Deep reinforcement learning identifies personalized intermittent androgen deprivation therapy for prostate cancer.

The evolution of drug resistance leads to treatment failure and tumor progression. Intermittent androgen deprivation therapy (IADT) helps responsive cancer cells compete with resistant cancer cells in intratumoral competition. However, conventional IADT is population-based, ignoring the heterogeneity of patients and cancer. Additionally, existing IADT relies on pre-determined thresholds of prostate-specific antigen to pause and resume treatment, which is not optimized for individual patients. To address these challenges, we framed a data-driven method in two steps. First, we developed a time-varied, mixed-effect and generative Lotka-Volterra (tM-GLV) model to account for the heterogeneity of the evolution mechanism and the pharmacokinetics of two ADT drugs Cyproterone acetate and Leuprolide acetate for individual patients. Then, we proposed a reinforcement-learning-enabled individualized IADT framework, namely, I$^{2}$ADT, to learn the patient-specific tumor dynamics and derive the optimal drug administration policy. Experiments with clinical trial data demonstrated that the proposed I$^{2}$ADT can significantly prolong the time to progression of prostate cancer patients with reduced cumulative drug dosage. We further validated the efficacy of the proposed methods with a recent pilot clinical trial data. Moreover, the adaptability of I$^{2}$ADT makes it a promising tool for other cancers with the availability of clinical data, where treatment regimens might need to be individualized based on patient characteristics and disease dynamics. Our research elucidates the application of deep reinforcement learning to identify personalized adaptive cancer therapy.

The Chinese Adaptation and Validation of the Adolescents' Ageism Toward Older Adults Scale.

Objectives: The purpose of this study was to translate and validate the adolescents' ageism toward older adults scale (AGES) in the Chinese cultural context and examine its psychometric properties among Chinese adolescents. Methods: The study consists of two phases with two separate samples. In phase one (sample 1: n = 407), exploratory factor analysis (EFA) is conducted to determine the factor structure of the C-AGES. In phase two (sample 2: n = 379), confirmatory factor analysis (CFA) is performed to confirm the factor structure and assess the model fit of the C-AGES. Results: EFA reveals a two-factor structure consisting of 17 items for the C-AGES. CFA in sample 2 confirms the factor structure and demonstrates good model fit. The C-AGES also exhibits high criterion validity, internal consistency, and cross-gender invariance. Discussion: The results suggest that the C-AGES is a valid measurement tool for assessing agism among Chinese adolescents.

Erratum: Roadmap for phase change materials in photonics and beyond.

[This corrects the article DOI: 10.1016/j.isci.2023.107946.].

Roadmap for phase change materials in photonics and beyond.

Phase Change Materials (PCMs) have demonstrated tremendous potential as a platform for achieving diverse functionalities in active and reconfigurable micro-nanophotonic devices across the electromagnetic spectrum, ranging from terahertz to visible frequencies. This comprehensive roadmap reviews the material and device aspects of PCMs, and their diverse applications in active and reconfigurable micro-nanophotonic devices across the electromagnetic spectrum. It discusses various device configurations and optimization techniques, including deep learning-based metasurface design. The integration of PCMs with Photonic Integrated Circuits and advanced electric-driven PCMs are explored. PCMs hold great promise for multifunctional device development, including applications in non-volatile memory, optical data storage, photonics, energy harvesting, biomedical technology, neuromorphic computing, thermal management, and flexible electronics.

Low-loss ultrafast and nonvolatile all-optical switch enabled by all-dielectric phase change materials.

All-optical switches show great potential to overcome the speed and power consumption limitations of electrical switching. Owing to its nonvolatile and superb cycle abilities, phase-change materials enabled all-optical switch (PC-AOS) is attracting much attention. However, realizing low-loss and ultrafast switching remains a challenge, because previous PC-AOS are mostly based on plasmonic metamaterials. The high thermal conductance of metallic materials disturbs the thermal accumulation for phase transition, and eventually decreases the switching speed to tens of nanoseconds. Here, we demonstrate an ultrafast switching (4.5 ps) and low-loss (2.8 dB) all-optical switch based on all-dielectric structure consisting of Ge2Sb2Te5 and photonic crystals. Its switching speed is approximately ten thousand times faster than the plasmonic one. A 5.4 dB on-off ratio at 1550 nm has been experimentally achieved. We believe that the proposed all-dielectric optical switch will accelerate the progress of ultrafast and energy-efficient photonic devices and systems.

The optimal design of clinical trials with potential biomarker effects: A novel computational approach.

As a future trend of healthcare, personalized medicine tailors medical treatments to individual patients. It requires to identify a subset of patients with the best response to treatment. The subset can be defined by a biomarker (eg, expression of a gene) and its cutoff value. Topics on subset identification have received massive attention. There are over two million hits by keyword searches on Google Scholar. However, designing clinical trials that utilize the discovered uncertain subsets/biomarkers is not trivial and rarely discussed in the literature. This leads to a gap between research results and real-world drug development. To fill in this gap, we formulate the problem of clinical trial design into an optimization problem involving high-dimensional integration, and propose a novel computational solution based on Monte Carlo and smoothing methods. Our method utilizes the modern techniques of general purpose computing on graphics processing units for large-scale parallel computing. Compared to a published method in three-dimensional problems, our approach is more accurate and 133 times faster. This advantage increases when dimensionality increases. Our method is scalable to higher dimensional problems since the precision bound of our estimated study power is a finite number not affected by dimensionality. To design clinical trials incorporating the potential biomarkers, users can use our software "DesignCTPB". This software can be found on Github and will be available as an R package on CRAN. Although our research is motivated by the design of clinical trials, the method can be used widely to solve other optimization problems involving high-dimensional integration.