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.

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.

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 explore the surgical treatment strategy for patients with thyroid cancer complicated with primary hyperparathyroidism(PHPT).Methods A retrospective analysis was conducted on the case data of thyroid cancer patients who underwent surgical treatment at Beijing Friendship Hospital,Capital Medical University from January 2016 to January 2022.Among them,22 patients with PHPT were included in study group,and 44 patients were randomly selected from thyroid cancer patients during the same period at a ratio of 1∶2 and included in control group.The operation time,intraoperative blood loss,hospital stay and occurrence of complications of two groups of patients were compared.Results The operation time of patients in study group was significantly longer than that in control group(P＜0.01).There was no statistically significant difference in intraoperative blood loss and hospital stay between two groups of patients(P＞0.05).There were 5 cases of temporary hypocalcemia in study group,12 cases of temporary hypocalcemia and 1 case of incision infection in control group.There was no statistically significant difference in incidence of postoperative complications between two groups of patients(P＞0.05).Conclusion Thyroid cancer complicated with PHPT increases the complexity of treatment.Through adequate preoperative diagnosis and reasonable surgical treatment strategies,the risks of secondary surgery and postoperative complications can be effectively reduced,and the prognosis of patients can be improved.

Objective:To evaluate the detection capability of the contrast-enhanced three-dimensional turbo spin-echo (CE-SPACE) sequence for brain metastases (BM), aiming to provide evidence for precise target delineation in stereotactic radiotherapy (SRT).Methods:A total of 123 BM patients who received radiotherapy at the Affiliated Cancer Hospital of Zhengzhou University from May to November 2024 were enrolled. All patients underwent contrast-enhanced (CE) MRI and CT scans in the same treatment position, with images rigidly registered in the Eclipse planning system. Two experienced radiation oncologists independently delineated BM lesions on CE-MPRAGE and CE-SPACE sequences in a blinded manner. Patients were divided into the delayed group (10 min, n=61) and a priority group (5 min, n=62) based on the time interval between gadolinium injection and CE-SPACE acquisition. The non-parametric Wilcoxon rank-sum test was used to compare the lesion counts and volume differences between the two imaging sequences. Point-biserial correlation analysis was performed to assess the correlation between the additional lesions identified by CE-SPACE and lesion volume. Results:The overall analysis demonstrated that CE-SPACE detected 421 BM lesions, achieving an 8.2% higher detection rate than CE-MPRAGE ( Z=3.78, P<0.001). In terms of lesion volume, the median BM lesions volume identified by CE-SPACE [0.30(0.07,1.53)cm 3] was 8.7% larger than that by CE-MPRAGE [0.23 (0.04, 1.34) cm 3] ( Z=12.88, P<0.001). CE-SPACE demonstrated superior sensitivity for lesions ≤ 0.06 cm3, with negative correlation between the number of additional lesions detected and lesion volume ( r=-0.104, P=0.034). Subgroup analysis revealed that in the delayed group, CE-SPACE detected significantly more lesions [median 2 (1, 3.5) vs. 2 (1, 3), P=0.002] and larger volumes [0.39 (0.08, 2.24) cm3 vs. 0.29 (0.05, 1.99) cm3, P<0.001] than CE-MPRAGE. In the priority group, CE-SPACE detected significantly larger lesion volumes [0.55 (0.09, 2.06) cm3 vs. 0.45 (0.08, 1.88) cm3, P<0.001], but no significant difference was observed in lesion counts between two sequences ( P=0.059). Conclusions:Three-dimensional CE-SPACE sequence offers superior detection sensitivity for small BM (≤ 0.06 cm3), providing crucial guidance for accurate target delineation in SRT.

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.

Objective:To evaluate the detection capability of the contrast-enhanced three-dimensional turbo spin-echo (CE-SPACE) sequence for brain metastases (BM), aiming to provide evidence for precise target delineation in stereotactic radiotherapy (SRT).Methods:A total of 123 BM patients who received radiotherapy at the Affiliated Cancer Hospital of Zhengzhou University from May to November 2024 were enrolled. All patients underwent contrast-enhanced (CE) MRI and CT scans in the same treatment position, with images rigidly registered in the Eclipse planning system. Two experienced radiation oncologists independently delineated BM lesions on CE-MPRAGE and CE-SPACE sequences in a blinded manner. Patients were divided into the delayed group (10 min, n=61) and a priority group (5 min, n=62) based on the time interval between gadolinium injection and CE-SPACE acquisition. The non-parametric Wilcoxon rank-sum test was used to compare the lesion counts and volume differences between the two imaging sequences. Point-biserial correlation analysis was performed to assess the correlation between the additional lesions identified by CE-SPACE and lesion volume. Results:The overall analysis demonstrated that CE-SPACE detected 421 BM lesions, achieving an 8.2% higher detection rate than CE-MPRAGE ( Z=3.78, P<0.001). In terms of lesion volume, the median BM lesions volume identified by CE-SPACE [0.30(0.07,1.53)cm 3] was 8.7% larger than that by CE-MPRAGE [0.23 (0.04, 1.34) cm 3] ( Z=12.88, P<0.001). CE-SPACE demonstrated superior sensitivity for lesions ≤ 0.06 cm3, with negative correlation between the number of additional lesions detected and lesion volume ( r=-0.104, P=0.034). Subgroup analysis revealed that in the delayed group, CE-SPACE detected significantly more lesions [median 2 (1, 3.5) vs. 2 (1, 3), P=0.002] and larger volumes [0.39 (0.08, 2.24) cm3 vs. 0.29 (0.05, 1.99) cm3, P<0.001] than CE-MPRAGE. In the priority group, CE-SPACE detected significantly larger lesion volumes [0.55 (0.09, 2.06) cm3 vs. 0.45 (0.08, 1.88) cm3, P<0.001], but no significant difference was observed in lesion counts between two sequences ( P=0.059). Conclusions:Three-dimensional CE-SPACE sequence offers superior detection sensitivity for small BM (≤ 0.06 cm3), providing crucial guidance for accurate target delineation in SRT.

Objective:To evaluate the application effect of intelligent management of health management (examination) institutions in Shanxi Province.Methods:In this study, a cross-sectional study was adopted. In October 2023, through convenient sampling, 300 health management (examination) institutions in Shanxi Province were investigated according to the four regions of northern Shanxi, central Shanxi, southern Shanxi and southeastern Shanxi, and the application information of intelligent management in the whole process from January to December 2022 was collected. According to the evaluation indicators in the documents of the National Health and Wellness Commission, six indicators were analyzed, including the completion rate of health questionnaire, the integrity of physical examination files, the filing rate of physical examination cycle, the evaluation rate of cardiovascular risk diseases, the re-examination rate of patients with chronic diseases (blood pressure, blood lipid and blood glucose indicators) and the follow-up rate of important abnormal results.Results:The completion rates of health questionnaires in northern Shanxi, central Shanxi, southern Shanxi and southeastern Shanxi were 54.9%, 84.2%, 93.2% and 98.2% ( χ 2=225 050.17, P<0.001), and the filing rates of health checkups in various regions were 89.1%, 94.7%, 94.0% and 94.5% ( χ 2=10 254.02, P<0.001). The filing rates of physical examination cycles in each region were 21.8%, 23.4%, 24.3% and 24.7% ( χ 2=734.15, P<0.001), and the assessment rates of cardiovascular risk diseases in each region were 84.5%, 85.4%, 80.2% and 86.2% ( χ 2=4 627.99, P<0.001). The reexamination rates of patients with chronic diseases (blood pressure, blood lipid and blood glucose indicators) in different regions were 38.8%, 39.7%, 39.3% and 39.5% ( χ 2=24.24, P<0.001), and the follow-up rates of important abnormal results were 81.7%, 84.1%, 82.9% and 83.9% ( χ 2=255.02, P<0.001). Conclusion:Shanxi health management (examination) institutions have achieved certain results in the application of intelligent management in the whole process, but there is still much room for improvement in the filing of physical examination cycle and re-examination of chronic diseases.

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 investigate the current status of nutritional support specialist nurses' knowledge, attitude and practice about nutrition nursing practice and analyze their influencing factors, so as to provide reference for optimizing and improving the level of nutritional support specialist nurses' knowledge, attitude and practice, and constructing a training program for nutritional support specialist nurses.Methods:From October to November 2023, 557 nutritional support specialist nurses in 28 provinces, autonomous regions, and municipalities directly under the central government of China were selected for the survey using the convenience sampling method. General information questionnaire and Nutrition Nursing Practice Knowledge, Attitude and Practice Questionnaire were used to conduct the survey. Multiple linear regression was used to explore the influencing factors of knowledge, attitude and practice.Results:A total of 557 questionnaires were distributed and 507 questionnaires were effectively recovered, with an effective recovery rate of 91.0%. The nutrition nursing practice knowledge dimension scores, attitude dimension scores, and practice dimension scores of 507 nutritional support specialist nurses were 30.00 (28.00, 31.00) , 47.00 (41.00, 50.00) , (22.65±6.77) , respectively. Multiple linear regression analyses showed that education was an influencing factor in the knowledge dimension of nutrition nursing practice among nutritional support nurse specialists ( P<0.05) ; title, hospital level, and years of experience as a nutritional support nurse specialist were influencing factors in the attitude dimension of nutrition nursing practice ( P<0.05) . Conclusions:Nutritional support specialist nurses have a good knowledge base of nutrition nursing practice and a positive attitude, but their practices need to be further improved. Knowledge, attitude, and practice are affected by multiple factors such as education, title, hospital level, and number of years in the professional field. In the process of nurse specialist training, managers should pay attention to the learning needs of nurses at different levels, carry out targeted training, establish a standard workflow for nurse specialists, and emphasize the construction of talents in primary hospitals, so as to promote the high-quality development of nutrition support specialist nurses.