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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

Recent studies have identified that metabolic reprogramming in lung adenocarcinoma cells, particularly lactate metabolism disorders, plays a crucial role in tumor development and immune therapy response. The accumulation of lactic acid not only provides energy support for the proliferation of tumor cells but also affects the function of immune cells by changing the tumor microenvironment, thereby promoting immune escape. Immunotherapy, especially the application of immune checkpoint inhibitors, has become an important strategy for treating lung adenocarcinoma. However, lactate metabolism disorders might affect the efficacy of immunotherapy, leading to resistance in some patients. Therefore, a thorough understanding of the mechanisms of lactic acid metabolism in lung adenocarcinoma and its impact on the response to immunotherapy is essential for developing new therapeutic strategies and improving the efficacy of immunotherapy. This review summarizes the role of lactate metabolism disorders in the development and immunotherapy of lung adenocarcinoma, discusses the potential role of lactic acid metabolism-related genes and pathways in lung adenocarcinoma, and explores the progress in therapeutic strategies targeting lactic acid metabolism regulation. This work aims to provide new insights for the treatment of lung adenocarcinoma.

Cervical cancer is the second most prevalent malignancy affecting women’s health globally, and the number of morbidity and mortality from cervical cancer continues to rise worldwide. The 5-year survival rate of patients with recurrent or metastatic cervical cancer is significantly reduced, and existing treatment modalities have low efficacy and high adverse effects, so there is a strong need for new, effective, and well-tolerated therapies. Antibody-drug conjugates (ADCs) are a new targeted therapeutic modality that can efficiently kill tumor cells. This review aims to summarize the composition, research, and development history and mechanism of action of ADCs, to review the research progress of ADCs in the treatment of cervical cancer, and to summarize and prospect the application of ADCs.

Huanglongbing (HLB) is a devastating citrus disease worldwide. A three-pronged approach to controlling HLB has been suggested, namely, removal of HLB-symptomatic trees, psyllid control, and replacement with HLB-free trees. However, such a strategy did not lead to successful HLB control in many citrus-producing regions, such as Florida. We hypothesize that this is because of the small-scale or incomprehensive implementation of the program; conversely, a comprehensive implementation of such a strategy at the regional level can successfully control HLB. To test our hypothesis, we investigated the effects of region-wide comprehensive implementation of this scheme to control HLB in Gannan region, China, with a total planted citrus acreage of over 110,000 ha from 2013 to 2019. With the region-wide implementation of comprehensive HLB management, the overall HLB incidence in Gannan decreased from 19.71% in 2014 to 3.86% in 2019. A partial implementation of such a program (without a comprehensive inoculum removal) at the regional level in Brazil resulted in HLB incidence increasing from 1.89% in 2010 to 19.02% in 2019. Using dynamic regression model analyses with data from both Brazil and China, we constructed a model to predict HLB incidence when all three components were applied at 100%. It was predicated that in a region-wide comprehensive implementation of such a program, HLB incidence would be controlled to a level of less than 1%. We conducted economic feasibility analyses and showed that average net profits were positive for groves that implemented the comprehensive strategy, but groves that did not implement it had negative net profits over a 10-year period. Overall, the key for the three-pronged program to successfully control HLB is the large scale (region-wide) and comprehensiveness in implementation. This study provides valuable information to control HLB and other economically important endemic diseases worldwide. [ABSTRACT FROM AUTHOR]