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 investigate the application and effectiveness of a zero-trust network architecture(ZTNA)in a hospital's smart-management platform,providing a practical reference for network-architecture optimization in smart-hospital initiatives.Methods A single-arm mode was involved in the deployment of ZTNA.An encrypted tunnel was established by the zero-trust proxy gateway,and the components for zero-trust terminal security,behavior management,firewall,identity authentication,security operation and analysis center were synergized with the help of a logical bus to form a security protection system of end-to-end trust assessment,dynamic access control,micro-isolation and visualization,and the integration and access to the hospital's intelligent management platform were realized by means of ticket injection.Results ZTNA markedly enhanced data protection for the platform,and significantly improved user experience by simplified authentication and enhanced support for mobile operation.Conclusion ZTNA ensures the security of kinds of hospital business systems,and lays a foundation for large comprehensive hospitals to construct cross-region,cross-institution and multi-center medical information platforms and open data sharing modes.[Chinese Medical Equipment Journal,2025,46(8):50-57]

Aim To observe the interventional effects of Polygala fallax Hemsl.(PFH)on diabetic kidney disease(DKD)rats and the regulatory mechanism of ferroptosis.Methods Thirty-six SD rats were ran-domly divided into the control group,DKD group,fer-rostatin-1(Fer-1)group,PFH-L group,PFH-M group,and PFH-H group,with six rats in each group.Model-ing was induced by a one-time intraperitoneal injection of 35 mg·kg-1 streptozocin(STZ)in combination with high-sugar and high-fat feed.Ferrostatin-1(25μmol·kg-1)was injected intraperitoneally in Fer-1 group.The PFH-L,PFH-M,and PFH-H groups were gavaged with 50,100 and 200 mg·kg-1 of alcoholic extracts of PFH respectively,and the control and DKD groups were gavaged with an equal volume of distilled water once a day for eight weeks.At the end of drug administration,blood glucose,24h-UP,BUN and Scr levels were measured in each group of rats.HE stai-ning and Masson staining were used to observe renal histopathological changes.ELISA was employed to de-termine the levels of total iron,MDA and GSH activity.IHC was used to observe the expression of Nrf2,SLC7A11,and GPX4 in renal tissues.Western blot was used to detect the protein expression of COL1A1,α-SMA,TGF-,FTH-1,TFR-1,Nrf2,SLC7A1 1,GPX4,in renal tissues.RT-qPCR was used to detect the ex-pression levels of Nrf2,SLC7A11,GPX4 mRNA in re-nal tissues.Results Compared with the control group,blood glucose,24h-UP,BUN,Scr increased(P＜0.05);glomerular volume increased,tubular dilata-tion and collagen fiber deposition were obvious;total i-ron,MDA content increased and GSH activity de-creased(P＜0.05);there was increased protein ex-pression of COL1A1,α-SMA,TGF-β,TFR-1 and de-creased protein expression of FTH-1(P＜0.01);there was decreased mRNA and protein expression of Nrf2,SLC7A11,GPX4 in DKD group rats(P＜0.01).Compared with the DKD group,blood glucose,24h-UP,BUN and Scr decreased(P＜0.05);renal tissue le-sions were significantly reduced;total iron and MDA content decreased,and GSH activity increased(P＜0.05);COL1A1,α-SMA,TGF-β,and TFR-1 protein expression decreased and FTH-1 protein expression in-creased(P＜0.05,P＜0.01);Nrf2,SLC7A11,GPX4 mRNA and protein expression increased in Fer-1 and PFH dose groups(P＜0.05,P＜0.01).Conclusions PFH attenuates renal histopathological injury in DKD rats,and the mechanism may be related to the regula-tion of the Nrf2/SLC7A11/GPX4 signaling pathway.

This study was aimed at evaluating the characteristics of H9N2 subtype avian influenza viruses(AIVs)origina-ting from wild birds in major wetlands in Fujian.Five H9N2 subtype AIVs isolated from fecal samples from wild birds in wet-lands of the Minjiang River,Jiulong River,Sandu Bay,Xinghua Bay,and Quanzhou Bay in Fujian were sequenced.Sequence a-nalysis of the HA genes of the five H9N2 subtype AIVs indicated that the five isolates shared 89.8％-99.4％nucleotide se-quence identity.All five isolates belonged to the same h9.4.2.5c evolutionary branch.The cleavage site motifs of HA were all PSRSSR ↓ GLF,thus indicating molecular characteristics of AIVs with low pathogenicity.The HA proteins of the viruses orig-inating from wild birds bore eight identical potential glycosylation sites,among which the glycosylation site at position 313 was located near the HA protein cleavage site.The 226th amino acid of HA in the receptor binding site was leucine in each virus,thus indicating that HAs of the five H9N2 subtype AIVs had mammalian sialic acid α-2,6 receptor binding affinity.In conclu-sion,the five H9N2 subtype AIVs originating from wild birds in Fujian had low pathogenicity,and the HAs had mammalian sialic acid α-2,6 receptor binding affinity.

Objective:To construct a thyroid nodule segmentation model using ultrasound dynamic images and explore its potential for assisting in the screening of thyroid nodules.Methods:A total of 126 patients with thyroid nodules（comprising 150 nodules）who were diagnosed and treated at Xuzhou Cancer Hospital from April 2024 to December 2024 were prospectively enrolled. Two-dimensional ultrasound was performed to capture short-axis and long-axis video images of thyroid nodules，forming a dynamic ultrasound image dataset. The dataset was divided into training，validation，and test sets in a ratio of 6∶1∶3. After the training loss curve converged，the model that performed well on the validation set was selected for testing. Three-fold cross-validation was employed for training and testing. All 300 ultrasound videos were divided into three subsets. In each experiment，two subsets were used as the training set，and one subset was used as the test set to evaluate the model's generalization ability. A collaborative spatiotemporal diffusion model was established based on the dynamic trends and tissue texture details of thyroid nodules. Six widely used segmentation metrics were employed to evaluate the model's application capabilities.Results:The study included 126 patients with 150 thyroid nodules，300 dynamic ultrasound images，and video lengths of 3-4 seconds per nodule，resulting in 12 312 segmented images. The size of the thyroid nodules was（10.7 ± 10.6）mm（transverse diameter）×（8.4 ± 6.3）mm（anteroposterior diameter）. Among the nodules，62（41.3%）had clear boundaries，while 88（58.7%）had indistinct boundaries；61（40.7%）exhibited regular shapes，while 89（59.3%）were irregular；66（44.0%）had a taller-than-wide aspect ratio；and 70（46.7%）showed microcalcifications. The collaborative diffusion model based on dynamic ultrasound image segmentation achieved the following scores：a Jaccard score of（69.22 ± 0.03）%，a Dice score of（79.16 ± 0.18）%，a Precision score of（86.70 ± 0.17）%，a Recall score of（77.82 ± 0.04）%，an Sα score of（85.26 ± 0.01）%，and an Eθmn score of（90.58 ± 0.17）%. Compared to other models，this model demonstrated significant improvements across all evaluation metrics，achieving the highest values in each metric with increments of over 8% and 1%，respectively. Conclusions:The collaborative diffusion model with a dynamic controller，constructed based on dynamic ultrasound images of thyroid nodules，demonstrates excellent performance in ultrasound image segmentation. It improves the accuracy of thyroid nodule screening，thereby providing a valuable auxiliary diagnostic tool for clinical practice.

Objective To explore the clinical effects and influencing factors of recombinant human growth hormone(rhGH)treatment in pediatric patients with growth hormone deficiency.Methods The study subjects were pediatric patients with growth hormone deficiency,all of whom received a combination therapy of stanozolol tablets 2 mg(qd)and 0.1 U·kg-1·d-1 of recombinant human growth hormone injection for a continuous period of one year.After one year,the patients were divided into active group and invalid group based on clinical outcomes.Comparisons were made between the two groups in terms of height,annual growth velocity(GV)and height standard deviation score(HtSDS)before and after treatment,and safety evaluations were conducted.Multivariate Logistic regression analysis was used to identify factors that influenced the treatment outcomes in pediatric patients with growth hormone deficiency.Results After one year of treatment,a total of 93 cases pediatric patients were included in the analysis,with 62 cases in the active group and 31 cases in the invalid group.The heights of the patients before and after treatment were(119.40±2.48)and(129.08±2.37)cm,respectively;the GV were(3.08±0.39)and(7.34±1.02)cm·year-1,respectively;the HtSDS were-2.45±0.49 and-1.68±0.35,respectively,with statistically significant differences observed for all comparisons(all P＜0.05).In the active and invalid groups,the proportions of patients with GV＜3.0 cm·year-1 before treatment were 32.26％and 58.06％,respectively;the proportions of patients with peak growth hormone(GH)levels＜5.0 ng·mL-1 before treatment were 30.65％and 54.84％,respectively;the average maternal heights were(158.83±5.76)and(155.48±6.58)cm,respectively,with statistically significant differences observed for all comparisons(all P＜0.05).The results of the Logistic regression model showed that GV＜3.0 cm·year-1 before treatment,peak GH levels＜5.0 ng·mL-1 before treatment,and a shorter maternal height were independent risk factors for poor treatment outcomes with rhGH in pediatric patients with growth hormone deficiency(all P＜0.05).No severe adverse reactions occurred during the treatment of any patient;seven patients experienced pain at the injection site,one patient had a slight increase in blood glucose,and five patients had mild decreased appetite.The total incidence of adverse drug reactions were 13.98％(13 cases/93 cases).Conclusion Treatment with rhGH for pediatric patients with growth hormone deficiency can significantly improve height development and has good safety,but it is influenced by factors such as growth velocity,peak GH levels,and maternal height.

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.

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