Objective To explore the functional impact of A-to-I editing in the seed region of miR-411 during post-myocardial infarction (MI) fibrosis and elucidate its therapeutic potential. Methods Integrating GEO database with myocardial RNA-seq data from MI mouse models, we identified dynamic A-to-I RNA editing in small noncoding RNAs across MI progression (1 day to 8 weeks post-MI). Four miRNAs exhibited differential editing rates between MI and controls, with miR-411 showing progressive editing enhancement at seed region position 4 (P＜0.01). This editing event was validated in both murine MI models and human heart failure specimens. Results The A-to-I editing ratio change of the 4th nucleotide in the seed region of miR-411 mainly occurs in cardiac fibroblasts rather than cardiomyocytes, and the editing at this site depends on ADAR2 rather than ADAR1. Edited miR-411 (ED-miR-411) diverged from wild-type miR-411 (WT-miR-411) in suppressing collagen-related pathways (extracellular matrix [ECM]-receptor interaction, collagen-containing ECM, ECM organization; P＜0.01) in cardiac fibroblasts. Mechanistically, dual-luciferase assays confirmed ED-miR-411 directly targeted the 3′UTR and suppressed expression of type Ⅱ transforming growth factor (TGF)-beta receptor (TGFBR2) and CD44, which were key drivers of TGF-β-mediated fibroblast activation. ED-miR-411 overexpression blunted TGF-β-induced collagen synthesis and myofibroblast proliferation (P＜0.05). In vivo, intramyocardial delivery of ED-miR-411 mimics at 1 week post-MI reduced fibrosis by 40% and improved ejection fraction by 15% (P＜0.01 vs controls), whereas WT-miR-411 showed no therapeutic effect. Conclusions A-to-I editing of miR-411 emerges as an endogenous anti-fibrotic mechanism by repressing TGFBR2 and CD44, thereby disrupting TGF-β signaling and ECM dysregulation. Our findings highlight ED-miR-411 as a novel RNA-based therapeutic candidate to mitigate post-infarction cardiac remodeling.

ObjectiveTo observe the short-term and long-term efficacy of traditional Chinese medicine （TCM） surgical operations in treating mixed hemorrhoids. MethodsA multi-center retrospective cohort study was conducted， collecting clinical data from 17，831 mixed hemorrhoid surgery patients in 8 top-tier TCM hospitals in Jiangsu Province. Standardized and structured datasets were obtained through artificial intelligence models. Patients who underwent western surgical treatment were categorized into the western surgery group （11,646 cases）， and those receiving TCM surgical operations were categorized into the TCM surgery group （6185 cases）. Propensity score matching （1∶1 matching） was used to balance baseline data between groups. The primary outcome was the one-year recurrence rate， and secondary outcomes included the main symptoms （rectal bleeding， degree of prolapse） and secondary symptoms （anal distension， anal edema， wound secretion and exudation， anal stenosis， residual skin tags， perianal itching， and anal pain） measured on days 7， 28， and 60 after discharge. ResultsAfter matching， 2194 patients were included in each group. Symptom scores showed that at 28 days after discharge， the TCM surgical group had superior improvement in rectal bleeding ［OR=5.786， 95%CI （3.092，10.827）］， degree of prolapse ［OR=4.510， 95%CI （1.649，12.333）］， and anal edema ［OR=3.188， 95%CI （1.295，7.845）］ compared to the western surgical group. At 60 days post-discharge， the TCM group still showed advantages in improving rectal bleeding ［OR=5.237， 95%CI （1.077，25.464）］ and anal pain ［OR=11.697， 95%CI （1.186，115.336）］ （P＜0.05）. Long-term follow-up showed that the one-year recurrence rate in the TCM surgery group was 1.1% （8/726）， while that in the western surgery group was 2.3% （10/444）， with no statistically significant difference between the two groups （P＞0.05）. ConclusionBased on real-world data， TCM surgical treatment for mixed hemorrhoids shows significant short-term symptom improvement， particularly in terms of hemostasis， reducing swelling， and alleviating prolapse of anal masses.

With the accelerating trend of population aging, the number of elderly patients with lung cancer continues to rise, and the disease burden is becoming increasingly heavy. The clinical management of these patients faces severe challenges due to their decreased physiological reserve, complex comorbidities, and significant individual heterogeneity. Consequently, under traditional diagnosis and treatment models, doctors often struggle to identify the individualized risks of elderly patients in a timely and comprehensive manner, which can easily lead to decision biases such as undertreatment or overtreatment. In view of this, this study advocates for the establishment of an umbrella decision-making model specifically tailored for elderly lung cancer patients. Grounded in a multidisciplinary team (MDT) platform, this model deeply integrates oncological indicators with the comprehensive geriatric assessment (CGA) system. By holistically considering multidimensional variables including tumor burden, organ function, frailty index, cognitive status, and social support, the model establishes an operational mechanism characterized by "single entry, precise stratification, and targeted selection". Accordingly, patients can be scientifically triaged into distinct intervention tiers, such as active surveillance, minimally invasive surgery, drug therapy, radiotherapy, and best supportive care, thereby achieving real-time alignment between treatment intensity and patient fitness. This article elaborates on the construction logic and key operational procedures of this novel decision-making framework, aiming to guide clinical practice beyond the limitations of a tumor-centric perspective toward a holistic, dynamic, whole-course management strategy. This transition seeks to ensure optimal quality of life and clinical net benefit for elderly patients alongside survival prolongation.

Objective:To construct a machine learning model based on localization CT-based radiomics, dosiomics and clinical features for predicting radiotherapy response in non-small cell lung cancer (NSCLC) and validate its application value.Methods:A retrospective case series study was conducted. A total of 138 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022 were selected. The efficacy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, and the patients were stratified according to the objective remission (complete remission+partial remission). Random stratified sampling was used to divide the 138 patients into a training group (96 cases) and an internal validation group (42 cases) at a ratio of 7∶3. Additionally, 33 patients who received radiotherapy at Jining Cancer Hospital from January 2019 to December 2022 were included as the external validation group. Based on the pre-radiotherapy data of the radiotherapy planning system, PyRadiomics software package was used to extract 107 radiomics features and 107 dosiomics features for each patient. Pearson correlation analysis and LASSO regression analysis were used for dimensionality reduction screening; the final selected features were weighted and integrated to generate radiomics-dosiomics scores (RDS), which were then input into logistic regression (LR), support vector machine (SVM), extremely randomized forest (Extra Trees), K-nearest neighbor algorithm (KNN), lightweight gradient boosting machine (Light GBM), and multi-layer perceptron (MLP) machine learning algorithms to construct 6 radiomics-dosiomics models (RDM) for predicting the objective remission. RECIST 1.1 standard was used to evaluate objective remission as the gold standard, receiver operating characteristic (ROC) curve of 6 RDM for predicting objective remission was plotted, and the optimal algorithm for RDM was selected. Univariate and multivariate logistic regression were performed on demographic characteristics, hematological indicators and radiotherapy parameters of the training group to screen independent risk factors for NSCLC patients who received radiotherapy but did not achieve objective remission. These factors were input into the optimal machine learning algorithm to construct a clinical model (CM). Combined with features from RDS and CM, the clinical feature-radiomics-dosiomics combined model (CRDM) was established, and the nomogram of the model for predicting objective remission in NSCLC patients with radiotherapy was drawn. ROC curves were used to evaluate the efficacy of CM, RDM and CRDM in predicting the objective remission in NSCLC patients with radiotherapy in the training group, internal validation group and external validation group.Results:Four radiomics features (including grayscale variance, low grayscale long-range operation emphasis, low grayscale area emphasis, and small area low grayscale area emphasis, all of which were texture features) and 6 dosiomics features [including 1 first-order feature (robust mean absolute deviation), 4 texture features (grayscale non-uniformity, large area emphasis, large area high grayscale emphasis, contrast) and 1 shape feature (shortest axis length)] were selected. ROC curve analysis showed that the area under the curve (AUC) of the RDM constructed using SVM algorithm for judging the objective remission in the training group and the internal validation group was 0.907 (95% CI: 0.836-0.977) and 0.822 (95% CI: 0.685-0.959), which were higher than RDM constructed using other algorithms, and the sensitivity (96.2% and 91.7%), specificity (78.6% and 76.7%) and accuracy (83.3% and 81.0%) at the optimal cut-off values were all higher. Considering the stability and generalization ability of the model, SVM algorithm was ultimately used to construct RDM, CM and CRDM uniformly. Based on training group data, univariate and multivariate logistic regression analysis showed that elevated platelet-to-lymphocyte ratio (PLR) ( OR = 1.001, 95% CI: 1.000-1.003, P = 0.035) and increased target volume of radiotherapy plan ( OR = 1.001, 95% CI: 1.000-1.001, P = 0.008) were independent risk factors for failure to achieve objective remission. ROC curve analysis showed that in the training group and the internal validation group, the AUC of CRDM predicting objective remission were 0.914 (95% CI: 0.856-0.972) and 0.864 (95% CI: 0.754-0.974), respectively, which were better than CM [AUC were 0.735 (95% CI: 0.612-0.857) and 0.697 (95% CI: 0.507-0.888)] and RDM, respectively. In the external validation group, the AUC of CRDM, CM and RDM were 0.778 (95% CI: 0.500-1.000), 0.667 (95% CI: 0.434-0.899) and 0.741 (95% CI: 0.463-1.000), respectively. Conclusions:The CRDM constructed by combining radiomics, dosiomics and clinical features can comprehensively and accurately evaluate the radiotherapy response of NSCLC patients, and may have important clinical application value in achieving precision medicine and optimizing treatment strategies.

Objective:To develop and validate a combined model integrating radiomics, dosiomics, and clinical parameters based on CT simulation and dosimetric images in order to predict the occurrence of radiation pneumonitis (RP) in patients with non-small cell lung cancer (NSCLC).Methods:A retrospective study was conducted on the clinic data of 143 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022. Patients were randomly stratified into a training group ( n = 100) and an internal validation group ( n = 43) at a 7∶3 ratio. Moreover, clinic data were collected from 34 NSCLC patients who received radiotherapy at the Jining Cancer Hospital between January 2019 and December 2022 as an external validation group. All three groups (the training group, internal validation, and external validation groups) were further categorized into two groups based on the RP severity (i.e., RP ≥ grade 2 and RP < grade 2). Their radiotherapy dose, CT simulation, and 3D dose distribution images were collected. Then, the total lung minus planning target volume (TL-PTV) was defined as the region of interest (ROI) for radiomics and dosiomic feature extraction, followed by feature dimensionality reduction. Consequently, key features associated with RP were determined. Four predictive models were developed using machine learning approaches (especially multilayer perceptron, MLP): a clinical model (CM), a radiomics model (RM), a dosiomics model (DM), and a radiomics and dosiomics nomogram (RDN), with a nomogram subsequently constructed. Ultimately, the performance and clinical feasibility of these models were assessed using receiver operating characteristic (ROC), area under the curve (AUC), and decision curve analysis (DCA). Results:A total of 1 834 radiomic features and 1 834 dosiomic features were extracted. Using the occurrence of RP ≥ grade 2 as the marker variable, 14 radiomic features, 15 dosiomic features, and three clinical features were selected from the training group to construct the prediction models (CM, RM, DM, and RDN). The performance and generalizability of these models were subsequently validated in both the internal validation and external validation groups. Specifically, the RDN exhibited AUCs of 0.915 (95% CI: 0.852-0.978), 0.879 (95% CI: 0.777-0.982), and 0.838 (95% CI: 0.701-0.975) in the three groups, respectively. A nomogram was established for RDN by integrating the radiomics score (R-score), dosiomics score (D-score), mean lung dose (MLD), V20, and V30. This nomogram allowed for individualized risk estimation of RP and facilitated personalized radiotherapy planning. Conclusions:The RDN model that is developed based on CT simulation and 3D dose distribution images and integrates radiomics, dosiomics, and clinical features can effectively predict the RP risk of NSCLC patients. The integration of multidimensional data contributes to the formation of the optimal predictive model, offering guidance for clinicians.

Acute pancreatitis(AP)is a common and severe digestive disease in dogs,characterized by a high recurrence rate and complications that significantly impact canine health.This study in-vestigates the mechanism by which adipose-derived stem cells(ADSCs)regulate the CHOP/Bcl-2 pathway to mitigate acute pancreatic injury in dogs.Twenty Beagle dogs were randomly divided in-to four groups:a Sham group,an AP model group,an ADSCs treatment group,and a conditioned medium(CM)treatment group.The AP model was established by injecting a mixed solution of trypsin and sodium taurocholate into the AP model group.The ADSCs treatment group received intravenous injections of ADSCs immediately following surgery,while the CM treatment group re-ceived conditioned medium preparations.Pancreatic tissue was collected 24 hours post-surgery,and changes in the CHOP/Bcl-2 pathway were assessed using histopathology,transmission electron microscopy,western blotting,fluorescence quantification,immunofluorescence,and TUNEL stai-ning.The results indicated that AP induced significant interstitial edema,hemorrhage,inflammato-ry cell infiltration,chromatin contraction,and endoplasmic reticulum swelling,the expression lev-els of GRP78 and CHOP were found to be elevated,whereas the expression of Bcl-2 was downreg-ulated.Additionally,the expression levels of BAX,JNK,Caspase-3,and Caspase-12 increased,leading to an increased rate of cell apoptosis.Such changes result in an elevated apoptosis rate and a further decrease in Bcl-2 expression.Both ADSCs and CM therapy were found to alleviate the path-ological damage in pancreatic tissue,resulting in downregulation of CHOP and apoptosis-related markers,upregulation of Bcl-2,and a reduction in the apoptosis rate.The results of this study sug-gest that ADSCs may mitigate acute pancreatic injury induced by trypsin and sodium taurocholate in AP dogs by modulating the CHOP/Bcl-2 pathway.Targeted modulation of the transcriptional activity of CHOP may offer novel therapeutic approaches for AP.

The incidence of age-related macular degeneration(AMD)is rising with the intensifying aging trend,be-coming a critical challenge that demands urgent solutions.Dry AMD(dAMD)accounts for approximately 80％of all AMD cases,and there is currently a lack of highly effective treatment options.In recent years,traditional Chinese medicine(TCM)has been proven to effectively treat dAMD through actions such as antioxidation,anti-apoptosis,anti-inflammation,and lipid-lowering.By reviewing domestic and international literature,this article discusses TCM monomers,extracts,and compound formulations for dAMD,analyzing their various mechanisms.Based on traditional TCM efficacy theories and in-tegrated with modern mechanisms of action,it targets the active components of TCM to elucidate the connection between effective medicinal targets of TCM and dAMD,thereby clarifying the efficacy and scientific basis of TCM monomers,com-pounds,and extracts in treating dAMD.The aim is to provide new perspectives for the prevention and clinical treatment of dAMD.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Aim Mouse model of myocardial hypertro-phy was established via intraperitoneal injection of iso-proterenol(ISO)in mice.This approach allows for an in-depth investigation into the pharmacological effects and mechanisms of action of emodin,offering novel in-sights and directions for the improvement of myocardial hypertrophy.Methods The mice were randomly di-vided into the following groups:control group(CON),emodin group(EMO),MAPK activator control group(EMO+Ani),model group(ISO),treatment group(ISO+EMO),and activator intervention group(ISO+EMO+Ani).After treatment with emodin and inter-vention with MAPK activator,the heart weight ratio and cardiac size of each group were observed.Hematoxy-lin-eosin(HE)staining was used to observe the patho-logical changes in cardiac tissue,and kits were utilized to measure the levels of GSH,LDH,and MDA in the serum.Western blot was employed to detect the protein expression levels of inflammatory and oxidative factors,as well as p-p38,p-ERK,p38,and ERK in cardiac tis-sue.Results Emodin can significantly inhibit the production of myocardial inflammatory and oxidative factors induced by ISO,thereby effectively alleviating the degree of myocardial hypertrophy and fibrosis.Af-ter the p38/ERK signaling pathway was specifically ac-tivated by farnesol,the improvement effect of emodin on myocardial hypertrophy was weakened.Further comparison revealed that,compared with the myocardi-al hypertrophy pathological model group,the pathologi-cal protein expression levels in the farnesol-treated group showed no significant difference,and were even higher in some indicators.Conclusion Emodin can effectively inhibit the release of inflammatory factors and improve the state of oxidative stress by modulating the p38/ERK signaling pathway,thereby exerting an ameliorative effect on myocardial hypertrophy.

Objective:To explore the potential risk factors for cancer in patients with adenomatous polyps based on tumor markers and polyp lesion characteristics.Methods:A retrospective analysis was conducted to collect clinical data of 115 patients with adenomatous intestinal polyps who visited Nanjing Jiangbei Hospital from November 2022 to November 2024. They were divided into a cancerous group (17 cases) and a non cancerous group (98 cases) based on whether they were cancerous or not. Clinical data such as tissue type and polyp site and tumor marker levels such as carcinoembryonic antigen (CEA) and cancer antigen 72-4 (CA72-4) were collected at the first visit of all patients. The potential risk factors of adenomatous intestinal polyp canceration were investigated by Logistic regression analysis.Results:Univariate analysis revealed that the proportion of villous tubular adenomas, central depression of polyps, and lobulated polyps in the cancerous group were higher than those in the non cancerous group. Serum levels of CEA and CA72-4 were also higher in the cancerous group than in the non cancerous group : 13/17 vs.47.96% (47/98), 7/17 vs. 15.31% (15/98), 6/17 vs. 8.16% (8/98), (6.41 ± 1.81) μg/L vs. (4.23 ± 1.48) μg/L, (6.98 ± 1.83) kU/L vs. (5.66 ± 1.78) kU/L, respectively. The difference was statistically significant ( P<0.05). The results of Logistic regression analysis showed that the histological subtype of villous tubular adenoma, central depression of polyps, lobulated polyps, and high levels of CEA and CA72-4 were independent risk factors for cancer in patients with adenomatous intestinal polyps ( P<0.05). A nomogram risk model was constructed based on the influencing factors of canceration in patients with adenomatous intestinal polyps. The calibration curve was drawn, and the calibration curve was similar to the Y-X straight line, suggesting that the evaluation results of the nomogram risk model were highly consistent with the actual observation results. The receiver operating characteristic (ROC) curve was drawn. The results showed that the area under the curve (AUC) of the nomogram risk model for evaluating the canceration of patients with adenomatous intestinal polyps was 0.956, and the evaluation value was high. The decision curve was drawn, with the threshold of high risk as the horizontal coordinate and the net rate of return as the vertical coordinate. The results showed that when the threshold was in the range of 0 - 0.85, 0.96 - 0.99, the net benefit rate of predicting the cancer risk of patients with adenomatous intestinal polyps was greater than 0 and the maximum net benefit rate was 0.148. Conclusions:The histological classification of villous tubular adenoma, central depression of polyps, lobulated polyps, and high levels of CEA and CA72-4 are independent risk factors for cancer in patients with adenomatous intestinal polyps; The evaluation efficiency of the column chart risk model constructed based on the above factors is good.