Rapid inverse design of metamaterials based on prescribed mechanical behavior through machine learning.

Designing and printing metamaterials with customizable architectures enables the realization of unprecedented mechanical behaviors that transcend those of their constituent materials. These behaviors are recorded in the form of response curves, with stress-strain curves describing their quasi-static footprint. However, existing inverse design approaches are yet matured to capture the full desired behaviors due to challenges stemmed from multiple design objectives, nonlinear behavior, and process-dependent manufacturing errors. Here, we report a rapid inverse design methodology, leveraging generative machine learning and desktop additive manufacturing, which enables the creation of nearly all possible uniaxial compressive stress‒strain curve cases while accounting for process-dependent errors from printing. Results show that mechanical behavior with full tailorability can be achieved with nearly 90% fidelity between target and experimentally measured results. Our approach represents a starting point to inverse design materials that meet prescribed yet complex behaviors and potentially bypasses iterative design-manufacturing cycles.

Enabling Chemo-Immunotherapy with HIFU in Canine Cancer Patients.

High intensity focused ultrasound (HIFU) is a promising non-invasive technique for treating solid tumors using thermal and histotripsy-based mechanical ablation. However, its clinical significance in different tumor types is not fully understood. To assess its therapeutic efficacy and immunomodulatory properties, we compared HIFU thermal ablation and histotripsy ablation in dogs with spontaneous tumors. We also evaluated the ability of non-ablative HIFU-based mild hyperthermia (40-45 ºC) to improve Doxorubicin delivery and immunomodulation. Our results showed that HIFU thermal ablation induced tumor remission in the majority of treated patients over 60 days, while histotripsy achieved partial response to stable disease persistence. The adverse effects of thermal ablation were minor to moderate, while histotripsy exposures were relatively well-tolerated. Furthermore, we observed a correlation between HIFU-therapeutic response and serum anti-tumor cytokine profiles and the presence of functionally active cytotoxic immune cells in patients. Similarly, Doxorubicin-treated patients showed improved drug delivery, efficacy, and anti-tumor immune responses with HIFU hyperthermia. In conclusion, our study demonstrates that depending on the tumor type and treatment parameters, HIFU treatments can enable tumor growth control, immune activation, and chemotherapy in veterinary patient. These findings have significant clinical implications and highlight the potential of HIFU as a promising cancer treatment approach.

A self-inspected adaptive SMOTE algorithm (SASMOTE) for highly imbalanced data classification in healthcare.

In many healthcare applications, datasets for classification may be highly imbalanced due to the rare occurrence of target events such as disease onset. The SMOTE (Synthetic Minority Over-sampling Technique) algorithm has been developed as an effective resampling method for imbalanced data classification by oversampling samples from the minority class. However, samples generated by SMOTE may be ambiguous, low-quality and non-separable with the majority class. To enhance the quality of generated samples, we proposed a novel self-inspected adaptive SMOTE (SASMOTE) model that leverages an adaptive nearest neighborhood selection algorithm to identify the "visible" nearest neighbors, which are used to generate samples likely to fall into the minority class. To further enhance the quality of the generated samples, an uncertainty elimination via self-inspection approach is introduced in the proposed SASMOTE model. Its objective is to filter out the generated samples that are highly uncertain and inseparable with the majority class. The effectiveness of the proposed algorithm is compared with existing SMOTE-based algorithms and demonstrated through two real-world case studies in healthcare, including risk gene discovery and fatal congenital heart disease prediction. By generating the higher quality synthetic samples, the proposed algorithm is able to help achieve better prediction performance (in terms of F1 score) on average compared to the other methods, which is promising to enhance the usability of machine learning models on highly imbalanced healthcare data.

Characteristics of circulating small noncoding RNAs in plasma and serum during human aging.

Objective: Aging is a complicated process that triggers age-related disease susceptibility through intercellular communication in the microenvironment. While the classic secretome of senescence-associated secretory phenotype (SASP) including soluble factors, growth factors, and extracellular matrix remodeling enzymes are known to impact tissue homeostasis during the aging process, the effects of novel SASP components, extracellular small noncoding RNAs (sncRNAs), on human aging are not well established. Methods: Here, by utilizing 446 small RNA-seq samples from plasma and serum of healthy donors found in the Extracellular RNA (exRNA) Atlas data repository, we correlated linear and nonlinear features between circulating sncRNAs expression and age by the maximal information coefficient (MIC) relationship determination. Age predictors were generated by ensemble machine learning methods (Adaptive Boosting, Gradient Boosting, and Random Forest) and core age-related sncRNAs were determined through weighted coefficients in machine learning models. Functional investigation was performed via target prediction of age-related miRNAs. Results: We observed the number of highly expressed transfer RNAs (tRNAs) and microRNAs (miRNAs) showed positive and negative associations with age respectively. Two-variable (sncRNA expression and individual age) relationships were detected by MIC and sncRNAs-based age predictors were established, resulting in a forecast performance where all R 2 values were greater than 0.96 and root-mean-square errors (RMSE) were less than 3.7 years in three ensemble machine learning methods. Furthermore, important age-related sncRNAs were identified based on modeling and the biological pathways of age-related miRNAs were characterized by their predicted targets, including multiple pathways in intercellular communication, cancer and immune regulation. Conclusion: In summary, this study provides valuable insights into circulating sncRNAs expression dynamics during human aging and may lead to advanced understanding of age-related sncRNAs functions with further elucidation.

A utility-based machine learning-driven personalized lifestyle recommendation for cardiovascular disease prevention.

In recent decades, cardiovascular disease (CVD) has become the leading cause of death in most countries of the world. Since many types of CVD are preventable by modifying lifestyle behaviors, the objective of this paper is to develop an effective personalized lifestyle recommendation algorithm for reducing the risk of common types of CVD. However, in practice, the underlying relationships between the risk factors (e.g., lifestyles, blood pressure, etc.) and disease onset is highly complex. It is also challenging to identify effective modification recommendations for different individuals due to individual's effort-benefits consideration and uncertainties in disease progression. Therefore, to address these challenges, this study developed a novel data-driven approach for personalized lifestyle behaviors recommendation based on machine learning and a personalized exponential utility function model. The contributions of this work can be summarized into three aspects: (1) a classification-based prediction model is implemented to predict the CVD risk based on the condition of risk factors; (2) the generative adversarial network (GAN) is incorporated to learn the underlying relationship between risk factors, as well as quantify the uncertainty of disease progression under lifestyle modifications; and (3) a novel personalized exponential utility function model is proposed to evaluate the modifications' utilities with respect to CVD risk reduction, individual's effort-benefits consideration, and disease progression uncertainty, as well as identify the optimal modification for each individual. The effectiveness of the proposed method is validated through an open-access CVD dataset. The results demonstrate that the personalized lifestyle modification recommended by the proposed methodology has the potential to effectively reduce the CVD risk. Thus, it is promising to be further applied to real-world cases for CVD prevention.

A multilayer network-enabled ultrasonic image series analysis approach for online cancer drug delivery monitoring.

The objective of this study is to develop an effective data-driven methodology for the online monitoring of cancer drug delivery guided by the ultrasonic images. To achieve this goal, effective image quantification and accurate feature extraction play a critical role on image-guided drug delivery (IGDD) monitoring. However, the existing image-guided approaches in such area are mainly focused on the analysis for individual images rather than the image series. In fact, the temporal patterns between consecutive images may contain critical information and it is necessary to be considered in the monitoring analysis. In addition, the conventional approaches, such as the pure intensity-based method, also do not sufficiently consider the effects of noise in the ultrasonic images, which also limits the monitoring sensitivity and accuracy. To address the challenges, this paper proposed a novel multilayer network-enabled IGDD (MNE-IGDD) monitoring approach. The contributions of the proposed method can be summarized into three aspects: (1) formulate the sequential ultrasound images to a multilayer network by the proposed spatial-regularized distance; (2) detect drug delivery area based on community detection algorithm of multilayer network; and (3) quantify the drug delivery progress by incorporating the image intensity-based features with the detected community. Both the detected communities and feature increment percentages are applied as the evaluation metric for validation. A simulation study was conducted and this method was also applied to a real-world mouse colon tumor treatment case study under three temperature conditions. Both simulation and the real-world case studies demonstrated that the proposed method is promising to achieve satisfactory monitoring performance in clinical trials.

A Spectral Fiedler Field-based Contrast Platform for Imaging of Nanoparticles in Colon Tumor.

The temporal and spatial patterns of nanoparticle that ferry both imaging and therapeutic agent in solid tumors is significantly influenced by target tissue movement, low spatial resolution, and inability to accurately define regions of interest (ROI) at certain tissue depths. These combine to limit and define nanoparticle untreated regions in tumors. Utilizing graph and matrix theories, the objective of this project was to develop a novel spectral Fiedler field (SFF) based-computational technology for nanoparticle mapping in tumors. The novelty of SFF lies in the utilization of the changes in the tumor topology from baseline for contrast variation assessment. Data suggest that SFF can enhance the spatiotemporal contrast compared to conventional method by 2-3 folds in tumors. Additionally, the SFF contrast is readily translatable for assessment of tumor drug distribution. Thus, our SFF computational platform has the potential for integration into devices that allow contrast and drug delivery applications.