Transient receptor potential channels in sensory mechanisms of the lower urinary tract.

Urine storage and excretion require a network of interactions in the urinary tract and the central nervous system (CNS), which is mediated by a reservoir of water in the bladder and the outlet to the bladder neck, urethra and external urethral sphincter. Through communicating and coordinating each other, micturition system eventually showed a switch-like activity pattern. At cervicothoracic and lumbosacral spine, the spinal reflex pathway of the lower urinary tract (LUT) received mechanosensory input from the urothelium to regulate the bladder contraction activity, thereby controlled urination voluntarily. Impairment of above-mentioned any level could result in lower urinary tract dysfunction (LUTD), placed a huge burden on patients and society. Specific expression of purinergic receptors and transient receptor potential (TRP) channels are thought to play an important role in urinary excretion in the lower urinary tract. This article reviewed the knowledge about the voiding reflex and described the role and function of TRP channels during voiding.

GSDMD/Drp1 signaling pathway mediates hippocampal synaptic damage and neural oscillation abnormalities in a mouse model of sepsis-associated encephalopathy.

BACKGROUND: Gasdermin D (GSDMD)-mediated pyroptotic cell death is implicated in the pathogenesis of cognitive deficits in sepsis-associated encephalopathy (SAE), yet the underlying mechanisms remain largely unclear. Dynamin-related protein 1 (Drp1) facilitates mitochondrial fission and ensures quality control to maintain cellular homeostasis during infection. This study aimed to investigate the potential role of the GSDMD/Drp1 signaling pathway in cognitive impairments in a mouse model of SAE. METHODS: C57BL/6 male mice were subjected to cecal ligation and puncture (CLP) to establish an animal model of SAE. In the interventional study, mice were treated with the GSDMD inhibitor necrosulfonamide (NSA) or the Drp1 inhibitor mitochondrial division inhibitor-1 (Mdivi-1). Surviving mice underwent behavioral tests, and hippocampal tissues were harvested for histological analysis and biochemical assays at corresponding time points. Haematoxylin-eosin staining and TUNEL assays were used to evaluate neuronal damage. Golgi staining was used to detect synaptic dendritic spine density. Additionally, transmission electron microscopy was performed to assess mitochondrial and synaptic morphology in the hippocampus. Local field potential recordings were conducted to detect network oscillations in the hippocampus. RESULTS: CLP induced the activation of GSDMD, an upregulation of Drp1, leading to associated mitochondrial impairment, neuroinflammation, as well as neuronal and synaptic damage. Consequently, these effects resulted in a reduction in neural oscillations in the hippocampus and significant learning and memory deficits in the mice. Notably, treatment with NSA or Mdivi-1 effectively prevented these GSDMD-mediated abnormalities. CONCLUSIONS: Our data indicate that the GSDMD/Drp1 signaling pathway is involved in cognitive deficits in a mouse model of SAE. Inhibiting GSDMD or Drp1 emerges as a potential therapeutic strategy to alleviate the observed synaptic damages and network oscillations abnormalities in the hippocampus of SAE mice.

Repeated (S)-ketamine administration ameliorates the spatial working memory impairment in mice with chronic pain: role of the gut microbiota-brain axis.

Chronic pain is commonly linked with diminished working memory. This study explores the impact of the anesthetic (S)-ketamine on spatial working memory in a chronic constriction injury (CCI) mouse model, focusing on gut microbiome. We found that multiple doses of (S)-ketamine, unlike a single dose, counteracted the reduced spontaneous alteration percentage (%SA) in the Y-maze spatial working memory test, without affecting mechanical or thermal pain sensitivity. Additionally, repeated (S)-ketamine treatments improved the abnormal composition of the gut microbiome (ß-diversity), as indicated by fecal 16S rRNA analysis, and increased levels of butyrate, a key gut - brain axis mediator. Protein analysis showed that these treatments also corrected the upregulated histone deacetylase 2 (HDAC2) and downregulated brain-derived neurotrophic factor (BDNF) in the hippocampi of CCI mice. Remarkably, fecal microbiota transplantation from mice treated repeatedly with (S)-ketamine to CCI mice restored %SA and hippocampal BDNF levels in CCI mice. Butyrate supplementation alone also improved %SA, BDNF, and HDAC2 levels in CCI mice. Furthermore, the TrkB receptor antagonist ANA-12 negated the beneficial effects of repeated (S)-ketamine on spatial working memory impairment in CCI mice. These results indicate that repeated (S)-ketamine administration ameliorates spatial working memory impairment in CCI mice, mediated by a gut microbiota - brain axis, primarily through the enhancement of hippocampal BDNF - TrkB signaling by butyrate.

Efficacy of high-dose-rate brachytherapy with different radiation source activities among cervical cancer patients and risk factors for long-term outcomes: A 6-year retrospective study.

PURPOSE: This study aimed to assess the impact of dose rates due to natural decay of Iridium-192 sources and the risk factors of clinical outcomes for cervical cancer patients treated with high-dose-rate (HDR) brachytherapy. METHODS AND MATERIALS: Four ninety-four patients were divided into relatively-high-radioactive (rHR), relatively-medium-radioactive (rMR), and relatively-low-radioactive (rLR) groups for retrospective treatment response comparison. The short-term outcomes were evaluated using the 1-month /3-month follow-up results based on RECIST 1.1. Local recurrence-free survival (LRFS) and metastatic recurrence-free survival (MRFS) were selected as long-term outcomes. A class of transformation models with adaptive lasso was applied to assess the risk factors of long-term outcomes. RESULTS: No significant difference was identified in short- or long-term outcomes of different radioactive groups. Subgroup analyses demonstrated similar findings. In multivariate factor analysis, advanced stage was significantly associated with higher risk of local recurrence and metastatic recurrence (HR = 1.66, 95%confidence interval [CI] = 1.14-2.43, p = 0.008; HR = 1.57, 95%CI = 1.23-2.00, p < 0.001). Significant associations were observed between local recurrence and pathology, and between metastatic recurrence and pre-treatment serum indices, respectively (HR = 8.62, 95%CI = 2.28-32.60, p = 0.002; HR = 1.98, 95%CI=1.20-2.26, p = 0.008). CONCLUSIONS: Overall, there was no significant difference in long- or short-term efficacy of the HDR brachytherapy among the groups with different levels of activity of radiation sources. Stage, pathology, and pretreatment serum indices were crucial factors that affected the long-term outcomes.

Mechanistic modelling of relative biological effectiveness of carbon ion beams and comparison with experiments.

Objective.Relative biological effectiveness (RBE) plays a vital role in carbon ion radiotherapy, which is a promising treatment method for reducing toxic effects on normal tissues and improving treatment efficacy. It is important to have an effective and precise way of obtaining RBE values to support clinical decisions. A method of calculating RBE from a mechanistic perspective is reported.Approach.Ratio of dose to obtain the same number of double strand breaks (DSBs) between different radiation types was used to evaluate RBE. Package gMicroMC was used to simulate DSB yields. The DSB inductions were then analyzed to calculate RBE. The RBE values were compared with experimental results.Main results.Furusawa's experiment yielded RBE values of 1.27, 2.22, 3.00 and 3.37 for carbon ion beam with dose-averaged LET of 30.3 keVµm-1, 54.5 keVµm-1, 88 keVµm-1and 137 keVµm-1, respectively. RBE values computed from gMicroMC simulations were 1.75, 2.22, 2.87 and 2.97. When it came to a more sophisticated carbon ion beam with 6 cm spread-out Bragg peak, RBE values were 1.61, 1.63, 2.19 and 2.36 for proximal, middle, distal and distal end part, respectively. Values simulated by gMicroMC were 1.50, 1.87, 2.19 and 2.34. The simulated results were in reasonable agreement with the experimental data.Significance.As a mechanistic way for the evaluation of RBE for carbon ion radiotherapy by combining the macroscopic simulation of energy spectrum and microscopic simulation of DNA damages, this work provides a promising tool for RBE calculation supporting clinical applications such as treatment planning.

Ultra-fast multi-parametric 4D-MRI image reconstruction for real-time applications using a downsampling-invariant deformable registration (D2R) model.

BACKGROUND AND PURPOSE: Motion estimation from severely downsampled 4D-MRI is essential for real-time imaging and tumor tracking. This simulation study developed a novel deep learning model for simultaneous MR image reconstruction and motion estimation, named the Downsampling-Invariant Deformable Registration (D2R) model. MATERIALS AND METHODS: Forty-three patients undergoing radiotherapy for liver tumors were recruited for model training and internal validation. Five prospective patients from another center were recruited for external validation. Patients received 4D-MRI scans and 3D MRI scans. The 4D-MRI was retrospectively down-sampled to simulate real-time acquisition. Motion estimation was performed using the proposed D2R model. The accuracy and robustness of the proposed D2R model and baseline methods, including Demons, Elastix, the parametric total variation (pTV) algorithm, and VoxelMorph, were compared. High-quality (HQ) 4D-MR images were also constructed using the D2R model for real-time imaging feasibility verification. The image quality and motion accuracy of the constructed HQ 4D-MRI were evaluated. RESULTS: The D2R model showed significantly superior and robust registration performance than all the baseline methods at downsampling factors up to 500. HQ T1-weighted and T2-weighted 4D-MR images were also successfully constructed with significantly improved image quality, sub-voxel level motion error, and real-time efficiency. External validation demonstrated the robustness and generalizability of the technique. CONCLUSION: In this study, we developed a novel D2R model for deformation estimation of downsampled 4D-MR images. HQ 4D-MR images were successfully constructed using the D2R model. This model may expand the clinical implementation of 4D-MRI for real-time motion management during liver cancer treatment.

3D bioprinted endothelial cell-microglia coculture for diabetic retinopathy modeling.

Diabetic retinopathy (DR) is a common diabetes complication leading to vision impairment or blindness due to retinal vasculature alterations. Hyperglycemia induces structural alterations, inflammation, and angiogenic factor upregulation. Current treatments targeting vascular endothelial growth factor are insufficient for approximately 20% of DR patients, necessitating alternative approaches. Microglia (MG), essential for retinal homeostasis, remains underexplored in DR. This study used digital light processing bioprinting to construct a 3D coculture model of endothelial cells (ECs) and MG under varying glucose conditions, with a hydrogel stiffness of 4.6-7.1 kPa to mimic the extracellular matrix property of retina plexiform. Our results showed that high glucose levels influenced both EC and microglial phenotypes, gene expression, and angiogenic potential. Increasing glucose from 5 mM to 25 mM reduces drug efficacy by 17% for Aflibercept in EC monoculture, and 25% and 30% for Aflibercept and Conbercept in EC-MG coculture, respectively, suggesting that diabetic condition and MG presence could interfere with drug responses. In conclusion, our findings emphasize the importance of cellular interactions and microenvironmental factors in DR therapy, aiming to identify novel strategies and improve understanding of MG's role in disease pathogenesis.

A generalizable new figure of merit for dose optimization in dual energy cone beam CT scanning protocols.

Objective. This study proposes and evaluates a new figure of merit (FOMn) for dose optimization of Dual-energy cone-beam CT (DE-CBCT) scanning protocols based on size-dependent modeling of radiation dose and multi-scale image quality.Approach. FOMn was defined using Z-score normalization and was proportional to the dose efficiency providing better multi-scale image quality, including comprehensive contrast-to-noise ratio (CCNR) and electron density (CED) for CatPhan604 inserts of various materials. Acrylic annuluses were combined with CatPhan604 to create four phantom sizes (diameters of the long axis are 200 mm, 270 mm, 350 mm, and 380 mm, respectively). DE-CBCT was decomposed using image-domain iterative methods based on Varian kV-CBCT images acquired using 25 protocols (100 kVp and 140 kVp combined with 5 tube currents).Main results. The accuracy of CED was approximately 1% for all protocols, but degraded monotonically with the increased phantom sizes. Combinations of lower voltage + higher current and higher voltage + lower current were optimal protocols balancing CCNR and dose. The most dose-efficient protocols for CED and CCNR were inconsistent, underlining the necessity of including multi-scale image quality in the evaluation and optimization of DE-CBCT. Pediatric and adult anthropomorphic phantom tests confirmed dose-efficiency of FOMn-recommended protocols.Significance. FOMn is a comprehensive metric that collectively evaluates radiation dose and multi-scale image quality for DE-CBCT. The models and data can also serve as lookup tables, suggesting personalized dose-efficient protocols for specific clinical imaging purposes.

Background-Aware Classification Activation Map for Weakly Supervised Object Localization.

Weakly supervised object localization (WSOL) relaxes the requirement of dense annotations for object localization by using image-level annotation to supervise the learning process. However, most WSOL methods only focus on forcing the object classifier to produce high activation score on object parts without considering the influence of background locations, causing excessive background activations and ill-pose background score searching. Based on this point, our work proposes a novel mechanism called the background-aware classification activation map (B-CAM) to add background awareness for WSOL training. Besides aggregating an object image-level feature for supervision, our B-CAM produces an additional background image-level feature to represent the pure-background sample. This additional feature can provide background cues for the object classifier to suppress the background activations on object localization maps. Moreover, our B-CAM also trained a background classifier with image-level annotation to produce adaptive background scores when determining the binary localization mask. Experiments indicate the effectiveness of the proposed B-CAM on four different types of WSOL benchmarks, including CUB-200, ILSVRC, OpenImages, and VOC2012 datasets.

Prognostic Factors Analysis of Metastatic Recurrence in Cervical Carcinoma Patients Treated with Definitive Radiotherapy: A Retrospective Study Using Mixture Cure Model.

OBJECTIVES: This study aims to identify prognostic factors associated with metastatic recurrence-free survival of cervical carcinoma (CC) patients treated with radical radiotherapy and assess the cure probability of radical radiotherapy from metastatic recurrence. METHODS: Data were from 446 cervical carcinoma patients with radical radiotherapy for an average follow up of 3.96 years. We applied a mixture cure model to investigate the association between metastatic recurrence and prognostic factors and the association between noncure probability and factors, respectively. A nonparametric test of cure probability under the framework of a mixture cure model was used to examine the significance of cure probability of the definitive radiotherapy treatment. Propensity-score-matched (PSM) pairs were generated to reduce bias in subgroup analysis. RESULTS: Patients in advanced stages (p = 0.005) and those with worse treatment responses in the 3rd month (p = 0.004) had higher metastatic recurrence rates. Nonparametric tests of the cure probability showed that 3-year cure probability from metastatic recurrence was significantly larger than 0, and 5-year cure probability was significantly larger than 0.7 but no larger than 0.8. The empirical cure probability by mixture cure model was 79.2% (95% CI: 78.6-79.9%) for the entire study population, and the overall median metastatic recurrence time for uncured patients (patients susceptible to metastatic recurrence) was 1.60 (95% CI: 1.51-1.69) years. Locally advanced/advanced stage was a risk factor but non-significant against the cure probability (OR = 1.078, p = 0.088). The interaction of age and activity of radioactive source were statistically significant in the incidence model (OR = 0.839, p = 0.025). In subgroup analysis, compared with high activity of radioactive source (HARS), low activity of radioactive source (LARS) significantly contributed to a 16.1% higher cure probability for patients greater than 53 years old, while cure probability was 12.2% lower for the younger patients. CONCLUSIONS: There was statistically significant evidence in the data showing the existence of a large amount of patients cured by the definitive radiotherapy treatment. HARS is a protective factor against metastatic recurrence for uncured patients, and young patients tend to benefit more than the elderly from the HARS treatment.

Simulation of a new respiratory phase sorting method for 4D-imaging using optical surface information towards precision radiotherapy.

BACKGROUND: Respiratory signal detection is critical for 4-dimensional (4D) imaging. This study proposes and evaluates a novel phase sorting method using optical surface imaging (OSI), aiming to improve the precision of radiotherapy. METHOD: Based on 4D Extended Cardiac-Torso (XCAT) digital phantom, OSI in point cloud format was generated from the body segmentation, and image projections were simulated using the geometries of Varian 4D kV cone-beam-CT (CBCT). Respiratory signals were extracted respectively from the segmented diaphragm image (reference method) and OSI respectively, where Gaussian Mixture Model and Principal Component Analysis (PCA) were used for image registration and dimension reduction respectively. Breathing frequencies were compared using Fast-Fourier-Transform. Consistency of 4DCBCT images reconstructed using Maximum Likelihood Expectation Maximization algorithm was also evaluated quantitatively, where high consistency can be suggested by lower Root-Mean-Square-Error (RMSE), Structural-Similarity-Index (SSIM) value closer to 1, and larger Peak-Signal-To-Noise-Ratio (PSNR) respectively. RESULTS: High consistency of breathing frequencies was observed between the diaphragm-based (0.232 Hz) and OSI-based (0.251 Hz) signals, with a slight discrepancy of 0.019Hz. Using end of expiration (EOE) and end of inspiration (EOI) phases as examples, the mean±1SD values of the 80 transverse, 100 coronal and 120 sagittal planes were 0.967, 0,972, 0.974 (SSIM); 1.657 ± 0.368, 1.464 ± 0.104, 1.479 ± 0.297 (RMSE); and 40.501 ± 1.737, 41.532 ± 1.464, 41.553 ± 1.910 (PSNR) for the EOE; and 0.969, 0.973, 0.973 (SSIM); 1.686 ± 0.278, 1.422 ± 0.089, 1.489 ± 0.238 (RMSE); and 40.535 ± 1.539, 41.605 ± 0.534, 41.401 ± 1.496 (PSNR) for EOI respectively. CONCLUSIONS: This work proposed and evaluated a novel respiratory phase sorting approach for 4D imaging using optical surface signals, which can potentially be applied to precision radiotherapy. Its potential advantages were non-ionizing, non-invasive, non-contact, and more compatible with various anatomic regions and treatment/imaging systems.

Fu-Zheng-Tong-Luo formula promotes autophagy and alleviates idiopathic pulmonary fibrosis by controlling the Janus kinase 2/signal transducer and activator of transcription 3 pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: Fu-Zheng-Tong-Luo (FZTL) formula is a Chinese herbal prescription which is used to treat idiopathic pulmonary fibrosis (IPF). We previously reported that the FZTL formula could improve IPF injury in rats; however, the mechanism remains unelucidated. AIM OF THE STUDY: To elucidate the effects and mechanisms of the FZTL formula on IPF. MATERIALS AND METHODS: The bleomycin-induced pulmonary fibrosis rat model and transforming growth factor-ß-induced lung fibroblast model were used. Histological changes and fibrosis formation were detected in the rat model after treatment with the FZTL formula. Furthermore, the effects of the FZTL formula on autophagy and lung fibroblast activation were determined. Moreover, the mechanism of FZTL was explored using transcriptomics analysis. RESULTS: We observed that FZTL alleviated IPF injury in rats and inhibited inflammatory responses and fibrosis formation in rats. Moreover, it promoted autophagy and inhibited lung fibroblast activation in vitro. Transcriptomics analysis revealed that FZTL regulates the Janus kinase 2 (JAK)/signal transducer and activator of the transcription 3 (STAT) signaling pathway. The JAK2/STAT3 signaling activator interleukin 6 inhibited the anti-fibroblast activation effect of the FZTL formula. Combined treatment with the JAK2 inhibitor (AZD1480) and autophagy inhibitor (3-methyladenine) did not enhance the antifibrotic effect of FZTL. CONCLUSIONS: The FZTL formula can inhibit IPF injury and lung fibroblast activation. Its effects are mediated via the JAK2/STAT3 signaling pathway. The FZTL formula may be a potential complementary therapy for pulmonary fibrosis.

Comprehensive prognostic modeling of locoregional recurrence after radiotherapy for patients with locoregionally advanced hypopharyngeal squamous cell carcinoma.

Purpose: To propose and evaluate a comprehensive modeling approach combing radiomics, dosiomics and clinical components, for more accurate prediction of locoregional recurrence risk after radiotherapy for patients with locoregionally advanced HPSCC. Materials and methods: Clinical data of 77 HPSCC patients were retrospectively investigated, whose median follow-up duration was 23.27 (4.83-81.40) months. From the planning CT and dose distribution, 1321 radiomics and dosiomics features were extracted respectively from planning gross tumor volume (PGTV) region each patient. After stability test, feature dimension was further reduced by Principal Component Analysis (PCA), yielding Radiomic and Dosiomic Principal Components (RPCs and DPCs) respectively. Multiple Cox regression models were constructed using various combinations of RPC, DPC and clinical variables as the predictors. Akaike information criterion (AIC) and C-index were used to evaluate the performance of Cox regression models. Results: PCA was performed on 338 radiomic and 873 dosiomic features that were tested as stable (ICC1 > 0.7 and ICC2 > 0.95), yielding 5 RPCs and DPCs respectively. Three comprehensive features (RPC0, P<0.01, DPC0, P<0.01 and DPC3, P<0.05) were found to be significant in the individual Radiomic or Dosiomic Cox regression models. The model combining the above features and clinical variable (total stage IVB) provided best risk stratification of locoregional recurrence (C-index, 0.815; 95%CI, 0.770-0.859) and prevailing balance between predictive accuracy and complexity (AIC, 143.65) than any other investigated models using either single factors or two combined components. Conclusion: This study provided quantitative tools and additional evidence for the personalized treatment selection and protocol optimization for HPSCC, a relatively rare cancer. By combining complementary information from radiomics, dosiomics, and clinical variables, the proposed comprehensive model provided more accurate prediction of locoregional recurrence risk after radiotherapy.

Dexpramipexole ameliorates cognitive deficits in sepsis-associated encephalopathy through suppressing mitochondria-mediated pyroptosis and apoptosis.

OBJECTIVES: This study was aimed at evaluating the effects of dexpramipexole (DPX) - a mitochondrial protectant that sustains mitochondrial function and energy production - on cognitive function in a mouse model of sepsis-associated encephalopathy (SAE) induced by peripheral administration of lipopolysaccharide (LPS) and examining the potential mechanisms. METHODS: C57BL/6 male mice were randomized into one of four treatment protocols: Con+Sal, Con+DPX, LPS+Sal or LPS+DPX. The mice were intraperitoneally (i.p.) injected with LPS or equivalent volumes of normal saline once daily for 3 consecutive days. To evaluate the protective effects of DPX, we administered DPX or normal saline i.p. to the mice once daily for 6 consecutive days. Six mice in each group were decapitated on day 7, and each brain was rapidly removed and separated into two halves for biochemical and histochemical analysis. The remaining surviving mice in each group were subjected to behavioral tests from days 7 to 10. RESULTS: Peripheral administration of LPS to mice led to learning and memory deficits in behavioral tests, which were associated with mitochondrial impairment and ATP depletion in the hippocampus. Repeated DPX treatment protected the mitochondria against LPS-induced morphological and functional impairment; inhibited the activation of the Nod-like receptor pyrin domain-containing 3 (NLRP3) inflammasome-caspase-1-dependent pyroptosis pathway and cytochrome c (Cyt-c)-caspase-3-dependent apoptosis pathway; and attenuated LPS-induced neuroinflammation and cell death in the hippocampus in SAE mice. CONCLUSIONS: Mitochondria-mediated pyroptosis and apoptosis are involved in the pathogenesis of cognitive deficits in a mouse model of SAE and DPX protects mitochondria and suppresses the mitochondria-medicated pyroptosis and apoptosis pathways, and ameliorates LPS-induced neuroinflammation and cognitive deficits. This study provides theoretical evidence supporting DPX for the treatment of SAE.

Efficiency and Safety of Baofei Granules in Chronic Obstructive Pulmonary Disease (Lung and Spleen Qi Deficiency Syndrome): A Multicenter, Randomized, Double-Blind, Placebo-Controlled Phase II Clinical Trial.

Background: Baofei Granules (BFGs) have been extensively applied in the clinical treatment of chronic obstructive pulmonary disease (COPD) and significantly have affected COPD patients with lung and spleen qi deficiency syndrome. However, the data from previous small-sample clinical trials are limited. This trial aimed to estimate the efficiency and safety of BFGs in COPD with lung and spleen qi deficiency syndrome. Methods: It is a multicenter, randomized, double-blind, placebo-controlled phase II clinical trial. The 216 stable COPD patients will be divided randomly in a ratio of 1:1. The whole trial period consists of a 4-week introductory period, a 52-week treatment period and a 48-week follow-up. Study visits occur every 4 weeks during the treatment period and every 12 weeks during the follow-up. All the subjects will receive 10g BFGs or placebo three times per day for 56 weeks and be followed up for 48 weeks. The primary efficiency evaluation outcome will be the frequency and duration of AECOPD, and the secondary efficiency evaluation outcome will be pulmonary function tests (PFTs), modified Medical Research Council (mMRC) dyspnoea scale, six-minute walking test (6MWT), COPD assessment test (CAT) score, traditional Chinese medicine (TCM) syndrome score, the frequency of emergency medication, BODE index, and the time to first Clinically important deterioration (CID). The safety evaluation outcomes will be adverse events (AEs), vital signs, physical examination, twelve-lead electrocardiogram (ECG), and laboratory examinations. All the data will be analyzed by SAS9.4. Discussion: This is the first and largest clinical trial that evaluates the efficiency and safety of BFGs for COPD with lung and spleen qi deficiency syndrome. It will provide valuable clinical evidence for recommendations on COPD management by the integrated TCM and western medicine. Trial Registration: CTR20211280. Date: June 09, 2021. http://www.chinadrugtrials.org.cn/clinicaltrials.searchlistdetail.dhtml?id=383a370ecd9f43d7af6f1c8585779e1a.

Deep learning-based synthetization of real-time in-treatment 4D images using surface motion and pretreatment images: A proof-of-concept study.

PURPOSE: To develop a deep learning model that maps body surface motion to internal anatomy deformation, which is potentially applicable to dose-free real-time 4D virtual image-guided radiotherapy based on skin surface data. METHODS: Body contours were segmented out of 4DCT images. Deformable image registration algorithm was used to register the end-of-exhalation (EOE) phase to other phases. Deformation vector field was dimension-reduced to the first two principal components (PCs). A deep learning model was trained to predict the two PC scores of each phase from surface displacement. The instant deformation field can then be reconstructed, warping EOE image to obtain real-time CT image. This approach was validated on 4D XCAT phantom, the public DIR-Lab, and 4D-Lung dataset respectively, with and without simulated noise. RESULTS: Validation accuracy of the tumor centroid trajectory was observed as 0.04 ± 0.02 mm on XCAT phantom. For the DIR-Lab dataset, 300 landmarks were annotated on the end-of-inhalation (EOI) images of each patient, and the mean displacements between their predicted and reference positions were below 2 mm for all studied cases. For the 4D-Lung dataset, the average dice coefficients ± std between predicted and reference tumor contours at EOI phase were 0.835 ± 0.092 for all studied cases. CONCLUSIONS: A deep learning-based approach was proposed and validated to predict internal anatomy deformation from the surface motion, which is potentially applicable to on-line target navigation for accurate radiotherapy based on real-time 4D skin surface data and pretreatment images.

Triplet Cross-Fusion Learning for Unpaired Image Denoising in Optical Coherence Tomography.

Optical coherence tomography (OCT) is a widely-used modality in clinical imaging, which suffers from the speckle noise inevitably. Deep learning has proven its superior capability in OCT image denoising, while the difficulty of acquiring a large number of well-registered OCT image pairs limits the developments of paired learning methods. To solve this problem, some unpaired learning methods have been proposed, where the denoising networks can be trained with unpaired OCT data. However, majority of them are modified from the cycleGAN framework. These cycleGAN-based methods train at least two generators and two discriminators, while only one generator is needed for the inference. The dual-generator and dual-discriminator structures of cycleGAN-based methods demand a large amount of computing resource, which may be redundant for OCT denoising tasks. In this work, we propose a novel triplet cross-fusion learning (TCFL) strategy for unpaired OCT image denoising. The model complexity of our strategy is much lower than those of the cycleGAN-based methods. During training, the clean components and the noise components from the triplet of three unpaired images are cross-fused, helping the network extract more speckle noise information to improve the denoising accuracy. Furthermore, the TCFL-based network which is trained with triplets can deal with limited training data scenarios. The results demonstrate that the TCFL strategy outperforms state-of-the-art unpaired methods both qualitatively and quantitatively, and even achieves denoising performance comparable with paired methods. Code is available at: https://github.com/gengmufeng/TCFL-OCT.

A generalized image quality improvement strategy of cone-beam CT using multiple spectral CT labels in Pix2pix GAN.

Objective.The quantitative and routine imaging capabilities of cone-beam CT (CBCT) are hindered from clinical applications due to the severe shading artifacts of scatter contamination. The scatter correction methods proposed in the literature only consider the anatomy of the scanned objects while disregarding the impact of incident x-ray energy spectra. The multiple-spectral model is in urgent need for CBCT scatter estimation.Approach.In this work, we incorporate the multiple spectral diagnostic multidetector CT labels into the pixel-to-pixel (Pix2pix) GAN to estimate accurate scatter distributions from CBCT projections acquired at various imaging volume sizes and x-ray energy spectra. The Pix2pix GAN combines the residual network as the generator and the PatchGAN as the discriminator to construct the correspondence between the scatter-contaminated projection and scatter distribution. The network architectures and loss function of Pix2pix GAN are optimized to achieve the best performance on projection-to-scatter transition.Results.The CBCT data of a head phantom and abdominal patients are applied to test the performance of the proposed method. The error of the corrected CBCT image using the proposed method is reduced from over 200 HU to be around 20 HU in both phantom and patient studies. The mean structural similarity index of the CT image is improved from 0.2 to around 0.9 after scatter correction using the proposed method compared with the MC-simulation method, which indicates a high similarity of the anatomy in the images before and after the proposed correction. The proposed method achieves higher accuracy of scatter estimation than using the Pix2pix GAN with the U-net generator.Significance.The proposed scheme is an effective solution to the multiple spectral CBCT scatter correction. The scatter-correction software using the proposed model will be available at:https://github.com/YangkangJiang/Cone-beam-CT-scatter-correction-tool.

Synergistically segmenting choroidal layer and vessel using deep learning for choroid structure analysis.

Objective. The choroid is the most vascularized structure in the human eye, whose layer structure and vessel distribution are both critical for the physiology of the retina, and disease pathogenesis of the eye. Although some works have used graph-based methods or convolutional neural networks to separate the choroid layer from the outer-choroid structure, few works focused on further distinguishing the inner-choroid structure, such as the choroid vessel and choroid stroma.Approach.Inspired by the multi-task learning strategy, in this paper, we propose a segmentation pipeline for choroid analysis which can separate the choroid layer from other structures and segment the choroid vessel synergistically. The key component of this pipeline is the proposed choroidal U-shape network (CUNet), which catches both correlation features and specific features between the choroid layer and the choroid vessel. Then pixel-wise classification is generated based on these two types of features to obtain choroid layer segmentation and vessel segmentation. Besides, the training process of CUNet is supervised by a proposed adaptive multi-task segmentation loss which adds a regularization term that is used to balance the performance of the two tasks.Main results.Experiments show the high performance (4% higher dice score) and less computational complexity (18.85 M lower size) of our proposed strategy.Significance.The high performance and generalization on both choroid layer and vessel segmentation indicate the clinical potential of our proposed pipeline.

A dual-supervised deformation estimation model (DDEM) for constructing ultra-quality 4D-MRI based on a commercial low-quality 4D-MRI for liver cancer radiation therapy.

BACKGROUND: Most available four-dimensional (4D)-magnetic resonance imaging (MRI) techniques are limited by insufficient image quality and long acquisition times or require specially designed sequences or hardware that are not available in the clinic. These limitations have greatly hindered the clinical implementation of 4D-MRI. PURPOSE: This study aims to develop a fast ultra-quality (UQ) 4D-MRI reconstruction method using a commercially available 4D-MRI sequence and dual-supervised deformation estimation model (DDEM). METHODS: Thirty-nine patients receiving radiotherapy for liver tumors were included. Each patient was scanned using a time-resolved imaging with interleaved stochastic trajectories (TWIST)-lumetric interpolated breath-hold examination (VIBE) MRI sequence to acquire 4D-magnetic resonance (MR) images. They also received 3D T1-/T2-weighted MRI scans as prior images, and UQ 4D-MRI at any instant was considered a deformation of them. A DDEM was developed to obtain a 4D deformable vector field (DVF) from 4D-MRI data, and the prior images were deformed using this 4D-DVF to generate UQ 4D-MR images. The registration accuracies of the DDEM, VoxelMorph (normalized cross-correlation [NCC] supervised), VoxelMorph (end-to-end point error [EPE] supervised), and the parametric total variation (pTV) algorithm were compared. Tumor motion on UQ 4D-MRI was evaluated quantitatively using region of interest (ROI) tracking errors, while image quality was evaluated using the contrast-to-noise ratio (CNR), lung-liver edge sharpness, and perceptual blur metric (PBM). RESULTS: The registration accuracy of the DDEM was significantly better than those of VoxelMorph (NCC supervised), VoxelMorph (EPE supervised), and the pTV algorithm (all, p < 0.001), with an inference time of 69.3 ± 5.9 ms. UQ 4D-MRI yielded ROI tracking errors of 0.79 ± 0.65, 0.50 ± 0.55, and 0.51 ± 0.58 mm in the superior-inferior, anterior-posterior, and mid-lateral directions, respectively. From the original 4D-MRI to UQ 4D-MRI, the CNR increased from 7.25 ± 4.89 to 18.86 ± 15.81; the lung-liver edge full-width-at-half-maximum decreased from 8.22 ± 3.17 to 3.65 ± 1.66 mm in the in-plane direction and from 8.79 ± 2.78 to 5.04 ± 1.67 mm in the cross-plane direction, and the PBM decreased from 0.68 ± 0.07 to 0.38 ± 0.01. CONCLUSION: This novel DDEM method successfully generated UQ 4D-MR images based on a commercial 4D-MRI sequence. It shows great promise for improving liver tumor motion management during radiation therapy.