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Abstract Background Diabetic retinopathy (DR), a prevalent complication in patients with type 2 diabetes, has attracted increasing attention. Recent studies have explored a plausible association between retinopathy and significant liver fibrosis. The aim of this investigation was to develop a sophisticated machine learning (ML) model, leveraging comprehensive clinical datasets, to forecast the likelihood of significant liver fibrosis in patients with retinopathy and to interpret the ML model by applying the SHapley Additive exPlanations (SHAP) method. Methods This inquiry was based on data from the National Health and Nutrition Examination Survey 2005–2008 cohort. Utilizing the Fibrosis-4 index (FIB-4), liver fibrosis was stratified across a spectrum of grades (F0-F4). The severity of retinopathy was determined using retinal imaging and segmented into four discrete gradations. A ten-fold cross-validation approach was used to gauge the propensity towards liver fibrosis. Eight ML methodologies were used: Extreme Gradient Boosting, Random Forest, multilayer perceptron, Support Vector Machines, Logistic Regression (LR), Plain Bayes, Decision Tree, and k-nearest neighbors. The efficacy of these models was gauged using metrics, such as the area under the curve (AUC). The SHAP method was deployed to unravel the intricacies of feature importance and explicate the inner workings of the ML model. Results The analysis included 5,364 participants, of whom 2,116 (39.45%) exhibited notable liver fibrosis. Following random allocation, 3,754 individuals were assigned to the training set and 1,610 were allocated to the validation cohort. Nine variables were curated for integration into the ML model. Among the eight ML models scrutinized, the LR model attained zenith in both AUC (0.867, 95% CI: 0.855–0.878) and F1 score (0.749, 95% CI: 0.732–0.767). In internal validation, this model sustained its superiority, with an AUC of 0.850 and an F1 score of 0.736, surpassing all other ML models. The SHAP methodology unveils the foremost factors through importance ranking. Conclusion Sophisticated ML models were crafted using clinical data to discern the propensity for significant liver fibrosis in patients with retinopathy and to intervene early. Practice implications Improved early detection of liver fibrosis risk in retinopathy patients enhances clinical intervention outcomes.

Abstract Objective This study aims to evaluate the efficacy and safety of 3D printing technology in brachytherapy for cervical cancer, comparing its outcomes with conventional free hand implantation brachytherapy. Methods A total of 50 cervical cancer patients treated at the First Affiliated Hospital of Gannan Medical College from January 2019 to July 2023 were included in this study. Patients were divided into two groups: 25 patients received intensity-modulated radiotherapy (IMRT) combined with 3D-printed brachytherapy, and 25 patients underwent IMRT combined with free hand brachytherapy implantation. Key indicators analyzed included short-term therapeutic effects, survival outcomes, operation times, the number of CT scans, the number of needles inserted, dosimetric parameters, and complications. Results The use of 3D-printed brachytherapy significantly improved the safety of radiation therapy operations, especially for large tumors (≥ 30 mm), by providing more precise dose distribution and reducing the radiation doses received by critical organs such as the bladder and rectum. Compared to the artificial implant group (88% prevalence), the 3D-printed brachytherapy group showed a significantly lower incidence of radiation enteritis (29.2% prevalence, p

IntroductionCervical cancer remains a significant challenge in oncology with an escalating demand for novel therapeutic strategies that can navigate the complexities of its pathophysiology. This study elucidated the antineoplastic effects of cryptotanshinone, a derivative of danshen (Salvia miltiorrhiza), a herb widely utilized in traditional Chinese medicine practices.MethodsEmploying a comprehensive multi-omics approach, including transcriptomic, proteomic, and bioinformatics analyses, we investigated the potential effects of cryptotanshinone on cervical cancer through data mining and computational analysis.Results and DiscussionOur results demonstrated that the potential of cryptotanshinone to disrupted cancer cell proliferation and induced apoptosis may be ascribed to its modulation of gene expression and interaction with specific protein networks. Furthermore, network pharmacology and pathway enrichment analyses identified critical hubs and signaling pathways, suggesting a multi-targeted mechanism of action. Furthermore, the establishment of a prognostic model, which is founded upon differentially expressed genes linked to cryptotanshinone treatment, underscores its promising role as both a prognostic biomarker and a therapeutic agent. These insights pave the way for the integration of cryptotanshinone into therapeutic regimens, offering a promising avenue for enhancing the efficacy of cervical cancer treatment and patient outcomes.

Rho GTPases are signaling macromolecules that are associated with developmental progression and pathogenesis of Fusarium graminearum. Generally, enzymatic activities of Rho GTPases are regulated by Rho GTPase guanine nucleotide exchange factors (RhoGEFs). In this study, we identified a putative RhoGEF encoding gene (FgBUD3) in F. graminearum database and proceeded further by using a functional genetic approach to generate FgBUD3 targeted gene deletion mutant. Phenotypic analysis results showed that the deletion of FgBUD3 caused severe reduction in growth of FgBUD3 mutant generated during this study. We also observed that the deletion of FgBUD3 completely abolished sexual reproduction and triggered the production of abnormal asexual spores with nearly no septum in ΔFgbud3 strain. Further results obtained from infection assays conducted during this research revealed that the FgBUD3 defective mutant lost its pathogenicity on wheat and hence, suggests FgBud3 plays an essential role in the pathogenicity of F. graminearum. Additional, results derived from yeast two-hybrid assays revealed that FgBud3 strongly interacted with FgRho4 compared to the interaction with FgRho2, FgRho3, and FgCdc42. Moreover, we found that FgBud3 interacted with both GTP-bound and GDP-bound form of FgRho4. From these results, we subsequently concluded that, the Rho4-interacting GEF protein FgBud3 crucially promotes vegetative growth, asexual and sexual development, cell division and pathogenicity in F. graminearum.