Evaluation of the anti-cancer efficacy of lipid nanoparticles containing siRNA against HPV16 E6/E7 combined with cisplatin in a xenograft model of cervical cancer.

OBJECTIVE: To investigate the anti-cancer efficacy of ENB101-LNP, an ionizable lipid nanoparticles (LNPs) encapsulating siRNA against E6/E7 of HPV 16, in combination therapy with cisplatin in cervical cancer in vitro and in vivo. METHODS: CaSki cells were treated with ENB101-LNP, cisplatin, or combination. Cell viability assessed the cytotoxicity of the treatment. HPV16 E6/E7 gene knockdown was verified with RT-PCR both in vitro and in vivo. HLA class I and PD-L1 were checked by flow cytometry. A xenograft model was made using CaSki cells in BALB/c nude mice. To evaluate anticancer efficacy, mice were grouped. ENB101-LNP was given three times weekly for 3 weeks intravenously, and cisplatin was given once weekly intraperitoneally. Tumor growth was monitored. On day 25, mice were euthanized; tumors were collected, weighed, and imaged. Tumor samples were analyzed through histopathology, immunostaining, and western blot. RESULTS: ENB101-LNP and cisplatin synergistically inhibit CaSki cell growth. The combination reduces HPV 16 E6/E7 mRNA and boosts p21 mRNA, p53, p21, and HLA class I proteins. In mice, the treatment significantly blocked tumor growth and promoted apoptosis. Tumor inhibition rates were 29.7% (1 mpk ENB101-LNP), 29.6% (3 mpk), 34.0% (cisplatin), 47.0% (1 mpk ENB101-LNP-cisplatin), and 68.8% (3 mpk ENB101-LNP-cisplatin). RT-PCR confirmed up to 80% knockdown of HPV16 E6/E7 in the ENB101-LNP groups. Immunohistochemistry revealed increased p53, p21, and HLA-A expression with ENB101-LNP treatments, alone or combined. CONCLUSION: The combination of ENB101-LNP, which inhibits E6/E7 of HPV 16, with cisplatin, demonstrated significant anticancer activity in the xenograft mouse model of cervical cancer.

Adaptive Data Selection-Based Machine Learning Algorithm for Prediction of Component Obsolescence.

Product obsolescence occurs in the manufacturing industry as new products with better performance or improved cost-effectiveness are developed. A proactive strategy for predicting component obsolescence can reduce manufacturing losses and lead to customer satisfaction. In this study, we propose a machine learning algorithm for a proactive strategy based on an adaptive data selection method to forecast the obsolescence of electronic diodes. Typical machine learning algorithms construct a single model for a dataset. By contrast, the proposed algorithm first determines a mathematical cover of the dataset via unsupervised clustering and subsequently constructs multiple models, each of which is trained with the data in one cover. For each data point in the test dataset, an optimal model is selected for regression. Results of empirical experiments show that the proposed method improves the obsolescence prediction accuracy and accelerates the training procedure. A novelty of this study is that it demonstrates the effectiveness of unsupervised clustering methods for improving supervised regression algorithms.

Forecasting Obsolescence of Components by Using a Clustering-Based Hybrid Machine-Learning Algorithm.

Product obsolescence occurs in every production line in the industry as better-performance or cost-effective products become available. A proactive strategy for obsolescence allows firms to prepare for such events and reduces the manufacturing loss, which eventually leads to positive customer satisfaction. We propose a machine learning-based algorithm to forecast the obsolescence date of electronic diodes, which has a limitation on the amount of data available. The proposed algorithm overcomes these limitations in two ways. First, an unsupervised clustering algorithm is applied to group the data based on their similarity and build independent machine-learning models specialized for each group. Second, a hybrid method including several reliable techniques is constructed to improve the prediction accuracy and overcome the limitation of the lack of data. It is empirically confirmed that the prediction accuracy of the obsolescence date for the electrical component data is improved through the proposed clustering-based hybrid method.

Fulvestrant-3-Boronic Acid (ZB716) Demonstrates Oral Bioavailability and Favorable Pharmacokinetic Profile in Preclinical ADME Studies.

Fulvestrant-3-boronic acid (ZB716), an oral selective estrogen receptor degrader (SERD) under clinical development, has been investigated in ADME studies to characterize its absorption, metabolism, and pharmacokinetics. ZB716 was found to have high plasma protein binding in human and animal plasma, and low intestinal mucosal permeability. ZB716 had high clearance in hepatocytes of all species tested. ZB716 was metabolized primarily by CYP2D6 and CYP3A. In human liver microsomes, ZB716 demonstrated relatively low inhibition of CYP1A2, 2C8, 2C9, 2C19, 2D6, and 3A4 (when testosterone was used as the substrate), and no inhibition of CYP2B6 and 3A4 (when midazolam was used as the substrate). In assays for enzyme activity, ZB716 induced CYP1A2, 2B6, and 3A4 in a concentration-dependent manner. Single-dose and repeated-dose pharmacokinetic studies in rats and dogs showed oral bioavailability, dose-proportional drug exposure, and drug accumulation as measured by maximum concentration and area under the concentration-time curve (AUC).

Two-Stage Latent Dynamics Modeling and Filtering for Characterizing Individual Walking and Running Patterns with Smartphone Sensors.

Recently, data from built-in sensors in smartphones have been readily available, and analyzing data for various types of health information from smartphone users has become a popular health care application area. Among relevant issues in the area, one of the most prominent topics is analyzing the characteristics of human movements. In this paper, we focus on characterizing the human movements of walking and running based on a novel machine learning approach. Since walking and running are human fundamental activities, analyzing their characteristics promptly and automatically during daily smartphone use is particularly valuable. In this paper, we propose a machine learning approach, referred to as 'two-stage latent dynamics modeling and filtering' (TS-LDMF) method, where we combine a latent space modeling stage with a nonlinear filtering stage, for characterizing individual dynamic walking and running patterns by analyzing smartphone sensor data. For the task of characterizing movements, the proposed method makes use of encoding the high-dimensional sequential data from movements into random variables in a low-dimensional latent space. The use of random variables in the latent space, often called latent variables, is particularly useful, because it is capable of conveying compressed information concerning movements and efficiently handling the uncertainty originating from high-dimensional sequential observation. Our experimental results show that the proposed use of two-stage latent dynamics modeling and filtering yields promising results for characterizing individual dynamic walking and running patterns.