Objective To explore the relationship between circadian rhythm genes and the occurrence, development, prognosis, and tumor microenvironment (TME) of lung adenocarcinoma (LUAD). Methods The Cancer Genome Atlas data were used to evaluate the expression, copy number variation, and somatic mutation frequency of circadian gene sets in LUAD. Gene ontology, Kyoto encyclopedia of genes and genomes, and gene set enrichment analysis were used to explore the potential mechanisms by which circadian rhythm genes affected LUAD progression. Cox regression, least absolute shrinkage and selection operator regression, support vector machine recursive feature elimination, and random forest screened circadian genes and established prognostic models, and on this basis constructed nomogram to predict patients’ 1-, 3-, and 5-year survival rates. Kaplan-Meier survival curves, receiver operating characteristic (ROC) curves, and time-dependent ROC curves were drawn to evaluate the predictive ability of the model, and the external dataset of GEO further verified the prognostic value of the prediction model. In addition, we evaluated the association of the prognostic model with immune cells and immune checkpoint genes. Single cell RNA sequencing (scRNA-seq) analysis was used to explore the molecular characteristics between prognostically relevant circadian genes and different immune cell populations in TME. Results Differentially expressed circadian rhythm genes were mainly enriched in biological processes related to cGMP-PKG signaling pathway, lipid and atherosclerosis, and JAK-STAT signaling pathway. Seven circadian rhythm genes: LGR4, CDK1, KLF10, ARNTL2, RORA, NPAS2, PTGDS were screened out, and a RiskScore model was established. According to the median RiskScore, samples were divided into a high-risk group and a low-risk group. Compared with patients in the low-risk group, patients in the high-risk group showed a poorer prognosis (P<0.001). Immunological characterization analysis showed that there were differences in the infiltration of multiple immune cells between the low-risk group and high-risk group. Most immune checkpoint genes had higher expression levels in the high-risk group than those in the low-risk group, and RiskScore was positively correlated with the expression of CD276, TNFSF4, PDCD1LG2, CD274, and TNFRSF9, and negatively correlated with the expression of CD40LG and TNFSF15. The scRNA-seq analysis showed that RORA and KLF10 were mainly expressed in natural killer cells. Conclusion The prognostic model based on seven feature circadian rhythm genes has certain predictive value for predicting survival of LUAD patients. Dysregulated expression of circadian genes may regulate the occurrence, progression as well as prognosis of LUAD through affecting TME, which provides a possible direction for finding potential strategies for treating LUAD from the perspective of mechanism by which circadian disorder affects immune cells.

Intermittent exotropia(IXT)is the most prevalent form of childhood strabismus, with an estimated prevalence of approximately 3.26% in the Chinese population. Although patients can intermittently maintain orthotropia, the deviation angle often fluctuates markedly, and frank exotropia may become evident during fatigue or lapses in attention. Without intervention, roughly 75% of cases progress over time. Management comprises surgical and non-surgical approaches. Surgery remains the most definitive treatment, however, the optimal timing is controversial, and postoperative outcomes may include under- or over-correction, necessitating additional procedures. Non-surgical options include observation, refractive correction, over-minus lens therapy, prisms, orthoptic exercises, and botulinum toxin-A injections. These modalities are particularly suitable for young, or uncooperative children, patients with small-angle, well-controlled deviations, or those seeking to defer surgery, in such cases, non-surgical treatment can maintain binocular alignment and preserve monocular function, thereby delaying or avoiding surgery. Because the efficacy of each non-surgical strategy varies, this review summarizes the current evidence on non-surgical treatment of IXT.

ObjectiveTo examine the influences of Shenfu injection on the endogenous metabolic byproducts in the myocardium of the rat model exhibiting chronic heart failure， thus deciphering the therapeutic mechanism of the Qi-reinforcing and Yang-warming method. MethodsSD rats were randomly allocated into a control group and a modeling group. Chronic heart failure with heart-Yang deficiency syndrome in rats was modeled by multi-point subcutaneous injection of isoproterenol， and the rats were fed for 14 days after modeling. The successfully modeled rats were randomized into model， Shenfu injection （6.0 mL·kg^-1）， and trimetazidine （10 mg·kg^-1） groups and treated with corresponding agents for 15 days. The control group and the model group were injected with equal doses of normal saline， and the samples were collected after the intervention was completed. Cardiac color ultrasound was performed. Hematoxylin-eosin （HE） staining was used to observe histopathological morphology， and the serum level of N-terminal pro-brain natriuretic peptide （NT-proBNP） was assessed by enzyme-linked immunosorbent assay （ELISA）. The mitochondrial morphological and structural changes of cardiomyocytes were observed by transmission electron microscopy， and the metabolic profiling was carried out by ultra high performance liquid chromatography-quantitative exactive-mass spectrometry （UHPLC-QE-MS）. Differential metabolites were screened and identified by orthogonal partial least squares-discriminant analysis （OPLS-DA） and other methods， and then the MetaboAnalyst database was used for further screening. The relevant biological pathways were obtained through pathway enrichment analysis. The receiver operating characteristic （ROC） curve was established to evaluate the diagnostic value of each potential biomarker for myocardial injury and the evaluation value for drug efficacy. ResultsThe results of color ultrasound showed that Shenfu Injection improved the cardiac function indexes of model rats （P<0.05）. The results of HE staining showed that Shenfu injection effectively alleviated the pathological phenomena such as myocardial tissue structure disorder and inflammatory cell infiltration in model rats. The results of ELISA showed that Shenfu injection effectively regulated the serum NT-proBNP level in the model rats. Transmission electron microscopy （TEM） showed that Shenfu injection effectively restored the mitochondrial morphological structure. The results of metabolomics showed that the metabolic phenotypes of myocardial samples presented markedly differences between groups. Nine differential metabolites could be significantly reversed in the Shenfu injection group， involving three metabolic pathways： pyruvate metabolism， histidine metabolism， and citric acid cycle （TCA cycle）. The results of ROC analysis showed that the area under the curve （AUC） values of all metabolites were between 0.75 and 1.0， indicating that the differential metabolites had high diagnostic accuracy for myocardial injury， and the changes in their expression levels could be used as potential markers for efficacy evaluation. ConclusionShenfu injection significantly alleviated the damage of cardiac function， myocardium， and mitochondrial structure in the rat model of chronic heart failure with heart-Yang deficiency syndrome by ameliorating energy metabolism remodeling. Reinforcing Qi and warming Yang is a key method for treating chronic heart failure with heart-Yang deficiency syndrome.

OBJECTIVE To analyze the impact of the diagnosis-related groups （DRG） payment reform on the length of stay and medical costs in patients with chronic obstructive pulmonary disease （COPD） in Kashgar region， aiming to provide localized empirical evidence for the optimization of regional medical insurance payment methods. METHODS Based on the inpatient settlement database of the Xinjiang Uygur Autonomous Region Healthcare Security Administration， settlement data of COPD inpatients from 17 medical institutions in Kashgar region between January 1， 2022， and December 31， 2024， were extracted. The overall changes in patients’ length of stay and costs were compared before and after the reform. Subsequently， interrupted time series analysis （ITSA） was employed to explore the impact of the DRG payment reform on these variables. RESULTS Following the reform， both the average length of stay and various cost decreased significantly compared to the pre-reform period （ P ＜0.001）. At the overall sample level， the average length of stay， average total cost， average drug cost， average medical service cost， and average examination cost per admission all demonstrated significant long-term downward trends after the reform （ P ＜0.05）. However， the decrease in average out-of-pocket costs and the increase in average consumable costs per admission were not statistically significant （ P ＞0.05）. In tertiary medical institutions， the average length of stay and all categories of costs （except average consumable costs per admission） exhibited significant long-term upward trends after the reform （ P ＜0.05）； conversely， in secondary and lower-level medical institutions， the average length of stay， average total cost， average drug cost， average medical service cost， and average examination cost per admission showed significant long-term downward trends （ P ＜0.05）. CONCLUSIONS The DRG payment reform has achieved an overall effect of reducing the length of stay and controlling costs in COPD patients from Kashgar region. However， the effects vary across different levels of medical institutions： secondary and lower-level institutions show a long-term downward trend in length of stay and costs， whereas tertiary institutions exhibit a long-term upward trend. Furthermore， patients’ out-of-pocket financial burden does not show significant improvement.

Hematologic malignancies,such as lymphoma,myeloma,and myeloid neoplasms,can occur in extramedullary tissues.Traditional histopathological morphology and immunohistochemical staining have lim-itations,including time-consuming specimen processing,prolonged reporting cycles,and relatively low sensi-tivity in cases of limited cell numbers.Flow cytometry offers significant advantages in detecting tissue sam-ples,such as rapid processing,shorter reporting cycles,and high accuracy and sensitivity,making it an effective complement to histopathological and immunohistochemical methods.However,the application of flow cytome-try in tissue sample detection currently lacks standardized protocols for sample collection and preservation,single-cell suspension preparation,antibody panel design for limited samples,data analysis,and result repor-ting.To promote the standardized application of flow cytometry in detecting hematologic tumor cells in tissue samples,the Cell Analysis Professional Committee of the Chinese Society of Biotechnology organized experts to develop the Chinese Expert Consensus on Flow Cytometry for Detecting Hematologic Tumor Cells in Tis-sue Samples(hereinafter referred to as the Consensus).This Consensus elaborates on the technical aspects of flow cytometry for tissue sample detection,covering sample processing,antibody panel design,data analysis,reporting content,and quality management.It particularly emphasizes recommended antibody panels and data analysis methods for flow cytometry when tissue sample cell counts are low.This article aims to interpret the key points of the Consensus to facilitate its better application in clinical practice.

Lymphoma is a highly heterogeneous malignancy characterized by complex molecular regulatory mechanisms that result in significant differences in aggressiveness and prognosis across its subtypes.Gene copy number alteration(CNA)analysis,an emerging technology,has become a pivotal tool in the precision re-search and management of lymphoma.By detecting DNA deletions,amplifications,and chromosomal copy number changes,CNA analysis addresses the limitations of traditional cytogenetic techniques,enhances the ac-curacy of subtype classification,and aids in evaluating tumor heterogeneity and disease progression.This re-view provides a comprehensive summary of CNA detection methods and their applications in lymphoma,with a focus on recent advancements in the field.It offers a comparative analysis of CNA detection techniques and discusses their role in precision diagnosis,subtype classification,monitoring disease progression,predicting therapeutic resistance,and assessing prognosis.Additionally,the review explores the potential applications of CNA analysis in uncovering molecular regulatory mechanisms,optimizing therapeutic strategies,and impro-ving patient survival outcomes.

Objective To determine the prognostic biomarkers and new therapeutic targets of the lung adenocarcinoma (LUAD), based on which to establish a prediction model for the survival of LUAD patients. Methods An integrative analysis was conducted on gene expression and clinicopathologic data of LUAD, which were obtained from the UCSC database. Subsequently, various methods, including screening of differentially expressed genes (DEGs), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Set Enrichment Analysis (GSEA), were employed to analyze the data. Cox regression and least absolute shrinkage and selection operator (LASSO) regression were used to establish an assessment model. Based on this model, we constructed a nomogram to predict the probable survival of LUAD patients at different time points (1-year, 2-year, 3-year, 5-year, and 10-year). Finally, we evaluated the predictive ability of our model using Kaplan-Meier survival curves, receiver operating characteristic (ROC) curves, and time-dependent ROC curves. The validation group further verified the prognostic value of the model. Results The different-grade pathological subtypes' DEGs were mainly enriched in biological processes such as metabolism of xenobiotics by cytochrome P450, natural killer cell-mediated cytotoxicity, antigen processing and presentation, and regulation of enzyme activity, which were closely related to tumor development. Through Cox regression and LASSO regression, we constructed a reliable prediction model consisting of a five-gene panel (MELTF, MAGEA1, FGF19, DKK4, C14ORF105). The model demonstrated excellent specificity and sensitivity in ROC curves, with an area under the curve (AUC) of 0.675. The time-dependent ROC analysis revealed AUC values of 0.893, 0.713, and 0.632 for 1-year, 3-year, and 5-year survival, respectively. The advantage of the model was also verified in the validation group. Additionally, we developed a nomogram that accurately predicted survival, as demonstrated by calibration curves and C-index. Conclusion We have developed a prognostic prediction model for LUAD consisting of five genes. This novel approach offers clinical practitioners a personalized tool for making informed decisions regarding the prognosis of their patients.

Objective To develop an artificial intelligence (AI)-driven lung cancer database by structuring and standardizing clinical data, enabling advanced data mining for lung cancer research, and providing high-quality data for real-world studies. Methods Building on the extensive clinical data resources of the Department of Thoracic Surgery at Peking Union Medical College Hospital, this study utilized machine learning techniques, particularly natural language processing (NLP), to automatically process unstructured data from electronic medical records, examination reports, and pathology reports, converting them into structured formats. Data governance and automated cleaning methods were employed to ensure data integrity and consistency. Results As of September 2024, the database included comprehensive data from 18 811 patients, encompassing inpatient and outpatient records, examination and pathology reports, physician orders, and follow-up information, creating a well-structured, multi-dimensional dataset with rich variables. The database’s real-time querying and multi-layer filtering functions enabled researchers to efficiently retrieve study data that meet specific criteria, significantly enhancing data processing speed and advancing research progress. In a real-world application exploring the prognosis of non-small cell lung cancer, the database facilitated the rapid analysis of prognostic factors. Research findings indicated that factors such as tumor staging and comorbidities had a significant impact on patient survival rates, further demonstrating the database’s value in clinical big data mining. Conclusion The AI-driven lung cancer database enhances data management and analysis efficiency, providing strong support for large-scale clinical research, retrospective studies, and disease management. With the ongoing integration of large language models and multi-modal data, the database’s precision and analytical capabilities are expected to improve further, providing stronger support for big data mining and real-world research of lung cancer.

This study selected 6 529 plant-based Chinese materia medica(PCMM) from Chinese Materia Medica as research subjects and applied a random permutation test to explore the overall correlation characteristics between nature and flavor, as well as the correlation characteristics after distinguishing different medicinal parts and harvest seasons. The results showed that the overall correlation characteristics between nature and flavor in PCMM were significantly associated in the following pairs: cold and bitter, cool and bitter, cool and astringent, cool and light, neutral and sweet, neutral and astringent, neutral and light, neutral and sour, hot and pungent, and warm and pungent. When analyzing the data by distinguishing medicinal parts and/or harvest seasons, new correlation patterns emerged, characterized by the disappearance of some significant correlations and the emergence of new ones. When analyzing by medicinal parts alone, significant correlations were found in the following cases: cold and light in leaves, cold and salty in barks, cool and sweet in fruits and seeds, neutral and pungent in whole herbs, neutral and salty in stems, and warm and salty in flowers. However, no significant correlations were found between cool and bitter in stems and other types of herbs, cool and astringent in fruits, seeds, flowers, and other types of herbs, cool and light in leaves, fruits, seeds, barks, flowers and other types of herbs, neutral and sweet in barks, neutral and astringent in whole herbs and stems, neutral and light in leaves, fruits, seeds, and flowers, neutral and sour in whole herbs, stems, barks, flowers, and other types of herbs, and hot and pungent in whole herbs, stems, flowers, and other types of herbs. When analyzing by harvest season alone, significant correlations were found in the following cases: cold and salty, and cool and sour in herbs harvested in winter, and neutral and salty in herbs harvested year-round. However, no significant correlation was found between cool and light in herbs harvested in winter. When considering both medicinal parts and harvest seasons, compared to the independent influence of medicinal parts, 14 new significant correlations emerged(e.g., the correlation between cool and bitter in stems harvested in spring), while 53 previously significant correlations disappeared(e.g., the correlation between cool and bitter in barks harvested in summer). Compared to the independent influence of harvest seasons, 11 new significant correlations appeared(e.g., the correlation between cold and light in barks harvested in autumn), while 50 previously significant correlations disappeared(e.g., the correlation between hot and pungent in leaves harvested in winter). This study is the first to reveal the influence of medicinal parts and harvest seasons on the correlation between nature and flavor in PCMM, which highlights that these two factors can interact and jointly affect nature-flavor correlations. Further research is needed to explore the underlying mechanisms. This study provides a deeper understanding of the inherent scientific connotations of herbal properties and offers a theoretical foundation for the cultivation and harvesting of PCMM.
Seasons ; Plants, Medicinal/growth & development* ; Drugs, Chinese Herbal/chemistry* ; Taste

Acute lung injury(ALI) is a critical clinical condition primarily characterized by refractory hypoxemia and infiltration of inflammatory cells in lung tissue, which can progress into a more severe form known as acute respiratory distress syndrome(ARDS). Immune cells and inflammatory cytokines play important roles in the progression of the disease. Due to its unclear pathogenesis and the lack of effective clinical treatments, ALI is associated with a high mortality rate and severely affects patients' quality of life, making the search for effective therapeutic agents particularly urgent. Ginseng Radix et Rhizoma, the dried root of the perennial herb Panax ginseng from the Araliaceae family, contains active ingredients such as saponins and polysaccharides, which possess various pharmacological effects including anti-tumor activity, immune regulation, and metabolic modulation. In recent years, studies have shown that ginsenosides exhibit notable effects in reducing inflammation, ameliorating epithelial and endothelial cell injury, and providing anticoagulant action, indicating their comprehensive role in alleviating lung injury. This review summarizes the pathogenesis of ALI and the molecular mechanisms through which ginsenosides act at different stages of ALI development. The aim is to provide a scientific reference for the development of ginsenoside-based drugs targeting ALI, as well as a theoretical basis for the clinical application of Ginseng Radix et Rhizoma in the treatment of ALI.
Ginsenosides/pharmacology* ; Humans ; Acute Lung Injury/immunology* ; Animals ; Panax/chemistry* ; Drugs, Chinese Herbal