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Development of versatile theranostic agents that simultaneously integrate therapeutic and diagnostic features remains a clinical urgent. Herein, we aimed to prepare uniform PEGylated (lactic-co-glycolic acid) (PLGA) microcapsules (PB@(Fe3O4@PEG-PLGA) MCs) with superparamagnetic Fe3O4 nanoparticles embedded in the shell and Prussian blue (PB) NPs inbuilt in the cavity via a premix membrane emulsification (PME) method. On account of the eligible geometry and multiple load capacity, these MCs could be used as efficient multi-modality contrast agents to simultaneously enhance the contrasts of US, MR and PAT imaging. In-built PB NPs furnished the MCs with excellent photothermal conversion property and embedded Fe3O4 NPs endowed the magnetic location for fabrication of targeted drug delivery system. Notably, after further in-situ encapsulation of antitumor drug of DOX, (PB+DOX)@(Fe3O4@PEG-PLGA) MCs possessed more unique advantages on achieving near infrared (NIR)-responsive drug delivery and magnetic-guided chemo-photothermal synergistic osteosarcoma therapy. In vitro and in vivo studies revealed these biocompatible (PB+DOX)@(Fe3O4@PEG-PLGA) MCs could effectively target to the tumor tissue with superior therapeutic effect against the invasion of osteosarcoma and alleviation of osteolytic lesions, which will be developed as a smart platform integrating multi-modality imaging capabilities and synergistic effect with high therapy efficacy.

The regenerating islet-derived (REG) protein family suppresses cell death and promotes cell proliferation. Here the authors report that the lncRNA REG1CP forms an RNA–DNA triplex at the promoter of REG3A gene to increase its expression.

Background: Lhasa is the main residence of Tibetans and one of the highest cities in the world. Its unique geography and ethnic population provide the chance to investigate the interactions among high altitude, ethnicity, and cardiac adaptation. Meanwhile, echocardiographic data about healthy Tibetans on a large scale are not available. This study aimed to analyze physiological factors related to ventricular size and valvular function in healthy Tibetans in Lhasa. Methods: A representative sample of residents in Tibet was recruited using a multistage cluster random sampling method. Two-dimensional echocardiographic measurements and Doppler evaluation for valvular function were performed. Healthy Tibetans in Lhasa constituted the study population. Associations between physiological parameters and ventricular dimensions in healthy Tibetans were analyzed by canonical correlation analysis. Factors related to valvular regurgitations were determined by logistic regression analysis. Results: The 454 healthy Tibetans (340 females and 114 male) in Lhasa were included in the final analysis. Canonical correlation analysis revealed that weight was positively correlated with the proximal right ventricular outflow diameter and the basal left ventricular linear dimension in both genders. Weight and pulse were negatively related to mild tricuspid regurgitation. Age was a positive factor for pulmonary and aortic regurgitations. The same was found between systolic blood pressure and mitral regurgitation. Conclusions: Weight is associated with ventricular size and valvular regurgitation in healthy Tibetans. It should be of more concern in research of high altitude population.

Abstract Background The association between plasma free fatty acid (FFA) and the outcomes in the heart failure (HF) patients remains unclear. Methods A cohort study among HF patients was performed. Plasma FFA was analyzed as both a continuous and a categorical variable (grouped by tertiles) to assess its association with composite cardiovascular (CV) death and HF hospitalization (CV death & HHP), CV death alone, and all-cause mortality (ACM) using Cox regression models. Subgroup analyses of HF patients with preserved ejection fraction (HFpEF) and mildly reduced/reduced ejection fraction (HFmrEF/HFrEF) were performed. This work also assessed the effectiveness of combining FFA and NT-pro BNP biomarkers for risk stratification by calculating the concordance index (C-index). Results Among the 4,109 HF patients, FFA levels exceeding 0.4–0.42 mmol/L were associated with increased risks of the three outcomes. Patients in the highest FFA tertile faced greater risks than those in the lowest tertile. Adjusted hazard ratios were 1.32 (95% CI: 1.11–1.58) for CV death & HHP, 1.45 (95% CI: 1.16–1.82) for CV death, and 1.39 (95% CI: 1.15–1.68) for ACM, with a maximum follow-up of 8 years (median: 25 months). Subgroup analyses revealed that elevated FFA levels consistently predicted worse outcomes in both HFmrEF/HFrEF and HFpEF patients. The C-index for predicting outcomes was significantly greater when NT-pro BNP and FFA were combined than when NT-pro BNP was used alone (P

Abstract Background Published data on whether post-stroke delirium (PSD) is an independent predictor of outcomes in patients with acute stroke are inconsistent and have not yet been synthesized and quantified via meta-analyses. Methods This systematic review and meta-analysis followed the Meta-analysis of Observational Studies in Epidemiology (MOOSE) and Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The study protocol involved a search of the PubMed, Embase, PsycINFO, and Medline databases from 1946 to November 1, 2023, of which prospective observational and case–control studies were included. The quality of the included studies was rated using the Newcastle Ottawa Scale. Pooled effect estimates calculated using a random-effects model were expressed as the odds ratios (ORs), hazard ratios (HRs), and standardized mean differences (SMDs) with 95% confidence intervals (CIs). The protocol was registered in PROSPERO (CRD42023472551). Results The search yielded 39 eligible articles comprising 3295 and 9643 patients with and without PSD, respectively. Thirty studies were high quality, while 9 had moderate quality. The primary analyses, adequately adjusting for predefined confounders, showed that PSD was significantly associated with mortality risk (average follow-up of 19.50 months; OR, 3.47; 95% CI, 2.35–5.12; I 2, 26.0%) and poor neurological function (average follow-up of 21.75 months; OR, 3.62; 95% CI, 2.15–6.09; I 2, 0). Secondary analyses, with or without inadequate adjustment, showed that PSD was significantly associated with prolonged hospital length of stay, increased risk of institutionalization, poor cognitive outcomes, and quality of life after discharge. Conclusions This systematic review and meta-analysis provides evidence that PSD was independently associated with mortality and poor neurological function after controlling for pre-specified confounders. The prevention of PSD remains a high clinical and research priority.

Abstract Purpose This study aims to develop an ensemble machine learning-based (EML-based) risk prediction model for radiation dermatitis (RD) in patients with head and neck cancer undergoing proton radiotherapy, with the goal of achieving superior predictive performance compared to traditional models. Materials and methods Data from 57 head and neck cancer patients treated with intensity-modulated proton therapy at Kaohsiung Chang Gung Memorial Hospital were analyzed. The study incorporated 11 clinical and 9 dosimetric parameters. Pearson’s correlation was used to eliminate highly correlated variables, followed by feature selection via LASSO to focus on potential RD predictors. Model training involved traditional logistic regression (LR) and advanced ensemble methods such as Random Forest and XGBoost, which were optimized through hyperparameter tuning. Results Feature selection identified six key predictors, including smoking history and specific dosimetric parameters. Ensemble machine learning models, particularly XGBoost, demonstrated superior performance, achieving the highest AUC of 0.890. Feature importance was assessed using SHAP (SHapley Additive exPlanations) values, which underscored the relevance of various clinical and dosimetric factors in predicting RD. Conclusion The study confirms that EML methods, especially XGBoost with its boosting algorithm, provide superior predictive accuracy, enhanced feature selection, and improved data handling compared to traditional LR. While LR offers greater interpretability, the precision and broader applicability of EML make it more suitable for complex medical prediction tasks, such as predicting radiation dermatitis. Given these advantages, EML is highly recommended for further research and application in clinical settings.

Introduction: The bidirectional relationship between the brain cortex and cardiovascular diseases (CVDs) remains inadequately explored. Methods: This study used bidirectional Mendelian randomization (MR) analysis to explore the interactions between nine phenotypes associated with hypertension, heart failure, atrial fibrillation (AF), and coronary heart disease (CHD), and brain cortex measurements. These measurements included total surface area (SA), average thickness (TH), and the SA and TH of 34 regions defined by the Desikan-Killiany atlas. The nine traits were obtained from sources such as the UK Biobank and FinnGen, etc., while MRI-derived traits of cortical structure were sourced from the ENIGMA Consortium. The primary estimate was obtained using the inverse-variance weighted approach. A false discovery rate adjustment was applied to the p-values (resulting in q-values) in the analyses of regional cortical structures. Results: A total of 1,260 two-sample MR analyses were conducted. Existing CHD demonstrated an influence on the SA of the banks of the superior temporal sulcus (bankssts) (q=0.018) and the superior frontal lobe (q=0.018), while hypertension was associated with changes in the TH of the lateral occipital region (q=0.02). Regarding the effects of the brain cortex on CVD incidence, total SA was significantly associated with the risk of CHD. Additionally, 16 and 3 regions exhibited significant effects on blood pressure and AF risk, respectively (q

Purpose This study enhances the efficiency of predicting complications in lung cancer patients receiving proton therapy by utilizing large language models (LLMs) and meta-analytical techniques for literature quality assessment. Materials and Methods We integrated systematic reviews with LLM evaluations, sourcing studies from Web of Science, PubMed, and Scopus, managed via EndNote X20. Inclusion and exclusion criteria ensured literature relevance. Techniques included meta-analysis, heterogeneity assessment using Cochran’s Q test and I 2 statistics, and subgroup analyses for different complications. Quality and bias risk were assessed using the PROBAST tool and further analyzed with models such as ChatGPT-4, Llama2-13b, and Llama3-8b. Evaluation metrics included AUC, accuracy, precision, recall, F1 score, and time efficiency (WPM). Results The meta-analysis revealed an overall effect size of 0.78 for model predictions, with high heterogeneity observed (I 2 = 72.88%, P < 0.001). Subgroup analysis for radiation-induced esophagitis and pneumonitis revealed predictive effect sizes of 0.79 and 0.77, respectively, with a heterogeneity index (I 2 ) of 0%, indicating that there were no significant differences among the models in predicting these specific complications. A literature assessment using LLMs demonstrated that ChatGPT-4 achieved the highest accuracy at 90%, significantly outperforming the Llama3 and Llama2 models, which had accuracies ranging from 44% to 62%. Additionally, LLM evaluations were conducted 3229 times faster than manual assessments were, markedly enhancing both efficiency and accuracy. The risk assessment results identified nine studies as high risk, three as low risk, and one as unknown, confirming the robustness of the ChatGPT-4 across various evaluation metrics. Conclusion This study demonstrated that the integration of large language models with meta-analysis techniques can significantly increase the efficiency of literature evaluations and reduce the time required for assessments, confirming that there are no significant differences among models in predicting post proton therapy complications in lung cancer patients.

Abstract CD4+CD25+ regulatory T cells (Tregs) play an important role in maintaining immune homeostasis in multiple sclerosis (MS). Hence, we aimed to explore the therapeutic efficacy and safety of adoptive cell therapy (ACT) utilizing induced antigen-specific Tregs in an animal model of MS, that is, in an experimental autoimmune encephalomyelitis (EAE) model. B cells from EAE model that were activated with soluble CD40L were used as antigen-presenting cells (APCs) to induce the differentiation of antigen-specific Tregs from naïve CD4 precursors, and then, a stepwise isolation of CD4+CD25highCD127low Tregs was performed using a flow sorter. All EAE mice were divided into Treg-treated group (2 × 104 cells in 0.2 mL per mouse, n = 14) and sham-treated group (0.2 mL normal saline (NS), n = 20), which were observed daily for clinical assessment, and for abnormal appearance for 6 weeks. Afterward, histological analysis, immunofluorescence and real-time PCR were performed. Compared to sham-treated mice, Treg-treated mice exhibited a significant decrease in disease severity scores and reduced inflammatory infiltration and demyelination in the spinal cord. Additionally, Tregs-treated mice demonstrated higher CCN3 protein and mRNA levels than sham-treated mice. The results of this preclinical study further support the therapeutic potential of this ACT approach in the treatment of MS.

Background The accurate selection of patients likely to respond to renal denervation (RDN) is crucial for optimizing treatment outcomes in patients with hypertension. This systematic review was designed to evaluate patient‐specific factors predicting the RDN response. Methods and Results We focused on individuals with hypertension who underwent RDN. Patients were categorized based on their baseline characteristics. The primary outcome was blood pressure (BP) reduction after RDN. Both randomized controlled trials and nonrandomized studies were included. We assessed the risk of bias using corresponding tools and further employed the Grading of Recommendations Assessment, Development, and Evaluation approach to assess the overall quality of evidence. A total of 50 studies were ultimately included in this systematic review, among which 17 studies were for meta‐analysis. Higher baseline heart rate and lower pulse wave velocity were shown to be associated with significant antihypertensive efficacy of RDN on 24‐hour systolic BP reduction (weighted mean difference, −4.05 [95% CI, −7.33 to −0.77]; weighted mean difference, −7.20 [95% CI, −9.79 to −4.62], respectively). In addition, based on qualitative analysis, higher baseline BP, orthostatic hypertension, impaired baroreflex sensitivity, and several biomarkers are also reported to be associated with significant BP reduction after RDN. Conclusions In patients with hypertension treated with the RDN, higher heart rate, and lower pulse wave velocity were associated with significant BP reduction after RDN. Other factors, including higher baseline BP, hypertensive patients with orthostatic hypertension, BP variability, impaired cardiac baroreflex sensitivity, and some biomarkers are also reported to be associated with a better BP response to RDN.

Abstract Machine learning algorithms were used to analyze the odds and predictors of complications of thyroid damage after radiation therapy in patients with head and neck cancer. This study used decision tree (DT), random forest (RF), and support vector machine (SVM) algorithms to evaluate predictors for the data of 137 head and neck cancer patients. Candidate factors included gender, age, thyroid volume, minimum dose, average dose, maximum dose, number of treatments, and relative volume of the organ receiving X dose (X: 10, 20, 30, 40, 50, 60 Gy). The algorithm was optimized according to these factors and tenfold cross-validation to analyze the state of thyroid damage and select the predictors of thyroid dysfunction. The importance of the predictors identified by the three machine learning algorithms was ranked: the top five predictors were age, thyroid volume, average dose, V50 and V60. Of these, age and volume were negatively correlated with thyroid damage, indicating that the greater the age and thyroid volume, the lower the risk of thyroid damage; the average dose, V50 and V60 were positively correlated with thyroid damage, indicating that the larger the average dose, V50 and V60, the higher the risk of thyroid damage. The RF algorithm was most accurate in predicting the probability of thyroid damage among the three algorithms optimized using the above factors. The Area under the receiver operating characteristic curve (AUC) was 0.827 and the accuracy (ACC) was 0.824. This study found that five predictors (age, thyroid volume, mean dose, V50 and V60) are important factors affecting the chance that patients with head and neck cancer who received radiation therapy will develop hypothyroidism. Using these factors as the prediction basis of the algorithm and using RF to predict the occurrence of hypothyroidism had the highest ACC, which was 82.4%. This algorithm is quite helpful in predicting the probability of radiotherapy complications. It also provides references for assisting medical decision-making in the future.