Adipose tissue is a critical energy reservoir in animals and humans, with multifaceted roles in endocrine regulation, immune response, and providing mechanical protection. Based on anatomical location and functional characteristics, adipose tissue can be categorized into distinct types, including white adipose tissue (WAT), brown adipose tissue (BAT), beige adipose tissue, and pink adipose tissue. Traditionally, adipose tissue research has centered on its morphological and functional properties as a whole. However, with the advent of single-cell transcriptomics, a new level of complexity in adipose tissue has been unveiled, showing that even under identical conditions, cells of the same type may exhibit significant variation in morphology, structure, function, and gene expression——phenomena collectively referred to as cellular heterogeneity. Single-cell transcriptomics, including techniques like single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq), enables in-depth analysis of the diversity and heterogeneity of adipocytes at the single-cell level. This high-resolution approach has not only deepened our understanding of adipocyte functionality but also facilitated the discovery of previously unidentified cell types and gene expression patterns that may play key roles in adipose tissue function. This review delves into the latest advances in the application of single-cell transcriptomics in elucidating the heterogeneity and diversity within adipose tissue, highlighting how these findings have redefined the understanding of cell subpopulations within different adipose depots. Moreover, the review explores how single-cell transcriptomic technologies have enabled the study of cellular communication pathways and differentiation trajectories among adipose cell subgroups. By mapping these interactions and differentiation processes, researchers gain insights into how distinct cellular subpopulations coordinate within adipose tissues, which is crucial for maintaining tissue homeostasis and function. Understanding these mechanisms is essential, as dysregulation in adipose cell interactions and differentiation underlies a range of metabolic disorders, including obesity and diabetes mellitus type 2. Furthermore, single-cell transcriptomics holds promising implications for identifying therapeutic targets; by pinpointing specific cell types and gene pathways involved in adipose tissue dysfunction, these technologies pave the way for developing targeted interventions aimed at modulating specific adipose subpopulations. In summary, this review provides a comprehensive analysis of the role of single-cell transcriptomic technologies in uncovering the heterogeneity and functional diversity of adipose tissues.

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 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.

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.

Objective To explore the research progress, research hotspot and development trend of tigecycline resistance based on the quantitative analysis and visualization function of CiteSpace. Methods The data were collected from 4,263 Chinese and English articles on tigecycline resistance in CNKI, Wanfang, VIP and Web of Science (WOS) databases from 2012 to 2022. CiteSpace 5.8.R3 software was used to analyze the cooperative network of authors, the cooperative network of countries and institutions, the total citation times of journals, and keywords included in the literature, to reveal the hotspots and trends of tigecycline resistance research. Results The number of articles published in English literature was higher than that in Chinese literature. China had the largest number of published documents, showing a significant international academic influence in this research field. Countries all over the world were concerned about the resistance of tigecycline, but Chinese literatures focused more on the clinical infection and prevention of tigecycline resistance, while English literatures placed special emphasis on the research about the drug resistance mechanism of tigecycline. Conclusion The research direction at home and abroad is basically the same, but the research focus has gradually shifted from the clinical treatment and monitoring of tigecycline to the molecular level of drug resistance mechanism.

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 investigate the incidence and influencing factors of falls among the elderly in Ningbo City, Zhejiang Province, so as to provide the basis for developing effective prevention strategies. Methods: The residents aged 60 years and above in Haishu District and Yuyao City of Ningbo City were selected by the multi-stage cluster random sampling method from June to October 2022. Demographic information, fall incidence in the past year, history of disease and self-rated health were collected through questionnaire surveys. Incidence of falls was descriptively analyzed, and factors affecting falls were identified using a multivariable logistic regression model stratified by gender and age. Results: A total of 1 275 elderly people were surveyed, including 635 men and 640 women. The median age was 72.00 (interquartile range, 13.00) years. In the past year, 158 residents fell, accounting for 12.39%. Additionally, 14 individuals experienced two or more falls, accounting for 8.86%. The incidence of falls was 14.69% in women, which was higher than the 10.08% in men (P<0.05). The incidence of falls was 14.86% in the elderly over 70 years, which was higher than the 9.39% in those aged 60 to 70 years (P<0.05). Multivariable logistic regression showed that the educational level (primary school and above, OR=0.501, 95%CI: 0.301-0.836), heart disease (present, OR=1.996, 95%CI: 1.076-3.703), and self-rated health status (good, OR=0.529, 95%CI: 0.319-0.875) were factors affecting falls in women; educational level (primary school and above, OR=0.514, 95%CI: 0.285-0.928) and self-rated health status (good, OR=0.456, 95%CI: 0.253-0.824) were factors affecting falls in residents aged 60 to 70 years. Conclusion Fall risk among the elderly is associated with gender, age, heart disease, educational level and self-rated health status, and the influencing factors for falls vary in different genders and ages.