in elderly meningioma patients was assessed using restricted cubic spline modeling(RCS),decision curves(DCA),and validated using an external validation cohort to assess the stability of the model.Results Meningioma WHO grade Ⅱand Ⅲ(OR=3.994,95％CI 1.963-8.126),postoperative hypoalbuminemia(OR=2.194,95％CI 1.079-4.462),and postoperative anemia(OR=2.117,95％CI 1.096-4.089)were identified as independent risk factors for poor postoperative prognosis in elderly meningioma patients(P<0.05),while the use of analgesic/sedative medications was a protective factor(OR=0.388,95％CI 0.201-0.748,P<0.05).The Hosmer-Lemeshow test indicated that the constructed multifactorial model had a good fit accuracy(P=0.161).The AUC for predicting poor postoperative prognosis in elderly meningioma patients for postoperative albumin and hemoglobin were 0.545(95％CI 0.472-0.617)and 0.632(95％CI 0.561-0.702),respectively,and showed a nonlinear dose-response relationship with prognosis(P<0.01).DCA analysis results showed that the net benefit rate of multifactorial model was higher than that of postoperative albumin and hemoglobin when the threshold probabilities were between 0.10 and 0.90.The AUC for predicting postoperative prognosis in the elderly meningioma patients in the modeling and validation cohorts were 0.810 and 0.819,respectively,and their calibration curves suggested good discrimination and accuracy.Conclusions Meningioma WHO grades Ⅱ and Ⅲ,postoperative anemia and hypoalbuminemia are independent risk factors for poor postoperative prognosis in elderly meningioma patients,while the use of analgesic/sedative drugs is a protective factor.The multifactorial model constructed based on these factors has a good predictive efficacy and credibility,and can be used as a reference for clinical decision-making.

Objective Ticks serve as vectors for transmitting Babesia microti.However,the specific mechanism remains unclear.This study aimed to investigate the effect of tick autophagy molecules on the proliferation of Babesia microti.Methods An experimental model of infected and uninfected mice was used to collect tick materials for proteomic analysis to identify differentially expressed autophagy-related molecules in Haemaphysalis longicornis.The cloning of the HlATG8 gene,protein expression,and production of polyclonal antibodies were completed.The HlATG8 gene was then knocked down using RNAi interference technology.Results The tick autophagy molecule,HlATG8,was identified and found to be significantly upregulated in ticks infected with Babesia microti.The load of Babesia microti in ticks increased significantly following the knockdown of the HlATG8 gene.Conclusions The tick autophagy molecule in Hae.longicornis,HlATG8,inhibits the proliferation of Babesia.

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.