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 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.

Objective To construct a "disease-syndrome combination" mathematical representation model for pulmonary nodules based on oral microbiome data, utilizing a multimodal data algorithm framework centered on dynamic systems theory. Furthermore, to compare predictive models under various algorithmic frameworks and validate the efficacy of the optimal model in predicting the presence of pulmonary nodules. Methods A total of 213 subjects were prospectively enrolled from July 2022 to March 2023 at the Hospital of Chengdu University of Traditional Chinese Medicine, Sichuan Cancer Hospital, and the Chengdu Integrated Traditional Chinese and Western Medicine Hospital. This cohort included 173 patients with pulmonary nodules and 40 healthy subjects. A novel multimodal data algorithm framework centered on dynamic systems theory, termed VAEGANTF (Variational Auto Encoder-Generative Adversarial Network-Transformer), was proposed. Subsequently, based on a multi-dimensional integrated dataset of “clinical features-syndrome elements-microorganisms”, all subjects were divided into training (70%) and testing (30%) sets for model construction and efficacy testing, respectively. Using pulmonary nodules as dependent variables, and combining candidate markers such as clinical features, lesion location, disease nature, and microbial genera, the independent variables were screened based on variable importance ranking after identifying and addressing multicollinearity. Missing values were then imputed, and data were standardized. Eight machine learning algorithms were then employed to construct pulmonary nodule risk prediction models: random forest, least absolute shrinkage and selection operator (LASSO) regression, support vector machine, multilayer perceptron, eXtreme Gradient Boosting (XGBoost), VAE-ViT (Vision Transformer), GAN-ViT, and VAEGANTF. K-fold cross-validation was used for model parameter tuning and optimization. The efficacy of the eight predictive models was evaluated using confusion matrices and receiver operating characteristic (ROC) curves, and the optimal model was selected. Finally, goodness-of-fit testing and decision curve analysis (DCA) were performed to evaluate the optimal model. Results There were no statistically significant differences between the two groups in demographic characteristics such as age and sex. The 213 subjects were randomly divided into training and testing sets (7 : 3), and prediction models were constructed using the eight machine learning algorithms. After excluding potential problems such as multicollinearity, a total of 301 clinical feature information, syndrome elements, and microbial genera markers were included for model construction. The area under the curve (AUC) values of the random forest, LASSO regression, support vector machine, multilayer perceptron, and VAE-ViT models did not reach 0.85, indicating poor efficacy. The AUC values of the XGBoost, GAN-ViT, and VAEGANTF models all reached above 0.85, with the VAEGANTF model exhibiting the highest AUC value (AUC=0.923). Goodness-of-fit testing indicated good calibration ability of the VAEGANTF model, and decision curve analysis showed a high degree of clinical benefit. The nomogram results showed that age, sex, heart, lung, Qixu, blood stasis, dampness, Porphyromonas genus, Granulicatella genus, Neisseria genus, Haemophilus genus, and Actinobacillus genus could be used as predictors. Conclusion The “disease-syndrome combination” risk prediction model for pulmonary nodules based on the VAEGANTF algorithm framework, which incorporates multi-dimensional data features of “clinical features-syndrome elements-microorganisms”, demonstrates better performance compared to other machine learning algorithms and has certain reference value for early non-invasive diagnosis of pulmonary nodules.

This study investigated the effects and action mechanism of tannins from Galla chinensis cream(TGCC) on the skin wound of rat tail. Male Sprague Dawley(SD) rats were randomly divided into a control group, model group, model+low-dose TGCC(50 mg per rat) group, model+high-dose TGCC group(100 mg per rat), and model+TGC+FAK inhibitor(Y15) cream(100 mg+10 mg per rat) group, with 10 rats in each group. After the rat tail skin injury model was successfully constructed, in the treatment group, corresponding drugs were applied to the wound surface, while in the control and model groups, the same amount of cream base as the TGCC group was applied by the same method. Then, sterile gauze was wrapped around the wound edge, and these operations were performed three times a day for 28 consecutive days. The wound healing status at the third, seventh, eleventh, fourteenth, twenty-first, and twenty-eighth days was recorded, and the wound healing rate and healing time were calculated. On the day after the last dose of medication, rat serum and tail skin wound tissue were collected for analyzing the activities of serum alanine aminotransferase(ALT), aspartate aminotransferase(AST), creatinine(CREA), urea, reactive oxygen species(ROS), interferon gamma(IFN-γ), interleukin(IL)-1β, IL-6, IL-4, IL-10, tumor necrosis factor(TNF)-α, as well as catalase(CAT), glutathione(GSH), lactate dehydrogenase(LDH), malondialdehyde(MDA), myeloperoxidase(MPO), superoxide dismutase(SOD), total antioxidant capacity(T-AOC), platelet endothelial cell adhesion molecule-1(CD31), and leukocyte differentiation antigen 34(CD34) in the wound tissue of rat tail skin. Hematoxylin-eosin, Masson, and sirius red staining were used to observe the morphological changes in the wound tissue of rat tail skin. The thickness of the epidermis, the number of fibroblasts and blood vessels, and the contents of collagen fibers, typeⅠ collagen(COLⅠ), and COLⅢ were calculated. The mRNA expressions of keratin 10(KRT10), KRT14, vascular endothelial growth factor(VEGF), fibroblast growth factor(FGF), epidermal growth factor(EGF), CD31, CD34, matrix metallopeptidase-2(MMP-2), MMP-9, COLⅠ, COLⅢ, desmin, fibroblast specific protein 1(FSP1), IFN-γ, IL-1β, TNF-α, IL-4, IL-6, and IL-10 in skin wound tissue were determined by quantitative real-time polymerase chain reaction(PCR). Western blot was utilized to detect the protein expressions of KRT10, KRT14, VEGF, FGF, EGF, MMP-2, MMP-9, COLⅠ, COLⅢ, desmin, FSP1, focal adhesion kinase(FAK), phosphorylated focal adhesion kinase(p-FAK), phosphatidylin-ositol-3-kinase(PI3K), phosphorylated phosphatidylin-ositol-3-kinase(p-PI3K), protein kinase B(Akt), phosphorylated protein kinase B(p-Akt), mammalian target of rapamycin(mTOR), and phosphorylated mammalian target of rapamycin(p-mTOR). The results manifest that TGCC can dramatically elevate the healing rate of rat tail wounds and shorten wound healing time. Besides, it can reduce serum ROS levels, the contents of MDA, MPO, and LDH in the rat skin wound tissue, as well as the serum IFN-γ, IL-1β, IL-6, and TNF-α levels and the mRNA expression levels of IFN-γ, IL-1β, IL-6, and TNF-α in the skin wound tissue. It can elevate the activities of CAT, GSH, SOD, and T-AOC in wound tissue, the IL-4 and IL-10 contents in serum, and the mRNA expressions of IL-4 and IL-10 in the wound tissue. In addition, TGGC can inhibit inflammatory cell infiltration and increase the epidermal thickness, counts of fibroblasts and blood vessels, and contents of collagen fibers, COLⅠ, and COLⅢ. Besides, TGCC can elevate the mRNA and protein expressions of epidermal differentiation markers(KRT10 and KRT14), endothelial cell markers(CD31 and CD34), angiogenesis and fibroblast proliferation, differentiation markers(VEGF, FGF, EGF, COLⅠ, COLⅢ, desmin, and FSP1), reduce the mRNA and protein expressions of gelatinases(MMP-2 and MMP-9), and increase protein expressions of p-FAK, p-PI3K, p-Akt, p-mTOR, as well as ratios of p-FAK/FAK, p-PI3K/PI3K, p-Akt/Akt, and p-mTOR/mTOR. These results suggest that TGCC can significantly facilitate skin wound healing, and its mechanism may be related to the activation of the FAK/PI3K/Akt/mTOR signaling pathway, inhibition of inflammatory cell infiltration in skin wound tissue, elevation of epidermal thickness, counts of fibroblasts and vessels, and contents of collagen fiber, COLⅠ, and COLⅢ, and reduction of MMP-2 and MMP-9 expressions, thus accelerating wound healing.
Animals ; Male ; Wound Healing/drug effects* ; Rats ; Rats, Sprague-Dawley ; Signal Transduction/drug effects* ; TOR Serine-Threonine Kinases/genetics* ; Phosphatidylinositol 3-Kinases/genetics* ; Skin/metabolism* ; Proto-Oncogene Proteins c-akt/genetics* ; Tannins/pharmacology* ; Humans ; Drugs, Chinese Herbal/administration & dosage* ; Focal Adhesion Kinase 1/genetics*

This study aims to explore the mechanism of lung and gut protection by Tanreqing Capsules on the mice infected with influenza virus based on "the lung-gut axis". A total of 110 C57BL/6J mice were randomized into control group, model group, oseltamivir group, and low-and high-dose Tanreqing Capsules groups. Ten mice in each group underwent body weight protection experiments, and the remaining 12 mice underwent experiments for mechanism exploration. Mice were infected with influenza virus A/Puerto Rico/08/1934(PR8) via nasal inhalation for the modeling. The lung tissue was collected on day 3 after gavage, and the lung tissue, colon tissue, and feces were collected on day 7 after gavage for subsequent testing. The results showed that Tanreqing Capsules alleviated the body weight reduction and increased the survival rate caused by PR8 infection. Compared with model group, Tanreqing Capsules can alleviate the lung injury by reducing the lung index, alleviating inflammation and edema in the lung tissue, down-regulating viral gene expression at the late stage of infection, reducing the percentage of neutrophils, and increasing the percentage of T cells. Tanreqing Capsules relieved the gut injury by restoring the colon length, increasing intestinal lumen mucin secretion, alleviating intestinal inflammation, and reducing goblet cell destruction. The gut microbiota analysis showed that Tanreqing Capsules increased species diversity compared with model group. At the phylum level, Tanreqing Capsules significantly increased the abundance of Firmicutes and Actinobacteria, while reducing the abundance of Bacteroidota and Proteobacteria to maintain gut microbiota balance. At the genus level, Tanreqing Capsules significantly increased the abundance of unclassified＿f＿Lachnospiraceae while reducing the abundance of Bacteroides, Eubacterium, and Phocaeicola to maintain gut microbiota balance. In conclusion, Tanreqing Capsules can alleviate mouse lung and gut injury caused by influenza virus infection and restore the balance of gut microbiota. Treating influenza from the lung and gut can provide new ideas for clinical practice.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Mice ; Lung/metabolism* ; Mice, Inbred C57BL ; Capsules ; Orthomyxoviridae Infections/virology* ; Gastrointestinal Microbiome/drug effects* ; Male ; Humans ; Female ; Influenza A virus/physiology* ; Influenza, Human/virology*

OBJECTIVES: To study the molecular mechanisms of LDH-loaded si-NEAT1 for regulating paclitaxel resistance and tumor-associated macrophage (TAM) polarization in breast cancer. METHODS: qRT-PCR and Western blotting were used to detect the expression of lncRNA NEAT1, miR-133b, and PD-L1 in breast cancer SKBR3 cells and paclitaxel-resistant SKBR3 cells (SKBR3-PR). The effects of transfection with si-NEAT1 and miR-133b mimics on MRP, MCRP and PD-L1 expressions and cell proliferation, migration and apoptosis were investigated using qRT-PCR, Western blotting, scratch and Transwell assays, and flow cytometry. Rescue experiments were conducted using si-NEAT1 and miR-133b inhibitor. Human THP-1 macrophages were cultured in the presence of conditioned media (CM) derived from SKBR3 and SKBR3-PR cells with or with si-NEAT1 transfection for comparison of IL-4-induced macrophage polarization by detecting the surface markers. LDH@si-NEAT1 nanocarriers were constructed, and their effects on MRP, MCRP and PD-L1 expressions and cell behaviors of the tumor cells were examined. THP-1 cells were treated with the CM from LDH@si-NEAT1-treated tumor cells, and the changes in their polarization were assessed. RESULTS: SKBR3-PR cells showered significantly upregulated NEAT1 and PD-L1 expressions and lowered miR-133b expression as compared with their parental cells. Transfection with si-NEAT1 and miR-133b mimics inhibited viability, promoted apoptosis and enhanced MRP and BCRP expressions in SKBR3-PR cells. NEAT1 knockdown obvious upregulated miR-133b and downregulated PD-L1, MRP and BCRP expressions. The CM from SKBR3-PR cells obviously promoted M2 polarization of THP-1 macrophages, which was significantly inhibited by CM from si-NEAT1-transfected cells. Treatment with LDH@si-NEAT1 effectively inhibited migration and invasion, promoted apoptosis, and reduced MRP, BCRP and PD-L1 expressions in the tumor cells. The CM from LDH@si-NEAT1-treated SKBR3-PR cells significantly downregulated Arg-1, CD163, IL-10, and PD-L1 and upregulated miR-133b expression in THP-1 macrophages. CONCLUSIONS LDH@si-NEAT1 reduces paclitaxel resistance of breast cancer cells and inhibits TAM polarization by targeting the miR-133b/PD-L1 axis.
Humans ; MicroRNAs/genetics* ; RNA, Long Noncoding/genetics* ; Paclitaxel/pharmacology* ; Breast Neoplasms/metabolism* ; Drug Resistance, Neoplasm ; B7-H1 Antigen/metabolism* ; Cell Line, Tumor ; Female ; Tumor-Associated Macrophages ; Apoptosis ; Cell Proliferation ; Macrophages ; Cell Movement

OBJECTIVE: Varied acupoint selections represent a potential cause of the uncertainty surrounding the efficacy of acupuncture for knee osteoarthritis (OA). Skin temperature, a guiding factor for acupoint selection, may help to address this issue. This study explored thermal sensitization of acupoints used for the treatment of knee OA. METHODS: This cross-sectional case-control study enrolled cases aged 45-75 years with symptomatic knee OA and age- and gender-matched non-knee OA controls in a 1:1 ratio. All participants underwent infrared thermographic imaging. The primary outcome was the relative skin temperature of acupoint (STA), and the secondary outcome was the absolute STA of 11 acupoints. The Z test was used to compare the relative and absolute STAs between the groups. Principal component analysis was used to extract the common factors (CFs, acupoint cluster) in the STAs. A general linear model was used to identify factors affecting the STA in the knee OA cases. For the group comparisons of relative STA, P < 0.0045 (adjusted for 11 acupoints through Bonferroni correction) was considered to indicate statistical significance. For other analyses, P < 0.05 was used as the threshold for statistical significance. RESULTS: The analysis included 308 participants, consisting of 151 cases (mean age: [64.58 ± 6.67] years; male: 25.83%; mean body mass index: [25.70 ± 3.16] kg/m²) and 157 controls (mean age: [63.37 ± 5.96] years; male: 26.11%; mean body mass index: [24.47 ± 2.84] kg/m²). The relative STAs of ST34 (P = 0.0001), EX-LE2 (P < 0.0001), EX-LE5 (P = 0.0006), SP10 (P < 0.0001), BL40 (P = 0.0012) and GB39 (P = 0.0037) were higher in the knee OA group. No difference was found in the STAs of ST35, ST36, SP9, GB33 and GB34. Four CFs were identified for relative STA in both groups. The acupoints within each CF were consistent between the groups. The mean values of the relative STAs across each CF were higher in the knee OA group. In the knee OA cases, no factors were observed to affect the relative STA, while age and gender were found to affect the absolute STA. CONCLUSION Among patients with knee OA, thermal sensitization occurs in the acupoints of the lower extremity, exhibiting localized and regional thermal consistencies. The thermally sensitized acupoints that we identified in this study, ST34, SP10, EX-LE2, EX-LE5, GB39 and BL40, may be good choices for the acupuncture treatment of knee OA. Please cite this article as: Tu JF, Wang XZ, Yan SY, Wang YR, Yang JW, Shi GX, Zhang WZ, Jing LN, Yang LS, Liu DH, Wang LQ, Mi BH. Thermal sensitization of acupoints in patients with knee osteoarthritis: A cross-sectional case-control study. J Integr Med. 2025; 23(3): 289-296.
Humans ; Osteoarthritis, Knee/physiopathology* ; Male ; Cross-Sectional Studies ; Middle Aged ; Female ; Acupuncture Points ; Case-Control Studies ; Aged ; Skin Temperature ; Acupuncture Therapy

Objective To investigate the effect of nobiletin on platelet-activating factor(PAF)metabolism in diabetic rats with renal injury.Methods Totally 72 rats were randomly divided into control group(n=10)and modeling group(n=62).The modeling group rats were induced to develop a diabetic rat model with renal injury and then further divided into the model group,aspirin group(20 mg/kg),and nobiletin low(50 mg/kg),medium(100 mg/kg),and high-dose(200 mg/kg)groups,each with 10 rats.After continuous oral administration for 6 weeks,rat body weight,kidney weight,and kidney index were measured.Histopathological assessments were conducted by using HE,periodic acid-Schiff staining(PAS),Masson staining,and transmission electron microscopy.Blood glucose levels,renal function,inflammatory factors,PAF and its regulatory factors were detected.Expression levels of PAF metabolism-related proteins,PAF-acetylhydrolase(PAFAH),PAF receptor(PAFR),and cholinephosphotransferase 1(CHPT1)in kidney tissues were assessed using Western blotting and immunohistochemistry.Results Following nobiletin intervention,rat body weight increased while kidney weight and kidney index decreased.Improvement in renal tissue pathology was observed,with reduced interstitial fibrosis and thinner basement membrane.Fasting blood glucose and glycated hemoglobin decreased,while fasting insulin showed no significant improvement.Urea nitrogen,blood creatinine,cystatin C,and 24-hour urinary protein excretion were reduced.Levels of interleukin(IL)-1α,IL-6,IL-8,and tumor necrosis factor(TNF-α)were lowered.PAF and its regulatory factors decreased.PAFR and CHPT1 expression decreased,while PAFAH increased.Conclusion Nobiletin can alleviate renal injury in diabetic rats with renal injury,improve kidney function,regulate blood glucose,and mitigate inflammatory response.Its mechanism may be associated with the modulation of platelet-activating factor metabolism.

Objective To propose a human life signal separation and reconstruction method based on optimized variational mode decomposition(VMD)to improve the accuracy and timeliness of the life detection radar in extracting and separating human life signals such as heartbeat and respiration.Methods Firstly,the particle swarm optimization algorithm was used to optimize the parameters of VMD,and the human life signal was decomposed into a series of intrinsic mode functions(IMFs);secondly,the alignment entropy of each IMF was calculated,the noise was removed based on the alignment entropy threshold,and the remaining components were reconstructed to form human life signals;finally,the method proposed was compared with infinite impulse response(IIR)filtering,VMD and complete ensemble empirical mode decomposition with adaptive nosie(CEEMDAN)to verify its performance.Results Under different noise levels the proposed method outperformed IIR filtering,VMD and CEEMDAN in evaluation metrics of signal-to-noise ratio and root-mean-square error,and behaved better than CEEMDAN in terms of computational time-consumption.Conclusion The proposed method realizes rapid separation and reconstruction of vital signals such as heartbeat and respiration while effectively filtering out the noise,which has broad application prospects in the fields of non-contact vital signs detection of bum/scald patients,infectious disease patients and newborns and the search and rescue of buried casualties after a disaster.[Chinese Medical Equipment Journal,2024,45(3):9-15]

Objective To construct a big data sharing platform for clinical scientific research to solve the problems of clinical research in decentralized application systems and data sharing safety.Methods A clinical research information data usage management system was developed through the formulation of management methods in line with the actual situation of the institution,normalized standard data usage processes and a data usage service team.Then a clinical scientific research big data sharing platform including the components for sharing environment construction,research application integration,data desensitization and encryption and file management was established based on the existing hospital systems,the requirements of clinical research data usage management and the habits of clinical researchers.Results The platform realized the balance between open sharing of clinical research data and data security control,which improved the efficiency of clinical researchers while reducing data security risks during data transmission and data analysis.Conclusion The clinical scientific research big data sharing platform meets the needs of clinical scientific research application and data security management,and provides references for the co-construction-sharing of medical big data resources.[Chinese Medical Equipment Journal,2024,45(4):27-31]