The lung collaterals form a network that branches from the lung meridian, traversing the lung system and extending across the body′s surface. Lung collateral disease refers to the structural alterations or dysfunction in these collaterals caused by external or internal pathogens. Research into the structural and physiological functions of children′s lung collaterals, as well as the pathogenesis and syndrome differentiation for treating lung collateral diseases in children, holds significant value in guiding the prevention and treatment of pediatric respiratory conditions. Drawing on the theory of collateral disease, the clinical insights of both historical and contemporary physicians, and modern research findings—while considering the unique physiological and pathological characteristics of children′s respiratory systems—this study provides a foundational summary of the morphology and spatial distribution of children′s lung collaterals. The characteristics of these collaterals are highlighted as thin, sparse, short, narrow, brittle, and tender. From this structural understanding, the unique physiological functions of children′s lung collaterals are analyzed. The study further explores the interactions between pathogenic factors and lung collaterals, elucidating the pathogenesis and progression of children′s lung collateral diseases. It proposes treatment principles centered on "seeking treatment in the collaterals and employing the method of unblocking collaterals, "which align with the unique features of pediatric lung collaterals. Common treatment approaches, and relevant prescriptions for managing these diseases are summarized. This paper lays the foundation for a theoretical system encompassing the structure, function, pathogenesis, and syndrome differentiation for treating children′s lung collateral diseases. It offers valuable insights for the clinical diagnosis and management of pediatric respiratory diseases linked to collateral dysfunction and serves as a reference for the systematic development of a broader theoretical framework for children′s collateral diseases.

Dysregulated RNA splicing is a well-recognized characteristic of colorectal cancer (CRC); however, its intricacies remain obscure, partly due to challenges in profiling full-length transcript variants at the single-cell level. Here, we employ high-depth long-read scRNA-seq to define the full-length transcriptome of colorectal epithelial cells in 12 CRC patients, revealing extensive isoform diversities and splicing alterations. Cancer cells exhibited increased transcript complexity, with widespread 3'-UTR shortening and reduced intron retention. Distinct splicing regulation patterns were observed between intrinsic-consensus molecular subtypes (iCMS), with iCMS3 displaying even higher splicing factor activities and more pronounced 3'-UTR shortening. Furthermore, we revealed substantial shifts in isoform usage that result in alterations of protein sequences from the same gene with distinct carcinogenic effects during tumorigenesis of CRC. Allele-specific expression analysis revealed dominant mutant allele expression in key oncogenes and tumor suppressors. Moreover, mutated PPIG was linked to widespread splicing dysregulation, and functional validation experiments confirmed its critical role in modulating RNA splicing and tumor-associated processes. Our findings highlight the transcriptomic plasticity in CRC and suggest novel candidate targets for splicing-based therapeutic strategies.
Humans ; Colorectal Neoplasms/metabolism* ; RNA Isoforms/metabolism* ; Single-Cell Analysis ; RNA Splicing ; Gene Expression Regulation, Neoplastic ; RNA, Neoplasm/metabolism* ; Transcriptome

Benzylisoquinoline alkaloids (BIAs) are a structurally diverse group of plant metabolites renowned for their pharmacological properties. However, sustainable sources for these compounds remain limited. Consequently, researchers are focusing on elucidating BIA biosynthetic pathways and genes to explore alternative sources using synthetic biology approaches. CYP80B, a family of cytochrome P450 (CYP450) enzymes, plays a crucial role in BIA biosynthesis. Previously reported CYP80Bs are known to catalyze the 3'-hydroxylation of (S)-N-methylcoclaurine, with the N-methyl group essential for catalytic activity. In this study, we successfully cloned a full-length CYP80B gene (StCYP80B) from Stephania tetrandra (S. tetrandra) and identified its function using a yeast heterologous expression system. Both in vivo yeast feeding and in vitro enzyme analysis demonstrated that StCYP80B could catalyze N-methylcoclaurine and coclaurine into their respective 3'-hydroxylated products. Notably, StCYP80B exhibited an expanded substrate selectivity compared to previously reported wild-type CYP80Bs, as it did not require an N-methyl group for hydroxylase activity. Furthermore, StCYP80B displayed a clear preference for the (S)-configuration. Co-expression of StCYP80B with the CYP450 reductases (CPRs, StCPR1, and StCPR2), also cloned from S. tetrandra, significantly enhanced the catalytic activity towards (S)-coclaurine. Site-directed mutagenesis of StCYP80B revealed that the residue H205 is crucial for coclaurine catalysis. Additionally, StCYP80B exhibited tissue-specific expression in plants. This study provides new genetic resources for the biosynthesis of BIAs and further elucidates their synthetic pathway in natural plant systems.
Cytochrome P-450 Enzyme System/chemistry* ; Benzylisoquinolines/chemistry* ; Hydroxylation ; Plant Proteins/chemistry* ; Alkaloids/metabolism* ; Stephania tetrandra/genetics*

The scientific interpretation of the theory of medicinal properties of TCM is a research hotspot in the modernization of TCM. It is of great value to clarify the property and degree of cold and heat in Chinese materia medica for guiding clinical precise medication. In recent years, the research on the cold and heat properties of Chinese materia medica has been carried out at the animal, cell and molecular levels. Based on the objective material basis of medicinal properties, from the perspective of biological effects such as thermodynamics and multiomics; with the help of infrared thermal imaging and other technologies for analysis; forming a variety of research models such as "property-structure relationship". Related research has developed from a single material component or index to a new model that tends to integrate multi-source information and multi-dimensional data. However, how to deal with the problems of large sample size, strong redundancy, high heterogeneity, and how to integrate multi-dimensional information are still research difficulties. With its powerful computing and learning ability, machine learning can show good discrimination and prediction ability in the study of cold and hot properties of Chinese materia medica, and play an important role in the study of cold and hot properties of Chinese materia medica. At present, the most widely used algorithms are linear discriminant analysis, Logistic discriminant analysis, support vector machine, decision tree, random forest and so on. The data dimension of the existing research needs to be enriched, the algorithm has room for further optimization, and a more detailed discriminant model of cold and hot properties of Chinese materia medica needs to be established.

Objective: To investigate the changes in the levels of interleukin-6 (IL-6) and let-7e in rats induced by coal mine dust, so as to provide the basis for the mechanism of coal worker's pneumoconiosis (CWP). Methods: Sixty-four clean and healthy male Sprague-Dawley rats were randomly divided into the control group, coal dust group, mixed dust group (mixed coal and silica dust) and quartz group. The rats in the control group were exposed to 1 mL physiological saline by non-exposure tracheal perfusion, and the rats in the dust-exposed groups were exposed to 1 mL dust suspension. Rats were sacrificed by anesthesia after 1 month and 6 months, lung tissue was observed using hematoxylin-eosin staining, the pathological change in the lungs was scored using the Szapiel scoring system, the levels of IL-6 in the bronchoalveolar lavage fluid were detected using enzyme-linked immunosorbent assay, and the expression of let-7e was determined by quantitative real-time PCR. Results: A month after exposure, a small amount of coal spots and inflammatory exudation were observed in the lung tissue of the coal dust group and the mixed dust group. The quartz group showed tissue structure destruction and mild fibrosis and thickening of alveolar septum. Six months after exposure, there were more coal spots and slightly thickened alveolar septum in the coal dust group, and hyperplasia of pulmonary interstitial fibers, destruction of alveolar structure and silica nodules were observed in the mixed dust group. In the quartz group, the alveolar structure was obviously destroyed, the interstitial fiber proliferation was significant and silica nodules were seen. Two-factor analysis of variance showed that the interaction between duration of exposure and dust type significantly influenced the pathological score of lung tissue, IL-6 levels, and let-7e expression levels (P<0.05). Under the same dust type, the pathological score of lung tissue and IL-6 levels were higher at 6 months after exposure than at 1 month, while the relative expression of let-7e was lower at 6 months after exposure than at 1 month (all P<0.05). Under the same duration of exposure, the pathological score of lung tissue and IL-6 levels were higher in the dust-exposed groups than in the control group, while the relative expression of let-7e was lower in the dust-exposed groups than in the control group (all P<0.05). Conclusions Coal dust can cause an increase in levels of IL-6 and a decrease in let-7e expression in rats. The type of dust and duration of exposure can interactively affect IL-6 and let-7e.

Cancer is one of the leading causes of morbidity and mortality worldwide. Due to population aging, lifestyle variation and other factors, the morbidity and mortality of cancer continue to rise in China, resulting in a serious public health problem threating people's health. In response to this increasingly serious problem, tremendous efforts have been made in China, including the development of a series of policies and measures for specific needs of cancer prevention and treatment. Currently, China's strategy to reduce the disease burden of cancer mainly focuses on primary and secondary prevention. This paper summarizes the current status of disease burden of cancer and the performance of three-grade cancer prevention in China.

In recent years, the incidence of cancer in China has been increasing steadily. Advancing primary prevention measures for cancers could be an effective strategy to curb this trend. Diet has been considered a modifiable and shared risk factor for various cancers. Studying dietary patterns, with consideration of the interactions between foods and nutrients, has a practical implication for cancer prevention. This study provided an overview of dietary pattern extraction methods, summarized the research findings on the association between dietary patterns and cancers in the digestive system, respiratory system, and genitourinary system, and elucidated the potential mechanisms underlying these associations, in order to provide scientific references for future research in this field.

Objective·To investigate the effect of ceria nanoparticles-polyethylene glycol(CeNP-PEG)on scavenging reactive oxygen species(ROS)and alleviating disease activity in dextran sulphate sodium(DSS)-induced colitis mice.Methods·CeNP was synthesized with the hydrates of cerium acetate,oleamine,and xylene,which was modified with polyethylene glycol-stearyl phosphatidylethanolamine(mPEG-DPSE)to obtain CeNP-PEG.Then CeNP-PEG was purified.The particle size and zeta potential of CeNP-PEG were measured by using transmission electron microscopy(TEM)and dynamic light scattering(DLS).Mouse macrophages(Raw264.7)were cultured in vitro and induced to a pro-inflammatory phenotype(M1 phenotype).M1 macrophages were treated with 0.5 μg/mL and 1.0 μg/mL CeNP-PEG,respectively,and then Western blotting was used to detect the expression changes of the proteins related with nuclear factor-κB(NF-κB)signaling pathway.DSS-induced colitis mice models were constructed,and CeNP-PEG(1.0 mg/mL)was intravenously administrated for 3 times via tail vein during the modeling period.Meanwhile,the body weight,fecal characteristics,and frequency of rectal bleeding in mice were monitored in the normal control group(Normal group),the model group(DSS group),and the CeNP-PEG treatment group.The disease activity index(DAI)was calculated to evaluate the intestinal inflammation.The level of ROS in mouse intestinal tissues was detected by dihydroethidine(DHE)staining and the mRNA expression levels of inflammatory cytokines interferon-γ(Ifn-γ),interleukin-6(Il-6),Il-1β and tumor necrosis factor-α(Tnf-α)were detected by real-time quantitative PCR(RT-qPCR).Results·The hydrated particle size of synthesized CeNP-PEG was(6.96±0.27)nm,and the average zeta potential was(-6.02±1.31)mV.Western blotting results showed that the expression of p-P65 increased in the pro-inflammatory macrophages compared with the control group.The expression of NF-κB inhibitor-α(IκB-α)decreased,and their expressions tended to recover after the intervention of different concentrations of CeNP-PEG.In the DSS-induced colitis models,mice in the CeNP-PEG treatment group lost less weight than those in the DSS group(P= 0.000)and had lower DAI scores(P=0.000).The RT-qPCR results of intestinal tissues showed that the mRNA levels of Ifn-γ,Il-1β,Il-6 and Tnf-α in the DSS group were significantly up-regulated compared with those in the Normal group(P=0.000),and all of them significantly decreased in the CeNP-PEG treatment group.The results of DHE staining showed that the fluorescence intensity of intestinal tissues in the DSS group was significantly enhanced than that in the Normal group,and the fluorescence intensity decreased in the CeNP-PEG treatment group.Conclusion·CeNP-PEG can inhibit the expression of intestinal inflammatory factors and the activation of NF-κB-related inflammatory pathway of pro-inflammatory macrophages,eliminate intestinal ROS,improve the intestinal inflammatory microenvironment,and alleviate the disease activity of DSS-induced colitis in mice.

Objective:To explore the value of predicting new-onset heart failure events in patients with hypertrophic cardiomyopathy (HCM) using clinical and cardiac magnetic resonance (CMR) features based on machine learning algorithms.Methods:The study was a retrospective cohort study. Patients with a confirmed diagnosis of HCM who underwent CMR examinations at Beijing Anzhen Hospital from May 2017 to March 2021 were selected and randomly divided into the training set and the validation set in a ratio of 7∶3. Clinical data and CMR parameters (including conventional parameters and radiomics features) were collected. The endpoint events were heart failure hospitalization and heart failure death, with follow-up ending in January 2023. Features with high stability and P value<0.05 in univariate Cox regression analysis were selected. Subsequently, three machine learning algorithms—random forest, decision tree, and XGBoost—were used to build heart failure event prediction models in the training set. The model performance was then evaluated using the independent validation set, with the performance assessed based on the concordance index. Results:A total of 462 patients were included, with a median age of 51 (39, 62) years, of whom 332 (71.9%) were male. There were 323 patients in the training set and 139 in the validation set. The median follow-up time was 42 (28, 52) months. A total of 44 patients (9.5% (44/462)) experienced endpoint events (8 cases of heart failure death and 36 cases of heart failure hospitalization), with 31 events in the training set and 13 in the validation set. Univariate Cox regression analysis identified 39 radiomic features, 4 conventional CMR parameters (left ventricular end-diastolic volume index, left ventricular end-systolic volume index, left ventricular ejection fraction, and late gadolinium enhancement ratio), and 1 clinical feature (history of non-sustained ventricular tachycardia) that could be included in the machine learning model. In the prediction models built with the training set, the concordance indices for the random forest, decision tree, and XGBoost models were 0.966 (95% CI 0.813-0.995), 0.956 (95% CI 0.796-0.992), and 0.973 (95% CI 0.823-0.996), respectively. In the validation set, the concordance indices for the random forest, decision tree, and XGBoost models were 0.854 (95% CI 0.557-0.964), 0.706 (95% CI 0.399-0.896), and 0.703 (95%CI 0.408-0.890), respectively. Conclusion:Integrating clinical and CMR features of HCM patients through machine learning aids in predicting heart failure events, with the random forest model showing superior performance.