Epigenetic modulation through BET bromodomain inhibitors as a novel therapeutic strategy for progranulin-deficient frontotemporal dementia.

Frontotemporal dementia (FTD) is a debilitating neurodegenerative disorder with currently no disease-modifying treatment options available. Mutations in GRN are one of the most common genetic causes of FTD, near ubiquitously resulting in progranulin (PGRN) haploinsufficiency. Small molecules that can restore PGRN protein to healthy levels in individuals bearing a heterozygous GRN mutation may thus have therapeutic value. Here, we show that epigenetic modulation through bromodomain and extra-terminal domain (BET) inhibitors (BETi) potently enhance PGRN protein levels, both intracellularly and secreted forms, in human central nervous system (CNS)-relevant cell types, including in microglia-like cells. In terms of potential for disease modification, we show BETi treatment effectively restores PGRN levels in neural cells with a GRN mutation known to cause PGRN haploinsufficiency and FTD. We demonstrate that BETi can rapidly and durably enhance PGRN in neural progenitor cells (NPCs) in a manner dependent upon BET protein expression, suggesting a gain-of-function mechanism. We further describe a CNS-optimized BETi chemotype that potently engages endogenous BRD4 and enhances PGRN expression in neuronal cells. Our results reveal a new epigenetic target for treating PGRN-deficient forms of FTD and provide mechanistic insight to aid in translating this discovery into therapeutics.

Wavelength encoding spectral imaging based on the combination of deeply learned filters and an RGB camera.

Hyperspectral imaging is a critical tool for gathering spatial-spectral information in various scientific research fields. As a result of improvements in spectral reconstruction algorithms, significant progress has been made in reconstructing hyperspectral images from commonly acquired RGB images. However, due to the limited input, reconstructing spectral information from RGB images is ill-posed. Furthermore, conventional camera color filter arrays (CFA) are designed for human perception and are not optimal for spectral reconstruction. To increase the diversity of wavelength encoding, we propose to place broadband encoding filters in front of the RGB camera. In this condition, the spectral sensitivity of the imaging system is determined by the filters and the camera itself. To achieve an optimal encoding scheme, we use an end-to-end optimization framework to automatically design the filters' transmittance functions and optimize the weights of the spectral reconstruction network. Simulation experiments show that our proposed spectral reconstruction network has excellent spectral mapping capabilities. Additionally, our novel joint wavelength encoding imaging framework is superior to traditional RGB imaging systems. We develop the deeply learned filter and conduct actual shooting experiments. The spectral reconstruction results have an attractive spatial resolution and spectral accuracy.

Reliable image dehazing by NeRF.

Image dehazing is a typical low-level visual task. With the continuous improvement of network performance and the introduction of various prior knowledge, the ability of image dehazing is becoming stronger. However, the existing dehazing methods have problems such as the inability to obtain real shooting datasets, unreliable dehazing processes, and the difficulty to deal with complex lighting scenes. To solve these problems, we propose a new haze model combining the optical scattering model and the computer graphics rendering. Based on the new haze model, we propose a high-quality and widely applicable dehazing dataset generation pipeline that does not require paired-data training and prior knowledge. We reconstruct the three-dimensional fog space with array camera and remove haze by thresholding voxel deletion. We use the Unreal Engine 5 to generate simulation datasets and the real shooting in laboratory to verify the effectiveness and the reliability of our generation pipeline. Through our pipeline, we can obtain wonderful dehaze results and dehaze datasets under various complex outdoors lighting conditions. We also propose a dehaze dataset enhancement method based on voxel control. Our pipeline and data enhancement are suitable for the latest algorithm model, these solutions can obtain better visual effects and objective indicators.

Safety of teriflunomide in Chinese adult patients with relapsing multiple sclerosis: A phase IV, 24-week multicenter study.

BACKGROUND: Disease-modifying therapies have been approved for the treatment of relapsing multiple sclerosis (RMS). The present study aims to examine the safety of teriflunomide in Chinese patients with RMS. METHODS: This non-randomized, multi-center, 24-week, prospective study enrolled RMS patients with variant (c.421C>A) or wild type ABCG2 who received once-daily oral teriflunomide 14 mg. The primary endpoint was the relationship between ABCG2 polymorphisms and teriflunomide exposure over 24 weeks. Safety was assessed over the 24-week treatment with teriflunomide. RESULTS: Eighty-two patients were assigned to variant (n = 42) and wild type groups (n = 40), respectively. Geometric mean and geometric standard deviation (SD) of pre-dose concentration (variant, 54.9 [38.0] µg/mL; wild type, 49.1 [32.0] µg/mL) and area under plasma concentration-time curve over a dosing interval (AUCtau) (variant, 1731.3 [769.0] µg∙h/mL; wild type, 1564.5 [1053.0] µg∙h/mL) values at steady state were approximately similar between the two groups. Safety profile was similar and well tolerated across variant and wild type groups in terms of rates of treatment emergent adverse events (TEAE), treatment-related TEAE, grade ≥3 TEAE, and serious adverse events (AEs). No new specific safety concerns or deaths were reported in the study. CONCLUSION: ABCG2 polymorphisms did not affect the steady-state exposure of teriflunomide, suggesting a similar efficacy and safety profile between variant and wild type RMS patients. REGISTRATION: NCT04410965, https://clinicaltrials.gov.

Retinal Thinning as a Marker of Disease Severity in Progressive Supranuclear Palsy.

OBJECTIVE: Progressive supranuclear palsy (PSP) involves a variety of visual symptoms that are thought to be partially caused by structural abnormalities of the retina. However, the relationship between retinal structural changes, disease severity, and intracranial alterations remains unknown. We investigated distinct retinal thinning patterns and their relationship with clinical severity and intracranial alterations in a PSP cohort. METHODS: We enrolled 19 patients with PSP (38 eyes) and 20 age-matched healthy controls (40 eyes). All of the participants underwent peripapillary and macular optical coherence tomography. Brain 11C-2ß-carbomethoxy-3ß-(4-fluorophenyl) tropane (11C-CFT) and 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography imaging were also performed in patients with PSP. We investigated the association between retinal thickness changes and clinical features, striatal dopamine transporter availability, and cerebral glucose metabolism. RESULTS: The peripapillary retinal nerve fiber layer (pRNFL) and macula were significantly thinner in patients with PSP than in controls. The thickness of the superior sector of the pRNFL demonstrated a significant negative relationship with the Movement Disorder Society-Unified Parkinson's Disease Rating Scale part III and Hoehn and Yahr staging scale scores. A significant negative correlation was found between outer inferior macular thickness and disease duration. Outer temporal macular thickness was positively correlated with Montreal Cognitive Assessment scores. In PSP, lower outer temporal macular thickness was also positively correlated with decreased dopamine transporter binding in the caudate. CONCLUSION: The pRNFL and macular thinning may be candidate markers for monitoring disease severity. Additionally, macular thinning may be an in vivo indicator of nigrostriatal dopaminergic cell degeneration in PSP patients.

Design of an optimized Alvarez lens based on the fifth-order polynomial combination.

This paper proposes an optimized design of the Alvarez lens by utilizing a combination of three fifth-order X-Y polynomials. It can effectively minimize the curvature of the lens surface to meet the manufacturing requirements. The phase modulation function and aberration of the proposed lens are evaluated by using first-order optical analysis. Simulations compare the proposed lens with the traditional Alvarez lens in terms of surface curvature, zoom capability, and imaging quality. The results demonstrate the exceptional performance of the proposed lens, achieving a remarkable 26.36% reduction in the maximum curvature of the Alvarez lens (with a coefficient A value of 4×10-4 and a diameter of 26 mm) while preserving its original zoom capability and imaging quality.

Image restoration for optical zooming system based on Alvarez lenses.

Alvarez lenses are known for their ability to achieve a broad range of optical power adjustment by utilizing complementary freeform surfaces. However, these lenses suffer from optical aberrations, which restrict their potential applications. To address this issue, we propose a field of view (FOV) attention image restoration model for continuous zooming. In order to simulate the degradation of optical zooming systems based on Alvarez lenses (OZA), a baseline OZA is designed where the polynomial for the Alvarez lenses consists of only three coefficients. By computing spatially varying point spread functions (PSFs), we simulate the degraded images of multiple zoom configurations and conduct restoration experiments. The results demonstrate that our approach surpasses the compared methods in the restoration of degraded images across various zoom configurations while also exhibiting strong generalization capabilities under untrained configurations.

ASF-Transformer: neutralizing the impact of atmospheric turbulence on optical imaging through alternating learning in the spatial and frequency domains.

Atmospheric turbulence, a pervasive and complex physical phenomenon, challenges optical imaging across various applications. This paper presents the Alternating Spatial-Frequency (ASF)-Transformer, a learning-based method for neutralizing the impact of atmospheric turbulence on optical imaging. Drawing inspiration from split-step propagation and correlated imaging principles, we propose the Alternating Learning in Spatial and Frequency domains (LASF) mechanism. This mechanism utilizes two specially designed transformer blocks that alternate between the spatial and Fourier domains. Assisted by the proposed patch FFT loss, our model can enhance the recovery of intricate textures without the need for generative adversarial networks (GANs). Evaluated across diverse test mediums, our model demonstrated state-of-the-art performance in comparison to recent methods. The ASF-Transformer diverges from mainstream GAN-based solutions, offering a new strategy to combat image degradation introduced by atmospheric turbulence. Additionally, this work provides insights into neural network architecture by integrating principles from optical theory, paving the way for innovative neural network designs in the future.

Direct distortion prediction method for AR-HUD dynamic distortion correction.

Dynamic distortion is one of the most critical factors affecting the experience of automotive augmented reality head-up displays (AR-HUDs). A wide range of views and the extensive display area result in extraordinarily complex distortions. Existing methods based on the neural network first obtain distorted images and then get the predistorted data for training mostly. This paper proposes a distortion prediction framework based on the neural network. It directly trains the network with the distorted data, realizing dynamic adaptation for AR-HUD distortion correction and avoiding errors in coordinate interpolation. Additionally, we predict the distortion offsets instead of the distortion coordinates and present a field of view (FOV)-weighted loss function based on the spatial-variance characteristic to further improve the prediction accuracy of distortion. Experiments show that our methods improve the prediction accuracy of AR-HUD dynamic distortion without increasing the network complexity or data processing overhead.

Nighttime Image Dehazing by Render.

Nighttime image dehazing presents unique challenges due to the unevenly distributed haze caused by the color change of artificial light sources. This results in multiple interferences, including atmospheric light, glow, and direct light, which make the complex scattering haze interference difficult to accurately distinguish and remove. Additionally, obtaining pairs of high-definition data for fog removal at night is a difficult task. These challenges make nighttime image dehazing a particularly challenging problem to solve. To address these challenges, we introduced the haze scattering formula to more accurately express the haze in three-dimensional space. We also proposed a novel data synthesis method using the latest CG textures and lumen lighting technology to build scenes where various hazes can be seen clearly through ray tracing. We converted the complex 3D scattering relationship transformation into a 2D image dataset to better learn the mapping from 3D haze to 2D haze. Additionally, we improved the existing neural network and established a night haze intensity evaluation label based on the idea of optical PSF. This allowed us to adjust the haze intensity of the rendered dataset according to the intensity of the real haze image and improve the accuracy of dehazing. Our experiments showed that our data construction and network improvement achieved better visual effects, objective indicators, and calculation speed.

External evaluation of the predictive performance of published population pharmacokinetic models of linezolid in adult patients.

OBJECTIVES: Several linezolid population pharmacokinetic (popPK) models have been established to facilitate optimal therapy; however, their extrapolated predictive performance to other clinical sites is unknown. This study aimed to externally evaluate the predictive performance of published pharmacokinetic models of linezolid in adult patients. METHODS: For the evaluation dataset, 150 samples were collected from 70 adult patients (72.9% of which were critically ill) treated with linezolid at our center. Twenty-five published popPK models were identified from PubMed and Embase. Model predictability was evaluated using prediction-based, simulation-based, and Bayesian forecasting-based approaches to assess model predictability. RESULTS: Prediction-based diagnostics found that the prediction error within ±30% (F30) was less than 40% in all models, indicating unsatisfactory predictability. The simulation-based prediction- and variability-corrected visual predictive check and normalized prediction distribution error test indicated large discrepancies between the observations and simulations in most of the models. Bayesian forecasting with one or two prior observations significantly improved the models' predictive performance. CONCLUSION: The published linezolid popPK models showed insufficient predictive ability. Therefore, their sole use is not recommended, and incorporating therapeutic drug monitoring of linezolid in clinical applications is necessary.

Snapshot hyperspectral imaging based on equalization designed DOE.

Hyperspectral imaging attempts to determine distinctive information in spatial and spectral domain of a target. Over the past few years, hyperspectral imaging systems have developed towards lighter and faster. In phase-coded hyperspectral imaging systems, a better coding aperture design can improve the spectral accuracy relatively. Using wave optics, we post an equalization designed phase-coded aperture to achieve desired equalization point spread functions (PSFs) which provides richer features for subsequent image reconstruction. During the reconstruction of images, our raised hyperspectral reconstruction network, CAFormer, achieves better results than the state-of-the-art networks with less computation by substituting self-attention with channel-attention. Our work revolves around the equalization design of the phase-coded aperture and optimizes the imaging process from three aspects: hardware design, reconstruction algorithm, and PSF calibration. Our work is putting snapshot compact hyperspectral technology closer to a practical application.

Elimination of varying chromatic aberrations based on diffractive optics.

In telescopic systems consisting of Alvarez lenses, chromatic aberrations vary with the magnifications and the fields of view. Computational imaging has developed rapidly in recent years, therefore we propose a method of optimizing the DOE and the post-processing neural network in 2 stages for achromatic aberrations. We apply the iterative algorithm and the gradient descent method to optimize the DOE, respectively, and then adopt U-Net to further optimize the results. The results show that the optimized DOEs improve the results, the gradient descent optimized DOE with U-Net performs the best and has a very robust and good performance in the case of simulated chromatic aberrations. The results also verify the validity of our algorithm.

Model Informed Development of SIM0295 in Patients with Gout and Hyperuricemia and Healthy Volunteers Using a Population Pharmacokinetics/ Pharmacodynamics Approach.

BACKGROUND: SIM0295, a novel inhibitor of human uric acid transporter 1 (hURAT1), is used to treat patients with gout and hyperuricemia. This study aimed to develop population pharmacokinetics and pharmacodynamics (popPK/PD) models of SIM0295 and explore potential covariates to inform clinical drug development. RESEARCH DESIGN AND METHODS: Data were obtained from four phase I studies conducted in healthy Korean and Chinese subjects and two phase II studies conducted in Korean patients with gout and hyperuricemia. The popPK/PD model of SIM0295 was developed using nonlinear mixed effects modeling. RESULTS: SIM0295 pharmacokinetics was described using a two-compartment model with the absorption of four transit compartments and first-order elimination. PK parameters were normalized to weight via allometric scaling. Food was identified as a factor significantly affecting the absorption rate, with no clinical relevance. The sigmoid Emax model with a semi-mechanism of inhibition of serum uric acid (sUA) reabsorption was used to describe the exposure-response relationship. Additionally, Monte Carlo simulations demonstrated that approimately 9 mg/day of SIM0295 for 7 days could achieve the maximum decrease in sUA. CONCLUSION: The established popPK/PD model characterized the dose-exposure-response relationship for SIM0295 in healthy subjects and patients with gout and hyperuricemia and could be used to inform the drug development.

External evaluation and systematic review of population pharmacokinetic models for high-dose methotrexate in cancer patients.

Several population pharmacokinetic (PPK) models have been established to optimize the therapeutic regimen and reduce the toxicity of high-dose methotrexate (HDMTX) in patients with cancer. However, their predictive performance when extrapolated to different clinical centers was unknown. In this study, we aimed to externally evaluate the predictive ability of HDMTX PPK models and determine the potential influencing factors. We searched the literature and determined the predictive performance of the selected models using methotrexate concentrations in 721 samples from 60 patients in the First Affiliated Hospital of the Navy Medical University. Prediction-based diagnostics and simulation-based normalized prediction distribution errors (NPDE) were used to evaluate the predictive performance of the models. The influence of prior information was also assessed using Bayesian forecasting, and the potential factors affecting model predictability were investigated. Thirty models extracted from published PPK studies were assessed. Prediction-based diagnostics showed that the number of compartments potentially influenced model transferability, and simulation-based NPDE indicated model misspecification. Bayesian forecasting significantly improved the predictive performance of the models. Various factors, including bioassays, covariates, and population diagnosis, influence model extrapolation. The published models were unsatisfactory for all prediction-based diagnostics, except for the 24 h methotrexate concentration monitoring and simulation-based diagnostics, making them inappropriate for direct extrapolation. Moreover, Bayesian forecasting combined therapeutic drug monitoring could improve the predictive performance of the models.

Parametric population pharmacokinetics of isoniazid: a systematic review.

INTRODUCTION: Isoniazid (INH) plays an important role in prevention and treatment of tuberculosis (TB). However, large pharmacokinetic (PK) variations are observed in patients receiving standard INH dosages. Considering the influence of PK variations on INH efficacy or adverse reactions, we reviewed the population PK studies of INH and explored significant covariates that influence INH PK. METHODS: The PubMed and Embase databases were systematically searched from their inception to 30 January 2023. PPK studies on INH using a parametric nonlinear mixed-effect approach were included in this review. The characteristics and identified significant covariates of the included studies were summarized. RESULTS: Twenty-one studies conducted in adults, and seven in pediatrics were included. A two-compartment model with first-order absorption and elimination was the frequently used structural model for INH. NAT2 genotype, body size, and age were identified as significant covariates affecting INH PK variation. The median clearance (CL) value in the fast metabolizers was 2.55-fold higher than that in the slow metabolizers. Infants and children had higher CL per weight values than adults with the same metabolic phenotype. In pediatric patients, CL value increased with postnatal age. CONCLUSIONS: Compared with slow metabolizers, the daily dose of INH should be increased by 200-600 mg in fast metabolizers. To achieve effective treatment, pediatric patients need a higher dose per kilogram than adults. Further PPK studies of anti-tuberculosis drugs are needed to comprehensively understand the covariates that affect their PK characteristics and to achieve accurate dose adjustments.

Is a Lower Dose of Rivaroxaban Required for Asians? A Systematic Review of a Population Pharmacokinetics and Pharmacodynamics Analysis of Rivaroxaban.

Rivaroxaban has been widely used to prevent and treat various thromboembolic diseases for more than a decade. However, whether a lower dose of rivaroxaban is required for Asians is still debatable. This review aimed to explore the potential ethnic difference in pharmacokinetic/pharmacodynamic (PK/PD) characteristics between Asians and Caucasians. A systematic search was conducted and twenty-four studies were identified, of which 10 were conducted on Asian adults, 11 on predominantly Caucasian adults, and 3 on Caucasian pediatrics. The apparent clearance (CL/F) of rivaroxaban in Caucasian adults with non-valvular atrial fibrillation (6.45-7.64 L/h) was about 31-43% higher than that in Asians (4.46-5.98 L/h) taking 10~20 mg rivaroxaban every 24 h. Moreover, there was no obvious difference in CL/F among Japanese, Chinese, Thai, and Irani people. Regarding PK/PD relationship, prothrombin time was linked to rivaroxaban concentration in a linear or near-linear manner, and Factor Xa activity was linked with the Emax model. The exposure-response relationship was comparable between Asians and Caucasians. Renal function has a significant influence on CL/F, and no covariate was recognized for exposure-response relationship. In conclusion, a lower dose of rivaroxaban might be required for Asians, and further studies are warranted to verify this ethnic difference to facilitate optimal dosing regimens.

Handling Delayed or Missed Dose of Antiseizure Medications: A Model-Informed Individual Remedial Dosing.

BACKGROUND AND OBJECTIVES: Delayed or missed antiseizure medications (ASMs) doses are common during long-term or lifelong antiepilepsy treatment. This study aims to explore optimal individualized remedial dosing regimens for delayed or missed doses of 11 commonly used ASMs. METHODS: To explore remedial dosing regimens, Monte Carlo simulation was used based on previously identified and published population pharmacokinetic models. Six remedial strategies for delayed or missed doses were investigated. The deviation time outside the individual therapeutic range was used to evaluate each remedial regimen. The influences of patients' demographics, concomitant medication, and scheduled dosing intervals on remedial regimens were assessed. RxODE and Shiny in R were used to perform Monte Carlo simulation and recommend individual remedial regimens. RESULTS: The recommended remedial regimens were highly correlated with delayed time, scheduled dosing interval, and half-life of the ASM. Moreover, the optimal remedial regimens for pediatric and adult patients were different. The renal function, along with concomitant medication that affects the clearance of the ASM, may also influence the remedial regimens. A web-based dashboard was developed to provide individualized remedial regimens for the delayed or missed dose, and a user-defined module with all parameters that could be defined flexibly by the user was also built. DISCUSSION: Monte Carlo simulation based on population pharmacokinetic models may provide a rational approach to propose remedial regimens for delayed or missed doses of ASMs in pediatric and adult patients with epilepsy.

Computational Optics for Mobile Terminals in Mass Production.

Correcting the optical aberrations and the manufacturing deviations of cameras is a challenging task. Due to the limitation on volume and the demand for mass production, existing mobile terminals cannot rectify optical degradation. In this work, we systematically construct the perturbed lens system model to illustrate the relationship between the deviated system parameters and the spatial frequency response (SFR) measured from photographs. To further address this issue, an optimization framework is proposed based on this model to build proxy cameras from the machining samples' SFRs. Engaging with the proxy cameras, we synthetic data pairs, which encode the optical aberrations and the random manufacturing biases, for training the learning-based algorithms. In correcting aberration, although promising results have been shown recently with convolutional neural networks, they are hard to generalize to stochastic machining biases. Therefore, we propose a dilated Omni-dimensional dynamic convolution (DOConv) and implement it in post-processing to account for the manufacturing degradation. Extensive experiments which evaluate multiple samples of two representative devices demonstrate that the proposed optimization framework accurately constructs the proxy camera. And the dynamic processing model is well-adapted to manufacturing deviations of different cameras, realizing perfect computational photography. The evaluation shows that the proposed method bridges the gap between optical design, system machining, and post-processing pipeline, shedding light on the joint of image signal reception (lens and sensor) and image signal processing (ISP).