Allicin in pregnancy diets modulates steroid metabolism in pregnant sows and placental sulphate metabolism promoting placental angiogenesis and foetal development

The low-birth-weight of piglets is an important factor affecting pig enterprises. The placenta, as a key organ for material exchange between mother and foetus, directly influences the growth and development of the foetus. Allicin exhibits various biological activities, including anti-inflammatory and antioxidant properties. It may also play a crucial role in enhancing sow reproductive performance and placental angiogenesis. In this study, we used 70 lactating Landrace × Yorkshire binary heterozygous sows to explore the effect of allicin on the reproductive performance of sows and placental development. The sows were randomly assigned into the Allicin group (Allicin), which was fed with a diet containing 0.25% allicin, and the negative control group, which was fed with basal feed. The experimental period lasted for 114 d from the date of mating to the end of farrowing. The results showed that the addition of allicin to the gestation diets increased the number of total born piglets, born alive piglets, and high-birth-weight piglets, reduced peripartum oxidative stress, alleviated dysregulation of glucose-lipid metabolism in sows, and increased the levels of antioxidant markers in the placenta. Differential analysis of metabolites in maternal plasma and placenta samples by non-targeted metabolomics revealed that allicin improved cholesterol metabolism, steroid biosynthesis, and increased plasma progesterone levels in sows. Allicin promoted sulphur metabolism, cysteine and methionine metabolism in placental samples and increased the hydrogen sulphide (H2S) content in the placenta. In addition, Quantitative Real-time PCR, Western blot and immunofluorescence results showed that allicin upregulated the expression of angiogenesis-related genes, VEGF-A, FLK 1 and Ang 1, in the placenta, implying that it promoted placental angiogenesis. These results indicate that supplementing the diet of pregnant sows with allicin reduces oxidative stress, alleviates dysregulation of glucose-lipid metabolism during the periparturient period, and promotes placental angiogenesis and foetal development by increasing plasma progesterone level and placental H2S content.

Full-dose whole-body PET synthesis from low-dose PET using high-efficiency denoising diffusion probabilistic model: PET consistency model

PURPOSE

Positron Emission Tomography (PET) has been a commonly used imaging modality in broad clinical applications. One of the most important tradeoffs in PET imaging is between image quality and radiation dose: high image quality comes with high radiation exposure. Improving image quality is desirable for all clinical applications while minimizing radiation exposure is needed to reduce risk to patients.

METHODS

We introduce PET Consistency Model (PET-CM), an efficient diffusion-based method for generating high-quality full-dose PET images from low-dose PET images. It employs a two-step process, adding Gaussian noise to full-dose PET images in the forward diffusion, and then denoising them using a PET Shifted-window Vision Transformer (PET-VIT) network in the reverse diffusion. The PET-VIT network learns a consistency function that enables direct denoising of Gaussian noise into clean full-dose PET images. PET-CM achieves state-of-the-art image quality while requiring significantly less computation time than other methods. Evaluation with normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR), multi-scale structure similarity index (SSIM), normalized cross-correlation (NCC), and clinical evaluation including Human Ranking Score (HRS) and Standardized Uptake Value (SUV) Error analysis shows its superiority in synthesizing full-dose PET images from low-dose inputs.

RESULTS

In experiments comparing eighth-dose to full-dose images, PET-CM demonstrated impressive performance with NMAE of 1.278 ± 0.122%, PSNR of 33.783 ± 0.824 dB, SSIM of 0.964 ± 0.009, NCC of 0.968 ± 0.011, HRS of 4.543, and SUV Error of 0.255 ± 0.318%, with an average generation time of 62 s per patient. This is a significant improvement compared to the state-of-the-art diffusion-based model with PET-CM reaching this result 12× faster. Similarly, in the quarter-dose to full-dose image experiments, PET-CM delivered competitive outcomes, achieving an NMAE of 0.973 ± 0.066%, PSNR of 36.172 ± 0.801 dB, SSIM of 0.984 ± 0.004, NCC of 0.990 ± 0.005, HRS of 4.428, and SUV Error of 0.151 ± 0.192% using the same generation process, which underlining its high quantitative and clinical precision in both denoising scenario.

CONCLUSIONS

We propose PET-CM, the first efficient diffusion-model-based method, for estimating full-dose PET images from low-dose images. PET-CM provides comparable quality to the state-of-the-art diffusion model with higher efficiency. By utilizing this approach, it becomes possible to maintain high-quality PET images suitable for clinical use while mitigating the risks associated with radiation. The code is availble at https://github.com/shaoyanpan/Full-dose-Whole-body-PET-Synthesis-from-Low-dose-PET-Using-Consistency-Model.

CBCT-based synthetic CT image generation using a diffusion model for CBCT-guided lung radiotherapy

BACKGROUND

Although cone beam computed tomography (CBCT) has lower resolution compared to planning CTs (pCT), its lower dose, higher high-contrast resolution, and shorter scanning time support its widespread use in clinical applications, especially in ensuring accurate patient positioning during the image-guided radiation therapy (IGRT) process.

PURPOSE

While CBCT is critical to IGRT, CBCT image quality can be compromised by severe stripe and scattering artifacts. Tumor movement secondary to respiratory motion also decreases CBCT resolution. In order to improve the image quality of CBCT, we propose a Lung Diffusion Model (L-DM) framework.

METHODS

Our proposed algorithm is based on a conditional diffusion model trained on pCT and deformed CBCT (dCBCT) image pairs to synthesize lung CT images from dCBCT images and benefit CBCT-based radiotherapy. dCBCT images were used as the constraint for the L-DM. The image quality and Hounsfield unit (HU) values of the synthetic CTs (sCT) images generated by the proposed L-DM were compared to three selected mainstream generation models.

RESULTS

We verified our model in both an institutional lung cancer dataset and a selected public dataset. Our L-DM showed significant improvement in the four metrics of mean absolute error (MAE), peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC), and structural similarity index measure (SSIM). In our institutional dataset, our proposed L-DM decreased the MAE from 101.47 to 37.87 HU and increased the PSNR from 24.97 to 29.89 dB, the NCC from 0.81 to 0.97, and the SSIM from 0.80 to 0.93. In the public dataset, our proposed L-DM decreased the MAE from 173.65 to 58.95 HU, while increasing the PSNR, NCC, and SSIM from 13.07 to 24.05 dB, 0.68 to 0.94, and 0.41 to 0.88, respectively.

CONCLUSIONS

The proposed L-DM significantly improved sCT image quality compared to the pre-correction CBCT and three mainstream generative models. Our model can benefit CBCT-based IGRT and other potential clinical applications as it increases the HU accuracy and decreases the artifacts from input CBCT images.

Diagnostic and prognostic value of parameters of erector spinae in patients with uremic sarcopenia

AIM

This study aimed to investigate whether computed tomography (CT)-measured erector spinae parameters (ESPs) have diagnostic, severity assessment, and prognostic predictive value in uremic sarcopenia (US).

MATERIALS AND METHODS

A total of 202 uremic patients were enrolled and divided into two groups: a control group and a sarcopenia group. Sarcopenia was classified into two types: severe and nonsevere. The area, volume, and density of the erector spinae (ES) were measured using chest CT images, and the relevant ESP, including the erector spinae index (ESI), total erector spinae volume (TESV), erector spinae density (ESD), and erector spinae gauge (ESG) were calculated. The occurrence of adverse events was followed-up for 36 months. The diagnostic value and severity of US were determined using the receiver operating characteristic (ROC) curve. Survival curves diagnosed using CT were plotted and compared with the curve drawn using the gold standard. Cox regression analysis was used to identify independent risk factors associated with survival in US.

RESULTS

With an area under the curve (AUC) of 0.840 and 0.739, the combined ESP has diagnostic value and the ability to assess the severity of US. There was no significant difference in the survival curve between the combined ESP for the diagnosis of US and the gold standard (P > 0.05). ESI is a standalone predictor of survival in patients with US.

CONCLUSION

ESP measured by CT has diagnostic values for US and its severity, as well as being a predictive value for the prognosis of US.

Unsupervised Bayesian Generation of Synthetic CT from CBCT Using Patient-Specific Score-Based Prior

BACKGROUND

Cone-beam computed tomography (CBCT) scans, performed fractionally (e.g., daily or weekly), are widely utilized for patient alignment in the image-guided radiotherapy (IGRT) process, thereby making it a potential imaging modality for the implementation of adaptive radiotherapy (ART) protocols. Nonetheless, significant artifacts and incorrect Hounsfield unit (HU) values hinder their application in quantitative tasks such as target and organ segmentations and dose calculation. Therefore, acquiring CT-quality images from the CBCT scans is essential to implement online ART in clinical settings.

PURPOSE

This work aims to develop an unsupervised learning method using the patient-specific diffusion model for CBCT-based synthetic CT (sCT) generation to improve the image quality of CBCT.

METHODS

The proposed method is in an unsupervised framework that utilizes a patient-specific score-based model as the image prior alongside a customized total variation (TV) regularization to enforce coherence across different transverse slices. The score-based model is unconditionally trained using the same patient's planning CT (pCT) images to characterize the manifold of CT-quality images and capture the unique anatomical information of the specific patient. The efficacy of the proposed method was assessed on images from anatomical sites including head and neck (H&N) cancer, pancreatic cancer, and lung cancer. The performance of the proposed CBCT correction method was evaluated using quantitative metrics including mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and normalized cross-correlation (NCC). Additionally, the proposed algorithm was benchmarked against two other unsupervised diffusion model-based CBCT correction algorithms.

CT-based synthetic iodine map generation using conditional denoising diffusion probabilistic model

BACKGROUND

Iodine maps, derived from image-processing of contrast-enhanced dual-energy computed tomography (DECT) scans, highlight the differences in tissue iodine intake. It finds multiple applications in radiology, including vascular imaging, pulmonary evaluation, kidney assessment, and cancer diagnosis. In radiation oncology, it can contribute to designing more accurate and personalized treatment plans. However, DECT scanners are not commonly available in radiation therapy centers. Additionally, the use of iodine contrast agents is not suitable for all patients, especially those allergic to iodine agents, posing further limitations to the accessibility of this technology.

PURPOSE

The purpose of this work is to generate synthetic iodine map images from non-contrast single-energy CT (SECT) images using conditional denoising diffusion probabilistic model (DDPM).

METHODS

One-hundered twenty-six head-and-neck patients' images were retrospectively investigated in this work. Each patient underwent non-contrast SECT and contrast DECT scans. Ground truth iodine maps were generated from contrast DECT scans using commercial software syngo.via installed in the clinic. A conditional DDPM was implemented in this work to synthesize iodine maps. Three-fold cross-validation was conducted, with each iteration selecting the data from 42 patients as the test dataset and the remainder as the training dataset. Pixel-to-pixel generative adversarial network (GAN) and CycleGAN served as reference methods for evaluating the proposed DDPM method.

RESULTS

The accuracy of the proposed DDPM was evaluated using three quantitative metrics: mean absolute error (MAE) (1.039 ± 0.345 mg/mL), structural similarity index measure (SSIM) (0.89 ± 0.10) and peak signal-to-noise ratio (PSNR) (25.4 ± 3.5 db) respectively. Compared to the reference methods, the proposed technique showcased superior performance across the evaluated metrics, further validated by the paired two-tailed t-tests.

CONCLUSION

The proposed conditional DDPM framework has demonstrated the feasibility of generating synthetic iodine map images from non-contrast SECT images. This method presents a potential clinical application, which is providing accurate iodine contrast map in instances where only non-contrast SECT is accessible.

Predicting Dental General Anesthesia Use among Children with Behavioral Health Conditions

OBJECTIVES

To evaluate how different data sources affect the performance of machine learning algorithms that predict dental general anesthesia use among children with behavioral health conditions.

STUDY DESIGN

Observational study using claims data.

METHODS

Using Medicaid claims from Partners For Kids (2013-2019), electronic medical record data, and the Ohio Child Opportunity Index, we conducted a retrospective cohort study of 12,410 children with behavioral health diagnoses. Four lasso-regularized logistic regression models were developed to predict dental general anesthesia use, each incorporating different data sources. Lift scores, or the ratio of positive predictive value to base case prevalence, were used to compare models, and a lift score of 2.5 was considered minimally acceptable for risk prediction.

RESULTS

Dental general anesthesia use ranged from 3.2% to 3.9% across models, which made it difficult for the machine learning models to achieve high positive predictive value. Model performance was best when either the electronic medical record (lift = 2.59) or Ohio Child Opportunity Index (lift = 2.56), but not both (lift = 2.34) or neither (lift = 1.87), was used.

CONCLUSIONS

Incorporating additional data sources improved machine learning model performance, and 2 models achieved satisfactory performance. The model using electronic medical record data could be applied in hospital-based settings, and the model using the Ohio Child Opportunity Index could be more valuable in community-based settings.

KNOWLEDGE TRANSFER STATEMENT

Machine learning was applied to satisfactorily predict which children with behavioral health diagnoses would require dental treatment under general anesthesia. Incorporating electronic medical record data or area-level social determinants of health data, but not both, improved the performance of the machine learning predictions. The 2 highest performing models could be applied by hospitals using medical record data or by organizations using area-level social determinants of health data to risk stratify the pediatric behavioral health population.

Image-domain material decomposition for dual-energy CT using unsupervised learning with data-fidelity loss

BACKGROUND

Dual-energy computed tomography (DECT) and material decomposition play vital roles in quantitative medical imaging. However, the decomposition process may suffer from significant noise amplification, leading to severely degraded image signal-to-noise ratios (SNRs). While existing iterative algorithms perform noise suppression using different image priors, these heuristic image priors cannot accurately represent the features of the target image manifold. Although deep learning-based decomposition methods have been reported, these methods are in the supervised-learning framework requiring paired data for training, which is not readily available in clinical settings.

PURPOSE

This work aims to develop an unsupervised-learning framework with data-measurement consistency for image-domain material decomposition in DECT.

METHODS

The proposed framework combines iterative decomposition and deep learning-based image prior in a generative adversarial network (GAN) architecture. In the generator module, a data-fidelity loss is introduced to enforce the measurement consistency in material decomposition. In the discriminator module, the discriminator is trained to differentiate the low-noise material-specific images from the high-noise images. In this scheme, paired images of DECT and ground-truth material-specific images are not required for the model training. Once trained, the generator can perform image-domain material decomposition with noise suppression in a single step.

RESULTS

In the simulation studies of head and lung digital phantoms, the proposed method reduced the standard deviation (SD) in decomposed images by 97% and 91% from the values in direct inversion results. It also generated decomposed images with structural similarity index measures (SSIMs) greater than 0.95 against the ground truth. In the clinical head and lung patient studies, the proposed method suppressed the SD by 95% and 93% compared to the decomposed images of matrix inversion.

CONCLUSIONS

Since the invention of DECT, noise amplification during material decomposition has been one of the biggest challenges, impeding its quantitative use in clinical practice. The proposed method performs accurate material decomposition with efficient noise suppression. Furthermore, the proposed method is within an unsupervised-learning framework, which does not require paired data for model training and resolves the issue of lack of ground-truth data in clinical scenarios.

Emerging anticancer potential and mechanisms of snake venom toxins: A review

Animal-derived venom, like snake venom, has been proven to be valuable natural resources for the drug development. Previously, snake venom was mainly investigated in its pharmacological activities in regulating coagulation, vasodilation, and cardiovascular function, and several marketed cardiovascular drugs were successfully developed from snake venom. In recent years, snake venom fractions have been demonstrated with anticancer properties of inducing apoptotic and autophagic cell death, restraining proliferation, suppressing angiogenesis, inhibiting cell adhesion and migration, improving immunity, and so on. A number of active anticancer enzymes and peptides have been identified from snake venom toxins, such as L-amino acid oxidases (LAAOs), phospholipase A2 (PLA2), metalloproteinases (MPs), three-finger toxins (3FTxs), serine proteinases (SPs), disintegrins, C-type lectin-like proteins (CTLPs), cell-penetrating peptides, cysteine-rich secretory proteins (CRISPs). In this review, we focus on summarizing these snake venom-derived anticancer components on their anticancer activities and underlying mechanisms. We will also discuss their potential to be developed as anticancer drugs in the future.

A phase I study of TAK-659 and paclitaxel in patients with taxane-refractory advanced solid tumors

BACKGROUND

Paclitaxel resistance limits durability of response in patients with initial clinical benefit. Overexpression of spleen tyrosine kinase (SYK) has been proposed as a possible resistance mechanism. This phase I trial evaluated the safety and preliminary activity of the SYK inhibitor TAK-659 combined with paclitaxel in patients with advanced taxane-refractory solid tumors.

PATIENTS AND METHODS

Patients with advanced solid tumors and prior progression on taxane-based therapy received intravenous infusion of paclitaxel on days 1, 8, and 15 plus oral TAK-659 daily in 28-day cycles. The dose-escalation phase included six cohorts treated at different dose levels; the dose-expansion phase included patients with ovarian cancer treated at the highest dose level. Toxicity was graded using the National Cancer Institute Common Terminology Criteria for Adverse Events version 5.0. Efficacy was evaluated using Response Evaluation Criteria in Solid Tumors version 1.1.

RESULTS

Our study included 49 patients. Maximum tolerated dose was not reached, but higher rates of adverse events were observed at higher dose levels. There were no treatment-related deaths. The most common treatment-related adverse events of any grade were increased aspartate aminotransferase (n = 31; 63%), increased alanine aminotransferase (n = 26; 53%), decreased neutrophil count (n = 26; 53%), and decreased white blood cell count (n = 26; 53%). Most adverse events were either grade 1 or 2. In the 44 patients with evaluable disease, 12 (27%) had stable disease as the best overall response, including three patients with prolonged stable disease, and 4 patients (9%) achieved a partial response.

CONCLUSIONS

The combination of paclitaxel and TAK-659 showed preliminary activity possibly overcoming resistance to taxane-based therapy as well as a tolerable safety profile in patients with advanced solid tumors.

[Clinical analysis of 9 children with refractory N-methyl-D-aspartate receptor antibody encephalitis children treated with tocilizumab]

Objective: To analyze the clinical features of children with refractory N-methyl-D-aspartate (NMDA) receptor antibody encephalitis treated with tocilizumab. Methods: Demographic and clinical manifeatations, immunotherapy and prognosis data of 9 children with refractory NMDA receptor antibody encephalitis who received tocilizumab in the Department of Pediatrics Neurology, XiangYa Hospital of Central South University from August 2021 to September 2023 were collected retrospectively. Prognosis was evaluated using the modified Rankin scale at initial diagnosis, at the initiation of tocilizumab treatment, and at the last follow-up. Treatment related complications, neuroimaging, and electroencephalography data were analyzed. Results: Among the 9 children, 6 were male and 3 were female, with an onset age of 4.2 (2.8, 8.7) years. At the onset of the disease, 9 children had a modified Rankin scale score of 5. When tocilizumab treatment was initiated, 7 children had a score of 5, and 2 children had a score of 4. The interval between the onset and initiation of tocilizumab treatment was 12 (5, 27) months, and the treatment frequency was 8 (5, 13) times. The follow-up time was 2.8 (1.5, 3.7) years. At the last follow-up, the symptoms of 9 children, including movement disorder, sleep disorder, consciousness disorder, silence and autonomic dysfunction, were improved to varying degrees, and none of them had seizures. At the last follow-up, 4 cases with a modified Rankin scale score of 0, 1 case with a score of 1, 2 cases with a score of 3, 1 case with a score of 4 and 1 case with a score of 5. The modified Rankin scale at the last follow-up was significantly different from that at the start of tocilizumab (Z=-2.56, P=0.014). All children had no serious adverse reactions during the treatment. Conclusions: After treatment with tocilizumab, the symptoms in patievts with refractory NMDA receptor antibody encephalitis, including movement disorder, sleep disorder, consciousness disorder, silence and autonomic dysfunction were improved, and none of them had seizures. The modified Rankin scale were improved, and the safety was good.

The COSPAR planetary protection policy for missions to Icy Worlds: A review of history, current scientific knowledge, and future directions

Recent discoveries related to the habitability and astrobiological relevance of the outer Solar System have expanded our understanding of where and how life may have originated. As a result, the Icy Worlds of the outer Solar System have become among the highest priority targets for future spacecraft missions dedicated to astrobiology-focused and/or direct life detection objectives. This, in turn, has led to a renewed interest in planetary protection concerns and policies for the exploration of these worlds and has been a topic of discussion within the COSPAR (Committee on Space Research) Panel on Planetary Protection. This paper summarizes the results of those discussions, reviewing the current knowledge and the history of planetary protection considerations for Icy Worlds as well as suggesting ways forward. Based on those discussions, we therefore suggest to (1) Establish a new definition for Icy Worlds for Planetary Protection that captures the outer Solar System moons and dwarf planets like Pluto, but excludes more primitive bodies such as comets, centaurs, and asteroids: Icy Worlds in our Solar System are defined as all bodies with an outermost layer that is believed to be greater than 50 % water ice by volume and have enough mass to assume a nearly round shape. (2) Establish indices for the lower limits of Earth life with regards to water activity (LLAw) and temperature (LLT) and apply them into all areas of the COSPAR Planetary Protection Policy. These values are currently set at 0.5 and -28 °C and were originally established for defining Mars Special Regions; (3) Establish LLT as a parameter to assign categorization for Icy Worlds missions. The suggested categorization will have a 1000-year period of biological exploration, to be applied to all Icy Worlds and not just Europa and Enceladus as is currently the case. (4) Have all missions consider the possibility of impact. Transient thermal anomalies caused by impact would be acceptable so long as there is less than 10-4 probability of a single microbe reaching deeper environments where temperature is >LLT in the period of biological exploration. (5) Restructure or remove Category II* from the policy as it becomes largely redundant with this new approach, (6) Establish that any sample return from an Icy World should be Category V restricted Earth return.

Synthetic CT generation from MRI using 3D transformer-based denoising diffusion model

BACKGROUND AND PURPOSE

Magnetic resonance imaging (MRI)-based synthetic computed tomography (sCT) simplifies radiation therapy treatment planning by eliminating the need for CT simulation and error-prone image registration, ultimately reducing patient radiation dose and setup uncertainty. In this work, we propose a MRI-to-CT transformer-based improved denoising diffusion probabilistic model (MC-IDDPM) to translate MRI into high-quality sCT to facilitate radiation treatment planning.

METHODS

MC-IDDPM implements diffusion processes with a shifted-window transformer network to generate sCT from MRI. The proposed model consists of two processes: a forward process, which involves adding Gaussian noise to real CT scans to create noisy images, and a reverse process, in which a shifted-window transformer V-net (Swin-Vnet) denoises the noisy CT scans conditioned on the MRI from the same patient to produce noise-free CT scans. With an optimally trained Swin-Vnet, the reverse diffusion process was used to generate noise-free sCT scans matching MRI anatomy. We evaluated the proposed method by generating sCT from MRI on an institutional brain dataset and an institutional prostate dataset. Quantitative evaluations were conducted using several metrics, including Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), Multi-scale Structure Similarity Index (SSIM), and Normalized Cross Correlation (NCC). Dosimetry analyses were also performed, including comparisons of mean dose and target dose coverages for 95% and 99%.

RESULTS

MC-IDDPM generated brain sCTs with state-of-the-art quantitative results with MAE 48.825 ± 21.491 HU, PSNR 26.491 ± 2.814 dB, SSIM 0.947 ± 0.032, and NCC 0.976 ± 0.019. For the prostate dataset: MAE 55.124 ± 9.414 HU, PSNR 28.708 ± 2.112 dB, SSIM 0.878 ± 0.040, and NCC 0.940 ± 0.039. MC-IDDPM demonstrates a statistically significant improvement (with p < 0.05) in most metrics when compared to competing networks, for both brain and prostate synthetic CT. Dosimetry analyses indicated that the target dose coverage differences by using CT and sCT were within ± 0.34%.

CONCLUSIONS

We have developed and validated a novel approach for generating CT images from routine MRIs using a transformer-based improved DDPM. This model effectively captures the complex relationship between CT and MRI images, allowing for robust and high-quality synthetic CT images to be generated in a matter of minutes. This approach has the potential to greatly simplify the treatment planning process for radiation therapy by eliminating the need for additional CT scans, reducing the amount of time patients spend in treatment planning, and enhancing the accuracy of treatment delivery.

Sudomotor dysfunction is associated with impaired left ventricular diastolic function in persons with type 2 diabetes: a cross-sectional study

BACKGROUND

The incidence of preserved ejection fraction heart failure has significantly increased in persons with type 2 diabetes mellitus (T2DM). Left ventricular (LV) diastolic dysfunction is an early and important manifestation of preserved ejection fraction heart failure. The onset of heart failure in persons with diabetes is associated with diabetic neuropathy. However, the relationship among sudomotor function, which is an early manifestation of small fiber neuropathy, and LV diastolic function remains unclear. This study aimed to explore the association between sudomotor function and LV diastolic function in persons with T2DM.

METHODS

In total, 699 persons with T2DM were enrolled and divided into three groups according to electrochemical skin conductance (ESC) assessed using the SUDOSCAN device: "no dysfunction" group (NSF), "moderate dysfunction" group (MDF), and "severe dysfunction" group (SDF). LV diastolic function was assessed using Doppler echocardiography. To evaluate the relationship between ESC and echocardiographic parameters, Pearson's correlation analysis was performed. Additionally, logistic regression analysis was used to determine the association between LV diastolic function and ESC. A receiver operating characteristic (ROC) curve was constructed to evaluate the performance of sudomotor function indicators in detecting impaired cardiac diastolic function.

RESULTS

There were 301 persons (43.06%) in the NSF group, 232 (33.19%) in the MDF group, and 166 (23.75%) in the SDF group. Compared to the NSF group, the MDF and SDF groups had higher A and E/e' and lower e' values (all p < 0.05). Pearson's correlation analysis showed that A and E/e' were negatively associated with foot ESC (FESC) and hand ESC (HESC), whereas e' was positively associated with FESC and HESC (all p < 0.05). After adjusting for confounding factors, binary logistic regression analysis showed that ESC was independently associated with impaired LV diastolic function (p = 0.003). The area under the ROC curve values for FESC and HESC were 0.621 and 0.635, respectively (both p < 0.05).

CONCLUSIONS

Deteriorating sudomotor function was associated with reduced diastolic function indicators. ESC can be used as a biomarker for detecting LV diastolic impairment.

CBCT-Based synthetic CT image generation using conditional denoising diffusion probabilistic model

BACKGROUND

Daily or weekly cone-beam computed tomography (CBCT) scans are commonly used for accurate patient positioning during the image-guided radiotherapy (IGRT) process, making it an ideal option for adaptive radiotherapy (ART) replanning. However, the presence of severe artifacts and inaccurate Hounsfield unit (HU) values prevent its use for quantitative applications such as organ segmentation and dose calculation. To enable the clinical practice of online ART, it is crucial to obtain CBCT scans with a quality comparable to that of a CT scan.

PURPOSE

This work aims to develop a conditional diffusion model to perform image translation from the CBCT to the CT distribution for the image quality improvement of CBCT.

METHODS

The proposed method is a conditional denoising diffusion probabilistic model (DDPM) that utilizes a time-embedded U-net architecture with residual and attention blocks to gradually transform the white Gaussian noise sample to the target CT distribution conditioned on the CBCT. The model was trained on deformed planning CT (dpCT) and CBCT image pairs, and its feasibility was verified in brain patient study and head-and-neck (H&N) patient study. The performance of the proposed algorithm was evaluated using mean absolute error (MAE), peak signal-to-noise ratio (PSNR) and normalized cross-correlation (NCC) metrics on generated synthetic CT (sCT) samples. The proposed method was also compared to four other diffusion model-based sCT generation methods.

RESULTS

In the brain patient study, the MAE, PSNR, and NCC of the generated sCT were 25.99 HU, 30.49 dB, and 0.99, respectively, compared to 40.63 HU, 27.87 dB, and 0.98 of the CBCT images. In the H&N patient study, the metrics were 32.56 HU, 27.65 dB, 0.98 and 38.99 HU, 27.00, 0.98 for sCT and CBCT, respectively. Compared to the other four diffusion models and one Cycle generative adversarial network (Cycle GAN), the proposed method showed superior results in both visual quality and quantitative analysis.

CONCLUSIONS

The proposed conditional DDPM method can generate sCT from CBCT with accurate HU numbers and reduced artifacts, enabling accurate CBCT-based organ segmentation and dose calculation for online ART.

High-resolution MRI synthesis using a data-driven framework with denoising diffusion probabilistic modeling

Objective. High-resolution magnetic resonance imaging (MRI) can enhance lesion diagnosis, prognosis, and delineation. However, gradient power and hardware limitations prohibit recording thin slices or sub-1 mm resolution. Furthermore, long scan time is not clinically acceptable. Conventional high-resolution images generated using statistical or analytical methods include the limitation of capturing complex, high-dimensional image data with intricate patterns and structures. This study aims to harness cutting-edge diffusion probabilistic deep learning techniques to create a framework for generating high-resolution MRI from low-resolution counterparts, improving the uncertainty of denoising diffusion probabilistic models (DDPM).Approach. DDPM includes two processes. The forward process employs a Markov chain to systematically introduce Gaussian noise to low-resolution MRI images. In the reverse process, a U-Net model is trained to denoise the forward process images and produce high-resolution images conditioned on the features of their low-resolution counterparts. The proposed framework was demonstrated using T2-weighted MRI images from institutional prostate patients and brain patients collected in the Brain Tumor Segmentation Challenge 2020 (BraTS2020).Main results. For the prostate dataset, the bicubic interpolation model (Bicubic), conditional generative-adversarial network (CGAN), and our proposed DDPM framework improved the noise quality measure from low-resolution images by 4.4%, 5.7%, and 12.8%, respectively. Our method enhanced the signal-to-noise ratios by 11.7%, surpassing Bicubic (9.8%) and CGAN (8.1%). In the BraTS2020 dataset, the proposed framework and Bicubic enhanced peak signal-to-noise ratio from resolution-degraded images by 9.1% and 5.8%. The multi-scale structural similarity indexes were 0.970 ± 0.019, 0.968 ± 0.022, and 0.967 ± 0.023 for the proposed method, CGAN, and Bicubic, respectively.Significance. This study explores a deep learning-based diffusion probabilistic framework for improving MR image resolution. Such a framework can be used to improve clinical workflow by obtaining high-resolution images without penalty of the long scan time. Future investigation will likely focus on prospectively testing the efficacy of this framework with different clinical indications.

Deep learning-based synthetic dose-weighted LET map generation for intensity modulated proton therapy

The advantage of proton therapy as compared to photon therapy stems from the Bragg peak effect, which allows protons to deposit most of their energy directly at the tumor while sparing healthy tissue. However, even with such benefits, proton therapy does present certain challenges. The biological effectiveness differences between protons and photons are not fully incorporated into clinical treatment planning processes. In current clinical practice, the relative biological effectiveness (RBE) between protons and photons is set as constant 1.1. Numerous studies have suggested that the RBE of protons can exhibit significant variability. Given these findings, there is a substantial interest in refining proton therapy treatment planning to better account for the variable RBE. Dose-average linear energy transfer (LETd) is a key physical parameter for evaluating the RBE of proton therapy and aids in optimizing proton treatment plans. Calculating precise LETddistributions necessitates the use of intricate physical models and the execution of specialized Monte-Carlo simulation software, which is a computationally intensive and time-consuming progress. In response to these challenges, we propose a deep learning based framework designed to predict the LETddistribution map using the dose distribution map. This approach aims to simplify the process and increase the speed of LETdmap generation in clinical settings. The proposed CycleGAN model has demonstrated superior performance over other GAN-based models. The mean absolute error (MAE), peak signal-to-noise ratio and normalized cross correlation of the LETdmaps generated by the proposed method are 0.096 ± 0.019 keVμm-1, 24.203 ± 2.683 dB, and 0.997 ± 0.002, respectively. The MAE of the proposed method in the clinical target volume, bladder, and rectum are 0.193 ± 0.103, 0.277 ± 0.112, and 0.211 ± 0.086 keVμm-1, respectively. The proposed framework has demonstrated the feasibility of generating synthetic LETdmaps from dose maps and has the potential to improve proton therapy planning by providing accurate LETdinformation.

Advances in understanding grapevine downy mildew: From pathogen infection to disease management

Plasmopara viticola is geographically widespread in grapevine-growing regions. Grapevine downy mildew disease, caused by this biotrophic pathogen, leads to considerable yield losses in viticulture annually. Because of the great significance of grapevine production and wine quality, research on this disease has been widely performed since its emergence in the 19th century. Here, we review and discuss recent understanding of this pathogen from multiple aspects, including its infection cycle, disease symptoms, genome decoding, effector biology, and management and control strategies. We highlight the identification and characterization of effector proteins with their biological roles in host-pathogen interaction, with a focus on sustainable control methods against P. viticola, especially the use of biocontrol agents and environmentally friendly compounds.

Dual RNA-Seq Reveals Temperature-Mediated Gene Reprogramming and Molecular Crosstalk between Grapevine and Lasiodiplodia theobromae

High temperatures associated with a fluctuating climate profoundly accelerate the occurrence of a myriad of plant diseases around the world. A comprehensive insight into how plants respond to pathogenic microorganisms under high-temperature stress is required for plant disease management, whereas the underlying mechanisms behind temperature-mediated plant immunity and pathogen pathogenicity are still unclear. Here, we evaluated the effect of high temperature on the development of grapevine canker disease and quantified the contribution of temperature variation to the gene transcription reprogramming of grapevine and its pathogenic agent Lasiodiplodia theobromae using a dual RNA-seq approach. The results showed that both grapevine and the pathogen displayed altered transcriptomes under different temperatures, and even the transcription of a plethora of genes from the two organisms responded in different directions and magnitudes. The transcription variability that arose due to temperature oscillation allowed us to identify a total of 26 grapevine gene modules and 17 fungal gene modules that were correlated with more than one gene module of the partner organism, which revealed an extensive web of plant-pathogen gene reprogramming during infection. More importantly, we identified a set of temperature-responsive genes that were transcriptionally orchestrated within the given gene modules. These genes are predicted to be involved in multiple cellular processes including protein folding, stress response regulation, and carbohydrate and peptide metabolisms in grapevine and porphyrin- and pteridine-containing compound metabolisms in L. theobromae, implying that in response to temperature oscillation, a complex web of signaling pathways in two organism cells is activated during infection. This study describes a co-transcription network of grapevine and L. theobromae in the context of considering temperature variation, which provides novel insights into deciphering the molecular mechanisms underlying temperature-modulated disease development.

Image-Domain Material Decomposition for Dual-energy CT using Unsupervised Learning with Data-fidelity Loss

BACKGROUND

Dual-energy CT (DECT) and material decomposition play vital roles in quantitative medical imaging. However, the decomposition process may suffer from significant noise amplification, leading to severely degraded image signal-to-noise ratios (SNRs). While existing iterative algorithms perform noise suppression using different image priors, these heuristic image priors cannot accurately represent the features of the target image manifold. Although deep learning-based decomposition methods have been reported, these methods are in the supervised-learning framework requiring paired data for training, which is not readily available in clinical settings.

PURPOSE

This work aims to develop an unsupervised-learning framework with data-measurement consistency for image-domain material decomposition in DECT.

Deep Learning and GAN-Synthesis for Auto-Segmentation of Pancreatic Cancer by Non-Enhanced CT for Adaptive Radiotherapy

PURPOSE/OBJECTIVE(S)

In conventional adaptive radiotherapy (ART) for pancreatic cancer, contrast-enhanced CT (CECT) helps to more precisely delineate primary gross tumor volume (GTV) than non-enhanced CT (NECT). However, frequent use of contrast medium can damage kidneys and prolong treatment time. Moreover, traditional manual delineation is labor-intensive and highly dependent on the experience of oncologists. Currently, automatic delineation based on deep learning with Generative Adversarial Networks (GAN)-based CT synthesis is one of the most feasible solutions to these problems.

MATERIALS/METHODS

A dataset of 35 pancreatic cancer patients was retrospectively collected from May 2021 to December 2022. All patients consist of a pair of NECT and CECT. We designed and developed an automatic delineation framework (Proposed) for GTV of pancreatic cancer based on Trans-cycleGAN and a modified 3D U-Net. TranscycleGAN can not only synthesize CECT from NECT, but can also augment the amount of CT images; then all real and synthesized CT images were used to train the modified 3D U-Net for automatic delineation of GTV; finally, our framework was able to automatically delineate GTV by NECT, but not only by CECT. Our framework was evaluated by dice similarity coefficient (DSC), 95% Harsdorff distance (95HD) and average surface distance (ASD) with oncologists' manual delineation ("gold standard").

RESULTS

The evaluation results were summarized in Table 1. The proposed framework achieved the best automatic delineation results by NECT, which was superior to that of CECT: 0.917 & 0.903 of DSC, 2.498mm & 3.029mm of HD95, 0.481mm & 0.534mm of ASD, p < 0.05 for DSC and HD95. Specifically, it is significantly superior to the automatic delineation results using U-Net by CECT 0.917 & 0.818 of DSC, 2.498mm & 13.228mm of HD95, 0.481mm & 3.633mm of ASD, p < 0.05 for DSC.

CONCLUSION

We proposed an automatic delineation framework for contouring GTV in ART of pancreatic cancer based on deep learning and Trans-cycleGAN network. This framework could automatically delineate GTV and achieve better performance with NECT compared to CECT. Our method could not only reduce the use of contrast medium, but also increase the precision and effectiveness of tumor delineation, which could have a positive impact on precision radiotherapy.

Efficacy and Safety of High-Dose Vitamin C Combined with Total Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer (HCCSC R02 Study)

PURPOSE/OBJECTIVE(S)

Forpatients with locally advanced rectal cancer (LARC), the standard treatment is fluoropyrimidine (FU) -based neoadjuvant chemoradiotherapy (NCRT) combined with curative surgery. The CAO/ARO/AIO-04 trial and FORWARC trial reported that the addition of oxaliplatin to FU -based NCRT contributed to improve pathologic complete response (pCR), nevertheless, increased the acute therapeutic toxicity. Some studies showed that vitamin C (VitC) had potential benefits on anti-tumor therapy and anti-inflammatory response. Therefore, we conducted this HCCSC R02 study to explore the efficacy and safety of adding a high-dose intravenous VitC to mFOLFOX6/XELOX -based NCRT in LARC.

MATERIALS/METHODS

HCCSCR02 study was designed as a prospective, single-center phase II trial, which including pts aged 18-75 years with stage II/III rectal adenocarcinoma, distance from anus ≤12cm. The enrollment criteria included: staged with MRI as cT3/cT4 or cN1/2, or mesorectal fascia involvement (MRF+), or difficult to preserve the anus. Patients with glucose-6-phosphate dehydrogenase enzyme(G6PD) deficiency were excluded. Pelvic intensity modulated radiation therapy (IMRT) was given in 45-50.4Gy/25-28 fractions. Concurrently, two cycles of chemotherapy (mFOLFOX6 or XELOX) were administered during IMRT, as well as intravenous VitC (24g) delivered daily after the end of each radiation therapy. Additional 2-3 cycles of mFOLFOX6 / XELOX were adopted between the completion of radiotherapy and surgery. The primary endpoint was pCR rate. The secondary endpoints included radiation-related toxicities, overall survival (OS) and disease-free survival (DFS). This study is still recruiting.

RESULTS

From May 15, 2021 to Feb 8, 2023, 19 pts were recruited and finished all the scheduled NCRT, of which the proportion of cT4, cT3, cN2, cN1 were 31.6%, 63.2%, 52.6%, 36.8%, respectively. In addition, 10 pts (52.6%) were diagnosed as MRF+ initially, and 8 pts (42.1%) had a lower primary tumor(≤5cm) who were considered difficult for anal preservation before NCRT. All subjects enrolled were confirmed to be proficient mismatch repair (pMMR). As a result, 18 pts underwent a total mesorectal excision (TME) all with R0-resection, and 8 pts were evaluated as pCR (44.4%, 8/18, confidence interval: 0.246-0.663), 11 as major pathological response rate (MPR) (61.6%, 11/18), respectively. The anus preservation rate in patients with lower diseases was 87.5% (7/8). One case accepted a watch-and-wait strategy because of clinical complete response (cCR). Overall, grade 3 toxicities were observed in 4 pts, including 3 leucopenia (15.8%, 3/19), 2 neutropenia (10.5%, 2/19) and 1 diarrhea (5.3%, 1/19). No grade 4 adverse event was observed.

CONCLUSION

The addition of high-dose VitC to the mFOLFOX6/XELOX-based NCRT in LARC showed a promising pCR, well tolerance, particularly low rate of diarrhea, thus warrants further investigation.

CLINICAL TRIAL INFORMATION

NCT04801511.

PD-L1 Inhibitors Combined with Thoracic Radiotherapy in First-Line Treatment of Extensive Stage Small Cell Lung Cancer: A Propensity Score-Matched, Real-World Study

PURPOSE/OBJECTIVE(S)

The CREST study showed that the addition of thoracic radiotherapy (TRT) could improve the survival of extensive stage small cell lung cancer (ES-SCLC), but whether TRT can bring survival benefit in the era of immunotherapy is controversial. This study aims to explore the efficacy and safety of adding TRT to the combination of PD-L1 inhibitors and chemotherapy.

MATERIALS/METHODS

Thepatients who received PD-L1 inhibitors combined with platinum-based chemotherapy as the first-line treatment of ES-SCLC from January 2019 to December 2021 were retrospectively collected. According to whether they received TRT, they were divided into two groups, and the follow-up analysis was performed. Propensity score matching (PSM) in with a 1:1 ratio was performed to balance the baseline characteristics of the two cohorts. The endpoints were progression-free survival (PFS) and OS.

RESULTS

A total of 211 patients with ES-SCLC were enrolled, of whom 70 (33.2%) patients received standard therapy plus TRT as first-line treatment, and 141 (66.8%) patients in the control group received PD-L1 inhibitors plus chemotherapy. After PSM, a total of 65 pairs of patients were enrolled in the analysis. There were no significant differences in baseline characteristics between the two groups of patients who received TRT and those who did not. In all patients, the median PFS (mPFS) in the TRT group and the non-TRT groupwere 9.5 months and 7.2 months, respectively, with HR = 0.60 (95% CI 0.41-0.87, p = 0.007). The median OS (mOS) in the TRT group was also significantly longer than that in the non-TRT group (24.1 months vs. 18.5 months, HR = 0.53, 95% CI 0.32-0.85, p = 0.009). Multivariable analysis showed that baseline liver metastasis and bone metastasis were independent prognostic factors for OS. In terms of safety, immunotherapy combined with thoracic radiotherapy increased the incidence of treatment-related pneumonia (p<0.001), most of which were grade 1-2.

CONCLUSION

This real-world study shows that adding TRT to durvalumab or atezolizumab plus chemotherapy significantly improves survival in ES-SCLC. It leads to more treatment-related pneumonia, but most of them can be relieved after symptomatic treatment. This treatment model deserves to be explored in prospective clinical trials.