Yantai Infectious Diseases Hospital

Vascular surgery is an important procedure that is carried out to treat diseases of the entire vascular system. Intraoperative hemostasis, precise vision of intricate vascular anatomy, restenosis, implant-associated infection, and inadequate endothelium regeneration are among the enduring difficulties that continue to limit vascular surgery. Advanced biomaterial techniques are required since conventional materials and imaging technologies often fail to meet these multifactorial limitations. By enabling synthetic nanomaterials with programmable physicochemical qualities that directly interact with biological systems at the molecular and cellular levels, recent developments in nanotechnology have sparked a paradigm shift in vascular surgery. The limitations of conventional contrast media are addressed by nanostructured contrast agents, such as superparamagnetic iron oxide nanoparticles, which significantly improve vascular imaging resolution and diagnostic sensitivity across magnetic resonance modalities. Biodegradable polymers and surface-engineered coatings are examples of controlled-release nanocarriers that enable targeted administration of antiproliferative and antithrombotic drugs, lowering systemic toxicity and minimizing restenosis. In addition to providing long-lasting antibacterial action to avoid surgical site infections, nano-engineered surface topographies and coatings on grafts and stents accelerate endothelialization and reduce thrombogenicity. Additionally, some nanomaterials’ inherent catalytic and redox modulation properties aid in better wound healing and inflammation resolution. With a focus on mechanistic insights into hemostasis, imaging enhancement, drug transport, implant integration, and regenerative processes, this review thoroughly summarizes recent preclinical and translational studies on the use of nanomaterials in vascular surgery. Furthermore, we outline the present obstacles to clinical translation, such as biocompatibility, long-term safety, and manufacturing scalability, and suggest future paths for incorporating nano-enabled techniques into evidence-based surgical practice.

BackgroundPredicting colorectal cancer (CRC) recurrence risk remains a challenge in clinical practice. Owing to the widespread use of radiomics in CRC diagnosis and treatment, some researchers recently explored the effectiveness of radiomics-based models in forecasting CRC recurrence risk. Nonetheless, the lack of systematic evidence of the efficacy of such models has hampered their clinical adoption.ObjectiveThis study aimed to explore the value of radiomics in predicting CRC recurrence, providing a scholarly rationale for developing more specific interventions.MethodsOverall, 4 databases (Embase, PubMed, the Cochrane Library, and Web of Science) were searched for relevant articles from inception to January 1, 2025. We included studies that developed or validated radiomics-based machine learning models for predicting CRC recurrence using computed tomography or magnetic resonance imaging and provided discriminative performance metrics (c-index). Nonoriginal articles, studies that did not develop a model, and those lacking clear outcome measures were excluded from the study. The quality of the included original studies was assessed using the Radiomics Quality Score. A bivariate mixed-effects model was used to conduct a meta-analysis in which the c-index values with 95% CI were pooled. For the meta-analysis, subgroup analyses were conducted separately on the validation and training sets.ResultsThis meta-analysis included 17 original studies involving 4600 patients with CRC. The quality of the identified studies was low (mean Radiomics Quality Score 13.23/36, SD 2.56), with limitations in prospective design and biological validation. In the validation set, the c-index values based on clinical features, radiomics features, and radiomics features combined with clinical features were 0.73 (95% CI 0.68‐0.79), 0.80 (95% CI 0.75‐0.85), and 0.83 (95% CI 0.79‐0.87), respectively. In the internal validation set, the c-index values based on clinical features, radiomics features, and radiomics features+clinical features were 0.70 (95% CI 0.61‐0.79), 0.83 (95% CI 0.78‐0.88), and 0.83 (95% CI 0.78‐0.88), respectively. Finally, in the external validation set, the c-index values based on clinical features, radiomics features, and radiomics features combined with clinical features were 0.76 (95% CI 0.70‐0.83), 0.75 (95% CI 0.66‐0.83), and 0.83 (95% CI 0.78‐0.88), respectively.ConclusionsRadiomics-based machine learning models, especially those integrating radiomics and clinical features, showed promising predictive performance for CRC recurrence risk. However, this study has several limitations, such as moderate study quality, limited sample size, and high heterogeneity in modeling approaches. These findings suggest the potential clinical value of integrated models in risk stratification and their potential to enhance personalized treatment, though further high-quality prospective studies are warranted.

PurposeEarly prediction of HBsAg seroclearance prior to the application of Peg-IFN-based therapy has important clinical implications. This study aims to construct a predictive model with baseline parameters for HBsAg seroclearance after Peg-IFN-based therapy in virally suppressed patients with HBeAg-negative chronic hepatitis B (CHB).Patients and MethodsFrom January 1, 2018 to May 1, 2023, we retrospectively enrolled 377 nucleos(t)ide analogue-suppressed patients with HBeAg-negative CHB who received a 48-week Peg-IFN-based therapy from 10 centers in China. A multivariate cox regression model was developed for predicting HBsAg seroclearance in a development cohort with 229 patients recruited from 5 centers, then validated in an independent validation cohort with 148 patients recruited from another 5 centers. This study is registered with ClinicalTrials.gov, number NCT06196632.ResultsIn the development and validation cohort, 17.9% (41/229) and 20.27% (30/148) of patients achieved HBsAg seroclearance, respectively. The best performing model was constructed by age (HR 0.962, 95% CI 0.928–0.997), baseline HBsAg (HR 0.998, 95% CI 0.997–0.999) and alanine aminotransferase (HR 1.008, 95% CI 1.003–1.012). It showed good predictive performance in predicting HBsAg seroclearance in both the development [area under the receiver operating characteristic curve (AUC) 0.842] and validation cohort (AUC 0.852). Using cut-off points of −2.7 and −1.3, it can identify HBeAg-negative CHB patients with high, intermediate and low incidence rate of HBsAg seroclearance.ConclusionA model was constructed with baseline parameters for predicting HBsAg seroclearance after Peg-IFN-based therapy in virally suppressed patients with HBeAg-negative CHB. It showed good predictive value and can provide guidance for the clinical application of Peg-IFN-based therapy.

BackgroundHealthcare workers (HCWs) are at an increased risk of latent tuberculosis infection (LTBI) and active TB due to their occupational exposure to tuberculosis (TB). There are limited studies to assess the baseline prevalence of LTBI among HCWs in coastal eastern China. The purpose of this study was to evaluate the prevalence and risk factors of LTBI among HCWs.MethodsA cross-sectional study with stratified cluster sampling method was conducted. All HCWs with TB exposure from the selected healthcare institutions were recruited to complete a standardized questionnaire and to undergo testing by the creation tuberculin skin test (C-TST). Univariate and multivariate logistic regression was employed to identify determinants associated with LTBI.ResultsA total of 342 HCWs were included in the analysis. The prevalence of LTBI was found to be 13.16% (95% CI: 9.76–17.21%). The results of multivariate regression analysis showed that healthcare institutions level (OR = 4.131, 95% CI:1.687–10.118, p = 0.002); physician (OR = 3.132, 95% CI:1.132–8.662, p = 0.028), laboratory technician (OR = 5.545, 95% CI:1.385–22.200, p = 0.016); working duration 10–14 years (OR = 5.721, 95% CI: 1.435–22.809, p = 0.013), 15–19 years (OR = 4.305, 95% CI: 1.028–18.032, p = 0.046) and 20 years above (OR = 4.047, 95% CI: 1.079–15.171, p = 0.038); household contact with TB (OR = 4.488, 95% CI: 1.051–19.166, p = 0.043) were significantly associated with a higher prevalence of LTBI.ConclusionsThe LTBI prevalence among HCWs in Yantai is relatively low. TB screening should be included in the annual health check-ups for HCWs, especially for high-risk groups such as frontline HCWs, long-serving workers, and those with household contact with TB, to reduce the risk of TB infection. These findings highlight the need to enhance infection control measures and active surveillance for at-risk HCWs, even in low TB incidence settings. Future research should focus on longitudinal studies to understand LTBI dynamics and the effectiveness of interventions in low-incidence settings.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12889-025-24926-z.