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Background: Our goal is to develop a nomogram model to predict overall survival (OS) for elderly esophageal squamous cell carcinoma (ESCC) patients receiving definitive radiotherapy (RT) or concurrent chemoradiotherapy (CRT), aiding clinicians in personalized treatment planning with a risk stratification system. Methods: A retrospective study was conducted on 718 elderly ESCC patients treated with RT or CRT at 10 medical centers (3JECROG) from January 2004 to November 2016. We identified independent prognostic factors using univariate and multifactorial Cox regression to construct a nomogram model. Its effectiveness was evaluated using concordance statistics (C-index), area under the curve (AUC), net reclassification index (NRI), and integrated discrimination improvement (IDI), and compared against the AJCC staging. Additionally, decision curve analysis (DCA) assessed the model's clinical benefit. Patients were stratified into low, intermediate, and high-risk groups using the nomogram, and their prognoses in various disease stages were analyzed. Results: Significant prognostic factors identified included diabetes, tumor volume (GTVp), tumor length, location, and clinical stages (T, N, M), and RT response. Multivariate analysis confirmed these as independent factors for OS. The nomogram outperformed AJCC staging in prediction accuracy and discrimination, evidenced by a higher C-index, better AUC, and significant NRI and IDI values. Patients categorized by the nomogram demonstrated distinct 5-year OS rates, with a higher C-index than AJCC staging (0.597 vs. 0.562). Conclusions: The study identified key prognostic factors for elderly ESCC patients receiving RT or CRT. The nomogram model, based on these factors, showed enhanced prediction performance, discrimination, and clinical utility compared to AJCC staging. This risk stratification provided more accurate survival predictions and aided in personalized risk management. [ABSTRACT FROM AUTHOR]

Radiation-induced skin toxicity, resulting from ionizing or nonionizing radiation, is a common skin disorder. However, the underlying relationship between skin microbiota and radiation-induced skin toxicity remains largely unexplored. Herein, we uncover the microbiota–skin interaction based on a genome-wide association study (GWAS) featuring 150 skin microbiota and three types of skin microenvironment. Summary datasets of human skin microbiota were extracted from the GWAS catalog database, and summary datasets of radiation-induced skin toxicity from the FinnGen biobank. Mendelian Randomization (MR) analysis was leveraged to sort out the causal link between skin microbiota and radiation-induced skin toxicity. We identified 33 causal connections between human skin microbiota and radiation-induced skin toxicity, including 19 positive and 14 negative causative directions. Among these potential associations, the genus Staphylococcus could serve as a common risk factor for radiation-induced skin toxicity, especially for radiodermatitis. And Streptococcus salivarius was identified as a potential protective factor against radiation-induced skin toxicity. Additional analysis indicated no pleiotropy, heterogeneity, or reverse causal relationship in the results. We comprehensively assessed potential associations of skin microbiota with radiation-induced skin toxicity and identified several suggestive links. Our results provide promising targets for the prevention and treatment of radiation-induced skin toxicity. [ABSTRACT FROM AUTHOR]

Objective: The objective of this study is to develop a machine learning model integrating clinical characteristics with radiomics and dosiomics data, aiming to assess their predictive utility in anticipating grade 2 or higher BMS occurrences in cervical cancer patients undergoing radiotherapy. Methods: A retrospective analysis was conducted on the clinical data, planning CT images, and radiotherapy planning documents of 106 cervical cancer patients who underwent radiotherapy at our hospital. The patients were randomly divided into training set and test set in an 8:2 ratio. The radiomic features and dosiomic features were extracted from the pelvic bone marrow (PBM) of planning CT images and radiotherapy planning documents, and the least absolute shrinkage and selection operator (LASSO) algorithm was employed to identify the best predictive characteristics. Subsequently, the dosiomic score (D-score) and the radiomic score (R-score) was calculated. Clinical predictors were identified through both univariate and multivariate logistic regression analysis. Predictive models were constructed by intergrating clinical predictors with DVH parameters, combining DVH parameters and R-score with clinical predictors, and amalgamating clinical predictors with both D-score and R-score. The predictive model's efficacy was assessed by plotting the receiver operating characteristic (ROC) curve and evaluating its performance through the area under the ROC curve (AUC), the calibration curve, and decision curve analysis (DCA). Results: Seven radiomic features and eight dosiomic features exhibited a strong correlation with the occurrence of BMS. Through univariate and multivariate logistic regression analyses, age, planning target volume (PTV) size and chemotherapy were identified as clinical predictors. The AUC values for the training and test sets were 0.751 and 0.743, respectively, surpassing those of clinical DVH R-score model (AUC=0.707 and 0.679) and clinical DVH model (AUC=0.650 and 0.638). Furthermore, the analysis of both the calibration and the DCA suggested that the combined model provided superior calibration and demonstrated a higher net clinical benefit. Conclusion: The combined model is of high diagnostic value in predicting the occurrence of BMS in patients with cervical cancer during radiotherapy. [ABSTRACT FROM AUTHOR]

Endophytes of plants have a wide range of strains comprising important sources of various bioactive metabolites. An endophytic fungus was isolated from honeysuckle, an important Chinese medicinal plant. The phylogenetic and physiological characterization indicated that the isolated strain JY2corresponded to Fusarium sp. The culture filtrate of JY2displayed antagonism activity against some pathogenic bacteria owing to the existence of antimicrobial compounds. The filtrate revealed the strongest in vitro antimicrobial activity on Pseudomonas aeruginosa by increasing the permeability of cell membranes. The antibacterial extract was fractionated and purifiedusing silica gel chromatography. Five different bioactive compounds were isolated by bioactivity-guided fractionation from the culture extracts of JY2, and preliminarily identified by HPLC-MS spectral data. These results suggest that Fusarium sp. features a potentially remarkable antimicrobial activity and could be valuable to discover the new drugs or agents for antimicrobial purposes.