Objective To analyze the differences in distribution of traditional Chinese medicine (TCM) syndrome elements and salivary microbiota between the individuals with pulmonary nodules and those without, and to explore the potential correlation between the distribution of TCM syndrome elements and salivary microbiota in patients with pulmonary nodules. Methods We retrospectively recruited 173 patients with pulmonary nodules (PN) and 40 healthy controls (HC). The four diagnostic information was collected from all participants, and syndrome differentiation method was used to analyze the distribution of TCM syndrome elements in both groups. Saliva samples were obtained from the subjects for 16S rRNA high-throughput sequencing to obtain differential microbiota and to explore the correlation between TCM syndrome elements and salivary microbiota in the evolution of the pulmonary nodule disease. Results The study found that in the PN group, the primary TCM syndrome elements related to disease location were the lung and liver, and the primary TCM syndrome elements related to disease nature were yin deficiency and phlegm. In the HC group, the primary TCM syndrome elements related to disease location were the lung and spleen, and the primary TCM syndrome elements related to disease nature were dampness and qi deficiency. There were differences between the two groups in the distribution of TCM syndrome elements related to disease location (lung, liver, kidney, exterior, heart) and disease nature (yin deficiency, phlegm, qi stagnation, qi deficiency, dampness, blood deficiency, heat, blood stasis) (P<0.05). The species abundance of the salivary microbiota was higher in the PN group than that in the HC group (P<0.05), and there was significant difference in community composition between the two groups (P<0.05). Correlation analysis using multiple methods, including Mantel test network heatmap analysis and Spearman correlation analysis and so on, the results showed that in the PN group, Prevotella and Porphyromonas were positively correlated with disease location in the lung, and Porphyromonas and Granulicatella were positively correlated with disease nature in yin deficiency (P<0.05). Conclusion The study concludes that there are notable differences in the distribution of TCM syndrome elements and the species abundance and composition of salivary microbiota between the patients with pulmonary nodules and the healthy individuals. The distinct external syndrome manifestations in patients with pulmonary nodules, compared to healthy individuals, may be a cascade event triggered by changes in the salivary microbiota. The dual correlation of Porphyromonas with both disease location and nature suggests that changes in its abundance may serve as an objective indicator for the improvement of symptoms in patients with yin deficiency-type pulmonary nodules.

ObjectiveTransfer RNA-derived fragments (tRFs) are a recently characterized and rapidly expanding class of small non-coding RNAs, typically ranging from 13 to 50 nucleotides in length. They are derived from mature or precursor tRNA molecules through specific cleavage events and have been implicated in a wide range of cellular processes. Increasing evidence indicates that tRFs play important regulatory roles in gene expression, primarily by interacting with target messenger RNAs (mRNAs) to induce transcript degradation, in a manner partially analogous to microRNAs (miRNAs). However, despite their emerging biological relevance and potential roles in disease mechanisms, there remains a significant lack of computational tools capable of systematically predicting the interaction landscape between tRFs and their target mRNAs. Existing databases often rely on limited interaction features and lack the flexibility to accommodate novel or user-defined tRF sequences. The primary goal of this study was to develop a machine learning based prediction algorithm that enables high-throughput, accurate identification of tRF:mRNA binding events, thereby facilitating the functional analysis of tRF regulatory networks. MethodsWe began by assembling a manually curated dataset of 38 687 experimentally verified tRF:mRNA interaction pairs and extracting seven biologically informed features for each pair: (1) AU content of the binding site, (2) site pairing status, (3) binding region location, (4) number of binding sites per mRNA, (5) length of the longest consecutive complementary stretch, (6) total binding region length, and (7) seed sequence complementarity. Using this dataset and feature set, we trained 4 distinct machine learning classifiers—logistic regression, random forest, decision tree, and a multilayer perceptron (MLP)—to compare their ability to discriminate true interactions from non-interactions. Each model’s performance was evaluated using overall accuracy, receiver operating characteristic (ROC) curves, and the corresponding area under the ROC curve (AUC). The MLP consistently achieved the highest AUC among the four, and was therefore selected as the backbone of our prediction framework, which we named tRF Prospect. For biological validation, we retrieved 3 high-throughput RNA-seq datasets from the gene expression omnibus (GEO) in which individual tRFs were overexpressed: AS-tDR-007333 (GSE184690), tRF-3004b (GSE197091), and tRF-20-S998LO9D (GSE208381). Differential expression analysis of each dataset identified genes downregulated upon tRF overexpression, which we designated as putative targets. We then compared the predictions generated by tRF Prospect against those from three established tools—tRFTar, tRForest, and tRFTarget—by quantifying the number of predicted targets for each tRF and assessing concordance with the experimentally derived gene sets. ResultsThe proposed algorithm achieved high predictive accuracy, with an AUC of 0.934. Functional validation was conducted using transcriptome-wide RNA-seq datasets from cells overexpressing specific tRFs, confirming the model’s ability to accurately predict biologically relevant downregulation of mRNA targets. When benchmarked against established tools such as tRFTar, tRForest, and tRFTarget, tRF Prospect consistently demonstrated superior performance, both in terms of predictive precision and sensitivity, as well as in identifying a higher number of true-positive interactions. Moreover, unlike static databases that are limited to precomputed results, tRF Prospect supports real-time prediction for any user-defined tRF sequence, enhancing its applicability in exploratory and hypothesis-driven research. ConclusionThis study introduces tRF Prospect as a powerful and flexible computational tool for investigating tRF:mRNA interactions. By leveraging the predictive strength of deep learning and incorporating a broad spectrum of interaction-relevant features, it addresses key limitations of existing platforms. Specifically, tRF Prospect: (1) expands the range of detectable tRF and target types; (2) improves prediction accuracy through multilayer perceptron model; and (3) allows for dynamic, user-driven analysis beyond database constraints. Although the current version emphasizes miRNA-like repression mechanisms and faces challenges in accurately capturing 5'UTR-associated binding events, it nonetheless provides a critical foundation for future studies aiming to unravel the complex roles of tRFs in gene regulation, cellular function, and disease pathogenesis.

The 2024 World Conference on Lung Cancer (WCLC) and the European Society for Medical Oncology (ESMO) Annual Meeting, two of the most prestigious events in oncology, have concluded sequentially. As the most authoritative annual gatherings in lung cancer and the entire oncology field, the WCLC and ESMO conferences brought together top oncology experts and scientists from around the world to share, discuss, and publish the latest cutting-edge advancements in oncology. In both conferences, lung cancer immunotherapy remained a hot topic of considerable interest. This article aims to summarize and discuss the important research progress on perioperative immunotherapy for non-small cell lung cancer reported at the two conferences.

Objective To reveal the scientific output and trends in pulmonary nodules/early-stage lung cancer prediction models. Methods Publications on predictive models of pulmonary nodules/early lung cancer between January 1, 2002 and June 3, 2023 were retrieved and extracted from CNKI, Wanfang, VIP and Web of Science database. CiteSpace 6.1.R3 and VOSviewer 1.6.18 were used to analyze the hotspots and theme trends. Results A marked increase in the number of publications related to pulmonary nodules/early-stage lung cancer prediction models was observed. A total of 12581 authors from 2711 institutions in 64 countries/regions published 2139 documents in 566 academic journals in English. A total of 282 articles from 1256 authors were published in 176 journals in Chinese. The Chinese and English journals which published the most pulmonary nodules/early-stage lung cancer prediction model-related papers were Journal of Clinical Radiology and Frontiers in Oncology, respectively. Chest was the most frequently cited journal. China and the United States were the leading countries in the field of pulmonary nodules/early-stage lung cancer prediction models. The institutions represented by Fudan University had significant academic influence in the field. Analysis of keywords revealed that multi-omics, nomogram, machine learning and artificial intelligence were the current focus of research. Conclusion Over the last two decades, research on risk-prediction models for pulmonary nodules/early-stage lung cancer has attracted increasing attention. Prognosis, machine learning, artificial intelligence, nomogram, and multi-omics technologies are both current hotspots and future trends in this field. In the future, in-depth explorations using different omics should increase the sensitivity and accuracy of pulmonary nodules/early-stage lung cancer prediction models. More high-quality future studies should be conducted to validate the efficacy and safety of pulmonary nodules/early-stage lung cancer prediction models further and reduce the global burden of lung cancer.

Objective To explore the recognition capabilities of electronic nose combined with machine learning in identifying the breath odor map of benign and malignant pulmonary nodules and Traditional Chinese Medicine (TCM) syndrome elements. Methods The study design was a single-center observational study. General data and four diagnostic information were collected from 108 patients with pulmonary nodules admitted to the Department of Cardiothoracic Surgery of Hospital of Chengdu University of TCM from April 2023 to March 2024. The patients' TCM disease location and nature distribution characteristics were analyzed using the syndrome differentiation method. The Cyranose 320 electronic nose was used to collect the odor profiles of oral exhalation, and five machine learning algorithms including random forest (RF), K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) were employed to identify the exhaled breath profiles of benign and malignant pulmonary nodules and different TCM syndromes. Results (1) The common disease locations in pulmonary nodules were ranked in descending order as liver, lung, and kidney; the common disease natures were ranked in descending order as Yin deficiency, phlegm, dampness, Qi stagnation, and blood deficiency. (2) The electronic nose combined with the RF algorithm had the best efficacy in identifying the exhaled breath profiles of benign and malignant pulmonary nodules, with an AUC of 0.91, accuracy of 86.36%, specificity of 75.00%, and sensitivity of 92.85%. (3) The electronic nose combined with RF, LR, or XGBoost algorithms could effectively identify the different TCM disease locations and natures of pulmonary nodules, with classification accuracy, specificity, and sensitivity generally exceeding 80.00%.Conclusion Electronic nose combined with machine learning not only has the potential capabilities to differentiate the benign and malignant pulmonary nodules, but also provides new technologies and methods for the objective diagnosis of TCM syndromes in pulmonary nodules.

Based on the theory of qi， blood and fluids， and taking into account of the pathogenesis evolution process from constraint to phlegm， stasis and then mass in pulmonary nodules， an attempt has been made to construct a three-dimensional differentiation system for pulmonary nodules from the dimensions of time and space. The temporal progression of the early， middle， and late stages of pulmonary nodules reflects the pathological changes from constraint to phlegm and then stasis in the metabolism disorders of qi， blood and fluid. The spatial structures such as size， density， and morphology of pulmonary nodules reflect the pathological states of the duration， severity， and primary and secondary conditions of qi， blood and fluid metabolism disorders. Based on the temporal progression， the therapeutic principles have been proposed， which are dispelling pathogenic factors and promoting the use of beneficial factors to interrupt the growth momentum in the early stage， removing turbidity and dispersing phlegm to reduce the degree of nodules in the middle stage， and dispersing nodulation and eliminating abnormalities in the late stage. Based on the spatial structures， the suggested therapeutic methods are using wind herbs， employing multiple approaches to treat phlegm， and promoting blood circulation to resolve stasis， so as to provide theoretical reference for the systematic diagnosis and treatment of pulmonary nodules in traditional Chinese medicine.

A novel pair of Z/E isomeric compounds with unprecedented carbon skeleton were isolated from an aqueous extract of Aspongopus chinensis Dallas by macroporous resin, silica gel, and semi-preparative high performance liquid chromatography (HPLC). Their structures were identified by nuclear magnetic resonance (NMR), Infrared spectroscopy (IR), Mass spectroscopy (MS) and other spectroscopic methods as (Z)-3-(but-1″-en-1″-yl)-1-(2ʹ-hydroxyethyl)-4-propylpyridin-1-ium, namely aspongopyridine A, and (E)-3-(but-1″-en-1″-yl)-1-(2ʹ-hydroxyethyl)-4-propylpyridin-1-ium, namely aspongopyridine B, respectively. Besides, the anti-inflammatory, anti-tumor, acetylcholinesterase inhibition and butyrylcholinesterase inhibition activities of the compounds 1 and 2 were evaluated. The results showed that compounds 1 and 2 have no anti-inflammatory, anti-tumor, and butyrylcholinesterase inhibition activities instead of weak acetylcholinesterase inhibition activity.

Objective To observe the clinical efficacy of FANG's scalp acupuncture combined with timing auricular point pressing therapy in the treatment of insomnia patients with maintenance hemodialysis(MHD).Methods A total of 70 patients with insomnia on MHD were randomly divided into observation group and control group,with 35 patients in each group.Both groups were given conventional treatment,the control group was given oral use of Estazolam Tablets on the basis of conventional treatment,and the observation group was given FANG's scalp acupuncture combined with timing auricular point pressing therapy.Both groups were treated for a total of 4 weeks of treatment.After 1 month of treatment,the clinical efficacy of the two groups was evaluated,and the changes in the Pittsburgh Sleep Quality Index(PSQI)score and the Kidney Disease Quality of Life Short Form(KDQOL-SF)score,as well as the scores of the Hamilton Depression Scale(HAMD)and the Hamilton Anxiety Scale(HAMA),were observed in the patients of the two groups before and after treatment.The changes in hemoglobin(Hb),serum creatinine(Scr),and blood urea nitrogen(BUN)levels were compared before and after treatment between the two groups,and the safety of the two groups was evaluated.Results(1)After treatment,the PSQI and KDQOL-SF scores of patients in the two groups were significantly improved(P＜0.05),and the observation group was significantly superior to the control group in improving PSQI and KDQOL-SF scores,and the difference was statistically significant(P＜0.05).(2)After treatment,the HAMD and HAMA scores of patients in the two groups were significantly improved(P＜0.05),and the observation group was significantly superior to the control group in improving HAMD and HAMA scores,and the differences were statistically significant(P＜0.05).(3)After treatment,the Hb,Scr,BUN levels of patients in the two groups were significantly improved(P＜0.05),and the observation group was significantly superior to the control group in improving Hb,Scr,BUN levels,and the differences were all statistically significant(P＜0.05).(4)The total effective rate was 77.14%(27/35)in the observation group and 62.86%(22/35)in the control group.The efficacy of the observation group was superior to that of the control group,and the difference was statistically significant(P＜0.05).(5)Comparison of the incidence of adverse reactions in the two groups of patients,the difference was not statistically significant(P＞0.05).Conclusion FANG's scalp acupuncture combined with timing auricular point pressing therapy in the treatment of insomnia patients with MHD can effectively improve the sleep quality of patients and alleviate anxiety and depression,so as to improve the quality of life of patients,with remarkable efficacy.

Objective To investigate the clinical effect of LUO's Nephropathy Recipe Ⅲ(composed of Sargassum,Astragali Radix,Salviae Miltiorrhizae Radix et Rhizoma,Rehmanniae Radix Praeparata,calcined Ostreae Concha,Houttuyniae Herba,Schizonepetae Spica,etc.)combined with conventional western medicine in treating stage 3-5 non-dialysis chronic kidney disease(CKD)of spleen-kidney deficiency with turbidity-toxin-stasis obstruction type.Methods A total of 180 patients with stage 3-5 non-dialysis CKD of spleen-kidney deficiency with turbidity-toxin-stasis obstruction type were randomly divided into observation group and control group,with 90 cases in each group.The control group was given conventional western medicine for symptomatic treatment,and the observation group was treated with LUO's Nephropathy RecipeⅢon the basis of treatment for the control group.The course of treatment for the two groups covered one month.Before and after treatment,the levels of serum inflammatory factors,renal function indicators and urine protein parameters in the two groups were observed.After treatment,the clinical efficacy and safety of the two groups were evaluated.Results(1)After one month of treatment,the total effective rate in the observation group was 95.56%(86/90)and that in the control group was 81.11%(73/90).The intergroup comparison(tested by chi-square test)showed that the efficacy of the observation group was significantly superior to that of the control group(P＜0.01).(2)After treatment,the serum levels of inflammatory factors of transforming growth factor β1(TGF-β1),monocyte chemotactic protein 1(MCP-1),and tumor necrosis factor α(TNF-α)in the two groups were significantly decreased compared with those before treatment(P＜0.05),and the decrease in the observation group was significantly superior to that in the control group(P＜0.01).(3)After treatment,the levels of renal function indicators of blood urea nitrogen(BUN),serum creatinine(Scr),blood uric acid(UA),and cystatin C(Cys-C)in the two groups were significantly decreased compared with those before treatment(P＜0.05),and the decrease in the observation group was significantly superior to that in the control group(P＜0.01).(4)After treatment,the levels of 24-hour urine protein quantification and urine microalbumin in the two groups were significantly decreased compared with those before treatment(P＜0.05),and the decrease in the observation group was significantly superior to that in the control group(P＜0.01).(5)The incidence of adverse reactions in the observation group was 4.44%(4/90),which was significantly lower than that of 15.56%(14/90)in the control group,and the difference was statistically significant between the two groups(P＜0.05).Conclusion LUO's Nephropathy Recipe Ⅲ combined with conventional western medicine exerts satisfactory efficacy in treating stage 3-5 non-dialysis CKD patients with spleen-kidney deficiency with turbidity-toxin-stasis obstruction syndrome type,and the therapy can significantly alleviate the inflammatory response,improve the renal function,decrease the urinary protein excretion of the patients,with high safety profile.

Objective To explore the impact of the three-level assistance model based on the narrative nursing theory on the mental health status of medical staff.Methods 140 medical staff working in a third class hospital in Xiangyang City were selected as the research object.The three-level assistance model based on narrative nursing theory was used to intervene them from September 2021 to July 2022.The symptom self-assessment scales before and after the intervention were compared.Results Before the intervention,the total score of SCL-90(156.37±32.56)points and the scores of various symptom factors of medical staff were higher;After the intervention,the total score of SCL-90(133.35±43.48)points and the scores of various symptom factors were lower than those before the intervention and the difference was statistically significant(P<0.05).Conclusion The three-level assistance model based on narrative nursing theory can reduce the total score of SCL-90 and the scores of various symptom factors,improve the mental health status and mental health level.