Adjuvant radiotherapy shows benefit in selected stage I uterine sarcoma: A risk scoring system based on a population analysis.

BACKGROUND: The potential therapeutic benefit of adjuvant radiotherapy for patients with stage I uterine sarcoma has not been clear. In this study, we aimed to develop a risk scoring model to select the subgroup of patients with stage I uterine sarcoma who might benefit from adjuvant radiotherapy. METHODS: Patients with stage I uterine sarcoma from the Surveillance, Epidemiology, and End Results program from 2010 to 2014 were retrospectively included in this analysis. Cox proportional hazards models were performed to identify risk factors. RESULTS: A total of 947 stage I uterine sarcoma patients were included. The 5-year disease-specific survival (DSS) of the overall cohort was 75.81%. Multivariate analysis identified stage (p = 0.013), tumor grade (p <0.001) and histology (p = 0.043) as independent prognostic factors for DSS, and these factors were used to generate the risk scoring model. The low-risk group presented a better DSS than the high-risk group (95.51% vs. 49.88%, p < 0.001). The addition of radiotherapy to surgery significantly increased the DSS in the high-risk group compared with surgery alone (78.06% vs. 46.88%, p = 0.022), but no significant survival benefit was observed in the low-risk group (98.36% vs. 100%, p = 0.766). CONCLUSIONS: Our risk scoring model based on stage, tumor grade, and histology predicted the outcome of patients with stage I uterine sarcoma cancer. This system may help to select stage I uterine sarcoma cancer patients who might benefit from adjuvant radiotherapy.

A Strategy for the Effective Optimization of Pharmaceutical Formulations Based on Parameter-Optimized Support Vector Machine Model.

Engineering pharmaceutical formulations is governed by a number of variables, and the finding of the optimal preparation is intricately linked to the exploration of a multiparametric space through a variety of optimization tasks. As a result, making such optimization activities simpler is a significant undertaking. For the purposes of this study, we suggested a prediction model that was based on least square support vector machine (LSSVM) and whose parameters were optimized using the particle swarm optimization algorithm (PSO-LSSVM model). Other in silico optimization methods were used and compared, including the LSSVM and the back propagation (BP) neural networks algorithm. PSO-LSSVM demonstrated the highest performance on the test dataset, with the lowest mean square error. In addition, two dosage forms, quercetin solid dispersion and apigenin nanoparticles, were selected as model formulations due to the wide range of formulation compositions and manufacturing factors used in their production. Three different models were used to predict the ideal formulations of two different dosage forms, and in real world, the Taguchi orthogonal design arrays were used to optimize the formulations of each dosage form. It is clear that the predicted performance of two formulations using PSO-LSSVM was both consistent with the outcomes of the Taguchi orthogonal planned experiment, demonstrating the model's good reliability and high usefulness. Together, our PSO-LSSVM prediction model has the potential to accurately predict the best possible formulations, reduce the reliance on experimental effort, accelerate the process of formulation design, and provide a low-cost solution to drug preparation optimization.

Prognostic factors and treatment comparison in small cell neuroendocrine carcinoma of the uterine cervix based on population analyses.

OBJECTIVE: We aimed to assess the impact of the treatment modality on the outcome of small cell neuroendocrine cervical carcinoma (SCNEC) using the Surveillance Epidemiology and End Results (SEER) database. METHODS: Patients from the SEER program between 1981 and 2014 were identified. Significant factors for cancer-specific survival (CSS) and overall survival (OS) were analyzed using the Kaplan-Meier survival and Cox regression methods. RESULTS: A total of 503 SCNEC patients were identified. The 5-year CSS and OS were 36.6% and 30.6%, respectively. The International Federation of Gynecology and Obstetrics (FIGO) stage I to IV distributions was 189 (37.6%), 108 (21.5%), 95 (18.9%), and 111 patients (22.0%), respectively. Within the patients with known treatment strategies, 177 (45.9%) were treated with radical surgery and 209 (54.1%) underwent primary radiotherapy. Local treatment strategies were independent prognostic factor for CSS and OS. The 5-year CSS for radical surgery and primary radiotherapy was 50.0% and 27.9%, respectively (P < .001). The 5-year OS for those who received radical surgery and primary radiotherapy was 57.8%, and 29.6%, respectively (P < .001). In FIGO stage I SCNEC, patients treated with radical surgery had superior CSS (P = .001) and OS (P = .003) than those with primary radiotherapy. However, in FIGO stage II and III SCNEC, there were no differences in CSS and OS with respect to different local treatment strategies. Our results also found that the addition of brachytherapy impacted OS in the FIGO stage III SENCE (P = .002). The 5-year CSS and OS of patients with FIGO IV were only 11.7% and 7.1%, respectively. CONCLUSIONS: SCNEC is a rare disease with aggressive clinical behavior. The findings indicate that radical surgery should be suggested for early-stage SCNEC and combining radiation therapy with brachytherapy should be suitable for patients with advanced stage.

Gene Expression Data Based Deep Learning Model for Accurate Prediction of Drug-Induced Liver Injury in Advance.

Drug-induced liver injury (DILI), one of the most common adverse effects, leads to drug development failure or withdrawal from the market in most cases, showing an emerging challenge that is to accurately predict DILI in the early stage. Recently, the vast amount of gene expression data provides us valuable information for distinguishing DILI on a genomic scale. Moreover, the deep learning algorithm is a powerful strategy to automatically learn important features from raw and noisy data and shows great success in the field of medical diagnosis. In this study, a gene expression data based deep learning model was developed to predict DILI in advance by using gene expression data associated with DILI collected from ArrayExpress and then optimized by feature gene selection and parameters optimization. In addition, the previous machine learning algorithm support vector machine (SVM) was also used to construct another prediction model based on the same data sets, comparing the model performance with the optimal DL model. Finally, the evaluation test using 198 randomly selected samples showed that the optimal DL model achieved 97.1% accuracy, 97.4% sensitivity, 96.8% specificity, 0.942 matthews correlation coefficient, and 0.989 area under the ROC curve, while the performance of SVM model only reached 88.9% accuracy, 78.8% sensitivity, 99.0% specificity, 0.794 matthews correlation coefficient, and 0.901 area under the ROC curve. Furthermore, external data sets verification and animal experiments were conducted to assess the optimal DL model performance. Finally, the predicted results of the optimal DL model were almost consistent with experiment results. These results indicated that our gene expression data based deep learning model could systematically and accurately predict DILI in advance. It could be a useful tool to provide safety information for drug discovery and clinical rational drug use in early stage and become an important part of drug safety assessment.

Preparation and optimization of poly (lactic acid) nanoparticles loaded with fisetin to improve anti-cancer therapy.

Fisetin is a natural flavonoid with promising antitumor activity, whereas its clinical application is limited by its hydrophobic property. In this study, we aimed to load fisetin into poly(lactic acid) (PLA) nanoparticles to increase fisetin's solubility and therapeutic efficacy. Based on spontaneous emulsification solvent diffusion (SESD) method, the formulation of PLA nanoparticles was optimized by two successive experimental designs. One-factor-at-a-time variation experiments were first applied to investigate the effects of four process variables on three responses, including drug encapsulation efficiency, average particles size and cumulative drug release ratio, followed by determining the possible ranges of these variables. Subsequently, the combinations of four variables at best levels were evaluated using a Taguchi orthogonal array design with regard to the same three responses. Eventually, the nanoparticle prepared by optimized procedure showed a narrow size distribution around 226.85 ±â€¯4.78 nm with a high encapsulation efficiency of 90.35%. The incorporation of fisetin in nanoparticles was subsequently confirmed by FT-IR and DSC spectroscopy. Furthermore, cytotoxicity assay against HCT116 colon cancer cells in vitro and antitumor test in a xenograft 4T1 breast cancer model in vivo demonstrated that the antitumor effect of drug-loaded nanoparticles was superior to that of free drug solution.

Corilagin Alleviates Nonalcoholic Fatty Liver Disease in High-Fat Diet-Induced C57BL/6 Mice by Ameliorating Oxidative Stress and Restoring Autophagic Flux.

Corilagin (Cori) possesses multiple biological activities. To determine whether Cori can exert protective effects against nonalcoholic fatty liver disease (NAFLD) and its potential mechanisms. C57BL/6 mice were fed with high-fat diet (HFD) alone or in combination with Cori (20 mg/kg, i.p.) and AML12 cells were exposed to 200 µM PA/OA with or without Cori (10 µM or 20 µM). Phenotypes and key indicators relevant to NAFLD were examined both in vivo and in vitro. In this study, Cori significantly ameliorated hepatic steatosis, confirmed by improved serum lipid profiles, and hepatic TC, TG contents, and the gene expression related to lipid metabolism in livers of HFD mice. Moreover, Cori attenuated HFD-mediated autophagy (including mitophagy) blockage by restoring autophagic flux, evidenced by increased number of autophagic double vesicles containing mitochondria, elevated LC3II protein levels, decreased p62 protein levels, as well as enhanced colocalization of autophagy-related protein (LC3, Parkin) and mitochondria. In accordance with this, Cori also reduced the accumulation of ROS and MDA levels, and enhanced the activities of antioxidative enzymes including SOD, GSH-Px, and CAT. In addition, Cori treatment improved mitochondrial dysfunction, evidenced by increased mitochondrial membrane potential (ΔΨm). In parallel with this, Cori decreased mitochondrial DNA oxidative damage, while increased mitochondrial biogenesis related transcription factors expression, mitochondrial DNA content and oxygen consumption rate (OCR). In conclusion, these results demonstrate that Cori is a potential candidate for the treatment of NAFLD via diminishing oxidative stress, restoring autophagic flux, as well as improving mitochondrial functions.

Curcumin suppresses Notch­1 signaling: Improvements in fatty liver and insulin resistance in rats.

Curcumin is a well­known phenolic substance and has many pharmacological effects associated with metabolism. However, the exact molecular mechanisms underlying this process have yet to be determined. The Notch pathway is a signal transduction pathway involved in energy metabolism. The present study aimed to investigate the effects of curcumin administration on glucose­lipid metabolism in rats subjected to a high fat diet, and investigate changes in Notch­1 signaling. Sprague­Dawley rats (n=40) were randomly divided into four groups (10 rats/group): Control diet group, high fat diet group, high fat diet plus curcumin low dose group and high fat diet plus curcumin high dose group. Following 8 weeks of treatment with curcumin (100 mg/kg in the low dose group and 200 mg/kg in the high dose group), serum metabolic markers and hepatic gene expression patterns were investigated. No differences in body weight following 8 weeks of curcumin administration (P>0.05) were observed; however, curcumin treatment did reduce visceral fat levels (peri­epididymal and peri­renal), and decreased cholesterol, triglyceride and low­density lipoprotein levels in serum compared with the high fat diet rats that did not receive curcumin (P<0.05, P<0.01). An oral glucose tolerance test and an intraperitoneal insulin tolerance test revealed that insulin resistance was reduced (P<0.05 or P<0.01) and tissue section analysis revealed that hepatosteatosis was attenuated following treatment with curcumin. Furthermore, the protein expression of Notch­1 and its downstream target Hes­1 were suppressed. These effects were also in parallel with an upregulation of fatty acid oxidation­associated gene expression, including peroxisome proliferator­activated receptor (PPAR)­α, carnitine palmitoyltransferase 1 and PPAR­Î³ (P<0.05). In addition, curcumin administration led to a downregulation in the expression of lipogenic genes, including sterol regulatory element­binding protein, fatty acid synthase and acetyl­CoA carboxylase (P<0.05). The expression of inflammation­associated genes, including nuclear factor­κB, tumor necrosis factor­α and prostaglandin­endoperoxide synthase 2 were also suppressed. The results of the present study suggest that the hepatic Notch­1 pathway can be suppressed via curcumin treatment, which may ameliorate fatty liver and insulin resistance in rats subjected to a high fat diet.