As the core vehicle for preserving and transmitting traditional Chinese medicine（TCM） academic thought and clinical experience， the establishment of a robust quality evaluation system for TCM clinical case reports is a crucial component in the current standardization and modernization of TCM. Based on the practical experience of constructing the China Clinical Cases Library of Traditional Chinese Medicine by the China Association of Chinese Medicine， this study conducted a comprehensive analysis of critical challenges， including insufficient authenticity and unfocused evaluation criteria. It proposed a three-dimensional evaluation framework grounded in the structure-process-outcome logic， encompassing three dimensions of authenticity and standardization， characteristics and advantages， application and translational impact. This framework integrated 12 key evaluation indicators in a systematic manner. The model preserved the academic characteristics of TCM syndrome differentiation and treatment， while aligning with modern scientific research standards， achieving a balance between individualized TCM experience and standardized evaluation. Concurrently， this study provided theoretical foundations and methodological guidance for evaluating the quality of TCM clinical cases， contributing significantly to the inheritance of TCM knowledge， evidence-based practice， and the reform of talent evaluation mechanisms.

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.

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*

Ulcerative colitis(UC) is a recurrent, chronic, nonspecific inflammatory bowel disease. The longer the course of the disease, the higher the risk of cancerization. In recent years, the incidence and mortality rates of colon cancer in China have been increasing year by year, seriously threatening the life and health of patients. Therefore, studying the mechanism of "inflammation cancer transformation" in UC and conducting early intervention is crucial. The "Kenang" theory is an important component of traditional Chinese medicine(TCM) theory of phlegm and blood stasis. It is based on the coexistence of phlegm and blood stasis in the body and deeply explores the pathogenic syndromes and characteristics of phlegm and blood stasis. Kenang is a pathological product formed when long-term Qi stagnation leads to the internal formation of phlegm and blood stasis, which is hidden deep within the body. It is characterized by being hidden, progressive, and difficult to treat. The etiology and pathogenesis of "inflammation cancer transformation" in UC are consistent with the connotation of the "Kenang" theory. The internal condition for the development of UC "inflammation cancer transformation" is the deficiency of healthy Qi, with Qi stagnation being the key pathological mechanism. Phlegm and blood stasis are the main pathogenic factors. Phlegm and blood stasis accumulate in the body over time and can produce cancer toxins. Due to the depletion of healthy Qi and a weakened constitution, the body is unable to limit the proliferation and invasion of cancer toxins, eventually leading to cancer transformation in UC. In clinical treatment, the focus should be on removing phlegm and blood stasis, with syndrome differentiation and treatment based on three basic principles: supporting healthy Qi to strengthen the body's foundation, resolving phlegm and blood stasis to break up the Kenang, and regulating Qi and blood to smooth the flow of energy and resolve stagnation. This approach helps to dismantle the Kenang, delay, block, or even reverse the cancerization process of UC, reduce the risk of "inflammation cancer transformation", improve the patient's quality of life, and provide new perspectives and strategies for early intervention in the development of colon cancer.
Humans ; Colitis, Ulcerative/immunology* ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal/therapeutic use* ; Cell Transformation, Neoplastic