Affiliated Hospital of Taishan Medical University

Hydrogen therapy as an emerging strategy for anticancer medicine is attracting attention. However, the limitations of effective hydrogen storage and release have hindered its development and application for hydrogen therapy. Herein, a high hydrogen storage nanoquadruplex (PdH0.2)4Se based on a palladium-selenium core has been designed and shows enhanced intratumoral accumulation via an enhanced permeability and retention (EPR) effect. High-efficiency hydrogen can be released from (PdH0.2)4Se, activated by near infrared irradiation (NIR), and combines with available selenium (Se) to produce highly toxic hydrogen selenide (H2Se), which in turn unbalances the GSH/GSSG ratio and induces ROS overproduction. The effect demonstrates that (PdH0.2)4Se irradiated by NIR significantly inhibits cancer cell proliferation, migration, invasion and angiogenesis in vitro. Furthermore, irradiation of (PdH0.2)4Se by NIR can induce significant ferroptosis of cancer cells by triggering mitochondrial dysfunction, ROS generation and lipid peroxidation-mediated oxidative damage in vitro. Finally, NIR irradiated (PdH0.2)4Se also exhibits tumor-targeted photothermal imaging, and inhibition of tumor growth in vivo activating cancer ferroptosis. Importantly, (PdH0.2)4Se demonstrates excellent safety and biocompatibility in vitro and in vivo. Thus together, our findings support the rational design of an effective hydrogen storage (PdH0.2)4Se nanoquadruplex with NIR-controlled release causing an unbalance of cellular GSH/GSSG and inducing cancer ferroptosis could be a highly efficient strategy for hydrogen-mediated cancer therapy.

This study aimed to develop and validate an interpretable machine learning (ML) model based on nnU-Net automated segmentation and computed tomography (CT) radiomics for preoperative risk stratification in thymic epithelial tumors (TETs). In this retrospective multicenter study, 764 patients with pathologically confirmed TETs were enrolled and divided into training, internal validation, and two external validation cohorts. An nnU-Net model was trained for automatic tumor segmentation, with performance assessed by the dice similarity coefficient (DSC). Radiomic features were extracted from the automated segmentations of venous-phase CT images, and least absolute shrinkage and selection operator (LASSO) regression was applied for feature selection. Predictive models, including radiomics-only, clinical-only, and a clinical-radiomics (combined) model, were constructed using five ML algorithms (RF, SVM, KNN, DT, and LR). Model performance was evaluated using the receiver operating characteristic (ROC) curve. Delong’s test was employed to compare these ML models and select the best-performing model as the final model. Calibration curve and decision curve analysis (DCA) were performed to assess clinical efficacy of the final model. The interpretability of the optimal model was elucidated using SHapley Additive exPlanations (SHAP). The nnU-Net segmentation model achieved excellent performance, with a DSC of 0.979 on the test cohort. Compared to the other four combined models, the RF-based combined model demonstrated superior predictive efficacy, yielding area under the curve (AUC) values of 0.941 (training), 0.884 (internal validation), 0.867 (external validation 1), and 0.872 (external validation 2). The calibration curves indicated excellent agreement between the RF-based model’s predictions and actual outcomes, and furthermore, DCA confirmed its superior net benefit over baseline strategies across a wide range of thresholds. SHAP tool identified 11 radiomic features and 3 clinical features as the most influential features, providing transparency into the model’s decision-making process. The nnU-Net framework enables accurate and efficient automatic segmentation of TETs. The proposed RF-based combined model, integrating clinical and radiomic features, provides a robust and interpretable tool for identifying the high-risk TETs, holding promise for supporting clinical decision-making towards personalized therapy.

Maintenance hemodialysis (MHD) patients commonly experience stigma during long-term treatment, yet the characteristics of their subgroups and the associated patterns with symptom burden remain unclear. This study aimed to identify latent subgroups of stigma among MHD patients and depict the symptom network structure of each subgroup. This cross-sectional study enrolled 558 MHD patients from two hospitals between March and December 2024. Stigma was assessed using the Social Impact Scale (SIS), symptom burden was assessed using the Modified Dialysis Symptom Index (DSI), subgroups of stigma were identified using latent profile analysis, and complex relationships between symptoms were explored using network analysis. Network comparison tests were performed to evaluate between-group differences in network connectivity. The study found that there were three subgroups of MHD patients with stigma: low stigma group (38.5%), moderate stigma group (49.6%), and high stigma group (11.8%). The network analysis showed that muscle pain, sadness, and chest pain were the core symptom nodes in the overall sample. Stable symptom networks were observed in the low and moderate stigma groups, with core symptoms differing by subgroup (physical symptoms in the low stigma group vs. emotional symptom in the moderate stigma group), though no significant difference in global network strength was found between these two groups. Stigma among MHD patients presents distinct heterogeneous subgroup characteristics, and there are core symptoms with high connection strength in the symptom network. The difficulty in recruiting patients with high stigma reflects the concealment of this population. Future studies should adopt longitudinal designs and expand the sample size of the high stigma subgroup to verify the value of core symptoms in symptom management. Not applicable.

Considering the clinical significance of brain tumor segmentation and the challenge of annotation scarcity, a self-supervised learning framework combining anatomical symmetry and cross-modal feature transfer (AnaCoMT+) is proposed. AnaCoMT+ innovatively integrates masked autoencoder and contrastive learning, which can extract biological structural features from annotated MRI images through a decoupled training strategy. AnaCoMT+ consists of a representation learning-based encoder, a self-supervised reconstruction head, a self-supervised projection head and a supervised segmentation head. The reconstruction head is employed to conduct cross-modal image reconstruction, thereby extracting the coordinated representations of multimodal images. The projection head is responsible for performing comparative learning of healthy brain regions and tumor regions, thereby enabling the model to focus on the feature expression of lesion areas. The novel design effectively resolves key limitations in current methods: (1) misalignment between pre-training targets and segmentation objectives, and (2) insufficient feature representation in semi-/self-supervised paradigms. Besides, in the construction of AnaCoMT+, in order to integrate and utilize multi-scale features, a multi-scale attention block (MSAB) and a hybrid attention block (HAB) are proposed, further improving the accuracy of brain tumor segmentation. Experimental results on the Brats 2019 dataset show that AnaCoMT+ can obtain an average Dice coefficient of 0.84, which is better than the existing methods.

The post-coronavirus disease 2019 (COVID-19) pulmonary sequelae have garnered public concern. We conducted a multicenter cross-sectional study in outpatient and health exam populations from 23 clinical centers (including university-affiliated/provincial general hospitals, municipal general hospitals, county hospitals, and specialized hospitals) in China (2019-2023), to assess temporal trends and potential influencing factors in the detection of CT-diagnosed pulmonary nodules, pleural effusion, pneumonia, and suspected lung tumors, cancer and viral pneumonia, clarifying pandemic impacts on lung health. Dynamic comparisons across key phases including initial outbreak, vaccine rollout, population-wide vaccination, and major adjustment of pandemic control policies, were performed. This study analyzed 1,616,750 clinical samples (1,102,605 outpatient, 514,145 health examination; 885,945 males, 730,805 females). Pulmonary nodule detection rose progressively, with surges in 2020-2021 and 2023, plateauing in 2021-2022. Outpatients and males showed steeper increases. University-affiliated/provincial hospitals had sharpest increases vs. municipal and county tiers. Specialized hospitals matched general hospital rates. AI boosted detection rates. CT-suspected lung tumors/cancer remained low and stable, unrelated to nodule trends. These results underscore 2019-2023 pulmonary nodule detection surges linked to SARS-CoV-2 infections and AI adoption. COVID-19 vaccination did not accelerate detection but may have slowed it short-term. Long-term studies on infection, vaccine impacts and pandemic-detected nodules' outcomes are urgently needed.

This article was published in Volume 67, issue 4 of publishing year 2023, with a mistake in Figure 3. The correct Figure 3 is the one included in this erratum.

Circulating tumor DNA (ctDNA) is the small fragments of DNA released by tumor cells into the blood. By detecting the abnormal changes in ctDNA in the blood, it can assist in the early screening of cancer, monitoring of treatment efficacy, early warning of recurrence, and guidance for individualized treatment. Using a systematic review and meta-analysis approach, in this study, we assessed the significant prognostic value of ctDNA in non-metastatic colorectal cancer (CRC) patients. Studies on the predictive value of ctDNA in CRC were considered. RevMan 5.3 was used to conduct meta-analyses and subgroup investigations classified by tumor population, two-year or three-year DFS, and tumor location. The analysis included 11 trials conducted with 605 individuals. Definitions of positive ctDNA: Detection of ≥ 1 tumor-informed mutation, or tumor methylation signature above threshold, or combined mutation and fragmentome analysis. Five studies investigated the associations between pre-operative ctDNA status and patient outcomes. Patients who had higher ctDNA levels had considerably lower DFS and OS, but these differences were not statistically significant. 10 studies investigated how post-operative ctDNA status affects patient outcomes. Patients with high levels of ctDNA had worse DFS and OS, regardless of the tumor population, two-year DFS, three-year DFS, or tumor location. Surgical resection or chemotherapy increased the rate of ctDNA+/-. The study was registered on PROSPERO (CRD42024575151). Post-operative ctDNA positivity is related to a poor outcome. Surgical excision or chemotherapy influences the ctDNA shift status. Circulating tumor DNA can serve as a promising prognostic indicator for patients with non-metastatic colorectal cancer. However, more studies are needed to assess the potential of ctDNA status with survival outcomes in Non-metastatic colorectal cancer.

BackgroundTotal hip arthroplasty (THA) is an effective treatment for end-stage hip disease, which can significantly relieve pain, rebuild joint function, and improve patients’ quality of life. Early post-operative functional exercises are essential to promote the recovery of hip function and minimize complications. However, some patients are afraid of exercise due to the risk of pain or dislocation of the prosthesis, resulting in poor adherence to rehabilitation exercises. Kinesiophobia has now become a key factor hindering the rehabilitation process and leading to poor functional recovery. Therefore, early identification of high-risk groups and implementation of targeted interventions are essential to improve patient prognosis.ObjectivesTo construct and validate a risk prediction model for post-operative kinesiophobia in total hip arthroplasty (THA) patients.MethodsThis study is a single-center cross-sectional study with only internal validation. Through the convenience sampling method, 205 patients who underwent total hip arthroplasty (THA) at the Second Affiliated Hospital of Shandong First Medical University from October 2024 to May 2025 were selected as the study subjects. Independent predictors associated with the occurrence of kinesiophobia were screened by univariate analysis and multifactorial logistic regression analysis. A nomogram risk prediction model was constructed using R software, and the Hosmer-Lemeshow test, receiver operating characteristic (ROC) curve, area under the curve (AUC), calibration curves, and decision analysis curves (DCA) were used to evaluate the model’s goodness-of-fit, discriminability, calibration, and clinical utility, respectively. The Bootstrap method was used to perform 1,000 repeated samplings for internal validation of the model.ResultsA total of 126 out of 205 post-operative THA patients developed kinesiophobia, and the incidence of kinesiophobia was 61.5%. Logistic regression showed that pain history duration, anxiety, hip function, coping style, and rehabilitation self-efficacy were the influencing factors for the development of kinesiophobia in post-operative patients with THA (P < 0.05). The AUC value of the constructed model was 0.947 (95% CI 0.919∼0.975), the specificity was 0.873, and the sensitivity was 0.911. The Hosmer-Lemeshow test (χ2 = 2.287, P = 0.971) and the calibration curve showed that the predicted probability of the model was consistent with the probability of the actual occurrence, and the model predicted the effect well; the DCA curve indicates that the model has good clinical utility. Internal validation using the Bootstrap method yielded an AUC value of 0.912 (95% CI 0.875–0.949).ConclusionTHA patients with a pain history duration (> 1 year), anxiety, poor hip function, low active coping, and poor rehabilitation self-efficacy are more likely to develop kinesiophobia after surgery. The constructed model has good predictive efficacy, which is helpful for early clinical identification of high-risk patients and provides a reference for developing individualized interventions.