Preoperative prediction of cervical cancer survival using a high-resolution MRI-based radiomics nomogram.

BACKGROUND: Cervical cancer patients receiving radiotherapy and chemotherapy require accurate survival prediction methods. The objective of this study was to develop a prognostic analysis model based on a radiomics score to predict overall survival (OS) in cervical cancer patients. METHODS: Predictive models were developed using data from 62 cervical cancer patients who underwent radical hysterectomy between June 2020 and June 2021. Radiological features were extracted from T2-weighted (T2W), T1-weighted (T1W), and diffusion-weighted (DW) magnetic resonance images prior to treatment. We obtained the radiomics score (rad-score) using least absolute shrinkage and selection operator (LASSO) regression and Cox's proportional hazard model. We divided the patients into low- and high-risk groups according to the critical rad-score value, and generated a nomogram incorporating radiological features. We evaluated the model's prediction performance using area under the receiver operating characteristic (ROC) curve (AUC) and classified the participants into high- and low-risk groups based on radiological characteristics. RESULTS: The 62 patients were divided into high-risk (n = 43) and low-risk (n = 19) groups based on the rad-score. Four feature parameters were selected via dimensionality reduction, and the scores were calculated after modeling. The AUC values of ROC curves for prediction of 3- and 5-year OS using the model were 0.84 and 0.93, respectively. CONCLUSION: Our nomogram incorporating a combination of radiological features demonstrated good performance in predicting cervical cancer OS. This study highlights the potential of radiomics analysis in improving survival prediction for cervical cancer patients. However, further studies on a larger scale and external validation cohorts are necessary to validate its potential clinical utility.

Natural Biologics Accelerate Healing of Diabetic Foot Ulcers by Regulating Oxidative Stress.

Difficult or even non-healing diabetic foot ulcers (DFU) are a global medical challenge. Although current treatments such as debridement, offloading, and infection control have resulted in partial improvement in DFU, the incidence, amputation, and mortality rates of DFU remain high. Therefore, there is an urgent need to find new or more effective drugs. Numerous studies have shown that oxidative stress plays an important role in the pathophysiology of DFU. The nuclear factor erythroid 2-related factor (Nrf2) signaling pathway and the advanced glycated end products (AGEs)-receptor for advanced glycation endproducts (RAGE), protein kinase C (PKC), polyol and hexosamine biochemical pathways play critical roles in the regulation of oxidative stress in the body. Targeting these pathways to restore redox balance can control and alleviate the occurrence and development of DFU. Natural biologics are a major source of potential drugs for these relevant targets, and their antioxidant potential has been extensively demonstrated. Here, we discussed the pathophysiological mechanism of oxidative stress in DFU, and identifiled natural biologics targeting these pathways to accelerate DFU healing, in order to provide a new or potential direction for clinical treatment, nursing and related basic research of DFU.

Development of a novel combined nomogram model integrating Rad-score, age and ECOG to predict the survival of patients with hepatocellular carcinoma treated by transcatheter arterial chemoembolization.

Background: Liver cancer is affecting more and more people's health. Transcatheter arterial chemoembolization (TACE) has become a routine treatment option, but the prognosis of patients is not optimistic. Effectively prediction of prognosis can provide clinicians with an objective basis for patient prognosis and timely adjustment of treatment strategies, thus improving the quality of patient survival. However, the current prediction methods have some limitations. Therefore, this study aims to develop a radiomics nomogram for predicting survival after TACE in patients with advanced hepatocellular carcinoma (HCC). Methods: Seventy advanced HCC patients treated with TACE were enrolled from January 2013 to July 2019. Clinical information included age, sex, and Eastern Cooperative Oncology Group (ECOG) score. Overall survival (OS) was confirmed by postoperative follow-up. Radiomics features were extracted using 3D Slicer (version 4.11.20210226) software, then obtain radiomics signature and calculate radiomics score (Rad-score) for each patient. Univariate and multivariate Cox regression were used to analyze the baseline clinical data of patients and establish clinical models. The obtained radiomics signature was incorporated into the clinical model to establish the radiomics nomogram. The predictive performance and calibration ability of the model were assessed by the area under the receiver operating characteristic (ROC) curve (AUC), C-index, and calibration curve. Results: Three significant features were selected from 851 radiomics features by the least absolute shrinkage and selection operator (LASSO) Cox regression model to construct the radiomics signature, and were significantly correlated with overall survival (P<0.001). Rad-score, age, and ECOG score were combined to construct a radiomics nomogram. The AUC, sensitivity, and specificity of the radiomics nomogram were 0.801 (95% CI: 0.693-0.909), 0.822 (95% CI: 0.674-0.915), and 0.720 (95% CI: 0.674-0.915), respectively. The C-index of the radiomics nomogram was 0.700 (95% CI: 0.547-0.853). Calibration curves showed better agreement between the predicted and actual probabilities in the radiomics nomogram among the 3 features. Conclusions: The Rad-score was a strong risk predictor of survival after TACE for HCC patients. The radiomics nomogram might be improved the predictive efficacy of survival after TACE and it may also provide assistance to physicians in making treatment decisions.

Digitoxin inhibits HeLa cell growth through the induction of G2/M cell cycle arrest and apoptosis in vitro and in vivo.

Cervical cancer is the fourth most common gynecological malignancy affecting the health of women worldwide and the second most common cause of cancer­related mortality among women in developing regions. Thus, the development of effective chemotherapeutic drugs for the treatment of cervical cancer has become an important issue in the medical field. The application of natural products for the prevention and treatment of various diseases, particularly cancer, has always attracted widespread attention. In the present study, a library of natural products composed of 78 single compounds was screened and it was found that digitoxin exhibited the highest cytotoxicity against HeLa cervical cancer cells with an IC50 value of 28 nM at 48 h. Furthermore, digitoxin exhibited extensive antitumor activities in a variety of malignant cell lines, including the lung cancer cell line, A549, the hepatoma cell line, MHCC97H, and the colon cancer cell line, HCT116. Mechanistically, digitoxin caused DNA double­stranded breaks (DSBs), inhibited the cell cycle at the G2/M phase via the ataxia telangiectasia mutated serine/threonine kinase (ATM)/ATM and Rad3­related serine/threonine kinase (ATR)­checkpoint kinase (CHK1)/checkpoint kinase 2 (CHK2)­Cdc25C pathway and ultimately triggered mitochondrial apoptosis, which was characterized by the disruption of Bax/Bcl­2, the release of cytochrome c and the sequential activation of caspases and poly(ADP­ribose) polymerase (PARP). In addition, the in vivo anticancer effect of digitoxin was confirmed in HeLa cell xenotransplantation models. On the whole, the findings of the present study demonstrate the efficacy of digitoxin against cervical cancer in vivo and elucidate its molecular mechanisms, including DSBs, cell cycle arrest and mitochondrial apoptosis. These results will contribute to the development of digitoxin as a chemotherapeutic agent in the treatment of cervical cancer.