Time series radiomics for the prediction of prostate cancer progression in patients on active surveillance.

Serial MRI is an essential assessment tool in prostate cancer (PCa) patients enrolled on active surveillance (AS). However, it has only moderate sensitivity for predicting histopathological tumour progression at follow-up, which is in part due to the subjective nature of its clinical reporting and variation among centres and readers. In this study, we used a long short-term memory (LSTM) recurrent neural network (RNN) to develop a time series radiomics (TSR) predictive model that analysed longitudinal changes in tumour-derived radiomic features across 297 scans from 76 AS patients, 28 with histopathological PCa progression and 48 with stable disease. Using leave-one-out cross-validation (LOOCV), we found that an LSTM-based model combining TSR and serial PSA density (AUC 0.86 [95% CI: 0.78-0.94]) significantly outperformed a model combining conventional delta-radiomics and delta-PSA density (0.75 [0.64-0.87]; p = 0.048) and achieved comparable performance to expert-performed serial MRI analysis using the Prostate Cancer Radiologic Estimation of Change in Sequential Evaluation (PRECISE) scoring system (0.84 [0.76-0.93]; p = 0.710). The proposed TSR framework, therefore, offers a feasible quantitative tool for standardising serial MRI assessment in PCa AS. It also presents a novel methodological approach to serial image analysis that can be used to support clinical decision-making in multiple scenarios, from continuous disease monitoring to treatment response evaluation. KEY POINTS: •LSTM RNN can be used to predict the outcome of PCa AS using time series changes in tumour-derived radiomic features and PSA density. •Using all available TSR features and serial PSA density yields a significantly better predictive performance compared to using just two time points within the delta-radiomics framework. •The concept of TSR can be applied to other clinical scenarios involving serial imaging, setting out a new field in AI-driven radiology research.

Pressure-Tuning Photothermal Synergy to Optimize the Photoelectronic Properties in Amorphous Halide Perovskite Cs3 Bi2 I9.

Effective modification of the structure and properties of halide perovskites via the pressure engineering strategy has attracted enormous interest in the past decade. However, sufficient effort and insights regarding the potential properties and applications of the high-pressure amorphous phase are still lacking. Here, the superior and tunable photoelectric properties that occur in the pressure-induced amorphization process of the halide perovskite Cs3 Bi2 I9 are demonstrated. With increasing pressure, the photocurrent with xenon lamp illumination exhibits a rapid increase and achieves an almost five orders of magnitude increment compared to its initial value. Impressively, a broadband photoresponse from 520 to 1650 nm with an optimal responsivity of 6.81 mA W-1 and fast response times of 95/96 ms at 1650 nm is achieved upon successive compression. The high-gain, fast, broadband, and dramatically enhanced photoresponse properties of Cs3 Bi2 I9 are the result of comprehensive photoconductive and photothermoelectric mechanisms, which are associated with enhanced orbital coupling caused by an increase in BiI interactions in the [BiI6 ]3- cluster, even in the amorphous state. These findings provide new insights for further exploring the potential properties and applications of amorphous perovskites.

Pressure-Tailored Self-Driven and Broadband Photoresponse in PbI2.

Photoelectric devices based on the photothermoelectric (PTE) effect show promising prospects for broadband detection without an external power supply. However, effective strategies are still required to regulate the conversion efficiency of light to heat and electricity. Herein, significantly enhanced photoresponse properties of PbI2 generated from a PTE mechanism via a high-pressure strategy are reported. PbI2 exhibits a stable, fast, self-driven, and broadband photoresponse at ≈980 nm. Intriguingly, the synergy of the photoconductivity and PTE mechanism is conducive to enhancing the photoelectric properties, and extending the detection bandwidth to the optical communication waveband (1650 nm) with an external bias. The dramatically enhanced photoresponse characteristics are attributed to narrowing of the band gap and a significantly decreased resistance, which originate from the enhancement of atomic orbital overlap owing to pressure-induced Pb-I bond contraction. These findings open up a new avenue toward designing self-driven and broadband photoelectric devices.

An Account of Models of Molecular Circuits for Associative Learning with Reinforcement Effect and Forced Dissociation.

The development of synthetic biology has enabled massive progress in biotechnology and in approaching research questions from a brand-new perspective. In particular, the design and study of gene regulatory networks in vitro, in vivo, and in silico have played an increasingly indispensable role in understanding and controlling biological phenomena. Among them, it is of great interest to understand how associative learning is formed at the molecular circuit level. Mathematical models are increasingly used to predict the behaviours of molecular circuits. Fernando's model, which is one of the first works in this line of research using the Hill equation, attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture. In this article, we carry out indepth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values. We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in Fernando's model. Our work can be readily used as reference for synthetic biologists who consider implementing circuits of this kind in biological systems.