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.

Platelets are indispensable for physiological hemostasis and pathological thrombus formation, and platelet adhesion to endothelial collagen is a critical initial step in thrombus formation, often overlooked in current antiplatelet therapies. This study aims to elucidate how ginsenoside CK enhances hemodynamic circulation, alleviates stasis, and proposes therapeutic mechanisms. Inspired by the effects on improving microcirculatory disturbances in an acute soft tissue injury model, CK was identified as a PHD2 inhibitor, effectively suppressing platelet adhesion to collagen. It was proposed that targeting PHD2 regulates collagen hydroxylation modification, thereby influencing the formation of its three-dimensional structure, reducing the binding affinity between VWF and collagen, and ultimately suppressing thrombotic events. The efficacy of this mechanism was subsequently confirmed through a mouse DIC model, demonstrating the feasibility of CK in alleviating circulatory disorders. It is worth noting that when Phd2 was knocked down in mice's lungs, pulmonary embolism was significantly reduced. Additionally, PHD2 inhibitors approved for other diseases have exhibited similar anti-thrombotic effects. Moreover, when PHD2 inhibitors were combined with aspirin, they more effectively inhibited arterial thrombosis in rats. The findings offer valuable insights into potential targets for developing antiplatelet drugs or expanding therapeutic applications for existing PHD2 inhibitors in treating thrombotic diseases.

Objective:To construct fused cancer cell/neutrophil membrane-coated polydopamine nanoparticles chelated with manganese ions (Ⅱ) (PMNP@FMs) and explore the potential for targeted pancreatic cancer fluorescence imaging and MRI.Methods:Cancer cell membranes fused with neutrophil membranes were encapsulated on the surface of polydopamine nanoparticles chelated with manganese ions (Ⅱ) (PMNPs) to prepare PMNP@FMs. The morphology, structure, and MRI performance of the product were characterized. The cytotoxicity of PMNP@FMs towards human pancreatic cancer cells (PANC-1) and normal human pancreatic ductal epithelial cells (hTERT-HPNE) was evaluated using cell counting kit (CCK)-8, and in vivo toxicity was assessed in healthy mice. PANC-1 pancreatic cancer xenograft nude mouse models were established for in vivo fluorescence imaging and MRI. Data were analyzed using the independent-sample t test, repeated measures analysis of variance and the least significance difference method. Results:PMNP@FMs exhibited a core-shell structure with a diameter of (112.81±8.64) nm, negative surface charge, and good dispersibility. The T 1 relaxivity of PMNPs was 18.81±0.22, which was 4.1 times higher than that of gadopentetate dimeglumine (Gd-DTPA) (4.55±0.24; t=75.54, P<0.001). Co-culture of PMNPs and PMNP@FMs with hTERT-HPNE and PANC-1 cells for 24 h resulted in cell viability above 90% within the concentration range of 0-500 μg/ml. PMNP@FMs did not affect mouse survival and showed no apparent organ damage. In vivo fluorescence imaging and MRI revealed that PMNP@FMs accumulated highly in tumors and reached the peak 24 h post intravenous administration (relative MR signal: 1.35±0.01, fluorescence intensity: (1.20±0.25)×10 10), surpassing the peak observed in the control group (1.22±0.01, (3.87±0.50)×10 9;F values: 11.03-188.01, t values: 18.20, 5.64, all P<0.05), with hepatic metabolism being the primary route of clearance. Conclusion:PMNP@FMs demonstrate a potential for targeted pancreatic cancer fluorescence imaging and MRI, offering promising prospect for precise diagnosis of early-stage pancreatic cancer.

Objective:To investigate the denoising performance of different deep learning (DL) strategies on low-dose brain 18F-FDG PET images. Methods:This retrospective methodological study was conducted on brain PET/CT images of 50 patients (35 males, 15 females, age 20-87 years) who received 3.7MBq/kg 18F-FDG at the Fifth Affiliated Hospital of Sun Yat-sen University between May 2023 and January 2024. Full-dose PET data were acquired with 2min scan. CT scans were acquired before PET scanning. Low-dose PET sinograms were generated by down-sampling the full-dose list mode data to 1/2, 1/4, and 1/20 of full-dose count level. Both full-dose and low-dose sinograms were reconstructed with random, CT-based attenuation and scatter corrections using the three-dimensional (3D) ordered-subsets expectation maximization (OSEM) algorithm (2 iterations, 20 subsets). A total of 4 DL denoising methods were established: (1) 3D conditional generative adversarial networks (GAN) using only low-dose PET as input (GAN-1); (2) 3D attention-based GAN (AttGAN) with low-dose PET input (AttGAN-1); (3) 3D AttGAN with low-dose PET and CT inputs (AttGAN-2); (4) 3D AttGAN with frequency-separation using low-dose PET and CT inputs (AttGAN-FS-2). For AttGAN-FS-2, during the frequency division process, high- and low-frequency components were extracted from the PET reconstructed images via Fourier transform, then inversed Fourier transform, denoised separately, and finally combined to produce the final denoised images. The dataset was separated into training (70%), validation (10%) and testing (20%) sets using simple random sampling without replacement with a fixed random seed. A 5-fold cross-validation scheme was then applied to test all 50 patients. Performance was evaluated against full-dose PET using normalized mean square error (NMSE), structural similarity (SSIM), peak signal-to-noise ratio (PSNR), contrast-to-noise ratio (CNR), SUV mean and SUV max bias of selected brain ROIs. Wilcoxon signed rank test was used to analyze the differences between the denoising methods. Results:AttGAN-FS-2 showed the best performance among all dose levels, with statistical difference as compared by low-dose PET and GAN-1 denoised images for NMSE, SSIM, PSNR, and CNR ( Z values: 2.92-6.15, all P<0.005). NMSE, SSIM quantitative evaluation results (median) of each model at 1/20 dose were: GAN-1: 0.08, 0.87, AttGAN-1: 0.08, 0.88, AttGAN-2: 0.07, 0.89, AttGAN-FS-2: 0.06, 0.91, respectively ( Z values: 3.24-5.77, all P<0.005). Conclusion:The DL-based method combined with multiple strategies AttGAN-FS-2 shows improved denoising performance for low-dose brain PET images.

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.

Chronic diseases constitute one of the most significant public health challenges globally.Addressing the coexistence of chronic conditions and fostering the development of positive adaptive behaviors is of utmost importance in enhancing self-management skills and healthcare outcomes.Selecting an appropriate theoretical model can offer valuable research insights and a robust framework for anticipating and intervening in patients' adaptive behaviors. Therefore, this paper summarizes the application of theoretical models pertinent to adaptive behaviors in patients with chronic diseases. It conducts a thorough analysis of the strengths and weaknesses of each theoretical model, considering aspects such as theoretical development, target application population, nursing interventions, and effect evaluation. Additionally, it proposes future prospects for the application of these models.In order to enhance awareness of disease adaptive behaviors and provide valuable insights for promoting the development and implementation of comprehensive health lifecycle care plans for chronic diseases.

Objective: To explore the correlation and lag effects of environmental factors on pediatric respiratory disease visits at hospital, so as to provide scientific basis for disease prediction and optimizing clinical diagnosis and treatment. Methods: Data from 503 889 pediatric respiratory disease outpatient and emergency visits a central hospital in Minhang District of Shanghai between 2017 and 2019, along with concurrent meteorological data were collected. A distributed lag non-linear models (DLNM) was constructed to explore the specific relationship between pediatric respiratory disease consultations and various environmental factors and to quantify the cumulative lag effects of environmental factors on respiratory disease consultations. Results: Among the environmental factors, temperature, fine particulate matter (PM 2.5 ), inhalable particulate matter (PM 10 ), nitrogen dioxide (NO 2), and sulfur dioxide (SO 2) were associated with pediatric respiratory disease visits. After adjusting for temperature, PM 2.5 and PM 10 concentrations did not show significant immediate or lag effects. The relative risk (RR) of pediatric respiratory disease visits increased with rising NO 2 concentrations. When NO 2 concentration ≥55 μg/m 3, significant immediate and lagged effects (lag 3, 5, and 7 days) were observed. The RR values were 1.05, 1.13, 1.17, and 1.21( P <0.05). The RR values showed an inverted “U” shaped relationship with SO 2 concentrations. When SO 2 concentration ≥5 μg/m 3, significant lagged effects (lag 3, 5, and 7 days) were observed. The RR values were 1.03 , 1.03, and 1.04 ( P <0.05). Conclusion High concentrations of NO 2 and SO 2 increase the risk of pediatric respiratory disease visits, with observable lag effects.

ObjectiveTo study the clinical characteristics and influencing factors of rheumatoid arthritis （RA） in the patients with cold dampness obstruction syndrome. MethodsThe RA patients treated in the Department of Traditional Chinese Medicine and Rheumatology of the China-Japan Friendship Hospital from August 2022 to June 2024 were selected. The demographic information， clinical data， laboratory test results， and traditional Chinese medicine （TCM） symptom information were collected for syndrome differentiation， on the basis of which the characteristics and influencing factors of cold dampness obstruction syndrome were analyzed. ResultsA total of 258 RA patients were selected in this study， including 88 （34.1%） patients with cold dampness obstruction syndrome， 53 （20.5%） patients with dampness and heat obstruction syndrome， 31 （12.0%） patients with wind dampness obstruction syndrome， 29 （11.2%） patients with liver-kidney deficiency syndrome， 19 （7.4%） patients with Qi-blood deficiency syndrome， 14 （5.4%） patients with phlegm-stasis obstruction syndrome， 15 （5.8%） patients with stasis obstructing collateral syndrome and 9 （3.5%） patients with Qi-Yin deficiency syndrome. The patients were assigned into two groups of cold dampness obstruction syndrome and other syndromes. The group of cold dampness obstruction syndrome had lower joint fever， 28-tender joint count （TJC28）， and 28-joint disease activity score （DAS28）-C-reactive protein （CRP） and higher central sensitization， cold feeling of joints， fear of wind and cold， cold limbs， and abdominal distention than the group of other syndromes （P<0.05）. The binary logistic regression analysis showed that central sensitization （OR 5.749， 95%CI 2.116-15.616， P<0.001） and DAS28-CRP （OR 0.600， 95% CI 0.418-0.862， P=0.006） were the independent factors influencing cold dampness obstruction syndrome in RA. ConclusionCold dampness obstruction syndrome is a common syndrome in RA patients. It is associated with central sensitization， cold feeling of joints， abdominal distension and may be a clinical syndrome associated with central sensitization.

AIM:To explore the central mechanisms by which metformin(Met)attenuates insulin resistance in high-fat diet(HF)-fed rats.METHODS:Forty healthy male Sprague-Dawley rats were randomly divided into 4 groups:normal chow(NC)group,HF group,HF+Met group,and HF+Met+SHU9119[melanocortin 4 receptor(MC4R)antagonist]group,with 10 rats per group.Treatments with HF and Met lasted for 12 weeks,while SHU9119 was injected for the last 10 d.Skeletal muscle AMP-activated protein kinase(AMPK)and silent information regulator 1(SIRT1)ex-pression and activity were measured,along with mitochondria oxidative stress markers,mitochondrial function and quanti-ty.Systemic and skeletal muscle insulin sensitivity were assessed using the average glucose infusion rate from 60 to 120 min(GIR60-120)and 2-deoxyglucose uptake(DGU)during hyperinsulinemic-euglycemic clamp.RESULTS:The rats in HF group exhibited significantly reduced expression and activity of AMPK/SIRT1 in skeletal muscles(P<0.05).More-over,mitochondrial oxidative stress markers,reactive oxygen species(ROS)and malondialdehyde(MDA),were marked-ly elevated(P<0.05),and the activity of antioxidant enzymes glutathione peroxidase(GPX)and manganese superoxide dismutase(MnSOD)was significantly decreased in HF group(P<0.05).There was also a notable decline in the activity of citrate synthase(P<0.05),a marker of mitochondrial oxidative capacity,and the copy number of mitochondrial DNA in HF group.These changes were correlated with significantly decreased GIR60-120 and DGU(P<0.05).Notably,Met treat-ment(HF+Met)restored the AMPK/SIRT1 expression and activity,improved mitochondrial function,and reduced oxida-tive stress,leading to improved insulin sensitivity(P<0.05).However,these beneficial effects of Met were reversed by the MC4R antagonist SHU9119 in HF+Met+SHU9119 group.CONCLUSION:Treatment with Met enhances skeletal muscle AMPK/SIRT1 expression and activity,reverses mitochondrial dysfunction,and improves insulin resistance in HF-fed rats.These effects might be mediated through the activation of hypothalamic MC4R.