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 In this study, a strain of colistin and tigecycline-resistant bacteria isolated in 2009 was analyzed, and the structure of drug-resistant plasmid and genetic environment were discussed, so as to provide basis for the prevention and control of multidrug-resistant bacteria. Methods A strain (GZ12244) with positive mcr and tet(M) was obtained by screening colistin and tigecycline resistance genes. Vitek-2 was used for strain identification, and the drug sensitivity test was carried out by broth dilution method. The molecular typing, drug resistance genes, insertion sequences, plasmid structure and genetic background were analyzed by genome-wide sequencing and bioinformatics. Results Strain GZ12244 is Klebsiella pneumoniae, which is resistant to colistin B, tigecycline, cefuroxime and tetracycline, and carries a variety of drug-resistant related genes such as mcr-1 and tet(M), and some of the drug-resistant genes with antibiotic efflux and antibiotic target change have amino acid substitution mutations. Mcr-1 and tet(M) coexist in a plasmid, and mcr-1 flanked by two insertion sequences ISApl1. There are insertion sequences such as IS15, IS1D and ISEc63 in the upstream and downstream of tet(M) gene. Conclusion Klebsiella pneumoniae GZ12244 is a multidrug-resistant strain. The drug-resistant gene exists in plasmid, and the mobile elements in upstream and downstream may spread the drug-resistant gene.

OBJECTIVE To analyze the traditional Chinese medicine （TCM） emergency management policy texts in China， reveal the characteristics， problems and improvement directions of Chinese medicine emergency management policies in China， and provide references and lessons for improving the level of Chinese medicine emergency management. METHODS Twenty-four TCM emergency management policy texts issued at the central level from 2016 to 2023 were coded and analyzed using Nvivo11 software to construct a three-dimensional analysis framework based on policy tools， stakeholders and policy strength. RESULTS In the policy tools dimension， the environmental type was the most （46.74%）， the supply type was the second （31.80%）， and the demand type was the least （21.46%）； in the stakeholder dimension， there were more healthcare institutions （40.63%） and government departments （31.25%）， and fewer healthcare workers （14.84%） and residents （13.28%）； in the policy strength dimension， the overall policy strength was poor， and the differences in effectiveness across policy instruments and stakeholders were more significant. The cross-cutting results showed that there was a certain degree of mismatch in policy instruments， stakeholders and policy strength. CONCLUSIONS The use of supply-oriented policy tools is slightly lacking， and the use of policy tools should be optimized in a coordinated manner； the distribution of stakeholders is relatively unbalanced， and synergies among stakeholders should be enhanced； the overall strength of policies is poor， and the top-level design of relevant policies should be improved.