Radiomics of pericoronary adipose tissue on computed tomography angiography predicts coronary heart disease in patients with type 2 diabetes mellitus.

BACKGROUND: Diabetes is a common chronic metabolic disease. The progression of the disease promotes vascular inflammation and the formation of atherosclerosis, leading to cardiovascular disease. The coronary artery perivascular adipose tissue attenuation index based on CCTA is a new noninvasive imaging biomarker that reflects the spatial changes in perivascular adipose tissue attenuation in CCTA images and the inflammation around the coronary arteries. In this study, a radiomics approach is proposed to extract a large number of image features from CCTA in a high-throughput manner and combined with clinical diagnostic data to explore the predictive ability of vascular perivascular adipose imaging data based on CCTA for coronary heart disease in diabetic patients. METHODS: R language was used for statistical analysis to screen the variables with significant differences. A presegmentation model was used for CCTA vessel segmentation, and the pericoronary adipose region was screened out. PyRadiomics was used to calculate the radiomics features of pericoronary adipose tissue, and SVM, DT and RF were used to model and analyze the clinical data and radiomics data. Model performance was evaluated using indicators such as PPV, FPR, AAC, and ROC. RESULTS: The results indicate that there are significant differences in age, blood pressure, and some biochemical indicators between diabetes patients with and without coronary heart disease. Among 1037 calculated radiomic parameters, 18.3% showed significant differences in imaging omics features. Three modeling methods were used to analyze different combinations of clinical information, internal vascular radiomics information and pericoronary vascular fat radiomics information. The results showed that the dataset of full data had the highest ACC values under different machine learning models. The support vector machine method showed the best specificity, sensitivity, and accuracy for this dataset. CONCLUSIONS: In this study, the clinical data and pericoronary radiomics data of CCTA were fused to predict the occurrence of coronary heart disease in diabetic patients. This provides information for the early detection of coronary heart disease in patients with diabetes and allows for timely intervention and treatment.

A pretreatment multiparametric MRI-based radiomics-clinical machine learning model for predicting radiation-induced temporal lobe injury in patients with nasopharyngeal carcinoma.

BACKGROUND: To establish and validate a machine learning model using pretreatment multiparametric magnetic resonance imaging-based radiomics data with clinical data to predict radiation-induced temporal lobe injury (RTLI) in patients with nasopharyngeal carcinoma (NPC) after intensity-modulated radiotherapy (IMRT). METHODS: Data from 230 patients with NPC who received IMRT (130 with RTLI and 130 without) were randomly divided into the training (n = 161) and validation cohort (n = 69) with a ratio of 7:3. Radiomics features were extracted from pretreatment apparent diffusion coefficient (ADC) map, T2-weighted imaging (T2WI), and CE-T1-weighted imaging (CE-T1WI). T-test, spearman rank correlation, and least absolute shrinkage and selection operator (LASSO) algorithm were employed to identify significant radiomics features. Clinical features were selected with univariate and multivariate analyses. Radiomics and clinical models were constructed using multiple machine learning classifiers, and a clinical-radiomics nomogram that combined clinical with radiomics features was developed. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were drawn to compare and verify the predictive performances of the clinical model, radiomics model, and clinical-radiomics nomogram. RESULTS: A total of 5064 radiomics features were extracted, from which 52 radiomics features were selected to construct the radiomics signature. The AUC of the radiomics signature based on multiparametric MRI was 0.980 in the training cohort and 0.969 in the validation cohort, outperforming the radiomics signature only based on T2WI and CE-T1WI (p < 0.05), which highlighted the significance of the DWI sequence in the prediction of temporal lobe injury. The area under the curve (AUC) of the clinical model was 0.895 in the training cohort and 0.905 in the validation cohort. The nomogram, which integrated radiomics and clinical features, demonstrated an impressive AUC value of 0.984 in the validation set; however, no statistically significant difference was observed compared to the radiomics model. The calibration curve and decision curve analysis of the nomogram demonstrated excellent predictive performance and clinical feasibility. CONCLUSIONS: The clinical-radiomics nomogram, integrating clinical features with radiomics features derived from pretreatment multiparametric MRI, exhibits compelling predictive performance for RTLI in patients diagnosed with NPC.

A greedy regularized block Kaczmarz method for accelerating reconstruction in magnetic particle imaging.

OBJECTIVE: Magnetic Particle Imaging (MPI) is an emerging medical tomographic imaging modality that enables real-time imaging with high sensitivity and high spatial and temporal resolution. For the system matrix reconstruction method, the MPI reconstruction problem is an ill-posed inverse problem that is commonly solved using the Kaczmarz algorithm. However, the high computation time of the Kaczmarz algorithm, which restricts MPI reconstruction speed, has limited the development of potential clinical applications for real-time MPI. In order to achieve fast reconstruction in real-time MPI, we propose a greedy regularized block Kaczmarz method (GRBK) which accelerates MPI reconstruction. APPROACH: GRBK is composed of a greedy partition strategy for the system matrix, which enables preprocessing of the system matrix into well-conditioned blocks to facilitate the convergence of the block Kaczmarz algorithm, and a regularized block Kaczmarz algorithm, which enables fast and accurate MPI image reconstruction at the same time. MAIN RESULTS: We quantitatively evaluated our GRBK using simulation data from three phantoms at 20dB, 30dB, and 40dB noise levels. The results showed that GRBK can improve reconstruction speed by single orders of magnitude compared to the prevalent regularized Kaczmarz algorithm including Tikhonov regularization, the non-negative Fused Lasso, and wavelet-based sparse model. We also evaluated our method on OpenMPIData, which is real MPI data. The results showed that our GRBK is better suited for real-time MPI reconstruction than current state-of-the-art reconstruction algorithms in terms of reconstruction speed as well as image quality. SIGNIFICANCE: Our proposed method is expected to be the preferred choice for potential applications of real-time MPI.

Erector spinae plane block versus paravertebral block on postoperative quality of recovery in obese patients undergoing laparoscopic sleeve gastrectomy: a randomized controlled trial.

Purpose: To compare the impact of erector spinae plane block (ESPB) and paravertebral block (PVB) on the quality of postoperative recovery (QoR) of patients following laparoscopic sleeve gastrectomy (LSG). Methods: A total of 110 patients who underwent elective LSG under general anesthesia were randomly assigned to receive either ultrasound-guided bilateral ESPB or PVB at T8 levels. Before anesthesia induction, 40 mL of 0.33% ropivacaine was administered. The primary outcome was the QoR-15 score at 24 hours postoperatively. Results: At 24 hours postoperatively, the QoR-15 score was comparable between the ESPB and PVB groups (131 (112-140) vs. 124 (111-142.5), P = 0.525). Consistently, there was no significant difference in QoR-15 scores at 48 hours postoperatively, numerical rating scale (NRS) pain scores at any postoperative time points, time to first ambulation, time to first anal exhaust, postoperative cumulative oxycodone consumption, and incidence of postoperative nausea and vomiting (PONV) between the two groups (all P > 0.05). No nerve block-related complications were observed in either group. Conclusion: In patients undergoing LSG, preoperative bilateral ultrasound-guided ESPB yields comparable postoperative recovery to preoperative bilateral ultrasound-guided PVB.

A systematic 3-D magnetic particle imaging simulation model for quantitative analysis of reconstruction image quality.

BACKGROUND AND OBJECTIVE: Magnetic particle imaging (MPI) is an emerging imaging technology in medical tomography that utilizes the nonlinear magnetization response of superparamagnetic iron oxide (SPIO) particles to determine the in vivo spatial distribution of nanoparticle contrast agents. The reconstruction image quality of MPI is determined by the characteristics of magnetic particles, the setting of the MPI scanner parameters, and the hardware interference of MPI systems. We explore a feasible method to systematically and quickly analyze the impact of these factors on MPI reconstruction image quality. METHODS: We propose a systematic 3-D MPI simulation model. The MPI simulation model has the capability of quickly producing the simulated reconstruction images of a scanned phantom, and quantitative analysis of MPI reconstruction image quality can be achieved by comparing the differences between the input image and output image. These factors are mainly classified as imaging parameters and interference parameters in our model. In order to reduce the computational time of the simulation model, we introduce GPU parallel programming to accelerate the processing of large complex matrix data. For ease of use, we also construct a reliable, high-performance, and open-source 3-D MPI simulation software tool based on our model. The efficiency of our model is evaluated by using OpenMPIData. To demonstrate the capabilities of our model, we conduct simulation experiments using parameters consistent with a real MPI scanner for improving MPI image quality. RESULTS: The experimental results show that our simulation model can systematically and quickly evaluate the impact of imaging parameters and interference parameters on MPI reconstruction image quality. CONCLUSIONS: We developed an easy-to-use and open-source 3-D MPI simulation software tool based on our simulation model incorporating all the stages of MPI formation, from signal acquisition to image reconstruction. In the future, our simulation model has potential guiding significance to practical MPI images.

Construction of Isoquinolone Scaffolds on DNA via Rhodium(III)-Catalyzed C-H Activation.

Isoquinolone is one of the most common heterocyclic core structures in countless natural products and many bioactive compounds. Here, a highly efficient approach to synthesize isoquinolone scaffolds on DNA via rhodium(III)-catalyzed C-H activation has been described. This chemistry transformation is robust and has shown good compatibility with DNA, which is suitable for DNA-encoded library synthesis.

Oxidative Dehydroxycyclization of Catechols with o-Mercaptoanilines to Access 1-Hydroxyphenothiazines.

The protocol of aerobic oxidative dehydroxycyclization installed in the synthesis of rarely studied 1-hydroxyphenothiazines from catechols and o-mercaptoanilines is presented. Utilizing a natural renewable low-toxicity gallic acid as an organocatalyst, this established transformation proceeded smoothly in an aqueous ethanol solution under mild conditions with good functional group compatibility and up to a 94% isolated yield. This protocol is also characterized by its operational simple workup involving only recrystallization, revealing its sustainability and synthetic practicability.

Instance-Specific Model Perturbation Improves Generalized Zero-Shot Learning.

Zero-shot learning (ZSL) refers to the design of predictive functions on new classes (unseen classes) of data that have never been seen during training. In a more practical scenario, generalized zero-shot learning (GZSL) requires predicting both seen and unseen classes accurately. In the absence of target samples, many GZSL models may overfit training data and are inclined to predict individuals as categories that have been seen in training. To alleviate this problem, we develop a parameter-wise adversarial training process that promotes robust recognition of seen classes while designing during the test a novel model perturbation mechanism to ensure sufficient sensitivity to unseen classes. Concretely, adversarial perturbation is conducted on the model to obtain instance-specific parameters so that predictions can be biased to unseen classes in the test. Meanwhile, the robust training encourages the model robustness, leading to nearly unaffected prediction for seen classes. Moreover, perturbations in the parameter space, computed from multiple individuals simultaneously, can be used to avoid the effect of perturbations that are too extreme and ruin the predictions. Comparison results on four benchmark ZSL data sets show the effective improvement that the proposed framework made on zero-shot methods with learned metrics.

Marine fungus Aspergillus c1. sp metabolite activates the HSF1/PGC-1α axis, inducing a thermogenic program for treating obesity.

Background and aims: Obesity is one of the most prevalent diseases worldwide with less ideal approved agents in clinic. Activating the HSF1/PGC-1α axis in adipose tissues has been reported to induce thermogenesis in mice, which presents a promising therapeutic avenue for obesity treatment. The present study aimed to identified novel natural HSF1 activator and evaluated the therapeutic effects of the newly discovered compound on obesity-associated metabolic disorders and the molecular mechanisms of these effects. Methods: Our previous reported HSF1/PGC-1α activator screening system was used to identify novel natural HSF1 activator. The PGC-1α luciferase activity, immunoblot, protein nuclear-translocation, immunofluorescence, chromatin immunoprecipitation assays were used to evaluate the activity of compound HN-001 in activating HSF1. The experiments of mitochondrial number measurement, TG assay and imaging, cellular metabolic assay, gene assays, and CRISPR/Cas 9 were applied for investigating the metabolic effect of HN-001 in C3H10-T1/2 adipocytes. The in vivo anti-obesity efficacies and beneficial metabolic effects of HN-001 were evaluated by performing body and fat mass quantification, plasma chemical analysis, GTT, ITT, cold tolerance test, thermogenesis analysis. Results: HN-001 dose- and time-dependently activated HSF1 and induced HSF1 nuclear translocation, resulting in an enhancement in binding with the gene Pgc-1α. This improvement induced activation of adipose thermogenesis and enhancement of mitochondrial oxidation capacity, thus inhibiting adipocyte maturation. Deletion of HSF1 in adipocytes impaired mitochondrial oxidation and abolished the above beneficial metabolic effects of HN-001, including adipocyte browning induction, improvements in mitogenesis and oxidation capacity, and lipid-lowering ability. In mice, HN-001 treatment efficiently alleviated diet-induced obesity and metabolic disorders. These changes were associated with increased body temperature in mice and activation of the HSF1/PGC-1α axis in adipose tissues. UCP1 expression and mitochondrial biogenesis were increased in both white and brown adipose tissues of HN-001-treated mice. Conclusion: These data indicate that HN-001 may have therapeutic potential for obesity-related metabolic diseases by increasing the capacity of energy expenditure in adipose tissues through a mechanism involving the HSF1/PGC-1α axis, which shed new light on the development of novel anti-obesity agents derived from marine sources.

Identification of a natural PLA2 inhibitor from the marine fungus Aspergillus sp. c1 for MAFLD treatment that suppressed lipotoxicity by inhibiting the IRE-1α/XBP-1s axis and JNK signaling.

Lipotoxicity is a pivotal factor that initiates and exacerbates liver injury and is involved in the development of metabolic-associated fatty liver disease (MAFLD). However, there are few reported lipotoxicity inhibitors. Here, we identified a natural anti-lipotoxicity candidate, HN-001, from the marine fungus Aspergillus sp. C1. HN-001 dose- and time- dependently reversed palmitic acid (PA)-induced hepatocyte death. This protection was associated with IRE-1α-mediated XBP-1 splicing inhibition, which resulted in suppression of XBP-1s nuclear translocation and transcriptional regulation. Knockdown of XBP-1s attenuated lipotoxicity, but no additional ameliorative effect of HN-001 on lipotoxicity was observed in XBP-1s knockdown hepatocytes. Notably, the ER stress and lipotoxicity amelioration was associated with PLA2. Both HN-001 and the PLA2 inhibitor MAFP inhibited PLA2 activity, reduced lysophosphatidylcholine (LPC) level, subsequently ameliorated lipotoxicity. In contrast, overexpression of PLA2 caused exacerbation of lipotoxicity and weakened the anti-lipotoxic effects of HN-001. Additionally, HN-001 treatment suppressed the downstream pro-apoptotic JNK pathway. In vivo, chronic administration of HN-001 (i.p.) in mice alleviated all manifestations of MAFLD, including hepatic steatosis, liver injury, inflammation, and fibrogenesis. These effects were correlated with PLA2/IRE-1α/XBP-1s axis and JNK signaling suppression. These data indicate that HN-001 has therapeutic potential for MAFLD because it suppresses lipotoxicity, and provide a natural structural basis for developing anti-MAFLD candidates.

Bioinspired Self-Assembly of Metalloporphyrins and Polyelectrolytes into Hierarchical Supramolecular Nanostructures for Enhanced Photocatalytic H2 Production in Water.

Polypeptides, as natural polyelectrolytes, are assembled into tailored proteins to integrate chromophores and catalytic sites for photosynthesis. Mimicking nature to create the water-soluble nanoassemblies from synthetic polyelectrolytes and photocatalytic molecular species for artificial photosynthesis is still rare. Here, we report the enhancement of the full-spectrum solar-light-driven H2 production within a supramolecular system built by the co-assembly of anionic metalloporphyrins with cationic polyelectrolytes in water. This supramolecular photocatalytic system achieves a H2 production rate of 793 and 685 µmol h-1 g-1 over 24 h with a combination of Mg or Zn porphyrin as photosensitizers and Cu porphyrin as a catalyst, which is more than 23 times higher than that of free molecular controls. With a photosensitizer to catalyst ratio of 10000 : 1, the highest H2 production rate of >51,700 µmol h-1 g-1 with a turnover number (TON) of >1,290 per molecular catalyst was achieved over 24 h irradiation. The hierarchical self-assembly not only enhances photostability through forming ordered stackings of the metalloporphyrins but also facilitates both energy and electron transfer from antenna molecules to catalysts, and therefore promotes the photocatalysis. This study provides structural and mechanistic insights into the self-assembly enhanced photostability and catalytic performance of supramolecular photocatalytic systems.

Joint ESPGHAN/NASPGHAN Guidelines on Childhood Eosinophilic Gastrointestinal Disorders Beyond Eosinophilic Esophagitis.

INTRODUCTION: Eosinophilic gastrointestinal disorders beyond eosinophilic esophagitis (non-EoE EGIDs) are rare chronic inflammatory disorders of the gastrointestinal (GI) tract. Diagnosis is based on clinical symptoms and histologic findings of eosinophilic inflammation after exclusion of a secondary cause or systemic disease. Currently, no guidelines exist for the evaluation of non-EoE EGIDs. Therefore, the European Society for Paediatric Gastroenterology, Hepatology and Nutrition (ESPGHAN) and the North American Society for Pediatric Gastroenterology, Hepatology and Nutrition (NASPGHAN) formed a task force group to provide consensus guidelines for childhood non-EoE EGIDs. METHODS: The working group was composed of pediatric gastroenterologists, adult gastroenterologists, allergists/immunologists, and pathologists. An extensive electronic literature search of the MEDLINE, EMBASE, and Cochrane databases was conducted up to February 2022. General methodology was used in the formulation of recommendations according to the Appraisal of Guidelines for Research and Evaluation (AGREE) II and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system to meet current standards of evidence assessment. RESULTS: The guidelines provide information on the current concept of non-EoE EGIDs, disease pathogenesis, epidemiology, clinical manifestations, diagnostic and disease surveillance procedures, and current treatment options. Thirty-four statements based on available evidence and 41 recommendations based on expert opinion and best clinical practices were developed. CONCLUSION: Non-EoE EGIDs literature is limited in scope and depth, making clear recommendations difficult. These consensus-based clinical practice guidelines are intended to assist clinicians caring for children affected by non-EoE EGIDs and to facilitate high-quality randomized controlled trials of various treatment modalities using standardized, uniform disease definitions.

The failure behavior of coal-rock combined body under compression-shear loading.

As shallow coal reserves continue to deplete rapidly, deep mining has become an unavoidable course of action. In the process of deep coal mining, affected by blasting, mining, and excavation, the coal-rock interface often encounters the action of compression-shear composite load. The interface crack directly affects the stability of the coal-rock structure. Uniaxial compression experiments have been conducted on rock and coal-like material with pre-existing interfacial oblique cracks to study the crack propagation criterion of the rock and coal-like material interface. An image acquisition system is used to record the surface speckle field of the whole process of specimen failure. The strain fields and stress intensity factor of the sample at different times are obtained by digital image correlation. At the same time, the stress field near the crack tip is calculated. The results show that with the increase of loading, two strains of localization bands are formed on the surface of the specimen. One of the strain-localized bands starts at the lower tip of the prefabricated crack and propagates along the vertical interface. The other starts at the upper tip of the prefabricated damage and propagates along the interface direction. It can be seen that the lower tip of the prefabricated crack enters into the crack fracture process zone earlier than the upper tip. The strain localization band narrows gradually with the load increase, and then macro cracks appear. The initiation of two tips is suitable for different fracture criteria. The lower tip is dominated by the maximum circumferential tensile stress, and the upper tip is dominated by shear stress. The specimens eventually fracture along the interface. The evolution of the strain field during the failure process of rock and coal-like material can reflect the generation and propagation of the crack.

STANet: Spatio-Temporal Adaptive Network and Clinical Prior Embedding Learning for 3D+T CMR Segmentation.

The segmentation of cardiac structure in magnetic resonance images (CMR) is paramount in diagnosing and managing cardiovascular illnesses, given its 3D+Time (3D+T) sequence. The existing deep learning methods are constrained in their ability to 3D+T CMR segmentation, due to: (1) Limited motion perception. The complexity of heart beating renders the motion perception in 3D+T CMR, including the long-range and cross-slice motions. The existing methods' local perception and slice-fixed perception directly limit the performance of 3D+T CMR perception. (2) Lack of labels. Due to the expensive labeling cost of the 3D+T CMR sequence, the labels of 3D+T CMR only contain the end-diastolic and end-systolic frames. The incomplete labeling scheme causes inefficient supervision. Hence, we propose a novel spatio-temporal adaptation network with clinical prior embedding learning (STANet) to ensure efficient spatio-temporal perception and optimization on 3D+T CMR segmentation. (1) A spatio-temporal adaptive convolution (STAC) treats the 3D+T CMR sequence as a whole for perception. The long-distance motion correlation is embedded into the structural perception by learnable weight regularization to balance long-range motion perception. The structural similarity is measured by cross-attention to adaptively correlate the cross-slice motion. (2) A clinical prior embedding learning strategy (CPE) is proposed to optimize the partially labeled 3D+T CMR segmentation dynamically by embedding clinical priors into optimization. STANet achieves outstanding performance with Dice of 0.917 and 0.94 on two public datasets (ACDC and STACOM), which indicates STANet has the potential to be incorporated into computer-aided diagnosis tools for clinical application.

Enhancement of mitochondrial energy metabolism by melatonin promotes vascularized skeletal muscle regeneration in a volumetric muscle loss model.

Volumetric muscle loss (VML) is a condition that results in the extensive loss of 20 % or more of skeletal muscle due to trauma or tumor ablation, leading to severe functional impairment and permanent disability. The current surgical interventions have limited functional regeneration of skeletal muscle due to the compromised self-repair mechanism. Melatonin has been reported to protect skeletal muscle from exercise-induced oxidative damage and holds great potential to treat muscle diseases. In this study, we hypothesize that melatonin can enhance myoblast differentiation and promote effective recovery of skeletal muscle following VML. In vitro administration of melatonin resulted in a significant enhancement of myogenesis in C2C12 myoblast cells, as evidenced by the up-regulation of myogenic marker genes in a dose-dependent manner. Further experiments revealed that silent information of regulator type 3 (SIRT3) played a critical role in the melatonin-enhanced myoblast differentiation through enhancement of mitochondrial energy metabolism and activation of mitochondrial antioxidant enzymes such as superoxide dismutase 2 (SOD2). Silencing of Sirt3 completely abrogated the protective effect of melatonin on the mitochondrial function of myoblasts, evidenced by the increased reactive oxygen species, decreased adenosine triphosphate production, and down-regulated myoblast-specific marker gene expression. In order to attain a protracted and consistent release, liposome-encapsuled melatonin was integrated into gelatin methacryloyl hydrogel (GelMA-Lipo@MT). The implantation of GelMA-Lipo@MT into a tibialis anterior muscle defect in a VML model effectively stimulated the formation of myofibers and new blood vessels in situ, while concurrently inhibiting fibrotic collagen deposition. The findings of this study indicate that the incorporation of melatonin with GelMA hydrogel has facilitated the de novo vascularized skeletal muscle regeneration by augmenting mitochondrial energy metabolism. This represents a promising approach for the development of skeletal muscle tissue engineering, which could be utilized for the treatment of VML and other severe muscle injuries.

An adaptive multi-frame parallel iterative method for accelerating real-time magnetic particle imaging reconstruction.

Objective. Real-time reconstruction of magnetic particle imaging (MPI) shows promising clinical applications. However, prevalent reconstruction methods are mainly based on serial iteration, which causes large delay in real-time reconstruction. In order to achieve lower latency in real-time MPI reconstruction, we propose a parallel method for accelerating the speed of reconstruction methods.Approach. The proposed method, named adaptive multi-frame parallel iterative method (AMPIM), enables the processing of multi-frame signals to multi-frame MPI images in parallel. To facilitate parallel computing, we further propose an acceleration strategy for parallel computation to improve the computational efficiency of our AMPIM.Main results. OpenMPIData was used to evaluate our AMPIM, and the results show that our AMPIM improves the reconstruction frame rate per second of real-time MPI reconstruction by two orders of magnitude compared to prevalent iterative algorithms including the Kaczmarz algorithm, the conjugate gradient normal residual algorithm, and the alternating direction method of multipliers algorithm. The reconstructed image using AMPIM has high contrast-to-noise with reducing artifacts.Significance. The AMPIM can parallelly optimize least squares problems with multiple right-hand sides by exploiting the dimension of the right-hand side. AMPIM has great potential for application in real-time MPI imaging with high imaging frame rate.

A transformer-based dual-domain network for reconstructing FOV extended cone-beam CT images from truncated sinograms in radiation therapy.

BACKGROUND AND OBJECTIVE: Cone-beam computed tomography (CBCT) is widely used in clinical radiotherapy, but its small field of view (sFOV) limits its application potential. In this study, a transformer-based dual-domain network (dual_swin), which combined image domain restoration and sinogram domain restoration, was proposed for the reconstruction of complete CBCT images with extended FOV from truncated sinograms. METHODS: The planning CT images with large FOV (LFOV) of 330 patients who received radiation therapy were collected. The synthetic CBCT (sCBCT) images with LFOV were generated from CT images by the trained cycleGAN network, and CBCT images with sFOV were obtained through forward projection, projection truncation, and filtered back projection (FBP), comprising the training and test data. The proposed dual_swin includes sinogram domain restoration, image domain restoration, and FBP layer, and the swin transformer blocks were used as the basic feature extraction module in the network to improve the global feature extraction ability. The proposed dual_swin was compared with the image domain method, the sinogram domain method, the U-Net based dual domain network (dual_Unet), and the traditional iterative reconstruction method based on prior image and conjugate gradient least-squares (CGLS) in the test of sCBCT images and clinical CBCT images. The HU accuracy and body contour accuracy of the predicted images by each method were evaluated. RESULTS: The images generated using the CGLS method were fuzzy and obtained the lowest structural similarity (SSIM) among all methods in the test of sCBCT and clinical CBCT images. The predicted images by the image domain methods are quite different from the ground truth and have low accuracy on HU value and body contour. In comparison with image domain methods, sinogram domain methods improved the accuracy of HU value and body contour but introduced secondary artifacts and distorted bone tissue. The proposed dual_swin achieved the highest HU and contour accuracy with mean absolute error (MAE) of 23.0 HU, SSIM of 95.7%, dice similarity coefficient (DSC) of 99.6%, and Hausdorff distance (HD) of 4.1 mm in the test of sCBCT images. In the test of clinical patients, images that were predicted by dual_swin yielded MAE, SSIM, DSC, and HD of 38.2 HU, 91.7%, 99.0%, and 5.4 mm, respectively. The predicted images by the proposed dual_swin has significantly higher accuracy than the other methods (P < 0.05). CONCLUSIONS: The proposed dual_swin can accurately reconstruct FOV extended CBCT images from the truncated sinogram to improve the application potential of CBCT images in radiotherapy.

Joint ESPGHAN/NASPGHAN Guidelines on Childhood Eosinophilic Gastrointestinal Disorders beyond Eosinophilic Esophagitis.

INTRODUCTION: Eosinophilic Gastrointestinal Disorders beyond Eosinophilic Esophagitis (non-EoE EGIDs) are rare chronic inflammatory disorders of the gastrointestinal (GI) tract. Diagnosis is based on clinical symptoms and histologic findings of eosinophilic inflammation after exclusion of a secondary cause or systemic disease. Currently, no guidelines exist for the evaluation of non-EoE EGIDs. Therefore, the European Society for Paediatric Gastroenterology, Hepatology and Nutrition (ESPGHAN) and the North American Society for Pediatric Gastroenterology, Hepatology and Nutrition (NASPGHAN) formed a task force group to provide consensus guidelines for childhood non-EoE EGIDs. METHODS: The working group was composed of pediatric gastroenterologists, adult gastroenterologists, allergists/immunologists, and pathologists. An extensive electronic literature search of the MEDLINE, EMBASE, and Cochrane databases was conducted up to February 2022. General methodology was used in the formulation of recommendations according to the Appraisal of Guidelines for Research and Evaluation (AGREE) II and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system to meet current standards of evidence assessment. RESULTS: The guidelines provide information on the current concept of non-EoE EGIDs, disease pathogenesis, epidemiology, clinical manifestations, diagnostic and disease surveillance procedures, and current treatment options. Thirty-four statements based on available evidence and 41 recommendations based on expert opinion and best clinical practices were developed. CONCLUSION: Non-EoE EGIDs literature is limited in scope and depth, making clear recommendations difficult. These consensus-based clinical practice guidelines are intended to assist clinicians caring for children affected by non-EoE EGIDs and to facilitate high-quality randomized controlled trials of various treatment modalities using standardized, uniform disease definitions.

Rebalanced Zero-Shot Learning.

Zero-shot learning (ZSL) aims to identify unseen classes with zero samples during training. Broadly speaking, present ZSL methods usually adopt class-level semantic labels and compare them with instance-level semantic predictions to infer unseen classes. However, we find that such existing models mostly produce imbalanced semantic predictions, i.e. these models could perform precisely for some semantics, but may not for others. To address the drawback, we aim to introduce an imbalanced learning framework into ZSL. However, we find that imbalanced ZSL has two unique challenges: (1) Its imbalanced predictions are highly correlated with the value of semantic labels rather than the number of samples as typically considered in the traditional imbalanced learning; (2) Different semantics follow quite different error distributions between classes. To mitigate these issues, we first formalize ZSL as an imbalanced regression problem which offers empirical evidences to interpret how semantic labels lead to imbalanced semantic predictions. We then propose a re-weighted loss termed Re-balanced Mean-Squared Error (ReMSE), which tracks the mean and variance of error distributions, thus ensuring rebalanced learning across classes. As a major contribution, we conduct a series of analyses showing that ReMSE is theoretically well established. Extensive experiments demonstrate that the proposed method effectively alleviates the imbalance in semantic prediction and outperforms many state-of-the-art ZSL methods.

Virtual Pain Unit Is Associated with Improvement of Postoperative Analgesia Quality: A Retrospective Single-Center Clinical Study.

INTRODUCTION: Acute postoperative pain is a major concern among surgical patients. Thus, this study established a new acute pain management model and compared the effects of the acute pain service (APS) model in 2020 and the virtual pain unit (VPU) model in 2021 on postoperative analgesia quality. METHODS: This retrospective, single-center clinical study involved 21,281 patients from 2020 to 2021. First, the patients were grouped on the basis of their pain management model (APS and VPU). The incidence of moderate to severe postoperative pain (MSPP) [numeric rating scale (NRS) score ≥ 5], postoperative nausea and vomiting (PONV), and postoperative dizziness were recorded. RESULTS: The VPU group recorded significantly lower MSPP incidence (1-12 months), PONV, and postoperative dizziness (1-10 months and 12 months) compared with the APS group. In addition, the annual average incidence of MSPP, PONV, and postoperative dizziness in the VPU group was significantly lower than in the APS group. CONCLUSIONS: The VPU model reduces the incidence of moderate to severe postoperative pain, nausea, vomiting, and dizziness; hence, it is a promising acute pain management model.