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Precision medicine that enables personalized treatment decision support has become an increasingly important research topic in chronic disease care. The main challenges in designing a treatment algorithm include modeling individual disease progression dynamics and designing adaptive treatment selection strategy. This study aims to develop an adaptive treatment selection framework tailored to an individual patient’s disease progression pattern and treatment response. We propose a Partially Observable Collaborative Model (POCM) to capture the individual variations in a heterogeneous population and optimize treatment outcomes in three stages. The POCM first infers the disease progression models by subgroup patterns using population data in stage one and then fine-tunes the models for individual patients with a small number of treatment trials in stage two. In stage three, we show how the treatment policies based on the Partially Observable Markov Decision Process (POMDP) can be tailored to individual patients by utilizing the disease models learned from the POCM. Using a simulated population of chronic depression patients, we show that the POCM can more accurately estimate the personal disease progression than the traditional method of solving a hidden Markov model. We also compare the POMDP treatment policies with other heuristic policies and demonstrate that the POCM-based policies give the highest net monetary benefits in majority of parameter settings. To conclude, the POCM method is a promising approach to model the chronic disease progression process and recommend a personalized treatment plan for individual patients in a heterogeneous population. 

High-performance optoelectronic devices, such as solar cells and light-emitting diodes, have been fabricated with lead halide perovskites owing to their superior carrier properties. However, charge transport in such optoelectronics is intrinsically directional due to the existence of p–n junctions, which thus lacks the potential to elucidate any perturbations in light or electricity during energy conversion. Here, with the presence of a LiCl additive in a formamidinium chloride (FACl) solution, the as-grown LiCl:FAPbCl 3 nanorods demonstrate greatly enhanced crystallinity and UV photoresponse as compared to pristine FAPbCl 3 nanostructures without the LiCl additive. Most importantly, the LiCl:FAPbCl 3 nanorod film exhibits unprecedented distinguishability to UV photons with different energies and oscillating intensities, in the form of bipolar and periodically oscillatory photocurrents. This work could advance the fundamental understanding of photoinduced carrier processes in halide perovskites and facilitate the development of novel UV-based optical communications. 

Materials in metastable states, such as amorphous ice and supercooled condensed matter, often exhibit exotic phenomena. To date, achieving metastability is usually accomplished by rapid quenching through a thermodynamic path function, namely, heating−cooling cycles. However, heat can be detrimental to organic-containing materials because it can induce degradation. Alternatively, the application of pressure can be used to achieve metastable states that are inaccessible via heating−cooling cycles. Here we report metastable states of 2D organic−inorganic hybrid perovskites reached through structural amorphization under compression followed by recrystallization via decompression. Remarkably, such pressure-derived metastable states in 2D hybrid perovskites exhibit enduring bandgap narrowing by as much as 8.2% with stability under ambient conditions. The achieved metastable states in 2D hybrid perovskites via compression−decompression cycles offer an alternative pathway toward manipulating the properties of these “soft” materials.