DROID: dose-ranging approach to optimizing dose in oncology drug development

In the era of targeted therapy, there has been increasing concern about the development of oncology drugs based on the “more is better” paradigm, developed decades ago for chemotherapy. Recently, the US Food and Drug Administration (FDA) initiated Project Optimus to reform the dose optimization and dose selection paradigm in oncology drug development. To accommodate this paradigm shifting, we propose a dose-ranging approach to optimizing dose (DROID) for oncology trials with targeted drugs. DROID leverages the well-established dose-ranging study framework, which has been routinely used to develop non-oncology drugs for decades, and bridges it with established oncology dose-finding designs to optimize the dose of oncology drugs. DROID consists of two seamlessly connected stages. In the first stage, patients are sequentially enrolled and adaptively assigned to investigational doses to establish the therapeutic dose range (TDR), defined as the range of doses with acceptable toxicity and efficacy profiles, and the recommended phase 2 dose set (RP2S). In the second stage, patients are randomized to the doses in RP2S to assess the dose–response relationship and identify the optimal dose. The simulation study shows that DROID substantially outperforms the conventional approach, providing a new paradigm to efficiently optimize the dose of targeted oncology drugs. DROID aligns with the approach of a randomized, parallel dose-response trial design recommended by the FDA in the Guidance on Optimizing the Dosage of Human Prescription Drugs and Biological Products for the Treatment of Oncologic Diseases

A Bayesian dose-finding design for phase I/II clinical trials with nonignorable dropouts

Phase I/II trials utilize both toxicity and efficacy data to achieve efficient dose finding. However, due to the requirement of assessing efficacy outcome, which often takes a long period of time to be evaluated, the duration of phase I/II trials is often longer than that of the conventional dose-finding trials. As a result, phase I/II trials are susceptible to the missing data problem caused by patient dropout, and the missing efficacy outcomes are often nonignorable in the sense that patients who do not experience treatment efficacy are more likely to drop out of the trial. We propose a Bayesian phase I/II trial design to accommodate nonignorable dropouts. We treat toxicity as a binary outcome and efficacy as a time-to-event outcome. We model the marginal distribution of toxicity using a logistic regression and jointly model the times to efficacy and dropout using proportional hazard models to adjust for nonignorable dropouts. The correlation between times to efficacy and dropout is modeled using a shared frailty. We propose a two-stage dose-finding algorithm to adaptively assign patients to desirable doses. Simulation studies show that the proposed design has desirable operating characteristics. Our design selects the target dose with a high probability and assigns most patients to the target dose

A Bayesian phase I/II clinical trial design in the presence of informative dropouts

A phase I/II trial design utilizes both toxicity and efficacy outcomes to make the decision of dose assignment for patients. Because assessing the efficacy endpoint often requires a relatively long follow-up time, phase I/II trials are more susceptible to the missing data problem caused by informative dropouts that are correlated with treatment efficacy and toxicity. In addition, patient outcomes may not be scored quickly enough to apply decision rules that choose treatments or doses for newly accrued patients. To address these issues, we propose a Bayesian phase I/II design that jointly models efficacy, toxicity, and dropout as time-to-event data. Correlations among the three time-to-event outcomes are taken into account by a shared frailty. This joint model strategy accounts for the informative dropouts and has an additional advantage of accommodating a high accrual rate without suspending patient enrollment when toxicity or efficacy outcomes require a long follow-up. Under the Bayesian paradigm, we continuously update the posterior estimate of the model and assign incoming patients to the most desirable dose based on an efficacy-toxicity trade-off utility. Simulation studies show that the proposed design has good operating characteristics with a high probability of selecting the target dose and assigning the most patients to the target dose

Treatment Comparisons in Adaptive Platform Trials Adjusting for Temporal Drift

An adaptive platform trial (APT) is a multi-arm trial in the context of a single disease where treatment arms are allowed to enter or leave the trial based on some decision rule. If a treatment enters the trial later than the control arm, there exist nonconcurrent controls who were not randomized between the two arms under comparison. As APTs typically take long periods of time to conduct, temporal drift may occur, which requires the treatment comparisons to be adjusted for this temporal change. Under the causal inference framework, we propose two approaches for treatment comparisons in APTs that account for temporal drift, both based on propensity score weighting. In particular, to address unmeasured confounders, one approach is doubly robust in the sense that it remains valid so long as either the propensity score model is correctly specified or the time effect model is correctly specified. Simulation study shows that our proposed approaches have desirable operating characteristics with well controlled Type I error rates and high power with or without unmeasured confounders

Enhancing Visibility in Nighttime Haze Images Using Guided APSF and Gradient Adaptive Convolution

Visibility in hazy nighttime scenes is frequently reduced by multiple factors, including low light, intense glow, light scattering, and the presence of multicolored light sources. Existing nighttime dehazing methods often struggle with handling glow or low-light conditions, resulting in either excessively dark visuals or unsuppressed glow outputs. In this paper, we enhance the visibility from a single nighttime haze image by suppressing glow and enhancing low-light regions. To handle glow effects, our framework learns from the rendered glow pairs. Specifically, a light source aware network is proposed to detect light sources of night images, followed by the APSF (Angular Point Spread Function)-guided glow rendering. Our framework is then trained on the rendered images, resulting in glow suppression. Moreover, we utilize gradient-adaptive convolution, to capture edges and textures in hazy scenes. By leveraging extracted edges and textures, we enhance the contrast of the scene without losing important structural details. To boost low-light intensity, our network learns an attention map, then adjusted by gamma correction. This attention has high values on low-light regions and low values on haze and glow regions. Extensive evaluation on real nighttime haze images, demonstrates the effectiveness of our method. Our experiments demonstrate that our method achieves a PSNR of 30.38dB, outperforming state-of-the-art methods by 13$\%$ on GTA5 nighttime haze dataset. Our data and code is available at: \url{https://github.com/jinyeying/nighttime_dehaze}.Comment: Accepted to ACM'MM2023, https://github.com/jinyeying/nighttime_dehaz

NightHaze: Nighttime Image Dehazing via Self-Prior Learning

Masked autoencoder (MAE) shows that severe augmentation during training produces robust representations for high-level tasks. This paper brings the MAE-like framework to nighttime image enhancement, demonstrating that severe augmentation during training produces strong network priors that are resilient to real-world night haze degradations. We propose a novel nighttime image dehazing method with self-prior learning. Our main novelty lies in the design of severe augmentation, which allows our model to learn robust priors. Unlike MAE that uses masking, we leverage two key challenging factors of nighttime images as augmentation: light effects and noise. During training, we intentionally degrade clear images by blending them with light effects as well as by adding noise, and subsequently restore the clear images. This enables our model to learn clear background priors. By increasing the noise values to approach as high as the pixel intensity values of the glow and light effect blended images, our augmentation becomes severe, resulting in stronger priors. While our self-prior learning is considerably effective in suppressing glow and revealing details of background scenes, in some cases, there are still some undesired artifacts that remain, particularly in the forms of over-suppression. To address these artifacts, we propose a self-refinement module based on the semi-supervised teacher-student framework. Our NightHaze, especially our MAE-like self-prior learning, shows that models trained with severe augmentation effectively improve the visibility of input haze images, approaching the clarity of clear nighttime images. Extensive experiments demonstrate that our NightHaze achieves state-of-the-art performance, outperforming existing nighttime image dehazing methods by a substantial margin of 15.5% for MUSIQ and 23.5% for ClipIQA

The role of health system governance in strengthening the rural health insurance system in China.

BACKGROUND: Systems of governance play a key role in the operation and performance of health systems. In the past six decades, China has made great advances in strengthening its health system, most notably in establishing a health insurance system that enables residents of rural areas to achieve access to essential services. Although there have been several studies of rural health insurance schemes, these have focused on coverage and service utilization, while much less attention has been given to the role of governance in designing and implementing these schemes. METHODS: Information from publications and policy documents relevant to the development of two rural health insurance policies in China was obtained, analysed, and synthesise. 92 documents on CMS (Cooperative Medical Scheme) or NCMS (New Rural Cooperative Medical Scheme) from four databases searched were included. Data extraction and synthesis of the information were guided by a framework that drew on that developed by the WHO to describe health system governance and leadership. RESULTS: We identified a series of governance practices that were supportive of progress, including the prioritisation by the central government of health system development and certain health policies within overall national development; strong government commitment combined with a hierarchal administrative system; clear policy goals coupled with the ability for local government to adopt policy measures that take account of local conditions; and the accumulation and use of the evidence generated from local practices. However these good practices were not seen in all governance domains. For example, poor collaboration between different government departments was shown to be a considerable challenge that undermined the operation of the insurance schemes. CONCLUSIONS: China's success in achieving scale up of CMS and NCMS has attracted considerable interest in many low and middle income countries (LMICs), especially with regard to the schemes' designs, coverage, and funding mechanisms. However, this study demonstrates that health systems governance may be critical to enable the development and operation of such schemes. Given that many LMICs are expanding health financing system to cover populations in rural areas or the informal sectors, we argue that strengthening specific practices in each governance domain could inform the adaptation of these schemes to other settings

Distinct MicroRNA Subcellular Size and Expression Patterns in Human Cancer Cells

Introduction. Small noncoding RNAs have important regulatory functions in different cell pathways. It is believed that most of them mainly play role in gene post-transcriptional regulation in the cytoplasm. Recent evidence suggests miRNA and siRNA activity in the nucleus. Here, we show distinct genome-wide sub-cellular localization distribution profiles of small noncoding RNAs in human breast cancer cells. Methods. We separated breast cancer cell nuclei from cytoplasm, and identified small RNA sequences using a high-throughput sequencing platform. To determine the relationship between miRNA sub-cellular distribution and cancer progression, we used microarray analysis to examine the miRNA expression levels in nucleus and cytoplasm of three human cell lines, one normal breast cell line and two breast cancer cell lines. Logistic regression and SVM were used for further analysis. Results. The sub-cellular distribution of small noncoding RNAs shows that numerous miRNAs and their isoforms (isomiR) not only locate to the cytoplasm but also appeare in the nucleus. Subsequent microarray analyses indicated that the miRNA nuclear-cytoplasmic-ratio is a significant characteristic of different cancer cell lines. Conclusions. Our results indicate that the sub-cellular distribution is important for miRNA function, and that the characterization of the small RNAs sub-cellular localizome may contribute to cancer research and diagnosis

Escaping from pollution: the effect of air quality on inter-city population mobility in China

China faces severe air pollution issues due to the rapid growth of the economy, causing concerns for human physical and mental health as well as behavioral changes. Such adverse impacts can be mediated by individual avoidance behaviors such as traveling from polluted cities to cleaner ones. This study utilizes smartphone-based location data and instrumental variable regression to try and find out how air quality affects population mobility. Our results confirm that air quality does affect the population outflows of cities. An increase of 100 points in the air quality index will cause a 49.60% increase in population outflow, and a rise of 1 μg m−3 in PM2.5 may cause a 0.47% rise in population outflow. Air pollution incidents can drive people to leave their cities 3 days or a week later by railway or road. The effect is heterogeneous among workdays, weekends and holidays. Our results imply that air quality management can be critical for urban tourism and environmental competitiveness